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Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

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Computer science articles within Scientific Reports

Matters Arising 07 May 2024 | Open Access

Reply to: Problems with two recent Petri net analyses of Neanderthal adhesive technology

• Sebastian Fajardo
• , Paul R. B. Kozowyk
•  &  Geeske H. J. Langejans

Article 07 May 2024 | Open Access

Measuring the prediction difficulty of individual cases in a dataset using machine learning

• Hyunjin Kwon
• , Matthew Greenberg
•  &  Joon Lee

Article 06 May 2024 | Open Access

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

• Tianyu Wang

An adaptable and personalized framework for top-N course recommendations in online learning

• Samina Amin
• , M. Irfan Uddin
•  &  Hamdan Ahmed Alzahrani

Causal impact evaluation of occupational safety policies on firms’ default using machine learning uplift modelling

• Berardino Barile
• , Marco Forti
•  &  Angelo Castaldo

Effective small crack detection based on tunnel crack characteristics and an anchor-free convolutional neural network

•  &  Chao Tang

Article 05 May 2024 | Open Access

Glaucoma detection using non-perfused areas in OCTA

• Julia Schottenhamml
• , Tobias Würfl
•  &  Christian Mardin

An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation

• , Jiawen He
•  &  Ruichun Tang

Article 04 May 2024 | Open Access

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

• , Jang Hyun Kim
•  &  Hae Sun Jung

Diffusion models for conditional generation of hypothetical new families of superconductors

• Samuel Yuan
•  &  S. V. Dordevic

Article 03 May 2024 | Open Access

Real-time data visualization of welding robot data and preparation for future of digital twin system

• Péter Magyar
• , János Hegedűs-Kuti
•  &  Gábor Farkas

An effective method for small objects detection based on MDFFAM and LKSPP

• Zhoutian Xu
• , Yadong Xu
•  &  Manyi Wang

Enhancing soccer goalkeepers penalty dive kinematics with instructional video and laterality insights in field conditions

• Rafael Luiz Martins Monteiro
• , Carlos Cesar Arruda dos Santos
•  &  Paulo Roberto Pereira Santiago

Multi-attention fusion transformer for single-image super-resolution

• Guanxing Li
• , Zhaotong Cui
•  &  Tianping Li

Article 02 May 2024 | Open Access

Accelerating material property prediction using generically complete isometry invariants

• Jonathan Balasingham
• , Viktor Zamaraev
•  &  Vitaliy Kurlin

The fine line between automation and augmentation in website usability evaluation

• Andrea Esposito
• , Giuseppe Desolda
•  &  Rosa Lanzilotti

A study of extractive summarization of long documents incorporating local topic and hierarchical information

• , Chuan Yang
•  &  Jia Li

Analysis of the retraining strategies for multi-label text message classification in call/contact center systems

• Katarzyna Poczeta
• , Mirosław Płaza
•  &  Maria Krechowicz

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

• , Bodong Cai
•  &  Wenzhe Jiao

Article 30 April 2024 | Open Access

Incremental high average-utility itemset mining: survey and challenges

• , Shengyi Yang
•  &  Tian Li

Article 29 April 2024 | Open Access

A RT-FDTD method of analyzing wireless propagation characteristics in underground mine

• Xiaoyan Song
• , Gaomin Zhang
•  &  Chang Zhou

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning

• Peiyuan Jiang
• , Chen Zeng
•  &  Jian Zhang

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms

• Andreas Goldschmied
• , Manuel Sigle
•  &  Karin Anne Lydia Müller

Article 27 April 2024 | Open Access

A self-supervised framework for cross-modal search in histopathology archives using scale harmonization

• Danial Maleki
• , Shahryar Rahnamayan
•  &  H. R. Tizhoosh

Article 26 April 2024 | Open Access

Research on the innovative application of Shen Embroidery cultural heritage based on convolutional neural network

•  &  Changyong Zhu

Rate splitting with semantics as a generalized multi-access framework for intelligent reflecting surfaces

• Senthil Kumar Jagatheesaperumal
• , Zhaohui Yang
•  &  Giancarlo Fortino

Spatial–temporal combination and multi-head flow-attention network for traffic flow prediction

• , Wenbo Liu
•  &  Panjing Li

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM

• Md Saef Ullah Miah
• , Md Mohsin Kabir
•  &  M. F. Mridha

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [ 82 Rb] PET for MACE prediction

• , Daniel Abler
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A GAN-BO-XGBoost model for high-quality patents identification

• Zengyuan Wu
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Multi-stage progressive detection method for water deficit detection in vertical greenery plants

•  &  Yuanxiang Huang

Article 25 April 2024 | Open Access

An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion

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Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks

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Edge computing based real-time Nephrops ( Nephrops norvegicus ) catch estimation in demersal trawls using object detection models

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Trait impulsivity influences behavioural and physiological responses to threat in a virtual environment

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An adaptive detection model for IPv6 extension header threats based on deterministic decision automaton

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•  &  Mingyue Ren

Article 24 April 2024 | Open Access

Uncertainty-driven mixture convolution and transformer network for remote sensing image super-resolution

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New method for calculating the windward area of irregular fragments

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MRI radiomics in head and neck cancer from reproducibility to combined approaches

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Article 23 April 2024 | Open Access

Co-ordinate-based positional embedding that captures resolution to enhance transformer’s performance in medical image analysis

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A new method based on YOLOv5 and multiscale data augmentation for visual inspection in substation

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Dual-branch feature encoding framework for infrared images super-resolution reconstruction

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The use of residual analysis to improve the error rate accuracy of machine translation

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SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

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Article 22 April 2024 | Open Access

A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach

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A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

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TO-UGDA: target-oriented unsupervised graph domain adaptation

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Multimodal decoding of error processing in a virtual reality flight simulation

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Article 21 April 2024 | Open Access

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

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Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

• Algorithms & data structures
• Artificial intelligence ( AI )
• Computer networking
• Database systems
• Human-computer interaction
• Information security (IS)
• Software engineering
• Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

• An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
• A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
• An evaluation of machine learning algorithms used for recommender systems in streaming services
• A review of approximation algorithm approaches for solving NP-hard problems
• An analysis of parallel algorithms for high-performance computing of genomic data
• The influence of data structures on optimal algorithm design and performance in Fintech
• A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
• A comparison of streaming algorithm performance for the detection of elephant flows
• A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
• Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
• Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
• A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
• A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
• Exploring the feasibility of applying information theory to feature extraction in retail datasets.
• Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

