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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, nanoflow: towards optimal large language model serving throughput.

The increasing usage of Large Language Models (LLMs) has resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users.

Distributed, Parallel, and Cluster Computing

A Survey of NL2SQL with Large Language Models: Where are we, and where are we going?

hkustdial/nl2sql_handbook • 9 Aug 2024

Translating users' natural language queries (NL) into SQL queries (i. e., NL2SQL) can significantly reduce barriers to accessing relational databases and support various commercial applications.

The Dawn of Natural Language to SQL: Are We Fully Ready?

hkustdial/nl2sql_survey • 3 Jun 2024

Translating users' natural language questions into SQL queries (i. e., NL2SQL) significantly lowers the barriers to accessing relational databases.

WhisperX: Time-Accurate Speech Transcription of Long-Form Audio

Large-scale, weakly-supervised speech recognition models, such as Whisper, have demonstrated impressive results on speech recognition across domains and languages.

Sound Audio and Speech Processing

Multi-modal Integrated Prediction and Decision-making with Adaptive Interaction Modality Explorations

Specifically, MIND leverages learning-based scenario predictions to obtain integrated predictions and decisions with social-consistent interaction modality and utilizes a modality-aware dynamic branching mechanism to generate scenario trees that efficiently capture the evolutions of distinctive interaction modalities with low variation of interaction uncertainty along the planning horizon.

GarmentCode: Programming Parametric Sewing Patterns

maria-korosteleva/garmentcode • 6 Jun 2023

In our prototype garment configurator, users can manipulate meaningful design parameters and body measurements, while the construction of pattern geometry is handled by garment programs implemented with GarmentCode.

Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs

Large Language Models (LLMs) have demonstrated great potential in robotic applications by providing essential general knowledge.

Quantitative 3D Map Accuracy Evaluation Hardware and Algorithm for LiDAR(-Inertial) SLAM

sangwoojung98/3d_map_evaluation • 19 Aug 2024

In this work, we propose a user-independent software/hardware system that can quantitatively evaluate the accuracy of a 3D pointcloud map acquired from LiDAR(-Inertial) SLAM.

SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension

MigVega/SLAM2REF • 28 Aug 2024

This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments.

ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning

hku-mars/rog-map • 28 Feb 2023

Recent advances in LiDAR technology have opened up new possibilities for robotic navigation.

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Computer Science is the study of computer systems and the impact that design, theory, and development have on society. The Computer Science Research Network on SSRN is an open access preprint server that provides a venue for authors to showcase their research papers in our digital library, speeding up the dissemination and providing the scholarly community access to groundbreaking working papers, early stage research and even peer reviewed or published journal articles. SSRN provides the opportunity to share different outputs of research such as preprints, preliminary or exploratory investigations, book chapters, PhD dissertations, course and teaching materials, presentations, and posters among others.  

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Making an Impact for a Better World

As computing continues to transform our world, the research we're pursuing at Stanford Computer Science seeks to ethically create, shape, and empower the new frontier. From the latest in robotics to foundation models to cryptocurrency, Stanford computer scientists are making an impact on the world beyond our academic walls. 

Faculty Spotlight:  Omer Reingold, the Rajeev Motwani Professor in Computer Science

"A computer scientist teaching a theater class is a bit unusual, I’ll grant you that. But is it so strange? For me, classifying different parts of campus to left-brain-versus-right-brain kind of thinking is just an unfortunate stereotype. I'd much rather go with ‘creativity is creativity is creativity.'" Read Omer Reingold's Story  

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Best Paper Award: "Breaking the Metric Voting Distortion Barrier"

Stanford professor, Moses Charikar, and his two co-authors, Kangning Wang (postdoc) and Prasanna Ramakrishnan (PhD student), win Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA24).

Click here to read more as Kangning and Prasanna discuss their passion for research, the challenges they faced, and the significance of this award.

A Robotic Diver Connects Human's Sight and Touch to the Deep Sea

The Future of AI Chat: Foundation Models and Responsible Innovation

Guest Percy Liang is an authority on AI who says that we are undergoing a paradigm shift in AI powered by foundation models, which are general-purpose models trained at immense scale, such as ChatGPT.

