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Computer Science Graduate Projects and Theses

Theses/dissertations from 2023 2023.

High-Performance Domain-Specific Library for Hydrologic Data Processing , Kalyan Bhetwal

Verifying Data Provenance During Workflow Execution for Scientific Reproducibility , Rizbanul Hasan

Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent , Ahmad Hojatimalekshah

Exploring the Capability of a Self-Supervised Conditional Image Generator for Image-to-Image Translation without Labeled Data: A Case Study in Mobile User Interface Design , Hailee Kiesecker

Fake News Detection Using Narrative Content and Discourse , Hongmin Kim

Anomaly Detection Using Graph Neural Network , Bishal Lakha

Sparse Format Conversion and Code Synthesis , Tobi Goodness Popoola

Portable Sparse Polyhedral Framework Code Generation Using Multi Level Intermediate Representation , Aaron St. George

Severity Measures for Assessing Error in Automatic Speech Recognition , Ryan Whetten

Theses/Dissertations from 2022 2022

Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design , James D. Beck

Meshfree Methods for PDEs on Surfaces , Andrew Michael Jones

Deep Learning of Microstructures , Amir Abbas Kazemzadeh Farizhandi

Long-Term Trends in Extreme Environmental Events with Changepoint Detection , Mintaek Lee

Structure Aware Smart Encoding and Decoding of Information in DNA , Shoshanna Llewellyn

Towards Making Transformer-Based Language Models Learn How Children Learn , Yousra Mahdy

Ontology-Based Formal Approach for Safety and Security Verification of Industrial Control Systems , Ramesh Neupane

Improving Children's Authentication Practices with Respect to Graphical Authentication Mechanism , Dhanush Kumar Ratakonda

Hate Speech Detection Using Textual and User Features , Rohan Raut

Automated Detection of Sockpuppet Accounts in Wikipedia , Mostofa Najmus Sakib

Characterization and Mitigation of False Information on the Web , Anu Shrestha

Sinusoidal Projection for 360° Image Compression and Triangular Discrete Cosine Transform Impact in the JPEG Pipeline , Iker Vazquez Lopez

Theses/Dissertations from 2021 2021

Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom , Garrett Allen

Fair and Efficient Consensus Protocols for Secure Blockchain Applications , Golam Dastoger Bashar

Why Don't You Act Your Age?: Recognizing the Stereotypical 8-12 Year Old Searcher by Their Search Behavior , Michael Green

Ensuring Consistency and Efficiency of the Incremental Unit Network in a Distributed Architecture , Mir Tahsin Imtiaz

Modeling Real and Fake News Sharing in Social Networks , Abishai Joy

Modeling and Analyzing Users' Privacy Disclosure Behavior to Generate Personalized Privacy Policies , A.K.M. Nuhil Mehdy

Into the Unknown: Exploration of Search Engines' Responses to Users with Depression and Anxiety , Ashlee Milton

Generating Test Inputs from String Constraints with an Automata-Based Solver , Marlin Roberts

A Case Study in Representing Scientific Applications ( GeoAc ) Using the Sparse Polyhedral Framework , Ravi Shankar

Actors for the Internet of Things , Arjun Shukla

Theses/Dissertations from 2020 2020

Towards Unifying Grounded and Distributional Semantics Using the Words-as-Classifiers Model of Lexical Semantics , Stacy Black

Improving Scientist Productivity, Architecture Portability, and Performance in ParFlow , Michael Burke

Polyhedral+Dataflow Graphs , Eddie C. Davis

Improving Spellchecking for Children: Correction and Design , Brody Downs

A Collection of Fast Algorithms for Scalar and Vector-Valued Data on Irregular Domains: Spherical Harmonic Analysis, Divergence-Free/Curl-Free Radial Basis Functions, and Implicit Surface Reconstruction , Kathryn Primrose Drake

Privacy-Preserving Protocol for Atomic Swap Between Blockchains , Kiran Gurung

Unsupervised Structural Graph Node Representation Learning , Mikel Joaristi

Detecting Undisclosed Paid Editing in Wikipedia , Nikesh Joshi

Do You Feel Me?: Learning Language from Humans with Robot Emotional Displays , David McNeill

Obtaining Real-World Benchmark Programs from Open-Source Repositories Through Abstract-Semantics Preserving Transformations , Maria Anne Rachel Paquin

Content Based Image Retrieval (CBIR) for Brand Logos , Enjal Parajuli

A Resilience Metric for Modern Power Distribution Systems , Tyler Bennett Phillips

Theses/Dissertations from 2019 2019

Edge-Assisted Workload-Aware Image Processing System , Anil Acharya

MINOS: Unsupervised Netflow-Based Detection of Infected and Attacked Hosts, and Attack Time in Large Networks , Mousume Bhowmick

Deviant: A Mutation Testing Tool for Solidity Smart Contracts , Patrick Chapman

Querying Over Encrypted Databases in a Cloud Environment , Jake Douglas

A Hybrid Model to Detect Fake News , Indhumathi Gurunathan

Suitability of Finite State Automata to Model String Constraints in Probablistic Symbolic Execution , Andrew Harris

UNICORN Framework: A User-Centric Approach Toward Formal Verification of Privacy Norms , Rezvan Joshaghani

Detection and Countermeasure of Saturation Attacks in Software-Defined Networks , Samer Yousef Khamaiseh

Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy , Manish Kumar

Application-Specific Memory Subsystem Benchmarking , Mahesh Lakshminarasimhan

Multilingual Information Retrieval: A Representation Building Perspective , Ion Madrazo

Improved Study of Side-Channel Attacks Using Recurrent Neural Networks , Muhammad Abu Naser Rony Chowdhury

Investigating the Effects of Social and Temporal Dynamics in Fitness Games on Children's Physical Activity , Ankita Samariya

BullyNet: Unmasking Cyberbullies on Social Networks , Aparna Sankaran

FALCON: Framework for Anomaly Detection In Industrial Control Systems , Subin Sapkota

Investigating Semantic Properties of Images Generated from Natural Language Using Neural Networks , Samuel Ward Schrader

Incremental Processing for Improving Conversational Grounding in a Chatbot , Aprajita Shukla

Estimating Error and Bias of Offline Recommender System Evaluation Results , Mucun Tian

Theses/Dissertations from 2018 2018

Leveraging Tiled Display for Big Data Visualization Using D3.js , Ujjwal Acharya

Fostering the Retrieval of Suitable Web Resources in Response to Children's Educational Search Tasks , Oghenemaro Deborah Anuyah

Privacy-Preserving Genomic Data Publishing via Differential Privacy , Tanya Khatri

Injecting Control Commands Through Sensory Channel: Attack and Defense , Farhad Rasapour

Strong Mutation-Based Test Generation of XACML Policies , Roshan Shrestha

Performance, Scalability, and Robustness in Distributed File Tree Copy , Christopher Robert Sutton

Using DNA For Data Storage: Encoding and Decoding Algorithm Development , Kelsey Suyehira

Detecting Saliency by Combining Speech and Object Detection in Indoor Environments , Kiran Thapa

Theses/Dissertations from 2017 2017

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews , Haritha Akella

Editing Behavior Analysis and Prediction of Active/Inactive Users in Wikipedia , Harish Arelli

CloudSkulk: Design of a Nested Virtual Machine Based Rootkit-in-the-Middle Attack , Joseph Anthony Connelly

Predicting Friendship Strength in Facebook , Nitish Dhakal

Privacy-Preserving Trajectory Data Publishing via Differential Privacy , Ishita Dwivedi

Cultivating Community Interactions in Citizen Science: Connecting People to Each Other and the Environment , Bret Allen Finley

Uncovering New Links Through Interaction Duration , Laxmi Amulya Gundala

Variance: Secure Two-Party Protocol for Solving Yao's Millionaires' Problem in Bitcoin , Joshua Holmes

A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases , Akshay Kansal

Integrity Coded Databases: Ensuring Correctness and Freshness of Outsourced Databases , Ujwal Karki

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display , Yuan Li

The Effects of Pair-Programming in a High School Introductory Computer Science Class , Ken Manship

Towards Automatic Repair of XACML Policies , Shuai Peng

Identification of Unknown Landscape Types Using CNN Transfer Learning , Ashish Sharma

Hand Gesture Recognition for Sign Language Transcription , Iker Vazquez Lopez

Learning to Code Music : Development of a Supplemental Unit for High School Computer Science , Kelsey Wright

Theses/Dissertations from 2016 2016

Identification of Small Endogenous Viral Elements within Host Genomes , Edward C. Davis Jr.

When the System Becomes Your Personal Docent: Curated Book Recommendations , Nevena Dragovic

Security Testing with Misuse Case Modeling , Samer Yousef Khamaiseh

Estimating Length Statistics of Aggregate Fried Potato Product via Electromagnetic Radiation Attenuation , Jesse Lovitt

Towards Multipurpose Readability Assessment , Ion Madrazo

Evaluation of Topic Models for Content-Based Popularity Prediction on Social Microblogs , Axel Magnuson

CEST: City Event Summarization using Twitter , Deepa Mallela

Developing an ABAC-Based Grant Proposal Workflow Management System , Milson Munakami

Phoenix and Hive as Alternatives to RDBMS , Diana Ornelas

Massively Parallel Algorithm for Solving the Eikonal Equation on Multiple Accelerator Platforms , Anup Shrestha

A Certificateless One-Way Group Key Agreement Protocol for Point-to-Point Email Encryption , Srisarguru Sridhar

Dynamic Machine Level Resource Allocation to Improve Tasking Performance Across Multiple Processes , Richard Walter Thatcher

Theses/Dissertations from 2015 2015

Developing an Application for Evolutionary Search for Computational Models of Cellular Development , Nicolas Scott Cornia

Accelerated Radar Signal Processing in Large Geophysical Datasets , Ravi Preesha Geetha

Integrity Coded Databases (ICDB) – Protecting Integrity for Outsourced Databases , Archana Nanjundarao

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Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations

Computer Science and Engineering Theses and Dissertations

Theses/dissertations from 2023 2023.

Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno

Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan

Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros

Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin

Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen

Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur

A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Graph Analysis on Social Networks , Shen Lu

Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo

Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi

Theses/Dissertations from 2022 2022

Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed

Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala

Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen

A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave

On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández

Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou

Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang

A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri

Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney

Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin

Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker

Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich

Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar

Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong

Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward

Theses/Dissertations from 2021 2021

Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar

Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia

Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto

Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis

Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill

Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja

Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana

Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev

Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.

Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski

Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov

Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai

Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri

Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco

Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam

Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava

A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza

Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar

Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams

Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari

Theses/Dissertations from 2020 2020

Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal

Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil

Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari

Composition of Atomic-Obligation Security Policies , Yan Cao Albright

Action Recognition Using the Motion Taxonomy , Maxat Alibayev

Sentiment Analysis in Peer Review , Zachariah J. Beasley

Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov

Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang

Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram

PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami

Composition of Atomic-Obligation Security Policies , Danielle Ferguson

Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel

The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann

Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez

Privacy-Preserving and Functional Information Systems , Thang Hoang

Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January

Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi

Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam

Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha

Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath

System Support of Concurrent Database Query Processing on a GPU , Hao Li

Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu

Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li

Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla

Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng

Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi

Models of Secure Software Enforcement and Development , Hernan M. Palombo

Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul

Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan

Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah

Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui

Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah

Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu

Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon

Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna

Theses/Dissertations from 2019 2019

Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur

Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed

Phenomena of Social Dynamics in Online Games , Essa Alhazmi

A Machine Learning Approach to Predicting Community Engagement on Social Media During Disasters , Adel Alshehri

Interactive Fitness Domains in Competitive Coevolutionary Algorithm , ATM Golam Bari

Measuring Influence Across Social Media Platforms: Empirical Analysis Using Symbolic Transfer Entropy , Abhishek Bhattacharjee

A Communication-Centric Framework for Post-Silicon System-on-chip Integration Debug , Yuting Cao

Authentication and SQL-Injection Prevention Techniques in Web Applications , Cagri Cetin

Multimodal Emotion Recognition Using 3D Facial Landmarks, Action Units, and Physiological Data , Diego Fabiano

Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations , Yongqiang Huang

A GPU-Based Framework for Parallel Spatial Indexing and Query Processing , Zhila Nouri Lewis

A Flexible, Natural Deduction, Automated Reasoner for Quick Deployment of Non-Classical Logic , Trisha Mukhopadhyay

An Efficient Run-time CFI Check for Embedded Processors to Detect and Prevent Control Flow Based Attacks , Srivarsha Polnati

Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain , John Rippetoe

Detecting Digitally Forged Faces in Online Videos , Neilesh Sambhu

Malicious Manipulation in Service-Oriented Network, Software, and Mobile Systems: Threats and Defenses , Dakun Shen

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Home > School, College, or Department > MCECS > Computer Science > Dissertations and Theses

Computer Science Dissertations and Theses

Theses/dissertations from 2024 2024.

MmWave RAT Optimization: MAC Layer Initial Access Design and Transport Layer Integration , Suresh Srinivasan (Dissertation)

Theses/Dissertations from 2023 2023

Seeing in the Dark: Towards Robust Pedestrian Detection at Nighttime , Afnan Althoupety (Dissertation)

A Deep Hierarchical Variational Autoencoder for World Models in Complex Reinforcement Learning Environments , Sriharshitha Ayyalasomayajula (Thesis)

Toward Efficient Rendering: A Neural Network Approach , Qiqi Hou (Dissertation)

Energy Auction with Non-Relational Persistence , Michael Ramez Howard (Thesis)

Implementing a Functional Logic Programming Language via the Fair Scheme , Andrew Michael Jost (Dissertation)

Multi-Agent Deep Reinforcement Learning for Radiation Localization , Benjamin Scott Totten (Thesis)

Theses/Dissertations from 2022 2022

Using Intrinsically-Typed Definitional Interpreters to Verify Compiler Optimizations in a Monadic Intermediate Language , Dani Barrack (Thesis)

An Automated Zoom Class Session Analysis Tool to Improve Education , Jack Arlo Cannon II (Thesis)

Scaling EPA-RIMM with Multicore System Management Interrupt Handlers , Alexander K. Freed (Thesis)

Unpaired Style Transfer Conditional Generative Adversarial Network for Scanned Document Generation , David Jonathan Hawbaker (Thesis)

Toward Analyzing the Diversity of Extractive Summaries , Aaron David Hudson (Thesis)

Making Curry with Rice: An Optimizing Curry Compiler , Steven Libby (Dissertation)

Domain Knowledge as Motion-Aware Inductive Bias for Deep Video Synthesis: Two Case Studies , Long Mai (Dissertation)

Theses/Dissertations from 2021 2021

Efficient Neuromorphic Algorithms for Gamma-Ray Spectrum Denoising and Radionuclide Identification , Merlin Phillip Carson (Thesis)

Storing Intermediate Results in Space and Time: SQL Graphs and Block Referencing , Basem Ibrahim Elazzabi (Dissertation)

Automated Test Generation for Validating SystemC Designs , Bin Lin (Dissertation)

Forecasting Optimal Parameters of the Broken Wing Butterfly Option Strategy Using Differential Evolution , David Munoz Constantine (Thesis)

Situate: An Agent-Based System for Situation Recognition , Max Henry Quinn (Dissertation)

Theses/Dissertations from 2020 2020

Multiple Diagram Navigation , Hisham Benotman (Dissertation)

Smart Contract Vulnerabilities on the Ethereum Blockchain: a Current Perspective , Daniel Steven Connelly (Thesis)

Extensible Performance-Aware Runtime Integrity Measurement , Brian G. Delgado (Dissertation)

Novel View Synthesis - a Neural Network Approach , Hoang Le (Dissertation)

Exploring the Potential of Sparse Coding for Machine Learning , Sheng Yang Lundquist (Dissertation)

Workflow Critical Path: a Data-Oriented Path Metric for Holistic HPC Workflows , Daniel D. Nguyen (Thesis)

Novel View Synthesis in Time and Space , Simon Niklaus (Dissertation)

Balancing Security, Performance and Deployability in Encrypted Search , David Joel Pouliot (Dissertation)

Theses/Dissertations from 2019 2019

A Secure Anti-Counterfeiting System using Near Field Communication, Public Key Cryptography, Blockchain, and Bayesian Games , Naif Saeed Alzahrani (Dissertation)

Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series , Logan Blakely (Thesis)

Local Radiance , Scott Peter Britell (Dissertation)

Correct-by-Construction Typechecking with Scope Graphs , Katherine Imhoff Casamento (Thesis)

Versatile Binary-level Concolic Testing , Bo Chen (Dissertation)

Crumpled and Abraded Encryption: Implementation and Provably Secure Construction , Scott Sherlock Griffy (Thesis)

Knowing Without Knowing: Real-Time Usage Identification of Computer Systems , Leila Mohammed Hawana (Thesis)

Design and Experimental Evaluation of DeepMarket: an Edge Computing Marketplace with Distributed TensorFlow Execution Capability , Soyoung Kim (Thesis)

Localizing Little Landmarks with Transfer Learning , Sharad Kumar (Thesis)

Context-Aware Wi-Fi Infrastructure-based Indoor Positioning Systems , Huy Phuong Tran (Dissertation)

Theses/Dissertations from 2018 2018

Bounding Box Improvement with Reinforcement Learning , Andrew Lewis Cleland (Thesis)

Sensing Building Structure Using UWB Radios for Disaster Recovery , Jeong Eun Lee (Dissertation)

Annotation-Enabled Interpretation and Analysis of Time-Series Data , Niveditha Venugopal (Thesis)

EPA-RIMM-V: Efficient Rootkit Detection for Virtualized Environments , Tejaswini Ajay Vibhute (Thesis)

Theses/Dissertations from 2017 2017

Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs , Erik Timothy Conser (Thesis)

Refining Bounding-Box Regression for Object Localization , Naomi Lynn Dickerson (Thesis)

Fully Generic Programming Over Closed Universes of Inductive-Recursive Types , Larry Diehl (Dissertation)

Communicating at Terahertz Frequencies , Farnoosh Moshirfatemi (Dissertation)

Designing In-Headset Authoring Tools for Virtual Reality Video , Cuong Nguyen (Dissertation)

Certifying Loop Pipelining Transformations in Behavioral Synthesis , Disha Puri (Dissertation)

Power-Aware Datacenter Networking and Optimization , Qing Yi (Dissertation)

Theses/Dissertations from 2016 2016

Identifying Relationships between Scientific Datasets , Abdussalam Alawini (Dissertation)

Information Representation and Computation of Spike Trains in Reservoir Computing Systems with Spiking Neurons and Analog Neurons , Amin Almassian (Thesis)

Investigations of an "Objectness" Measure for Object Localization , Lewis Richard James Coates (Thesis)

Image Stitching: Handling Parallax, Stereopsis, and Video , Fan Zhang (Dissertation)

Theses/Dissertations from 2015 2015

Novel Methods for Learning and Adaptation in Chemical Reaction Networks , Peter Banda (Dissertation)

Post-silicon Functional Validation with Virtual Prototypes , Kai Cong (Dissertation)

Novel Cryptographic Primitives and Protocols for Censorship Resistance , Kevin Patrick Dyer (Dissertation)

Hardware/Software Interface Assurance with Conformance Checking , Li Lei (Dissertation)

Leveraging Contextual Relationships Between Objects for Localization , Clinton Leif Olson (Thesis)

The Performance of Random Prototypes in Hierarchical Models of Vision , Kendall Lee Stewart (Thesis)

Tweakable Ciphers: Constructions and Applications , Robert Seth Terashima (Dissertation)

Scalable Equivalence Checking for Behavioral Synthesis , Zhenkun Yang (Dissertation)

Theses/Dissertations from 2014 2014

The Nax Language: Unifying Functional Programming and Logical Reasoning in a Language based on Mendler-style Recursion Schemes and Term-indexed Types , Ki Yung Ahn (Dissertation)

Using Spammers' Computing Resources for Volunteer Computing , Thai Le Quy Bui (Thesis)

Towards Constructing Interactive Virtual Worlds , Francis Chang (Dissertation)

System-wide Performance Analysis for Virtualization , Deron Eugene Jensen (Thesis)

Advances in Piecewise Smooth Image Reconstruction , Ralf Juengling (Dissertation)

Interpretable Machine Learning and Sparse Coding for Computer Vision , Will Landecker (Dissertation)

Optimizing Data Movement in Hybrid Analytic Systems , Patrick Michael Leyshock (Dissertation)

Ranked Similarity Search of Scientific Datasets: An Information Retrieval Approach , Veronika Margaret Megler (Dissertation)

Using GIST Features to Constrain Search in Object Detection , Joanna Browne Solmon (Thesis)

The Role of Prototype Learning in Hierarchical Models of Vision , Michael David Thomure (Dissertation)

Theses/Dissertations from 2013 2013

Object Detection and Recognition in Natural Settings , George William Dittmar (Thesis)

Trust-but-Verify: Guaranteeing the Integrity of User-generated Content in Online Applications , Akshay Dua (Dissertation)

Equivalence Checking for High-Assurance Behavioral Synthesis , Kecheng Hao (Dissertation)

Type Classes and Instance Chains: A Relational Approach , John Garrett Morris (Dissertation)

Theses/Dissertations from 2012 2012

Using Dataflow Optimization Techniques with a Monadic Intermediate Language , Justin George Bailey (Thesis)

A Survey and Analysis of Solutions to the Oblivious Memory Access Problem , Erin Elizabeth Chapman (Thesis)

A Data-Descriptive Feedback Framework for Data Stream Management Systems , Rafael J. Fernández Moctezuma (Dissertation)

Extending Relativistic Programming to Multiple Writers , Philip William Howard (Dissertation)

The Basic Scheme for the Evaluation of Functional Logic Programs , Arthur Peters (Thesis)

The Link Between Image Segmentation and Image Recognition , Karan Sharma (Thesis)

Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures , Josh Triplett (Dissertation)

Theses/Dissertations from 2011 2011

Conceptual Modeling of Data with Provenance , David William Archer (Dissertation)

Low-latency Estimates for Window-Aggregate Queries over Data Streams , Amit Bhat (Thesis)

Information Processing in Two-Dimensional Cellular Automata , Martin Cenek (Dissertation)

Scalable and Efficient Tasking for Dynamic Sensor Networks , Thanh Xuan Dang (Dissertation)

On the Effect of Topology on Learning and Generalization in Random Automata Networks , Alireza Goudarzi (Thesis)

HOLCF '11: A Definitional Domain Theory for Verifying Functional Programs , Brian Charles Huffman (Dissertation)

A Functional Approach to Memory-Safe Operating Systems , Rebekah Leslie (Dissertation)

Factoring Semiprimes Using PG2N Prime Graph Multiagent Search , Keith Eirik Wilson (Thesis)

High Speed Wireless Networking for 60GHz , Candy Yiu (Dissertation)

Theses/Dissertations from 2010 2010

Extensible Scheduling in a Haskell-based Operating System , Kenneth William Graunke (Thesis)

Addressing Automated Adversaries of Network Applications , Edward Leo Kaiser (Dissertation)

An Automata-Theoretic Approach to Hardware/Software Co-verification , Juncao Li (Dissertation)

Practical Type Inference for the GADT Type System , Chuan-kai Lin (Dissertation)

Scalable event tracking on high-end parallel systems , Kathryn Marie Mohror (Dissertation)

Performance Analysis of Hybrid CPU/GPU Environments , Michael Shawn Smith (Thesis)

Theses/Dissertations from 2009 2009

Computational Techniques for Reducing Spectra of the Giant Planets in Our Solar System , Holly L. Grimes (Thesis)

Programmer Friendly Refactoring Tools , Emerson Murphy-Hill (Dissertation)

A Framework for Superimposed Applications : Techniques to Represent, Access, Transform, and Interchange Bi-level Information , Sudarshan Srivivasa Murthy (Dissertation)

Graphical User Interfaces as Updatable Views , James Felger Terwilliger (Dissertation)

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Computer Science Theses & Dissertations

Theses and dissertations published by graduate students in the Department of Computer Science, College of Sciences, Old Dominion University, since Fall 2016 are available in this collection. Backfiles of all dissertations (and some theses) have also been added.

