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Hiring CS Graduates: What We Learned from Employers
Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.
A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature
Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.
Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts
Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.
A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science
Gender diversity in computer science at a large public r1 research university: reporting on a self-study.
With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.
Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects
Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.
Creativity in CS1: A Literature Review
Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.
CATS: Customizable Abstractive Topic-based Summarization
Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.
Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis
Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.
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Join the community, trending research, fast-livo: fast and tightly-coupled sparse-direct lidar-inertial-visual odometry.
hku-mars/fast-livo • 2 Mar 2022
The LIO subsystem registers raw points (instead of feature points on e. g., edges or planes) of a new scan to an incrementally-built point cloud map.
DB-GPT: Empowering Database Interactions with Private Large Language Models
eosphoros-ai/db-gpt • 29 Dec 2023
Our extensive experiments and user studies confirm that DB-GPT represents a paradigm shift in database interactions, offering a more natural, efficient, and secure way to engage with data repositories.
LCB-net: Long-Context Biasing for Audio-Visual Speech Recognition
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The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides.
Sound Multimedia Audio and Speech Processing
The Sky Above The Clouds
Technology ecosystems often undergo significant transformations as they mature.
Distributed, Parallel, and Cluster Computing
Automated Unit Test Improvement using Large Language Models at Meta
Codium-ai/cover-agent • 14 Feb 2024
This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests.
Software Engineering
Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs
In this letter, we address the problem of enabling LLMs to comprehend Area Graph, a text-based map representation, in order to enhance their applicability in the field of mobile robotics.
SingVisio: Visual Analytics of Diffusion Model for Singing Voice Conversion
In this study, we present SingVisio, an interactive visual analysis system that aims to explain the diffusion model used in singing voice conversion.
Sound Human-Computer Interaction Audio and Speech Processing
Leveraging Diverse Semantic-based Audio Pretrained Models for Singing Voice Conversion
We discover that the knowledge of different models is diverse and can be complementary for SVC.
Sound Audio and Speech Processing
CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking
Time delay neural network (TDNN) has been proven to be efficient for speaker verification.
Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC
TinyMPC/TinyMPC • 26 Mar 2024
Conic constraints appear in many important control applications like legged locomotion, robotic manipulation, and autonomous rocket landing.
Robotics Systems and Control Systems and Control Optimization and Control
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Computer science articles from across Nature Portfolio
Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.
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Meta’s AI translation model embraces overlooked languages
More than 7,000 languages are in use throughout the world, but popular translation tools cannot deal with most of them. A translation model that was tested on under-represented languages takes a key step towards a solution.
- David I. Adelani
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Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation
- Guoxi Liang
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Emergent digital bio-computation through spatial diffusion and engineered bacteria
Biological computing is a promising field with potential applications in biosafety, environmental monitoring, and personalized medicine. Here the authors create bio-computers using engineered E. coli colonies that respond to chemical gradients, producing different logic functions depending on how they are spatially arranged.
- Alex J. H. Fedorec
- Neythen J. Treloar
- Chris P. Barnes
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Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning
Simulating responses of a full particle physics detector with high granularity is computationally very expensive. Here, the authors develop a deep generative model that is able to model a detector with millions of information channel with good performances, reducing both storage demand and CPU time.
- Baran Hashemi
- Nikolai Hartmann
- Thomas Kuhr
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Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana
- Daniele Raimondi
- Antoine Passemiers
- Yves Moreau
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Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning
- Haozhou Yin
- Daokuan Ren
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Software cost estimation predication using a convolutional neural network and particle swarm optimization algorithm
- Moatasem. M. Draz
- Safaa. M. Azzam
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Accelerating AI: the cutting-edge chips powering the computing 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
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Who owns your voice? Scarlett Johansson OpenAI complaint raises questions
In the age of artificial intelligence, situations are emerging that challenge the laws over rights to a persona.
- Nicola Jones
Anglo-American bias could make generative AI an invisible intellectual cage
- Queenie Luo
- Michael Puett
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AlphaFold3 — why did Nature publish it without its code?
Criticism of our decision to publish AlphaFold3 raises important questions. We welcome readers’ views.
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Back to basics to open the black box
Most research efforts in machine learning focus on performance and are detached from an explanation of the behaviour of the model. We call for going back to basics of machine learning methods, with more focus on the development of a basic understanding grounded in statistical theory.
