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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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Join the community, trending research, pygwalker: on-the-fly assistant for exploratory visual data analysis.
Kanaries/pygwalker • 17 Jun 2024
However, the gap between common programmatic analysis (e. g., within computational notebooks) and exploratory visual analysis leads to a disjointed and inefficient data analysis experience.
Human-Computer Interaction
HookChain: A new perspective for Bypassing EDR Solutions
helviojunior/hookchain • 4 Apr 2024
In the current digital security ecosystem, where threats evolve rapidly and with complexity, companies developing Endpoint Detection and Response (EDR) solutions are in constant search for innovations that not only keep up but also anticipate emerging attack vectors.
Cryptography and Security Networking and Internet Architecture Operating Systems
NanoFlow: Towards Optimal Large Language Model Serving Throughput
The increasing usage of Large Language Models (LLMs) has resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users.
Distributed, Parallel, and Cluster Computing
Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)
Thinklab-SJTU/Bench2Drive • 26 Feb 2024
As a result, Think2Drive is able to run in an expert-level proficiency in CARLA v2 within 3 days of training on a single A6000 GPU, and to our best knowledge, so far there is no reported success (100\% route completion)on CARLA v2.
COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
ethz-asl/COIN-LIO • 2 Oct 2023
To effectively leverage intensity as an additional modality, we present a novel feature selection scheme that detects uninformative directions in the point cloud registration and explicitly selects patches with complementary image information.
Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs
Large Language Models (LLMs) have demonstrated great potential in robotic applications by providing essential general knowledge.
An Optimizing Framework on MLIR for Efficient FPGA-based Accelerator Generation
sjtu-zhao-lab/pom • 10 Jan 2024
First, most HLS tools depend exclusively on a single-level IR to perform all the optimizations, introducing excessive information into the IR and making debugging an arduous task.
Hardware Architecture Programming Languages
Robots with Attitude: Singularity-Free Quaternion-Based Model-Predictive Control for Agile Legged Robots
zixinz990/quaternion-mpc • 16 Sep 2024
We present a model-predictive control (MPC) framework for legged robots that avoids the singularities associated with common three-parameter attitude representations like Euler angles during large-angle rotations.
LCB-net: Long-Context Biasing for Audio-Visual Speech Recognition
The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides.
Sound Multimedia Audio and Speech Processing
Better Write Amplification for Streaming Data Processing
ytsaurus/ytsaurus • 5 Jun 2023
Many current applications have to perform data processing in a streaming fashion.
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Realizing full-body control of humanoid robots
Using deep reinforcement learning, flexible skills and behaviours emerge in humanoid robots, as demonstrated in two recent reports.
- Guangliang Li
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Holistic approach to carbon capture bridges the ‘Valley of Death’
Carbon-capture technology often founders at the point when basic research is translated into practical applications. A computational modelling platform called PrISMa solves this problem by considering the needs of all stakeholders.
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Federated influencer learning for secure and efficient collaborative learning in realistic medical database environment
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Grape clusters detection based on multi-scale feature fusion and augmentation
Sex estimation using skull silhouette images from postmortem computed tomography by deep learning
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Multi-view graph contrastive learning for social recommendation
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An innovative approach for QoS-aware web service composition using whale optimization algorithm
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Nuclear power for AI: what it will take to reopen Three Mile Island safely
As Microsoft strikes a deal to restart a reactor at the notorious power station, Nature talks to nuclear specialists about the unprecedented process.
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Audio long read: A day in the life of the world’s fastest supercomputer
Frontier, the first machine to break supercomputing’s exascale speed barrier, is giving researchers unprecedented insights into everything from atoms to galaxies.
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Bigger AI chatbots more inclined to spew nonsense — and people don't always realize
Artificial-intelligence models are improving overall but are more likely to answer every question, leading to wrong answers.
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How to stop a looming ‘splinternet’
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The lost data: how AI systems censor LGBTQ+ content in the name of safety
Many AI companies implement safety systems to protect users from offensive or inaccurate content. Though well intentioned, these filters can exacerbate existing inequalities, and data shows that they have disproportionately removed LGBTQ+ content.