In late Fall 2023 or Spring 2024, all theses will be digitized and available here. In the meantime, consult the Library Catalog to find older items in print.

Theses/Dissertations from 2023 2023

Dissertation: Inverse Mappers for QCD Global Analysis , Manal Almaeen

Thesis: Assessing the Prevalence and Archival Rate of URIs to Git Hosting Platforms in Scholarly Publications , Emily Escamilla

Thesis: Supporting Account-based Queries for Archived Instagram Posts , Himarsha R. Jayanetti

Dissertation: Detecting Malware With Securedeep Accelerator via Processor Side-Channel Fingerprinting for Internet of Things , Zhuoran Li

Dissertation: Tracing and Segmentation of Molecular Patterns in 3-Dimensional Cryo-ET/EM Density Maps Through Algorithmic Image Processing and Deep Learning-Based Techniques , Salim Sazzed

Dissertation: Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems , Polykarpos Thomadakis

Theses/Dissertations from 2022 2022

Dissertation: Machine Learning-Based Event Generator , Yasir Alanazi

Thesis: Using Ensemble Learning Techniques to Solve the Blind Drift Calibration Problem , Devin Scott Drake

Dissertation: A Relevance Model for Threat-Centric Ranking of Cybersecurity Vulnerabilities , Corren G. McCoy

Dissertation: Evaluation of Generative Models for Predicting Microstructure Geometries in Laser Powder Bed Fusion Additive Manufacturing , Andy Ramlatchan

Thesis: TransParsCit: A Transformer-Based Citation Parser Trained on Large-Scale Synthesized Data , MD Sami Uddin

Dissertation: Towards Privacy and Security Concerns of Adversarial Examples in Deep Hashing Image Retrieval , Yanru Xiao

Theses/Dissertations from 2021 2021

Dissertation: MOVE: Mobile Observers Variants and Extensions , Ryan Florin

Dissertation: Improving Collection Understanding for Web Archives with Storytelling: Shining Light Into Dark and Stormy Archives , Shawn M. Jones

Dissertation: A Unified Framework for Parallel Anisotropic Mesh Adaptation , Christos Tsolakis

Theses/Dissertations from 2020 2020

Dissertation: MementoMap: A Web Archive Profiling Framework for Efficient Memento Routing , Sawood Alam

Dissertation: A Framework for Verifying the Fixity of Archived Web Resources , Mohamed Aturban

Thesis: Parallelization of the Advancing Front Local Reconnection Mesh Generation Software Using a Pseudo-Constrained Parallel Data Refinement Method , Kevin Mark Garner Jr.

Dissertation: Towards Dynamic Vehicular Clouds , Aida Ghazizadeh

Dissertation: Bootstrapping Web Archive Collections From Micro-Collections in Social Media , Alexander C. Nwala

Dissertation: Automatic Linear and Curvilinear Mesh Generation Driven by Validity Fidelity and Topological Guarantees , Jing Xu

Theses/Dissertations from 2019 2019

Dissertation: Expanding the Usage of Web Archives by Recommending Archived Webpages Using Only the URI , Lulwah M. Alkwai

Dissertation: Highly Accurate Fragment Library for Protein Fold Recognition , Wessam Elhefnawy

Dissertation: Scalable Parallel Delaunay Image-to-Mesh Conversion for Shared and Distributed Memory Architectures , Daming Feng

Dissertation: Aggregating Private and Public Web Archives Using the Mementity Framework , Matthew R. Kelly

Thesis: Enhancing Portability in High Performance Computing: Designing Fast Scientific Code with Longevity , Jason Orender

Thesis: Novel Use of Neural Networks to Identify and Detect Electrical Infrastructure Performance , Evan Pierre Savaria

Theses/Dissertations from 2018 2018

Dissertation: New Methods to Improve Protein Structure Modeling , Maha Abdelrasoul

Dissertation: Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems , Hassan Hadi Latheeth AL-Maksousy

Thesis: To Relive the Web: A Framework for the Transformation and Archival Replay of Web Pages , John Andrew Berlin

Thesis: Supporting Big Data at the Vehicular Edge , Lloyd Decker

Thesis: Deep Learning for Segmentation Of 3D Cryo-EM Images , Devin Reid Haslam

Dissertation: FlexStream: SDN-Based Framework for Programmable and Flexible Adaptive Video Streaming , Ibrahim Ben Mustafa

Thesis: Novel Technique for Gait Analysis Using Two Waist Mounted Gyroscopes , Ahmed Nasr

Dissertation: Leveraging Resources on Anonymous Mobile Edge Nodes , Ahmed Salem

Theses/Dissertations from 2017 2017

Dissertation: SenSys: A Smartphone-Based Framework for ITS applications , Abdulla Ahmed Alasaadi

Dissertation: ItsBlue: A Distributed Bluetooth-Based Framework for Intelligent Transportation Systems , Ahmed Awad Alghamdi

Dissertation: Finite Element Modeling Driven by Health Care and Aerospace Applications , Fotios Drakopoulos

Dissertation: Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures , Kamesh Arumugam Karunanithi

Thesis: Multi-GPU Accelerated High-Fidelity Simulations of Beam-Beam Effects in Particle Colliders , Naga Sai Ravi Teja Majeti

Theses/Dissertations from 2016 2016

Dissertation: Using Web Archives to Enrich the Live Web Experience Through Storytelling , Yasmin AlNoamany

Thesis: Magnopark, Smart Parking Detection Based on Cellphone Magnetic Sensor , Maryam Arab

Dissertation: Scripts in a Frame: A Framework for Archiving Deferred Representations , Justin F. Brunelle

Dissertation: Machine Learning Methods for Brain Image Analysis , Ahmed Fakhry

Dissertation: Novel Monte Carlo Methods for Large-Scale Linear Algebra Operations , Hao Ji

Dissertation: Machine Learning Methods for Medical and Biological Image Computing , Rongjian Li

Dissertation: Toward Open and Programmable Wireless Network Edge , Mostafa Uddin

Thesis: An Optimized Multiple Right-Hand Side Dslash Kernel for Intel Xeon Phi , Aaron Walden

Dissertation: Towards Aggregating Time-Discounted Information in Sensor Networks , Xianping Wang

Dissertation: A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications , Wenlu Zhang

Theses/Dissertations from 2015 2015

Dissertation: Efficient Algorithms for Prokaryotic Whole Genome Assembly and Finishing , Abhishek Biswas

Dissertation: De Novo Protein Structure Modeling and Energy Function Design , Lin Chen

Dissertation: High Performance Large Graph Analytics by Enhancing Locality , Naga Shailaja Dasari

Thesis: Avoiding Spoilers on Mediawiki Fan Sites Using Memento , Shawn M. Jones

Dissertation: Energy Harvesting-Aware Design for Wireless Nanonetworks , Shahram Mohrehkesh

Thesis: Parallel Two-Dimensional Unstructured Anisotropic Delaunay Mesh Generation for Aerospace Applications , Juliette Kelly Pardue

Dissertation: Detecting, Modeling, and Predicting User Temporal Intention , Hany M. SalahEldeen

Dissertation: Wireless Networking for Vehicle to Infrastructure Communication and Automatic Incident Detection , Sarwar Aziz Sha-Mohammad

Dissertation: Computational Development for Secondary Structure Detection From Three-Dimensional Images of Cryo-Electron Microscopy , Dong Si

Thesis: Mobile Cloud Computing Based Non Rigid Registration for Image Guided Surgery , Arun Brahmavar Vishwanatha

Theses/Dissertations from 2014 2014

Dissertation: Web Archive Services Framework for Tighter Integration Between the Past and Present Web , Ahmed AlSum

Dissertation: Modeling Stem Cell Population Dynamics , Samiur Arif

Dissertation: A Framework for Web Object Self-Preservation , Charles L. Cartledge

Dissertation: Document Classification in Support of Automated Metadata Extraction Form Heterogeneous Collections , Paul K. Flynn

Dissertation: Resource Allocation in Vehicular Cloud Computing , Puya Ghazizadeh

Thesis: Generating Combinatorial Objects- A New Perspective , Alexander Chizoma Nwala

Dissertation: Enhancing Understanding of Discrete Event Simulation Models Through Analysis , Kara Ann Olson

Dissertation: Scalable Reasoning for Knowledge Bases Subject to Changes , Hui Shi

Dissertation: Improving Structural Features Prediction in Protein Structure Modeling , Ashraf Yaseen

Thesis: Computational Analysis of Gene Expression and Connectivity Patterns in the Convoluted Structures of Mouse Cerebellum , Tao Zeng

Theses/Dissertations from 2013 2013

Thesis: HTTP Mailbox - Asynchronous Restful Communication , Sawood Alam

Dissertation: TDMA Slot Reservation in Cluster-Based VANETs , Mohammad Salem Almalag

Thesis: Protein Loop Length Estimation From Medium Resolution Cryoem Images , Andrew R. McKnight

Theses/Dissertations from 2012 2012

Dissertation: De Novo Protein Structure Modeling from Cryoem Data Through a Dynamic Programming Algorithm in the Secondary Structure Topology Graph , Kamal H. Al Nasr

Dissertation: FRIEND: A Cyber-Physical System for Traffic Flow Related Information Aggregation and Dissemination , Samy S. El-Tawab

Thesis: An Extensible Framework for Creating Personal Archives of Web Resources Requiring Authentication , Matthew Ryan Kelly

Thesis: Visualizing Digital Collections at Archive-It , Kalpesh Padia

Theses/Dissertations from 2011 2011

Dissertation: A Framework for Incident Detection and notification in Vehicular Ad-Hoc Networks , Mahmoud Abuelela

Dissertation: A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks , Mohammad Hadi Arbabi

Thesis: A Probabilistic Analysis of Misparking in Reservation Based Parking Garages , Vikas G. Ashok

Thesis: A Penalty-Based Approach to Handling Cluster Sizing in Mobile Ad Hoc Networks , Ryan Florin

Dissertation: Data Aggregation and Dissemination in Vehicular Ad-Hoc Networks , Khaled Ibrahim

Dissertation: Using the Web Infrastructure for Real Time Recovery of Missing Web Pages , Martin Klein

Theses/Dissertations from 2010 2010

Dissertation: A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks , Hady S. Abdel Salam

Thesis: Merging Schemas in a Collaborative Faceted Classification System , Jianxiang Li

Thesis: XPath-Based Template Language for Describing the Placement of Metadata within a Document , Vijay Kumar Musham

Dissertation: Providing Location Security in Vehicular Ad Hoc Networks , Gongjun Yan

Theses/Dissertations from 2009 2009

Dissertation: Algorithms for Vertex-Weighted Matching in Graphs , Mahantesh Halappanavar

Theses/Dissertations from 2008 2008

Thesis: Using Timed-Release Cryptography to Mitigate Preservation Risk of Embargo Periods , Rabia Haq

Dissertation: Biology-Inspired Approach for Communal Behavior in Massively Deployed Sensor Networks , Kennie H. Jones

Dissertation: Biological Networks: Modeling and Structural Analysis , Emad Y. Ramadan

Dissertation: Integrating Preservation Functions Into the Web Server , Joan A. Smith

Theses/Dissertations from 2007 2007

Dissertation: FreeLib: A Self-Sustainable Peer-to-Peer Digital Library Framework for Evolving Communities , Ashraf A. Amrou

Thesis: Channel Management in Heterogeneous Cellular Networks , Mohammad Hadi Arbabi

Dissertation: Diagnosing Reading strategies: Paraphrase Recognition , Chutima Boonthum

Thesis: Investigating Real-Time Sonar Performance Predictions Using Beowulf Clustering , Charles Lane Cartledge

Dissertation: Lazy Preservation: Reconstructing Websites from the Web Infrastructure , Frank McCown

Theses/Dissertations from 2006 2006

Dissertation: Group Key Management in Wireless Ad-Hoc and Sensor Networks , Mohammed A. Moharrum

Dissertation: Template-Based Metadata Extraction for Heterogeneous Collection , Jianfeng Tang

Theses/Dissertations from 2005 2005

Dissertation: Collaborative Caching for efficient and Robust Certificate Authority Services in Mobile Ad-Hoc Networks , Laith Abdulaziz Al-Sulaiman

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Home > FACULTIES > Computer Science > CSD-ETD

Computer Science Theses and Dissertations

This collection contains theses and dissertations from the Department of Computer Science, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository

Theses/Dissertations from 2024 2024

A Target-Based and A Targetless Extrinsic Calibration Methods for Thermal Camera and 3D LiDAR , Farhad Dalirani

Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications , Sudipta Singha Roy

Theses/Dissertations from 2023 2023

Classification of DDoS Attack with Machine Learning Architectures and Exploratory Analysis , Amreen Anbar

Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces , Sepehr Asgarian

Improved Protein Sequence Alignments Using Deep Learning , Seyed Sepehr Ashrafzadeh

INVESTIGATING IMPROVEMENTS TO MESH INDEXING , Anurag Bhattacharjee

Algorithms and Software for Oligonucleotide Design , Qin Dong

Framework for Assessing Information System Security Posture Risks , Syed Waqas Hamdani

De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling , Fang Han

Local Model Agnostic XAI Methodologies Applied to Breast Cancer Malignancy Predictions , Heather Hartley

A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit , Rongji He

A Novel Method for Assessment of Batch Effect on single cell RNA sequencing data , Behnam Jabbarizadeh

Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs , Taabish Jeshani

Citation Polarity Identification From Scientific Articles Using Deep Learning Methods , Souvik Kundu

Denoising-Based Domain Adaptation Network for EEG Source Imaging , Runze Li

Decoy-Target Database Strategy and False Discovery Rate Analysis for Glycan Identification , Xiaoou Li

DpNovo: A DEEP LEARNING MODEL COMBINED WITH DYNAMIC PROGRAMMING FOR DE NOVO PEPTIDE SEQUENCING , Yizhou Li

Developing A Smart Home Surveillance System Using Autonomous Drones , Chongju Mai

Look-Ahead Selective Plasticity for Continual Learning , Rouzbeh Meshkinnejad

The Two Visual Processing Streams Through The Lens Of Deep Neural Networks , Aidasadat Mirebrahimi Tafreshi

Source-free Domain Adaptation for Sleep Stage Classification , Yasmin Niknam

Data Heterogeneity and Its Implications for Fairness , Ghazaleh Noroozi

Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System for Efficient, Sustainable, and Self-Adaptive Urban Environments , Elham Okhovat

Evaluating the Likelihood of Bug Inducing Commits Using Metrics Trend Analysis , Parul Parul

On Computing Optimal Repairs for Conditional Independence , Alireza Pirhadi

Open-Set Source-Free Domain Adaptation in Fundus Images Analysis , Masoud Pourreza

Migration in Edge Computing , Arshin Rezazadeh

A Modified Hopfield Network for the K-Median Problem , Cody Rossiter

Predicting Network Failures with AI Techniques , Chandrika Saha

Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective , Sajal Saha

An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support , Mozhgan Salimiparsa

Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition , Amir Hesam Salimnia

Global Cyber Attack Forecast using AI Techniques , Nusrat Kabir Samia

IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION , Rafael David Segistan Canizales

A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods , Kayvan Shabani

Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network , Sareh Soltani Nejad

Smartphone Loss Prevention System Using BLE and GPS Technology , Noshin Tasnim

A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets) , Yasmen M. Wahba

Reducing Negative Transfer of Random Data in Source-Free Unsupervised Domain Adaptation , Anthony Wong

Deep Neural Methods for True/Pseudo- Invasion Classification in Colorectal Polyp Whole-Slide Images , Zhiyuan Yang

Developing a Relay-based Autonomous Drone Delivery System , Muhammad Zakar

Learning Mortality Risk for COVID-19 Using Machine Learning and Statistical Methods , Shaoshi Zhang

Machine Learning Techniques for Improved Functional Brain Parcellation , Da Zhi

Theses/Dissertations from 2022 2022

The Design and Implementation of a High-Performance Polynomial System Solver , Alexander Brandt

Defining Service Level Agreements in Serverless Computing , Mohamed Elsakhawy

Algorithms for Regular Chains of Dimension One , Juan P. Gonzalez Trochez

Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications , Susmitha Hanumanthu

Multi-Device Data Analysis for Fault Localization in Electrical Distribution Grids , Jacob D L Hunte

Towards Parking Lot Occupancy Assessment Using Aerial Imagery and Computer Vision , John Jewell

Potential of Vision Transformers for Advanced Driver-Assistance Systems: An Evaluative Approach , Andrew Katoch

Psychological Understanding of Textual journals using Natural Language Processing approaches , Amirmohammad Kazemeinizadeh

Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems , Nima Khairdoost

Solving Challenges in Deep Unsupervised Methods for Anomaly Detection , Vahid Reza Khazaie

Developing an Efficient Real-Time Terrestrial Infrastructure Inspection System Using Autonomous Drones and Deep Learning , Marlin Manka

Predictive Modelling For Topic Handling Of Natural Language Dialogue With Virtual Agents , Lareina Milambiling

Improving Deep Entity Resolution by Constraints , Soudeh Nilforoushan

Respiratory Pattern Analysis for COVID-19 Digital Screening Using AI Techniques , Annita Tahsin Priyoti

Extracting Microservice Dependencies Using Log Analysis , Andres O. Rodriguez Ishida

False Discovery Rate Analysis for Glycopeptide Identification , Shun Saito

Towards a Generalization of Fulton's Intersection Multiplicity Algorithm , Ryan Sandford

An Investigation Into Time Gazed At Traffic Objects By Drivers , Kolby R. Sarson

Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives , Ibrahim Mohammed Sayem

A Unified Representation and Deep Learning Architecture for Persuasive Essays in English , Muhammad Tawsif Sazid

Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP) , Aakriti Sharma

Advances in the Automatic Detection of Optimization Opportunities in Computer Programs , Delaram Talaashrafi

Reputation-Based Trust Assessment of Transacting Service Components , Konstantinos Tsiounis

Fully Autonomous UAV Exploration in Confined and Connectionless Environments , Kirk P. Vander Ploeg

Three Contributions to the Theory and Practice of Optimizing Compilers , Linxiao Wang

Developing Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach , Wumian Wang

Predicting and Modifying Memorability of Images , Mohammad Younesi

Theses/Dissertations from 2021 2021

Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches , Mahtab Ahmed

A Physical Layer Framework for a Smart City Using Accumulative Bayesian Machine Learning , Razan E. AlFar

Load Balancing and Resource Allocation in Smart Cities using Reinforcement Learning , Aseel AlOrbani

Contrastive Learning of Auditory Representations , Haider Al-Tahan

Cache-Friendly, Modular and Parallel Schemes For Computing Subresultant Chains , Mohammadali Asadi

Protein Interaction Sites Prediction using Deep Learning , Sourajit Basak

Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis , William A. Beldman

Improving Reader Motivation with Machine Learning , Tanner A. Bohn

A Black-box Approach for Containerized Microservice Monitoring in Fog Computing , Shi Chang

Visualization and Interpretation of Protein Interactions , Dipanjan Chatterjee

A Framework for Characterising Performance in Multi-Class Classification Problems with Applications in Cancer Single Cell RNA Sequencing , Erik R. Christensen

Exploratory Search with Archetype-based Language Models , Brent D. Davis

Evolutionary Design of Search and Triage Interfaces for Large Document Sets , Jonathan A. Demelo

Building Effective Network Security Frameworks using Deep Transfer Learning Techniques , Harsh Dhillon

A Deep Topical N-gram Model and Topic Discovery on COVID-19 News and Research Manuscripts , Yuan Du

Automatic extraction of requirements-related information from regulatory documents cited in the project contract , Sara Fotouhi

Developing a Resource and Energy Efficient Real-time Delivery Scheduling Framework for a Network of Autonomous Drones , Gopi Gugan

A Visual Analytics System for Rapid Sensemaking of Scientific Documents , Amirreza Haghverdiloo Barzegar

Calibration Between Eye Tracker and Stereoscopic Vision System Employing a Linear Closed-Form Perspective-n-Point (PNP) Algorithm , Mohammad Karami

Fuzzy and Probabilistic Rule-Based Approaches to Identify Fault Prone Files , Piyush Kumar Korlepara

Parallel Arbitrary-precision Integer Arithmetic , Davood Mohajerani

A Technique for Evaluating the Health Status of a Software Module Using Process Metrics , . Ria

Visual Analytics for Performing Complex Tasks with Electronic Health Records , Neda Rostamzadeh

Predictive Model of Driver's Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems , Mohsen Shirpour

A Generative-Discriminative Approach to Human Brain Mapping , Deepanshu Wadhwa

WesternAccelerator：Rapid Development of Microservices , Haoran Wei

A Lightweight and Explainable Citation Recommendation System , Juncheng Yin

Mitosis Detection from Pathology Images , Jinhang Zhang

Theses/Dissertations from 2020 2020

Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury , Sheikh S. Abdullah

Towards the Development of Network Service Cost Modeling-An ISP Perspective , Yasmeen Ali

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Home > Computer Science > CompSci TDs > Masters Theses

Computer Science Masters Theses

Theses from 2024 2024.