CS Faculty & Their Research

Explore our network of faculty members and the innovation conceived by their research. They are shaping a new era of solutions and the next generation of thought leaders and entrepreneurs.

Meet Our Faculty & Their Research

Stanford Computer Science faculty members work on the world's most pressing problems, in conjunction with other leaders across multiple fields. Fueled by academic and industry cross-collaborations, they form a network and culture of innovation.

The Emmy Award-winning video looks back at a remarkable six decades of AI work at Stanford University.

Stanford has been a leader in AI almost since the day the term was dreamed up by John McCarthy in the 1950s. McCarthy would join the Stanford faculty in 1962 and found the Stanford Artificial Intelligence Lab (SAIL), initiating a six-decades-plus legacy of innovation. Over the years, the field has grown to welcome a diversity of researchers and areas of exploration, including robotics, autonomous vehicles, medical diagnostics, natural language processing, and more. All the while, Stanford has been at the forefront in research and in educating the next generation of innovators in AI. Artificial intelligence would not be what it is today without Stanford.  

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Stanford Computer Science has a legacy of working with industry to advance real-world solutions. Membership in our affiliate programs provides companies with access to the research, faculty, and students to accelerate their innovations.

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Connecting Students & Research: Jump In

At Stanford, students do amazing research. Their projects are widely recognized as some of the best in the world. Stanford's reputation as one of the top CS programs comes in large part from this. If you're a student with a passion for participating in meaningful research, our CURIS and LINXS programs are designed to get you started.

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The Stanford LINXS Program is an eight-week summer residential program that brings innovative undergraduates, who are currently attending Historically Black Colleges & Universities and Hispanic Serving Institutions, to Stanford for an immersive academic research and graduate school preparation experience. 

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CURIS is the undergraduate research program of Stanford's Computer Science Department. Each summer, 100+ undergraduates conduct and participate in computer science research advised and mentored by faculty and PhD students.  

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Computer science articles within Scientific Reports

Article 10 September 2024 | Open Access

Weld seam object detection system based on the fusion of 2D images and 3D point clouds using interpretable neural networks

• Shengbo Wang
• , Zengxu Li
•  &  Yaobin Yue

Aversion to external feedback suffices to ensure agent alignment

• Paulo Garcia

The analysis of art design under improved convolutional neural network based on the Internet of Things technology

Computer vision and statistical insights into cycling near miss dynamics.

• Mohamed Ibrahim

A multi-view multi-label fast model for Auricularia cornea phenotype identification and classification

• Yinghang Xu
• , Shizheng Qu
•  &  Zhuoshi Li

Nanophotonic structure inverse design for switching application using deep learning

• Ehsan Adibnia
• , Majid Ghadrdan
•  &  Mohammad Ali Mansouri-Birjandi

Solving linear systems on quantum hardware with hybrid HHL ++

• Romina Yalovetzky
• , Pierre Minssen
•  &  Marco Pistoia

Article 09 September 2024 | Open Access

Enhancing early Parkinson’s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database

• Vincenzo Dentamaro
• , Donato Impedovo
•  &  Paolo Taurisano

Energy and throughput aware adequate routing for wireless sensor networks using integrated game theory method

• M. Vivek Kumar
•  &  O. Saraniya

Computer vision quickly identifies radio signals with unlimited accuracy

• Michael C. Kleder

Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies

• Nihil Patel
• , Adrian Celaya
•  &  David T. Fuentes

Article 08 September 2024 | Open Access

Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification

• B. N. Jagadesh
• , N. V. RajaSekhar Reddy
•  &  Ch. Koteswararao

Article 06 September 2024 | Open Access

Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection

• Shafiqul Islam
• , Gordon C. Wishart
•  &  Haider Raza

A machine learning based EMA-DCPM algorithm for production scheduling

• , Haibin Liu
•  &  Mingfei Li

Fine classification of rice fields in high-resolution remote sensing images

• Lingyuan Zhao
• , Zifei Luo
•  &  Yan Zhang

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection

• Zicong Yang
• , Aitong Jin
•  &  Yan Liu

Article 05 September 2024 | Open Access

Novel concept-based image captioning models using LSTM and multi-encoder transformer architecture

