Computer Science Domain Research Articles

DETERMINING STUDENT'S ONLINE ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES

Predicting student's academic performance during online learning has been considered a major task during the pandemic period. During the online mode of learning, academic activities have been affected in such a way that the management of educational institutions has planned to design support systems for predicting the student's performance to reduce the dropout ratio of the students and bring improvement in academic activities. During COVID-19, the main challenge is maintaining student's grades by predicting their academic performance using different techniques such as Education Data Mining and Learning Analytics. Different features have been identified related to the teaching mechanisms in online learning, which have a great impact on the improvement of academic performance. A high-quality dataset helps us to generate productive results, which in turn helps us to make effective decisions for promoting high-quality education. In this research, five prediction models for predicting academic performance have been proposed by collecting an imbalanced dataset of 350 students from the same computer science domain. After applying pre-processing techniques for cleaning the data, machine learning models have been applied, including K-Nearest Neighbor Classifier, Decision Tree, Random Forest, Support Vector Classifier, and Gaussian Naive Bayes. Results have been predicted for an imbalanced and balanced dataset after feature selection. Support Vector classifier has produced the best results in a balanced dataset with selected features by giving an accuracy of 96.89%.

An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model

Novelty is a critical characteristic of innovative scientific articles, and accurately identifying novelty can facilitate the early detection of scientific breakthroughs. However, existing methods for measuring novelty have two main limitations: (1) Metadata-based approaches, such as citation analysis, are retrospective and do not alleviate the pressures of the peer review process or enable timely tracking of scientific progress; (2) Content-based methods have not adequately addressed the inherent uncertainty between the qualitative concept of novelty and the textual representation of papers. To address these issues, we propose a practical and effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model, referred to as MNSA-ITMCM. In this framework, papers are represented as topic combinations, and novelty is reflected in the organic reorganization of these topics. We use the BERTopic model to generate semantically informed topics, and then apply a topic selection algorithm based on maximum marginal relevance to obtain a topic combination that balances similarity and diversity. Furthermore, we leverage the cloud model from fuzzy mathematics to quantify novelty, overcoming the uncertainty inherent in natural language expression and topic modeling to improve the accuracy of novelty measurement. To validate the effectiveness of our framework, we conducted empirical evaluations on papers from the Cell 2021 journal (biomedical domain) and the ICLR 2023 conference (computer science domain). Through correlation analysis and prediction error analysis, our framework demonstrated the ability to identify different types of novel papers and accurately predict their novelty levels. The proposed framework is applicable across diverse scientific disciplines and publication venues, benefiting researchers, librarians, science evaluation agencies, policymakers, and funding organizations by improving the efficiency and comprehensiveness of identifying novelty research.

Exploring the Potential of Neutrosophic Topological Spaces in Computer Science

Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as developing efficient algorithms for NTS operations. Overall, this paper aims to stimulate further exploration of how neutrosophic topological spaces can contribute to advancements in various computer science domains.

Foreword for the Collection of Papers from the Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy

The 2022 Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy brought together research experts from many domains of social sciences, demography, public policy, statistics, and computer science to address key challenges in the use of the differentially private Census noisy measurement files to support social research and policy decisions.

Low-resource multi-granularity academic function recognition based on multiple prompt knowledge

PurposeFine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.Design/methodology/approachSpecifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.FindingsExtensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.Originality/valueIn addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases. This study emphasizes the importance of feature analysis and selection in ML modeling and demonstrates the efficacy of our gradient-boosted statistical feature-selection workflow for training predictive models. The models are fine-tuned through Bayesian optimization, using features derived solely from the chemical compositions of the materials data, before the model predictions are evaluated against literature values. We have collated ca. 35,000 Tc values and the performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our methodology is superior to the majority of alternative methods. In a 10-fold cross-validation, our regression model realized an R2 of (0.92 ± 0.01), an MAE of (40.8 ± 1.9) K, and an RMSE of (80.0 ± 5.0) K. We demonstrate the utility of our ML model through case studies that forecast Tc values for rare-earth intermetallic compounds and generate magnetic phase diagrams for various chemical systems. These case studies highlight the importance of a systematic approach to feature analysis and selection in enhancing both the predictive capability and interpretability of ML models, while being devoid of potential human bias. They demonstrate the advantages of such an approach over a mere reliance on algorithmic complexity and a black-box treatment in ML-based modeling within the domain of computational materials science.

