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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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Categories within Computer Science

  • cs.AI - Artificial Intelligence ( new , recent , current month ) Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
  • cs.CL - Computation and Language ( new , recent , current month ) Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
  • cs.CC - Computational Complexity ( new , recent , current month ) Covers models of computation, complexity classes, structural complexity, complexity tradeoffs, upper and lower bounds. Roughly includes material in ACM Subject Classes F.1 (computation by abstract devices), F.2.3 (tradeoffs among complexity measures), and F.4.3 (formal languages), although some material in formal languages may be more appropriate for Logic in Computer Science. Some material in F.2.1 and F.2.2, may also be appropriate here, but is more likely to have Data Structures and Algorithms as the primary subject area.
  • cs.CE - Computational Engineering, Finance, and Science ( new , recent , current month ) Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
  • cs.CG - Computational Geometry ( new , recent , current month ) Roughly includes material in ACM Subject Classes I.3.5 and F.2.2.
  • cs.GT - Computer Science and Game Theory ( new , recent , current month ) Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
  • cs.CV - Computer Vision and Pattern Recognition ( new , recent , current month ) Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
  • cs.CY - Computers and Society ( new , recent , current month ) Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
  • cs.CR - Cryptography and Security ( new , recent , current month ) Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
  • cs.DS - Data Structures and Algorithms ( new , recent , current month ) Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
  • cs.DB - Databases ( new , recent , current month ) Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
  • cs.DL - Digital Libraries ( new , recent , current month ) Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
  • cs.DM - Discrete Mathematics ( new , recent , current month ) Covers combinatorics, graph theory, applications of probability. Roughly includes material in ACM Subject Classes G.2 and G.3.
  • cs.DC - Distributed, Parallel, and Cluster Computing ( new , recent , current month ) Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
  • cs.ET - Emerging Technologies ( new , recent , current month ) Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
  • cs.FL - Formal Languages and Automata Theory ( new , recent , current month ) Covers automata theory, formal language theory, grammars, and combinatorics on words. This roughly corresponds to ACM Subject Classes F.1.1, and F.4.3. Papers dealing with computational complexity should go to cs.CC; papers dealing with logic should go to cs.LO.
  • cs.GL - General Literature ( new , recent , current month ) Covers introductory material, survey material, predictions of future trends, biographies, and miscellaneous computer-science related material. Roughly includes all of ACM Subject Class A, except it does not include conference proceedings (which will be listed in the appropriate subject area).
  • cs.GR - Graphics ( new , recent , current month ) Covers all aspects of computer graphics. Roughly includes material in all of ACM Subject Class I.3, except that I.3.5 is is likely to have Computational Geometry as the primary subject area.
  • cs.AR - Hardware Architecture ( new , recent , current month ) Covers systems organization and hardware architecture. Roughly includes material in ACM Subject Classes C.0, C.1, and C.5.
  • cs.HC - Human-Computer Interaction ( new , recent , current month ) Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
  • cs.IR - Information Retrieval ( new , recent , current month ) Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
  • cs.IT - Information Theory ( new , recent , current month ) Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
  • cs.LO - Logic in Computer Science ( new , recent , current month ) Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
  • cs.LG - Machine Learning ( new , recent , current month ) Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
  • cs.MS - Mathematical Software ( new , recent , current month ) Roughly includes material in ACM Subject Class G.4.
  • cs.MA - Multiagent Systems ( new , recent , current month ) Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
  • cs.MM - Multimedia ( new , recent , current month ) Roughly includes material in ACM Subject Class H.5.1.
  • cs.NI - Networking and Internet Architecture ( new , recent , current month ) Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
  • cs.NE - Neural and Evolutionary Computing ( new , recent , current month ) Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
  • cs.NA - Numerical Analysis ( new , recent , current month ) cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
  • cs.OS - Operating Systems ( new , recent , current month ) Roughly includes material in ACM Subject Classes D.4.1, D.4.2., D.4.3, D.4.4, D.4.5, D.4.7, and D.4.9.
  • cs.OH - Other Computer Science ( new , recent , current month ) This is the classification to use for documents that do not fit anywhere else.
  • cs.PF - Performance ( new , recent , current month ) Covers performance measurement and evaluation, queueing, and simulation. Roughly includes material in ACM Subject Classes D.4.8 and K.6.2.
  • cs.PL - Programming Languages ( new , recent , current month ) Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
  • cs.RO - Robotics ( new , recent , current month ) Roughly includes material in ACM Subject Class I.2.9.
  • cs.SI - Social and Information Networks ( new , recent , current month ) Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
  • cs.SE - Software Engineering ( new , recent , current month ) Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
  • cs.SD - Sound ( new , recent , current month ) Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
  • cs.SC - Symbolic Computation ( new , recent , current month ) Roughly includes material in ACM Subject Class I.1.
  • cs.SY - Systems and Control ( new , recent , current month ) cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
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SciSpace Resources

Top 16 International Computer Science Journals — A Template Guide

Monali Ghosh

MS Word, LaTeX Templates and Author Guidelines

This post is part of a series of blogs with links to Word, LaTeX templates and author instructions of top journals around the world in more than 25 core subjects in academia.

Getting started with your Research Paper Formatting

Writing a successful research paper is more than just communicating your knowledge . Most of the journals prescribe detailed set of authoring guidelines to apply on your content before you submit. Many research papers even get rejected for not following the guidelines of the journal (a reason why we built SciSpace (Formerly Typeset) — a platform to automatically apply 100% journal guidelines on your content).

To get you quickly started with your research paper formatting, this blog article lists journal formats and authoring guidelines of top international journals in Computer Science. You can find the links to MS Word template as well as LaTeX template of each of the journal here. You can also find the access link to the detailed author guidelines set by the journal. Feel free to check it out, share with friends and comment on the article.

Science-Journals

Top International Computer Science Journals

We have used "Impact Factor" and various other parameters to rank the journals( Source ).

1. IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. It covers research in computer vision and image understanding, pattern analysis and recognition, and machine intelligence. machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition.

Impact Factor — 5.694 (2013)

Journal Abbreviation — IEEE Trans. Pattern Anal. Mach. Intell.

Download MS Word Template here

Download LaTeX Template here

Check out the detailed Author Guidelines here

2. Artificial Intelligence

Artificial Intelligence is a scientific journal on artificial intelligence research. It was established in 1970 and is published by Elsevier.

Impact Factor — 3.333 (2015)

Find instructions for MS Word Template here

** The journal doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a few click s .

3. Communications of the ACM

Communications of the ACM is the monthly Journal of the Association for Computing Machinery (ACM). The focus is on the practical implications of advances in information technology and associated management issues; ACM also publishes a variety of more theoretical journals.

Impact Factor — 3.301 (2015)

Journal Abbreviation — Commun ACM

** The journal doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click.

4. Computer

Computer is an IEEE Computer Society practitioner-oriented magazine and contains peer-reviewed articles, regular columns and interviews on current computing-related issues.