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According to current researches, much of the electric power is being consumed by the server cooling system at the Data Center. Moreover, the power consumption rate increases when the number of the equipments and servers expands. Thus, the proposed server operation system has been designed to decrease power consumption rate and CO emission volume by minimizing the number of these equipments and simplifying the physical composition of the system. Virtualization technology was adopted in both designing and implementation phases to improve resource efficiency of the system. As a result, significant amount has been saved while constructing the server operation system in this paper. System’s performance has been evaluated using a virtual machine prior to its practical use through test bed experiments and the results confirms our expectation that the virtual hardwares will work as efficiently as actual ones. *As tracked by ISI/Clarivate | |
Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages. | |
— The proliferation of “Smart Cities” initiatives around the world is a part of the strategic response by governments to the challenges and opportunities of increasing urbanization and the rise of cities as the nexus of societal development. This JISA Thematic Series presents significant research contributions related to the design and development of Infrastructure, Services and Applications for the Smart City and Urban context. |
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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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Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations
Computer Science and Engineering Theses and Dissertations
Theses/dissertations from 2024 2024.
Automatic Image-Based Nutritional Calculator App , Kejvi Cupa
Individual Behavioral Modeling Across Games of Strategy , Logan Fields
Semi-automated Cell Annotation Framework Using Deep Learning , Abhiram Kandiyana
Predicting Gender of Author Using Large Language Models (LLMs) , Satya Uday Sanku
Context-aware Affective Behavior Modeling and Analytics , Md Taufeeq Uddin
Exploring the Use of Enhanced SWAD Towards Building Learned Models that Generalize Better to Unseen Sources , Brandon M. Weinhofer
Theses/Dissertations from 2023 2023
Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno
Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan
V2V and V2I Based Safety and Platooning Algorithms for Connected and Autonomous Vehicles , Omkar Dokur
Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros
Exploring Scalability of Multimodal User Interface Design in Virtual and Augmented Reality , Sarah M. Garcia
Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin
Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen
Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur
A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.
Graph Analysis on Social Networks , Shen Lu
Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera
Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo
Secure Reconfigurable Computing Paradigms for the Next Generation of Artificial Intelligence and Machine Learning Applications , Brooks Olney
Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi
Theses/Dissertations from 2022 2022
Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed
Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala
Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen
An Internet of Medical Things (IoMT) Approach for Remote Assessment of Head and Neck Cancer Patients , Ruchitha Chinthala
A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave
On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández
Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou
Developing Reinforcement Learning Algorithms for Robots to Aim and Pour Solid Objects , Haoxuan Li
Computing Group-By and Aggregate in Massively Parallel Systems , Chengcheng Mou
Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang
A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri
Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney
Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin
Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker
Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich
Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar
Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong
Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward
Exploration of Energy Efficient Computing for Data-Intensive Applications , Md Adnan Zaman
Theses/Dissertations from 2021 2021
Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar
Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia
Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto
Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis
Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey
AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi
Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill
Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja
Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana
Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev
Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.
Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski
Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov
Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai
Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri
Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco
Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam
Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava
A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza
Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar
Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams
Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari
Theses/Dissertations from 2020 2020
Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal
Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil
Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari
Composition of Atomic-Obligation Security Policies , Yan Cao Albright
Action Recognition Using the Motion Taxonomy , Maxat Alibayev
Sentiment Analysis in Peer Review , Zachariah J. Beasley
Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov
Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang
Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram
PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami
Composition of Atomic-Obligation Security Policies , Danielle Ferguson
Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel
The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann
Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez
Privacy-Preserving and Functional Information Systems , Thang Hoang
Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January
Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi
Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam
Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha
Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath
System Support of Concurrent Database Query Processing on a GPU , Hao Li
Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu
Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li
Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla
Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng
Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi
Models of Secure Software Enforcement and Development , Hernan M. Palombo
Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos
Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul
Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan
Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah
Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui
Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah
Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu
Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon
Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna
Theses/Dissertations from 2019 2019
Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur
Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed
Phenomena of Social Dynamics in Online Games , Essa Alhazmi
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Reference management. Clean and simple.
The top list of computer science research databases
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.