Third-Person View Attention Prediction in Natural Scenarios With Weak Information Dependency and Human-Scene Interaction Mechanism

First-person view attention has been widely studied in computer science domain since 1990s while third-person view attention in natural scenarios begins to gain the intensive interest until 2015. This paper focuses on the problem of third-person view attention prediction in natural scenarios where a human freely performs daily activities without constraints. To handle the two insuffiencies of existing methods: (i) assuming some extra information (except for input images) are given in advance and (ii) ignoring the importance of human-scene interaction, this paper proposes a model with weak information dependency, which helps to alleviate annotation costs. In addition, a transformer-based human-scene interaction mechanism is proposed to explore the global and long-dependency contexts between the human and scene. The pipeline of the proposed model is firstly extracting human and scene features, then inferring human attention probability map by fusing human and scene features via a transformer-based network, and finally predicting human attention object based on human attention probability map and object detection. The experiments on two public datasets validate the effectiveness of our model.

Experimental Study for Performance Measurement of Space-Shared and Time-Shared Scheduling Algorithm with Virtual Machine and Varying Length Jobs using CloudSim

Cloud Computing is one of the growing domains of computer science where researchers across the world are working to get deep insight into this domain and to develop new algorithms in various aspects of computing to make it more easily accessible to the users. It provides a simple, web-based, architectural neutral and heterogeneous platform where the users can access the services and resources as per their requirements on a pay per use model with ease. Scheduling is a process through which one process is allowed to use the computing resources while another process is delayed because of unavailability of any resources. Scheduling mechanism makes the system more efficient, faster and fairer. In this paper, I have studied Space-Shared and Time-Shared Scheduling algorithm for Virtual Machine and different length tasks using CloudSim to evaluate the performance based on various parameters like turnaround time (TT), execution time (ET) and waiting time (WT). The objective behind this work is to analyze and evaluate the effectiveness of both algorithms with respect to scheduling the resources.

Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: Tutorial.

Families of individuals with neurodevelopmental disabilities or differences (NDDs) often struggle to find reliable health information on the web. NDDs encompass various conditions affecting up to 14% of children in high-income countries, and most individuals present with complex phenotypes and related conditions. It is challenging for their families to develop literacy solely by searching information on the internet. While in-person coaching can enhance care, it is only available to a minority of those with NDDs. Chatbots, or computer programs that simulate conversation, have emerged in the commercial sector as useful tools for answering questions, but their use in health care remains limited. To address this challenge, the researchers developed a chatbot named CAMI (Coaching Assistant for Medical/Health Information) that can provide information about trusted resources covering core knowledge and services relevant to families of individuals with NDDs. The chatbot was developed, in collaboration with individuals with lived experience, to provide information about trusted resources covering core knowledge and services that may be of interest. The developers used the Django framework (Django Software Foundation) for the development and used a knowledge graph to depict the key entities in NDDs and their relationships to allow the chatbot to suggest web resources that may be related to the user queries. To identify NDD domain-specific entities from user input, a combination of standard sources (the Unified Medical Language System) and other entities were used which were identified by health professionals as well as collaborators. Although most entities were identified in the text, some were not captured in the system and therefore went undetected. Nonetheless, the chatbot was able to provide resources addressing most user queries related to NDDs. The researchers found that enriching the vocabulary with synonyms and lay language terms for specific subdomains enhanced entity detection. By using a data set of numerous individuals with NDDs, the researchers developed a knowledge graph that established meaningful connections between entities, allowing the chatbot to present related symptoms, diagnoses, and resources. To the researchers' knowledge, CAMI is the first chatbot to provide resources related to NDDs. Our work highlighted the importance of engaging end users to supplement standard generic ontologies to named entities for language recognition. It also demonstrates that complex medical and health-related information can be integrated using knowledge graphs and leveraging existing large datasets. This has multiple implications: generalizability to other health domains as well as reducing the need for experts and optimizing their input while keeping health care professionals in the loop. The researchers' work also shows how health and computer science domains need to collaborate to achieve the granularity needed to make chatbots truly useful and impactful.