Impact Factor — 1.438 (2013)

5. IEEE Transactions on Computers

IEEE Transactions on Computers is a monthly peer-reviewed scientific journal covering all aspects of computer design. It was established in 1952 and is published by the IEEE Computer Society.

Impact Factor — 1.473 (2013)

Journal Abbreviation — IEEE Trans. Comput.

6. IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined.

Impact Factor — 5.545 (2013)

Journal Abbreviation — IEEE Trans. Evolut. Comput.

7. IEEE Transactions on Fuzzy Systems

IEEE Transactions on Fuzzy Systems is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design or applications of fuzzy systems ranging from hardware to software, including significant technical achievements, exploratory developments, or performance studies of fielded systems based on fuzzy models.

Impact Factor — 6.701 (2016)

Journal Abbreviation — IEEE Trans. Fuzzy Syst.

8. Journal of Cryptology

The Journal of Cryptology is a scientific journal in the field of cryptology and cryptography. The journal is published quarterly by the International Association for Cryptologic Research.

Impact Factor — 1.021(2015)

** The journal doesn’t provide you a LaTeX template .The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click and download the LaTeX version.

9. IEEE Transactions on Information Theory

IEEE Transactions on Information Theory is a monthly peer-reviewed scientific journal published by the IEEE Information Theory Society. It covers information theory and the mathematics of communications.

Impact Factor — 2.65 (2013)

Journal Abbreviation — IEEE Trans. Inf. Theory

Check out MS Word Template here

Check out LaTeX Template here

10. IEEE Transactions on Neural Networks and Learning Systems

IEEE Transactions on Neural Networks and Learning Systems is a monthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design, and applications of neural networks and related learning systems.

Impact Factor — 4.37 (2013)

Journal Abbreviation — IEEE Trans. Neural Netw. Learn. Syst

11. Journal of the ACM

The Journal of the ACM is a peer-reviewed scientific journal covering computer science in general, especially theoretical aspects. It is an official journal of the Association for Computing Machinery.

Impact Factor — 2.353 (2011)

Journal Abbreviation — J. ACM

12. Journal of Artificial Intelligence Research

The Journal of Artificial Intelligence Research is an open access peer-reviewed scientific journal covering research in all areas of artificial intelligence. Paper volumes are printed by the AAAI Press.

Impact Factor — 1.691 (2010)

Journal Abbreviation — J. Artif. Intell. Res

13. Journal of Functional Programming

The Journal of Functional Programming is a peer-reviewed scientific journal covering the design, implementation, and application of functional programming languages, spanning the range from mathematical theory to industrial practice.

Impact Factor — 1.357(2015)

** The journal doesn’t provide you a LaTeX template . The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace , format it to the journal guidelines in a click and download your document in LaTeX format.

14. International Journal of Computer Vision

The International Journal of Computer Vision (IJCV) is a journal published by Springer.

Impact Factor — 3.623 (2012)

Journal Abbreviation — IJCV

Find LaTeX instructions here

Find MS Word instructions here

** The journal doesn’t provide you a LaTeX template . The journal also doesn’t provide you a MS Word template. You have to follow the author guidelines to format your document. You can also write your document on SciSpace and format it to the journal guidelines in a click.

15. Journal of Machine Learning Research

The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning.

Impact Factor — 2.45(2015)

16. SIAM Journal on Computing (SICOMP)

The SIAM Journal on Computing ( SICOMP ) is a scientific journal focusing on the mathematical and formal aspects of computer science. It is published by the Society for Industrial and Applied Mathematics (SIAM).

Journal Abbreviation — SIAM J. Comput.

If you found this list useful, please do share top computer science journals’ templates with your fellow researchers, academics and colleagues.

A research writing tool that helps you follow 100% guidelines

Adhering to fuzzy journal guidelines that runs to hundreds of pages is every researcher’s nightmare. That’s where SciSpace comes in.

SciSpace has around 14000 journal templates and enables you to format or re-format your research paper to all of the journal guidelines with 100% accuracy. What more, you save loads of your time while doing it .

SciSpace also has various University thesis, assignments and top international Conferences’ templates. Check it out here .

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WorldCIST 2021: Trends and Applications in Information Systems and Technologies pp 13–22 Cite as

Five Hundred Most-Cited Papers in the Computer Sciences: Trends, Relationships and Common Factors

  • Phoey Lee Teh   ORCID: orcid.org/0000-0002-7787-1299 19 &
  • Peter Heard   ORCID: orcid.org/0000-0002-5135-7822 20  
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  • First Online: 29 March 2021

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1366)

This study reveals common factors among highly cited papers in the computer sciences. The 500 most cited papers in the computer sciences published between January 2013 and December 2017 were downloaded from the Web of Science (WoS). Data on the number of citations, number of authors, article length and subject sub-discipline were extracted and analyzed in order to identify trends, relationships and common features. Correlations between common factors were analyzed. The 500 papers were cited a total of 10,926 times: the average number of citations per paper was 21.82 citations. A correlation was found between author credibility (defined in terms of the QS University Ranking of the first named author’s affiliation) and the number of citations. Authors from universities ranked 350 or higher were more cited than those from lower ranked universities. Relationships were also found between journal ranking and both the number of authors and the article length. Higher ranked journals tend to have a greater number of authors, but were of shorter length. The article length was also found to be correlated with the number of authors and the QS Subject Ranking of the first author’s affiliation. The proportion of articles in higher ranked journals (journal quartile), the length of articles and the number of citations per page were all found to correlate to the sub-discipline area (Information Systems; Software Engineering; Artificial Intelligence; Interdisciplinary Applications; and Theory and Methods).

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Teh, P.L., Heard, P. (2021). Five Hundred Most-Cited Papers in the Computer Sciences: Trends, Relationships and Common Factors. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_2

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Mendeley Blog

The top 10 research papers in computer science by mendeley readership..

Since we recently announced our $10001 Binary Battle to promote applications built on the Mendeley API ( now including PLoS as well), I decided to take a look at the data to see what people have to work with. My analysis focused on our second largest discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this one so that I could look at the data with fresh eyes, and also because it’s got some really cool papers to talk about. Here’s what I found: What I found was a fascinating list of topics, with many of the expected fundamental papers like Shannon’s Theory of Information and the Google paper, a strong showing from Mapreduce and machine learning, but also some interesting hints that augmented reality may be becoming more of an actual reality soon.

best computer science research papers

LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon’s information theory paper (#7) or the paper describing the concept that became Google (#3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their subdiscipline. In fact, AI researchers contributed the majority of readership to 6 out of the top 10 papers. Presumably, those interested in popular topics such as machine learning list themselves under AI, which explains the strength of this subdiscipline, whereas papers like the Mapreduce one or the Google paper appeal to a broad range of subdisciplines, giving those papers a smaller numbers spread across more subdisciplines. Professor Blei is also a bit of a superstar, so that didn’t hurt. (the irony of a manually-categorized list with an LDA paper at the top has not escaped us)

2. MapReduce : Simplified Data Processing on Large Clusters (available full-text)

best computer science research papers

It’s no surprise to see this in the Top 10 either, given the huge appeal of this parallelization technique for breaking down huge computations into easily executable and recombinable chunks. The importance of the monolithic “Big Iron” supercomputer has been on the wane for decades. The interesting thing about this paper is that had some of the lowest readership scores of the top papers within a subdiscipline, but folks from across the entire spectrum of computer science are reading it. This is perhaps expected for such a general purpose technique, but given the above it’s strange that there are no AI readers of this paper at all.