- Diego Marcondes
- Adilson Simonis
- Junior Barrera
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Open research in computer science
Spanning networks and communications to security and cryptology to big data, complexity, and analytics, SpringerOpen and BMC publish one of the leading open access portfolios in computer science. Learn about our journals and the research we publish here on this page.
Highly-cited recent articles
According to current researches, much of the electric power is being consumed by the server cooling system at the Data Center. Moreover, the power consumption rate increases when the number of the equipments and servers expands. Thus, the proposed server operation system has been designed to decrease power consumption rate and CO emission volume by minimizing the number of these equipments and simplifying the physical composition of the system. Virtualization technology was adopted in both designing and implementation phases to improve resource efficiency of the system. As a result, significant amount has been saved while constructing the server operation system in this paper. System’s performance has been evaluated using a virtual machine prior to its practical use through test bed experiments and the results confirms our expectation that the virtual hardwares will work as efficiently as actual ones. *As tracked by ISI/Clarivate | |
Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages. | |
— The proliferation of “Smart Cities” initiatives around the world is a part of the strategic response by governments to the challenges and opportunities of increasing urbanization and the rise of cities as the nexus of societal development. This JISA Thematic Series presents significant research contributions related to the design and development of Infrastructure, Services and Applications for the Smart City and Urban context. |
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- IEEE Xplore Digital Library Provides full-text access to IEEE transactions, IEEE and IEE journals, magazines, and conference proceedings published since 1988, and all current IEEE standards; brings additional search and access features to IEEE/IEE digital library users more... less... Institute of Electrical and Electronics Engineering
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Computer Science Department Dissertations Collection
Dissertations from 2024 2024.
Enabling Privacy and Trust in Edge AI Systems , Akanksha Atrey, Computer Science
Generative Language Models for Personalized Information Understanding , Pengshan Cai, Computer Science
Towards Automatic and Robust Variational Inference , Tomas Geffner, Computer Science
Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping , Samer Nashed, Computer Science
Policy Gradient Methods: Analysis, Misconceptions, and Improvements , Christopher P. Nota, Computer Science
Data to science with AI and human-in-the-loop , Gustavo Perez Sarabia, Computer Science
Question Answering By Case-Based Reasoning With Textual Evidence , Dung N. Thai, Computer Science
Dissertations from 2023 2023
An Introspective Approach for Competence-Aware Autonomy , Connor Basich, Computer Science
Foundations of Node Representation Learning , Sudhanshu Chanpuriya, Computer Science
Learning to See with Minimal Human Supervision , Zezhou Cheng, Computer Science
IMPROVING USER EXPERIENCE BY OPTIMIZING CLOUD SERVICES , Ishita Dasgupta, Computer Science
Automating the Formal Verification of Software , Emily First, Computer Science
Learning from Sequential User Data: Models and Sample-efficient Algorithms , Aritra Ghosh, Computer Science
Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data , Mahmood Jasim, Computer Science
Rigorous Experimentation For Reinforcement Learning , Scott M. Jordan, Computer Science
Towards Robust Long-form Text Generation Systems , Kalpesh Krishna, Computer Science
Emerging Trustworthiness Issues in Distributed Learning Systems , Hamid Mozaffari, Computer Science
TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP , Milad nasresfahani, Computer Science
Evidence Assisted Learning for Clinical Decision Support Systems , Bhanu Pratap Singh Rawat, Computer Science
DESIGN AND ANALYSIS OF CONTENT CACHING SYSTEMS , Anirudh Sabnis, Computer Science
Quantifying and Enhancing the Security of Federated Learning , Virat Vishnu Shejwalkar, Computer Science
Effective and Efficient Transfer Learning in the Era of Large Language Models , Tu Vu, Computer Science
Data-driven Modeling and Analytics for Greening the Energy Ecosystem , John Wamburu, Computer Science
Bayesian Structural Causal Inference with Probabilistic Programming , Sam A. Witty, Computer Science
LEARNING TO RIG CHARACTERS , Zhan Xu, Computer Science
GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS , Dongxu Zhang, Computer Science
Dissertations from 2022 2022
COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN , Raghavendra K. Addanki, Computer Science
MEASURING NETWORK INTERFERENCE AND MITIGATING IT WITH DNS ENCRYPTION , Seyed Arian Akhavan Niaki, Computer Science
Few-Shot Natural Language Processing by Meta-Learning Without Labeled Data , Trapit Bansal, Computer Science
Communicative Information Visualizations: How to make data more understandable by the general public , Alyxander Burns, Computer Science
REINFORCEMENT LEARNING FOR NON-STATIONARY PROBLEMS , Yash Chandak, Computer Science
Modeling the Multi-mode Distribution in Self-Supervised Language Models , Haw-Shiuan Chang, Computer Science
Nonparametric Contextual Reasoning for Question Answering over Large Knowledge Bases , Rajarshi Das, Computer Science
Languages and Compilers for Writing Efficient High-Performance Computing Applications , Abhinav Jangda, Computer Science
Controllable Neural Synthesis for Natural Images and Vector Art , Difan Liu, Computer Science
Probabilistic Commonsense Knowledge , Xiang Li, Computer Science
DISTRIBUTED LEARNING ALGORITHMS: COMMUNICATION EFFICIENCY AND ERROR RESILIENCE , Raj Kumar Maity, Computer Science
Practical Methods for High-Dimensional Data Publication with Differential Privacy , Ryan H. McKenna, Computer Science
Incremental Non-Greedy Clustering at Scale , Nicholas Monath, Computer Science
High-Quality Automatic Program Repair , Manish Motwani, Computer Science
Unobtrusive Assessment of Upper-Limb Motor Impairment Using Wearable Inertial Sensors , Brandon R. Oubre, Computer Science
Mixture Models in Machine Learning , Soumyabrata Pal, Computer Science
Decision Making with Limited Data , Kieu My Phan, Computer Science
Neural Approaches for Language-Agnostic Search and Recommendation , Hamed Rezanejad Asl Bonab, Computer Science
Low Resource Language Understanding in Voice Assistants , Subendhu Rongali, Computer Science
Enabling Daily Tracking of Individual’s Cognitive State With Eyewear , Soha Rostaminia, Computer Science
LABELED MODULES IN PROGRAMS THAT EVOLVE , Anil K. Saini, Computer Science
Reliable Decision-Making with Imprecise Models , Sandhya Saisubramanian, Computer Science
Data Scarcity in Event Analysis and Abusive Language Detection , Sheikh Muhammad Sarwar, Computer Science
Representation Learning for Shape Decomposition, By Shape Decomposition , Gopal Sharma, Computer Science
Metareasoning for Planning and Execution in Autonomous Systems , Justin Svegliato, Computer Science
Approximate Bayesian Deep Learning for Resource-Constrained Environments , Meet Prakash Vadera, Computer Science
ANSWER SIMILARITY GROUPING AND DIVERSIFICATION IN QUESTION ANSWERING SYSTEMS , Lakshmi Nair Vikraman, Computer Science
Dissertations from 2021 2021
Neural Approaches to Feedback in Information Retrieval , Keping Bi, Computer Science
Sociolinguistically Driven Approaches for Just Natural Language Processing , Su Lin Blodgett, Computer Science
Enabling Declarative and Scalable Prescriptive Analytics in Relational Data , Matteo Brucato, Computer Science
Neural Methods for Answer Passage Retrieval over Sparse Collections , Daniel Cohen, Computer Science
Utilizing Graph Structure for Machine Learning , Stefan Dernbach, Computer Science
Enhancing Usability and Explainability of Data Systems , Anna Fariha, Computer Science
Algorithms to Exploit Data Sparsity , Larkin H. Flodin, Computer Science
3D Shape Understanding and Generation , Matheus Gadelha, Computer Science
Robust Algorithms for Clustering with Applications to Data Integration , Sainyam Galhotra, Computer Science
Improving Evaluation Methods for Causal Modeling , Amanda Gentzel, Computer Science
SAFE AND PRACTICAL MACHINE LEARNING , Stephen J. Giguere, Computer Science
COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING , Craig Stuart Greenberg, Computer Science
Natural Language Processing for Lexical Corpus Analysis , Abram Kaufman Handler, Computer Science
Social Measurement and Causal Inference with Text , Katherine A. Keith, Computer Science
Concentration Inequalities in the Wild: Case Studies in Blockchain & Reinforcement Learning , A. Pinar Ozisik, Computer Science
Resource Allocation in Distributed Service Networks , Nitish Kumar Panigrahy, Computer Science
History Modeling for Conversational Information Retrieval , Chen Qu, Computer Science
Design and Implementation of Algorithms for Traffic Classification , Fatemeh Rezaei, Computer Science
SCALING DOWN THE ENERGY COST OF CONNECTING EVERYDAY OBJECTS TO THE INTERNET , Mohammad Rostami, Computer Science
Deep Learning Models for Irregularly Sampled and Incomplete Time Series , Satya Narayan Shukla, Computer Science
Traffic engineering in planet-scale cloud networks , Rachee Singh, Computer Science
Video Adaptation for High-Quality Content Delivery , Kevin Spiteri, Computer Science
Learning from Limited Labeled Data for Visual Recognition , Jong-Chyi Su, Computer Science
Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications , Amee Trivedi, Computer Science
Geometric Representation Learning , Luke Vilnis, Computer Science
Understanding of Visual Domains via the Lens of Natural Language , Chenyun Wu, Computer Science
Towards Practical Differentially Private Mechanism Design and Deployment , Dan Zhang, Computer Science
Audio-driven Character Animation , Yang Zhou, Computer Science
Dissertations from 2020 2020
Noise-Aware Inference for Differential Privacy , Garrett Bernstein, Computer Science
Motion Segmentation - Segmentation of Independently Moving Objects in Video , Pia Katalin Bideau, Computer Science
An Empirical Assessment of the Effectiveness of Deception for Cyber Defense , Kimberly J. Ferguson-Walter, Computer Science
Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems , Richard Freedman, Computer Science
Improving Reinforcement Learning Techniques by Leveraging Prior Experience , Francisco M. Garcia, Computer Science
Optimization and Training of Generational Garbage Collectors , Nicholas Jacek, Computer Science
Understanding the Dynamic Visual World: From Motion to Semantics , Huaizu Jiang, Computer Science
Improving Face Clustering in Videos , SouYoung Jin, Computer Science
Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction , Ariel Kobren, Computer Science
Learning Latent Characteristics of Data and Models using Item Response Theory , John P. Lalor, Computer Science
Higher-Order Representations for Visual Recognition , Tsung-Yu Lin, Computer Science
Learning from Irregularly-Sampled Time Series , Steven Cheng-Xian Li, Computer Science
Dynamic Composition of Functions for Modular Learning , Clemens GB Rosenbaum, Computer Science
Improving Visual Recognition With Unlabeled Data , Aruni Roy Chowdhury, Computer Science
Deep Neural Networks for 3D Processing and High-Dimensional Filtering , Hang Su, Computer Science
Towards Optimized Traffic Provisioning and Adaptive Cache Management for Content Delivery , Aditya Sundarrajan, Computer Science
The Limits of Location Privacy in Mobile Devices , Keen Yuun Sung, Computer Science
ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS , David Tench, Computer Science
System Design for Digital Experimentation and Explanation Generation , Emma Tosch, Computer Science
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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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Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations
Computer Science and Engineering Theses and Dissertations
Theses/dissertations from 2023 2023.
Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno
Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan
Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros
Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin
Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen
Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur
A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.
Graph Analysis on Social Networks , Shen Lu
Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera
Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo
Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai NG Lugo
Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi
Theses/Dissertations from 2022 2022
Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed
Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala
Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen
A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave
On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández
Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou
Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang
A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri
Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney
Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin
Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker
Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich
Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar
Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong
Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward
Theses/Dissertations from 2021 2021
Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar
Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia
Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto
Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis
Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey
AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi
Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill
Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja
Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana
Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev
Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.
Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski
Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov
Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai
Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri
Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco
Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam
Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava
A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza
Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar
Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams
Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari
Theses/Dissertations from 2020 2020
Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal
Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil
Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari
Composition of Atomic-Obligation Security Policies , Yan Cao Albright
Action Recognition Using the Motion Taxonomy , Maxat Alibayev
Sentiment Analysis in Peer Review , Zachariah J. Beasley
Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov
Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang
Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram
PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami
Composition of Atomic-Obligation Security Policies , Danielle Ferguson
Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel
The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann
Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez
Privacy-Preserving and Functional Information Systems , Thang Hoang
Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January
Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi
Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam
Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha
Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath
System Support of Concurrent Database Query Processing on a GPU , Hao Li
Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu
Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li
Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla
Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng
Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi
Models of Secure Software Enforcement and Development , Hernan M. Palombo
Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos
Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul
Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan
Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah
Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui
Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah
Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu
Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon
Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna
Theses/Dissertations from 2019 2019
Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur
Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed
Phenomena of Social Dynamics in Online Games , Essa Alhazmi
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Interactive Fitness Domains in Competitive Coevolutionary Algorithm , ATM Golam Bari
Measuring Influence Across Social Media Platforms: Empirical Analysis Using Symbolic Transfer Entropy , Abhishek Bhattacharjee
A Communication-Centric Framework for Post-Silicon System-on-chip Integration Debug , Yuting Cao
Authentication and SQL-Injection Prevention Techniques in Web Applications , Cagri Cetin
Multimodal Emotion Recognition Using 3D Facial Landmarks, Action Units, and Physiological Data , Diego Fabiano
Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations , Yongqiang Huang
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Computer Science Theses and Dissertations
This collection contains theses and dissertations from the Department of Computer Science, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository
Theses/Dissertations from 2024 2024
A Target-Based and A Targetless Extrinsic Calibration Methods for Thermal Camera and 3D LiDAR , Farhad Dalirani
Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications , Sudipta Singha Roy
Theses/Dissertations from 2023 2023
Classification of DDoS Attack with Machine Learning Architectures and Exploratory Analysis , Amreen Anbar
Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces , Sepehr Asgarian
Improved Protein Sequence Alignments Using Deep Learning , Seyed Sepehr Ashrafzadeh
INVESTIGATING IMPROVEMENTS TO MESH INDEXING , Anurag Bhattacharjee
Algorithms and Software for Oligonucleotide Design , Qin Dong
Framework for Assessing Information System Security Posture Risks , Syed Waqas Hamdani
De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling , Fang Han
Local Model Agnostic XAI Methodologies Applied to Breast Cancer Malignancy Predictions , Heather Hartley
A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit , Rongji He
A Novel Method for Assessment of Batch Effect on single cell RNA sequencing data , Behnam Jabbarizadeh
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs , Taabish Jeshani
Citation Polarity Identification From Scientific Articles Using Deep Learning Methods , Souvik Kundu
Denoising-Based Domain Adaptation Network for EEG Source Imaging , Runze Li
Decoy-Target Database Strategy and False Discovery Rate Analysis for Glycan Identification , Xiaoou Li
DpNovo: A DEEP LEARNING MODEL COMBINED WITH DYNAMIC PROGRAMMING FOR DE NOVO PEPTIDE SEQUENCING , Yizhou Li
Developing A Smart Home Surveillance System Using Autonomous Drones , Chongju Mai
Look-Ahead Selective Plasticity for Continual Learning , Rouzbeh Meshkinnejad
The Two Visual Processing Streams Through The Lens Of Deep Neural Networks , Aidasadat Mirebrahimi Tafreshi
Source-free Domain Adaptation for Sleep Stage Classification , Yasmin Niknam
Data Heterogeneity and Its Implications for Fairness , Ghazaleh Noroozi
Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System for Efficient, Sustainable, and Self-Adaptive Urban Environments , Elham Okhovat
Evaluating the Likelihood of Bug Inducing Commits Using Metrics Trend Analysis , Parul Parul
On Computing Optimal Repairs for Conditional Independence , Alireza Pirhadi
Open-Set Source-Free Domain Adaptation in Fundus Images Analysis , Masoud Pourreza
Migration in Edge Computing , Arshin Rezazadeh
A Modified Hopfield Network for the K-Median Problem , Cody Rossiter
Predicting Network Failures with AI Techniques , Chandrika Saha
Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective , Sajal Saha
An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support , Mozhgan Salimiparsa
Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition , Amir Hesam Salimnia
Global Cyber Attack Forecast using AI Techniques , Nusrat Kabir Samia
IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION , Rafael David Segistan Canizales
A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods , Kayvan Shabani
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network , Sareh Soltani Nejad
Smartphone Loss Prevention System Using BLE and GPS Technology , Noshin Tasnim
A Hybrid Continual Machine Learning Model for Efficient Hierarchical Classification of Domain-Specific Text in The Presence of Class Overlap (Case Study: IT Support Tickets) , Yasmen M. Wahba
Reducing Negative Transfer of Random Data in Source-Free Unsupervised Domain Adaptation , Anthony Wong
Deep Neural Methods for True/Pseudo- Invasion Classification in Colorectal Polyp Whole-Slide Images , Zhiyuan Yang
Developing a Relay-based Autonomous Drone Delivery System , Muhammad Zakar
Learning Mortality Risk for COVID-19 Using Machine Learning and Statistical Methods , Shaoshi Zhang
Machine Learning Techniques for Improved Functional Brain Parcellation , Da Zhi
Theses/Dissertations from 2022 2022
The Design and Implementation of a High-Performance Polynomial System Solver , Alexander Brandt
Defining Service Level Agreements in Serverless Computing , Mohamed Elsakhawy
Algorithms for Regular Chains of Dimension One , Juan P. Gonzalez Trochez
Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications , Susmitha Hanumanthu
Multi-Device Data Analysis for Fault Localization in Electrical Distribution Grids , Jacob D L Hunte
Towards Parking Lot Occupancy Assessment Using Aerial Imagery and Computer Vision , John Jewell
Potential of Vision Transformers for Advanced Driver-Assistance Systems: An Evaluative Approach , Andrew Katoch
Psychological Understanding of Textual journals using Natural Language Processing approaches , Amirmohammad Kazemeinizadeh
Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems , Nima Khairdoost
Solving Challenges in Deep Unsupervised Methods for Anomaly Detection , Vahid Reza Khazaie
Developing an Efficient Real-Time Terrestrial Infrastructure Inspection System Using Autonomous Drones and Deep Learning , Marlin Manka
Predictive Modelling For Topic Handling Of Natural Language Dialogue With Virtual Agents , Lareina Milambiling
Improving Deep Entity Resolution by Constraints , Soudeh Nilforoushan
Respiratory Pattern Analysis for COVID-19 Digital Screening Using AI Techniques , Annita Tahsin Priyoti
Extracting Microservice Dependencies Using Log Analysis , Andres O. Rodriguez Ishida
False Discovery Rate Analysis for Glycopeptide Identification , Shun Saito
Towards a Generalization of Fulton's Intersection Multiplicity Algorithm , Ryan Sandford
An Investigation Into Time Gazed At Traffic Objects By Drivers , Kolby R. Sarson
Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives , Ibrahim Mohammed Sayem
A Unified Representation and Deep Learning Architecture for Persuasive Essays in English , Muhammad Tawsif Sazid
Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP) , Aakriti Sharma
Advances in the Automatic Detection of Optimization Opportunities in Computer Programs , Delaram Talaashrafi
Reputation-Based Trust Assessment of Transacting Service Components , Konstantinos Tsiounis
Fully Autonomous UAV Exploration in Confined and Connectionless Environments , Kirk P. Vander Ploeg
Three Contributions to the Theory and Practice of Optimizing Compilers , Linxiao Wang
Developing Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach , Wumian Wang
Predicting and Modifying Memorability of Images , Mohammad Younesi
Theses/Dissertations from 2021 2021
Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches , Mahtab Ahmed
A Physical Layer Framework for a Smart City Using Accumulative Bayesian Machine Learning , Razan E. AlFar
Load Balancing and Resource Allocation in Smart Cities using Reinforcement Learning , Aseel AlOrbani
Contrastive Learning of Auditory Representations , Haider Al-Tahan
Cache-Friendly, Modular and Parallel Schemes For Computing Subresultant Chains , Mohammadali Asadi
Protein Interaction Sites Prediction using Deep Learning , Sourajit Basak
Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis , William A. Beldman
Improving Reader Motivation with Machine Learning , Tanner A. Bohn
A Black-box Approach for Containerized Microservice Monitoring in Fog Computing , Shi Chang
Visualization and Interpretation of Protein Interactions , Dipanjan Chatterjee
A Framework for Characterising Performance in Multi-Class Classification Problems with Applications in Cancer Single Cell RNA Sequencing , Erik R. Christensen
Exploratory Search with Archetype-based Language Models , Brent D. Davis
Evolutionary Design of Search and Triage Interfaces for Large Document Sets , Jonathan A. Demelo
Building Effective Network Security Frameworks using Deep Transfer Learning Techniques , Harsh Dhillon
A Deep Topical N-gram Model and Topic Discovery on COVID-19 News and Research Manuscripts , Yuan Du
Automatic extraction of requirements-related information from regulatory documents cited in the project contract , Sara Fotouhi