Besides the interdisciplinary research databases Web of Science and Scopus there are also academic databases specifically dedicated to computer science. We have compiled a list of the top 4 research databases with a special focus on computer science to help you find research papers, scholarly articles, and conference papers fast.
ACM Digital Library is the clear number one when it comes to academic databases for computer science. The ACM Full-Text Collection currently has 540,000+ articles, while the ACM Guide to Computing Literature holds more than 2.8+ million bibliographic entries.
- Coverage: 2.8+ million articles
- Abstracts: ✔
- Related articles: ✘
- References: ✔
- Cited by: ✔
- Full text: ✔ (requires institutional subscription)
- Export formats: BibTeX, EndNote
Pro tip: Use a reference manager like Paperpile to keep track of all your sources. Paperpile integrates with ACM Digital Library and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons and later cite them in thousands of citation styles:
IEEE Xplore holds more than 4.7 million research articles from the fields of electrical engineering, computer science, and electronics. It not only covers articles published in scholarly journals, but also conference papers, technical standards, as well as some books.
- Coverage: 4.7+ million articles
- Export formats: BibTeX, RIS
Hosted at the University of Trier, Germany, dbpl has become an indispensable resource in the field of computer science. Its index covers journal articles, conference and workshop proceedings, as well as monographs.
- Coverage: 4.3 million articles
- Abstracts: ✘
- References: ✘
- Cited by: ✘
- Full text: ✘ (Links to publisher websites available)
- Export formats: RIS, BibTeX
Springer's Lecture Notes in Computer Science is the number one publishing source for conference proceedings covering all areas of computer science.
- Coverage: 415,000+ articles
- Export formats: RIS, EndNote, BibTeX
Hosted at the University of Trier, Germany, dbpl has become an indispensable resource in the field of computer science. It's index covers journal articles, conference and workshop proceedings, as well as monographs.
Microsoft Academic was a free academic search engine developed by Microsoft Research. It had more than 13.9 million articles indexed. It was shut down in 2022.
EEE Xplore holds more than 4.7 million research articles from the fields of electrical engineering, computer science, and electronics. It not only covers articles published in scholarly journals, but also conference papers, technical standards, as well as some books.
Journal of Computer Science
Aims and scope.
The Journal of Computer Science (JCS) is dedicated to advancing computer science by publishing high-quality research and review articles that span both theoretical foundations and practical applications in information, computation, and computer systems. With a commitment to excellence, JCS offers a platform for researchers, scholars, and industry professionals to share their insights and contribute to the ongoing evolution of computer science. Published on a monthly basis, JCS provides up-to-date insights into this ever-evolving discipline.
It is with great pleasure that we announce the SGAMR Annual Awards 2020. This award is given annually to Researchers and Reviewers of International Journal of Structural Glass and Advanced Materials Research (SGAMR) who have shown innovative contributions and promising research as well as others who have excelled in their Editorial duties.
This special issue "Neuroinflammation and COVID-19" aims to provide a space for debate in the face of the growing evidence on the affectation of the nervous system by COVID-19, supported by original studies and case series.
The SGAMR Editorial Board is pleased to announce the inauguration of the yearly “SGAMR Young Researcher Award” (SGAMR-YRA). The best paper published by a young researcher will be selected by a journal committee, from the Editorial Board.
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Computer Science Article Databases
- ACM Digital Library This link opens in a new window This site provides access to tables of contents, abstracts, reviews, and full text of every article ever published by ACM and bibliograhic citations from major publishers in computing.
- Compendex This link opens in a new window Compendex is the most comprehensive bibliographic database of scientific and technical engineering research available, covering all engineering disciplines. It includes millions of bibliographic citations and abstracts from thousands of engineering journals and conference proceedings. When combined with the Engineering Index Backfile (1884-1969), Compendex covers well over 120 years of core engineering literature.
- IEEE Xplore This link opens in a new window Provides full-text access to IEEE transactions, IEEE and IEE journals, magazines, and conference proceedings published since 1988, and all current IEEE standards; brings additional search and access features to IEEE/IEE digital library users. Browsable by books & e-books, conference publications, education and learning, journals and magazines, standards and by topic. Also provides links to IEEE standards, IEEE spectrum and other sites.