3. The Anatomy of a large-scale hypertextual search engine (available full-text)

best computer science research papers

In this paper, Google founders Sergey Brin and Larry Page discuss how Google was created and how it initially worked. This is another paper that has high readership across a broad swath of disciplines, including AI, but wasn’t dominated by any one discipline. I would expect that the largest share of readers have it in their library mostly out of curiosity rather than direct relevance to their research. It’s a fascinating piece of history related to something that has now become part of our every day lives.

4. Distinctive Image Features from Scale-Invariant Keypoints

best computer science research papers

This paper was new to me, although I’m sure it’s not new to many of you. This paper describes how to identify objects in a video stream without regard to how near or far away they are or how they’re oriented with respect to the camera. AI again drove the popularity of this paper in large part and to understand why, think “ Augmented Reality “. AR is the futuristic idea most familiar to the average sci-fi enthusiast as Terminator-vision . Given the strong interest in the topic, AR could be closer than we think, but we’ll probably use it to layer Groupon deals over shops we pass by instead of building unstoppable fighting machines.

5. Reinforcement Learning: An Introduction (available full-text)

best computer science research papers

This is another machine learning paper and its presence in the top 10 is primarily due to AI, with a small contribution from folks listing neural networks as their discipline, most likely due to the paper being published in IEEE Transactions on Neural Networks. Reinforcement learning is essentially a technique that borrows from biology, where the behavior of an intelligent agent is is controlled by the amount of positive stimuli, or reinforcement, it receives in an environment where there are many different interacting positive and negative stimuli. This is how we’ll teach the robots behaviors in a human fashion, before they rise up and destroy us.

6. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (available full-text)

best computer science research papers

Popular among AI and information retrieval researchers, this paper discusses recommendation algorithms and classifies them into collaborative, content-based, or hybrid. While I wouldn’t call this paper a groundbreaking event of the caliber of the Shannon paper above, I can certainly understand why it makes such a strong showing here. If you’re using Mendeley, you’re using both collaborative and content-based discovery methods!

7. A Mathematical Theory of Communication (available full-text)

best computer science research papers

Now we’re back to more fundamental papers. I would really have expected this to be at least number 3 or 4, but the strong showing by the AI discipline for the machine learning papers in spots 1, 4, and 5 pushed it down. This paper discusses the theory of sending communications down a noisy channel and demonstrates a few key engineering parameters, such as entropy, which is the range of states of a given communication. It’s one of the more fundamental papers of computer science, founding the field of information theory and enabling the development of the very tubes through which you received this web page you’re reading now. It’s also the first place the word “bit”, short for binary digit, is found in the published literature.

8. The Semantic Web (available full-text)

best computer science research papers

In The Semantic Web, Tim Berners-Lee, Sir Tim, the inventor of the World Wide Web, describes his vision for the web of the future. Now, 10 years later, it’s fascinating to look back though it and see on which points the web has delivered on its promise and how far away we still remain in so many others. This is different from the other papers above in that it’s a descriptive piece, not primary research as above, but still deserves it’s place in the list and readership will only grow as we get ever closer to his vision.

9. Convex Optimization (available full-text)

best computer science research papers

This is a very popular book on a widely used optimization technique in signal processing. Convex optimization tries to find the provably optimal solution to an optimization problem, as opposed to a nearby maximum or minimum. While this seems like a highly specialized niche area, it’s of importance to machine learning and AI researchers, so it was able to pull in a nice readership on Mendeley. Professor Boyd has a very popular set of video classes at Stanford on the subject, which probably gave this a little boost, as well. The point here is that print publications aren’t the only way of communicating your ideas. Videos of techniques at SciVee or JoVE or recorded lectures ( previously ) can really help spread awareness of your research.

10. Object recognition from local scale-invariant features (available in full-text)

best computer science research papers

This is another paper on the same topic as paper #4, and it’s by the same author. Looking across subdisciplines as we did here, it’s not surprising to see two related papers, of interest to the main driving discipline, appear twice. Adding the readers from this paper to the #4 paper would be enough to put it in the #2 spot, just below the LDA paper.

Conclusions

So what’s the moral of the story? Well, there are a few things to note. First of all, it shows that Mendeley readership data is good enough to reveal both papers of long-standing importance as well as interesting upcoming trends. Fun stuff can be done with this! How about a Mendeley leaderboard? You could grab the number of readers for each paper published by members of your group, and have some friendly competition to see who can get the most readers, month-over-month. Comparing yourself against others in terms of readers per paper could put a big smile on your face, or it could be a gentle nudge to get out to more conferences or maybe record a video of your technique for JoVE or Khan Academy or just Youtube.

Another thing to note is that these results don’t necessarily mean that AI researchers are the most influential researchers or the most numerous, just the best at being accounted for. To make sure you’re counted properly, be sure you list your subdiscipline on your profile, or if you can’t find your exact one, pick the closest one, like the machine learning folks did with the AI subdiscipline. We recognize that almost everyone does interdisciplinary work these days. We’re working on a more flexible discipline assignment system, but for now, just pick your favorite one.

These stats were derived from the entire readership history, so they do reflect a founder effect to some degree. Limiting the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars.

Technical details: To do this analysis I queried the Mendeley database, analyzed the data using R , and prepared the figures with Tableau Public . A similar analysis can be done dynamically using the Mendeley API . The API returns JSON, which can be imported into R using the fine RJSONIO package from Duncan Temple Lang and Carl Boettiger is implementing the Mendeley API in R . You could also interface with the Google Visualization API to make motion charts showing a dynamic representation of this multi-dimensional data. There’s all kinds of stuff you could do, so go have some fun with it. I know I did.

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2 thoughts on “ the top 10 research papers in computer science by mendeley readership. ”.

You might consider revisiting the subdiscipline list, e.g. split computer vision, robotics and machine learning from AI, since the latest is a fuzzy and uncertain concept. Neural networks could be combined with machine learning, though.

Especially in fast-growing fields like computer science, discipline will always be a somewhat fuzzy concept. We are working on a way for people to assign themselves and papers to disciplines in a more flexible way.

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You should be reading academic computer science papers

You read documentation and tutorials to become a better programmer, but if you really want to be cutting-edge, academic research is where it's at.

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[Ed. note: While we take some time to rest up over the holidays and prepare for next year, we are re-publishing our top ten posts for the year. This is our number one post of 2022! Thanks for reading and we’ll see you in the new year. ]

As working programmers, you need to keep learning all the time. You check out tutorials, documentation, Stack Overflow questions, anything you can find that will help you write code and keep your skills current. But how often do you find yourself digging into academic computer science papers to improve your programming chops?