• Applying deep learning algorithms for speech recognition in speech-impaired children
• A review of the impact of artificial intelligence on decision-making processes in stock valuation
• An evaluation of reinforcement learning algorithms used in the production of video games
• An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
• An analysis of the ethical and social implications of artificial intelligence-based automated marking
• The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
• An examination of the use of artificial intelligence in orthopaedic surgery
• The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
• An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
• A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

• An analysis of the impact of 5G technology on internet penetration in rural Tanzania
• Assessing the role of software-defined networking (SDN) in modern cloud-based computing
• A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
• Exploring the influence of cloud computing on security risks in fintech.
• An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
• Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
• An evaluation of the challenges and opportunities in 6G wireless network adoption
• The role of network congestion control algorithms in improving network performance on streaming platforms
• An analysis of network coding-based approaches for data security
• Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Topics & Ideas: Database Systems

• An analysis of big data management systems and technologies used in B2B marketing
• The impact of NoSQL databases on data management and analysis in smart cities
• An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
• Exploring the role of data warehousing and business intelligence in global consultancies
• An analysis of the use of graph databases for data modelling and analysis in recommendation systems
• The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
• An examination of the challenges and opportunities of distributed databases in supply chain management
• Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
• An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
• Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

• An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments is in my favour. May i get the proper material about that ?

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13 Research Papers Accepted to ICML 2021

Papers from CS researchers have been accepted to the 38th International Conference on Machine Learning (ICML 2021). 

Associate Professor Daniel Hsu was one of the publication chairs of the conference and Assistant Professor Elham Azizi helped organize the 2021 ICML Workshop on Computational Biology . The workshop highlighted how machine learning approaches can be tailored to making both translational and basic scientific discoveries with biological data.

Below are the abstracts and links to the accepted papers.

A Proxy Variable View of Shared Confounding Yixin Wang Columbia University , David Blei Columbia University

Causal inference from observational data can be biased by unobserved confounders. Confounders—the variables that affect both the treatments and the outcome—induce spurious non-causal correlations between the two. Without additional conditions, unobserved confounders generally make causal quantities hard to identify. In this paper, we focus on the setting where there are many treatments with shared confounding, and we study under what conditions is causal identification possible. The key observation is that we can view subsets of treatments as proxies of the unobserved confounder and identify the intervention distributions of the rest. Moreover, while existing identification formulas for proxy variables involve solving integral equations, we show that one can circumvent the need for such solutions by directly modeling the data. Finally, we extend these results to an expanded class of causal graphs, those with other confounders and selection variables.

Unsupervised Representation Learning via Neural Activation Coding Yookoon Park Columbia University , Sangho Lee Seoul National University , Gunhee Kim Seoul National University , David Blei Columbia University

We present neural activation coding (NAC) as a novel approach for learning deep representations from unlabeled data for downstream applications. We argue that the deep encoder should maximize its nonlinear expressivity on the data for downstream predictors to take full advantage of its representation power. To this end, NAC maximizes the mutual information between activation patterns of the encoder and the data over a noisy communication channel. We show that learning for a noise-robust activation code increases the number of distinct linear regions of ReLU encoders, hence the maximum nonlinear expressivity. More interestingly, NAC learns both continuous and discrete representations of data, which we respectively evaluate on two downstream tasks: (i) linear classification on CIFAR-10 and ImageNet-1K and (ii) nearest neighbor retrieval on CIFAR-10 and FLICKR-25K. Empirical results show that NAC attains better or comparable performance on both tasks over recent baselines including SimCLR and DistillHash. In addition, NAC pretraining provides significant benefits to the training of deep generative models. Our code is available at https://github.com/yookoon/nac.

The Logical Options Framework Brandon Araki MIT , Xiao Li MIT , Kiran Vodrahalli Columbia University , Jonathan DeCastro Toyota Research Institute , Micah Fry MIT Lincoln Laboratory , Daniela Rus MIT CSAIL

Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF’s learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps on our benchmarks. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning Yonghan Jung Columbia University , Jin Tian Columbia University , Elias Bareinboim Columbia University

General methods have been developed for estimating causal effects from observational data under causal assumptions encoded in the form of a causal graph. Most of this literature assumes that the underlying causal graph is completely specified. However, only observational data is available in most practical settings, which means that one can learn at most a Markov equivalence class (MEC) of the underlying causal graph. In this paper, we study the problem of causal estimation from a MEC represented by a partial ancestral graph (PAG), which is learnable from observational data. We develop a general estimator for any identifiable causal effects in a PAG. The result fills a gap for an end-to-end solution to causal inference from observational data to effects estimation. Specifically, we develop a complete identification algorithm that derives an influence function for any identifiable causal effects from PAGs. We then construct a double/debiased machine learning (DML) estimator that is robust to model misspecification and biases in nuisance function estimation, permitting the use of modern machine learning techniques. Simulation results corroborate with the theory.

Environment Inference for Invariant Learning  Elliot Creager University of Toronto , Joern Jacobsen Apple Inc. , Richard Zemel Columbia University

Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumption in this area is that the training examples are partitioned into  domains'' or environments”. Our focus is on the more common setting where such partitions are not provided. We propose EIIL, a general framework for domain-invariant learning that incorporates Environment Inference to directly infer partitions that are maximally informative for downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on the CMNIST benchmark without using environment labels, and significantly outperforms ERM on worst-group performance in the Waterbirds dataset. Finally, we establish connections between EIIL and algorithmic fairness, which enables EIIL to improve accuracy and calibration in a fair prediction problem.

SketchEmbedNet: Learning Novel Concepts by Imitating Drawings Alex Wang University of Toronto , Mengye Ren University of Toronto , Richard Zemel Columbia University

Sketch drawings capture the salient information of visual concepts. Previous work has shown that neural networks are capable of producing sketches of natural objects drawn from a small number of classes. While earlier approaches focus on generation quality or retrieval, we explore properties of image representations learned by training a model to produce sketches of images. We show that this generative, class-agnostic model produces informative embeddings of images from novel examples, classes, and even novel datasets in a few-shot setting. Additionally, we find that these learned representations exhibit interesting structure and compositionality.

