research paper related to computer science

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30 Interesting Computer Science Research Paper Topics

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• Biotechnology, medicine, and computer science
• Neuron networks and machine learning
• Big data analysis
• Virtual reality and its connection to human perception
• The success of computer-assisted education
• Computer assistance in support services
• Database architecture and management
• Human-computer interactions. The importance of usability
• The limits of computation and communication
• Computers and media. Where is the line between art and math modeling?
• Why there are so much programming languages?
• Digital security versus private information
• Encrypting and decrypting
• Quantum computers. Are they the future?
• Is the evolution of search algorithms finished?
• The importance of open source software
• Portable gadgets and the peculiarities of software development for them
• Cloud storages: advantages and disadvantages
• Computer viruses: the main principles of work and the hazards
• DDOS attacks, their danger on the global scale and their prevention
• Is SCRUM methodology the best-invented one for computer science?
• The online medicine apps: can they sometimes substitute the treatment of real doctors?
• 5G Wireless System: is it the future?
• Windows, macOS, UNIX – what OS is the most perspective now?
• Biometric systems and recognizing
• Ethical hacking. Who are the “white hat hackers”?
• Cyborgs: is it sci-fi or nearest future?
• The ATM and bank security
• The evolution of torrents
• What is blockchain?

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• SIGCSE Top 10 Paper Awards

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

At 50th anniversary sigcse symposium, leading computer science education group highlights research that has shaped the field.

New York, NY, March 2, 2019 – 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:

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. 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.

2. “ Improving the CS1 experience with pair programming ” (2003) Nachiappan Nagappan, Laurie Williams, Miriam Ferzli, Eric Wiebe, Kai Yang, Carol Miller, Suzanne Balik, North Carolina State University 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.

3. “ Undergraduate women in computer science: experience, motivation and culture ” (1997) Allan Fisher, Jane Margolis, Faye Miller, Carnegie Mellon University 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.

4. “ A Multi-institutional Study of Peer Instruction in Introductory Computing ” (2016) Leo Porter, Beth Simon, University of California, San Diego; Dennis Bouvier, Southern Illinois University; Quintin Cutts, University of Glasgow; Scott Grissom, Grand Valley State University; Cynthia Lee, Stanford University; Robert McCartney, University of Connecticut; Daniel Zingaro, University of Toronto 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.

5. " The introductory programming course in computer science: ten principles " (1978) G. Michael Schneider, University of Minnesota 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 CS1—Computer Programming 1—as described by the ACM Curriculum Committee on Computer Science.

6. “ Constructivism in computer science education ” (1998) Mordechai Ben-Ari, Weizmann Institute of 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.

7. “ Using software testing to move students from trial-and-error to reflection-in-action ” (2004) Stephen H. Edwards, Virginia Tech 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.

8. “ What should we teach in an introductory programming course ” (1974) David Gries, Cornell University 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.

9. “ Contributing to success in an introductory computer science course: a study of twelve factors ” (2001) Brenda Cantwell Wilson, Murray State University; Sharon Shrock, Southern Illinois University 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.

10. “ Teaching objects-first in introductory computer science ” (2003) Stephen Cooper, Saint Joseph's University; Wanda Dann, Ithaca College; Randy Pausch Carnegie Mellon University 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.

About SIGCSE

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 the quality of 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 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.

Contact: Adrienne Decker 585-475-4653 [email protected]

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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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Research Reports on Computer Science

Research Reports on Computer Science mainly reports on innovative research results that cover novel theories, technologies and engineering applications in the fields of computer science and engineering. The journal considers contributions in the form of original research papers, short communications and review articles on cross-cutting theories, technologies and real-world applications in line with the journal scope.

Topics of interest include, but are not limited to: evolutionary computing, data analytics, computer architecture, software engineering, database technology, big data processing, quantum computers, artificial intelligence, artificial neural networks, virtual reality, machine learning and automated reasoning, Internet of Things and cloud computing, intelligent human-machine interface, soft computing, etc.

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by Satendra Kumar Jain, Anil Kumar Gupta

ABSTRACT: Reference evapotranspiration (ETo) is a valuable factor in the hydrological process and its estimation is a sophisticated and nonlinear problem. In this study, the utility of multilayer perceptron...