Enabling smart healthcare applications through visible light communication networks , Jack Manhardt

Time series anomaly detection using generative adversarial networks , Shyam Sundar Saravanan

Theses from 2023 2023

DYNAMIC DISCOUNTED SATISFICING BASED DRIVER DECISION PREDICTION IN SEQUENTIAL TAXI REQUESTS , Sree Pooja Akula

MAT: Genetic Algorithms Based Multi-Objective Adversarial Attack on Multi-Task Deep Neural Networks , Nikola Andric

COMPUTER VISION IN ADVERSE CONDITIONS: SMALL OBJECTS, LOW-RESOLUTION IMAGES, AND EDGE DEPLOYMENT , Raja Sunkara

Theses from 2022 2022

Maximising social welfare in selfish multi-modal routing using strategic information design for quantal response travelers , Sainath Sanga

Man-in-the-Middle Attacks on MQTT based IoT networks , Henry C. Wong

Theses from 2021 2021

Biochemical assay invariant attestation for the security of cyber-physical digital microfluidic biochips , Fredrick Eugene Love II

Theses from 2020 2020

On predicting stopping time of human sequential decision-making using discounted satisficing heuristic , Mounica Devaguptapu

Theses from 2019 2019

Advanced techniques for improving canonical genetic programming , Adam Tyler Harter

Evolved parameterized selection for evolutionary algorithms , Samuel Nathan Richter

Design and implementation of applications over delay tolerant networks for disaster and battlefield environment , Karthikeyan Sachidanandam

Theses from 2018 2018

Mixed-criticality real-time task scheduling with graceful degradation , Samsil Arefin

CARD: Concealed and remote discovery of IoT devices in victims' home networks , Sammie Lee Bush

Multiple security domain non deducibility in the FREEDM smart grid infrastructure , Manish Jaisinghani

Reputation and credit based incentive mechanism for data-centric message delivery in delay tolerant networks , Himanshu Jethawa

Solidification rate detection through solid-liquid interface tracking , Wei Luo

Cloud transactions and caching for improved performance in clouds and DTNs , Dileep Mardham

Cyber-physical security of an electric microgrid , Prashanth Palaniswamy

An approach for formal analysis of the security of a water treatment testbed , Sai Sidharth Patlolla

Analyzing large scale trajectory data to identify users with similar behavior , Tyler Clark Percy

Precise energy efficient scheduling of mixed-criticality tasks & sustainable mixed-criticality scheduling , Sai Sruti

A network tomography approach for traffic monitoring in smart cities , Ruoxi Zhang

Improved CRPD analysis and a secure scheduler against information leakage in real-time systems , Ying Zhang

Theses from 2017 2017

Cyber-physical security of a chemical plant , Prakash Rao Dunaka

UFace: Your universal password no one can see , Nicholas Steven Hilbert

Multi stage recovery from large scale failure in interdependent networks , Maria Angelin John Bosco

Multiple security domain model of a vehicle in an automated vehicle system , Uday Ganesh Kanteti

Personalizing education with algorithmic course selection , Tyler Morrow

Decodable network coding in wireless network , Junwei Su

Multiple security domain nondeducibility air traffic surveillance systems , Anusha Thudimilla

Theses from 2016 2016

Automated design of boolean satisfiability solvers employing evolutionary computation , Alex Raymond Bertels

Care-Chair: Opportunistic health assessment with smart sensing on chair backrest , Rakesh Kumar

Theses from 2015 2015

Dependability analysis and recovery support for smart grids , Isam Abdulmunem Alobaidi

Sensor authentication in collaborating sensor networks , Jake Uriah Bielefeldt

Argumentation based collaborative software architecture design and intelligent analysis of software architecture rationale , NagaPrashanth Chanda

A Gaussian mixture model for automated vesicle fusion detection and classification , Haohan Li

Hyper-heuristics for the automated design of black-box search algorithms , Matthew Allen Martin

Aerial vehicle trajectory design for spatio-temporal task satisfaction and aggregation based on utility metric , Amarender Reddy Mekala

Design and implementation of a broker for cloud additive manufacturing services , Venkata Prashant Modekurthy

Cyber security research frameworks for coevolutionary network defense , George Daniel Rush

Energy disaggregation in NIALM using hidden Markov models , Anusha Sankara

Theses from 2014 2014

Crime pattern detection using online social media , Raja Ashok Bolla

Energy efficient scheduling and allocation of tasks in sensor cloud , Rashmi Dalvi

A cloud brokerage architecture for efficient cloud service selection , Venkata Nagarjuna Dondapati

Access control delegation in the clouds , Pavani Gorantla

Evolving decision trees for the categorization of software , Jasenko Hosic

M-Grid : A distributed framework for multidimensional indexing and querying of location based big data , Shashank Kumar

Privacy preservation using spherical chord , Doyal Tapan Mukherjee

Top-K with diversity-M data retrieval in wireless sensor networks , Kiran Kumar Puram

On temporal and frequency responses of smartphone accelerometers for explosives detection , Srinivas Chakravarthi Thandu

Efficient data access in mobile cloud computing , Siva Naga Venkata Chaitanya Vemulapalli

An empirical study on symptoms of heavier internet usage among young adults , SaiPreethi Vishwanathan

Theses from 2013 2013

Sybil detection in vehicular networks , Muhammad Ibrahim Almutaz

Argumentation placement recommendation and relevancy assessment in an intelligent argumentation system , Nian Liu

Security analysis of a cyber physical system : a car example , Jason Madden

Efficient integrity verification of replicated data in cloud , Raghul Mukundan

Search-based model summarization , Lokesh Krishna Ravichandran

Hybridizing and applying computational intelligence techniques , Jeffery Scott Shelburg

Secure design defects detection and correction , Wenquan Wang

Theses from 2012 2012

Robust evolutionary algorithms , Brian Wesley Goldman

Semantic preserving text tepresentation and its applications in text clustering , Michael Howard

Vehicle path verification using wireless sensor networks , Gerry W. Howser

Distributed and collaborative watermarking in relational data , Prakash Kumar

Theses from 2011 2011

A social network of service providers for trust and identity management in the Cloud , Makarand Bhonsle

Adaptive rule-based malware detection employing learning classifier systems , Jonathan Joseph Blount

A low-cost motion tracking system for virtual reality applications , Abhinav Chadda

Optimization of textual affect entity relation models , Ajith Cherukad Jose

MELOC - memory and location optimized caching for mobile Ad hoc networks , Lekshmi Manian Chidambaram

A framework for transparent depression classification in college settings via mining internet usage patterns , Raghavendra Kotikalapudi

An incentive based approach to detect selfish nodes in Mobile P2P network , Hemanth Meka

Location privacy policy management system , Arej Awodha Muhammed

Exploring join caching in programming codes to reduce runtime execution , Swetha Surapaneni

Theses from 2010 2010

Event detection from click-through data via query clustering , Prabhu Kumar Angajala

Population control in evolutionary algorithms , Jason Edward Cook

Dynamic ant colony optimization for globally optimizing consumer preferences , Pavitra Dhruvanarayana

EtherAnnotate: a transparent malware analysis tool for integrating dynamic and static examination , Joshua Michael Eads

Representation and validation of domain and range restrictions in a relational database driven ontology maintenance system , Patrick Garrett. Edgett

Cloud security requirements analysis and security policy development using a high-order object-oriented modeling technique , Kenneth Kofi Fletcher

Multi axis slicing for rapid prototyping , Divya Kanakanala

Content based image retrieval for bio-medical images , Vikas Nahar

2-D path planning for direct laser deposition process , Swathi Routhu

Contribution-based priority assessment in a web-based intelligent argumentation network for collaborative software development , Maithili Satyavolu

An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems , Shivakar Vulli

Intelligent computational argumentation for evaluating performance scores in multi-criteria decision making , Rubal Wanchoo

Minimize end-to-end delay through cross-layer optimization in multi-hop wireless sensor networks , Yibo Xu

Theses from 2009 2009

Information flow properties for cyber-physical systems , Rav Akella

Exploring the use of a commercial game engine for the development of educational software , Hussain Alafaireet

Automated offspring sizing in evolutionary algorithms , André Chidi Nwamba

Theses from 2008 2008

Image analysis techniques for vertebra anomaly detection in X-ray images , Mohammed Das

Cross-layer design through joint routing and link allocation in wireless sensor networks , Xuan Gong

A time series classifier , Christopher Mark Gore

An economic incentive based routing protocol incorporating quality of service for mobile peer-to-peer networks , Anil Jade

Incorporation of evidences in an intelligent argumentation network for collaborative engineering design , Ekta Khudkhudia

PrESerD - Privacy ensured service discovery in mobile peer-to-peer environment , Santhosh Muthyapu

Co-optimization: a generalization of coevolution , Travis Service

Critical infrastructure protection and the Domain Name Service (DNS) system , Mark Edward Snyder

Co-evolutionary automated software correction: a proof of concept , Joshua Lee Wilkerson

Theses from 2007 2007

A light-weight middleware framework for fault-tolerant and secure distributed applications , Ian Jacob Baird

Symbolic time series analysis using hidden Markov models , Nikhil Bhardwaj

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This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog .

MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.

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Intracellular sensor spatial multiplexing via RNA scaffolds 

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aBBRate: automating BBR congestion control attack exploration using a model-based approach.

Analysis of named entity recognition & entity linking in historical text

Annotating decision analyses using semantic web technologies

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ARID: affinity representing instance descriptors.

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Theses and Dissertations

Theses/dissertations from 2023 2023.

Integrative analysis of cell-free DNA liquid biopsy data , Irfan Alahi

Deep Learning for Tomographic Image Reconstruction Guided by Generative Models and Image Science , Sayantan Bhadra

Feature-Oriented Hardware Design , Justin Deters

Injective Mapping under Constraints , Xingyi Du

Consequences and Incentives of Fair Learning , Andrew Estornell

Learning in Large Interactive Environments , Hai S. Le

TFA inference: Using mathematical modeling of gene expression data to infer the activity of transcription factors , Cynthia Ma

Learning from User Interactions and Providing Guidance in Visual Data Analysis , Shayan Monadjemi

Translating Deep Learning into Scientific Domains: Case Studies in Bioactivation, Quantum Chemistry, and Clinical Studies , Kathryn Sarullo

An Efficient Task-Parallel Platform for Interactive Applications , Kyle Singer

Models and Algorithms for Real-Time Systems , Abhishek Singh

Understanding Societal Values of ChatGPT , Yidan Tang

Throughput Optimizations for Irregular Dataflow Streaming Applications on Wide-SIMD Architectures , Stephen William Timcheck

Adversarial Patch Attacks on Deep Reinforcement Learning Algorithms , Peizhen Tong

Honesty Is Not Always the Best Policy: Defending Against Membership Inference Attacks on Genomic Data , Rajagopal Venkatesaramani

Real-time Analysis of Aerosol Size Distributions with the Fast Integrated Mobility Spectrometer (FIMS) , Daisy Wang

Feature Selection from Clinical Surveys Using Semantic Textual Similarity , Benjamin Warner

An Assistive Interface For Displaying Novice's Code History , Ruiwei Xiao

Model-based Deep Learning for Computational Imaging , Xiaojian Xu

Securing Autonomous Driving: Addressing Adversarial Attacks and Defenses , Jinghan Yang

Evaluating the Problem Solving Abilities of ChatGPT , Fankun Zeng

PathFormer: Interpretable and Powerful Graph Transformer for Gene Network Analysis , Qihang Zhao, Zehao Dong, Muhan Zhang, Philip Payne, Michael Province, Carlos Cruchaga, Tianyu Zhao, Yixin Chen, and Fuhai Li

Theses/Dissertations from 2022 2022

Tell It Slant: Investigating the Engagement, Discourse, and Popularity of Data Visualization in Online Communities , Emma Baker

A Reconfigurable FPGA Overlay Architecture for Matrix-Matrix Multiplication , Zihao Chen

Smart Sensing and Clinical Predictions with Wearables: From Physiological Signals to Mental Health , Ruixuan Dai

Dynamic Continuous Distributed Constraint Optimization Problems , Khoi Hoang

The Effects of Host-like Environmental Signals and Gene Expression on Capsule Growth in Cryptococcus neoformans , Yu Min Jung

Application of Crowdsourcing and Machine Learning to Predict Sentiments in Textual Student Feedback in Large Computer Science Classes , Robert Kasumba

Measuring the Effectiveness of Light Concentration with the Catoptric Surface , Samatha Kodali

Applying HLS to FPGA Data Preprocessing in the Advanced Particle-astrophysics Telescope , Meagan Konst

Application of Neural Networks to Predict Patient-Specific Cellular Parameters in Computational Cardiac Models , Chang Hi Lee

Scalable Software Infrastructure for the Lab and a Specific Investigation of the Yeast Transcription Factor Eds1 , Chase Mateusiak

Design & Analysis of Mixed-mode Integrated Circuit for Pulse-shape Discrimination , Bryan Orabutt

Modeling Metastasis in Breast Cancer Patients Using EHR Data, the Area Deprivation Index (ADI), and Machine Learning Models , Vishesh Patel

Speeding up the quantification of Contrast Sensitivity functions using Multidimensional Bayesian Active Learning , Shohaib Shaffiey

Integrating Physical Models and Deep Priors for Computational Imaging , Yu Sun

Human-Centered Machine Learning: Algorithm Design and Human Behavior , Wei Tang

Scheduling for High Throughput and Small Latency in Parallel and Distributed Systems , Zhe Wang

Design and Analysis of Strategic Behavior in Networks , Sixie Yu

Geometric Algorithms for Modeling Plant Roots from Images , Dan Zeng

Theses/Dissertations from 2021 2021

Using Computer Vision to Track Anatomical Structures During Cochlear Implant Surgery , Nicholas Bach

Bayesian Quadrature with Prior Information: Modeling and Policies , Henry Chai

Machine Learning in Complex Scientific Domains: Hospitalization Records, Drug Interactions, Predictive Modeling and Fairness for Class Imbalanced Data , Arghya Datta

Improving additional adversarial robustness for classification , Michael Guo

Deep learning for automatic microscopy image analysis , Shenghua He

Control Flow Integrity for Real-time Embedded Systems , Yuqian Huo

Mapping Transcription Factor Networks and Elucidating Their Biological Determinants , Yiming Kang

Predicting Patient Outcomes with Machine Learning for Diverse Health Data , Dingwen Li

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Home > College of Natural Sciences > COMPUTERSCI-ENGINEERING > COMPUTERSCI-ENGINEERING-ETD

Computer Science and Engineering Theses, Projects, and Dissertations

Theses/projects/dissertations from 2023 2023.

CLASSIFICATION OF LARGE SCALE FISH DATASET BY DEEP NEURAL NETWORKS , Priyanka Adapa

GEOSPATIAL WILDFIRE RISK PREDICTION USING DEEP LEARNING , Abner Alberto Benavides

HUMAN SUSPICIOUS ACTIVITY DETECTION , Nilamben Bhuva

MAX FIT EVENT MANAGEMENT WITH SALESFORCE , AKSHAY DAGWAR

MELANOMA DETECTION BASED ON DEEP LEARNING NETWORKS , Sanjay Devaraneni

Heart Disease Prediction Using Binary Classification , Virendra Sunil Devare

CLASSIFICATION OF THORAX DISEASES FROM CHEST X-RAY IMAGES , Sharad Jayusukhbhai Dobariya

WEB BASED MANAGEMENT SYSTEM FOR HOUSING SOCIETY , Likhitha Reddy Eddala

Sales and Stock Management System , Rashmika Gaddam Ms

CONTACTLESS FOOD ORDERING SYSTEM , Rishivar Kumar Goli

RESTAURANT MANAGEMENT WEBSITE , Akhil Sai Gollapudi

DISEASE OF LUNG INFECTION DETECTION USING CNN MODEL -BAYESIAN OPTIMIZATION , poojitha gutha

DATA POISONING ATTACKS ON PHASOR MEASUREMENT UNIT DATA , Rutuja Sanjeev Haridas

CRIME MAPPING ANALYSIS USING WEB APPLICATION. , Lavanya Krishnappa

A LONG-TERM FUNDS PREDICTOR BASED ON DEEP LEARNING , SHUIYI KUANG

LIVER SEGMENTATION AND LESION DETECTION IN MEDICAL IMAGES USING A DEEP LEARNING-BASED U-NET MODEL , Kaushik Mahida

PHASOR MEASUREMENT UNIT DATA VISUALIZATION , Nikhila Mandava

TWITTER POLICING , Hemanth Kumar Medisetty

TRANSACTION MANAGEMENT SYSYEM FOR A PUBLISHER , HASSAIN SHAREEF MOHAMMED JR

LOBANGU: AN OPTICAL CHARACTER RECOGNITION RECEIPT MANAGEMENT APP FOR HEALTH CENTER PHARMACIES IN THE D.R.CONGO AND SURROUNDING EASTERN AFRICAN COUNTRIES , Bénis Munganga

PREDICTIVE MODEL FOR CFPB CONSUMER COMPLAINTS , Vyshnavi Nalluri

REVIEW CLASSIFICATION USING NATURAL LANGUAGE PROCESSING AND DEEP LEARNING , Brian Nazareth

Brain Tumor Detection Using MRI Images , Mayur Patel

QUIZ WEB APPLICATION , Dipti Rathod

HYPOTHYROID DISEASE ANALYSIS BY USING MACHINE LEARNING , SANJANA SEELAM

Pillow Based Sleep Tracking Device Using Raspberry Pi , Venkatachalam Seviappan

FINSERV ANDROID APPLICATION , Harsh Piyushkumar Shah

AUTOMATED MEDICAL NOTES LABELLING AND CLASSIFICATION USING MACHINE LEARNING , Akhil Prabhakar Thota

GENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF MACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET , Asitha Thumpati

GOVERNMENT AID PORTAL , Darshan Togadiya

GENERAL POPULATION PROJECTION MODEL WITH CENSUS POPULATION DATA , Takenori Tsuruga

LUNG LESION SEGMENTATION USING DEEP LEARNING APPROACHES , Sree Snigdha Tummala

DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING , Saranya Valleri

Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation , Peter Wright

Theses/Projects/Dissertations from 2022 2022

LEARN PROGRAMMING IN VIRTUAL REALITY? A PROJECT FOR COMPUTER SCIENCE STUDENTS , Benjamin Alexander

LUNG CANCER TYPE CLASSIFICATION , Mohit Ramajibhai Ankoliya

HIGH-RISK PREDICTION FOR COVID-19 PATIENTS USING MACHINE LEARNING , Raja Kajuluri

IMPROVING INDIA’S TRAFFIC MANAGEMENT USING INTELLIGENT TRANSPORTATION SYSTEMS , Umesh Makhloga

DETECTION OF EPILEPSY USING MACHINE LEARNING , Balamurugan Murugesan

SOCIAL MOBILE APPLICATION: UDROP , Mahmoud Oraiqat

Improved Sensor-Based Human Activity Recognition Via Hybrid Convolutional and Recurrent Neural Networks , Sonia Perez-Gamboa

College of Education FileMaker Extraction and End-User Database Development , Andrew Tran

DEEP LEARNING EDGE DETECTION IN IMAGE INPAINTING , Zheng Zheng

Theses/Projects/Dissertations from 2021 2021

A General Conversational Chatbot , Vipin Nambiar

Verification System , Paras Nigam

DESKTOP APPLICATION FOR THE PUZZLE BOARD GAME “RUSH HOUR” , Huanqing Nong

Ahmedabad City App , Rushabh Picha

COMPUTER SURVEILLANCE SYSTEM USING WI-FI FOR ANDROID , Shashank Reddy Saireddy

ANDROID PARKING SYSTEM , Vishesh Reddy Sripati

Sentiment Analysis: Stock Index Prediction with Multi-task Learning and Word Polarity Over Time , Yue Zhou