• Asmaa A. E. Osman
• , Mohamed A. Wahby Shalaby
•  &  Khaled M. Elsayed

An improved sand cat swarm optimization with lens opposition-based learning and sparrow search algorithm

• Yanguang Cai
• , Changle Guo
•  &  Xiang Chen

Trust, trustworthiness and AI governance

• Christian Lahusen
• , Martino Maggetti
•  &  Marija Slavkovik

Lightweight deep neural network for radio frequency interference detection and segmentation in synthetic aperture radar

• Fenghao Zheng
• , Zhongmin Zhang
•  &  Dang Zhang

A PV cell defect detector combined with transformer and attention mechanism

•  &  Zhenzhen Lv

An encryption algorithm for color images based on an improved dual-chaotic system combined with DNA encoding

• , Tingting Liu
•  &  Jun Yin

Article 04 September 2024 | Open Access

Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning

• Seung Hwan Lee
• , Jisu Jeon
•  &  Kwang Gi Kim

Fog-assisted de-duplicated data exchange in distributed edge computing networks

• Ghawar Said
• , Anwar Ghani
•  &  Do-Hyeun Kim

Multi-modality multi-task model for mRS prediction using diffusion-weighted resonance imaging

• In-Seo Park
• , Seongheon Kim
•  &  Chulho Kim

Formal analysis of signal protocol based on logic of events theory

• , Meihua Xiao
•  &  Ruihan Xu

Livestream sales prediction based on an interpretable deep-learning model

•  &  Xian Zhang

ERABiLNet: enhanced residual attention with bidirectional long short-term memory

• Koteeswaran Seerangan
• , Malarvizhi Nandagopal
•  &  Shitharth Selvarajan

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets

• Omneya Attallah

APIS: a paired CT-MRI dataset for ischemic stroke segmentation - methods and challenges

• Santiago Gómez
• , Edgar Rangel
•  &  Fabio Martínez

Advancing sentiment classification through a population game model approach

• Neha Punetha
•  &  Goonjan Jain

Flying foxes optimization with reinforcement learning for vehicle detection in UAV imagery

• Naif Almakayeel

A two-stage defect detection method for unevenly illuminated self-adhesive printed materials

• Guifeng Peng
•  &  Qing Jiang

Urban traffic tiny object detection via attention and multi-scale feature driven in UAV-vision

• Yangyang Wang
• , Jie Zhang
•  &  Jian Zhou

Article 03 September 2024 | Open Access

An improve crested porcupine algorithm for UAV delivery path planning in challenging environments

• Shenglin Liu
• , Zikai Jin
•  &  Huimin Lu

Designing a reliable machine learning system for accurately estimating the ultimate condition of FRP-confined concrete

• Meysam Alizamir
• , Aliakbar Gholampour
•  &  Woo-tai Jung

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model

• Moneerah Alotaibi
• , Amal Alshardan
•  &  Alaa O. Khadidos

Article 02 September 2024 | Open Access

Optimization of the convolutional neural network classification model under the background of innovative art teaching models

•  &  Shuguang Xu

Fuchs’ Endothelial Corneal Dystrophy evaluation using a high-resolution wavefront sensor

• Carolina Belda-Para
• , Gonzalo Velarde-Rodríguez
•  &  José M. Rodríguez-Ramos

Short-term natural gas load forecasting based on EL-VMD-Transformer-ResLSTM

• Mingzhi Zhao
• , Guangrong Guo
•  &  Ziyi Wang

A rigorous workflow and comparative analysis for accurate determination of vitrinite reflectance using data-driven approaches in the Persian Gulf region

• Parsa Kharazi Esfahani
• , Hadi Mahdavi Basir
•  &  Ahmad Reza Rabbani

Article 31 August 2024 | Open Access

Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations

• Seokhyeon Heo
• , Kyeong Eun Uhm
•  &  Jongmin Lee

Identifying alternately poisoning attacks in federated learning online using trajectory anomaly detection method