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Home > FACULTIES > Computer Science > CSD-ETD
Computer Science Theses and Dissertations
This collection contains theses and dissertations from the Department of Computer Science, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository
Theses/Dissertations from 2024 2024
ASSESSMENT OF AI-GENERATED IMAGES USING COMPUTATIONAL METRICS AND HUMAN CENTRIC ANALYSIS , Memoona Aziz Ms.
Approximation Algorithms for High Multiplicity Strip Packing, Thief Orienteering, and K-Median , Andrew Bloch-Hansen
A Target-Based and A Targetless Extrinsic Calibration Methods for Thermal Camera and 3D LiDAR , Farhad Dalirani
Efficient Algorithms and Parallel Implementations for Power Series Multiplication , Seyed Abdol Hamid Fathi
Understanding Protein Deep Learning Models through Explainability , Zahra Fazel
Using Driver Gaze and On-Road Driving Data for Predicting Driver Maneuvers in Advanced Driving Assistance Systems , Farzan Heidari
Protein-Protein Interaction Prediction , SeyedMohsen Hosseini
Container Migration: A Perfomance Evaluation Between MIGrror AND Pre-copy , Xinwen Liang
UTILIZING MACHINE LEARNING TECHNIQUES FOR DISPERSION MEASURE ESTIMATION IN FAST RADIO BURSTS STUDIES , Hosein Rajabi
Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications , Sudipta Singha Roy
Framework for Bug Inducing Commit Prediction Using Quality Metrics , Alireza Tavakkoli Barzoki
Knowledge-grounded Natural Language Understanding of Biomedical and Clinical Literature , Xindi Wang
Theses/Dissertations from 2023 2023
Classification of DDoS Attack with Machine Learning Architectures and Exploratory Analysis , Amreen Anbar
Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces , Sepehr Asgarian
Improved Protein Sequence Alignments Using Deep Learning , Seyed Sepehr Ashrafzadeh
INVESTIGATING IMPROVEMENTS TO MESH INDEXING , Anurag Bhattacharjee
Algorithms and Software for Oligonucleotide Design , Qin Dong
Framework for Assessing Information System Security Posture Risks , Syed Waqas Hamdani
De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling , Fang Han
Local Model Agnostic XAI Methodologies Applied to Breast Cancer Malignancy Predictions , Heather Hartley
A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit , Rongji He
A Novel Method for Assessment of Batch Effect on single cell RNA sequencing data , Behnam Jabbarizadeh
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs , Taabish Jeshani
Citation Polarity Identification From Scientific Articles Using Deep Learning Methods , Souvik Kundu
Denoising-Based Domain Adaptation Network for EEG Source Imaging , Runze Li
Decoy-Target Database Strategy and False Discovery Rate Analysis for Glycan Identification , Xiaoou Li
DpNovo: A DEEP LEARNING MODEL COMBINED WITH DYNAMIC PROGRAMMING FOR DE NOVO PEPTIDE SEQUENCING , Yizhou Li
Developing A Smart Home Surveillance System Using Autonomous Drones , Chongju Mai
Look-Ahead Selective Plasticity for Continual Learning , Rouzbeh Meshkinnejad
The Two Visual Processing Streams Through The Lens Of Deep Neural Networks , Aidasadat Mirebrahimi Tafreshi
Source-free Domain Adaptation for Sleep Stage Classification , Yasmin Niknam
Data Heterogeneity and Its Implications for Fairness , Ghazaleh Noroozi
Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System for Efficient, Sustainable, and Self-Adaptive Urban Environments , Elham Okhovat
Evaluating the Likelihood of Bug Inducing Commits Using Metrics Trend Analysis , Parul Parul
On Computing Optimal Repairs for Conditional Independence , Alireza Pirhadi
Open-Set Source-Free Domain Adaptation in Fundus Images Analysis , Masoud Pourreza
Migration in Edge Computing , Arshin Rezazadeh
A Modified Hopfield Network for the K-Median Problem , Cody Rossiter
Predicting Network Failures with AI Techniques , Chandrika Saha
Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective , Sajal Saha
An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support , Mozhgan Salimiparsa
Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition , Amir Hesam Salimnia
Global Cyber Attack Forecast using AI Techniques , Nusrat Kabir Samia
IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION , Rafael David Segistan Canizales
A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods , Kayvan Shabani
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network , Sareh Soltani Nejad
Smartphone Loss Prevention System Using BLE and GPS Technology , Noshin Tasnim
A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets) , Yasmen M. Wahba
Reducing Negative Transfer of Random Data in Source-Free Unsupervised Domain Adaptation , Anthony Wong