Theses/Projects/Dissertations from 2020 2020

BUBBLE-IN DIGITAL TESTING SYSTEM , Chaz Hampton

FEEDBACK REVIEW SYSTEM USING SENTIMENT ANALYSIS , Vineeth Kukkamalla

WEB APPLICATION FOR MOVIE PERFORMANCE PREDICTION , Devalkumar Patel

Theses/Projects/Dissertations from 2019 2019

REVIEWS TO RATING CONVERSION AND ANALYSIS USING MACHINE LEARNING TECHNIQUES , Charitha Chanamolu

EASY EXAM , SARTHAK DABHI

EXTRACT TRANSFORM AND LOADING TOOL FOR EMAIL , Amit Rajiv Lawanghare

VEHICLE INFORMATION SYSTEM USING BLOCKCHAIN , Amey Zulkanthiwar

Theses/Projects/Dissertations from 2018 2018

USING AUTOENCODER TO REDUCE THE LENGTH OF THE AUTISM DIAGNOSTIC OBSERVATION SCHEDULE (ADOS) , Sara Hussain Daghustani

California State University, San Bernardino Chatbot , Krutarth Desai

ORGANIZE EVENTS MOBILE APPLICATION , Thakshak Mani Chandra Reddy Gudimetla

SOCIAL NETWORK FOR SOFTWARE DEVELOPERS , Sanket Prabhakar Jadhav

VIRTUALIZED CLOUD PLATFORM MANAGEMENT USING A COMBINED NEURAL NETWORK AND WAVELET TRANSFORM STRATEGY , Chunyu Liu

INTER PROCESS COMMUNICATION BETWEEN TWO SERVERS USING MPICH , Nagabhavana Narla

SENSOR-BASED HUMAN ACTIVITY RECOGNITION USING BIDIRECTIONAL LSTM FOR CLOSELY RELATED ACTIVITIES , Arumugam Thendramil Pavai

NEURAL NETWORK ON VIRTUALIZATION SYSTEM, AS A WAY TO MANAGE FAILURE EVENTS OCCURRENCE ON CLOUD COMPUTING , Khoi Minh Pham

EPICCONFIGURATOR COMPUTER CONFIGURATOR AND CMS PLATFORM , IVO A. TANTAMANGO

STUDY ON THE PATTERN RECOGNITION ENHANCEMENT FOR MATRIX FACTORIZATIONS WITH AUTOMATIC RELEVANCE DETERMINATION , hau tao

Theses/Projects/Dissertations from 2017 2017

CHILDREN’S SOCIAL NETWORK: KIDS CLUB , Eiman Alrashoud

MULTI-WAY COMMUNICATION SYSTEM , S. Chinnam

WEB APPLICATION FOR GRADUATE COURSE RECOMMENDATION SYSTEM , Sayali Dhumal

MOBILE APPLICATION FOR ATTENDANCE SYSTEM COYOTE-ATTENDANCE , Sindhu Hari

WEB APPLICATION FOR GRADUATE COURSE ADVISING SYSTEM , Sanjay Karrolla

Custom T-Shirt Designs , Ranjan Khadka

STUDENT CLASS WAITING LIST ENROLLMENT , AISHWARYA LACHAGARI

ANDROID MOBILE APPLICATION FOR HOSPITAL EXECUTIVES , Vihitha Nalagatla

PIPPIN MACHINE , Kiran Reddy Pamulaparthy

SOUND MODE APPLICATION , Sindhuja Pogaku

I2MAPREDUCE: DATA MINING FOR BIG DATA , Vishnu Vardhan Reddy Sherikar

COMPARING AND IMPROVING FACIAL RECOGNITION METHOD , Brandon Luis Sierra

NATURAL LANGUAGE PROCESSING BASED GENERATOR OF TESTING INSTRUMENTS , Qianqian Wang

AUTOMATIC GENERATION OF WEB APPLICATIONS AND MANAGEMENT SYSTEM , Yu Zhou

Theses/Projects/Dissertations from 2016 2016

CLOTH - MODELING, DEFORMATION, AND SIMULATION , Thanh Ho

CoyoteLab - Linux Containers for Educational Use , Michael D. Korcha

PACKET FILTER APPROACH TO DETECT DENIAL OF SERVICE ATTACKS , Essa Yahya M Muharish

DATA MINING: TRACKING SUSPICIOUS LOGGING ACTIVITY USING HADOOP , Bir Apaar Singh Sodhi

Theses/Projects/Dissertations from 2015 2015

APPLY DATA CLUSTERING TO GENE EXPRESSION DATA , Abdullah Jameel Abualhamayl Mr.

Density Based Data Clustering , Rayan Albarakati

Developing Java Programs on Android Mobile Phones Using Speech Recognition , Santhrushna Gande

THE DESIGN AND IMPLEMENTATION OF AN ADAPTIVE CHESS GAME , Mehdi Peiravi

CALIFORNIA STATE UNIVERSITY SAN BERNARDINO WiN GPS , Francisco A. Ron

ESTIMATION ON GIBBS ENTROPY FOR AN ENSEMBLE , Lekhya Sai Sake

A WEB-BASED TEMPERATURE MONITORING SYSTEM FOR THE COLLEGE OF ARTS AND LETTERS , Rigoberto Solorio

ANTICS: A CROSS-PLATFORM MOBILE GAME , Gerren D. Willis

Theses/Projects/Dissertations from 2014 2014

Introducing Non-Determinism to the Parallel C Compiler , Rowen Concepcion

THE I: A CLIENT-BASED POINT-AND-CLICK PUZZLE GAME , Aldo Lewis

Interactive Student Planner Application , NII TETTEH TACKIE YARBOI

ANDROID MOBILE APPLICATION FOR CREST COMMUNITY CHURCH IN RIVERSIDE , Ran Wei

Proton Computed Tomography: Matrix Data Generation Through General Purpose Graphics Processing Unit Reconstruction , micah witt

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Computer Science Senior Theses

Theses/dissertations from 2023 2023.

Utilizing Natural Language Processing for Automated Clinical Text Review: Identification of Care Preference Documentation in Patients’ Discharge Summaries , Saksham Arora

GeoRCF-GN: Geography-Aware State Prediction in Dynamic Networks , Barkin Cavdaroglu

Connecting Linguistic Expressions and Pain Relief Through Transformer Model Construction and Analysis , Sarah M. Chacko

Cyclic Mixed-Radix Dense Gray Codes , Jessica Cheng

NOVEL GENERALIZATIONS AND ALGORITHMS FOR THE MAX-k-COVERAGE PROBLEM , Luc Cote

An Empirical Study of Locality-Sensitive Hashing to Approximate the Minimum Spanning Tree , Elizabeth Crocker

Investigating English-Language Dialect-Adjusted Models , Samiha Datta

Interpreting Business Strategy and Market Dynamics: A Multi-Method AI Approach , Lobna Jbeniani

An Algorithmic Approach to Jazz Guitar Voice-Leading Chord Fingerings , Matthew B. Keating

Counterfactual Replacement Analysis for Interpretation of Blackbox Sexism Classification Models , Anders Knospe

Sarcasm Detection in English and Arabic Tweets Using Transformer Models , Rishik Lad

Unmasking Bias: Investigating Strategies for Minimizing Discrimination in AI Models , Julia L. Martin

Data-Optimized Spatial Field Predictions for Robotic Adaptive Sampling: A Gaussian Process Approach , Zachary Nathan

Predictive AI for the S&P 500 Index , Jacqueline Rose Perry

Deep Learning for Skin Photoaging , Gokul Srinivasan

Utilizing Mixed Graphical Network Models to Explore Parent Psychological Symptoms and Their Centrality to Parent Mental Health in Households with High Child Screen Usage , Piper F. Stacey, Nicholas C. Jacobson, and Damien Lekkas

Exploring Improvements to Space-Bounded Derandomization from Better Pseudorandom Generators , Boxian Wang

Stereotypes and Language Models: Understanding how language models encode stereotypes, debiasing language models, and examining how stereotypes affect conversations , Brian C. Wang

The Dilemma of Disclosure: Designing Interpersonal Informatics Tools for Mood Tracking , Daniel Earl Westphal

Theses/Dissertations from 2022 2022

Towards a Computational Model of Narrative on Social Media , Anne Bailey

Entity Based Sentiment Analysis for Textual Health Advice , Dae Lim Chung

Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems , Daniel DiPietro

Leveraging Context Patterns for Medical Entity Classification , Garrett Johnston

Destabilizing Terrorist Networks , John Keane

Determining American Sign Language Joint Trajectory Similarity Using Dynamic Time Warping (DTW) , Rohith Mandavilli

Designing narrative-based interfaces for collective action: A case study using Amazon, climate change, and consumer behavior , Catherine Parnell

Machine Learning and the Network Analysis of Ethereum Trading Data , Santosh Sivakumar

TorSH: Obfuscating consumer Internet-of-Things traffic with a collaborative smart-home router network , Adam Vandenbussche

Analyzing Behavioral Adaptation to COVID-19 And Return To Pre-Pandemic Baselines in a Cohort of College Seniors , Vlado Vojdanovski

Theses/Dissertations from 2021 2021

The Discrete-Event Modeling of Administrative Claims (DEMAC) System: Dynamically modeling the U.S. healthcare delivery system with Medicare claims data to improve end-of-life care , Rachael Chacko

An inside vs. outside classification system for Wi-Fi IoT devices , Paul Gralla

Exploring the Long Tail , Joseph H. Hajjar

Counting and Sampling Small Structures in Graph and Hypergraph Data Streams , Themistoklis Haris

Exploring the Relationship Between Intrinsic Motivation and Receptivity to mHealth Interventions , Sarah Hong

Lexical Complexity Prediction with Assembly Models , Aadil Islam

Deterring Intellectual Property Thieves: Algorithmic Generation of Adversary-Aware Fake Knowledge Graphs , Snow Kang

Exploring the Use of Social Media to Infer Relationships Between Demographics, Psychographics and Vaccine Hesitancy , Abhimanyu Kapur

Fine-Grained Detection of Hate Speech Using BERToxic , Yakoob Khan

Examining Polarized COVID-19 Twitter Discussion Using Inverse Reinforcement Learning , Sydney Lister

A configurable social network for running IRB-approved experiments , Mihovil Mandic

Improving existing methods for calculating embodied carbon emissions in trade through feature discovery: an information theoretic approach , Sam Morton

Impulse Method for Shallow Water Simulation , Evan Muscatel

Classifying Common Knee Rehabilitation Exercise Mistakes Using IMU Data , Fedor Myagkov

Object Manipulation with Modular Planar Tensegrity Robots , Maxine Perroni-Scharf

Physically Based Rendering Techniques to Visualize Thin-Film Smoothed Particle Hydrodynamics Fluid Simulations , Aditya H. Prasad

Interpreting Attention-Based Models for Natural Language Processing , Steven J. Signorelli Jr

Implementation and Optimization of PEG Parsers for Use on FPGAs , Shikhar Sinha

Informative Journaling Application (Unwind) for Ambient Awareness on Mood in Young Adults to Reduce Anxiety and Depression: A randomized, placebo-controlled trial , Jalen Wang

Producing Easy-to-Verify Proofs of Linearizability , Ugur Yavuz

Theses/Dissertations from 2020 2020

Information Network Navigation , Ryan W. Blankemeier

Memory constraints in cued-recall-dependent learning and performance tasks: Why do humans struggle with simple yet memory-intensive tasks? , Jack L. Burgess

Push-relabel algorithms for computing perfect matchings of regular bipartite multigraphs , Benjamin J. Coleman

A Clustering Algorithm for Early Prediction of Controversial Reddit Posts , Abenezer Daniel Dara

Towards Ryser's Conjecture: Bounds on the Cardinality of Partitioned Intersecting Hypergraphs , Anna E. Dodson

Mining Academic Publications to Predict Automation , Elena A. Doty

Digital Legacies for Digital Natives , Katie Goldstein

Predicting Influencer Virality on Twitter , Danah K. Han

A Critical Audit of Accuracy and Demographic Biases within Toxicity Detection Tools , Jiachen Jiang

Probabilistic Error Upper Bounds For Distributed Statistical Estimation , Matthew Jin

Learning Humor Through AI: A Study on New Yorker's Cartoon Caption Contests Using Deep Learning , Ray Tianyu Li

A computational approach to analyzing and detecting trans-exclusionary radical feminists (TERFs) on Twitter , Christina T. Lu

VR-Notes: A Perspective-Based, Multimedia Annotation System in Virtual Reality , Justin Luo

Query Free Adversarial Transfer via Undertrained Surrogates , Christopher S. Miller

Restoring Humanity to Those Dying Below: An Inquiry Concerning the Ethics of Autonomous Weapons Systems , Juliette A. Pouchol

Regression-based motion planning , Josiah K. Putman

Autonomous Eye Tracking in Octopus bimaculoides , Mark Andrew Taylor

Label Noise Reduction Without Assumptions , Jason Wei

Automatic Generation of Input Grammars Using Symbolic Execution , Linda Xiao

Theses/Dissertations from 2019 2019

Convergence Times of Decentralized Graph Coloring Algorithms , Paul B. de Supinski

Orthogonal Array Sampling for Monte Carlo Based Rendering , Afnan Enayet

Fair Algorithms for Clustering , Nicolas J. Flores

Evaluating the Efficacy of Magnetometer-Based Vehicle Sensors , Luke A. Hudspeth

Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts , Steven Jiang

Twitter Bot Detection in the Context of the 2018 US Senate Elections , Wes Kendrick

Application of Binary Search to Video Annotation and Behavior Tracking for the Social Sciences , Caitlyn Lee

Is Augmented Reality in Denial of the Convention? Examining the Presence of Uncanny Valley in Augmented Reality , Sung Jun Park

Comparing brain-like representations learned by vanilla, residual, and recurrent CNN architectures , Cara E. Van Uden

Theses/Dissertations from 2018 2018

Securing, Standardizing, and Simplifying Electronic Health Record Audit Logs Through Permissioned Blockchain Technology , Jessie Anderson

IPv6 Security Issues in Linux and FreeBSD Kernels: A 20-year Retrospective , Jack R. Cardwell

Thinking Inside the Box: Converting Encapsulated PostScript to Scalable Vector Graphics , Trevor L. Davis

Overlaying Virtual Scale Models on Real Environments Without the Use of Peripherals , George Hito

Robotic Laundry Folding , Evan Honnold

Balancing patient control and practical access policy for electronic health records via blockchain technology , Elena Horton

Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization , Bruno Korbar

Full and Dense Cyclic Gray codes in Mixed Radices , Devina Kumar

Navigating Virtual Reality Using Only Your Gazes and Mind , Christopher J. Kymn

The Next Generation of EMPRESS: A Metadata Management System For Accelerated Scientific Discovery at Exascale , Margaret R. Lawson

DartDraw: The Design and Implementation of Global State Management, User Interaction Management, and Text in a React-Redux Drawing Application , Collin M. McKinney

PyMOL Plugin to Build Protein Structures Based on Natural TERM Overlaps , Noah T. Paravicini

Reflections on Building DartDraw: A React + Redux Vector-Based Graphics Editor , Elisabeth G. Pillsbury

Theses/Dissertations from 2017 2017

Accuracy and Racial Biases of Recidivism Prediction Instruments , Julia J. Dressel

Dense Gray Codes in Mixed Radices , Jessica C. Fan

Using Computational Models to Understand ASD Facial Expression Recognition Patterns , Irene L. Feng

OpenCollab: A Blockchain Based Protocol to Incentivize Open Source Software Development , Yondon Fu

ShareABEL: Secure Sharing of mHealth Data through Cryptographically-Enforced Access Control , Emily Greene

Cryptographic transfer of sensor data from the Amulet to a smartphone , David B. Harmon

A HoloLens Application to Aid People who are Visually Impaired in Navigation Tasks , Jonathan L. Huang

NovenaNetwork: A Catholic Social Media iOS Application for Praying Novenas as a Community , Marissa Le Coz

Assemble.live: Designing for Schisms in Large Groups in Audio/Video Calls , Benjamin P. Packer

Scene classification from degraded images: comparing human and computer vision performance , Tim M. Tadros

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Home > Robinson College of Business > Computer Information Systems > CIS_DISS

Computer Information Systems Dissertations

Dissertations from 2023 2023.

Investigating the Effectiveness of Algorithmic Interventions in Health Decision Making , Jung min Lee

Dissertations from 2022 2022

Organizational Intelligence in Digital Innovation: Evidence from Georgia State University , Khaleed M. Fuad

The Impact of Corporate Engagement in Open-Source Enterprise Systems Community on Release Performance , Peiwei Li

Essays on IT and Platform Governance from External Stakeholder Perspectives , Junyoung Park

Deciding to Fail: Three Essays about Self-Interest in Systems , Pengcheng Wang

Investigating Decentralization, Incentives, and Configurations of Blockchain Governance , Rongen Zhang

Dissertations from 2021 2021

Decision-Making Dilemma in Human-Automation Interaction: Who Should Grasp Authority, Human or Intelligent Systems? , Xiaocong Cui

Empirical Essays on Crowd-based Digital Platforms and Product Innovation Performance , Heeseung Lee

Collective Attention Allocation for Innovation Productivity in Open-Source Software Projects: A Configurational Perspective , Yanran Liu

Network Effects and Decentralized Governance in Public Blockchain Ecosystems , Yukun Yang

Dissertations from 2020 2020

Managing Technical Debt in Agile Software Development Projects , Maheshwar Boodraj

Essays on Motivations and Motivational Affordances in the Context of Health Information Technology , Hyoungyong Choi

Technical and Behavioral Interventions for Medication Adherence through Mobile Health , Xinying Liu

Conversational AI Agents: Investigating AI-Specific Characteristics that Induce Anthropomorphism and Trust in Human-AI Interaction , Kambiz Saffarizadeh

Dissertations from 2019 2019

Explaining the Privacy Paradox through Identifying Boundary Conditions of the Relationship between Privacy Concerns and Disclosure Behaviors , Tawfiq Alashoor

Unravelling the Influence of Online Social Context on Consumer Health Information Technology (CHIT) Implementations , Amrita George

Essays on Technology in Presence of Globalization , Joshua Madden

Three Essays on Digital Innovation from an Intellectual Property Rights Perspective , Zhitao Yin

Dissertations from 2018 2018

A "Practice-Based" Theory of the Firm: A Practice Theory Perspective to Organizational Strategy Development and Knowledge Management , Sayed Mahdi Almohri

Extracting Business Value of IT and Identifying IT Innovation in Large Institutional Settings Undergoing Regulatory Change , Jessica Pye

Three Empirical Essays on Health Informatics and Analytics , Youyou Tao

Classification And Analysis Of Mobile Health Evaluation Through Taxonomy and Method Development , Alan T. Yang

Dissertations from 2017 2017

Integrating online-offline interactions to explain societal challenges , Christine Abdalla Mikhaeil

Dissertations from 2016 2016

Managing Enterprise Systems Post Implementation through Competency Centers: An Inquiry into Assemblage and Emergence , Arun Aryal

Essays on Health Information Technology: Insights from Analyses of Big Datasets , Langtao Chen

Three Essays on the Empowerment Role of Information Technology in Healthcare Services , Liwei Chen

Towards a Better Comprehension of Adaptation to Information and Communication Technologies: A Multi-level Approach , Najma Saidani

Smart Interventions for Effective Medication Adherence , Neetu Singh

Dissertations from 2015 2015

An Event-based Analysis Framework for Open Source Software Development Projects , Tianjie Deng

Affect and Decision Making in Troubled Information Technology Projects , Hyung Koo Lee

Risks, Controls and Business Value of IT-Enabled Interfirm and Intrafirm Processes , Chaitanya Sambhara

Dissertations from 2014 2014

Traveling of Requirements in the Development of Packaged Software: An Investigation of Work Design and Uncertainty , Thomas Gregory

Genres of Inquiry in Design Science Research: Applying Search Conference to Contemporary Information Systems Security Theory , Mala Kaul

The Role of Regret and Its Applications in IS Decision Making , EunHee Park

New Perspectives on Implementing Health Information Technology , Sumantra Sarkar

Dissertations from 2013 2013

An Investigation of the Relationships between Goals and Software Project Escalation: Insights from Goal Setting and Goal Orientation Theories , Jong Seok Lee

The Impact of IT-Enabled and Team Relational Coordination on Patient Satisfaction , Darryl S. Romanow

Dissertations from 2012 2012

Knowledge Worker Behavioral Responses and Job Outcomes in Mandatory Enterprise System Use Contexts , Robert Hornyak

The Management of Distance in Distributed-work , Chauvet Mathieu

Realizing Shared Services - A Punctuated Process Analysis of a Public IT Department , Tim Olsen

A Requirements-Based Exploration of Open-Source Software Development Projects – Towards a Natural Language Processing Software Analysis Framework , Radu Vlas

Dissertations from 2011 2011

Health Information Systems Affordances: How the Materiality of Information Technology Enables and Constrains the Work Practices of Clinicians , Chad Anderson

Towards Information Polycentricity Theory: Investigation of a Hospital Revenue Cycle , Rajendra Singh

Examining Scholarly Influence: A Study in Hirsch Metrics and Social Network Analysis , Hirotoshi Takeda

Dissertations from 2010 2010

What Support Does Information and Communication Technology (ICT) Offer to Organizational Improvisation During Crisis Response ? , Anouck Adrot

Quality in IS Research: Theory and Validation of Constructs for Service, Information, and System , Yi Ding