While the tutorials can help you write code right now, it’s the academic papers that can help you understand where programming came from and where it’s going. Every programming feature, from the null pointer (aka the billion dollar mistake ) to objects (via Smalltalk ) has been built on a foundation of research that stretches back to the 1960s (and earlier). Future innovations will be built on the research of today.

We spoke to three of the members of the Papers We Love team, an online repository of their favorite computer science scholarship.

Zeeshan Lakhani, an engineering director at BlockFi, Darren Newton, an engineering team lead at Datadog, and David Ashby, a staff engineer at SageSure, all met while working at a company called Arc90. They found that none of them had formal training in computer science, but they all wanted to learn more. All three came from humanities and arts disciplines: Ashby has an English degree with a history minor, Newton went to art school twice, and Lakhani went to film school for undergrad before getting a master’s degree in music and audio engineering. All of those fields of study rely heavily on reading texts that built the foundation of the discipline as to understand the theory that underlies all practice.

Like any good student of the humanities, they went looking for answers in the archives. “I had a latent librarian inside,” said Newton. “So I'm always interested in the historical source material for the things that I do.”

Surveying history

As part of learning more about the history of programming, Ashby was reading Tracy Kidder’s Soul of a New Machine , about the race to design a 32-bit microcomputer in the late 70s. It covered both the engineering culture at the time and the problems and concepts those engineers wrestled with. This was before the time of mass-market CPUs and standard motherboard components, so a lot of what we take for granted today was still being worked out.

In Kidder’s book, Lakhani, Newton, and Ashby saw a whole history of computer science that they had no connection with, so they decided to try reading a foundational paper: Tony Hoare ’s “ Communicating Sequential Processes ” from 1978. They were working on Clojure and Clojurescript at the time, so this seemed relevant. When they sat down to discuss the paper, they realized they didn’t even know how to approach understanding it. “It was like, I can't understand half of this formalism, but maybe the intro is pretty good,” said Lakhani. “But we need someone like David Nolen to explain this to us.”

Nolen was an acquaintance who worked for The New York Times . He gave a talk there about Clojure and other Lisp-like languages, referencing a lot of John McCarthy’s early papers. Hearing this explanation with the academic context started turning a few gears in their minds. That’s when the idea of Papers We Love was born.

Knowing the history of the computing concepts that you use every day unlocks a lot of understanding into how they work at a practical level. The tools that you use, from databases to programming languages, are built on a foundation of academic research. “Understanding the roots of the things you're working on unlocks a lot of knowledge that you're not going to get purely just by using every day because you don't understand the paths that they didn't go down,” said Ashby.

There’s a talk they love that Bret Victor gave in 2013 called “ The Future of Programming .” He’s dressed like an engineer from the 70s, white button-up, khakis, pocket protector. He starts giving his talk using an overhead projector that has the name of the talk. He adjusts the slide and it reveals that the date is 1973. He goes on to talk about all the great things coming out of research, all the things that are going to shake up computer science. And they’re all things that the audience is still dealing with, like the move from sequential execution to concurrent models.

“The top theme was that it takes a long time,” said Lakhani. “There's a lot of things that are old that are new again, over and over and over.” The same problems are still relevant, whether because the problems are harder than once thought or because the research into those problems has been widely shared.

The trio behind Papers We Love aren’t alone in discovering a love for computing’s history. There is an increased interest in retrocomputing , engineers looking at the systems of the past to learn more about the practice of technology. It’s the flipside of looking at older papers; you look at the old hardware and software programmers used and work on it with a present-day mindset. “A lot of people are spinning up these ancient operating systems on Raspberry PIs and working with them,” said Newton. “Like spinning up an old Smalltalk VM on a Raspberry PI or recreating a PDP-10.”

When you see these issues in their initial contexts, like reading the research papers that tried to address them, you can get a better perspective on where you are now. That can lead to all sorts of epiphanies. “Oh, objects do the things they do because of Smalltalk back in the 80s,” said Ashby. “And that's why big systems look like that. And that's why Java looks like that.”

That new understanding can help you solve the problems that you face now.

The future of programming (today)

There’s more to reading research papers than understanding history; you can find new ways to solve problems by reading current research. “The idea of Stack Overflow is: someone else has had your problem before,” said Ashby. “Academic papers are: someone else has thought about this problem before.”

If your work involves building variations of the same old CRUD app in new spaces, then maybe research papers won’t help you. But if you are trying to solve the unique problems of your industry, then some of the research in those problem spaces may help you overcome them. “I find papers to expand the idea of what's possible with the work you do,” said Ashby. “They can help you appreciate that there are other ways to solve these problems.”

For Newton and his colleagues at Datadog, academic papers are an integral part of their work. Their monitoring software has to process a lot of information in real time to give engineers a view of their applications and the stack they run on. “We are very concerned with performance algorithms and better ways to do statistics on large volumes of data ,” said Newton. “We need to rely on academic research for some of that.”

Just because research exists, of course, it doesn’t mean your problems are automatically solved. Sometimes a single paper only gets you part of the solution. “I was at Comcast where we wanted to leverage load balancing work that we do in terms of routing,” said Lakhani. “We ended up applying three different kinds of papers that didn't know each other. We put semantics into network packets, routed them based on another paper via a specific protocol, and implemented a bunch of IETF specs. Part of this work now lives in a Rust library people can run today.” It's finding threads in academic work and braiding them together to solve the problems at hand.

Without reading those papers, Lakhani’s team wouldn’t have been able to design such an effective solution. Perhaps they would have gotten there on their own. But imagine the amount of work to research those three concepts; there’s no need to redo their work if it’s already been done. It’s standing on the shoulders of giants, as the saying goes, and if you’re on top of the research in your field, you know exactly which giants to stand on.

A map of the giants’ shoulders

Naturally, being a graduate of the humanities myself, I wanted to know which were the giants of computer science, those papers that would be on the syllabus if you were to construct a humanities-style curricula for a class. Think of it as a map of which giant shoulders you could stand on to get ahead.

It turns out, I’m not the first to wonder what’s in the computer science canon. In 1996, Phillip Laplante wrote Great Papers in Computer Science , which might be a bit outdated at this point. For a more recent take on the same thing, the trio recommend Ideas That Created the Future , published last year. Lakhani, who is now doing a PhD in computer science at Carnegie Mellon University (my alma mater), points out that there was a course when he arrived that covered the important papers of the field.

In a way, this canon is exactly what the Papers We Love repo aims to create. It contains papers and links to papers organized by topic. The group welcomes new pull requests with academic papers that you all love and want to see spotlighted.

Here are a few papers (and talks) that they recommended to anyone wanting to get started reading the research:

  • Dynamo: Amazon’s Highly Available Key-value Store
  • A Unified Theory of Garbage Collection
  • Communicating Sequential Processes
  • Out of the Tar Pit

Of course, there are many more.