• Zhiying Ding
• , Wenshuo Wang
•  &  Chunxiao Mu

Article 30 August 2024 | Open Access

Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans

• , Hai Zhong
•  &  Guo Dong Pang

Improving privacy-preserving multi-faceted long short-term memory for accurate evaluation of encrypted time-series MRI images in heart disease

• Lenka Čepová
• , Muniyandy Elangovan
•  &  Faruq Mohammad

Enhancing source code classification effectiveness via prompt learning incorporating knowledge features

• , Senlin Luo
•  &  Zhengjun Li

CTNet: a convolutional transformer network for EEG-based motor imagery classification

• , Xiaolu Jiang
•  &  Sujun Weng

Augmented sets of output differences and new distinguishers for SPN ciphers

• Rahul Girme
• , Raghvendra Rohit
•  &  Santanu Sarkar

A novel classification algorithm for customer churn prediction based on hybrid Ensemble-Fusion model

• Chenggang He
•  &  Chris H. Q. Ding

Article 29 August 2024 | Open Access

Effective weight optimization strategy for precise deep learning forecasting models using EvoLearn approach

• , Ashima Anand
•  &  Rajender Parsad

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Journal of Computer Science

Aims and scope.

The Journal of Computer Science (JCS) is dedicated to advancing computer science by publishing high-quality research and review articles that span both theoretical foundations and practical applications in information, computation, and computer systems. With a commitment to excellence, JCS offers a platform for researchers, scholars, and industry professionals to share their insights and contribute to the ongoing evolution of computer science. Published on a monthly basis, JCS provides up-to-date insights into this ever-evolving discipline.

It is with great pleasure that we announce the SGAMR Annual Awards 2020. This award is given annually to Researchers and Reviewers of International Journal of Structural Glass and Advanced Materials Research (SGAMR) who have shown innovative contributions and promising research as well as others who have excelled in their Editorial duties.

This special issue "Neuroinflammation and COVID-19" aims to provide a space for debate in the face of the growing evidence on the affectation of the nervous system by COVID-19, supported by original studies and case series.

The SGAMR Editorial Board is pleased to announce the inauguration of the yearly “SGAMR Young Researcher Award” (SGAMR-YRA). The best paper published by a young researcher will be selected by a journal committee, from the Editorial Board.

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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
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• Computer Architecture and Hardware Design
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• Multi-agent Systems
• Evolutionary Robotics
• Quantum Machine Learning
• Computational Social Science
• Explainable Recommender Systems.
• Artificial Intelligence and its applications
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• Cybersecurity threats and solutions
• Internet of Things and its impact on society
• Virtual and Augmented Reality and its uses
• Blockchain Technology and its potential in various industries
• Web Development and Design
• Digital Marketing and its effectiveness
• Big Data and Analytics
• Software Development Life Cycle
• Gaming Development and its growth
• Network Administration and Maintenance
• Machine Learning and its uses
• Data Warehousing and Mining
• Computer Architecture and Design
• Computer Graphics and Animation
• Quantum Computing and its potential
• Data Structures and Algorithms
• Computer Vision and Image Processing
• Robotics and its applications
• Operating Systems and its functions
• Information Theory and Coding
• Compiler Design and Optimization
• Computer Forensics and Cyber Crime Investigation
• Distributed Computing and its significance
• Artificial Neural Networks and Deep Learning
• Cloud Storage and Backup
• Programming Languages and their significance
• Computer Simulation and Modeling
• Computer Networks and its types
• Information Security and its types
• Computer-based Training and eLearning
• Medical Imaging and its uses
• Social Media Analysis and its applications
• Human Resource Information Systems
• Computer-Aided Design and Manufacturing
• Multimedia Systems and Applications
• Geographic Information Systems and its uses
• Computer-Assisted Language Learning
• Mobile Device Management and Security
• Data Compression and its types
• Knowledge Management Systems
• Text Mining and its uses
• Cyber Warfare and its consequences
• Wireless Networks and its advantages
• Computer Ethics and its importance
• Computational Linguistics and its applications
• Autonomous Systems and Robotics
• Information Visualization and its importance
• Geographic Information Retrieval and Mapping
• Business Intelligence and its benefits
• Digital Libraries and their significance
• Artificial Life and Evolutionary Computation
• Computer Music and its types
• Virtual Teams and Collaboration
• Computer Games and Learning
• Semantic Web and its applications
• Electronic Commerce and its advantages
• Multimedia Databases and their significance
• Computer Science Education and its importance
• Computer-Assisted Translation and Interpretation
• Ambient Intelligence and Smart Homes
• Autonomous Agents and Multi-Agent Systems.