Developing a Resource and Energy Efficient Real-time Delivery Scheduling Framework for a Network of Autonomous Drones , Gopi Gugan
A Visual Analytics System for Rapid Sensemaking of Scientific Documents , Amirreza Haghverdiloo Barzegar
Calibration Between Eye Tracker and Stereoscopic Vision System Employing a Linear Closed-Form Perspective-n-Point (PNP) Algorithm , Mohammad Karami
Fuzzy and Probabilistic Rule-Based Approaches to Identify Fault Prone Files , Piyush Kumar Korlepara
Parallel Arbitrary-precision Integer Arithmetic , Davood Mohajerani
A Technique for Evaluating the Health Status of a Software Module Using Process Metrics , . Ria
Visual Analytics for Performing Complex Tasks with Electronic Health Records , Neda Rostamzadeh
Predictive Model of Driver's Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems , Mohsen Shirpour
A Generative-Discriminative Approach to Human Brain Mapping , Deepanshu Wadhwa
WesternAccelerator:Rapid Development of Microservices , Haoran Wei
A Lightweight and Explainable Citation Recommendation System , Juncheng Yin
Mitosis Detection from Pathology Images , Jinhang Zhang
Theses/Dissertations from 2020 2020
Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury , Sheikh S. Abdullah
Towards the Development of Network Service Cost Modeling-An ISP Perspective , Yasmeen Ali
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Computer Science > Computation and Language
Title: rag vs fine-tuning: pipelines, tradeoffs, and a case study on agriculture.
Abstract: There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning. RAG augments the prompt with the external data, while fine-Tuning incorporates the additional knowledge into the model itself. However, the pros and cons of both approaches are not well understood. In this paper, we propose a pipeline for fine-tuning and RAG, and present the tradeoffs of both for multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4. Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results. We propose metrics to assess the performance of different stages of the RAG and fine-Tuning pipeline. We conduct an in-depth study on an agricultural dataset. Agriculture as an industry has not seen much penetration of AI, and we study a potentially disruptive application - what if we could provide location-specific insights to a farmer? Our results show the effectiveness of our dataset generation pipeline in capturing geographic-specific knowledge, and the quantitative and qualitative benefits of RAG and fine-tuning. We see an accuracy increase of over 6 p.p. when fine-tuning the model and this is cumulative with RAG, which increases accuracy by 5 p.p. further. In one particular experiment, we also demonstrate that the fine-tuned model leverages information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%. Overall, the results point to how systems built using LLMs can be adapted to respond and incorporate knowledge across a dimension that is critical for a specific industry, paving the way for further applications of LLMs in other industrial domains.
Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
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The state of AI in 2023: Generative AI’s breakout year
You have reached a page with older survey data. please see our 2024 survey results here ..
The latest annual McKinsey Global Survey on the current state of AI confirms the explosive growth of generative AI (gen AI) tools . Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas. What’s more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.
The organizations that have already embedded AI capabilities have been the first to explore gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of gen AI tools. 1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption.
The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.
Table of Contents
- It’s early days still, but use of gen AI is already widespread
- Leading companies are already ahead with gen AI
- AI-related talent needs shift, and AI’s workforce effects are expected to be substantial
- With all eyes on gen AI, AI adoption and impact remain steady
About the research
1. it’s early days still, but use of gen ai is already widespread.
The findings from the survey—which was in the field in mid-April 2023—show that, despite gen AI’s nascent public availability, experimentation with the tools is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.
Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research , these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.
In these early days, expectations for gen AI’s impact are high : three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.
Responses show many organizations not yet addressing potential risks from gen AI
According to the survey, few companies seem fully prepared for the widespread use of gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.
2. Leading companies are already ahead with gen AI
The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.
AI high performers are much more likely than others to use AI in product and service development.
Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely new businesses or sources of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.
As we’ve seen in previous years , these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.
While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.
The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.
Many specialized MLOps technologies and practices may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver—and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.
3. AI-related talent needs shift, and AI’s workforce effects are expected to be substantial
Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.
The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.
Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.