Effect of Digital Enablement of Business-to-Business Exchange on Customer Outcomes: The Role of Information Systems Quality and Relationship Characteristics , Stephen M. Du

How and Why do IT Professionals Leave their Salaried Employment to Start a Company? , Mourmant Gaetan

A Novel Approach to Ontology Management , Jong Woo Kim

Investigating the Relationship between IT and Organizations: A Research Trilogy , Benoit Raymond

The Role of Stakeholder Perceptions during IT-Enabled Change: An Investigation of Technology Frames of Reference in a Sales Process Innovation Project , Brett Young

Dissertations from 2009 2009

An Examination of the Deaf Effect Response to Bad News Reporting in Information Systems Projects , Michael John Cuellar

Exploring IT-Based Knowledge Sharing Practices: Representing Knowledge within and across Projects , Alina Maria Dulipovici

Studies on Adaptation to Information Systems: Multiple Roles and Coping Strategies , Christophe Elie-Dit-Cosaque

A User-Centered Perspective on Information Technologies in Museums , Jessie Pallud

Trusting IT Artifacts: How Trust Affects our Use of Technology , Anthony Osborn Vance

Controlling Telework: An Exploratory Investigation of Portfolios of Control Applied to Remote Knowledge Workers , Jijie Wang

A Multidimensional and Visual Exploration Approach to Project Portfolio Management , Guangzhi Zheng

Dissertations from 2008 2008

Digital Integration: Understanding the Concept and its Environmental Predictors , Ricardo M. Checchi

Managing the Tension between Standardization and Customization in IT-enabled Service Provisioning: A Sensemaking Perspective , Mark O. Lewis

Patient Monitoring via Mobile Ad Hoc Network: Power Management, Reliability, and Delays , Sweta Sneha

Dissertations from 2007 2007

A Contextualist Approach to Telehealth Innovations , Sunyoung Cho

Escalation of Commitment in Information Technology Projects: A Goal Setting Theory Perspective , Vijay Kasi

Generating User-centric Dynamic and Adaptable Knowledge Models for World Wide Web , Li Lei

Improving Practices in a Small Software Firm: An Ambidextrous Perspective , Nannette Napier

Bad News Reporting on Troubled IT Projects: The Role of Personal, Situational, and Organizational Factors , Chongwoo Park

Individual-Technology Fit: Matching Individual Characteristics and Features of Biometric Interface Technologies with Performance , Adriane Randolph

Dissertations from 2006 2006

A Study of the Quality of Service in Group Oriented Mobile Transactions , Punit Ahluwalia

Leverage Points for Addressing Digital Inequality: An Extended Theory of Planned Behavior Perspective , JJ Po-An Hsieh

Exploratory and Exploitative Knowledge Sharing in Interorganizational Relationships , Ghiyoung Im

Business Process Integration: A Socio-Cognitive Process Model and a Support System , Radhika Jain

Upgrading Packaged Software: An Exploratory Study of Decisions, Impacts, and Coping Strategies from the Perspectives of Stakeholders , Huoy Min Khoo

A Process to Reuse Experiences via Narratives Among Software Project Managers , Stacie Clark Petter

Dissertations from 2005 2005

New Perspectives on the System Usage Construct , Andrew Burton-Jones

Modeling Dynamics in Agile Software Development , Lan Cao

Reuse of Scenario Specifications Using an Automated Relational Learner , Han-Gyun Woo

Dissertations from 2004 2004

Access Anytime Anyplace: An Empircal Investigation of Patterns of Technology Use in Nomadic Computing Environments , Karlene C. Cousins

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A list of completed theses and new thesis topics from the Computer Vision Group.

Are you about to start a BSc or MSc thesis? Please read our instructions for preparing and delivering your work.

Below we list possible thesis topics for Bachelor and Master students in the areas of Computer Vision, Machine Learning, Deep Learning and Pattern Recognition. The project descriptions leave plenty of room for your own ideas. If you would like to discuss a topic in detail, please contact the supervisor listed below and Prof. Paolo Favaro to schedule a meeting. Note that for MSc students in Computer Science it is required that the official advisor is a professor in CS.

AI deconvolution of light microscopy images

Level: master.

Background Light microscopy became an indispensable tool in life sciences research. Deconvolution is an important image processing step in improving the quality of microscopy images for removing out-of-focus light, higher resolution, and beter signal to noise ratio. Currently classical deconvolution methods, such as regularisation or blind deconvolution, are implemented in numerous commercial software packages and widely used in research. Recently AI deconvolution algorithms have been introduced and being currently actively developed, as they showed a high application potential.

Aim Adaptation of available AI algorithms for deconvolution of microscopy images. Validation of these methods against state-of-the -art commercially available deconvolution software.

Material and Methods Student will implement and further develop available AI deconvolution methods and acquire test microscopy images of different modalities. Performance of developed AI algorithms will be validated against available commercial deconvolution software.

• Al algorithm development and implementation: 50%.
• Data acquisition: 10%.
• Comparison of performance: 40 %.

Requirements

• Interest in imaging.
• Solid knowledge of AI.
• Good programming skills.

Supervisors Paolo Favaro, Guillaume Witz, Yury Belyaev.

Institutes Computer Vison Group, Digital Science Lab, Microscopy imaging Center.

Contact Yury Belyaev, Microscopy imaging Center, [email protected] , + 41 78 899 0110.

Instance segmentation of cryo-ET images

Level: bachelor/master.

In the 1600s, a pioneering Dutch scientist named Antonie van Leeuwenhoek embarked on a remarkable journey that would forever transform our understanding of the natural world. Armed with a simple yet ingenious invention, the light microscope, he delved into uncharted territory, peering through its lens to reveal the hidden wonders of microscopic structures. Fast forward to today, where cryo-electron tomography (cryo-ET) has emerged as a groundbreaking technique, allowing researchers to study proteins within their natural cellular environments. Proteins, functioning as vital nano-machines, play crucial roles in life and understanding their localization and interactions is key to both basic research and disease comprehension. However, cryo-ET images pose challenges due to inherent noise and a scarcity of annotated data for training deep learning models.

Credit: S. Albert et al./PNAS (CC BY 4.0)

To address these challenges, this project aims to develop a self-supervised pipeline utilizing diffusion models for instance segmentation in cryo-ET images. By leveraging the power of diffusion models, which iteratively diffuse information to capture underlying patterns, the pipeline aims to refine and accurately segment cryo-ET images. Self-supervised learning, which relies on unlabeled data, reduces the dependence on extensive manual annotations. Successful implementation of this pipeline could revolutionize the field of structural biology, facilitating the analysis of protein distribution and organization within cellular contexts. Moreover, it has the potential to alleviate the limitations posed by limited annotated data, enabling more efficient extraction of valuable information from cryo-ET images and advancing biomedical applications by enhancing our understanding of protein behavior.

Methods The segmentation pipeline for cryo-electron tomography (cryo-ET) images consists of two stages: training a diffusion model for image generation and training an instance segmentation U-Net using synthetic and real segmentation masks.

    1. Diffusion Model Training:         a. Data Collection: Collect and curate cryo-ET image datasets from the EMPIAR             database (https://www.ebi.ac.uk/empiar/).         b. Architecture Design: Select an appropriate architecture for the diffusion model.         c. Model Evaluation: Cryo-ET experts will help assess image quality and fidelity             through visual inspection and quantitative measures     2. Building the Segmentation dataset:         a. Synthetic and real mask generation: Use the trained diffusion model to generate             synthetic cryo-ET images. The diffusion process will be seeded from either a real             or a synthetic segmentation mask. This will yield to pairs of cryo-ET images and             segmentation masks.     3. Instance Segmentation U-Net Training:         a. Architecture Design: Choose an appropriate instance segmentation U-Net             architecture.         b. Model Evaluation: Evaluate the trained U-Net using precision, recall, and F1             score metrics.

By combining the diffusion model for cryo-ET image generation and the instance segmentation U-Net, this pipeline provides an efficient and accurate approach to segment structures in cryo-ET images, facilitating further analysis and interpretation.

References     1. Kwon, Diana. "The secret lives of cells-as never seen before." Nature 598.7882 (2021):         558-560.     2. Moebel, Emmanuel, et al. "Deep learning improves macromolecule identification in 3D         cellular cryo-electron tomograms." Nature methods 18.11 (2021): 1386-1394.     3. Rice, Gavin, et al. "TomoTwin: generalized 3D localization of macromolecules in         cryo-electron tomograms with structural data mining." Nature Methods (2023): 1-10.

Contacts Prof. Thomas Lemmin Institute of Biochemistry and Molecular Medicine Bühlstrasse 28, 3012 Bern ( [email protected] )

Prof. Paolo Favaro Institute of Computer Science Neubrückstrasse 10 3012 Bern ( [email protected] )

Adding and removing multiple sclerosis lesions with to imaging with diffusion networks

Background multiple sclerosis lesions are the result of demyelination: they appear as dark spots on t1 weighted mri imaging and as bright spots on flair mri imaging.  image analysis for ms patients requires both the accurate detection of new and enhancing lesions, and the assessment of  atrophy via local thickness and/or volume changes in the cortex.  detection of new and growing lesions is possible using deep learning, but made difficult by the relative lack of training data: meanwhile cortical morphometry can be affected by the presence of lesions, meaning that removing lesions prior to morphometry may be more robust.  existing ‘lesion filling’ methods are rather crude, yielding unrealistic-appearing brains where the borders of the removed lesions are clearly visible., aim: denoising diffusion networks are the current gold standard in mri image generation [1]: we aim to leverage this technology to remove and add lesions to existing mri images.  this will allow us to create realistic synthetic mri images for training and validating ms lesion segmentation algorithms, and for investigating the sensitivity of morphometry software to the presence of ms lesions at a variety of lesion load levels., materials and methods: a large, annotated, heterogeneous dataset of mri data from ms patients, as well as images of healthy controls without white matter lesions, will be available for developing the method.  the student will work in a research group with a long track record in applying deep learning methods to neuroimaging data, as well as experience training denoising diffusion networks..

Nature of the Thesis:

Literature review: 10%

Replication of Blob Loss paper: 10%

Implementation of the sliding window metrics:10%

Training on MS lesion segmentation task: 30%

Extension to other datasets: 20%

Results analysis: 20%

Fig. Results of an existing lesion filling algorithm, showing inadequate performance

Requirements:

Interest/Experience with image processing

Python programming knowledge (Pytorch bonus)

Interest in neuroimaging

Supervisor(s):

PD. Dr. Richard McKinley

Institutes: Diagnostic and Interventional Neuroradiology

Center for Artificial Intelligence in Medicine (CAIM), University of Bern

References: [1] Brain Imaging Generation with Latent Diffusion Models , Pinaya et al, Accepted in the Deep Generative Models workshop @ MICCAI 2022 , https://arxiv.org/abs/2209.07162

Contact : PD Dr Richard McKinley, Support Centre for Advanced Neuroimaging ( [email protected] )

Improving metrics and loss functions for targets with imbalanced size: sliding window Dice coefficient and loss.

Background The Dice coefficient is the most commonly used metric for segmentation quality in medical imaging, and a differentiable version of the coefficient is often used as a loss function, in particular for small target classes such as multiple sclerosis lesions.  Dice coefficient has the benefit that it is applicable in instances where the target class is in the minority (for example, in case of segmenting small lesions).  However, if lesion sizes are mixed, the loss and metric is biased towards performance on large lesions, leading smaller lesions to be missed and harming overall lesion detection.  A recently proposed loss function (blob loss[1]) aims to combat this by treating each connected component of a lesion mask separately, and claims improvements over Dice loss on lesion detection scores in a variety of tasks.

Aim: The aim of this thesisis twofold.  First, to benchmark blob loss against a simple, potentially superior loss for instance detection: sliding window Dice loss, in which the Dice loss is calculated over a sliding window across the area/volume of the medical image.  Second, we will investigate whether a sliding window Dice coefficient is better corellated with lesion-wise detection metrics than Dice coefficient and may serve as an alternative metric capturing both global and instance-wise detection.

Materials and Methods: A large, annotated, heterogeneous dataset of MRI data from MS patients will be available for benchmarking the method, as well as our existing codebases for MS lesion segmentation.  Extension of the method to other diseases and datasets (such as covered in the blob loss paper) will make the method more plausible for publication.  The student will work alongside clinicians and engineers carrying out research in multiple sclerosis lesion segmentation, in particular in the context of our running project supported by the CAIM grant.

Fig. An  annotated MS lesion case, showing the variety of lesion sizes

References: [1] blob loss: instance imbalance aware loss functions for semantic segmentation, Kofler et al, https://arxiv.org/abs/2205.08209

Idempotent and partial skull-stripping in multispectral MRI imaging

Background Skull stripping (or brain extraction) refers to the masking of non-brain tissue from structural MRI imaging.  Since 3D MRI sequences allow reconstruction of facial features, many data providers supply data only after skull-stripping, making this a vital tool in data sharing.  Furthermore, skull-stripping is an important pre-processing step in many neuroimaging pipelines, even in the deep-learning era: while many methods could now operate on data with skull present, they have been trained only on skull-stripped data and therefore produce spurious results on data with the skull present.

High-quality skull-stripping algorithms based on deep learning are now widely available: the most prominent example is HD-BET [1].  A major downside of HD-BET is its behaviour on datasets to which skull-stripping has already been applied: in this case the algorithm falsely identifies brain tissue as skull and masks it.  A skull-stripping algorithm F not exhibiting this behaviour would  be idempotent: F(F(x)) = F(x) for any image x.  Furthermore, legacy datasets from before the availability of high-quality skull-stripping algorithms may still contain images which have been inadequately skull-stripped: currently the only solution to improve the skull-stripping on this data is to go back to the original datasource or to manually correct the skull-stripping, which is time-consuming and prone to error. 

Aim: In this project, the student will develop an idempotent skull-stripping network which can also handle partially skull-stripped inputs.  In the best case, the network will operate well on a large subset of the data we work with (e.g. structural MRI, diffusion-weighted MRI, Perfusion-weighted MRI,  susceptibility-weighted MRI, at a variety of field strengths) to maximize the future applicability of the network across the teams in our group.

Materials and Methods: Multiple datasets, both publicly available and internal (encompassing thousands of 3D volumes) will be available. Silver standard reference data for standard sequences at 1.5T and 3T can be generated using existing tools such as HD-BET: for other sequences and field strengths semi-supervised learning or methods improving robustness to domain shift may be employed.  Robustness to partial skull-stripping may be induced by a combination of learning theory and model-based approaches.

Dataset curation: 10%

Idempotent skull-stripping model building: 30%

Modelling of partial skull-stripping:10%

Extension of model to handle partial skull: 30%

Results analysis: 10%

Fig. An example of failed skull-stripping requiring manual correction

References: [1] Isensee, F, Schell, M, Pflueger, I, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp . 2019; 40: 4952– 4964. https://doi.org/10.1002/hbm.24750

Automated leaf detection and leaf area estimation (for Arabidopsis thaliana)

Correlating plant phenotypes such as leaf area or number of leaves to the genotype (i.e. changes in DNA) is a common goal for plant breeders and molecular biologists. Such data can not only help to understand fundamental processes in nature, but also can help to improve ecotypes, e.g., to perform better under climate change, or reduce fertiliser input. However, collecting data for many plants is very time consuming and automated data acquisition is necessary.

The project aims at building a machine learning model to automatically detect plants in top-view images (see examples below), segment their leaves (see Fig C) and to estimate the leaf area. This information will then be used to determine the leaf area of different Arabidopsis ecotypes. The project will be carried out in collaboration with researchers of the Institute of Plant Sciences at the University of Bern. It will also involve the design and creation of a dataset of plant top-views with the corresponding annotation (provided by experts at the Institute of Plant Sciences).

Contact: Prof. Dr. Paolo Favaro ( [email protected] )

Master Projects at the ARTORG Center

The Gerontechnology and Rehabilitation group at the ARTORG Center for Biomedical Engineering is offering multiple MSc thesis projects to students, which are interested in working with real patient data, artificial intelligence and machine learning algorithms. The goal of these projects is to transfer the findings to the clinic in order to solve today’s healthcare problems and thus to improve the quality of life of patients. Assessment of Digital Biomarkers at Home by Radar.  [PDF] Comparison of Radar, Seismograph and Ballistocardiography and to Monitor Sleep at Home.   [PDF] Sentimental Analysis in Speech.  [PDF] Contact: Dr. Stephan Gerber ( [email protected] )

Internship in Computational Imaging at Prophesee

A 6 month intership at Prophesee, Grenoble is offered to a talented Master Student.

The topic of the internship is working on burst imaging following the work of Sam Hasinoff , and exploring ways to improve it using event-based vision.

A compensation to cover the expenses of living in Grenoble is offered. Only students that have legal rights to work in France can apply.

Anyone interested can send an email with the CV to Daniele Perrone ( [email protected] ).

Using machine learning applied to wearables to predict mental health

This Master’s project lies at the intersection of psychiatry and computer science and aims to use machine learning techniques to improve health. Using sensors to detect sleep and waking behavior has as of yet unexplored potential to reveal insights into health.  In this study, we make use of a watch-like device, called an actigraph, which tracks motion to quantify sleep behavior and waking activity. Participants in the study consist of healthy and depressed adolescents and wear actigraphs for a year during which time we query their mental health status monthly using online questionnaires.  For this masters thesis we aim to make use of machine learning methods to predict mental health based on the data from the actigraph. The ability to predict mental health crises based on sleep and wake behavior would provide an opportunity for intervention, significantly impacting the lives of patients and their families. This Masters thesis is a collaboration between Professor Paolo Favaro at the Institute of Computer Science ( [email protected] ) and Dr Leila Tarokh at the Universitäre Psychiatrische Dienste (UPD) ( [email protected] ).  We are looking for a highly motivated individual interested in bridging disciplines. 

Bachelor or Master Projects at the ARTORG Center

The Gerontechnology and Rehabilitation group at the ARTORG Center for Biomedical Engineering is offering multiple BSc- and MSc thesis projects to students, which are interested in working with real patient data, artificial intelligence and machine learning algorithms. The goal of these projects is to transfer the findings to the clinic in order to solve today’s healthcare problems and thus to improve the quality of life of patients. Machine Learning Based Gait-Parameter Extraction by Using Simple Rangefinder Technology.  [PDF] Detection of Motion in Video Recordings   [PDF] Home-Monitoring of Elderly by Radar  [PDF] Gait feature detection in Parkinson's Disease  [PDF] Development of an arthroscopic training device using virtual reality  [PDF] Contact: Dr. Stephan Gerber ( [email protected] ), Michael Single ( [email protected]. ch )

Dynamic Transformer

Level: bachelor.

Visual Transformers have obtained state of the art classification accuracies [ViT, DeiT, T2T, BoTNet]. Mixture of experts could be used to increase the capacity of a neural network by learning instance dependent execution pathways in a network [MoE]. In this research project we aim to push the transformers to their limit and combine their dynamic attention with MoEs, compared to Switch Transformer [Switch], we will use a much more efficient formulation of mixing [CondConv, DynamicConv] and we will use this idea in the attention part of the transformer, not the fully connected layer.

• Input dependent attention kernel generation for better transformer layers.

Publication Opportunity: Dynamic Neural Networks Meets Computer Vision (a CVPR 2021 Workshop)

Extensions:

• The same idea could be extended to other ViT/Transformer based models [DETR, SETR, LSTR, TrackFormer, BERT]

Related Papers:

• Visual Transformers: Token-based Image Representation and Processing for Computer Vision [ViT]
• DeiT: Data-efficient Image Transformers [DeiT]
• Bottleneck Transformers for Visual Recognition [BoTNet]
• Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet [T2TViT]
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer [MoE]
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity [Switch]
• CondConv: Conditionally Parameterized Convolutions for Efficient Inference [CondConv]
• Dynamic Convolution: Attention over Convolution Kernels [DynamicConv]
• End-to-End Object Detection with Transformers [DETR]
• Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [SETR]
• End-to-end Lane Shape Prediction with Transformers [LSTR]
• TrackFormer: Multi-Object Tracking with Transformers [TrackFormer]
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [BERT]

Contact: Sepehr Sameni

Visual Transformers have obtained state of the art classification accuracies for 2d images[ViT, DeiT, T2T, BoTNet]. In this project, we aim to extend the same ideas to 3d data (videos), which requires a more efficient attention mechanism [Performer, Axial, Linformer]. In order to accelerate the training process, we could use [Multigrid] technique.

• Better video understanding by attention blocks.

Publication Opportunity: LOVEU (a CVPR workshop) , Holistic Video Understanding (a CVPR workshop) , ActivityNet (a CVPR workshop)

• Rethinking Attention with Performers [Performer]
• Axial Attention in Multidimensional Transformers [Axial]
• Linformer: Self-Attention with Linear Complexity [Linformer]
• A Multigrid Method for Efficiently Training Video Models [Multigrid]

GIRAFFE is a newly introduced GAN that can generate scenes via composition with minimal supervision [GIRAFFE]. Generative methods can implicitly learn interpretable representation as can be seen in GAN image interpretations [GANSpace, GanLatentDiscovery]. Decoding GIRAFFE could give us per-object interpretable representations that could be used for scene manipulation, data augmentation, scene understanding, semantic segmentation, pose estimation [iNeRF], and more. 

In order to invert a GIRAFFE model, we will first train the generative model on Clevr and CompCars datasets, then we add a decoder to the pipeline and train this autoencoder. We can make the task easier by knowing the number of objects in the scene and/or knowing their positions. 