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Title: can llms generate novel research ideas a large-scale human study with 100+ nlp researchers.

Abstract: Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.

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

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

New York, NY, March 2, 2019 – As a capstone to its 50th annual SIGCSE Technical Symposium , leaders of the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE) are celebrating the ideas that have shaped the field by recognizing a select group of publications with a “Top Ten Symposium Papers of All Time Award.” The top ten papers were chosen from among the best papers that were presented at the SIGCSE Technical Symposium over the last 49 years.

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

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

The Top Ten Symposium Papers are:

1. “ Identifying student misconceptions of programming ” (2010) Lisa C. Kaczmarczyk, Elizabeth R. Petrick, University of California, San Diego; Philip East, University of Northern Iowa; Geoffrey L. Herman, University of Illinois at Urbana-Champaign Computing educators are often baffled by the misconceptions that their CS1 students hold. We need to understand these misconceptions more clearly in order to help students form correct conceptions. This paper describes one stage in the development of a concept inventory for Computing Fundamentals: investigation of student misconceptions in a series of core CS1 topics previously identified as both important and difficult. Formal interviews with students revealed four distinct themes, each containing many interesting misconceptions.

2. “ Improving the CS1 experience with pair programming ” (2003) Nachiappan Nagappan, Laurie Williams, Miriam Ferzli, Eric Wiebe, Kai Yang, Carol Miller, Suzanne Balik, North Carolina State University Pair programming is a practice in which two programmers work collaboratively at one computer, on the same design, algorithm, or code. Prior research indicates that pair programmers produce higher quality code in essentially half the time taken by solo programmers. The authors organized an experiment to assess the efficacy of pair programming in an introductory Computer Science course. Their results indicate that pair programming creates a laboratory environment conducive to more advanced, active learning than traditional labs; students and lab instructors report labs to be more productive and less frustrating.

3. “ Undergraduate women in computer science: experience, motivation and culture ” (1997) Allan Fisher, Jane Margolis, Faye Miller, Carnegie Mellon University During a year-long study, the authors examined the experiences of undergraduate women studying computer science at Carnegie Mellon University, with a specific eye toward understanding the influences and processes whereby they attach themselves to or detach themselves from the field. This report, midway through the two-year project, recaps the goals and methods of the study, reports on their progress and preliminary conclusions, and sketches their plans for the final year and the future beyond this particular project.

4. “ A Multi-institutional Study of Peer Instruction in Introductory Computing ” (2016) Leo Porter, Beth Simon, University of California, San Diego; Dennis Bouvier, Southern Illinois University; Quintin Cutts, University of Glasgow; Scott Grissom, Grand Valley State University; Cynthia Lee, Stanford University; Robert McCartney, University of Connecticut; Daniel Zingaro, University of Toronto Peer Instruction (PI) is a student-centric pedagogy in which students move from the role of passive listeners to active participants in the classroom. This paper adds to this body of knowledge by examining outcomes from seven introductory programming instructors: three novices to PI and four with a range of PI experience. Through common measurements of student perceptions, the authors provide evidence that introductory computing instructors can successfully implement PI in their classrooms.

5. " The introductory programming course in computer science: ten principles " (1978) G. Michael Schneider, University of Minnesota Schneider describes the crucial goals of any introductory programming course while leaving to the reader the design of a specific course to meet these goals. This paper presents ten essential objectives of an initial programming course in Computer Science, regardless of who is teaching or where it is being taught. Schneider attempts to provide an in-depth, philosophical framework for the course called CS1—Computer Programming 1—as described by the ACM Curriculum Committee on Computer Science.