Deep Neural Methods for True/Pseudo- Invasion Classification in Colorectal Polyp Whole-Slide Images , Zhiyuan Yang
Developing a Relay-based Autonomous Drone Delivery System , Muhammad Zakar
Learning Mortality Risk for COVID-19 Using Machine Learning and Statistical Methods , Shaoshi Zhang
Machine Learning Techniques for Improved Functional Brain Parcellation , Da Zhi
Theses/Dissertations from 2022 2022
The Design and Implementation of a High-Performance Polynomial System Solver , Alexander Brandt
Defining Service Level Agreements in Serverless Computing , Mohamed Elsakhawy
Algorithms for Regular Chains of Dimension One , Juan P. Gonzalez Trochez
Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications , Susmitha Hanumanthu
Multi-Device Data Analysis for Fault Localization in Electrical Distribution Grids , Jacob D L Hunte
Towards Parking Lot Occupancy Assessment Using Aerial Imagery and Computer Vision , John Jewell
Potential of Vision Transformers for Advanced Driver-Assistance Systems: An Evaluative Approach , Andrew Katoch
Psychological Understanding of Textual journals using Natural Language Processing approaches , Amirmohammad Kazemeinizadeh
Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems , Nima Khairdoost
Solving Challenges in Deep Unsupervised Methods for Anomaly Detection , Vahid Reza Khazaie
Developing an Efficient Real-Time Terrestrial Infrastructure Inspection System Using Autonomous Drones and Deep Learning , Marlin Manka
Predictive Modelling For Topic Handling Of Natural Language Dialogue With Virtual Agents , Lareina Milambiling
Improving Deep Entity Resolution by Constraints , Soudeh Nilforoushan
Respiratory Pattern Analysis for COVID-19 Digital Screening Using AI Techniques , Annita Tahsin Priyoti
Extracting Microservice Dependencies Using Log Analysis , Andres O. Rodriguez Ishida
False Discovery Rate Analysis for Glycopeptide Identification , Shun Saito
Towards a Generalization of Fulton's Intersection Multiplicity Algorithm , Ryan Sandford
An Investigation Into Time Gazed At Traffic Objects By Drivers , Kolby R. Sarson
Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives , Ibrahim Mohammed Sayem
A Unified Representation and Deep Learning Architecture for Persuasive Essays in English , Muhammad Tawsif Sazid
Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP) , Aakriti Sharma
Advances in the Automatic Detection of Optimization Opportunities in Computer Programs , Delaram Talaashrafi
Reputation-Based Trust Assessment of Transacting Service Components , Konstantinos Tsiounis
Fully Autonomous UAV Exploration in Confined and Connectionless Environments , Kirk P. Vander Ploeg
Three Contributions to the Theory and Practice of Optimizing Compilers , Linxiao Wang
Developing Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach , Wumian Wang
Predicting and Modifying Memorability of Images , Mohammad Younesi
Theses/Dissertations from 2021 2021
Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches , Mahtab Ahmed
A Physical Layer Framework for a Smart City Using Accumulative Bayesian Machine Learning , Razan E. AlFar
Load Balancing and Resource Allocation in Smart Cities using Reinforcement Learning , Aseel AlOrbani
Contrastive Learning of Auditory Representations , Haider Al-Tahan
Cache-Friendly, Modular and Parallel Schemes For Computing Subresultant Chains , Mohammadali Asadi
Protein Interaction Sites Prediction using Deep Learning , Sourajit Basak
Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis , William A. Beldman
Improving Reader Motivation with Machine Learning , Tanner A. Bohn
A Black-box Approach for Containerized Microservice Monitoring in Fog Computing , Shi Chang
Visualization and Interpretation of Protein Interactions , Dipanjan Chatterjee
A Framework for Characterising Performance in Multi-Class Classification Problems with Applications in Cancer Single Cell RNA Sequencing , Erik R. Christensen
Exploratory Search with Archetype-based Language Models , Brent D. Davis
Evolutionary Design of Search and Triage Interfaces for Large Document Sets , Jonathan A. Demelo
Building Effective Network Security Frameworks using Deep Transfer Learning Techniques , Harsh Dhillon
A Deep Topical N-gram Model and Topic Discovery on COVID-19 News and Research Manuscripts , Yuan Du
Automatic extraction of requirements-related information from regulatory documents cited in the project contract , Sara Fotouhi
Developing a Resource and Energy Efficient Real-time Delivery Scheduling Framework for a Network of Autonomous Drones , Gopi Gugan