Goals:  

Scene Manipulation and Decomposition by Inverting the GIRAFFE 

Publication Opportunity:  DynaVis 2021 (a CVPR workshop on Dynamic Scene Reconstruction)  

Related Papers: 

• GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields [GIRAFFE] 
• Neural Scene Graphs for Dynamic Scenes 
• pixelNeRF: Neural Radiance Fields from One or Few Images [pixelNeRF] 
• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis [NeRF] 
• Neural Volume Rendering: NeRF And Beyond 
• GANSpace: Discovering Interpretable GAN Controls [GANSpace] 
• Unsupervised Discovery of Interpretable Directions in the GAN Latent Space [GanLatentDiscovery] 
• Inverting Neural Radiance Fields for Pose Estimation [iNeRF] 

Quantized ViT

Visual Transformers have obtained state of the art classification accuracies [ViT, CLIP, DeiT], but the best ViT models are extremely compute heavy and running them even only for inference (not doing backpropagation) is expensive. Running transformers cheaply by quantization is not a new problem and it has been tackled before for BERT [BERT] in NLP [Q-BERT, Q8BERT, TernaryBERT, BinaryBERT]. In this project we will be trying to quantize pretrained ViT models. 

Quantizing ViT models for faster inference and smaller models without losing accuracy 

Publication Opportunity:  Binary Networks for Computer Vision 2021 (a CVPR workshop)  

Extensions:  

• Having a fast pipeline for image inference with ViT will allow us to dig deep into the attention of ViT and analyze it, we might be able to prune some attention heads or replace them with static patterns (like local convolution or dilated patterns), We might be even able to replace the transformer with performer and increase the throughput even more [Performer]. 
• The same idea could be extended to other ViT based models [DETR, SETR, LSTR, TrackFormer, CPTR, BoTNet, T2TViT] 
• Learning Transferable Visual Models From Natural Language Supervision [CLIP] 
• Visual Transformers: Token-based Image Representation and Processing for Computer Vision [ViT] 
• DeiT: Data-efficient Image Transformers [DeiT] 
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [BERT] 
• Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT [Q-BERT] 
• Q8BERT: Quantized 8Bit BERT [Q8BERT] 
• TernaryBERT: Distillation-aware Ultra-low Bit BERT [TernaryBERT] 
• BinaryBERT: Pushing the Limit of BERT Quantization [BinaryBERT] 
• Rethinking Attention with Performers [Performer] 
• End-to-End Object Detection with Transformers [DETR] 
• Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [SETR] 
• End-to-end Lane Shape Prediction with Transformers [LSTR] 
• TrackFormer: Multi-Object Tracking with Transformers [TrackFormer] 
• CPTR: Full Transformer Network for Image Captioning [CPTR] 
• Bottleneck Transformers for Visual Recognition [BoTNet] 
• Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet [T2TViT] 

Multimodal Contrastive Learning

Recently contrastive learning has gained a lot of attention for self-supervised image representation learning [SimCLR, MoCo]. Contrastive learning could be extended to multimodal data, like videos (images and audio) [CMC, CoCLR]. Most contrastive methods require large batch sizes (or large memory pools) which makes them expensive for training. In this project we are going to use non batch size dependent contrastive methods [SwAV, BYOL, SimSiam] to train multimodal representation extractors. 

Our main goal is to compare the proposed method with the CMC baseline, so we will be working with STL10, ImageNet, UCF101, HMDB51, and NYU Depth-V2 datasets. 

Inspired by the recent works on smaller datasets [ConVIRT, CPD], to accelerate the training speed, we could start with two pretrained single-modal models and finetune them with the proposed method.  

• Extending SwAV to multimodal datasets 
• Grasping a better understanding of the BYOL 

Publication Opportunity:  MULA 2021 (a CVPR workshop on Multimodal Learning and Applications)  

• Most knowledge distillation methods for contrastive learners also use large batch sizes (or memory pools) [CRD, SEED], the proposed method could be extended for knowledge distillation. 
• One could easily extend this idea to multiview learning, for example one could have two different networks working on the same input and train them with contrastive learning, this may lead to better models [DeiT] by cross-model inductive biases communications. 
• Self-supervised Co-training for Video Representation Learning [CoCLR] 
• Learning Spatiotemporal Features via Video and Text Pair Discrimination [CPD] 
• Audio-Visual Instance Discrimination with Cross-Modal Agreement [AVID-CMA] 
• Self-Supervised Learning by Cross-Modal Audio-Video Clustering [XDC] 
• Contrastive Multiview Coding [CPC] 
• Contrastive Learning of Medical Visual Representations from Paired Images and Text [ConVIRT] 
• A Simple Framework for Contrastive Learning of Visual Representations [SimCLR] 
• Momentum Contrast for Unsupervised Visual Representation Learning [MoCo] 
• Bootstrap your own latent: A new approach to self-supervised Learning [BYOL] 
• Exploring Simple Siamese Representation Learning [SimSiam] 
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments [SwAV] 
• Contrastive Representation Distillation [CRD] 
• SEED: Self-supervised Distillation For Visual Representation [SEED] 

Robustness of Neural Networks

Neural Networks have been found to achieve surprising performance in several tasks such as classification, detection and segmentation. However, they are also very sensitive to small (controlled) changes to the input. It has been shown that some changes to an image that are not visible to the naked eye may lead the network to output an incorrect label. This thesis will focus on studying recent progress in this area and aim to build a procedure for a trained network to self-assess its reliability in classification or one of the popular computer vision tasks.

Contact: Paolo Favaro

Masters projects at sitem center

The Personalised Medicine Research Group at the sitem Center for Translational Medicine and Biomedical Entrepreneurship is offering multiple MSc thesis projects to the biomed eng MSc students that may also be of interest to the computer science students. Automated quantification of cartilage quality for hip treatment decision support.  PDF Automated quantification of massive rotator cuff tears from MRI. PDF Deep learning-based segmentation and fat fraction analysis of the shoulder muscles using quantitative MRI. PDF Unsupervised Domain Adaption for Cross-Modality Hip Joint Segmentation. PDF Contact:  Dr. Kate Gerber

Internships/Master thesis @ Chronocam

3-6 months internships on event-based computer vision. Chronocam is a rapidly growing startup developing event-based technology, with more than 15 PhDs working on problems like tracking, detection, classification, SLAM, etc. Event-based computer vision has the potential to solve many long-standing problems in traditional computer vision, and this is a super exciting time as this potential is becoming more and more tangible in many real-world applications. For next year we are looking for motivated Master and PhD students with good software engineering skills (C++ and/or python), and preferable good computer vision and deep learning background. PhD internships will be more research focused and possibly lead to a publication.  For each intern we offer a compensation to cover the expenses of living in Paris.  List of some of the topics we want to explore:

• Photo-realistic image synthesis and super-resolution from event-based data (PhD)
• Self-supervised representation learning (PhD)
• End-to-end Feature Learning for Event-based Data
• Bio-inspired Filtering using Spiking Networks
• On-the fly Compression of Event-based Streams for Low-Power IoT Cameras
• Tracking of Multiple Objects with a Dual-Frequency Tracker
• Event-based Autofocus
• Stabilizing an Event-based Stream using an IMU
• Crowd Monitoring for Low-power IoT Cameras
• Road Extraction from an Event-based Camera Mounted in a Car for Autonomous Driving
• Sign detection from an Event-based Camera Mounted in a Car for Autonomous Driving
• High-frequency Eye Tracking

Email with attached CV to Daniele Perrone at  [email protected] .

Contact: Daniele Perrone

Object Detection in 3D Point Clouds

Today we have many 3D scanning techniques that allow us to capture the shape and appearance of objects. It is easier than ever to scan real 3D objects and transform them into a digital model for further processing, such as modeling, rendering or animation. However, the output of a 3D scanner is often a raw point cloud with little to no annotations. The unstructured nature of the point cloud representation makes it difficult for processing, e.g. surface reconstruction. One application is the detection and segmentation of an object of interest.  In this project, the student is challenged to design a system that takes a point cloud (a 3D scan) as input and outputs the names of objects contained in the scan. This output can then be used to eliminate outliers or points that belong to the background. The approach involves collecting a large dataset of 3D scans and training a neural network on it.

Contact: Adrian Wälchli

Shape Reconstruction from a Single RGB Image or Depth Map

A photograph accurately captures the world in a moment of time and from a specific perspective. Since it is a projection of the 3D space to a 2D image plane, the depth information is lost. Is it possible to restore it, given only a single photograph? In general, the answer is no. This problem is ill-posed, meaning that many different plausible depth maps exist, and there is no way of telling which one is the correct one.  However, if we cover one of our eyes, we are still able to recognize objects and estimate how far away they are. This motivates the exploration of an approach where prior knowledge can be leveraged to reduce the ill-posedness of the problem. Such a prior could be learned by a deep neural network, trained with many images and depth maps.

CNN Based Deblurring on Mobile

Deblurring finds many applications in our everyday life. It is particularly useful when taking pictures on handheld devices (e.g. smartphones) where camera shake can degrade important details. Therefore, it is desired to have a good deblurring algorithm implemented directly in the device.  In this project, the student will implement and optimize a state-of-the-art deblurring method based on a deep neural network for deployment on mobile phones (Android).  The goal is to reduce the number of network weights in order to reduce the memory footprint while preserving the quality of the deblurred images. The result will be a camera app that automatically deblurs the pictures, giving the user a choice of keeping the original or the deblurred image.

Depth from Blur

If an object in front of the camera or the camera itself moves while the aperture is open, the region of motion becomes blurred because the incoming light is accumulated in different positions across the sensor. If there is camera motion, there is also parallax. Thus, a motion blurred image contains depth information.  In this project, the student will tackle the problem of recovering a depth-map from a motion-blurred image. This includes the collection of a large dataset of blurred- and sharp images or videos using a pair or triplet of GoPro action cameras. Two cameras will be used in stereo to estimate the depth map, and the third captures the blurred frames. This data is then used to train a convolutional neural network that will predict the depth map from the blurry image.

Unsupervised Clustering Based on Pretext Tasks

The idea of this project is that we have two types of neural networks that work together: There is one network A that assigns images to k clusters and k (simple) networks of type B perform a self-supervised task on those clusters. The goal of all the networks is to make the k networks of type B perform well on the task. The assumption is that clustering in semantically similar groups will help the networks of type B to perform well. This could be done on the MNIST dataset with B being linear classifiers and the task being rotation prediction.

Adversarial Data-Augmentation

The student designs a data augmentation network that transforms training images in such a way that image realism is preserved (e.g. with a constrained spatial transformer network) and the transformed images are more difficult to classify (trained via adversarial loss against an image classifier). The model will be evaluated for different data settings (especially in the low data regime), for example on the MNIST and CIFAR datasets.

Unsupervised Learning of Lip-reading from Videos

People with sensory impairment (hearing, speech, vision) depend heavily on assistive technologies to communicate and navigate in everyday life. The mass production of media content today makes it impossible to manually translate everything into a common language for assistive technologies, e.g. captions or sign language.  In this project, the student employs a neural network to learn a representation for lip-movement in videos in an unsupervised fashion, possibly with an encoder-decoder structure where the decoder reconstructs the audio signal. This requires collecting a large dataset of videos (e.g. from YouTube) of speakers or conversations where lip movement is visible. The outcome will be a neural network that learns an audio-visual representation of lip movement in videos, which can then be leveraged to generate captions for hearing impaired persons.

Learning to Generate Topographic Maps from Satellite Images

Satellite images have many applications, e.g. in meteorology, geography, education, cartography and warfare. They are an accurate and detailed depiction of the surface of the earth from above. Although it is relatively simple to collect many satellite images in an automated way, challenges arise when processing them for use in navigation and cartography. The idea of this project is to automatically convert an arbitrary satellite image, of e.g. a city, to a map of simple 2D shapes (streets, houses, forests) and label them with colors (semantic segmentation). The student will collect a dataset of satellite image and topological maps and train a deep neural network that learns to map from one domain to the other. The data could be obtained from a Google Maps database or similar.

New Variables of Brain Morphometry: the Potential and Limitations of CNN Regression

Timo blattner · sept. 2022.

The calculation of variables of brain morphology is computationally very expensive and time-consuming. A previous work showed the feasibility of ex- tracting the variables directly from T1-weighted brain MRI images using a con- volutional neural network. We used significantly more data and extended their model to a new set of neuromorphological variables, which could become inter- esting biomarkers in the future for the diagnosis of brain diseases. The model shows for nearly all subjects a less than 5% mean relative absolute error. This high relative accuracy can be attributed to the low morphological variance be- tween subjects and the ability of the model to predict the cortical atrophy age trend. The model however fails to capture all the variance in the data and shows large regional differences. We attribute these limitations in part to the moderate to poor reliability of the ground truth generated by FreeSurfer. We further investigated the effects of training data size and model complexity on this regression task and found that the size of the dataset had a significant impact on performance, while deeper models did not perform better. Lack of interpretability and dependence on a silver ground truth are the main drawbacks of this direct regression approach.

Home Monitoring by Radar

Lars ziegler · sept. 2022.

Detection and tracking of humans via UWB radars is a promising and continuously evolving field with great potential for medical technology. This contactless method of acquiring data of a patients movement patterns is ideal for in home application. As irregularities in a patients movement patterns are an indicator for various health problems including neurodegenerative diseases, the insight this data could provide may enable earlier detection of such problems. In this thesis a signal processing pipeline is presented with which a persons movement is modeled. During an experiment 142 measurements were recorded by two separate radar systems and one lidar system which each consisted of multiple sensors. The models that were calculated on these measurements by the signal processing pipeline were used to predict the times when a person stood up or sat down. The predictions showed an accuracy of 72.2%.

Revisiting non-learning based 3D reconstruction from multiple images

Aaron sägesser · oct. 2021.

Arthroscopy consists of challenging tasks and requires skills that even today, young surgeons still train directly throughout the surgery. Existing simulators are expensive and rarely available. Through the growing potential of virtual reality(VR) (head-mounted) devices for simulation and their applicability in the medical context, these devices have become a promising alternative that would be orders of magnitude cheaper and could be made widely available. To build a VR-based training device for arthroscopy is the overall aim of our project, as this would be of great benefit and might even be applicable in other minimally invasive surgery (MIS). This thesis marks a first step of the project with its focus to explore and compare well-known algorithms in a multi-view stereo (MVS) based 3D reconstruction with respect to imagery acquired by an arthroscopic camera. Simultaneously with this reconstruction, we aim to gain essential measures to compare the VR environment to the real world, as validation of the realism of future VR tasks. We evaluate 3 different feature extraction algorithms with 3 different matching techniques and 2 different algorithms for the estimation of the fundamental (F) matrix. The evaluation of these 18 different setups is made with a reconstruction pipeline embedded in a jupyter notebook implemented in python based on common computer vision libraries and compared with imagery generated with a mobile phone as well as with the reconstruction results of state-of-the-art (SOTA) structure-from-motion (SfM) software COLMAP and Multi-View Environment (MVE). Our comparative analysis manifests the challenges of heavy distortion, the fish-eye shape and weak image quality of arthroscopic imagery, as all results are substantially worse using this data. However, there are huge differences regarding the different setups. Scale Invariant Feature Transform (SIFT) and Oriented FAST Rotated BRIEF (ORB) in combination with k-Nearest Neighbour (kNN) matching and Least Median of Squares (LMedS) present the most promising results. Overall, the 3D reconstruction pipeline is a useful tool to foster the process of gaining measurements from the arthroscopic exploration device and to complement the comparative research in this context.

Examination of Unsupervised Representation Learning by Predicting Image Rotations

Eric lagger · sept. 2020.

In recent years deep convolutional neural networks achieved a lot of progress. To train such a network a lot of data is required and in supervised learning algorithms it is necessary that the data is labeled. To label data there is a lot of human work needed and this takes a lot of time and money to be done. To avoid the inconveniences that come with this we would like to find systems that don’t need labeled data and therefore are unsupervised learning algorithms. This is the importance of unsupervised algorithms, even though their outcome is not yet on the same qualitative level as supervised algorithms. In this thesis we will discuss an approach of such a system and compare the results to other papers. A deep convolutional neural network is trained to learn the rotations that have been applied to a picture. So we take a large amount of images and apply some simple rotations and the task of the network is to discover in which direction the image has been rotated. The data doesn’t need to be labeled to any category or anything else. As long as all the pictures are upside down we hope to find some high dimensional patterns for the network to learn.

StitchNet: Image Stitching using Autoencoders and Deep Convolutional Neural Networks

Maurice rupp · sept. 2019.

This thesis explores the prospect of artificial neural networks for image processing tasks. More specifically, it aims to achieve the goal of stitching multiple overlapping images to form a bigger, panoramic picture. Until now, this task is solely approached with ”classical”, hardcoded algorithms while deep learning is at most used for specific subtasks. This thesis introduces a novel end-to-end neural network approach to image stitching called StitchNet, which uses a pre-trained autoencoder and deep convolutional networks. Additionally to presenting several new datasets for the task of supervised image stitching with each 120’000 training and 5’000 validation samples, this thesis also conducts various experiments with different kinds of existing networks designed for image superresolution and image segmentation adapted to the task of image stitching. StitchNet outperforms most of the adapted networks in both quantitative as well as qualitative results.

Facial Expression Recognition in the Wild

Luca rolshoven · sept. 2019.

The idea of inferring the emotional state of a subject by looking at their face is nothing new. Neither is the idea of automating this process using computers. Researchers used to computationally extract handcrafted features from face images that had proven themselves to be effective and then used machine learning techniques to classify the facial expressions using these features. Recently, there has been a trend towards using deeplearning and especially Convolutional Neural Networks (CNNs) for the classification of these facial expressions. Researchers were able to achieve good results on images that were taken in laboratories under the same or at least similar conditions. However, these models do not perform very well on more arbitrary face images with different head poses and illumination. This thesis aims to show the challenges of Facial Expression Recognition (FER) in this wild setting. It presents the currently used datasets and the present state-of-the-art results on one of the biggest facial expression datasets currently available. The contributions of this thesis are twofold. Firstly, I analyze three famous neural network architectures and their effectiveness on the classification of facial expressions. Secondly, I present two modifications of one of these networks that lead to the proposed STN-COV model. While this model does not outperform all of the current state-of-the-art models, it does beat several ones of them.

A Study of 3D Reconstruction of Varying Objects with Deformable Parts Models

Raoul grossenbacher · july 2019.

This work covers a new approach to 3D reconstruction. In traditional 3D reconstruction one uses multiple images of the same object to calculate a 3D model by taking information gained from the differences between the images, like camera position, illumination of the images, rotation of the object and so on, to compute a point cloud representing the object. The characteristic trait shared by all these approaches is that one can almost change everything about the image, but it is not possible to change the object itself, because one needs to find correspondences between the images. To be able to use different instances of the same object, we used a 3D DPM model that can find different parts of an object in an image, thereby detecting the correspondences between the different pictures, which we then can use to calculate the 3D model. To take this theory to practise, we gave a 3D DPM model, which was trained to detect cars, pictures of different car brands, where no pair of images showed the same vehicle and used the detected correspondences and the Factorization Method to compute the 3D point cloud. This technique leads to a completely new approach in 3D reconstruction, because changing the object itself was never done before.

Motion deblurring in the wild replication and improvements

Alvaro juan lahiguera · jan. 2019, coma outcome prediction with convolutional neural networks, stefan jonas · oct. 2018, automatic correction of self-introduced errors in source code, sven kellenberger · aug. 2018, neural face transfer: training a deep neural network to face-swap, till nikolaus schnabel · july 2018.

This thesis explores the field of artificial neural networks with realistic looking visual outputs. It aims at morphing face pictures of a specific identity to look like another individual by only modifying key features, such as eye color, while leaving identity-independent features unchanged. Prior works have covered the topic of symmetric translation between two specific domains but failed to optimize it on faces where only parts of the image may be changed. This work applies a face masking operation to the output at training time, which forces the image generator to preserve colors while altering the face, fitting it naturally inside the unmorphed surroundings. Various experiments are conducted including an ablation study on the final setting, decreasing the baseline identity switching performance from 81.7% to 75.8 % whilst improving the average χ2 color distance from 0.551 to 0.434. The provided code-based software gives users easy access to apply this neural face swap to images and videos of arbitrary crop and brings Computer Vision one step closer to replacing Computer Graphics in this specific area.

A Study of the Importance of Parts in the Deformable Parts Model

Sammer puran · june 2017, self-similarity as a meta feature, lucas husi · april 2017, a study of 3d deformable parts models for detection and pose-estimation, simon jenni · march 2015, amodal leaf segmentation, nicolas maier · nov. 2023.

Plant phenotyping is the process of measuring and analyzing various traits of plants. It provides essential information on how genetic and environmental factors affect plant growth and development. Manual phenotyping is highly time-consuming; therefore, many computer vision and machine learning based methods have been proposed in the past years to perform this task automatically based on images of the plants. However, the publicly available datasets (in particular, of Arabidopsis thaliana) are limited in size and diversity, making them unsuitable to generalize to new unseen environments. In this work, we propose a complete pipeline able to automatically extract traits of interest from an image of Arabidopsis thaliana. Our method uses a minimal amount of existing annotated data from a source domain to generate a large synthetic dataset adapted to a different target domain (e.g., different backgrounds, lighting conditions, and plant layouts). In addition, unlike the source dataset, the synthetic one provides ground-truth annotations for the occluded parts of the leaves, which are relevant when measuring some characteristics of the plant, e.g., its total area. This synthetic dataset is then used to train a model to perform amodal instance segmentation of the leaves to obtain the total area, leaf count, and color of each plant. To validate our approach, we create a small dataset composed of manually annotated real images of Arabidopsis thaliana, which is used to assess the performance of the models.