6. “ Constructivism in computer science education ” (1998) Mordechai Ben-Ari, Weizmann Institute of Science Constructivism is a theory of learning which claims that students construct knowledge rather than merely receive and store knowledge transmitted by the teacher. Constructivism has been extremely influential in science and mathematics education, but not in computer science education (CSE). This paper surveys constructivism in the context of CSE, and shows how the theory can supply a theoretical basis for debating issues and evaluating proposals.

7. “ Using software testing to move students from trial-and-error to reflection-in-action ” (2004) Stephen H. Edwards, Virginia Tech Introductory computer science students have relied on a trial and error approach to fixing errors and debugging for too long. Moving to a reflection in action strategy can help students become more successful. Traditional programming assignments are usually assessed in a way that ignores the skills needed for reflection in action, but software testing promotes the hypothesis-forming and experimental validation that are central to this mode of learning. By changing the way assignments are assessed--where students are responsible for demonstrating correctness through testing, and then assessed on how well they achieve this goal--it is possible to reinforce desired skills. Automated feedback can also play a valuable role in encouraging students while also showing them where they can improve.

8. “ What should we teach in an introductory programming course ” (1974) David Gries, Cornell University Gries argues that an introductory course (and its successor) in programming should be concerned with three aspects of programming: 1. How to solve problems, 2. How to describe an algorithmic solution to a problem, and 3. How to verify that an algorithm is correct. In this paper he discusses mainly the first two aspects. He notes that the third is just as important, but if the first two are carried out in a systematic fashion, the third is much easier than commonly supposed.

9. “ Contributing to success in an introductory computer science course: a study of twelve factors ” (2001) Brenda Cantwell Wilson, Murray State University; Sharon Shrock, Southern Illinois University This study was conducted to determine factors that promote success in an introductory college computer science course. The model included twelve possible predictive factors including math background, attribution for success/failure (luck, effort, difficulty of task, and ability), domain specific self-efficacy, encouragement, comfort level in the course, work style preference, previous programming experience, previous non-programming computer experience, and gender. Subjects included 105 students enrolled in a CS1 introductory computer science course at a midwestern university. The study revealed three predictive factors in the following order of importance: comfort level, math, and attribution to luck for success/failure.

10. “ Teaching objects-first in introductory computer science ” (2003) Stephen Cooper, Saint Joseph's University; Wanda Dann, Ithaca College; Randy Pausch Carnegie Mellon University An objects-first strategy for teaching introductory computer science courses is receiving increased attention from CS educators. In this paper, the authors discuss the challenge of the objects-first strategy and present a new approach that attempts to meet this challenge. The approach is centered on the visualization of objects and their behaviors using a 3D animation environment. Statistical data as well as informal observations are summarized to show evidence of student performance as a result of this approach. A comparison is made of the pedagogical aspects of this new approach with that of other relevant work.

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

About SIGCSE

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

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

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

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Exploring Coffee Science with Professor of Chemical Engineering Bill Ristenpart 

• by John Metcalfe, Bay Area News Group
• September 09, 2024

Coffee — it gives us energy, facilitates conversation and accounts for more than $200 billion in global revenue . But do we truly understand it?   

Hardly, says Bill Ristenpart . "There are so many unanswered questions about coffee," he says. "Because of the lack of academic investment, there are a lot of fundamental things that are not well understood to this day."

Ristenpart should know. He and colleague Tonya Kuhl are professors of chemical engineering and directors of the  Coffee Center at UC Davis , the only physical institute devoted to coffee research and education in America. Opened  this spring , the center includes a pilot roastery, brewing and espresso lab, green bean storage and a sensory-and-cupping workshop — everything you need for  advanced coffee experimentation  under one roof.   

How do you precisely roast beans? What to do with coffee's waste byproducts? The Coffee Center is designed to test such questions, as well as prepare new generations for rewarding jobs in the field of java.   

"If you want to run a winery, there are a few universities in the state where you can go recruit people from, UC Davis being one of them," says Ristenpart. "But the coffee industry, which is even bigger than wine, traditionally has not had ready-to-recruit people who have knowledge of coffee to come work in R&D or product development."   