A Visual Analytics System for Rapid Sensemaking of Scientific Documents , Amirreza Haghverdiloo Barzegar
Calibration Between Eye Tracker and Stereoscopic Vision System Employing a Linear Closed-Form Perspective-n-Point (PNP) Algorithm , Mohammad Karami
Fuzzy and Probabilistic Rule-Based Approaches to Identify Fault Prone Files , Piyush Kumar Korlepara
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New research project will create AI audit tools to combat misinformation
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Posted on 8 May 2024
New project will work with stakeholders including non-experts to develop tools that will stop AI presenting false or invented information as fact.
The Participatory Harm Auditing Workbenches and Methodologies (PHAWM) project will develop new methods for maximising the potential benefits of predictive and generative AI while minimising their potential for harm arising from bias and misinformation.
The project will pioneer participatory AI auditing. This is where non-experts, including regulators, end-users and those people most likely to be affected by decisions made by AI systems, will play a role in ensuring that those systems provide fair and reliable outputs.
New tools to support the auditing process will be developed in partnership with these stakeholders. The project will also create new training resources to help encourage widespread adoption of the tools.
Collaboration
The Department of Computer Science is part of the project consortium, which is led by the University of Glasgow and includes the Universities of Edinburgh, Sheffield, Stirling and Strathclyde and King’s College London. Funded by Responsible AI UK, the project brings together 25 researchers from these universities with 23 partner organisations.
York’s expertise in cyber security and privacy will be central to the project. The auditing tools created will help develop more robust and reliable AI systems, but they must consider security and privacy aspects from the start.
Dr Siamak Shahandashti , Senior Lecturer in the Department of Computer Science, will lead this element of the project. “The underlying training data used to train the AI models may contain sensitive private information such as health records,” he said. “We’re taking a 'privacy-by-design' approach to developing AI auditing mechanisms and tools, ensuring that the preservation of privacy is taken into account from the outset, rather than patched on as an afterthought.”
Dr Simone Stumpf, of the University of Glasgow’s School of Computing Science, is the project’s principal investigator. She said: “Auditing the outputs of AI can be a powerful tool to help develop more robust and reliable systems, but until now auditing has been unevenly applied and left mainly in the hands of experts. The PHAWM project will put auditing power in the hands of people who best understand the potential impact…That will help produce fairer and more robust outcomes for end-users and help ensure that AI technologies meet their regulatory obligations.”
Societal impact
Professor of Artificial Intelligence Gopal Ramchurn, from the University of Southampton and CEO of RAi UK, said the projects are multi-disciplinary and bring together computer and social scientists, alongside other specialists.
He added: “These projects are the keystones of the Responsible AI UK programme and have been chosen because they address the most pressing challenges that society faces with the rapid advances in AI. The projects will deliver interdisciplinary research that looks to address the complex socio-technical challenges that already exist or are emerging with the use of generative AI and other forms of AI deployed in the real-world.
“The concerns around AI are not just for governments and industry to deal with – it is important that AI experts engage with researchers and policymakers to ensure we can better anticipate the issues that will be caused by AI.”
Related links
- Sia Shahandashti's research
- Cyber security and privacy research group
- MSc in Cyber Security
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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 ...
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.
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 ...
Part 1: Orientation to Small Group Systems Chapter 1: Small Groups as the Heart of Society Chapter 2: Groups as Structured Open Systems Part 2: Foundations of Small Group Communication Chapter 3: Communication Principles for Group Members... more. Download. by Gloria Galanes. Computer Science.
arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.