If you’re intimidated by starting on a paper, then check out some of Papers We Love’s presentations , which offer a primer on how to understand a paper. The whole idea of these talks is borne out of that first frustration with a paper, then finding a path through it with someone else’s help. “They've gotten the CliffsNotes,” says Lakhani. “Now they can attack the paper and really understand it.”

The Papers We Love community continues to try to build a bridge between industry and academia. Everyone benefits—the industry gets access to new solutions without having to wait for someone else to implement and open-source them, and academics get to see their ideas tested and implemented in real situations.

“One of the goals of Papers We Love is to make it where you find out about stuff a little bit faster,” said Lakhani. “Maybe that changes things.”

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Google Scholar reveals its most influential papers for 2021

Early clinical observations of COVID-19 and its mortality risk factors among the most cited output, while a five-year-old AI paper continues to command attention.

best computer science research papers

Examples of using SSD, an object-detection algorithm described in a highly cited artificial intelligence paper. Credit: Wei Liu et al. European Conference on Computer Vision (2016)

24 August 2021

best computer science research papers

Wei Liu et al. European Conference on Computer Vision (2016)

Examples of using SSD, an object-detection algorithm described in a highly cited artificial intelligence paper.

COVID-19-related papers have eclipsed artificial intelligence research in the annual listing of the most highly-cited publications in the Google Scholar database. The most highly cited COVID-19 paper, published in The Lancet in early 2020, has garnered more than 30,000 citations to date (see below for paper summary).

But, in the database of almost 400 million academic papers and other scholarly literature, even it fell a long way short of the most highly cited paper of the last five years, ‘Deep Residual Learning for Image Recognition’, published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition by a team from Microsoft in 2016.

The five-year-old paper’s astonishing ascendancy continues, from 25,256 citations in 2019 to 49,301 citations in 2020 to 82,588 citations in 2021. We wrote about it last year here .

The 2021 Google Scholar Metrics ranking tracks papers published between 2016 and 2020, and includes citations from all articles that were indexed in Google Scholar as of July 2020. Google Scholar is the largest database in the world of its kind.

Below we describe selections from Google Scholar’s most highly-cited articles for 2021. COVID-19 research dominated new arrivals in the list, but we’re also featuring a popular AI paper from 2016, and research that provides an economical shortcut to seeing patterns of human genetic variation, also from 2016.

See our coverage of the 2019 and 2020 lists.

‘Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China’

30,529 citations

Published in February 2020, this is one of the earliest papers to describe the clinical characteristics of COVID-19. It was authored by researchers in China and doctors working in hospitals in Wuhan, the city where COVID-19 was first detected in late 2019.

The team, from institutions such as the Jin Yin-tan Hospital in Wuhan and China-Japan Friendship Hospital in Beijing, reviewed the clinical and nursing reports, chest X-rays and lab results of the first 41 COVID-19 patients. They noted that the novel virus acts similarly to SARS and MERS, in that it causes pneumonia, but is different in that it seldom manifests as a runny nose or intestinal symptoms.

The final sentences of the paper call for robust and rapid testing, because of the likelihood of the disease spreading out of control:

“Reliable quick pathogen tests and feasible differential diagnosis based on clinical description are crucial for clinicians in their first contact with suspected patients. Because of the pandemic potential of 2019-nCoV, careful surveillance is essential to monitor its future host adaption, viral evolution, infectivity, transmissibility, and pathogenicity.”

The paper has been referenced or cited in almost 100 policy documents to date , including several released by the World Health Organization on topics such as mask-wearing and clinical care of patients with severe symptoms .

‘Clinical Characteristics of Coronavirus Disease 2019 in China’

New England Journal of Medicine

19,656 citations

Published online in February 2020, this study was a retrospective review of medical records for 1,099 COVID-19 cases reported to the National Health Commission of the People's Republic of China between 11 December 2019 and 29 January 2020.

The team, which included almost 40 researchers from China from institutions such as the Guangzhou Medical University in Guangzhou and Wuhan Jinyintan Hospital in Wuhan, accessed electronic medical records from 552 hospitals in mainland China to summarise exposure risk, signs and symptoms, laboratory and radiologic findings related to COVID-19 infection.

The study garnered a lot of media attention based on the evidence it put forward that men might be more severely impacted by disease – 58% of the patient cohort were male.

However, as Sharon Begley reported for STAT , “It’s possible the apparent sex imbalance reflects patterns of travel and contacts that make men more likely to be exposed to carriers of the virus, not any inherent biological differences. It’s also possible the apparent worse disease severity in men could skew the data.”

A paper published in JAMA around the same time by researchers in the United States reported that, among hospitalized patients, there is “a slight predominance of men”.

A Nature Communications meta-analysis , published in December 2020, looked at 92 studies covering more than three million patients and concluded that, while males and females appeared to be susceptible to infection, men were 2.84 times more likely to be end up in intensive care and 1.39 times more likely to die from the disease.

‘Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study’

17,047 citations

Published in March 2020, The Lancet described this study as the first time researchers have examined risk factors associated with severe symptoms and death in hospitalised or deceased patients. Of the 191 patients studied, 137 were discharged from hospital and 54 died.

The study, by researchers from hospitals in China, also presented new data on viral shedding – information that informed early understanding of how the virus spreads and can be detected over the cause of infection.

“The extended viral shedding noted in our study has important implications for guiding decisions around isolation precautions and antiviral treatment in patients with confirmed COVID-19 infection,” said co-lead author, Bin Cao, from the China-Japan Friendship Hospital and Capital Medical University in Beijing.

“However, we need to be clear that viral shedding time should not be confused with other self-isolation guidance for people who may have been exposed to COVID-19 but do not have symptoms, as this guidance is based on the incubation time of the virus.”

‘A Novel Coronavirus from Patients with Pneumonia in China, 2019’

The New England journal of medicine

16,194 citations

On 31 December 2019, the Chinese Center for Disease Control and Prevention (China CDC) dispatched a rapid response team to accompany health authorities in Hubei province and Wuhan city in conducting COVID-19 investigations.

This study, published in January 2020, reported the results of that investigation, including the clinical features of the pneumonia of two patients.

Described by Jose Manuel Jimenez-Guardeño, a researcher in the Department of Infectious Diseases at King's College London , UK and colleagues in an article for The Conversation as “the article that released this virus to the world”, the paper details how the virus was isolated from patients with pneumonia in Wuhan in cell cultures.

“In fact, actual photographs of SARS-CoV-2 were shown to the world for the first time here,” say Jimenez-Guardeño and his co-authors .

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The study authors urged that more epidemiologic investigations were needed in order to characterize transmission modes, reproduction intervals and other characteristics of the virus to inform strategies to control and stop its spread.