Universal Template for Few-Shot Dataset Generalization Eleni Triantafillou University of Toronto , Hugo Larochelle Google Brain , Richard Zemel Columbia University , Vincent Dumoulin Google

Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from \emph{new datasets} using only a few examples. To this end, we propose to utilize the diverse training set to construct a \emph{universal template}: a partial model that can define a wide array of dataset-specialized models, by plugging in appropriate components. For each new few-shot classification problem, our approach therefore only requires inferring a small number of parameters to insert into the universal template. We design a separate network that produces an initialization of those parameters for each given task, and we then fine-tune its proposed initialization via a few steps of gradient descent. Our approach is more parameter-efficient, scalable and adaptable compared to previous methods, and achieves the state-of-the-art on the challenging Meta-Dataset benchmark.

On Monotonic Linear Interpolation of Neural Network Parameters James Lucas University of Toronto , Juhan Bae University of Toronto, Michael Zhang University of Toronto , Stanislav Fort Google AI , Richard Zemel Columbia University , Roger Grosse University of Toronto

Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decrease in the training objective. This Monotonic Linear Interpolation (MLI) property, first observed by Goodfellow et al. 2014, persists in spite of the non-convex objectives and highly non-linear training dynamics of neural networks. Extending this work, we evaluate several hypotheses for this property that, to our knowledge, have not yet been explored. Using tools from differential geometry, we draw connections between the interpolated paths in function space and the monotonicity of the network — providing sufficient conditions for the MLI property under mean squared error. While the MLI property holds under various settings (e.g., network architectures and learning problems), we show in practice that networks violating the MLI property can be produced systematically, by encouraging the weights to move far from initialization. The MLI property raises important questions about the loss landscape geometry of neural networks and highlights the need to further study their global properties.

A Computational Framework For Slang Generation Zhewei Sun University of Toronto , Richard Zemel Columbia University , Yang Xu University of Toronto

Slang is a common type of informal language, but its flexible nature and paucity of data resources present challenges for existing natural language systems. We take an initial step toward machine generation of slang by developing a framework that models the speaker’s word choice in slang context. Our framework encodes novel slang meaning by relating the conventional and slang senses of a word while incorporating syntactic and contextual knowledge in slang usage. We construct the framework using a combination of probabilistic inference and neural contrastive learning. We perform rigorous evaluations on three slang dictionaries and show that our approach not only outperforms state-of-the-art language models, but also better predicts the historical emergence of slang word usages from 1960s to 2000s. We interpret the proposed models and find that the contrastively learned semantic space is sensitive to the similarities between slang and conventional senses of words. Our work creates opportunities for the automated generation and interpretation of informal language.

Wandering Within A World: Online Contextualized Few-Shot Learning Mengye Ren University of Toronto , Michael Iuzzolino Google Research , Michael Mozer Google Research , Richard Zemel Columbia University

We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and instead models are evaluated online while learning novel classes. As in the real world, where the presence of spatiotemporal context helps us retrieve learned skills in the past, our online few-shot learning setting also features an underlying context that changes throughout time. Object classes are correlated within a context and inferring the correct context can lead to better performance. Building upon this setting, we propose a new few-shot learning dataset based on large scale indoor imagery that mimics the visual experience of an agent wandering within a world. Furthermore, we convert popular few-shot learning approaches into online versions and we also propose a new contextual prototypical memory model that can make use of spatiotemporal contextual information from the recent past.

Bayesian Few-Shot Classification With One-Vs-Each Polya-Gamma Augmented Gaussian Processes Jake Snell University of Toronto , Richard Zemel Columbia University

Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. Bayesian methods are well-suited to tackling the fundamental issue of overfitting in the few-shot scenario because they allow practitioners to specify prior beliefs and update those beliefs in light of observed data. Contemporary approaches to Bayesian few-shot classification maintain a posterior distribution over model parameters, which is slow and requires storage that scales with model size. Instead, we propose a Gaussian process classifier based on a novel combination of Pólya-Gamma augmentation and the one-vs-each softmax approximation that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks.

Theoretical Bounds On Estimation Error For Meta-Learning James Lucas University of Toronto , Mengye Ren University of Toronto , Irene Kameni African Master for Mathematical Sciences , Toni Pitassi Columbia University , Richard Zemel Columbia University

Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be adapted to more realistic settings where train and test distributions differ. Unfortunately, there is severely limited theoretical support for these algorithms and little is known about the difficulty of these problems. In this work, we provide novel information-theoretic lower-bounds on minimax rates of convergence for algorithms that are trained on data from multiple sources and tested on novel data. Our bounds depend intuitively on the information shared between sources of data, and characterize the difficulty of learning in this setting for arbitrary algorithms. We demonstrate these bounds on a hierarchical Bayesian model of meta-learning, computing both upper and lower bounds on parameter estimation via maximum-a-posteriori inference.

A PAC-Bayesian Approach To Generalization Bounds For Graph Neural Networks Renjie Liao University of Toronto , Raquel Urtasun University of Toronto , Richard Zemel Columbia University

In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPGNNs), via a PAC-Bayesian approach. Our result reveals that the maximum node degree and spectral norm of the weights govern the generalization bounds of both models. We also show that our bound for GCNs is a natural generalization of the results developed in  arXiv:1707.09564v2  [cs.LG] for fully-connected and convolutional neural networks. For message passing GNNs, our PAC-Bayes bound improves over the Rademacher complexity based bound in  arXiv:2002.06157v1  [cs.LG], showing a tighter dependency on the maximum node degree and the maximum hidden dimension. The key ingredients of our proofs are a perturbation analysis of GNNs and the generalization of PAC-Bayes analysis to non-homogeneous GNNs. We perform an empirical study on several real-world graph datasets and verify that our PAC-Bayes bound is tighter than others.

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Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

