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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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Home » 500+ Computer Science Research Topics
500+ Computer Science Research Topics
Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.
Computer Science Research Topics
Computer Science Research Topics are as follows:
- Using machine learning to detect and prevent cyber attacks
- Developing algorithms for optimized resource allocation in cloud computing
- Investigating the use of blockchain technology for secure and decentralized data storage
- Developing intelligent chatbots for customer service
- Investigating the effectiveness of deep learning for natural language processing
- Developing algorithms for detecting and removing fake news from social media
- Investigating the impact of social media on mental health
- Developing algorithms for efficient image and video compression
- Investigating the use of big data analytics for predictive maintenance in manufacturing
- Developing algorithms for identifying and mitigating bias in machine learning models
- Investigating the ethical implications of autonomous vehicles
- Developing algorithms for detecting and preventing cyberbullying
- Investigating the use of machine learning for personalized medicine
- Developing algorithms for efficient and accurate speech recognition
- Investigating the impact of social media on political polarization
- Developing algorithms for sentiment analysis in social media data
- Investigating the use of virtual reality in education
- Developing algorithms for efficient data encryption and decryption
- Investigating the impact of technology on workplace productivity
- Developing algorithms for detecting and mitigating deepfakes
- Investigating the use of artificial intelligence in financial trading
- Developing algorithms for efficient database management
- Investigating the effectiveness of online learning platforms
- Developing algorithms for efficient and accurate facial recognition
- Investigating the use of machine learning for predicting weather patterns
- Developing algorithms for efficient and secure data transfer
- Investigating the impact of technology on social skills and communication
- Developing algorithms for efficient and accurate object recognition
- Investigating the use of machine learning for fraud detection in finance
- Developing algorithms for efficient and secure authentication systems
- Investigating the impact of technology on privacy and surveillance
- Developing algorithms for efficient and accurate handwriting recognition
- Investigating the use of machine learning for predicting stock prices
- Developing algorithms for efficient and secure biometric identification
- Investigating the impact of technology on mental health and well-being
- Developing algorithms for efficient and accurate language translation
- Investigating the use of machine learning for personalized advertising
- Developing algorithms for efficient and secure payment systems
- Investigating the impact of technology on the job market and automation
- Developing algorithms for efficient and accurate object tracking
- Investigating the use of machine learning for predicting disease outbreaks
- Developing algorithms for efficient and secure access control
- Investigating the impact of technology on human behavior and decision making
- Developing algorithms for efficient and accurate sound recognition
- Investigating the use of machine learning for predicting customer behavior
- Developing algorithms for efficient and secure data backup and recovery
- Investigating the impact of technology on education and learning outcomes
- Developing algorithms for efficient and accurate emotion recognition
- Investigating the use of machine learning for improving healthcare outcomes
- Developing algorithms for efficient and secure supply chain management
- Investigating the impact of technology on cultural and societal norms
- Developing algorithms for efficient and accurate gesture recognition
- Investigating the use of machine learning for predicting consumer demand
- Developing algorithms for efficient and secure cloud storage
- Investigating the impact of technology on environmental sustainability
- Developing algorithms for efficient and accurate voice recognition
- Investigating the use of machine learning for improving transportation systems
- Developing algorithms for efficient and secure mobile device management
- Investigating the impact of technology on social inequality and access to resources
- Machine learning for healthcare diagnosis and treatment
- Machine Learning for Cybersecurity
- Machine learning for personalized medicine
- Cybersecurity threats and defense strategies
- Big data analytics for business intelligence
- Blockchain technology and its applications
- Human-computer interaction in virtual reality environments
- Artificial intelligence for autonomous vehicles
- Natural language processing for chatbots
- Cloud computing and its impact on the IT industry
- Internet of Things (IoT) and smart homes
- Robotics and automation in manufacturing
- Augmented reality and its potential in education
- Data mining techniques for customer relationship management
- Computer vision for object recognition and tracking
- Quantum computing and its applications in cryptography
- Social media analytics and sentiment analysis
- Recommender systems for personalized content delivery
- Mobile computing and its impact on society
- Bioinformatics and genomic data analysis
- Deep learning for image and speech recognition
- Digital signal processing and audio processing algorithms
- Cloud storage and data security in the cloud
- Wearable technology and its impact on healthcare
- Computational linguistics for natural language understanding
- Cognitive computing for decision support systems
- Cyber-physical systems and their applications
- Edge computing and its impact on IoT
- Machine learning for fraud detection
- Cryptography and its role in secure communication
- Cybersecurity risks in the era of the Internet of Things
- Natural language generation for automated report writing
- 3D printing and its impact on manufacturing
- Virtual assistants and their applications in daily life
- Cloud-based gaming and its impact on the gaming industry
- Computer networks and their security issues
- Cyber forensics and its role in criminal investigations
- Machine learning for predictive maintenance in industrial settings
- Augmented reality for cultural heritage preservation
- Human-robot interaction and its applications
- Data visualization and its impact on decision-making
- Cybersecurity in financial systems and blockchain
- Computer graphics and animation techniques
- Biometrics and its role in secure authentication
- Cloud-based e-learning platforms and their impact on education
- Natural language processing for machine translation
- Machine learning for predictive maintenance in healthcare
- Cybersecurity and privacy issues in social media
- Computer vision for medical image analysis
- Natural language generation for content creation
- Cybersecurity challenges in cloud computing
- Human-robot collaboration in manufacturing
- Data mining for predicting customer churn
- Artificial intelligence for autonomous drones
- Cybersecurity risks in the healthcare industry
- Machine learning for speech synthesis
- Edge computing for low-latency applications
- Virtual reality for mental health therapy
- Quantum computing and its applications in finance
- Biomedical engineering and its applications
- Cybersecurity in autonomous systems
- Machine learning for predictive maintenance in transportation
- Computer vision for object detection in autonomous driving
- Augmented reality for industrial training and simulations
- Cloud-based cybersecurity solutions for small businesses
- Natural language processing for knowledge management
- Machine learning for personalized advertising
- Cybersecurity in the supply chain management
- Cybersecurity risks in the energy sector
- Computer vision for facial recognition
- Natural language processing for social media analysis
- Machine learning for sentiment analysis in customer reviews
- Explainable Artificial Intelligence
- Quantum Computing
- Blockchain Technology
- Human-Computer Interaction
- Natural Language Processing
- Cloud Computing
- Robotics and Automation
- Augmented Reality and Virtual Reality
- Cyber-Physical Systems
- Computational Neuroscience
- Big Data Analytics
- Computer Vision
- Cryptography and Network Security
- Internet of Things
- Computer Graphics and Visualization
- Artificial Intelligence for Game Design
- Computational Biology
- Social Network Analysis
- Bioinformatics
- Distributed Systems and Middleware
- Information Retrieval and Data Mining
- Computer Networks
- Mobile Computing and Wireless Networks
- Software Engineering
- Database Systems
- Parallel and Distributed Computing
- Human-Robot Interaction
- Intelligent Transportation Systems
- High-Performance Computing
- Cyber-Physical Security
- Deep Learning
- Sensor Networks
- Multi-Agent Systems
- Human-Centered Computing
- Wearable Computing
- Knowledge Representation and Reasoning
- Adaptive Systems
- Brain-Computer Interface
- Health Informatics
- Cognitive Computing
- Cybersecurity and Privacy
- Internet Security
- Cybercrime and Digital Forensics
- Cloud Security
- Cryptocurrencies and Digital Payments
- Machine Learning for Natural Language Generation
- Cognitive Robotics
- Neural Networks
- Semantic Web
- Image Processing
- Cyber Threat Intelligence
- Secure Mobile Computing
- Cybersecurity Education and Training
- Privacy Preserving Techniques
- Cyber-Physical Systems Security
- Virtualization and Containerization
- Machine Learning for Computer Vision
- Network Function Virtualization
- Cybersecurity Risk Management
- Information Security Governance
- Intrusion Detection and Prevention
- Biometric Authentication
- Machine Learning for Predictive Maintenance
- Security in Cloud-based Environments
- Cybersecurity for Industrial Control Systems
- Smart Grid Security
- Software Defined Networking
- Quantum Cryptography
- Security in the Internet of Things
- Natural language processing for sentiment analysis
- Blockchain technology for secure data sharing
- Developing efficient algorithms for big data analysis
- Cybersecurity for internet of things (IoT) devices
- Human-robot interaction for industrial automation
- Image recognition for autonomous vehicles
- Social media analytics for marketing strategy
- Quantum computing for solving complex problems
- Biometric authentication for secure access control
- Augmented reality for education and training
- Intelligent transportation systems for traffic management
- Predictive modeling for financial markets
- Cloud computing for scalable data storage and processing
- Virtual reality for therapy and mental health treatment
- Data visualization for business intelligence
- Recommender systems for personalized product recommendations
- Speech recognition for voice-controlled devices
- Mobile computing for real-time location-based services
- Neural networks for predicting user behavior
- Genetic algorithms for optimization problems