ProQuiz

ProQuiz is an app designed for educational purposes, specifically to help users prepare for multiple-choice exams, especially in the field of computer science. The app aims to make studying enjoyable by incorporating elements of gamification. The primary objective of ProQuiz is to assist users in getting ready for subjective tests used in admissions and recruitment processes, with a focus on the computer science domain. The app is organized into three main modules: computer science, reasoning, and analytical skills. Each of these modules covers various sub-categories to address different subjects. During the quiz, users have access to helpful features such as hints, the ability to skip questions, and a pause/life-lines option. These features can be used only once per user. The app also provides feedback on progress throughout the quiz, and upon completion, it displays the user's results. The overall aim is to provide users with an engaging platform for practicing and enhancing their knowledge in a variety of subjects related to computer science. This research paper examines the development and effectiveness of ProQuiz. Through user testing and analysis, we evaluate its usability and engagement. Additionally, we propose enhancements, including personalized learning paths and social features. This paper contributes insights into innovative educational technology and suggests avenues for future improvement of ProQuiz.

CYBERCRIMES, LAW, AND CYBER FORENSICS: COMPARATIVE STUDY OF INDIAN, BRITISH, AND AMERICAN CONTEXTS

New forms of crime have emerged with the rising prevalence of computer misuse. Some of them include the use of cutting-edge tools and methods for criminal conspiracy. From spreading fraud through email to stealing and reselling intellectual property, cybercrime covers a lot of ground. The domain of computer science known as "cyber forensics" is considered to be among the most essential subfields. It is concerned with the investigation of cybercrime. The concept of cyber forensics is broken down into its parts by the author in this paper. This paper focuses on the significance of the topic as well as some of the methods and instruments that are used by cyber forensic investigators. The objective of cyber forensics is to conduct an examination of electronic content in a manner that is forensically sound with the goals of preserving, recovering, analyzing, identifying, and presenting facts and views about digital information. Cyber forensics evidence undergoes the same processes as other digital evidence and serves as supporting evidence in judicial proceedings. Cyber forensics is often linked to the investigation of cybercrime, but it may also be utilized in legal actions involving private parties. This paper demonstrates the necessity for further investigation into how cyber forensics might be used to enhance the identification of cybercrime. This research will help fill in some of the gaps in our knowledge of how cyber forensics investigations are conducted in India and explore cyber security policy in the United Kingdom, the United States, and India to identify both similarities and differences. Keywords: Cybercrime, Cyber Forensics, Policy

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition.

Emotional recognition is a pivotal research domain in computer and cognitive science. Recent advancements have led to various emotion recognition methods, leveraging data from diverse sources like speech, facial expressions, electroencephalogram (EEG), electrocardiogram, and eye tracking (ET). This article introduces a novel emotion recognition framework, primarily targeting the analysis of users' psychological reactions and stimuli. It is important to note that the stimuli eliciting emotional responses are as critical as the responses themselves. Hence, our approach synergizes stimulus data with physical and physiological signals, pioneering a multimodal method for emotional cognition. Our proposed framework unites stimulus source data with physiological signals, aiming to enhance the accuracy and robustness of emotion recognition through data integration. We initiated an emotional cognition experiment to gather EEG and ET data alongside recording emotional responses. Building on this, we developed the Emotion-Multimodal Fusion Neural Network (E-MFNN), optimized for multimodal data fusion to process both stimulus and physiological data. We conducted extensive comparisons between our framework's outcomes and those from existing models, also assessing various algorithmic approaches within our framework. This comparison underscores our framework's efficacy in multimodal emotion recognition. The source code is publicly available at https://figshare.com/s/8833d837871c78542b29.