• Using machine learning to detect and prevent cyber attacks
• Developing algorithms for optimized resource allocation in cloud computing
• Investigating the use of blockchain technology for secure and decentralized data storage
• Developing intelligent chatbots for customer service
• Investigating the effectiveness of deep learning for natural language processing
• Developing algorithms for detecting and removing fake news from social media
• Investigating the impact of social media on mental health
• Developing algorithms for efficient image and video compression
• Investigating the use of big data analytics for predictive maintenance in manufacturing
• Developing algorithms for identifying and mitigating bias in machine learning models
• Investigating the ethical implications of autonomous vehicles
• Developing algorithms for detecting and preventing cyberbullying
• Investigating the use of machine learning for personalized medicine
• Developing algorithms for efficient and accurate speech recognition
• Investigating the impact of social media on political polarization
• Developing algorithms for sentiment analysis in social media data
• Investigating the use of virtual reality in education
• Developing algorithms for efficient data encryption and decryption
• Investigating the impact of technology on workplace productivity
• Developing algorithms for detecting and mitigating deepfakes
• Investigating the use of artificial intelligence in financial trading
• Developing algorithms for efficient database management
• Investigating the effectiveness of online learning platforms
• Developing algorithms for efficient and accurate facial recognition
• Investigating the use of machine learning for predicting weather patterns
• Developing algorithms for efficient and secure data transfer
• Investigating the impact of technology on social skills and communication
• Developing algorithms for efficient and accurate object recognition
• Investigating the use of machine learning for fraud detection in finance
• Developing algorithms for efficient and secure authentication systems
• Investigating the impact of technology on privacy and surveillance
• Developing algorithms for efficient and accurate handwriting recognition
• Investigating the use of machine learning for predicting stock prices
• Developing algorithms for efficient and secure biometric identification
• Investigating the impact of technology on mental health and well-being
• Developing algorithms for efficient and accurate language translation
• Investigating the use of machine learning for personalized advertising
• Developing algorithms for efficient and secure payment systems
• Investigating the impact of technology on the job market and automation
• Developing algorithms for efficient and accurate object tracking
• Investigating the use of machine learning for predicting disease outbreaks
• Developing algorithms for efficient and secure access control
• Investigating the impact of technology on human behavior and decision making
• Developing algorithms for efficient and accurate sound recognition
• Investigating the use of machine learning for predicting customer behavior
• Developing algorithms for efficient and secure data backup and recovery
• Investigating the impact of technology on education and learning outcomes
• Developing algorithms for efficient and accurate emotion recognition
• Investigating the use of machine learning for improving healthcare outcomes
• Developing algorithms for efficient and secure supply chain management
• Investigating the impact of technology on cultural and societal norms
• Developing algorithms for efficient and accurate gesture recognition
• Investigating the use of machine learning for predicting consumer demand
• Developing algorithms for efficient and secure cloud storage
• Investigating the impact of technology on environmental sustainability
• Developing algorithms for efficient and accurate voice recognition
• Investigating the use of machine learning for improving transportation systems
• Developing algorithms for efficient and secure mobile device management
• Investigating the impact of technology on social inequality and access to resources
• Machine learning for healthcare diagnosis and treatment
• Machine Learning for Cybersecurity
• Machine learning for personalized medicine
• Cybersecurity threats and defense strategies
• Big data analytics for business intelligence
• Blockchain technology and its applications
• Human-computer interaction in virtual reality environments
• Artificial intelligence for autonomous vehicles
• Natural language processing for chatbots
• Cloud computing and its impact on the IT industry
• Internet of Things (IoT) and smart homes
• Robotics and automation in manufacturing
• Augmented reality and its potential in education
• Data mining techniques for customer relationship management
• Computer vision for object recognition and tracking
• Quantum computing and its applications in cryptography
• Social media analytics and sentiment analysis
• Recommender systems for personalized content delivery
• Mobile computing and its impact on society
• Bioinformatics and genomic data analysis
• Deep learning for image and speech recognition
• Digital signal processing and audio processing algorithms
• Cloud storage and data security in the cloud
• Wearable technology and its impact on healthcare
• Computational linguistics for natural language understanding
• Cognitive computing for decision support systems
• Cyber-physical systems and their applications
• Edge computing and its impact on IoT
• Machine learning for fraud detection
• Cryptography and its role in secure communication
• Cybersecurity risks in the era of the Internet of Things
• Natural language generation for automated report writing
• 3D printing and its impact on manufacturing
• Virtual assistants and their applications in daily life
• Cloud-based gaming and its impact on the gaming industry
• Computer networks and their security issues
• Cyber forensics and its role in criminal investigations
• Machine learning for predictive maintenance in industrial settings
• Augmented reality for cultural heritage preservation
• Human-robot interaction and its applications
• Data visualization and its impact on decision-making
• Cybersecurity in financial systems and blockchain
• Computer graphics and animation techniques
• Biometrics and its role in secure authentication
• Cloud-based e-learning platforms and their impact on education
• Natural language processing for machine translation
• Machine learning for predictive maintenance in healthcare
• Cybersecurity and privacy issues in social media
• Computer vision for medical image analysis
• Natural language generation for content creation
• Cybersecurity challenges in cloud computing
• Human-robot collaboration in manufacturing
• Data mining for predicting customer churn
• Artificial intelligence for autonomous drones
• Cybersecurity risks in the healthcare industry
• Machine learning for speech synthesis
• Edge computing for low-latency applications
• Virtual reality for mental health therapy
• Quantum computing and its applications in finance
• Biomedical engineering and its applications
• Cybersecurity in autonomous systems
• Machine learning for predictive maintenance in transportation
• Computer vision for object detection in autonomous driving
• Augmented reality for industrial training and simulations
• Cloud-based cybersecurity solutions for small businesses
• Natural language processing for knowledge management
• Machine learning for personalized advertising
• Cybersecurity in the supply chain management
• Cybersecurity risks in the energy sector
• Computer vision for facial recognition
• Natural language processing for social media analysis
• Machine learning for sentiment analysis in customer reviews
• Explainable Artificial Intelligence
• Quantum Computing
• Blockchain Technology
• Human-Computer Interaction
• Natural Language Processing
• Cloud Computing
• Robotics and Automation
• Augmented Reality and Virtual Reality
• Cyber-Physical Systems
• Computational Neuroscience
• Big Data Analytics
• Computer Vision
• Cryptography and Network Security
• Internet of Things
• Computer Graphics and Visualization
• Artificial Intelligence for Game Design
• Computational Biology
• Social Network Analysis
• Bioinformatics
• Distributed Systems and Middleware
• Information Retrieval and Data Mining
• Computer Networks
• Mobile Computing and Wireless Networks
• Software Engineering
• Database Systems
• Parallel and Distributed Computing
• Human-Robot Interaction
• Intelligent Transportation Systems
• High-Performance Computing
• Cyber-Physical Security
• Deep Learning
• Sensor Networks
• Multi-Agent Systems
• Human-Centered Computing
• Wearable Computing
• Knowledge Representation and Reasoning
• Adaptive Systems
• Brain-Computer Interface
• Health Informatics
• Cognitive Computing
• Cybersecurity and Privacy
• Internet Security
• Cybercrime and Digital Forensics
• Cloud Security
• Cryptocurrencies and Digital Payments
• Machine Learning for Natural Language Generation
• Cognitive Robotics
• Neural Networks
• Semantic Web
• Image Processing
• Cyber Threat Intelligence
• Secure Mobile Computing
• Cybersecurity Education and Training
• Privacy Preserving Techniques
• Cyber-Physical Systems Security
• Virtualization and Containerization
• Machine Learning for Computer Vision
• Network Function Virtualization
• Cybersecurity Risk Management
• Information Security Governance
• Intrusion Detection and Prevention
• Biometric Authentication
• Machine Learning for Predictive Maintenance
• Security in Cloud-based Environments
• Cybersecurity for Industrial Control Systems
• Smart Grid Security
• Software Defined Networking
• Quantum Cryptography
• Security in the Internet of Things
• Natural language processing for sentiment analysis
• Blockchain technology for secure data sharing
• Developing efficient algorithms for big data analysis
• Cybersecurity for internet of things (IoT) devices
• Human-robot interaction for industrial automation
• Image recognition for autonomous vehicles
• Social media analytics for marketing strategy
• Quantum computing for solving complex problems
• Biometric authentication for secure access control
• Augmented reality for education and training
• Intelligent transportation systems for traffic management
• Predictive modeling for financial markets