- Distributed computing for parallel processing
- Internet of things (IoT) for smart cities
- Wireless sensor networks for environmental monitoring
- Cloud-based gaming for high-performance gaming
- Social network analysis for identifying influencers
- Autonomous systems for agriculture
- Robotics for disaster response
- Data mining for customer segmentation
- Computer graphics for visual effects in movies and video games
- Virtual assistants for personalized customer service
- Natural language understanding for chatbots
- 3D printing for manufacturing prototypes
- Artificial intelligence for stock trading
- Machine learning for weather forecasting
- Biomedical engineering for prosthetics and implants
- Cybersecurity for financial institutions
- Machine learning for energy consumption optimization
- Computer vision for object tracking
- Natural language processing for document summarization
- Wearable technology for health and fitness monitoring
- Internet of things (IoT) for home automation
- Reinforcement learning for robotics control
- Big data analytics for customer insights
- Machine learning for supply chain optimization
- Natural language processing for legal document analysis
- Artificial intelligence for drug discovery
- Computer vision for object recognition in robotics
- Data mining for customer churn prediction
- Autonomous systems for space exploration
- Robotics for agriculture automation
- Machine learning for predicting earthquakes
- Natural language processing for sentiment analysis in customer reviews
- Big data analytics for predicting natural disasters
- Internet of things (IoT) for remote patient monitoring
- Blockchain technology for digital identity management
- Machine learning for predicting wildfire spread
- Computer vision for gesture recognition
- Natural language processing for automated translation
- Big data analytics for fraud detection in banking
- Internet of things (IoT) for smart homes
- Robotics for warehouse automation
- Machine learning for predicting air pollution
- Natural language processing for medical record analysis
- Augmented reality for architectural design
- Big data analytics for predicting traffic congestion
- Machine learning for predicting customer lifetime value
- Developing algorithms for efficient and accurate text recognition
- Natural Language Processing for Virtual Assistants
- Natural Language Processing for Sentiment Analysis in Social Media
- Explainable Artificial Intelligence (XAI) for Trust and Transparency
- Deep Learning for Image and Video Retrieval
- Edge Computing for Internet of Things (IoT) Applications
- Data Science for Social Media Analytics
- Cybersecurity for Critical Infrastructure Protection
- Natural Language Processing for Text Classification
- Quantum Computing for Optimization Problems
- Machine Learning for Personalized Health Monitoring
- Computer Vision for Autonomous Driving
- Blockchain Technology for Supply Chain Management
- Augmented Reality for Education and Training
- Natural Language Processing for Sentiment Analysis
- Machine Learning for Personalized Marketing
- Big Data Analytics for Financial Fraud Detection
- Cybersecurity for Cloud Security Assessment
- Artificial Intelligence for Natural Language Understanding
- Blockchain Technology for Decentralized Applications
- Virtual Reality for Cultural Heritage Preservation
- Natural Language Processing for Named Entity Recognition
- Machine Learning for Customer Churn Prediction
- Big Data Analytics for Social Network Analysis
- Cybersecurity for Intrusion Detection and Prevention
- Artificial Intelligence for Robotics and Automation
- Blockchain Technology for Digital Identity Management
- Virtual Reality for Rehabilitation and Therapy
- Natural Language Processing for Text Summarization
- Machine Learning for Credit Risk Assessment
- Big Data Analytics for Fraud Detection in Healthcare
- Cybersecurity for Internet Privacy Protection
- Artificial Intelligence for Game Design and Development
- Blockchain Technology for Decentralized Social Networks
- Virtual Reality for Marketing and Advertising
- Natural Language Processing for Opinion Mining
- Machine Learning for Anomaly Detection
- Big Data Analytics for Predictive Maintenance in Transportation
- Cybersecurity for Network Security Management
- Artificial Intelligence for Personalized News and Content Delivery
- Blockchain Technology for Cryptocurrency Mining
- Virtual Reality for Architectural Design and Visualization
- Natural Language Processing for Machine Translation
- Machine Learning for Automated Image Captioning
- Big Data Analytics for Stock Market Prediction
- Cybersecurity for Biometric Authentication Systems
- Artificial Intelligence for Human-Robot Interaction
- Blockchain Technology for Smart Grids
- Virtual Reality for Sports Training and Simulation
- Natural Language Processing for Question Answering Systems
- Machine Learning for Sentiment Analysis in Customer Feedback
- Big Data Analytics for Predictive Maintenance in Manufacturing
- Cybersecurity for Cloud-Based Systems
- Artificial Intelligence for Automated Journalism
- Blockchain Technology for Intellectual Property Management
- Virtual Reality for Therapy and Rehabilitation
- Natural Language Processing for Language Generation
- Machine Learning for Customer Lifetime Value Prediction
- Big Data Analytics for Predictive Maintenance in Energy Systems
- Cybersecurity for Secure Mobile Communication
- Artificial Intelligence for Emotion Recognition
- Blockchain Technology for Digital Asset Trading
- Virtual Reality for Automotive Design and Visualization
- Natural Language Processing for Semantic Web
- Machine Learning for Fraud Detection in Financial Transactions
- Big Data Analytics for Social Media Monitoring
- Cybersecurity for Cloud Storage and Sharing
- Artificial Intelligence for Personalized Education
- Blockchain Technology for Secure Online Voting Systems
- Virtual Reality for Cultural Tourism
- Natural Language Processing for Chatbot Communication
- Machine Learning for Medical Diagnosis and Treatment
- Big Data Analytics for Environmental Monitoring and Management.
- Cybersecurity for Cloud Computing Environments
- Virtual Reality for Training and Simulation
- Big Data Analytics for Sports Performance Analysis
- Cybersecurity for Internet of Things (IoT) Devices
- Artificial Intelligence for Traffic Management and Control
- Blockchain Technology for Smart Contracts
- Natural Language Processing for Document Summarization
- Machine Learning for Image and Video Recognition
- Blockchain Technology for Digital Asset Management
- Virtual Reality for Entertainment and Gaming
- Natural Language Processing for Opinion Mining in Online Reviews
- Machine Learning for Customer Relationship Management
- Big Data Analytics for Environmental Monitoring and Management
- Cybersecurity for Network Traffic Analysis and Monitoring
- Artificial Intelligence for Natural Language Generation
- Blockchain Technology for Supply Chain Transparency and Traceability
- Virtual Reality for Design and Visualization
- Natural Language Processing for Speech Recognition
- Machine Learning for Recommendation Systems
- Big Data Analytics for Customer Segmentation and Targeting
- Cybersecurity for Biometric Authentication
- Artificial Intelligence for Human-Computer Interaction
- Blockchain Technology for Decentralized Finance (DeFi)
- Virtual Reality for Tourism and Cultural Heritage
- Machine Learning for Cybersecurity Threat Detection and Prevention
- Big Data Analytics for Healthcare Cost Reduction
- Cybersecurity for Data Privacy and Protection
- Artificial Intelligence for Autonomous Vehicles
- Blockchain Technology for Cryptocurrency and Blockchain Security
- Virtual Reality for Real Estate Visualization
- Natural Language Processing for Question Answering
- Big Data Analytics for Financial Markets Prediction
- Cybersecurity for Cloud-Based Machine Learning Systems
- Artificial Intelligence for Personalized Advertising
- Blockchain Technology for Digital Identity Verification
- Virtual Reality for Cultural and Language Learning
- Natural Language Processing for Semantic Analysis
- Machine Learning for Business Forecasting
- Big Data Analytics for Social Media Marketing
- Artificial Intelligence for Content Generation
- Blockchain Technology for Smart Cities
- Virtual Reality for Historical Reconstruction
- Natural Language Processing for Knowledge Graph Construction
- Machine Learning for Speech Synthesis
- Big Data Analytics for Traffic Optimization
- Artificial Intelligence for Social Robotics
- Blockchain Technology for Healthcare Data Management
- Virtual Reality for Disaster Preparedness and Response
- Natural Language Processing for Multilingual Communication
- Machine Learning for Emotion Recognition
- Big Data Analytics for Human Resources Management
- Cybersecurity for Mobile App Security
- Artificial Intelligence for Financial Planning and Investment
- Blockchain Technology for Energy Management
- Virtual Reality for Cultural Preservation and Heritage.
- Big Data Analytics for Healthcare Management
- Cybersecurity in the Internet of Things (IoT)
- Artificial Intelligence for Predictive Maintenance
- Computational Biology for Drug Discovery
- Virtual Reality for Mental Health Treatment
- Machine Learning for Sentiment Analysis in Social Media
- Human-Computer Interaction for User Experience Design
- Cloud Computing for Disaster Recovery
- Quantum Computing for Cryptography
- Intelligent Transportation Systems for Smart Cities
- Cybersecurity for Autonomous Vehicles
- Artificial Intelligence for Fraud Detection in Financial Systems
- Social Network Analysis for Marketing Campaigns
- Cloud Computing for Video Game Streaming
- Machine Learning for Speech Recognition
- Augmented Reality for Architecture and Design
- Natural Language Processing for Customer Service Chatbots
- Machine Learning for Climate Change Prediction
- Big Data Analytics for Social Sciences
- Artificial Intelligence for Energy Management
- Virtual Reality for Tourism and Travel
- Cybersecurity for Smart Grids
- Machine Learning for Image Recognition
- Augmented Reality for Sports Training
- Natural Language Processing for Content Creation
- Cloud Computing for High-Performance Computing
- Artificial Intelligence for Personalized Medicine
- Virtual Reality for Architecture and Design
- Augmented Reality for Product Visualization
- Natural Language Processing for Language Translation
- Cybersecurity for Cloud Computing
- Artificial Intelligence for Supply Chain Optimization
- Blockchain Technology for Digital Voting Systems
- Virtual Reality for Job Training
- Augmented Reality for Retail Shopping
- Natural Language Processing for Sentiment Analysis in Customer Feedback
- Cloud Computing for Mobile Application Development
- Artificial Intelligence for Cybersecurity Threat Detection
- Blockchain Technology for Intellectual Property Protection
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CVPR 2024 Announces Best Paper Award Winners
This year, from more than 11,500 paper submissions, the CVPR 2024 Awards Committee selected the following 10 winners for the honor of Best Papers during the Awards Program at CVPR 2024, taking place now through 21 June at the Seattle Convention Center in Seattle, Wash., U.S.A.