Looking specifically at gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what our recent research suggests: while the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.
AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.
4. With all eyes on gen AI, AI adoption and impact remain steady
While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.
Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.
The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner; Bryce Hall , an associate partner in the Washington, DC, office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, based in the Chicago and London offices, respectively.
They wish to thank Shivani Gupta, Abhisek Jena, Begum Ortaoglu, Barr Seitz, and Li Zhang for their contributions to this work.
This article was edited by Heather Hanselman, an editor in the Atlanta office.
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· Development of ideas into research questions;
· How to find, read, evaluate and referee other research;
· Design and evaluation of experiments and appropriate use of statistics;
· Ethics, the principles of science and examples of science gone wrong.
Much of the book is a step-by-step guide to effective communication, with advice on:
· Writing style and editing;
· Figures, graphs and tables;
· Mathematics and algorithms;
· Literature reviews and referees’ reports;
· Structuring of arguments and results into papers and theses;
· Writing of other professional documents;
· Presentation of talks and posters.
Written in an accessible style and including handy checklists and exercises, Writing for Computer Science is not only an introduction to the doing and describing of research, but is a valuable reference for working scientists in the computing and mathematical sciences.
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Computer science: Subject, fundamental research problems, methodology, structure, and applied problems
Seventy Years of Computer Science
The Mathematical Origins of Modern Computing
- Effective Communication
- Organization
- Presentation of Ideas
- Scientific Research
- Writing Style
Table of contents (17 chapters)
Front matter, introduction.
Justin Zobel
Getting Started
Reading and reviewing, hypotheses, questions, and evidence, writing a paper, style specifics, punctuation, mathematics, graphs, figures, and tables, other professional writing, experimentation, statistical principles, presentations, back matter.
“This is a comprehensive guide on research methods and how to produce a scientific publication detailing one’s research in computer science … . a must-read for those doing research in CS and related fields. It will greatly benefit anyone who is involved in any kind of scientific research, as the examples are only from the CS field. Students, researchers, scientists, and other academicians involved in scientific research will improve both their research methods and writing by reading this book.” (Alexis Leon, Computing Reviews, July, 2015)
Authors and Affiliations
About the author.
Justin Zobel is Head of the University of Melbourne's Department of Computing & Information Systems. He received his PhD from the University of Melbourne and for many years was based at RMIT University, where he led the Search Engine group. As a researcher, Professor Zobel is best known for his role in the development of algorithms for efficient web search. His current research areas include search, measurement and evaluation, bioinformatics, fundamental algorithms and data structures and compression. He is an author of around 200 papers, has written three texts on postgraduate study and research methods and is an associate editor of ACM Transactions on Information Systems, Information Processing & Management, and Information Retrieval.
Bibliographic Information
Book Title : Writing for Computer Science
Authors : Justin Zobel
DOI : https://doi.org/10.1007/978-1-4471-6639-9
Publisher : Springer London
eBook Packages : Computer Science , Computer Science (R0)
Copyright Information : Springer-Verlag London 2014
Softcover ISBN : 978-1-4471-6638-2 Published: 17 February 2015
eBook ISBN : 978-1-4471-6639-9 Published: 09 February 2015
Edition Number : 3
Number of Pages : XIII, 284
Number of Illustrations : 28 b/w illustrations
Topics : Popular Computer Science , Computer Science, general
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Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. ... Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG ...
arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.
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Papers We Love (PWL) is a community built around reading, discussing and learning more about academic computer science papers. This repository serves as a directory of some of the best papers the community can find, bringing together documents scattered across the web. You can also visit the Papers We Love site for more info.
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Open research in computer science. Spanning networks and communications to security and cryptology to big data, complexity, and analytics, SpringerOpen and BMC publish one of the leading open access portfolios in computer science. Learn about our journals and the research we publish here on this page.
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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 ...
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Computer Science > Computation and Language. arXiv:2401.08406 (cs) ... In this paper, we propose a pipeline for fine-tuning and RAG, and present the tradeoffs of both for multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4. Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and ...
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About the research. The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions ...
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Extensive guidance on writing and presentation skills for researchers and practitioners in the field of Computer Science. A comprehensive introduction to research methods and scientific writing for computer scientists. An overview of the skills that a student needs to become an effective researcher. Includes supplementary material: sn.pub/extras.
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