Between running tests, teaching chemical engineering and sipping black coffee, Ristenpart took time to chat about this groundbreaking research center.   

Q:  So, the idea for the Coffee Center sort of originated from a course you teach?   

A:  My colleague and I developed an undergraduate class called  The Design of Coffee . Students get to roast coffee and measure the pH and taste it. The course culminates in an engineering design contest where they have to make one liter of the best-tasting coffee, as judged by a blind panel, using the least amount of electrical energy. 

The course went from 16 students in 2013 to 2,091 students this past academic year — it's now the most popular course at UC Davis. When just by random luck a whole building on campus became available, the timing was perfect, because the class had exploded in popularity. The administration said, "If you can raise the funding to renovate it, you can have the building for a coffee center."   

Q:  Is everyone at the center just slamming coffee all the time, getting wired?   

A:  We're all jittery and our hands are shaking, yeah. No – not really. But I do drink a lot of coffee. I like to joke they made me director of the Coffee Center because I drink the most on campus.   

Q:  You've called coffee arguably the most important beverage on the planet. Why is that?   

A:  It's certainly consumed a lot. ( Ed: Coffee is ranked among the world's most-consumed beverages, next to water and tea. ) If you just look at the size of the economy, it matters a lot – there are a lot of jobs provided through coffee. And of course, companies are all powered by coffee – it really provides a lot of energy, so to speak, for the entire economy.   

Q: What are some of the unsolved questions you're investigating?   

A: The chemistry that occurs during roasting is hideously complicated. There are these series of complicated reactions in parallel that have a huge impact on the concentrations of more than a thousand different molecules identified as contributing to the flavor and aroma of coffee. But the precise details of how to change a specific roast profile – temperature versus time in the roaster – right now is rules of thumb and trial and error, not a fundamental, mechanistic understanding.   

Q: And you're looking at sustainability, as well, when it comes to cascara (the fruit surrounding the coffee bean)?   

A: It's kind of amazing, when you think about it: A coffee farm plants coffee trees that take about three years before producing any fruit. They water and fertilize and prune and maintain them and laboriously harvest them, and the very first thing they do is take all the coffee cherries, strip off the fruit – which is about half the mass – and throw it away. Responsible guys compost it, and the irresponsible guys just throw it into the nearest river and let it float away, which causes terrible problems.   

What we're doing, with collaboration with (researchers in) Santa Barbara, is extracting flavor molecules and sugar and caffeine from fresh cascara. We've made a killer cascara syrup, where we just add a little sugar and a little lemon juice to balance the pH, and it tastes phenomenal. It's amazing on blueberry pancakes. If you like cocktails, it tastes really good in daiquiris. And what's really cool is it's super-caffeinated. I drink a lot of coffee and can have a couple of shots of espresso, no problem. But I drink a tablespoon of the syrup and, man, I'm jittery, I'm wired.   

Q: Neat. Any other real-world applications the center has pioneered?   

A: There's a whole spin-off tech company from the Coffee Center. I had two computer-science majors who were both Q certified – Q certification is like being the sommeliers of the coffee world. It's the most rigorous training, and there are only about 500 in the United States. They wrote software to quantify roast score – how brown the coffee is, which people in the coffee industry care about a lot – using the camera on your cellphone. They got seed funding and are now at RoastPic.com .   

Q:  What will be the coffee trends of the future?   

A:  A very obvious one that's already on a lot of people's radar is cold brew exploding. Last year, Starbucks said 75 percent of their drinks sold in North America were  cold rather than hot . I can certainly see that among the students here – when they show up in lecture, most of the time they have some kind of cold beverage, a Frappucino-type thing. Drinking hot, black coffee – that's certainly becoming a minority, at least here on campus.   

So, I think there's going to be more innovation in the cold-brew space. We have a lot of research funded right now and are going to do a deep dive into cold brew. We're looking at chilled hot brew versus true cold brew made with cold water. And there are definitely differences in how it tastes, which is pretty fascinating. 

Read the original article on SiliconValley.com  

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