Best Papers
- “ Generative Image Dynamics ” Authors: Zhengqi Li, Richard Tucker, Noah Snavely, Aleksander Holynski The paper presents a new approach for modeling natural oscillation dynamics from a single still picture. This approach produces photo-realistic animations from a single picture and significantly outperforms prior baselines. It also demonstrates potential to enable several downstream applications such as creating seamlessly looping or interactive image dynamics.
- “ Rich Human Feedback for Text-to-Image Generation ” Authors: Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy Dj Dvijotham, Katherine M. Collins, Yiwen Luo, Yang Li, Kai J. Kohlhoff, Deepak Ramachandran, and Vidhya Navalpakkam This paper highlights the first rich human feedback dataset for image generation. Authors designed and trained a multimodal Transformer to predict the rich human feedback and demonstrated some instances to improve image generation.
Honorable mention papers included, “ EventPS: Real-Time Photometric Stereo Using an Event Camera ” and “ pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. ”
Best Student Papers
- “ Mip-Splatting: Alias-free 3D Gaussian Splatting ” Authors: Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, Andreas Geiger This paper introduces Mip-Splatting, a technique improving 3D Gaussian Splatting (3DGS) with a 3D smoothing filter and a 2D Mip filter for alias-free rendering at any scale. This approach significantly outperforms state-of-the-art methods in out-of-distribution scenarios, when testing at sampling rates different from training, resulting in better generalization to out-of-distribution camera poses and zoom factors.
- “ BioCLIP: A Vision Foundation Model for the Tree of Life ” Authors: Samuel Stevens, Jiaman Wu, Matthew J. Thompson, Elizabeth G. Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M. Dahdul, Charles Stewart, Tanya Berger-Wolf, Wei-Lun Chao, and Yu Su This paper offers TREEOFLIFE-10M and BIOCLIP, a large-scale diverse biology image dataset and a foundation model for the tree of life, respectively. This work shows BIOCLIP is a strong fine-grained classifier for biology in both zero- and few-shot settings.
There also were four honorable mentions in this category this year: “ SpiderMatch: 3D Shape Matching with Global Optimality and Geometric Consistency ”; “ Image Processing GNN: Breaking Rigidity in Super-Resolution; Objects as Volumes: A Stochastic Geometry View of Opaque Solids ;” and “ Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods. ”
“We are honored to recognize the CVPR 2024 Best Paper Awards winners,” said David Crandall, Professor of Computer Science at Indiana University, Bloomington, Ind., U.S.A., and CVPR 2024 Program Co-Chair. “The 10 papers selected this year – double the number awarded in 2023 – are a testament to the continued growth of CVPR and the field, and to all of the advances that await.”
Additionally, the IEEE Computer Society (CS), a CVPR organizing sponsor, announced the Technical Community on Pattern Analysis and Machine Intelligence (TCPAMI) Awards at this year’s conference. The following were recognized for their achievements:
- 2024 Recipient : “ Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation ” Authors: Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
- 2024 Recipient : Angjoo Kanazawa, Carl Vondrick
- 2024 Recipient : Andrea Vedaldi
“The TCPAMI Awards demonstrate the lasting impact and influence of CVPR research and researchers,” said Walter J. Scheirer, University of Notre Dame, Notre Dame, Ind., U.S.A., and CVPR 2024 General Chair. “The contributions of these leaders have helped to shape and drive forward continued advancements in the field. We are proud to recognize these achievements and congratulate them on their success.”
About the CVPR 2024 The Computer Vision and Pattern Recognition Conference (CVPR) is the preeminent computer vision event for new research in support of artificial intelligence (AI), machine learning (ML), augmented, virtual and mixed reality (AR/VR/MR), deep learning, and much more. Sponsored by the IEEE Computer Society (CS) and the Computer Vision Foundation (CVF), CVPR delivers the important advances in all areas of computer vision and pattern recognition and the various fields and industries they impact. With a first-in-class technical program, including tutorials and workshops, a leading-edge expo, and robust networking opportunities, CVPR, which is annually attended by more than 10,000 scientists and engineers, creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation.
CVPR 2024 takes place 17-21 June at the Seattle Convention Center in Seattle, Wash., U.S.A., and participants may also access sessions virtually. For more information about CVPR 2024, visit cvpr.thecvf.com .
About the Computer Vision Foundation The Computer Vision Foundation (CVF) is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV). Visit thecvf.com for more information.
About the IEEE Computer Society Engaging computer engineers, scientists, academia, and industry professionals from all areas and levels of computing, the IEEE Computer Society (CS) serves as the world’s largest and most established professional organization of its type. IEEE CS sets the standard for the education and engagement that fuels continued global technological advancement. Through conferences, publications, and programs that inspire dialogue, debate, and collaboration, IEEE CS empowers, shapes, and guides the future of not only its 375,000+ community members, but the greater industry, enabling new opportunities to better serve our world. Visit computer.org for more information.
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| Title | Type | --> | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | journal | 37.044 Q1 | 39 | 3 | 13 | 897 | 955 | 13 | 100.11 | 299.00 | 27.78 | ||
| 2 | conference and proceedings | 12.263 | 356 | 1607 | 1987 | 91756 | 38161 | 1983 | 19.21 | 57.10 | 25.95 | ||
| 3 | conference and proceedings | 10.331 | 531 | 1613 | 5199 | 91574 | 135771 | 5194 | 21.76 | 56.77 | 24.99 | ||
| 4 | book series | 9.129 Q1 | 30 | 13 | 37 | 234 | 370 | 4 | 0.36 | 18.00 | 0.00 | ||
| 5 | journal | 8.707 Q1 | 263 | 271 | 724 | 12672 | 11814 | 706 | 14.83 | 46.76 | 26.38 | ||
| 6 | journal | 8.119 Q1 | 16 | 21 | 46 | 1045 | 1720 | 44 | 18.17 | 49.76 | 30.68 | ||
| 7 | book series | 7.993 Q1 | 12 | 0 | 2 | 0 | 33 | 2 | 0.00 | 0.00 | 0.00 | ||
| 8 | journal | 7.951 Q1 | 116 | 201 | 441 | 11275 | 8533 | 347 | 20.56 | 56.09 | 31.96 | ||
| 9 | journal | 7.766 Q1 | 251 | 268 | 848 | 17376 | 7936 | 848 | 8.80 | 64.84 | 20.96 | ||
| 10 | journal | 6.668 Q1 | 221 | 218 | 471 | 15857 | 8320 | 457 | 16.03 | 72.74 | 26.08 | ||
| 11 | journal | 6.601 Q1 | 101 | 94 | 297 | 3891 | 5224 | 233 | 16.40 | 41.39 | 24.66 | ||
| 12 | journal | 6.280 Q1 | 213 | 308 | 531 | 51722 | 13816 | 531 | 25.70 | 167.93 | 24.57 | ||
| 13 | journal | 6.158 Q1 | 417 | 1132 | 1248 | 79813 | 26320 | 1236 | 19.32 | 70.51 | 25.30 | ||
| 14 | journal | 6.085 Q1 | 325 | 65 | 303 | 11573 | 7186 | 297 | 28.12 | 178.05 | 22.15 | ||
| 15 | journal | 6.069 Q1 | 20 | 2 | 7 | 287 | 109 | 7 | 20.60 | 143.50 | 25.00 | ||
| 16 | journal | 5.940 Q1 | 67 | 176 | 428 | 8504 | 7872 | 304 | 14.59 | 48.32 | 25.08 | ||
| 17 | journal | 5.926 Q1 | 188 | 167 | 460 | 1879 | 5302 | 420 | 10.00 | 11.25 | 23.93 | ||
| 18 | journal | 5.775 Q1 | 177 | 95 | 464 | 9818 | 13119 | 450 | 24.28 | 103.35 | 28.01 | ||
| 19 | journal | 5.647 Q1 | 156 | 377 | 462 | 31043 | 11404 | 450 | 20.93 | 82.34 | 28.05 | ||
| 20 | journal | 5.641 Q1 | 185 | 797 | 2103 | 31222 | 26174 | 2103 | 10.59 | 39.17 | 28.60 | ||
| 21 | journal | 5.631 Q1 | 282 | 341 | 599 | 3566 | 5764 | 482 | 7.40 | 10.46 | 22.95 | ||
| 22 | journal | 5.371 Q1 | 251 | 1086 | 1927 | 28831 | 19313 | 1927 | 9.40 | 26.55 | 26.35 | ||
| 23 | journal | 5.305 Q1 | 172 | 56 | 239 | 4036 | 2142 | 220 | 7.93 | 72.07 | 40.08 | ||
| 24 | journal | 5.209 Q1 | 210 | 250 | 292 | 8233 | 4154 | 287 | 11.82 | 32.93 | 28.27 | ||
| 25 | journal | 4.954 Q1 | 46 | 192 | 492 | 15899 | 4995 | 487 | 10.21 | 82.81 | 13.32 | ||
| 26 | journal | 4.952 Q1 | 27 | 2 | 7 | 375 | 145 | 7 | 7.50 | 187.50 | 25.00 | ||
| 27 | journal | 4.948 Q1 | 148 | 60 | 106 | 4376 | 1539 | 93 | 14.31 | 72.93 | 42.02 | ||
| 28 | journal | 4.896 Q1 | 211 | 86 | 251 | 2535 | 3339 | 218 | 7.76 | 29.48 | 21.63 | ||