• Cloud computing for scalable data storage and processing
• Virtual reality for therapy and mental health treatment
• Data visualization for business intelligence
• Recommender systems for personalized product recommendations
• Speech recognition for voice-controlled devices
• Mobile computing for real-time location-based services
• Neural networks for predicting user behavior
• Genetic algorithms for optimization problems
• Distributed computing for parallel processing
• Internet of things (IoT) for smart cities
• Wireless sensor networks for environmental monitoring
• Cloud-based gaming for high-performance gaming
• Social network analysis for identifying influencers
• Autonomous systems for agriculture
• Robotics for disaster response
• Data mining for customer segmentation
• Computer graphics for visual effects in movies and video games
• Virtual assistants for personalized customer service
• Natural language understanding for chatbots
• 3D printing for manufacturing prototypes
• Artificial intelligence for stock trading
• Machine learning for weather forecasting
• Biomedical engineering for prosthetics and implants
• Cybersecurity for financial institutions
• Machine learning for energy consumption optimization
• Computer vision for object tracking
• Natural language processing for document summarization
• Wearable technology for health and fitness monitoring
• Internet of things (IoT) for home automation
• Reinforcement learning for robotics control
• Big data analytics for customer insights
• Machine learning for supply chain optimization
• Natural language processing for legal document analysis
• Artificial intelligence for drug discovery
• Computer vision for object recognition in robotics
• Data mining for customer churn prediction
• Autonomous systems for space exploration
• Robotics for agriculture automation
• Machine learning for predicting earthquakes
• Natural language processing for sentiment analysis in customer reviews
• Big data analytics for predicting natural disasters
• Internet of things (IoT) for remote patient monitoring
• Blockchain technology for digital identity management
• Machine learning for predicting wildfire spread
• Computer vision for gesture recognition
• Natural language processing for automated translation
• Big data analytics for fraud detection in banking
• Internet of things (IoT) for smart homes
• Robotics for warehouse automation
• Machine learning for predicting air pollution
• Natural language processing for medical record analysis
• Augmented reality for architectural design
• Big data analytics for predicting traffic congestion
• Machine learning for predicting customer lifetime value
• Developing algorithms for efficient and accurate text recognition
• Natural Language Processing for Virtual Assistants
• Natural Language Processing for Sentiment Analysis in Social Media
• Explainable Artificial Intelligence (XAI) for Trust and Transparency
• Deep Learning for Image and Video Retrieval
• Edge Computing for Internet of Things (IoT) Applications
• Data Science for Social Media Analytics
• Cybersecurity for Critical Infrastructure Protection
• Natural Language Processing for Text Classification
• Quantum Computing for Optimization Problems
• Machine Learning for Personalized Health Monitoring
• Computer Vision for Autonomous Driving
• Blockchain Technology for Supply Chain Management
• Augmented Reality for Education and Training
• Natural Language Processing for Sentiment Analysis
• Machine Learning for Personalized Marketing
• Big Data Analytics for Financial Fraud Detection
• Cybersecurity for Cloud Security Assessment
• Artificial Intelligence for Natural Language Understanding
• Blockchain Technology for Decentralized Applications
• Virtual Reality for Cultural Heritage Preservation
• Natural Language Processing for Named Entity Recognition
• Machine Learning for Customer Churn Prediction
• Big Data Analytics for Social Network Analysis
• Cybersecurity for Intrusion Detection and Prevention
• Artificial Intelligence for Robotics and Automation
• Blockchain Technology for Digital Identity Management
• Virtual Reality for Rehabilitation and Therapy
• Natural Language Processing for Text Summarization
• Machine Learning for Credit Risk Assessment
• Big Data Analytics for Fraud Detection in Healthcare
• Cybersecurity for Internet Privacy Protection
• Artificial Intelligence for Game Design and Development
• Blockchain Technology for Decentralized Social Networks
• Virtual Reality for Marketing and Advertising
• Natural Language Processing for Opinion Mining
• Machine Learning for Anomaly Detection
• Big Data Analytics for Predictive Maintenance in Transportation
• Cybersecurity for Network Security Management
• Artificial Intelligence for Personalized News and Content Delivery
• Blockchain Technology for Cryptocurrency Mining
• Virtual Reality for Architectural Design and Visualization
• Natural Language Processing for Machine Translation
• Machine Learning for Automated Image Captioning
• Big Data Analytics for Stock Market Prediction
• Cybersecurity for Biometric Authentication Systems
• Artificial Intelligence for Human-Robot Interaction
• Blockchain Technology for Smart Grids
• Virtual Reality for Sports Training and Simulation
• Natural Language Processing for Question Answering Systems
• Machine Learning for Sentiment Analysis in Customer Feedback
• Big Data Analytics for Predictive Maintenance in Manufacturing
• Cybersecurity for Cloud-Based Systems
• Artificial Intelligence for Automated Journalism
• Blockchain Technology for Intellectual Property Management
• Virtual Reality for Therapy and Rehabilitation
• Natural Language Processing for Language Generation
• Machine Learning for Customer Lifetime Value Prediction
• Big Data Analytics for Predictive Maintenance in Energy Systems
• Cybersecurity for Secure Mobile Communication
• Artificial Intelligence for Emotion Recognition
• Blockchain Technology for Digital Asset Trading
• Virtual Reality for Automotive Design and Visualization
• Natural Language Processing for Semantic Web
• Machine Learning for Fraud Detection in Financial Transactions
• Big Data Analytics for Social Media Monitoring
• Cybersecurity for Cloud Storage and Sharing
• Artificial Intelligence for Personalized Education
• Blockchain Technology for Secure Online Voting Systems
• Virtual Reality for Cultural Tourism
• Natural Language Processing for Chatbot Communication
• Machine Learning for Medical Diagnosis and Treatment
• Big Data Analytics for Environmental Monitoring and Management.
• Cybersecurity for Cloud Computing Environments
• Virtual Reality for Training and Simulation
• Big Data Analytics for Sports Performance Analysis
• Cybersecurity for Internet of Things (IoT) Devices
• Artificial Intelligence for Traffic Management and Control
• Blockchain Technology for Smart Contracts
• Natural Language Processing for Document Summarization
• Machine Learning for Image and Video Recognition
• Blockchain Technology for Digital Asset Management
• Virtual Reality for Entertainment and Gaming
• Natural Language Processing for Opinion Mining in Online Reviews
• Machine Learning for Customer Relationship Management
• Big Data Analytics for Environmental Monitoring and Management
• Cybersecurity for Network Traffic Analysis and Monitoring
• Artificial Intelligence for Natural Language Generation
• Blockchain Technology for Supply Chain Transparency and Traceability
• Virtual Reality for Design and Visualization
• Natural Language Processing for Speech Recognition
• Machine Learning for Recommendation Systems
• Big Data Analytics for Customer Segmentation and Targeting
• Cybersecurity for Biometric Authentication
• Artificial Intelligence for Human-Computer Interaction
• Blockchain Technology for Decentralized Finance (DeFi)
• Virtual Reality for Tourism and Cultural Heritage
• Machine Learning for Cybersecurity Threat Detection and Prevention
• Big Data Analytics for Healthcare Cost Reduction
• Cybersecurity for Data Privacy and Protection
• Artificial Intelligence for Autonomous Vehicles
• Blockchain Technology for Cryptocurrency and Blockchain Security
• Virtual Reality for Real Estate Visualization
• Natural Language Processing for Question Answering
• Big Data Analytics for Financial Markets Prediction
• Cybersecurity for Cloud-Based Machine Learning Systems
• Artificial Intelligence for Personalized Advertising
• Blockchain Technology for Digital Identity Verification
• Virtual Reality for Cultural and Language Learning
• Natural Language Processing for Semantic Analysis
• Machine Learning for Business Forecasting
• Big Data Analytics for Social Media Marketing
• Artificial Intelligence for Content Generation
• Blockchain Technology for Smart Cities
• Virtual Reality for Historical Reconstruction
• Natural Language Processing for Knowledge Graph Construction
• Machine Learning for Speech Synthesis
• Big Data Analytics for Traffic Optimization
• Artificial Intelligence for Social Robotics
• Blockchain Technology for Healthcare Data Management
• Virtual Reality for Disaster Preparedness and Response
• Natural Language Processing for Multilingual Communication
• Machine Learning for Emotion Recognition
• Big Data Analytics for Human Resources Management
• Cybersecurity for Mobile App Security
• Artificial Intelligence for Financial Planning and Investment
• Blockchain Technology for Energy Management
• Virtual Reality for Cultural Preservation and Heritage.
• Big Data Analytics for Healthcare Management
• Cybersecurity in the Internet of Things (IoT)
• Artificial Intelligence for Predictive Maintenance
• Computational Biology for Drug Discovery
• Virtual Reality for Mental Health Treatment
• Machine Learning for Sentiment Analysis in Social Media
• Human-Computer Interaction for User Experience Design
• Cloud Computing for Disaster Recovery
• Quantum Computing for Cryptography
• Intelligent Transportation Systems for Smart Cities
• Cybersecurity for Autonomous Vehicles
• Artificial Intelligence for Fraud Detection in Financial Systems
• Social Network Analysis for Marketing Campaigns
• Cloud Computing for Video Game Streaming
• Machine Learning for Speech Recognition
• Augmented Reality for Architecture and Design
• Natural Language Processing for Customer Service Chatbots
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• Artificial Intelligence and its applications
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Journal of Computer Science