‘SSD: Single Shot MultiBox Detector’

European Conference on Computer Vision

15,368 citations

A change of pace from recent COVID-19 studies, this paper, led by Wei Liu from the University of North Carolina at Chapel Hill and published in 2016, remains one of the most highly cited in the field of artificial intelligence (AI). It describes a new method for detecting objects in images or video footage using a single deep neural network – a set of AI algorithms inspired by the neurological processes that fire in the human cerebral cortex.

The approach, called the Single Shot MultiBox Detector, or SSD, has been described as faster than Faster R-CNN – another object detection technology that was described in a very highly cited paper published in 2015 ( see our coverage here ).

SSD works by dividing the image into a grid, with each grid cell responsible for detecting objects within that part of the image. As the name indicates, the network is able to identify all objects within an image in a single pass, allowing for real-time analysis.

SSD is now one of a handful of object detection technologies that are now available. YOLO (You Only Look Once) is a similar single-shot object detection algorithm, whereas R-CNN and Faster R-CNN use a two-step approach , which involves first identifying the regions where objects might be, and then detecting them.

‘Analysis of protein-coding genetic variation in 60,706 humans’

7,696 citations

Led by Monkol Lek from the University of Sydney in Australia and Daniel MacArthur from the Broad Institute of MIT and Harvard University , this 2016 paper presents an open-access catalogue of more than 60,000 human exome sequences (exomes are the coding portions of genes) from people of European, African, South Asian, East Asian, and Latinx ancestry.

The collection was compiled as part of the Exome Aggregation Consortium project, run by an international group of researchers with a focus on exome sequencing. As exomes only make up about 2% of the human genome , the approach has been praised for being able to highlight patterns of genetic variation, including known disease-related variants, in a more cost-effective way than whole-genome sequencing.

Presented at a 2015 genomics conference, the catalogue encompasses 7.4 million genetic variants, which can be used to identify those connected to rare diseases. “Large-scale reference datasets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes,” Lek said when the paper was published.

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Papers We Love ( PWL ) is a community built around reading, discussing and learning more about academic computer science papers. This repository serves as a directory of some of the best papers the community can find, bringing together documents scattered across the web. You can also visit the Papers We Love site for more info.

Due to licenses we cannot always host the papers themselves (when we do, you will see a 📜 emoji next to its title in the directory README) but we can provide links to their locations.

If you enjoy the papers, perhaps stop by a local chapter meetup and join in on the vibrant discussions around them. You can also discuss PWL events, the content in this repository, and/or anything related to PWL on our Discord server.

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best computer science research papers

13 Research Papers Accepted to ICML 2021

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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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Press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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Latest Computer Science Research Topics for 2024

Home Blog Programming Latest Computer Science Research Topics for 2024

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Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

Top Computer Science Research Topics

Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

  • Apache NiFi:  A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
  • Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
  • OpenFog Consortium:  An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

  • Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
  • Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
  • Research Trend:  The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

Tips and Tricks to Write Computer Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

  • Know your interest.
  • Choose the topic wisely.
  • Make proper research about the demand of the topic.
  • Get proper references.
  • Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore.

One of the most important trends is using cutting-edge technology to address current issues. For instance, new IIoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies. 

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.  

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut. 

 There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more. 

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Reference management. Clean and simple.

The top list of academic search engines

academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

  • Coverage: approx. 200 million articles
  • Abstracts: only a snippet of the abstract is available
  • Related articles: ✔
  • References: ✔
  • Cited by: ✔
  • Links to full text: ✔
  • Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

Search interface of Google Scholar

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

  • Coverage: approx. 136 million articles (contains duplicates)
  • Abstracts: ✔
  • Related articles: ✘
  • References: ✘
  • Cited by: ✘
  • Export formats: RIS, BibTeX

Search interface of Bielefeld Academic Search Engine aka BASE

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

  • Coverage: approx. 136 million articles
  • Links to full text: ✔ (all articles in CORE are open access)
  • Export formats: BibTeX

Search interface of the CORE academic search engine

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

  • Coverage: approx. 200 million articles and reports
  • Links to full text: ✔ (available for some databases)
  • Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Search interface of Science.gov

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

  • Coverage: approx. 40 million articles
  • Export formats: APA, MLA, Chicago, BibTeX

Search interface of Semantic Scholar

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

  • Coverage: no detailed statistics available, approx. 100 million articles
  • Abstracts: only snippets of the abstract are available
  • Export formats: APA, MLA, RIS, BibTeX

Search interface of Baidu Scholar

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

  • Coverage: no detailed statistics available, approx. 1 billion documents
  • Abstracts: only snippets of the article are available
  • Export formats: not available

Search interface of RefSeek

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

best computer science research papers

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

best computer science research papers

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CS&E Colloquium: Fundamental Problems in AI: Transferability, Compressibility and Generalization

The computer science colloquium takes place on Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker,  Tomer Galanti  ( MIT ), will be giving a talk titled " Fundamental Problems in AI: Transferability, Compressibility and Generalization ".

In this talk, we delve into several fundamental questions in deep learning. We start by addressing the question, "What are good representations of data?" Recent studies have shown that the representations learned by a single classifier over multiple classes can be easily adapted to new classes with very few samples. We offer a compelling explanation for this behavior by drawing a relationship between transferability and an emergent property known as neural collapse. Later, we explore why certain architectures, such as convolutional networks, outperform fully-connected networks, providing theoretical support for how their inherent sparsity aids learning with fewer samples. Lastly, I present recent findings on how training hyperparameters implicitly control the ranks of weight matrices, consequently affecting the model's compressibility and the dimensionality of the learned features. Additionally, I will describe how this research integrates into a broader research program where I aim to develop realistic models of contemporary learning settings to guide practices in deep learning and artificial intelligence. Utilizing both theory and experiments, I study fundamental questions in the field of deep learning, including why certain architectural choices improve performance or convergence rates, when transfer learning and self-supervised learning work, and what kinds of data representations are learned in practical settings.

Tomer Galanti

Keller Hall  3-180

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Computer Science

Computer science course offerings in high school spur more students to coding degrees, by jeffrey r. young     feb 6, 2024.

Computer Science Course Offerings in High School Spur More Students to Coding Degrees

Oksana Klymenko / Shutterstock

In recent years high schools across the country have been adding computer science courses, and there is a movement to make them ubiquitous. A new study of an unusually rich dataset in Maryland found that such efforts can have a significant impact when it comes to getting more students to go on to careers in coding, and in bringing more diversity to the field.

The study, published as a working paper this month, found that taking a high-quality computer science course in high school increased the chance that the student goes on to major in computer science in college by 10 percentage points, and increased the chance that the student would finish a CS degree program by 5 percentage points.

“It’s not surprising in some ways,” says the lead researcher on the study, Jing Liu. “But we need the numbers so we can show it concretely.”

Liu, who is an assistant professor of education policy at the University of Maryland at College Park, surmises that taking a class in computer science helps some students overcome popular misconceptions about coding.

“It’s like math anxiety — they think they can’t do it,” the professor says of some students. And he knows that feeling firsthand. “I took my first CS course in grad school, and before that I totally thought I was not a CS person,” he says. “Just exposing people to the actual curriculum can overcome fears.”