| 29 | journal | 4.863 Q1 | 230 | 476 | 1342 | 15250 | 16073 | 1340 | 10.44 | 32.04 | 24.53 | ||
| 30 | journal | 4.696 Q1 | 83 | 240 | 510 | 10198 | 6783 | 458 | 14.59 | 42.49 | 29.34 | ||
| 31 | journal | 4.633 Q1 | 36 | 18 | 70 | 2223 | 1203 | 70 | 13.64 | 123.50 | 21.88 | ||
| 32 | journal | 4.621 Q1 | 88 | 109 | 395 | 7159 | 3822 | 395 | 11.10 | 65.68 | 32.99 | ||
| 33 | journal | 4.608 Q1 | 27 | 63 | 110 | 3855 | 1648 | 106 | 19.84 | 61.19 | 32.10 | ||
| 34 | journal | 4.501 Q1 | 329 | 1007 | 1752 | 27160 | 12219 | 1751 | 6.50 | 26.97 | 20.35 | ||
| 35 | journal | 4.496 Q1 | 36 | 3 | 9 | 883 | 244 | 9 | 14.20 | 294.33 | 30.00 | ||
| 36 | journal | 4.420 Q1 | 193 | 1035 | 2489 | 30287 | 32448 | 2432 | 12.46 | 29.26 | 26.92 | ||
| 37 | journal | 4.346 Q1 | 190 | 92 | 216 | 5524 | 2531 | 208 | 9.24 | 60.04 | 17.11 | ||
| 38 | journal | 4.273 Q1 | 84 | 232 | 508 | 11375 | 8001 | 434 | 12.51 | 49.03 | 37.71 | ||
| 39 | journal | 4.204 Q1 | 218 | 426 | 1070 | 16592 | 11768 | 1062 | 10.99 | 38.95 | 29.27 | ||
| 40 | journal | 4.176 Q1 | 185 | 89 | 218 | 6841 | 1687 | 216 | 6.45 | 76.87 | 26.42 | ||
| 41 | journal | 4.170 Q1 | 251 | 1818 | 1894 | 46076 | 23364 | 1884 | 11.83 | 25.34 | 28.16 | ||
| 42 | journal | 4.112 Q1 | 171 | 273 | 813 | 19887 | 11160 | 809 | 12.77 | 72.85 | 27.89 | ||
| 43 | journal | 4.105 Q1 | 271 | 60 | 207 | 4991 | 2019 | 205 | 7.27 | 83.18 | 28.14 | ||
| 44 | journal | 4.006 Q1 | 56 | 97 | 232 | 5911 | 5002 | 232 | 15.63 | 60.94 | 29.27 | ||
| 45 | journal | 3.992 Q1 | 171 | 682 | 1809 | 30712 | 17917 | 1803 | 10.19 | 45.03 | 27.64 | ||
| 46 | journal | 3.896 Q1 | 151 | 282 | 683 | 2268 | 6608 | 655 | 7.61 | 8.04 | 29.01 | ||
| 47 | journal | 3.824 Q1 | 128 | 89 | 121 | 8012 | 1961 | 109 | 12.39 | 90.02 | 25.98 | ||
| 48 | journal | 3.818 Q1 | 141 | 94 | 332 | 4946 | 3733 | 322 | 11.48 | 52.62 | 26.38 | ||
| 49 | journal | 3.760 Q1 | 187 | 289 | 777 | 21339 | 10766 | 772 | 11.83 | 73.84 | 28.50 | ||
| 50 | journal | 3.703 Q1 | 259 | 362 | 1038 | 16564 | 12917 | 1037 | 9.83 | 45.76 | 28.12 |
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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.
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The top 10 research papers in computer science by mendeley readership..
Since we recently announced our $10001 Binary Battle to promote applications built on the Mendeley API ( now including PLoS as well), I decided to take a look at the data to see what people have to work with. My analysis focused on our second largest discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this one so that I could look at the data with fresh eyes, and also because it’s got some really cool papers to talk about. Here’s what I found: What I found was a fascinating list of topics, with many of the expected fundamental papers like Shannon’s Theory of Information and the Google paper, a strong showing from Mapreduce and machine learning, but also some interesting hints that augmented reality may be becoming more of an actual reality soon.
LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon’s information theory paper (#7) or the paper describing the concept that became Google (#3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their subdiscipline. In fact, AI researchers contributed the majority of readership to 6 out of the top 10 papers. Presumably, those interested in popular topics such as machine learning list themselves under AI, which explains the strength of this subdiscipline, whereas papers like the Mapreduce one or the Google paper appeal to a broad range of subdisciplines, giving those papers a smaller numbers spread across more subdisciplines. Professor Blei is also a bit of a superstar, so that didn’t hurt. (the irony of a manually-categorized list with an LDA paper at the top has not escaped us)
2. MapReduce : Simplified Data Processing on Large Clusters (available full-text)
It’s no surprise to see this in the Top 10 either, given the huge appeal of this parallelization technique for breaking down huge computations into easily executable and recombinable chunks. The importance of the monolithic “Big Iron” supercomputer has been on the wane for decades. The interesting thing about this paper is that had some of the lowest readership scores of the top papers within a subdiscipline, but folks from across the entire spectrum of computer science are reading it. This is perhaps expected for such a general purpose technique, but given the above it’s strange that there are no AI readers of this paper at all.
3. The Anatomy of a large-scale hypertextual search engine (available full-text)
In this paper, Google founders Sergey Brin and Larry Page discuss how Google was created and how it initially worked. This is another paper that has high readership across a broad swath of disciplines, including AI, but wasn’t dominated by any one discipline. I would expect that the largest share of readers have it in their library mostly out of curiosity rather than direct relevance to their research. It’s a fascinating piece of history related to something that has now become part of our every day lives.
4. Distinctive Image Features from Scale-Invariant Keypoints
This paper was new to me, although I’m sure it’s not new to many of you. This paper describes how to identify objects in a video stream without regard to how near or far away they are or how they’re oriented with respect to the camera. AI again drove the popularity of this paper in large part and to understand why, think “ Augmented Reality “. AR is the futuristic idea most familiar to the average sci-fi enthusiast as Terminator-vision . Given the strong interest in the topic, AR could be closer than we think, but we’ll probably use it to layer Groupon deals over shops we pass by instead of building unstoppable fighting machines.
5. Reinforcement Learning: An Introduction (available full-text)
This is another machine learning paper and its presence in the top 10 is primarily due to AI, with a small contribution from folks listing neural networks as their discipline, most likely due to the paper being published in IEEE Transactions on Neural Networks. Reinforcement learning is essentially a technique that borrows from biology, where the behavior of an intelligent agent is is controlled by the amount of positive stimuli, or reinforcement, it receives in an environment where there are many different interacting positive and negative stimuli. This is how we’ll teach the robots behaviors in a human fashion, before they rise up and destroy us.
6. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (available full-text)
Popular among AI and information retrieval researchers, this paper discusses recommendation algorithms and classifies them into collaborative, content-based, or hybrid. While I wouldn’t call this paper a groundbreaking event of the caliber of the Shannon paper above, I can certainly understand why it makes such a strong showing here. If you’re using Mendeley, you’re using both collaborative and content-based discovery methods!
7. A Mathematical Theory of Communication (available full-text)
Now we’re back to more fundamental papers. I would really have expected this to be at least number 3 or 4, but the strong showing by the AI discipline for the machine learning papers in spots 1, 4, and 5 pushed it down. This paper discusses the theory of sending communications down a noisy channel and demonstrates a few key engineering parameters, such as entropy, which is the range of states of a given communication. It’s one of the more fundamental papers of computer science, founding the field of information theory and enabling the development of the very tubes through which you received this web page you’re reading now. It’s also the first place the word “bit”, short for binary digit, is found in the published literature.
8. The Semantic Web (available full-text)
In The Semantic Web, Tim Berners-Lee, Sir Tim, the inventor of the World Wide Web, describes his vision for the web of the future. Now, 10 years later, it’s fascinating to look back though it and see on which points the web has delivered on its promise and how far away we still remain in so many others. This is different from the other papers above in that it’s a descriptive piece, not primary research as above, but still deserves it’s place in the list and readership will only grow as we get ever closer to his vision.