Assessment of movement and pose in a hospital bed by ambient and wearable sensor technology in healthy subjects

Tony licata · sept. 2022.

The use of automated systems describing the human motion has become possible in various domains. Most of the proposed systems are designed to work with people moving around in a standing position. Because such system could be interesting in a medical environment, we propose in this work a pipeline that can effectively predict human motion from people lying on beds. The proposed pipeline is tested with a data set composed of 41 participants executing 7 predefined tasks in a bed. The motion of the participants is measured with video cameras, accelerometers and pressure mat. Various experiments are carried with the information retrieved from the data set. Two approaches combining the data from the different measure technologies are explored. The performance of the different carried experiments is measured, and the proposed pipeline is composed with components providing the best results. Later on, we show that the proposed pipeline only needs to use the video cameras, which make the proposed environment easier to implement in real life situations.

Machine Learning Based Prediction of Mental Health Using Wearable-measured Time Series

Seyedeh sharareh mirzargar · sept. 2022.

Depression is the second major cause for years spent in disability and has a growing prevalence in adolescents. The recent Covid-19 pandemic has intensified the situation and limited in-person patient monitoring due to distancing measures. Recent advances in wearable devices have made it possible to record the rest/activity cycle remotely with high precision and in real-world contexts. We aim to use machine learning methods to predict an individual's mental health based on wearable-measured sleep and physical activity. Predicting an impending mental health crisis of an adolescent allows for prompt intervention, detection of depression onset or its recursion, and remote monitoring. To achieve this goal, we train three primary forecasting models; linear regression, random forest, and light gradient boosted machine (LightGBM); and two deep learning models; block recurrent neural network (block RNN) and temporal convolutional network (TCN); on Actigraph measurements to forecast mental health in terms of depression, anxiety, sleepiness, stress, sleep quality, and behavioral problems. Our models achieve a high forecasting performance, the random forest being the winner to reach an accuracy of 98% for forecasting the trait anxiety. We perform extensive experiments to evaluate the models' performance in accuracy, generalization, and feature utilization, using a naive forecaster as the baseline. Our analysis shows minimal mental health changes over two months, making the prediction task easily achievable. Due to these minimal changes in mental health, the models tend to primarily use the historical values of mental health evaluation instead of Actigraph features. At the time of this master thesis, the data acquisition step is still in progress. In future work, we plan to train the models on the complete dataset using a longer forecasting horizon to increase the level of mental health changes and perform transfer learning to compensate for the small dataset size. This interdisciplinary project demonstrates the opportunities and challenges in machine learning based prediction of mental health, paving the way toward using the same techniques to forecast other mental disorders such as internalizing disorder, Parkinson's disease, Alzheimer's disease, etc. and improving the quality of life for individuals who have some mental disorder.

CNN Spike Detector: Detection of Spikes in Intracranial EEG using Convolutional Neural Networks

Stefan jonas · oct. 2021.

The detection of interictal epileptiform discharges in the visual analysis of electroencephalography (EEG) is an important but very difficult, tedious, and time-consuming task. There have been decades of research on computer-assisted detection algorithms, most recently focused on using Convolutional Neural Networks (CNNs). In this thesis, we present the CNN Spike Detector, a convolutional neural network to detect spikes in intracranial EEG. Our dataset of 70 intracranial EEG recordings from 26 subjects with epilepsy introduces new challenges in this research field. We report cross-validation results with a mean AUC of 0.926 (+- 0.04), an area under the precision-recall curve (AUPRC) of 0.652 (+- 0.10) and 12.3 (+- 7.47) false positive epochs per minute for a sensitivity of 80%. A visual examination of false positive segments is performed to understand the model behavior leading to a relatively high false detection rate. We notice issues with the evaluation measures and highlight a major limitation of the common approach of detecting spikes using short segments, namely that the network is not capable to consider the greater context of the segment with regards to its origination. For this reason, we present the Context Model, an extension in which the CNN Spike Detector is supplied with additional information about the channel. Results show promising but limited performance improvements. This thesis provides important findings about the spike detection task for intracranial EEG and lays out promising future research directions to develop a network capable of assisting experts in real-world clinical applications.

PolitBERT - Deepfake Detection of American Politicians using Natural Language Processing

Maurice rupp · april 2021.

This thesis explores the application of modern Natural Language Processing techniques to the detection of artificially generated videos of popular American politicians. Instead of focusing on detecting anomalies and artifacts in images and sounds, this thesis focuses on detecting irregularities and inconsistencies in the words themselves, opening up a new possibility to detect fake content. A novel, domain-adapted, pre-trained version of the language model BERT combined with several mechanisms to overcome severe dataset imbalances yielded the best quantitative as well as qualitative results. Additionally to the creation of the biggest publicly available dataset of English-speaking politicians consisting of 1.5 M sentences from over 1000 persons, this thesis conducts various experiments with different kinds of text classification and sequence processing algorithms applied to the political domain. Furthermore, multiple ablations to manage severe data imbalance are presented and evaluated.

A Study on the Inversion of Generative Adversarial Networks

Ramona beck · march 2021.

The desire to use generative adversarial networks (GANs) for real-world tasks such as object segmentation or image manipulation is increasing as synthesis quality improves, which has given rise to an emerging research area called GAN inversion that focuses on exploring methods for embedding real images into the latent space of a GAN. In this work, we investigate different GAN inversion approaches using an existing generative model architecture that takes a completely unsupervised approach to object segmentation and is based on StyleGAN2. In particular, we propose and analyze algorithms for embedding real images into the different latent spaces Z, W, and W+ of StyleGAN following an optimization-based inversion approach, while also investigating a novel approach that allows fine-tuning of the generator during the inversion process. Furthermore, we investigate a hybrid and a learning-based inversion approach, where in the former we train an encoder with embeddings optimized by our best optimization-based inversion approach, and in the latter we define an autoencoder, consisting of an encoder and the generator of our generative model as a decoder, and train it to map an image into the latent space. We demonstrate the effectiveness of our methods as well as their limitations through a quantitative comparison with existing inversion methods and by conducting extensive qualitative and quantitative experiments with synthetic data as well as real images from a complex image dataset. We show that we achieve qualitatively satisfying embeddings in the W and W+ spaces with our optimization-based algorithms, that fine-tuning the generator during the inversion process leads to qualitatively better embeddings in all latent spaces studied, and that the learning-based approach also benefits from a variable generator as well as a pre-training with our hybrid approach. Furthermore, we evaluate our approaches on the object segmentation task and show that both our optimization-based and our hybrid and learning-based methods are able to generate meaningful embeddings that achieve reasonable object segmentations. Overall, our proposed methods illustrate the potential that lies in the GAN inversion and its application to real-world tasks, especially in the relaxed version of the GAN inversion where the weights of the generator are allowed to vary.

Multi-scale Momentum Contrast for Self-supervised Image Classification

Zhao xueqi · dec. 2020.

With the maturity of supervised learning technology, people gradually shift the research focus to the field of self-supervised learning. ”Momentum Contrast” (MoCo) proposes a new self-supervised learning method and raises the correct rate of self-supervised learning to a new level. Inspired by another article ”Representation Learning by Learning to Count”, if a picture is divided into four parts and passed through a neural network, it is possible to further improve the accuracy of MoCo. Different from the original MoCo, this MoCo variant (Multi-scale MoCo) does not directly pass the image through the encoder after the augmented images. Multi-scale MoCo crops and resizes the augmented images, and the obtained four parts are respectively passed through the encoder and then summed (upsampled version do not do resize to input but resize the contrastive samples). This method of images crop is not only used for queue q but also used for comparison queue k, otherwise the weights of queue k might be damaged during the moment update. This will further discussed in the experiments chapter between downsampled Multi-scale version and downsampled both Multi-scale version. Human beings also have the same principle of object recognition: when human beings see something they are familiar with, even if the object is not fully displayed, people can still guess the object itself with a high probability. Because of this, Multi-scale MoCo applies this concept to the pretext part of MoCo, hoping to obtain better feature extraction. In this thesis, there are three versions of Multi-scale MoCo, downsampled input samples version, downsampled input samples and contrast samples version and upsampled input samples version. The differences between these versions will be described in more detail later. The neural network architecture comparison includes ResNet50 , and the tested data set is STL-10. The weights obtained in pretext will be transferred to self-supervised learning, and in the process of self-supervised learning, the weights of other layers except the final linear layer are frozen without changing (these weights come from pretext).

Self-Supervised Learning Using Siamese Networks and Binary Classifier

Dušan mihajlov · march 2020.

In this thesis, we present several approaches for training a convolutional neural network using only unlabeled data. Our autonomously supervised learning algorithms are based on connections between image patch i. e. zoomed image and its original. Using the siamese architecture neural network we aim to recognize, if the image patch, which is input to the first neural network part, comes from the same image presented to the second neural network part. By applying transformations to both images, and different zoom sizes at different positions, we force the network to extract high level features using its convolutional layers. At the top of our siamese architecture, we have a simple binary classifier that measures the difference between feature maps that we extract and makes a decision. Thus, the only way that the classifier will solve the task correctly is when our convolutional layers are extracting useful representations. Those representations we can than use to solve many different tasks that are related to the data used for unsupervised training. As the main benchmark for all of our models, we used STL10 dataset, where we train a linear classifier on the top of our convolutional layers with a small amount of manually labeled images, which is a widely used benchmark for unsupervised learning tasks. We also combine our idea with recent work on the same topic, and the network called RotNet, which makes use of image rotations and therefore forces the network to learn rotation dependent features from the dataset. As a result of this combination we create a new procedure that outperforms original RotNet.

Learning Object Representations by Mixing Scenes

Lukas zbinden · may 2019.

In the digital age of ever increasing data amassment and accessibility, the demand for scalable machine learning models effective at refining the new oil is unprecedented. Unsupervised representation learning methods present a promising approach to exploit this invaluable yet unlabeled digital resource at scale. However, a majority of these approaches focuses on synthetic or simplified datasets of images. What if a method could learn directly from natural Internet-scale image data? In this thesis, we propose a novel approach for unsupervised learning of object representations by mixing natural image scenes. Without any human help, our method mixes visually similar images to synthesize new realistic scenes using adversarial training. In this process the model learns to represent and understand the objects prevalent in natural image data and makes them available for downstream applications. For example, it enables the transfer of objects from one scene to another. Through qualitative experiments on complex image data we show the effectiveness of our method along with its limitations. Moreover, we benchmark our approach quantitatively against state-of-the-art works on the STL-10 dataset. Our proposed method demonstrates the potential that lies in learning representations directly from natural image data and reinforces it as a promising avenue for future research.

Representation Learning using Semantic Distances

Markus roth · may 2019, zero-shot learning using generative adversarial networks, hamed hemati · dec. 2018, dimensionality reduction via cnns - learning the distance between images, ioannis glampedakis · sept. 2018, learning to play othello using deep reinforcement learning and self play, thomas simon steinmann · sept. 2018, aba-j interactive multi-modality tissue sectionto-volume alignment: a brain atlasing toolkit for imagej, felix meyenhofer · march 2018, learning visual odometry with recurrent neural networks, adrian wälchli · feb. 2018.

In computer vision, Visual Odometry is the problem of recovering the camera motion from a video. It is related to Structure from Motion, the problem of reconstructing the 3D geometry from a collection of images. Decades of research in these areas have brought successful algorithms that are used in applications like autonomous navigation, motion capture, augmented reality and others. Despite the success of these prior works in real-world environments, their robustness is highly dependent on manual calibration and the magnitude of noise present in the images in form of, e.g., non-Lambertian surfaces, dynamic motion and other forms of ambiguity. This thesis explores an alternative approach to the Visual Odometry problem via Deep Learning, that is, a specific form of machine learning with artificial neural networks. It describes and focuses on the implementation of a recent work that proposes the use of Recurrent Neural Networks to learn dependencies over time due to the sequential nature of the input. Together with a convolutional neural network that extracts motion features from the input stream, the recurrent part accumulates knowledge from the past to make camera pose estimations at each point in time. An analysis on the performance of this system is carried out on real and synthetic data. The evaluation covers several ways of training the network as well as the impact and limitations of the recurrent connection for Visual Odometry.

Crime location and timing prediction

Bernard swart · jan. 2018, from cartoons to real images: an approach to unsupervised visual representation learning, simon jenni · feb. 2017, automatic and large-scale assessment of fluid in retinal oct volume, nina mujkanovic · dec. 2016, segmentation in 3d using eye-tracking technology, michele wyss · july 2016, accurate scale thresholding via logarithmic total variation prior, remo diethelm · aug. 2014, novel techniques for robust and generalizable machine learning, abdelhak lemkhenter · sept. 2023.

Neural networks have transcended their status of powerful proof-of-concept machine learning into the realm of a highly disruptive technology that has revolutionized many quantitative fields such as drug discovery, autonomous vehicles, and machine translation. Today, it is nearly impossible to go a single day without interacting with a neural network-powered application. From search engines to on-device photo-processing, neural networks have become the go-to solution thanks to recent advances in computational hardware and an unprecedented scale of training data. Larger and less curated datasets, typically obtained through web crawling, have greatly propelled the capabilities of neural networks forward. However, this increase in scale amplifies certain challenges associated with training such models. Beyond toy or carefully curated datasets, data in the wild is plagued with biases, imbalances, and various noisy components. Given the larger size of modern neural networks, such models run the risk of learning spurious correlations that fail to generalize beyond their training data. This thesis addresses the problem of training more robust and generalizable machine learning models across a wide range of learning paradigms for medical time series and computer vision tasks. The former is a typical example of a low signal-to-noise ratio data modality with a high degree of variability between subjects and datasets. There, we tailor the training scheme to focus on robust patterns that generalize to new subjects and ignore the noisier and subject-specific patterns. To achieve this, we first introduce a physiologically inspired unsupervised training task and then extend it by explicitly optimizing for cross-dataset generalization using meta-learning. In the context of image classification, we address the challenge of training semi-supervised models under class imbalance by designing a novel label refinement strategy with higher local sensitivity to minority class samples while preserving the global data distribution. Lastly, we introduce a new Generative Adversarial Networks training loss. Such generative models could be applied to improve the training of subsequent models in the low data regime by augmenting the dataset using generated samples. Unfortunately, GAN training relies on a delicate balance between its components, making it prone mode collapse. Our contribution consists of defining a more principled GAN loss whose gradients incentivize the generator model to seek out missing modes in its distribution. All in all, this thesis tackles the challenge of training more robust machine learning models that can generalize beyond their training data. This necessitates the development of methods specifically tailored to handle the diverse biases and spurious correlations inherent in the data. It is important to note that achieving greater generalizability in models goes beyond simply increasing the volume of data; it requires meticulous consideration of training objectives and model architecture. By tackling these challenges, this research contributes to advancing the field of machine learning and underscores the significance of thoughtful design in obtaining more resilient and versatile models.

Automated Sleep Scoring, Deep Learning and Physician Supervision

Luigi fiorillo · oct. 2022.

Sleep plays a crucial role in human well-being. Polysomnography is used in sleep medicine as a diagnostic tool, so as to objectively analyze the quality of sleep. Sleep scoring is the procedure of extracting sleep cycle information from the wholenight electrophysiological signals. The scoring is done worldwide by the sleep physicians according to the official American Academy of Sleep Medicine (AASM) scoring manual. In the last decades, a wide variety of deep learning based algorithms have been proposed to automatise the sleep scoring task. In this thesis we study the reasons why these algorithms fail to be introduced in the daily clinical routine, with the perspective of bridging the existing gap between the automatic sleep scoring models and the sleep physicians. In this light, the primary step is the design of a simplified sleep scoring architecture, also providing an estimate of the model uncertainty. Beside achieving results on par with most up-to-date scoring systems, we demonstrate the efficiency of ensemble learning based algorithms, together with label smoothing techniques, in both enhancing the performance and calibrating the simplified scoring model. We introduced an uncertainty estimate procedure, so as to identify the most challenging sleep stage predictions, and to quantify the disagreement between the predictions given by the model and the annotation given by the physicians. In this thesis we also propose a novel method to integrate the inter-scorer variability into the training procedure of a sleep scoring model. We clearly show that a deep learning model is able to encode this variability, so as to better adapt to the consensus of a group of scorers-physicians. We finally address the generalization ability of a deep learning based sleep scoring system, further studying its resilience to the sleep complexity and to the AASM scoring rules. We can state that there is no need to train the algorithm strictly following the AASM guidelines. Most importantly, using data from multiple data centers results in a better performing model compared with training on a single data cohort. The variability among different scorers and data centers needs to be taken into account, more than the variability among sleep disorders.

Learning Representations for Controllable Image Restoration

Givi meishvili · march 2022.

Deep Convolutional Neural Networks have sparked a renaissance in all the sub-fields of computer vision. Tremendous progress has been made in the area of image restoration. The research community has pushed the boundaries of image deblurring, super-resolution, and denoising. However, given a distorted image, most existing methods typically produce a single restored output. The tasks mentioned above are inherently ill-posed, leading to an infinite number of plausible solutions. This thesis focuses on designing image restoration techniques capable of producing multiple restored results and granting users more control over the restoration process. Towards this goal, we demonstrate how one could leverage the power of unsupervised representation learning. Image restoration is vital when applied to distorted images of human faces due to their social significance. Generative Adversarial Networks enable an unprecedented level of generated facial details combined with smooth latent space. We leverage the power of GANs towards the goal of learning controllable neural face representations. We demonstrate how to learn an inverse mapping from image space to these latent representations, tuning these representations towards a specific task, and finally manipulating latent codes in these spaces. For example, we show how GANs and their inverse mappings enable the restoration and editing of faces in the context of extreme face super-resolution and the generation of novel view sharp videos from a single motion-blurred image of a face. This thesis also addresses more general blind super-resolution, denoising, and scratch removal problems, where blur kernels and noise levels are unknown. We resort to contrastive representation learning and first learn the latent space of degradations. We demonstrate that the learned representation allows inference of ground-truth degradation parameters and can guide the restoration process. Moreover, it enables control over the amount of deblurring and denoising in the restoration via manipulation of latent degradation features.

Learning Generalizable Visual Patterns Without Human Supervision

Simon jenni · oct. 2021.

Owing to the existence of large labeled datasets, Deep Convolutional Neural Networks have ushered in a renaissance in computer vision. However, almost all of the visual data we generate daily - several human lives worth of it - remains unlabeled and thus out of reach of today’s dominant supervised learning paradigm. This thesis focuses on techniques that steer deep models towards learning generalizable visual patterns without human supervision. Our primary tool in this endeavor is the design of Self-Supervised Learning tasks, i.e., pretext-tasks for which labels do not involve human labor. Besides enabling the learning from large amounts of unlabeled data, we demonstrate how self-supervision can capture relevant patterns that supervised learning largely misses. For example, we design learning tasks that learn deep representations capturing shape from images, motion from video, and 3D pose features from multi-view data. Notably, these tasks’ design follows a common principle: The recognition of data transformations. The strong performance of the learned representations on downstream vision tasks such as classification, segmentation, action recognition, or pose estimation validate this pretext-task design. This thesis also explores the use of Generative Adversarial Networks (GANs) for unsupervised representation learning. Besides leveraging generative adversarial learning to define image transformation for self-supervised learning tasks, we also address training instabilities of GANs through the use of noise. While unsupervised techniques can significantly reduce the burden of supervision, in the end, we still rely on some annotated examples to fine-tune learned representations towards a target task. To improve the learning from scarce or noisy labels, we describe a supervised learning algorithm with improved generalization in these challenging settings.

Learning Interpretable Representations of Images

Attila szabó · june 2019.

Computers represent images with pixels and each pixel contains three numbers for red, green and blue colour values. These numbers are meaningless for humans and they are mostly useless when used directly with classical machine learning techniques like linear classifiers. Interpretable representations are the attributes that humans understand: the colour of the hair, viewpoint of a car or the 3D shape of the object in the scene. Many computer vision tasks can be viewed as learning interpretable representations, for example a supervised classification algorithm directly learns to represent images with their class labels. In this work we aim to learn interpretable representations (or features) indirectly with lower levels of supervision. This approach has the advantage of cost savings on dataset annotations and the flexibility of using the features for multiple follow-up tasks. We made contributions in three main areas: weakly supervised learning, unsupervised learning and 3D reconstruction. In the weakly supervised case we use image pairs as supervision. Each pair shares a common attribute and differs in a varying attribute. We propose a training method that learns to separate the attributes into separate feature vectors. These features then are used for attribute transfer and classification. We also show theoretical results on the ambiguities of the learning task and the ways to avoid degenerate solutions. We show a method for unsupervised representation learning, that separates semantically meaningful concepts. We explain and show ablation studies how the components of our proposed method work: a mixing autoencoder, a generative adversarial net and a classifier. We propose a method for learning single image 3D reconstruction. It is done using only the images, no human annotation, stereo, synthetic renderings or ground truth depth map is needed. We train a generative model that learns the 3D shape distribution and an encoder to reconstruct the 3D shape. For that we exploit the notion of image realism. It means that the 3D reconstruction of the object has to look realistic when it is rendered from different random angles. We prove the efficacy of our method from first principles.

Learning Controllable Representations for Image Synthesis

Qiyang hu · june 2019.