The study found that taking a computer science course had the greatest impact for female students, Black students and those from low socioeconomic backgrounds. Liu sees that as evidence that increasing CS offerings in high school is helping to address well-known disparities in the tech world. “We need more women and we need more students of color in coding,” he says. “We are far from achieving equity in this space.”

An estimated 57 percent of U.S. high schools offer an introductory computer science course, according to an analysis last year by the nonprofit Code.org. Maryland recently made it a statewide requirement that all high schools offer at least one high-quality computer science course, though much of the data analyzed in the study covers a period before that law went into effect.

While offering courses is a key first step, says Cameron Lee Conrad, a University of Maryland doctoral student who also worked on the study, the research points to the importance of encouraging a broader mix of students to actually take the CS courses. “Take-up rates are higher among students who have high math test scores, students who are male, white students — all the students you’d expect it to be,” he says. That trend has been identified nationwide as well . So schools need to do more work to increase broad participation in the courses they’ve started offering, he adds. “Strengthening preparedness is critical,” he argues, noting that strengthening fundamental math education in K-12 schools will help more students be ready for computer science courses.

The researchers say there’s another challenge as more schools around the country start to offer CS courses: finding qualified teachers.

“Very few teachers are qualified to teach CS,” says Liu, noting that many schools have tapped math teachers to start up their computer science offerings, but often more training is needed. “How do we get them motivated and compensated?”

The researchers worked with the Maryland Center for Computing Education to do the study, working with a few state datasets that have been linked, including information from schools, colleges and workforce data. They say their study is the first one to offer “causal evidence” that taking a course in high school leads to students going on to further study and work in computer science.

Jeffrey R. Young ( @jryoung ) is an editor and reporter at EdSurge and host of the EdSurge Podcast . He can be reached at jeff [at] edsurge [dot] com

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Top ten computer science education research papers recognized

SIGCSE Symposium highlights research that has shaped the field

Association for Computing Machinery

As a capstone to its 50th annual SIGCSE Technical Symposium, leaders of the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE) are celebrating the ideas that have shaped the field by recognizing a select group of publications with a "Top Ten Symposium Papers of All Time Award." The top ten papers were chosen from among the best papers that were presented at the SIGCSE Technical Symposium over the last 49 years.

As part of the Top Ten announcement today in Minneapolis, the coauthors of each top paper will receive a plaque, free conference registration for one co-author to accept the award and up to a total of $2,000 that can be used toward travel for all authors of the top ranked paper.

"In 1969, the year of our first SIGCSE symposium, computing education was a niche specialty" explains SIGCSE Board Chair Amber Settle of DePaul University, of Chicago, USA. "Today, it is an essential skill students need to prepare for the workforce. Computing has become one of the most popular majors in higher education, and more and more students are being introduced to computing in K-12 settings. The Top Ten Symposium Papers of All Time Award will emphasize the outstanding research that underpins and informs how students of all ages learn computing. We also believe that highlighting excellent research will inspire others to enter the computing education field and make their own contributions."

The Top Ten Symposium Papers are:

Computing educators are often baffled by the misconceptions that their CS1 students hold. We need to understand these misconceptions more clearly in order to help students form correct conceptions. This paper describes one stage in the development of a concept inventory for Computing Fundamentals: investigation of student misconceptions in a series of core CS1 topics previously identified as both important and difficult. Formal interviews with students revealed four distinct themes, each containing many interesting misconceptions.

Pair programming is a practice in which two programmers work collaboratively at one computer, on the same design, algorithm, or code. Prior research indicates that pair programmers produce higher quality code in essentially half the time taken by solo programmers. The authors organized an experiment to assess the efficacy of pair programming in an introductory Computer Science course. Their results indicate that pair programming creates a laboratory environment conducive to more advanced, active learning than traditional labs; students and lab instructors report labs to be more productive and less frustrating.

During a year-long study, the authors examined the experiences of undergraduate women studying computer science at Carnegie Mellon University, with a specific eye toward understanding the influences and processes whereby they attach themselves to or detach themselves from the field. This report, midway through the two-year project, recaps the goals and methods of the study, reports on their progress and preliminary conclusions, and sketches their plans for the final year and the future beyond this particular project.

Peer Instruction (PI) is a student-centric pedagogy in which students move from the role of passive listeners to active participants in the classroom. This paper adds to this body of knowledge by examining outcomes from seven introductory programming instructors: three novices to PI and four with a range of PI experience. Through common measurements of student perceptions, the authors provide evidence that introductory computing instructors can successfully implement PI in their classrooms.

Schneider describes the crucial goals of any introductory programming course while leaving to the reader the design of a specific course to meet these goals. This paper presents ten essential objectives of an initial programming course in Computer Science, regardless of who is teaching or where it is being taught. Schneider attempts to provide an in-depth, philosophical framework for the course called CSI -- Computer Programming I -- as described by the ACM Curriculum Committee on Computer Science.

Constructivism is a theory of learning which claims that students construct knowledge rather than merely receive and store knowledge transmitted by the teacher. Constructivism has been extremely influential in science and mathematics education, but not in computer science education (CSE). This paper surveys constructivism in the context of CSE, and shows how the theory can supply a theoretical basis for debating issues and evaluating proposals.

Introductory computer science students have relied on a trial and error approach to fixing errors and debugging for too long. Moving to a reflection in action strategy can help students become more successful. Traditional programming assignments are usually assessed in a way that ignores the skills needed for reflection in action, but software testing promotes the hypothesis-forming and experimental validation that are central to this mode of learning. By changing the way assignments are assessed--where students are responsible for demonstrating correctness through testing, and then assessed on how well they achieve this goal--it is possible to reinforce desired skills. Automated feedback can also play a valuable role in encouraging students while also showing them where they can improve.

Gries argues that an introductory course (and its successor) in programming should be concerned with three aspects of programming: 1. How to solve problems, 2. How to describe an algorithmic solution to a problem, and 3. How to verify that an algorithm is correct. In this paper he discusses mainly the first two aspects. He notes that the third is just as important, but if the first two are carried out in a systematic fashion, the third is much easier than commonly supposed.

This study was conducted to determine factors that promote success in an introductory college computer science course. The model included twelve possible predictive factors including math background, attribution for success/failure (luck, effort, difficulty of task, and ability), domain specific self-efficacy, encouragement, comfort level in the course, work style preference, previous programming experience, previous non-programming computer experience, and gender. Subjects included 105 students enrolled in a CS1 introductory computer science course at a midwestern university. The study revealed three predictive factors in the following order of importance: comfort level, math, and attribution to luck for success/failure.

An objects-first strategy for teaching introductory computer science courses is receiving increased attention from CS educators. In this paper, the authors discuss the challenge of the objects-first strategy and present a new approach that attempts to meet this challenge. The approach is centered on the visualization of objects and their behaviors using a 3D animation environment. Statistical data as well as informal observations are summarized to show evidence of student performance as a result of this approach. A comparison is made of the pedagogical aspects of this new approach with that of other relevant work.