9. Convex Optimization (available full-text)
This is a very popular book on a widely used optimization technique in signal processing. Convex optimization tries to find the provably optimal solution to an optimization problem, as opposed to a nearby maximum or minimum. While this seems like a highly specialized niche area, it’s of importance to machine learning and AI researchers, so it was able to pull in a nice readership on Mendeley. Professor Boyd has a very popular set of video classes at Stanford on the subject, which probably gave this a little boost, as well. The point here is that print publications aren’t the only way of communicating your ideas. Videos of techniques at SciVee or JoVE or recorded lectures ( previously ) can really help spread awareness of your research.
10. Object recognition from local scale-invariant features (available in full-text)
This is another paper on the same topic as paper #4, and it’s by the same author. Looking across subdisciplines as we did here, it’s not surprising to see two related papers, of interest to the main driving discipline, appear twice. Adding the readers from this paper to the #4 paper would be enough to put it in the #2 spot, just below the LDA paper.
Conclusions
So what’s the moral of the story? Well, there are a few things to note. First of all, it shows that Mendeley readership data is good enough to reveal both papers of long-standing importance as well as interesting upcoming trends. Fun stuff can be done with this! How about a Mendeley leaderboard? You could grab the number of readers for each paper published by members of your group, and have some friendly competition to see who can get the most readers, month-over-month. Comparing yourself against others in terms of readers per paper could put a big smile on your face, or it could be a gentle nudge to get out to more conferences or maybe record a video of your technique for JoVE or Khan Academy or just Youtube.
Another thing to note is that these results don’t necessarily mean that AI researchers are the most influential researchers or the most numerous, just the best at being accounted for. To make sure you’re counted properly, be sure you list your subdiscipline on your profile, or if you can’t find your exact one, pick the closest one, like the machine learning folks did with the AI subdiscipline. We recognize that almost everyone does interdisciplinary work these days. We’re working on a more flexible discipline assignment system, but for now, just pick your favorite one.
These stats were derived from the entire readership history, so they do reflect a founder effect to some degree. Limiting the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars.
Technical details: To do this analysis I queried the Mendeley database, analyzed the data using R , and prepared the figures with Tableau Public . A similar analysis can be done dynamically using the Mendeley API . The API returns JSON, which can be imported into R using the fine RJSONIO package from Duncan Temple Lang and Carl Boettiger is implementing the Mendeley API in R . You could also interface with the Google Visualization API to make motion charts showing a dynamic representation of this multi-dimensional data. There’s all kinds of stuff you could do, so go have some fun with it. I know I did.
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2 thoughts on “ the top 10 research papers in computer science by mendeley readership. ”.
You might consider revisiting the subdiscipline list, e.g. split computer vision, robotics and machine learning from AI, since the latest is a fuzzy and uncertain concept. Neural networks could be combined with machine learning, though.
Especially in fast-growing fields like computer science, discipline will always be a somewhat fuzzy concept. We are working on a way for people to assign themselves and papers to disciplines in a more flexible way.
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Five Hundred Most-Cited Papers in the Computer Sciences: Trends, Relationships and Common Factors
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- Phoey Lee Teh ORCID: orcid.org/0000-0002-7787-1299 19 &
- Peter Heard ORCID: orcid.org/0000-0002-5135-7822 20
Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1366))
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This study reveals common factors among highly cited papers in the computer sciences. The 500 most cited papers in the computer sciences published between January 2013 and December 2017 were downloaded from the Web of Science (WoS). Data on the number of citations, number of authors, article length and subject sub-discipline were extracted and analyzed in order to identify trends, relationships and common features. Correlations between common factors were analyzed. The 500 papers were cited a total of 10,926 times: the average number of citations per paper was 21.82 citations. A correlation was found between author credibility (defined in terms of the QS University Ranking of the first named author’s affiliation) and the number of citations. Authors from universities ranked 350 or higher were more cited than those from lower ranked universities. Relationships were also found between journal ranking and both the number of authors and the article length. Higher ranked journals tend to have a greater number of authors, but were of shorter length. The article length was also found to be correlated with the number of authors and the QS Subject Ranking of the first author’s affiliation. The proportion of articles in higher ranked journals (journal quartile), the length of articles and the number of citations per page were all found to correlate to the sub-discipline area (Information Systems; Software Engineering; Artificial Intelligence; Interdisciplinary Applications; and Theory and Methods).
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Teh, P.L., Heard, P. (2021). Five Hundred Most-Cited Papers in the Computer Sciences: Trends, Relationships and Common Factors. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_2
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Latest Computer Science Research Topics for 2024
Home Blog Programming Latest Computer Science Research Topics for 2024
Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!
As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?
For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.
Top 12 Computer Science Research Topics for 2024
Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.
| 1. Integrated Blockchain and Edge Computing Systems | 7. Natural Language Processing Techniques |
| 2. Survey on Edge Computing Systems and Tools | 8. Lightweight Integrated Blockchain (ELIB) Model |
| 3. Evolutionary Algorithms and their Applications | 9. Big Data Analytics in the Industrial Internet of Things |
| 4. Fog Computing and Related Edge Computing Paradigms | 10. Machine Learning Algorithms |
| 5. Artificial Intelligence (AI) | 11. Digital Image Processing: |
| 6. Data Mining | 12. Robotics |
1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges
Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.
As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!
Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.
Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.
2. A Survey on Edge Computing Systems and Tools
With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.
Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.
Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?
As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:
- Apache NiFi: A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
- Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
- OpenFog Consortium: An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.
3. Machine Learning: Algorithms, Real-world Applications, and Research Directions
Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?
To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.
You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.
During the research, you will work on and study
- Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
- Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
- Research Trend: The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.
While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.
Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?
4. Evolutionary Algorithms and their Applications to Engineering Problems
Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.
This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.
Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.
The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.
5. The Role of Big Data Analytics in the Industrial Internet of Things
Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.
This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.
Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.
The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.
6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy
Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.
ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.
But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.
Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.
7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics
Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.
Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.
But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.
Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.
8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey
If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.
Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.
As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.
9. Artificial Intelligence (AI)
The field of artificial intelligence studies how to build machines with human-like cognitive abilities and it is one of the trending research topics in computer science . Unlike humans, AI technology can handle massive amounts of data in many ways. Some important areas of AI where more research is needed include:
- Deep learning: Within the field of Machine Learning, Deep Learning mimics the inner workings of the human brain to process and apply judgements based on input.
- Reinforcement learning: With artificial intelligence, a machine can learn things in a manner akin to human learning through a process called reinforcement learning.
- Natural Language processing (NLP): While it is evident that humans are capable of vocal communication, machines are also capable of doing so now! This is referred to as "natural language processing," in which computers interpret and analyse spoken words.
10. Digital Image Processing
Digital image processing is the process of processing digital images using computer algorithms. Recent research topics in computer science around digital image processing are grounded in these techniques. Digital image processing, a subset of digital signal processing, is superior to analogue image processing and has numerous advantages. It allows several algorithms to be applied to the input data and avoids issues like noise accumulation and signal distortion during processing. Digital image processing comes in a variety of forms for research. The most recent thesis and research topics in digital image processing are listed below:
- Image Acquisition
- Image Enhancement
- Image Restoration
- Color Image Processing
- Wavelets and Multi Resolution Processing
- Compression
- Morphological Processing
11. Data Mining
The method by which valuable information is taken out of the raw data is called data mining. Using various data mining tools and techniques, data mining is used to complete many tasks, including association rule development, prediction analysis, and clustering. The most effective method for extracting valuable information from unprocessed data in data mining technologies is clustering. The clustering process allows for the analysis of relevant information from a dataset by grouping similar and dissimilar types of data. Data mining offers a wide range of trending computer science research topics for undergraduates :
- Data Spectroscopic Clustering
- Asymmetric spectral clustering
- Model-based Text Clustering
- Parallel Spectral Clustering in Distributed System
- Self-Tuning Spectral Clustering
12. Robotics
We explore how robots interact with their environments, surrounding objects, other robots, and humans they are assisting through the research, design, and construction of a wide range of robot systems in the field of robotics. Numerous academic fields, including mathematics, physics, biology, and computer science, are used in robotics. Artificial intelligence (AI), physics simulation, and advanced sensor processing (such as computer vision) are some of the key technologies from computer science. Msc computer science project topic s focus on below mentioned areas around Robotics:
- Human Robot collaboration
- Swarm Robotics
- Robot learning and adaptation
- Soft Robotics
- Ethical considerations in Robotics
How to Choose the Right Computer Science Research Topics?
Choosing the research areas in computer science could be overwhelming. You can follow the below mentioned tips in your pursuit:
- Chase Your Curiosity: Think about what in the tech world keeps you up at night, in a good way. If it makes you go "hmm," that's the stuff to dive into.
- Tech Trouble Hunt: Hunt for the tech troubles that bug you. You know, those things that make you mutter, "There's gotta be a better way!" That's your golden research nugget.
- Interact with Nerds: Grab a coffee (or your beverage of choice) and have a laid-back chat with the tech geeks around you. They might spill the beans on cool problems or untapped areas in computer science.
- Resource Reality Check: Before diving in, do a quick reality check. Make sure your chosen topic isn't a resource-hungry beast. You want something you can tackle without summoning a tech army.
- Tech Time Travel: Imagine you have a time machine. What future tech would blow your mind? Research that takes you on a journey to the future is like a time travel adventure.