Aims and scope.

The Journal of Computer Science (JCS) is dedicated to advancing computer science by publishing high-quality research and review articles that span both theoretical foundations and practical applications in information, computation, and computer systems. With a commitment to excellence, JCS offers a platform for researchers, scholars, and industry professionals to share their insights and contribute to the ongoing evolution of computer science. Published on a monthly basis, JCS provides up-to-date insights into this ever-evolving discipline.

Science Publications is pleased to announce the launch of a new open access journal, Journal of Adaptive Structures. JAS brings together emerging technologies for adaptive smart structures, including advanced materials, smart actuation, sensing and control, to pursue the progressive adoption of the major scientific achievements in this multidisciplinary field on-board of commercial aircraft.

It is with great pleasure that we announce the SGAMR Annual Awards 2020. This award is given annually to Researchers and Reviewers of International Journal of Structural Glass and Advanced Materials Research (SGAMR) who have shown innovative contributions and promising research as well as others who have excelled in their Editorial duties.

This special issue "Neuroinflammation and COVID-19" aims to provide a space for debate in the face of the growing evidence on the affectation of the nervous system by COVID-19, supported by original studies and case series.

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University of Chicago Computer Science Researchers To Present Ten Papers at CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems is a premier international conference where researchers and practitioners gather to discuss the latest research in human-computer interaction. Held annually, CHI brings together experts from academia and industry to present groundbreaking research, share insights, and explore future directions in the field.

This year’s conference, CHI 2024, will see a remarkable showcase of innovative research from students and faculty at the University of Chicago Department of Computer Science . Three papers, including one each from Associate Professor Blase Ur’s group, Associate Professor Pedro Lopes’ group, and Associate Professor Marshini Chetty’s group, received best paper awards. Another paper from Lopes’ group also received an honorable mention.

Some works, like papers featuring Neubauer Professor Nick Feamster and Associate Professor Marshini Chetty, are also collaborations with faculty from The Law School and the Harris School of Public Policy , highlighting the interdisciplinary work that often takes place in the department. Each paper span a diverse range of topics, including contextual notifications for highlighting fairness and bias in data science, in-depth studies of online content moderation policies, investigations into compliance with privacy regulations, AI for the well-being of workers, the introduction of a design space for writing assistants, groundbreaking advancements in haptic interfaces, and innovative approaches to promoting digital well-being through leveraging material receipts for screen-time reflection. The work reflects the department’s commitment to advancing knowledge and addressing real-world challenges in the realm of computing.