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ABSTRACT: This paper described the detailed study for the detection and monitoring of accidents in hilly areas. In the past few decades, road accidents are a major cause of death in suburban hilly areas....

by Medisetti Yashwanth Sai Krishna, Suresh Kumar Gawre

ABSTRACT: Machine learning (ML) integrated with development and operations (DevOps) is the key to solving the problem of deploying the latest machine learning models. This paper proposes one of the ways of...

by Sai Venkat Boyapati, Maddirala Sai Karthik, Konakanchi Subrahmanyam, B Ramachandra Reddy

ABSTRACT: It is very important to understand the market drifts in the wake of booming civilization and ever-changing market requirements. The principal purpose of the study is the prediction of house prices...

by Shivendra Kumar Pandey, Buddha Singh

ABSTRACT: Due to the advancement of electronics engineering technology, many types of sensors have been developed. But sensors are still battery-powered devices. Once the battery is dead, the sensors are of...

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ABSTRACT: The optimization of electric vehicles (EVs) utilizing meta-heuristics has arisen as the way to propel state-of-the-art advancements, making ready for boundless reception, and reforming the flow...

by Satyajeet Rajaram Shinge, Urmila N. Shrawankar

ABSTRACT: Companies across industries increasingly depend upon cloud computing to manage their Industrial Internet of Things (IIoT) technology. Machines are connected over a network in the IIoT. Cloud...

by Swatej Patil, Mayur Patil, Kotadi Chinnaiah

ABSTRACT: The term "phishing" is often used to describe an attempt to obtain confidential data such as passwords or credit card details by impersonating a trustworthy source. In most cases, the term refers...

by Shatakshi Kokate, Urmila Shrawankar

ABSTRACT: The Internet of Things (IoT) network generates a lot of data and cloud servers collect that data. The server then analyzes the collected data and based on the findings, provides appropriate...

by Vijay Bhaskar Semwal , Yash Gupta

ABSTRACT: One of the most popular ways of representing any robotic model mathematically is through Denavit-Hartenberg (DH) parameter table. And the most common way of finding a forward kinematics solution...

by Swati Chaudhari, Archana Thakur, Alpana Rajan

ABSTRACT: In information technology (IT) security, defence in depth is considered the best practice. Protecting data at rest or in transit is a part of the defence in depth approach. Confidentiality, data...

by Agash Uthayasuriyan, Hema Chandran G, Kavvin UV, Sabbineni Hema Mahitha , Jeyakumar G

ABSTRACT: Machine Learning (ML) and Evolutionary Computing (EC) are the two most popular computational methodologies in computer science to solve learning and optimization problems around us, respectively....

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Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

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Utilizing convolutional neural networks to classify monkeypox skin lesions

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Deep neural architecture for natural language image synthesis for Tamil text using BASEGAN and hybrid super resolution GAN (HSRGAN)

An efficient churn prediction model using gradient boosting machine and metaheuristic optimization

• Ibrahim AlShourbaji
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A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification

• Dequan Wang
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Attention-based bidirectional-long short-term memory for abnormal human activity detection

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Gender and culture bias in letters of recommendation for computer science and data science masters programs

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News and Comment

AI finally beats humans at a real-life sport — drone racing

The new system combines simulation with onboard sensing and computation.

Could the Universe be a giant quantum computer?

Computational rules might describe the evolution of the cosmos better than the dynamical equations of physics — but only if they are given a quantum twist.

• David L. Chandler

Why scientists are delving into the virtual world

Virtual-reality software and headsets are increasingly being used by researchers to form deeper collaborations or work remotely.

• Rachael Pells

If AI becomes conscious: here’s how researchers will know

A checklist derived from six neuroscience-based theories of consciousness could aid in the assessment.

• Mariana Lenharo

Allow patents on AI-generated inventions — for the good of science

The current global mishmash of rules on whether innovations made using artificial intelligence are patentable impedes AI-rich fields such as drug discovery.