- Dream Big, Start Small: Your topic doesn't have to change the world on day one. Dream big, but start small. The best research often grows from tiny, curious seeds.
- Be the Tech Rebel: Don't be afraid to be a bit rebellious. If everyone's zigging, you might want to zag. The most exciting discoveries often happen off the beaten path.
- Make it Fun: Lastly, make sure it's fun. If you're going to spend time on it, might as well enjoy the ride. Fun research is the best research.
Tips and Tricks to Write Computer Science Research Topics
Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.
- Know your interest.
- Choose the topic wisely.
- Make proper research about the demand of the topic.
- Get proper references.
- Discuss with experts.
By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.
Why is Research in Computer Science Important?
Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.
1. Innovation in Technology
Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.
2. Problem-Solving Capabilities
From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.
3. Enhancing Human Life
Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.
4. Security Assurance
As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.
From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore. One of the most important trends is using cutting-edge technology to address current issues. For instance, new IoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.
Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.
With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies.
Frequently Asked Questions (FAQs)
Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.
Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut.
There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more.
Ramulu Enugurthi
Ramulu Enugurthi, a distinguished computer science expert with an M.Tech from IIT Madras, brings over 15 years of software development excellence. Their versatile career spans gaming, fintech, e-commerce, fashion commerce, mobility, and edtech, showcasing adaptability in multifaceted domains. Proficient in building distributed and microservices architectures, Ramulu is renowned for tackling modern tech challenges innovatively. Beyond technical prowess, he is a mentor, sharing invaluable insights with the next generation of developers. Ramulu's journey of growth, innovation, and unwavering commitment to excellence continues to inspire aspiring technologists.
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Computer Science > Computation and Language
Title: the prompt report: a systematic survey of prompting techniques.
Abstract: Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes a prompt due to the area's nascency. This paper establishes a structured understanding of prompts, by assembling a taxonomy of prompting techniques and analyzing their use. We present a comprehensive vocabulary of 33 vocabulary terms, a taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities. We further present a meta-analysis of the entire literature on natural language prefix-prompting.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | [cs.CL] |
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Volume 630 Issue 8017, 20 June 2024
Birds of prey, such as the red-tailed hawk ( Buteo jamaicensis ) pictured on the cover, spend much of their time in the air soaring. In this week’s issue, Emma Schachner and colleagues reveal a link between the wings and respiratory system that helps these birds maintain this mode of flying. The researchers focused on the subpectoral diverticulum (SPD), an extension of the respiratory system that dives between the main muscles responsible for wing flapping and forms air sacs on the chest beneath the wings. The team found that the SPD is present in most soaring birds but is absent in other species. Soaring birds can inflate the SPD air sacs at will and the sacs reduce the energy required to keep the wings outstretched when gliding.
Cover image: Raymond Gilbert
The Sustainable Development Goals: can they be made smarter?
Accounting for factors such as artificial intelligence in a more ambitious set of goals has a lot of merit — as long as urgency is not lost on the existing ones.
Advertisement
Human neuroscience is entering a new era — it mustn’t forget its human dimension
The field is taking a leap forward thanks to innovative technologies, such as artificial intelligence. Researchers must improve consent procedures and public involvement.
Put people at the heart of schizophrenia research
Scientists, health-care professionals, carers and individuals affected by the condition must work more closely with one another to improve people’s lives.
- Constanza Morén
We can make the UK a science superpower — with a radical political manifesto
As the election campaign in the United Kingdom marches on, politicians are hardly discussing the country’s global standing in science. Here’s why that needs to change.
Research Highlights
Ancient graves reveal taxes’ sharp bite nearly 3,000 years ago.
Buried items show that the poor got poorer as the Assyrian empire and its bureaucracy swelled.
A win for global action: harm from some ozone-eroding gases starts to fall
Emissions reductions achieved under the landmark Montreal Protocol mean that the ozone hole could close sooner than expected.
CRISPR improves a crop that feeds billions
The gene-editing system, normally used to disrupt a gene, is applied to improve the function of a gene in rice.
An object in space is emitting microwaves — and baffling scientists
Data recorded at an observatory in Chile do not fit with a black hole, a supernova, a pair of merging stars or anything else.
News in Focus
Cyberattacks are hitting research institutions — with devastating effects.
Hackers are targeting universities and research institutes with ransomware, leaving staff and students without the ability to work.
How a few days in space can disrupt a person’s biology
Trove of health data from space tourists and astronauts reveals the effects of microgravity, radiation and more.
- Heidi Ledford
AI and Euro 2024: VAR is shaking up football — and it’s not going away
Sports physicist Eric Goff explains how updates to the technology can help referees to make the toughest calls.
- Sumeet Kulkarni
CERN’s $17-billion supercollider in question as top funder criticizes cost
Germany has raised doubts about the affordability of the Large Hadron Collider’s planned successor.
- Davide Castelvecchi
What’s the best way to tackle climate change? An ‘evidence bank’ could help scientists find answers
Synthesizing research on which policies are most effective is a key priority in climate science, advocates say.
- Helen Pearson
Sleep deprivation disrupts memory: here’s why
Study in rats shows that a key brain signal linked to memory formation deteriorates after broken sleep.
How cutting-edge computer chips are speeding up the AI revolution
Engineers are harnessing the powers of graphics processing units (GPUs) and more, with a bevy of tricks to meet the computational demands of artificial intelligence.
- Dan Garisto
‘It can feel like there’s no way out’ — political scientists face pushback on their work
In a year in which numerous countries are going to the polls, many election-watching scientists are under pressure.
- Dyani Lewis
- Alison Abbott
Books & Arts
Book review, the climate crisis is solvable, but human rights must trump profits.
Huge planetary problems were fixed in the past, yielding lessons for the current climate crisis — yet this time a solution is justice.
- Friederike Otto
Sparrow massacres and Cuban vaccines: Books in Brief
Andrew Robinson reviews five of the best science picks.
- Andrew Robinson
Extending the Sustainable Development Goals to 2050 — a road map
The world should redouble its efforts on the SDGs, not abandon them. Here’s how to progress the United Nations’ agenda towards 2050.
- Francesco Fuso Nerini
- Mariana Mazzucato
- Jeffrey Sachs
Why museums should repatriate fossils
The legacy of a palaeontology expedition into Native American lands 150 years ago should prompt a rethink of where and how fossil collections are curated.
- Lukas Rieppel
Correspondence
Boycotting academics in israel is counterproductive.
- Simone Shamay-Tsoory
- Mouna Maroun
Earth-surface monitoring is at risk — more imaging tools are urgently needed
- Etienne Berthier
- Jeffrey S. Kargel
- Michael Zemp
I was prevented from attending my own conference: visa processes need urgent reform
- Felix Moronta-Barrios
The global refugee crisis is above all a human tragedy — but it affects wildlife, too
- Andrew D. Walde
- Gift S. Demaya
- Luca Luiselli
It’s time to talk about menstruation and fieldwork
Cringing colleagues can be an occupational hazard when pre-trip discussions cover periods — others welcome frank conversations.
- Cameron Walker
Technology Feature
Push and pull: how to measure the forces that sculpt embryos.
A steadily growing toolbox is giving researchers the ability to monitor and measure the physical forces that shape embryonic development.
- Michael Eisenstein
Collection:
Where I Work
How i’ve helped to discover nearly 40 species in the amazon.
Rodolfo Salas-Gismondi conducts palaeontology on a riverbank.
- Virginia Gewin
News & Views
Air sacs reduce energy costs for soaring birds.
Certain air sacs have evolved in multiple lineages of soaring birds, and it emerges that these probably function to reduce the force required from the major flight muscles as they hold the wings in place during gliding and soaring.
- Bret W. Tobalske
Precision dating pinpoints time between use of ancient fireplaces
Knowing the occupation timescales for ancient sites offers insights into population dynamics. A dating approach now establishes the time frame during which prehistoric hearths were in use at a high level of precision.
- Ségolène Vandevelde
Ribosomes unexpectedly moonlight as activators of angiogenin enzyme
The enzyme angiogenin functions in stress responses and aids the formation of blood vessels. It emerges that the ribosome takes a break from its usual role of making proteins to activate angiogenin and position it to cleave transfer RNA.
- Pavel Ivanov
‘Fighting fire with fire’ — using LLMs to combat LLM hallucinations
The number of errors produced by an LLM can be reduced by grouping its outputs into semantically similar clusters. Remarkably, this task can be performed by a second LLM, and the method’s efficacy can be evaluated by a third.
- Karin Verspoor
Father’s diet influences son’s metabolic health through sperm RNA
DNA from organelles called mitochondria is not inherited from the father. But mitochondrial RNAs that sense paternal diet and mitochondrial quality are delivered from sperm to egg, affecting offspring metabolism.
James Clerk Maxwell’s ode to bubble blowing
Curious volcanic activity confounds tourists near Naples, and Maxwell reviews a textbook on bubbles, in the weekly dip into Nature ’s archive.
Graphene combines computer logic and memory in a single device
A sheet of graphene sandwiched between electrolytes can host independently tunable proton and electron currents — setting the stage for a device that serves both computer-memory and logic functions.
- Manu Jaiswal
Perspectives
Language is primarily a tool for communication rather than thought.
Evidence from neuroscience and related fields suggests that language and thought processes operate in distinct networks in the human brain and that language is optimized for communication and not for complex thought.