Bias In Data Science

Best Paper Award Harrison et al., 2024. JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists.

Although the paper has won an award, the team will be presenting the paper virtually at the conference, rather than traveling to Hawai’i, as a show of solidarity with the community’s protests over the conference’s impact on the local community.

Content Moderation

Schaffner et al., 2024. Community Guidelines Make This the Best Party on the Internet: An In-Depth Study of Online Platforms’ Content Moderation Policies.

Compliance and Privacy Regulations

Tran et al., 2024. Measuring Compliance with the California Consumer Privacy Act Over Space and Time.

AI and Worker’s Well-being

Best Paper Award Das Swain et al., 2024. Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers.

Design Space for Writing Assistants

Lee at el., 2024. A Design Space for Intelligent and Interactive Writing Assistants.

Pushing Boundaries in Haptic Interfaces

Best Paper Award Nith et al., 2024. SplitBody: Reducing Mental Workload while Multitasking via Muscle Stimulation.

Honorable Mention Tanaka et al., 2024. Haptic Source-effector: Full-body Haptics via Non-invasive Brain Stimulation.

Teng et al., 2024. Haptic Permeability: Adding Holes to Tactile Devices Improves Dexterity. Marzursky et al., 2024. Stick&Slip: Altering Fingerpad Friction via Liquid Coatings.

The Human-Computer Integration Lab (directed by Associate Professor Pedro Lopes ) makes a significant impact with not one, but four papers showcasing groundbreaking advancements in haptic interfaces:

Tangible Intervention for Digital Well-being

Sathya et al., 2024. Attention Receipts: Utilizing the Materiality of Receipts to Improve Screen-time Reflection on YouTube.

The University of Chicago Department of Computer Science continues to make significant contributions to the field of human-computer interaction, as evidenced by the diverse and impactful research that will be showcased at CHI 2024 in May. These researchers’ papers exemplify the department’s dedication to advancing knowledge, fostering innovation, and addressing pressing societal issues through computing research.

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How ‘Computer Science for All’ initiatives affect students’ college and career outcomes

Subscribe to the brown center on education policy newsletter, jing liu , jing liu assistant professor of education policy - university of maryland-college park @drjingliu cameron conrad , and cameron conrad ph.d. candidate - university of maryland david blazar david blazar associate professor - university of maryland @david_blazar.

May 1, 2024

• Thirty states have adopted “CS for All” policies, which require all high schools to offer CS coursework, while eight states have gone a step further in requiring all students to take CS as a high school graduation requirement.
• Exposure to high school CS coursework raises CS BA receipt and improves early-career labor market outcomes.
• Although positive long-run impacts of CS course exposure are stronger for historically underrepresented groups, CS course take-up rates are lower among these students.

As the U.S. economy becomes more digitalized, employment in computer and information technology occupations is projected to grow by 10% in the next decade. In response to this increasing demand for technology skills, many states are working to expand CS learning opportunities across K-12 classrooms. Thirty states have adopted “CS for All” policies, which require all high schools to offer CS coursework, while eight states have gone a step further in requiring all students to take CS as a high school graduation requirement. Despite heightened policy interest in CS access, however, research is surprisingly sparse on how the expansion of CS coursework affects postsecondary and labor market outcomes.

In a recent working paper , we aim to fill this research gap by investigating the impact of the expansion of high school CS course offerings. The State of Maryland, which is the context of our study, has rapidly expanded CS course offerings over the last decade. A 2018 law further requires all Maryland high schools to offer at least one “high-quality” CS course aligned with rigorous K-12 CS standards. These “high-quality” courses include foundational courses such as Computer Science Essentials, AP courses such as AP Computer Science Principles, and more specialized programming courses. They are also closely aligned with Code.org’s definition of “foundational” CS courses.

Using rich longitudinal data from the Maryland Longitudinal Data System (MLDS) Center, our study is among the first that offers causal evidence on how access to these “high-quality” CS courses in high school affects college major choice and early-career earnings. Our research design exploits the fact that high schools adopted CS courses at different points in time. In a nutshell, we compare cohorts of students who were exposed to CS to prior cohorts of students from the same high school that were not exposed to CS. This design allows us to estimate causal impacts of both CS course offering and CS course-taking.

Finding #1: Exposure to high school CS coursework raises CS BA receipt and improves early-career labor market outcomes.

Taking a high school CS course leads to a large, five-percentage-point increase in students’ likelihood of earning a bachelor’s degree in CS. Figure 1 shows the estimated effects of taking a CS course in high school on the likelihood of completing a bachelor’s degree for a selection of subjects. Though only the point estimate on CS majors is statistically significant, the negative coefficients for other STEM fields, social sciences, and humanities suggest students may be switching from these fields into CS.

We also find positive impacts on employment and earnings at age 24: High schools offering high-quality CS courses raise students’ likelihood of being employed by 2.6 percentage points and annual earnings by about eight percent. Importantly, these estimates are based on all students, not just those who pursue CS in their later studies or careers.

These findings on degree receipt are similar in magnitude to prior research on STEM course-taking. For example, one study has found that taking advanced secondary science coursework raises STEM degree receipt for male students by six percentage points. In another study, taking advanced secondary mathematics coursework raises engineering degree receipt by nine percentage points for females. Along with these studies, our study suggests that policies that expand high school STEM offerings can increase degree receipt in related fields.

Finding #2: Effects on CS BA receipt and earnings are similar or larger for students who are historically underrepresented in the CS field.

CS, along with many other STEM fields, have historically been dominated by workers who are white or male, and recent policy efforts have been intended to make CS learning and career options more accessible for all . On receipt of bachelor’s degrees in CS, high school CS course-taking has similar and sometimes even larger effects for females, students from lower socioeconomic status (SES) backgrounds, and Black students relative to peer groups that have been better represented in CS fields historically. For example, taking a CS course raises receipt of CS bachelor’s degrees by seven percentage points for Black students, five percentage points for Hispanic students, and six percentages for white students, which are statistically indistinguishable from each other. The biggest difference comes from subgroup analysis by SES. The effect size is nearly eight percentage points for low SES students, while only two percentage points for their counterparts (though it is not statistically significant).