Annual Best Paper Award Announced

Today SIGCSE officers also announced the inauguration of an annual SIGCSE Test of Time Award. The first award will be presented at the 2020 SIGCSE Symposium and recognize research publications that have had wide-ranging impact on the field.

The Special Interest Group on Computer Science Education of the Association for Computing Machinery (ACM SIGCSE) is a community of approximately 2,600 people who, in addition to their specialization within computing, have a strong interest in quality computing education. SIGCSE provides a forum for educators to discuss the problems concerned with the development, implementation, and/or evaluation of computing programs, curricula, and courses, as well as syllabi, laboratories, and other elements of teaching and pedagogy.

ACM, the Association for Computing Machinery, is the world's largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field's challenges. ACM strengthens the computing profession's collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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  1. computer science Latest Research Papers

    14255 (FIVE YEARS 3547) H-INDEX 73 (FIVE YEARS 9) Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords Hiring CS Graduates: What We Learned from Employers ACM Transactions on Computing Education 10.1145/3474623 2022 Vol 22 (1) pp. 1-20 Author (s): Anna Stepanova Alexis Weaver Joanna Lahey

  2. Computer Science

    Computer Science Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's cs listings specific year/month: Catch-up: Changes since: , view results abstracts Search within the cs archive

  3. Top Ten Computer Science Education Research Papers of the Last 50 Years

    1. " Identifying student misconceptions of programming " (2010) Lisa C. Kaczmarczyk, Elizabeth R. Petrick, University of California, San Diego; Philip East, University of Northern Iowa; Geoffrey L. Herman, University of Illinois at Urbana-Champaign Computing educators are often baffled by the misconceptions that their CS1 students hold.

  4. Top 16 international Computer Science journals

    1. IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Transactions on Pattern Analysis and Machine Intelligence is a monthly peer-reviewed scientific journal published by the IEEE Computer Society.

  5. Computer science

    Amin Karimi Charalambos Poullis Research Open Access 18 Feb 2024 Scientific Reports Volume: 14, P: 4028 Efficient content caching for 5G assisted vehicular networks Faareh Ahmed Badr Alsamani...

  6. 533984 PDFs

    Explore the latest full-text research PDFs, articles, conference papers, preprints and more on COMPUTER SCIENCE. Find methods information, sources, references or conduct a literature review on ...

  7. Five Hundred Most-Cited Papers in the Computer Sciences ...

    1 Citations Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1366) Abstract This study reveals common factors among highly cited papers in the computer sciences. The 500 most cited papers in the computer sciences published between January 2013 and December 2017 were downloaded from the Web of Science (WoS).

  8. Journal of Computer Science

    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.

  9. PDF Top Ten Computer Science Education Research Papers of The Last 50 Years

    the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE) are celebrating the ideas that have shaped the field by recognizing a select group of publications with a "Top Ten Symposium Papers of All Time Award." The top ten papers were chosen from among the best papers that were presented at ...

  10. The latest in Computer Science

    Top Social New Greatest Trending Research Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU NVIDIA/cutlass • 9 Jan 2023 We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra.

  11. The Top 10 research papers in computer science by Mendeley readership

    1. Latent Dirichlet Allocation (available full-text) LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon's information theory paper (#7) or the paper describing the concept that became Google (#3).

  12. You should be reading academic computer science papers

    The future of programming (today) There's more to reading research papers than understanding history; you can find new ways to solve problems by reading current research. "The idea of Stack Overflow is: someone else has had your problem before," said Ashby. "Academic papers are: someone else has thought about this problem before.".

  13. Computer Science Review

    Top cited Most downloaded Most popular Review articleFull text access Sustainable computing across datacenters: A review of enabling models and techniques Muhammad Zakarya, ... Rahim Khan May 2024 View PDF Review articleFull text access Secret sharing: A comprehensive survey, taxonomy and applications Arup Kumar Chattopadhyay, ... Sukumar Nandi

  14. Computer Science and Engineering

    As recommender systems are a very popular domain for researchers from Computer Science, Engineering,Data Science,and Information Science backgrounds. It is used extensively on various...

  15. Best Computer Science Journals Ranking

    Best Computer Science Journals The ranking of best journals for Computer Science was published by Research.com, one of the prominent websites for computer science research providing trusted data on scientific contributions since 2014.

  16. Google Scholar reveals its most influential papers for 2021

    The five-year-old paper's astonishing ascendancy continues, from 25,256 citations in 2019 to 49,301 citations in 2020 to 82,588 citations in 2021. We wrote about it last year here. The 2021 ...

  17. GitHub

    Best Paper Awards in Computer Science Facebook Google Scholar (choose a subcategory) Microsoft Research Functional Programming Books Review MIT's Artificial Intelligence Lab Publications MIT's Distributed System's Reading Group arXiv Paper Repository SciRate cat-v.org y-archive netlib Services Engineering Reading List

  18. The top list of computer science research databases

    The top list of computer science research databases Content: 1. ACM Digital Library 2. IEEE Xplore Digital Library 3. dblp computer science bibliography 4. Springer Lecture Notes in Computer Science (LNCS) Frequently Asked Questions about computer science research databases Related Articles

  19. 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 ...

  20. Journal Rankings on Computer Science

    International Scientific Journal & Country Ranking. SCImago Institutions Rankings SCImago Media Rankings SCImago Iber SCImago Research Centers Ranking SCImago Graphica Ediciones Profesionales de la Información

  21. Latest Computer Science Research Topics for 2024

    1. Innovation in Technology Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world.

  22. The best academic search engines [Update 2024]

    Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

  23. CS&E Colloquium: Fundamental Problems in AI: Transferability

    The computer science colloquium takes place on Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Tomer Galanti (MIT), will be giving a talk titled "Fundamental Problems in AI: Transferability, Compressibility and Generalization".AbstractIn this talk, we delve into several fundamental questions in deep learning. We start by addressing the question, "What are good representations of data ...

  24. Computer Science Course Offerings in High School Spur More Students to

    A new study of an unusually rich dataset in Maryland found that such efforts can have a significant impact when it comes to getting more students to go on to careers in coding, and in bringing more diversity to the field. The study, published as a working paper this month, found that taking a high-quality computer science course in high school ...

  25. Best Online Computer Science Degrees Of 2024

    Best Online Bachelor's in Computer Science Degree Options. Oregon State University. Saint Leo University. Baker College. Western Governors University. Maryville University of Saint Louis ...

  26. The best universities for computer science in 5 global regions

    Asia. Computer science education is thriving at universities in many Asian countries. The largest continent by size and population, Asia accounts for 52% of global growth in revenues at tech ...

  27. Top ten computer science education research p

    This paper presents ten essential objectives of an initial programming course in Computer Science, regardless of who is teaching or where it is being taught. Schneider attempts to provide an in ...