- Evelina Fedorenko
- Steven T. Piantadosi
- Edward A. F. Gibson
Large-scale neurophysiology and single-cell profiling in human neuroscience
This Perspective considers the implications of advances in human physiology, single-cell and spatial transcriptomics and long-term culture of resected human brain tissue for the study of network-level activity in human neuroscience.
- Anthony T. Lee
- Edward F. Chang
- Tomasz J. Nowakowski
A molecular and cellular perspective on human brain evolution and tempo
This Perspective views brain development in terms of developmental tempo along the human lineage and reviews the contributions of recent technical advances to our understanding of neurodevelopment.
- Feline W. Lindhout
- Fenna M. Krienen
- Madeline A. Lancaster
A secondary atmosphere on the rocky exoplanet 55 Cancri e
The thermal emission spectrum of the rocky exoplanet 55 Cancri e obtained by the NIRCAM and MIRI instruments aboard the JWST indicates that it has a secondary volatile-rich atmosphere, possibly arising from a magma ocean.
- Aaron Bello-Arufe
- Brice-Olivier Demory
A site-resolved two-dimensional quantum simulator with hundreds of trapped ions
In this work, stable trapping of a two-dimensional Wigner crystal of above 500 ions is achieved, and the quantum simulation of 300 ions with individual state detection demonstrated.
Control of proton transport and hydrogenation in double-gated graphene
Independent control of the electric field and charge-carrier density in double-gated graphene allows the decoupling of proton transport and lattice hydrogenation, enabling both accelerated proton transport and proton-based logic operations.
- M. Lozada-Hidalgo
Detecting hallucinations in large language models using semantic entropy
Hallucinations (confabulations) in large language model systems can be tackled by measuring uncertainty about the meanings of generated responses rather than the text itself to improve question-answering accuracy.
- Sebastian Farquhar
- Jannik Kossen
Acceleration of radiative recombination for efficient perovskite LEDs
A dual-additive crystallization method using PyNI and 5AVA as additives results in highly efficient 3D perovskite films with enhanced photoluminescence quantum efficiencies and external quantum efficiencies, and hence increased LED performance.
- Mengmeng Li
- Yingguo Yang
- Jianpu Wang
Reproducible graphene synthesis by oxygen-free chemical vapour deposition
Assessment of surface contamination shows that trace oxygen is a key factor influencing the trajectory and quality of graphene grown by low-pressure chemical vapour deposition, with oxygen-free synthesis showing increased reproducibility and quality.
- Jacob Amontree
- Xingzhou Yan
Atomic dynamics of electrified solid–liquid interfaces in liquid-cell TEM
The development of advanced polymer electrochemical liquid cells for transmission electron microscopy allows direct monitoring of the atomic dynamics of electrified solid–liquid interfaces during copper-catalysed CO 2 electroreduction reactions.
- Qiubo Zhang
- Zhigang Song
- Haimei Zheng
Work hardening in colloidal crystals
Deformation of soft colloidal crystals lead to work hardening, similar to that seen in the deformation of metals.
- Seongsoo Kim
- Ilya Svetlizky
- Frans Spaepen
Capturing carbon dioxide from air with charged-sorbents
Charged-sorbents are a new class of designer sorbent materials for the capture of carbon dioxide from the atmosphere, and can be regenerated at low temperatures with direct heating generation using renewable electricity.
- Huaiguang Li
- Mary E. Zick
- Alexander C. Forse
Microbial competition for phosphorus limits the CO 2 response of a mature forest
Microbial pre-emption of mineralized soil P limits the capacity of trees for increased P uptake and assimilation under elevated CO 2 and therefore restricts their capacity to sequester extra C.
- Mingkai Jiang
- Kristine Y. Crous
- David S. Ellsworth
The time between Palaeolithic hearths
High-resolution time differences between six Middle Palaeolithic hearths from El Salt Unit x (Spain) obtained through archaeomagnetic and archaeostratigraphic analyses show sometimes decade-long intervals between hearths.
- Ángela Herrejón-Lagunilla
- Juan José Villalaín
- Ángel Carrancho
The respiratory system influences flight mechanics in soaring birds
An investigation of the subpectoral diverticulum—an inflatable air sac structure between the major flight muscles—in 68 avian species reveals that the respiratory system has a role in the mechanics of flight in soaring birds.
- Emma R. Schachner
- Andrew J. Moore
- Karl T. Bates
Myelin plasticity in the ventral tegmental area is required for opioid reward
Oligodendrogenesis is shown to be involved in reward learning, with dopaminergic neuronal activity-regulated myelin plasticity being an important reward circuit modification.
- Belgin Yalçın
- Matthew B. Pomrenze
- Michelle Monje
Descending networks transform command signals into population motor control
Command-like descending neurons in Drosophila melanogaster recruit additional descending neuronal networks to co-ordinate behaviours that require multiple motor subroutines controlling numerous body parts.
- Jonas Braun
- Femke Hurtak
- Pavan Ramdya
A body–brain circuit that regulates body inflammatory responses
The body–brain axis regulates body pro-inflammatory and anti-inflammatory immune responses following an immune insult.
- Mengtong Li
- Charles S. Zuker
Mental navigation in the primate entorhinal cortex
Measurements of activity in the entorhinal cortex of monkeys indicate that the recruitment of a cognitive map is a key part of mental navigation, and that cognitive maps can support behavior in the absence of external sensory input.
- Sujaya Neupane
- Mehrdad Jazayeri
A virally encoded high-resolution screen of cytomegalovirus dependencies
A genetic screen that expresses single guide RNA libraries targeting host genes in the human cytomegalovirus genome enables identification of host factors and provides insights into their roles during the viral replication cycle.
- Yaara Finkel
- Aharon Nachshon
- Noam Stern-Ginossar
Epigenetic inheritance of diet-induced and sperm-borne mitochondrial RNAs
A study shows that epididymal spermatozoa are sensitive to preconception diet, identifies mitochondrial tRNAs and their fragments as sperm-borne factors and demonstrates epigenetic inheritance of mitochondrial tRNAs.
- M. Gomez-Velazquez
- R. Teperino
Selective haematological cancer eradication with preserved haematopoiesis
An antibody–drug conjugate that targets the pan-haematopoietic marker CD45 combined with transplanted stem cells engineered to be shielded from it can eradicate leukaemic cells while preserving haematopoiesis.
- Simon Garaudé
- Romina Marone
- Lukas T. Jeker
Profiling phagosome proteins identifies PD-L1 as a fungal-binding receptor
Proximity labelling of phagosomal contents is used to identify proteins that localize to phagosomes in host–microorganism interactions.
- Avradip Chatterjee
- David M. Underhill
Strand-resolved mutagenicity of DNA damage and repair
How strand-asymmetric processes such as replication and transcription interact with DNA damage to drive mechanisms of repair and mutagenesis is explored.
- Craig J. Anderson
- Lana Talmane
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DNA mismatch and damage patterns revealed by single-molecule sequencing
A DNA sequencing method with single-molecule fidelity detects mismatches and damage present in only one of the two DNA strands with patterns that are both similar and distinct compared to known mutation patterns.
- Mei Hong Liu
- Benjamin M. Costa
- Gilad D. Evrony
Kainate receptor channel opening and gating mechanism
Structures of the kainate receptor GluK2 with and without concanavilin A and BPAM344 show how these ligands modulate channel activity and reveal the molecular basis of kainate receptor gating.
- Shanti Pal Gangwar
- Maria V. Yelshanskaya
- Alexander I. Sobolevsky
Structural mechanism of angiogenin activation by the ribosome
Angiogenin binds to the ribosomal A site to cleave tRNA.
- Anna B. Loveland
- Cha San Koh
- Andrei A. Korostelev
Amendments & Corrections
Author correction: nuclear genetic control of mtdna copy number and heteroplasmy in humans.
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Author Correction: Myt1l safeguards neuronal identity by actively repressing many non-neuronal fates
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Author Correction: High-speed and large-scale intrinsically stretchable integrated circuits
- Donglai Zhong
Publisher Correction: Interim analyses of a first-in-human phase 1/2 mRNA trial for propionic acidaemia
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- Andreas Schulze
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Changing climate patterns have caused a monumental shift in the world’s agricultural processes.
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Evaluating ChatGPT-4 Vision on Brazil's National Undergraduate Computer Science Exam
- Mendonça, Nabor C.
The recent integration of visual capabilities into Large Language Models (LLMs) has the potential to play a pivotal role in science and technology education, where visual elements such as diagrams, charts, and tables are commonly used to improve the learning experience. This study investigates the performance of ChatGPT-4 Vision, OpenAI's most advanced visual model at the time the study was conducted, on the Bachelor in Computer Science section of Brazil's 2021 National Undergraduate Exam (ENADE). By presenting the model with the exam's open and multiple-choice questions in their original image format and allowing for reassessment in response to differing answer keys, we were able to evaluate the model's reasoning and self-reflecting capabilities in a large-scale academic assessment involving textual and visual content. ChatGPT-4 Vision significantly outperformed the average exam participant, positioning itself within the top 10 best score percentile. While it excelled in questions that incorporated visual elements, it also encountered challenges with question interpretation, logical reasoning, and visual acuity. The involvement of an independent expert panel to review cases of disagreement between the model and the answer key revealed some poorly constructed questions containing vague or ambiguous statements, calling attention to the critical need for improved question design in future exams. Our findings suggest that while ChatGPT-4 Vision shows promise in multimodal academic evaluations, human oversight remains crucial for verifying the model's accuracy and ensuring the fairness of high-stakes educational exams. The paper's research materials are publicly available at https://github.com/nabormendonca/gpt-4v-enade-cs-2021.