A striking difference across groups emerges when we look at earnings outcomes. In Figure 2, we plot the impact of CS course-taking in high school on earnings for different student demographic groups. The overall effect is also included for comparison. We find that high schools offering CS coursework increase earnings at age 24 by eight percentage points overall (see Figure 2 below). Even more, when we break these estimates out by different student subgroups, we find these earnings effects are driven by historically underrepresented subgroups, including females, those from low SES backgrounds, and Black students. Together, these findings suggest that exposing students to CS coursework in high school can be an effective approach for increasing the supply of CS degree recipients and professionals in the labor market, with enhanced earnings particularly for historically underrepresented groups.

Finding #3: Although positive long-run impacts of CS course exposure are stronger for historically underrepresented groups, CS course take-up rates are lower among these students.

Given the range of long-run benefits of taking a high school CS course, which student groups are most likely to take advantage of this opportunity? We find that high schools offering CS raises the chance of students taking a CS course by about six percentage points for all students, on average, as not all students end up enrolling in the class when offered. Specifically, CS course take-up increases by about 13 percentage points for Asian students, but only six percentage points for Black students and four percentage points for Hispanic students. The contrast is similarly stark for students at different achievement levels: Based on their prior math achievement, the top 25% of students have an increased CS course-taking rate almost three times higher than that of the bottom 25% of students. We also observe slightly lower CS course take-up rates for females and students from low SES backgrounds compared to their counterparts, but the differences are not statistically significant.

Overall, although underrepresented groups of students benefit from taking a CS course if they enroll, they may be less likely to take on this opportunity compared to their peers. Similar lower participation rates among under-represented groups are also observed in other states and nationally as well. For example, in California , female students are much less likely to take CS despite having similar access relative to male peers. National data also show that male students are more than twice as likely to take CS compared to females. Thus, it is critical to identify and remove the barriers underrepresented students face regarding CS course enrollment.

Rapid expansion of CS courses raises questions about the capacity of schools and teachers to fulfill new requirements.

The number of states requiring all high schools to offer CS coursework has increased from just four states in 2017 to 30 states in 2023. We can also expect more states to follow the existing eight states that have mandated CS courses as a requirement for high school graduation. As Maryland and many other states continue their efforts in expanding CS courses in K-12 schools, this fast growth also raises concerns about whether schools are prepared to implement new CS requirements. In particular, a major bottleneck is the shortages of qualified CS teachers, a challenge also faced by many other countries that try to expand CS education. This is not surprising, given that individuals with CS skills and qualifications often have other highly paid job alternatives.

The limited supply of CS teachers also has implications for the current CS teacher workforce. Our own analysis of Maryland data suggests that most high schools that offer CS courses only have one CS teacher, meaning that they do not have colleagues to do co-planning or to share the challenges they encounter in their CS classrooms. In addition, a large share of Maryland CS teachers teaches out of their certified fields without adequate preparation for the necessary skills needed to teach CS.

These staffing challenges require school systems to concentrate efforts to boost the supply of qualified CS teachers as well as provide support to existing CS teachers to ensure quality CS instruction. States may consider allocating funding to expand traditional and alternative pathways to CS teacher certification. Focusing on professional development that provides training on CS content and pedagogy for existing teachers who will be new to teaching CS may also offer the potential to promote impactful CS instruction.

Taken as a whole, our research offers reasons for optimism about the potential of “CS for All” policies, especially given the positive effects of CS course-taking on CS degree recipients and early-career earnings for underrepresented student groups. However, policymakers must also be mindful of the way course expansion policies are implemented and improve the supply of qualified CS teachers to ensure all students benefit from these opportunities.

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A guide for early-career researchers in computational science

by Samuel Jarman, SciencePOD

In recent years, a growing number of students have embraced scientific computation as an integral component of their graduate research. Yet since many of them are new to the field, they often have little to no coding experience, or any prior knowledge of computational tools. For many students starting out in the field, this can seem daunting, and leaves them unsure of where to start.

In a new article published in The European Physical Journal Plus , a team led by Idil Ismail, a current graduate student at the University of Warwick, UK, present an introductory guide to the field for researchers embarking on new careers.

The team's work will help new graduate students to navigate the complexities of scientific computation science as they begin their journey in computational science research, and could ultimately help the wider field to become more transparent and inclusive.

Modern computational science is now being used in a wide array of subject areas: including mathematics, physics, chemistry, engineering, and the life sciences . Yet despite their many differences, these different branches of the field share many of the same techniques, which graduate students will need to learn regardless of the area they decide to pursue.

In their article, Ismail's team aim to highlight the universal skills, themes, and methods widely used by computational scientists. In nine carefully structured sections, they cover a broad spectrum of important techniques: including scientific programming, machine learning , and Bash scripting, With its approachable and instructive tone, the article is not intended as an exhaustive guide.

Instead, it acts as a useful starting point: signposting readers to more in-depth sources, and encouraging them to expand their knowledge by seeking out information for themselves. Altogether, the team's work will help early-career computational scientists to build a toolkit of indispensable skills, and will be a valuable resource for any graduate student entering the field.

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Scientists create robot snails that can move independently using tracks or work together to climb

by Bob Yirka , Tech Xplore

A team of roboticists at the Chinese University of Hong Kong has created a robot snail with a helmet-like shell that moves by rolling around on bulldozer-like tracks. They have published a paper on their research in Nature Communications .

Over the past several years, robot engineers have developed a wide variety of robots. Some can fly, others can swim and more can move across floors or landscapes. In this new effort, the researchers noted that the flying and swimming robots are able to move about in three-dimensional space, while those that walk, roll or crawl are limited to two dimensions. They sought to bridge that gap by developing a type of robot that could create its own three-dimensional space.

The result was a robot that looks like a snail, with a metal shell and a track that allows it to move around. In action, it resembles a WWI-era tank. But what sets the snail-like robot apart is the retractable suction cup that sits between its tracks. Its purpose is to allow the robot to adhere to another robot just like it.

Several robots can connect by using the suction cup to cling to the helmet of the robot ahead to form a train. They can also create stair steps that allow others of their kind to climb up and over objects. Because each of the robots has its own processor, it can work on its own or with others—when working together, the robots communicate with one another, helping to coordinate activities.

The research team notes that the tracks allow the robots to move across a wide variety of surfaces, both smooth and uneven. There are also magnets embedded in the tracks that help the robots climb on top of each other to create steps or other structures. Once they have the correct placement, they deploy the suction cup to hold fast.

The team found the robots capable of moving independently, climbing up steps as a collective and even making their way across gaps like ants do when forming bridges. They note that currently the robots are remotely controlled, but the researchers hope the robots will eventually be able to work autonomously. The team believes they could be used in field research , conducting search and rescue, or even in space missions as planetary probes.

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