• Ryan Abbott

Optimal crystal structure solutions

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Top research articles in 2022 in the area of Computer Science & Engineering

Computer Science & Information Technology

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• ACM Digital Library Conference proceedings, books, articles, magazines, newsletters, and multimedia titles relating to computing and information technology. Coverage: 1947 - present more... less... ACM Digital Library contains bibliographic information on ACM publications since 1947 and includes full-text of documents where available. Works published by affiliated organizations are included on a selected basis.
• IEEE Xplore Digital Library Resources for computer science, electrical engineering, electronics engineering, and mechanical engineering. Coverage: 1988 - present more... less... IEEE Xplore provides access to journals, annual conference proceedings, and IEEE technical standards.
• DBLP Computer Science Bibliography From the University of Trier, the "DBLP indexes more than 2.6 million articles and contains many links to home pages of computer scientists."
• ACL Anthology Browse through papers on the study of computational linguistics and natural language processing
• Eurographics Digital Library From the European Association for Computer Graphics.
• CiteSeerX A scientific literature digital library and search engine focusing on literature in computer and information science. Coverage: varies more... less... CiteSeerX attempts to provide algorithms, metadata, services, techniques, and software that can be used in other digital libraries. CiteSeer indexes PostScript and PDF research articles on the Web.

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• ABI/INFORM ProQuest access to articles on business and management from U.S. and international journals and trade magazines, including company histories, competitive intelligence, and product development. Coverage: 1971 - present more... less... Includes scholarly journals, trade magazines, working papers, market research, and company profiles.
• Factiva News, business magazines, trade journals, newsletters, and television and radio transcripts. Coverage: 1970s - present. Varies by publication more... less... Factiva forbids storing or using search results in research applications such as data mining or trend analysis. Note: If you are at a public workstation you will be asked for your WatIAM (Quest) User ID and Password.
• Nexis Uni News, legal, and business sources from around the world, Find news, legislation, case law, statutes, company profiles, market & industry reports. Coverage: 1970s - present more... less... Note: If you are at a public workstation you will be asked for your WatIAM (Quest) User ID and Password.

Multidisciplinary research databases

Not sure where to start? The databases below cover many disciplines including math, business, economics, health, life science, physical science, and technology.

• Scopus Peer-reviewed literature from scientific journals, books and conference proceedings, covering the fields of science, technology, medicine, social sciences, and arts and humanities. Coverage: 1966 - present
• Web of Science Articles and citations in the sciences, social sciences, arts, and humanities. Coverage: Varies more... less... Web of Science is comprised of several databases. The Science Citation Index Expanded (SCI) covers journals in the medical, physical and natural sciences, and engineering fields. The entire database extends back to 1899. The Social Sciences Citation Index (SSCI) covers journals in the social sciences. The entire database extends back to 1898. The Arts & Humanities Citation Index (AHCI) covers journals in the arts and humanities. It also selectively covers relevant items from science and technical journals. The entire database extends back to 1975.
• Google Scholar Google Scholar is a search engine finds scholarly information from many sources (however, not everything in Google Scholar is scholarly). To access materials paid for by your library, go to Google Scholar, then choose Settings and click "Library Links" to add the University of Waterloo. more... less... Google Scholar is a search engine that emphasizes scholarly information, particularly in the sciences and technology. It draws from academic publishers, professional societies, preprint repositories and universities. Note: Access To access materials paid for by your library, go to Google Scholar, then choose Settings and click "Library Links." The off-campus user will first need to login via "Get access from anywhere."
• arXiv A pre-print server which hosts papers (that have not been peer reviewed) relating to physics, mathematics, computer science, nonlinear sciences, qualitative biology and statistics Coverage: 1991 - present
• JSTOR Provides access to back issues of journals in the humanities, social sciences, and physical sciences, many of which date from the 1800s. Coverage: varies (excludes current 3 to 5 years)
• ProQuest A platform with many databases of journal indexes and abstracts, as well as some with full text Coverage: Varies more... less... This online platform hosts multiple resources.
• EBSCOhost A platform with many databases of journal indexes and abstracts, as well as some with full text Coverage: varies more... less... This online platform hosts multiple resources. Note: Offline digital lending: Requires Adobe Digital Editions.

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