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Bachelor's Degree in Computer Science
Why pursue a bachelor's degree in computer science.
The concentration in Computer Science is designed to teach students skills and ideas they will use immediately and in the future. Because information technology affects every aspect of society, graduates with computer science degrees have open to them an enormous variety of careers—engineering, teaching, medicine, law, basic science, entertainment, management, and countless others.
At Harvard College, students choose a "concentration," which is what we call a major. All prospective undergraduate students, including those intending to study engineering and applied sciences, apply directly to Harvard College . During your sophomore spring you’ll declare a concentration, or field of study. You may choose from 50 concentrations and 49 secondary field (from Harvard DSO website ).
All undergraduates in Computer Science at Harvard are candidates for the Bachelor of Arts degree (A.B.) . With the knowledge that it requires extra course work, you can consider the more intensive A.B./S.M. option through a concurrent masters degree.
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A.B. in Computer Science
The basic degree requirements are eleven to fourteen 4-credit courses in mathematics, theoretical computer science, computer software, and other areas of computer science. Math courses cover linear algebra, single variable calculus and probability/statistics. Students who place out of part or all of the introductory calculus sequence, Mathematics 1ab, reduce their concentration requirements to 11 courses.
Computer Science Secondary Field
A lightweight way of getting official recognition within Harvard for work in two fields is to do one or the other as a secondary field. For Computer Science, this involves taking 4 courses in the secondary field. Learn more about the computer science secondary field .
A.B./S.M. in Computer Science
Our AB/SM degree program is for currently enrolled Harvard College students only. Students who are eligible for Advanced Standing on the basis of A.P. tests before entering Harvard may be able to apply for admission to the S.M. program of the Graduate School of Arts and Sciences and graduate in four years with both a bachelor’s and master’s degree (not necessarily in the same field).
Beginning with the class of 2022, students have the opportunity to apply to the Graduate School of Arts and Sciences for a master’s degree pursued concurrently with the bachelor’s degree. As part of the concurrent degree program , students will be allowed to double-count up to sixteen credits (normally, four courses) for the Bachelor of Arts and the Master of Science. An undergraduate pursuing the concurrent degree must complete both of these degrees by the end of eight terms of residency, or the equivalent.
The Mind, Brain, and Behavior Program (MBB)
Students interested in addressing questions of neuroscience and cognition from the perspective of computer science may pursue a special program of study affiliated with the University-wide Mind, Brain, and Behavior Initiative, that allows them to participate in a variety of related activities. (Similar programs are available through the Anthropology, History and Science, Human Evolutionary Biology, Linguistics, Neurobiology, Philosophy, and Psychology concentrations.) Requirements for this honors-only program are based on those of the computer science Requirements for Honors Eligibility. See the handbook entry for more information and also Frequently Asked Questions about the MBB Track . This is an honors track program: students are eligible for English Honors.
Why study CS at Harvard? What’s different about pursuing CS in a liberal arts setting?
Get the answer to these questions and learn more about CS .
Prerequisites
Learn about the prerequisites for the concentration on our First-Year Exploration page . Students interested in concentrating in computer science can refer to our Sophomore Advising page and request to be matched with a Peer Concentration Advisor (PCA). PCAs serve as peer advisors for pre-concentrators (and current concentrators), providing a valuable perspective and helping students to discover additional resources and opportunities.
Requirements
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Research Opportunities in Computer Science
As part of your Bio/Biomedical Engineering coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to take part in or participate in some extraordinary projects. Learn more about research opportunities at Harvard SEA S.
Learn about the research interests of our Computer Science faculty .
Computer Science Career Paths
Learn about potential career paths for students for students concentrating in Computer Science .
Computer Science & Society
Harvard Computer Science has several programs that allow undergraduate students to think about the broader issues in tech and CS.
Computer Science Clubs and Organizations
SEAS-affiliated student organizations are critical to the overall growth of our concentrators as engineering and applied science professionals. These organizations enable our students to pursue passion projects and events in areas of interest that are complementary to the current formal academic curriculum. Learn more about computer science student clubs and organizations .
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Computer Science (since January 1993). For a specific paper, enter the identifier into the top right search box.. Browse: new (most recent mailing, with abstracts) ; recent (last 5 mailings) ; current month's listings; specific year/month:
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 ...
The ranking of best journals for Computer Science was published by Research.com, one of the prominent websites for computer science research providing trusted data on scientific contributions since 2014. The position in the ranking is based on a unique bibliometric score created by Research.com which is computed using the estimated h-index and ...
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 ...
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.
Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. Papers With Code highlights trending Computer Science research and the code to implement it.
Autonomous chemical research with large language models. Coscientist is an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs experiments by incorporating ...
Computer Science Research Topics are as follows: Using machine learning to detect and prevent cyber attacks. Developing algorithms for optimized resource allocation in cloud computing. Investigating the use of blockchain technology for secure and decentralized data storage. Developing intelligent chatbots for customer service.
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 ...
Computer Science Review publishes research surveys and expository overviews of open problems in computer science. All articles are aimed at a general computer science audience seeking a full and expert overview of the latest developments across computer science research. Articles from other fields …. View full aims & scope.
SEATTLE, 19 June 2024 - Today, during the 2024 Computer Vision and Pattern Recognition (CVPR) Conference opening session, the CVPR Awards Committee announced the winners of its prestigious Best Paper Awards, which annually recognize top research in computer vision, artificial intelligence (AI), machine learning (ML), augmented, virtual and mixed reality (AR/VR/MR), deep learning, and much more.
Journal Rankings on Computer Science. Display journals with at least. Citable Docs. (3years) Apply. Download data. 1 - 50 of 2039. Title.
Mapping Computer Science Research: Trends, Influences, and Predictions Mohammed Almutairi University of Notre Dame Notre Dame, USA [email protected] Ozioma Collins Oguine University of Notre Dame Notre Dame, USA [email protected] Abstract This paper explores the current trending research areas in the field of Computer Science (CS) and investigates the
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 ( 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 ...
Explore 89 research articles published in the Journal Computer Science Review (Elsevier BV) in the year 2021. The journal publishes majorly in the area (s): Computer science & The Internet. Over the lifetime, 381 publication (s) have been published in the journal receiving 23400 citation (s).
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 ...
1. Latent Dirichlet Allocation (available full-text) LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon's information theory paper (#7) or the paper describing the concept that became Google (#3).
The 500 most cited papers in the computer sciences published between January 2013 and December 2017 were downloaded from the Web of Science (WoS). Data on the number of citations, number of authors, article length and subject sub-discipline were extracted and analyzed in order to identify trends, relationships and common features.
An e-print service which presents papers in physics, mathematics, nonlinear science, computer science, quantitative biology, quantitative finance, and statistics. arXiv.org is a fully automated electronic archive and distribution server for research papers which functions as a means of communicating ongoing research information in these subject ...
Top 12 Computer Science Research Topics for 2024 Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.
Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes ...
Recent papers in Computer Science. Top Papers; Most Cited Papers; Most Downloaded Papers; ... elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function (Li, 2014) on PDBbind v2007 achieves a Pear ...
Top 10 research papers popular with people that like "Cloud Computing" and AI - perhaps the top 10 papers in the Hacker News readership. In my field of CS, and in actual theoretical CS, there are hundreds of seminal papers that are so much better and more profound than any of these; this list seems ridiculously specialised by comparison.
Where can we find the research paper on Computer Science? ... Post all of your math-learning resources here. Questions, no matter how basic, will be answered (to the best ability of the online subscribers). --- We're no longer participating in the protest against excessive API fees, but many other subreddits are; check out the progress [among ...
Procedia Computer Science offers a highly recognized Open Access platform where conference papers can be published and archived. Conference Authors receive maximum exposure as their work is made freely accessible to millions of researchers. Procedia Computer Science is indexed in Scopus, the Web of Science Conference Proceedings Citation Index ...
Research Highlight 11 Jun 2024 CRISPR improves a crop that feeds billions The gene-editing system, normally used to disrupt a gene, is applied to improve the function of a gene in rice.
The recent integration of visual capabilities into Large Language Models (LLMs) has the potential to play a pivotal role in science and technology education, where visual elements such as diagrams, charts, and tables are commonly used to improve the learning experience. This study investigates the performance of ChatGPT-4 Vision, OpenAI's most advanced visual model at the time the study was ...
All papers must be submitted electronically via the Transportation Research Part B: Methodological(TRB) online submission system Each author of the manuscript must follow the journal's Guide for Authors. To ensure that all manuscripts are correctly identified for inclusion in the special issue, it is important to select "VSI: Travel ...
Learn about our Computer Science concentrators > Apply to Harvard College > A.B. in Computer Science. The basic degree requirements are eleven to fourteen 4-credit courses in mathematics, theoretical computer science, computer software, and other areas of computer science. Math courses cover linear algebra, single variable calculus and ...