In this thesis, our focus is learning a controllable representation and applying the learned controllable feature representation on images synthesis, video generation, and even 3D reconstruction. We propose different methods to disentangle the feature representation in neural network and analyze the challenges in disentanglement such as reference ambiguity and shortcut problem when using the weak label. We use the disentangled feature representation to transfer attributes between images such as exchanging hairstyle between two face images. Furthermore, we study the problem of how another type of feature, sketch, works in a neural network. The sketch can provide shape and contour of an object such as the silhouette of the side-view face. We leverage the silhouette constraint to improve the 3D face reconstruction from 2D images. The sketch can also provide the moving directions of one object, thus we investigate how one can manipulate the object to follow the trajectory provided by a user sketch. We propose a method to automatically generate video clips from a single image input using the sketch as motion and trajectory guidance to animate the object in that image. We demonstrate the efficiency of our approaches on several synthetic and real datasets.

Beyond Supervised Representation Learning

Mehdi noroozi · jan. 2019.

The complexity of any information processing task is highly dependent on the space where data is represented. Unfortunately, pixel space is not appropriate for the computer vision tasks such as object classification. The traditional computer vision approaches involve a multi-stage pipeline where at first images are transformed to a feature space through a handcrafted function and then consequenced by the solution in the feature space. The challenge with this approach is the complexity of designing handcrafted functions that extract robust features. The deep learning based approaches address this issue by end-to-end training of a neural network for some tasks that lets the network to discover the appropriate representation for the training tasks automatically. It turns out that image classification task on large scale annotated datasets yields a representation transferable to other computer vision tasks. However, supervised representation learning is limited to annotations. In this thesis we study self-supervised representation learning where the goal is to alleviate these limitations by substituting the classification task with pseudo tasks where the labels come for free. We discuss self-supervised learning by solving jigsaw puzzles that uses context as supervisory signal. The rational behind this task is that the network requires to extract features about object parts and their spatial configurations to solve the jigsaw puzzles. We also discuss a method for representation learning that uses an artificial supervisory signal based on counting visual primitives. This supervisory signal is obtained from an equivariance relation. We use two image transformations in the context of counting: scaling and tiling. The first transformation exploits the fact that the number of visual primitives should be invariant to scale. The second transformation allows us to equate the total number of visual primitives in each tile to that in the whole image. The most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. We discuss a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific finetuned model. Finally, we study the problem of multi-task representation learning. A naive approach to enhance the representation learned by a task is to train the task jointly with other tasks that capture orthogonal attributes. Having a diverse set of auxiliary tasks, imposes challenges on multi-task training from scratch. We propose a framework that allows us to combine arbitrarily different feature spaces into a single deep neural network. We reduce the auxiliary tasks to classification tasks and the multi-task learning to multi-label classification task consequently. Nevertheless, combining multiple representation space without being aware of the target task might be suboptimal. As our second contribution, we show empirically that this is indeed the case and propose to combine multiple tasks after the fine-tuning on the target task.

Motion Deblurring from a Single Image

Meiguang jin · dec. 2018.

With the information explosion, a tremendous amount photos is captured and shared via social media everyday. Technically, a photo requires a finite exposure to accumulate light from the scene. Thus, objects moving during the exposure generate motion blur in a photo. Motion blur is an image degradation that makes visual content less interpretable and is therefore often seen as a nuisance. Although motion blur can be reduced by setting a short exposure time, an insufficient amount of light has to be compensated through increasing the sensor’s sensitivity, which will inevitably bring large amount of sensor noise. Thus this motivates the necessity of removing motion blur computationally. Motion deblurring is an important problem in computer vision and it is challenging due to its ill-posed nature, which means the solution is not well defined. Mathematically, a blurry image caused by uniform motion is formed by the convolution operation between a blur kernel and a latent sharp image. Potentially there are infinite pairs of blur kernel and latent sharp image that can result in the same blurry image. Hence, some prior knowledge or regularization is required to address this problem. Even if the blur kernel is known, restoring the latent sharp image is still difficult as the high frequency information has been removed. Although we can model the uniform motion deblurring problem mathematically, it can only address the camera in-plane translational motion. Practically, motion is more complicated and can be non-uniform. Non-uniform motion blur can come from many sources, camera out-of-plane rotation, scene depth change, object motion and so on. Thus, it is more challenging to remove non-uniform motion blur. In this thesis, our focus is motion blur removal. We aim to address four challenging motion deblurring problems. We start from the noise blind image deblurring scenario where blur kernel is known but the noise level is unknown. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0−1 loss to address this problem. Then we study the blind uniform motion deblurring scenario where both the blur kernel and the latent sharp image are unknown. We explore the relative scale ambiguity between the latent sharp image and blur kernel to address this issue. Moreover, we study the face deblurring problem and introduce a novel deep learning network architecture to solve it. We also address the general motion deblurring problem and particularly we aim at recovering a sequence of 7 frames each depicting some instantaneous motion of the objects in the scene.

Towards a Novel Paradigm in Blind Deconvolution: From Natural to Cartooned Image Statistics

Daniele perrone · july 2015.

In this thesis we study the blind deconvolution problem. Blind deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Recently it has been shown that a class of blind deconvolution formulations and image priors has the no-blur solution as global minimum. Despite this shortcoming, algorithms based on these formulations and priors can successfully solve blind deconvolution. In this thesis we show that a suitable initialization can exploit the non-convexity of the problem and yield the desired solution. Based on these conclusions, we propose a novel “vanilla” algorithm stripped of any enhancement typically used in the literature. Our algorithm, despite its simplicity, is able to compete with the top performers on several datasets. We have also investigated a remarkable behavior of a 1998 algorithm, whose formulation has the no-blur solution as global minimum: even when initialized at the no-blur solution, it converges to the correct solution. We show that this behavior is caused by an apparently insignificant implementation strategy that makes the algorithm no longer minimize the original cost functional. We also demonstrate that this strategy improves the results of our “vanilla” algorithm. Finally, we present a study of image priors for blind deconvolution. We provide experimental evidence supporting the recent belief that a good image prior is one that leads to a good blur estimate rather than being a good natural image statistical model. By focusing the attention on the blur estimation alone, we show that good blur estimates can be obtained even when using images quite different from the true sharp image. This allows using image priors, such as those leading to “cartooned” images, that avoid the no-blur solution. By using an image prior that produces “cartooned” images we achieve state-of-the-art results on different publicly available datasets. We therefore suggests a shift of paradigm in blind deconvolution: from modeling natural image statistics to modeling cartooned image statistics.

New Perspectives on Uncalibrated Photometric Stereo

Thoma papadhimitri · june 2014.

This thesis investigates the problem of 3D reconstruction of a scene from 2D images. In particular, we focus on photometric stereo which is a technique that computes the 3D geometry from at least three images taken from the same viewpoint and under different illumination conditions. When the illumination is unknown (uncalibrated photometric stereo) the problem is ambiguous: different combinations of geometry and illumination can generate the same images. First, we solve the ambiguity by exploiting the Lambertian reflectance maxima. These are points defined on curved surfaces where the normals are parallel to the light direction. Then, we propose a solution that can be computed in closed-form and thus very efficiently. Our algorithm is also very robust and yields always the same estimate regardless of the initial ambiguity. We validate our method on real world experiments and achieve state-of-art results. In this thesis we also solve for the first time the uncalibrated photometric stereo problem under the perspective projection model. We show that unlike in the orthographic case, one can uniquely reconstruct the normals of the object and the lights given only the input images and the camera calibration (focal length and image center). We also propose a very efficient algorithm which we validate on synthetic and real world experiments and show that the proposed technique is a generalization of the orthographic case. Finally, we investigate the uncalibrated photometric stereo problem in the case where the lights are distributed near the scene. In this case we propose an alternating minimization technique which converges quickly and overcomes the limitations of prior work that assumes distant illumination. We show experimentally that adopting a near-light model for real world scenes yields very accurate reconstructions.

Computer Science Thesis Oral

April 5, 2024 2:00pm — 4:00pm.

Location: In Person and Virtual - ET - Reddy Conference Room, Gates Hillman 4405 and Zoom

Speaker: MATT BUTROVICH , Ph.D. Candidate, Computer Science Department, Carnegie Mellon University https://mattbutrovi.ch/

On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments

The rise in computer storage and network performance means that disk I/O and network communication are often no longer bottlenecks in database management systems (DBMSs). Instead, the overheads associated with operating system (OS) services (e.g., system calls, thread scheduling, and data movement from kernel-space) limit query processing responsiveness. User-space applications can elide these overheads with a kernel-bypass design. However, extracting benefits from kernel-bypass frameworks is challenging, and the libraries are incompatible with standard deployment and debugging tools. 

This thesis presents an alternative in user-bypass: a design that extends OS behavior for DBMS-specific features, including observability, networking, and query execution. Historically, DBMS developers avoid kernel extensions for safety and security reasons, but recent improvements in OS extensibility present new opportunities. With user-bypass, developers write safe, event-driven programs to push DBMS logic into the kernel and avoid user-space overheads. There are two ways to to invoke user-bypass logic: (1) when a DBMS in user-space invokes these programs, user-bypass provides behavior similar to a new OS system call, albeit without kernel modifications. In contrast, (2) when an OS thread or interrupt triggers these programs in kernel-space, user-bypass inserts DBMS logic into the kernel stack.

First, we present a framework that employs user-bypass to collect training data for self-driving DBMSs efficiently. User-bypass programs reduce the number of round trips to kernel-space to retrieve performance counters and other system metrics. Next, we present a database proxy that applies user-bypass to support features like connection pooling and workload replication while reducing data copying and user-space thread scheduling. User-bypass programs embed DBMS network protocol logic in multiple layers of the OS network stack, applying DBMS proxy logic in a kernel-space fast path. Lastly, we present an embedded DBMS for future user-bypass applications. We discuss the design decisions, environment challenges, and performance characteristics of a DBMS that offers ACID transactions over multi-versioned data in kernel-space. We also explore applications of this user-bypass DBMS and compare them to modern user-space systems.

The techniques proposed in this thesis show user-bypass benefits across multiple DBMS design disciplines and provide a template for future DBMS and OS co-design.

Thesis Committee:

Andrew Pavlo (Chair) Jignesh M. Patel Justine Sherry Samuel Madden (Massachusetts Institute of Technology)  

In Person and Zoom Participation.  See announcement.

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Essay on Computer and its Uses for School Students and Children

500+ words essay on computer.

In this essay on computer, we are going to discuss some useful things about computers. The modern-day computer has become an important part of our daily life. Also, their usage has increased much fold during the last decade. Nowadays, they use the computer in every office whether private or government. Mankind is using computers for over many decades now. Also, they are used in many fields like agriculture, designing, machinery making, defense and many more. Above all, they have revolutionized the whole world.

History of Computers

It is very difficult to find the exact origin of computers. But according to some experts computer exists at the time of world war-II. Also, at that time they were used for keeping data. But, it was for only government use and not for public use. Above all, in the beginning, the computer was a very large and heavy machine.

Working of a Computer 

The computer runs on a three-step cycle namely input, process, and output. Also, the computer follows this cycle in every process it was asked to do. In simple words, the process can be explained in this way. The data which we feed into the computer is input, the work CPU do is process and the result which the computer give is output.

Components and Types of Computer

The simple computer basically consists of CPU, monitor, mouse, and keyboard . Also, there are hundreds of other computer parts that can be attached to it. These other parts include a printer, laser pen, scanner , etc.

The computer is categorized into many different types like supercomputers, mainframes, personal computers (desktop), PDAs, laptop, etc. The mobile phone is also a type of computer because it fulfills all the criteria of being a computer.

Get the huge list of more than 500 Essay Topics and Ideas

Uses of Computer in Various Fields

As the usage of computer increased it became a necessity for almost every field to use computers for their operations. Also, they have made working and sorting things easier. Below we are mentioning some of the important fields that use a computer in their daily operation.

Medical Field

They use computers to diagnose diseases, run tests and for finding the cure for deadly diseases . Also, they are able to find a cure for many diseases because of computers.

Whether it’s scientific research, space research or any social research computers help in all of them. Also, due to them, we are able to keep a check on the environment , space, and society. Space research helped us to explore the galaxies. While scientific research has helped us to locate resources and various other useful resources from the earth.

For any country, his defence is most important for the safety and security of its people. Also, computer in this field helps the country’s security agencies to detect a threat which can be harmful in the future. Above all the defense industry use them to keep surveillance on our enemy.

Threats from a Computer

Computers have become a necessity also, they have become a threat too. This is due to hackers who steal your private data and leak them on internet. Also, anyone can access this data. Apart from that, there are other threats like viruses, spams, bug and many other problems.

The computer is a very important machine that has become a useful part of our life. Also, the computers have twin-faces on one side it’s a boon and on the other side, it’s a bane. Its uses completely depend upon you. Apart from that, a day in the future will come when human civilization won’t be able to survive without computers as we depend on them too much. Till now it is a great discovery of mankind that has helped in saving thousands and millions of lives.

Frequently Asked Questions on Computer

Q.1  What is a computer?

A.1 A computer is an electronic device or machine that makes our work easier. Also, they help us in many ways.

Q.2 Mention various fields where computers are used?

A.2  Computers are majorly used in defense, medicine, and for research purposes.

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Dissertations / Theses on the topic 'Computer literacy – Study and teaching (Higher)'

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Wittwer, Kristin. "Teaching computer literacy for visually impaired students in higher education." Virtual Press, 1991. http://liblink.bsu.edu/uhtbin/catkey/834646.

Mansourian, Lida. "The Association Between Exposure to Computer Instruction and Changes in Attitudes Toward Computers." Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc331898/.

Wong, Ming-fai Patrick, and 黃明暉. "Integrating computer literacy across different subjects." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29599908.

Anderson, Glenda K. (Glenda Kay). "The Association Between Systematic Exposure to Information About Computers and Attitude Changes Among Students Who Are Non-Computer Majors." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc332169/.

Chaipraparl, Pornpun. "Thai High School Compute Literacy: A Content Analysis." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc330995/.

Chuvessiriporn, Suttichai. "Hospitality Students' Attitudes and Behavioral Intentions toward Learning and Using Computer Technology." Thesis, University of North Texas, 1999. https://digital.library.unt.edu/ark:/67531/metadc2279/.

Smith, Christina Louise. "Technology Literacy Skills Needed in Further Education and/or Work: A Delphi Study of High School Graduates’ Perspectives." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5776.

Jones, Janet. "Multiliteracies for academic purposes : a metafunctional exploration of intersemiosis and multimodality in university textbook and computer-based learning resources in science." University of Sydney, 2006. http://hdl.handle.net/2123/2259.

Greenfield, Robert Wayne. "The development of a curriculum for a high school course in computer literacy." CSUSB ScholarWorks, 1997. https://scholarworks.lib.csusb.edu/etd-project/1189.

Freehling, Seth. "The usage of Internet technologies by high school students in the completion of educational tasks outside of the school setting." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2940.

Broughton, Beverly Arlene. "An evaluation of a curriculum response to the State of Florida mandate for computer literacy at a large comprehensive high school in Dade County, Florida." FIU Digital Commons, 1991. http://digitalcommons.fiu.edu/etd/1815.

Fye, Carmen Michelle. "Composition and technology: Examining liminal spaces online." CSUSB ScholarWorks, 2001. https://scholarworks.lib.csusb.edu/etd-project/1950.

Schlebusch, Carlie Luzaan. "An exploration of grades 10 - 12 computer applications technology teachers' problem-solving skills in the Free State." Thesis, Welkom: Central University of Technology, Free State, 2014. http://hdl.handle.net/11462/678.

Free, Loretta Dianna. "Improving academic literacy at higher education." Thesis, Nelson Mandela Metropolitan University, 2008. http://hdl.handle.net/10948/839.

Haberle, Nikky. "Developing an evaluative framework for information literacy interventions." Thesis, Cape Technikon, 2001. http://hdl.handle.net/20.500.11838/1892.

Downey, Annie L. "The State of the Field of Critical Information Literacy in Higher Education." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc799537/.

Mobarak, Barbara Ann. "The development of a computer literacy curriculum for California charter schools." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2683.

Kearns, Hugh. "Effect of interaction between computer anxiety, locus of control and course structure on achievement in a computer literacy course." Title page, abstract and table of contents only, 1995. http://web4.library.adelaide.edu.au/theses/09EDM/09edmk24.pdf.

Grady, Paula Northam. ""Hands-on" computer workshops for improving microcomputer literacy : feasibility studies, design, layout, workbooks." Virtual Press, 1988. http://liblink.bsu.edu/uhtbin/catkey/539636.

Overstreet, Penni Kaye. "Computer literacy in master of public administration classes." CSUSB ScholarWorks, 1990. https://scholarworks.lib.csusb.edu/etd-project/556.

Oliverius, Thomas Michael. "Developing a middle school unit on computer literacy." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1225.

Atkins, Anthony T. "Digital deficit : literacy, technology, and teacher training in rhetoric and composition programs." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1301627.

Townsend, Rodwell. "The national curriculum statement on writing practice design for grades 11 and 12: implications for academic writing in higher education." Thesis, Nelson Mandela Metropolitan University, 2010. http://hdl.handle.net/10948/1125.

Reynolds, Lisa Marie. "An Empirical Study of Software Debugging Games with Introductory Students." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804874/.

Picard, Michelle Yvette. "Academic literacy right from the start?: a critical realist study of the way university literacy is constructed at a Gulf university." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004121.

Nasseh, Bizhan. "A study of the computer-based distance education in higher education institutions in Indiana." Virtual Press, 1996. http://liblink.bsu.edu/uhtbin/catkey/1036821.

Jones, Michael William. "An extended case study on the introductory teaching of programming." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/166317/.

Daly, Kelly Sue. "A web page of curricular resources for the computer literacy class: Grades 7 - 9." CSUSB ScholarWorks, 2001. https://scholarworks.lib.csusb.edu/etd-project/1959.

Amos, Trevor. "The development of academic literacy in the first-year psychology course at Rhodes University: an assessment of the tutorial programme." Thesis, Rhodes University, 1998. http://hdl.handle.net/10962/d1002432.

Wilson, Diane Easter. "A model curriculum for an associate of science in computer science, based on the ACM model, AACJC and CSAB guidelines." Virtual Press, 1991. http://liblink.bsu.edu/uhtbin/catkey/770947.

Michaud, Meredith Esther. "Information Literacy in the First Year of Higher Education: Faculty Expectations and Student Practices." PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/3079.

Funcke, Matthew. "Developing high-fidelity mental models of programming concepts using manipulatives and interactive metaphors." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1017929.

Borchers, Tracy Schneider. "A study to define secondary computer literacy programs: Implications for restructuring vocational education policy directions." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1059.

Mungthaisong, Sornchai. "Constructing EFL literacy practices : a qualitative investigation in intertextual talk in Thai university language classes /." Title page, abstract and table of contents only, 2003. http://web4.library.adelaide.edu.au/theses/09PH/09phm9962.pdf.

Smit, Talita C. "The role of African literature in enhancing critical literacy in first-generation entrants at the University of Namibia." Thesis, Stellenbosch : Stellenbosch University, 2009. http://hdl.handle.net/10019.1/1211.

Ho, Wai-pan Anthony, and 何慧彬. "Integrating information literacy into the curriculum: collaboration between university library and faculty." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B40039821.

Hackmack, Karin Erna. "An investigation into understanding of academic literacies of students registered in Early Childhood Development courses." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1013548.

Alman, Lourdes Fraga. "The Effects of a Computer-mediated Intervention on "At-risk" Preschool Students' Receptive Vocabulary and Computer Literacy Skills." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4372/.

Parker, Rembert N. "An introduction to computer programming for complete beginners using HTML, JavaScript, and C#." CardinalScholar 1.0, 2008. http://liblink.bsu.edu/uhtbin/catkey/1465970.

Ottati, Daniela F. "Geographical Literacy, Attitudes, and Experiences of Freshman Students: A Qualitative Study at Florida International University." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/1851.

Thomson, Carol Irene. "Changing words and worlds?: a phenomenological study of the acquisition of an academic literacy." Thesis, Rhodes University, 2008. http://hdl.handle.net/10962/d1003327.

Snyder, Brian Lyn. "A study of pedagogical approaches to teaching problem solving." Thesis, Kansas State University, 1985. http://hdl.handle.net/2097/9880.

Taylor, Bernard Wayne. "A Study of Anxiety Reducing Teaching Methods and Computer Anxiety among Community College Students." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc277692/.

Vogt, Karen Fay. "The use of technology in meeting science reform criteria: Can web-based instruction promote scientific literacy?" CSUSB ScholarWorks, 1999. https://scholarworks.lib.csusb.edu/etd-project/1861.

Shieh, Li-Ting. "A learning project : the development of sustainable support in the use of instructional technology." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=118288.

Fu, Jun. "Fostering digital literacy through web-based collaborative inquiry learning." HKBU Institutional Repository, 2011. http://repository.hkbu.edu.hk/etd_ra/1238.

Steinman-Veres, Marla. "Computer-aided instruction and simulations." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63891.

Mwansa, Patrick. "Perceptions of computer programming students on interactive environments for teaching object-oriented concepts using Java." Thesis, Cape Peninsula University of Technology, 2017. http://hdl.handle.net/20.500.11838/2536.

Cournoyer, Richard John. "The Application of Parametric Software into the Undergraduate Computer-Aided Manufacturing Environment." Digital WPI, 1999. https://digitalcommons.wpi.edu/etd-theses/1078.

Kirsten, Monica. "Multilingual/multicultural aspects of visual literacy and interpretation in multimodal educational communication." Thesis, University of the Western Cape, 2004. http://etd.uwc.ac.za/index.php?module=etd&amp.

Intensive Writing Experience for Thesis and Dissertation Writers - Elmore Family School of Electrical and Computer Engineering - Purdue University

Intensive Writing Experience for Thesis and Dissertation Writers

• May 14-16 (in-person)
• May 21-23 (virtual)
• May 28-30 (in-person)
• June 4-6 (in-person)