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.
This conceptual research paper is written to discuss the implementation of the A.D.A.B model in technology -based and technical subjects such as Computer Science, Engineering, Technical and so on ...
ytsaurus/ytsaurus • 5 Jun 2023. Many current applications have to perform data processing in a streaming fashion. Distributed, Parallel, and Cluster Computing. 1,854. 0.11 stars / hour. Paper. Code. Papers With Code highlights trending Computer Science research and the code to implement it.
The Emerging Interest Group on Reproducibility and Replicability (REP) provides a distinct community of researchers focused on the reproducibility, sustainability, independent verification, model testing, benchmark data, guidelines and standard; while simultaneously promoting best practice across the ACM by working with interested parties to include their requirements into the tools ...
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 ...
Computers. Computers is an international, scientific, peer-reviewed, open access journal of computer science, including computer and network architecture and computer-human interaction as its main foci, published monthly online by MDPI. Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
We are the home to prestigious publications that deliver insights from the brightest minds in computing. Our digital library has over 930,000+ articles, from a range of topics including award-winning special issues. All of Computing. Computer. IEEE Open Journal of the Computer Society. IEEE Transactions on Computers.
Open research in computer science. Spanning networks and communications to security and cryptology to big data, complexity, and analytics, SpringerOpen and BMC publish one of the leading open access portfolios in computer science. Learn about our journals and the research we publish here on this page.
Welcome to the Computing Research Repository (CoRR) in arXiv. The Computer Science section of arXiv was established in 1998 through a partnership of the Association for Computing Machinery, the Networked Computer Science Technical Reference Library, and arXiv. You can view the subject category descriptions and browse papers from the main CS ...
SN Computer Science is a broad-based, hybrid, peer reviewed journal that publishes original research in all the disciplines of computer science including various inter-disciplinary aspects. The journal aims to be a global forum of, for, and by the community and offers: Rapid peer review under the expert guidance of a global Editorial Board; No color or page charges and free submission
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 ...
Journal of Computer Science and Technology is an international platform publishing high quality, refereed papers in all aspects of computer science and technology. Sponsored by the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), and China Computer Federation (CCF). Jointly published by ICT, CAS and Springer on a ...
Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies, Anwesh Tuladhar. PDF. Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy, Troi André Williams. PDF. Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones, Armin Ziaie Tabari
Get 30 days free. 1. ACM Digital Library. ACM Digital Library is the clear number one when it comes to academic databases for computer science. The ACM Full-Text Collection currently has 540,000+ articles, while the ACM Guide to Computing Literature holds more than 2.8+ million bibliographic entries. Coverage: 2.8+ million articles. Abstracts: .
The Journal of Computer Science (JCS) is dedicated to advancing computer science by publishing high-quality research and review articles that span both theoretical foundations and practical applications in information, computation, and computer systems. With a commitment to excellence, JCS offers a platform for researchers, scholars, and ...
Computer Science Research Resources: Find Articles & Papers ... Search for articles, conference paper, and report information in all areas of engineering. Full-text is often available through direct download. Scopus This link opens in a new window. Search periodicals, conference proceedings, technical reports, trade literature, patents, books ...
Theses/Dissertations from 2023. PDF. Classification of DDoS Attack with Machine Learning Architectures and Exploratory Analysis, Amreen Anbar. PDF. Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces, Sepehr Asgarian. PDF.
The Department of Computer Science is part of the project consortium, which is led by the University of Glasgow and includes the Universities of Edinburgh, Sheffield, Stirling and Strathclyde and King's College London. Funded by Responsible AI UK, the project brings together 25 researchers from these universities with 23 partner organisations.
SAKURA internet Research Center and COGNANO envision that these research findings will open new possibilities for AI-driven drug discovery, contributing to advancements in medical science and ...
The ranking of best conferences in Kenya for Computer Science was devised by Research.com, one of the prominent sites for Computer Science research supplying credible data on scientific output since 2014. The position in the ranking is based on Impact Score values aggregated on 21-12-2022. ... Paper submission open. Rank Conference Details ...