Robustness and reproducibility for AI learning in biomedical sciences: RENOIR

Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibility and poor generalisation of findings, undermining scientific value and contributing to global research waste. To address these issues and focusing on the learning aspect of the AI field, we present RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR offers automated generation of transparent and usable reports, aiming to enhance the quality and reproducibility of AI studies. To demonstrate the versatility of our tool, we applied it to benchmark datasets from health, computer science, and STEM (Science, Technology, Engineering, and Mathematics) domains. Furthermore, we showcase RENOIR’s successful application in recently published studies, where it identified classifiers for SET2D and TP53 mutation status in cancer. Finally, we present a use case where RENOIR was employed to address a significant pharmacological challenge—predicting drug efficacy. RENOIR is freely available at https://github.com/alebarberis/renoir.

Research on the Application of Natural Language Processing Technology in English Translation Quality Control

Abstract Machine translation is an important research area in natural language processing. In this paper, we propose an innovative fusion translation model that combines the slicing method and the alignment method and select articles from different domains for experiments to explore the translation effects of the proposed method. The experiments show that the proposed CRF sequence annotation cut-scoring method performs excellently, with F-values of 86.7%, 89.0%, and 87.4% for the three domains of computer science, civil engineering, and medicine, respectively. The hybrid alignment method based on the length and lexical information also performs well, both in terms of correctness and recall and the hybrid alignment method is able to obtain better results than the length or lexical information methods alone. Putting the proposed method into the translation accuracy test, the BLEU score of the cut-and-align fusion translation model is improved to 11.08, while the NIST score is improved to 3.6468, which is a significant improvement in the translation quality of English.

Reaching quality and efficiency with a parameter-efficient controllable sentence simplification approach

The task of Automatic Text Simplification (ATS) aims to transform texts to improve their readability and comprehensibility. Current solutions are based on Large Language Models (LLM). These models have high performance but require powerful computing resources and large amounts of data to be fine-tuned when working in specific and technical domains. This prevents most researchers from adapting the models to their area of study. The main contributions of this research are as follows: (1) proposing an accurate solution when powerful resources are not available, using the transfer learning capabilities across different domains with a set of linguistic features using a reduced size pre-trained language model (T5-small) and making it accessible to a broader range of researchers and individuals; (2) the evaluation of our model on two well-known datasets, Turkcorpus and ASSET, and the analysis of the influence of control tokens on the SimpleText corpus, focusing on the domains of Computer Science and Medicine. Finally, a detailed discussion comparing our approach with state-of-the-art models for sentence simplification is included.

AS2LS: Adaptive Anatomical Structure-Based Two-Layer Level Set Framework for Medical Image Segmentation.

Medical images often exhibit intricate structures, inhomogeneous intensity, significant noise and blurred edges, presenting challenges for medical image segmentation. Several segmentation algorithms grounded in mathematics, computer science, and medical domains have been proposed to address this matter; nevertheless, there is still considerable scope for improvement. This paper proposes a novel adaptive anatomical structure-based two-layer level set framework (AS2LS) for segmenting organs with concentric structures, such as the left ventricle and the fundus. By adaptive fitting region and edge intensity information, the AS2LS achieves high accuracy in segmenting complex medical images characterized by inhomogeneous intensity, blurred boundaries and interference from surrounding organs. Moreover, we introduce a novel two-layer level set representation based on anatomical structures, coupled with a two-stage level set evolution algorithm. Experimental results demonstrate the superior accuracy of AS2LS in comparison to representative level set methods and deep learning methods.

Hacking gender in computer‐supported collaborative learning: The experience of being in mixed‐gender teams at a computer science hackathon

AbstractBackgroundGender stereotypes about women and men are prevalent in computer science (CS). The study's goal was to investigate the role of gender bias in computer‐supported collaborative learning (CSCL) in a CS context by elaborating on gendered experiences in the perception of individual and team performance in mixed‐gender teams in a hackathon.DatasetThe dataset of this study was collected at a 3‐day CSCL hackathon aimed at gaining knowledge on designing educational games. We assigned the 28 participants of the hackathon to mixed‐gender groups and asked them to fill out a questionnaire, including collective self‐esteem scales, before the start. During the hackathon, we again asked the participants to complete team progress evaluation surveys individually after each workday. Lastly, we interviewed 11 participants to elaborate on the quantitative findings with qualitative data.MethodologyWe applied an exploratory mixed‐method approach using quantitative survey data at several time points during the hackathon, which was analysed with clustering and descriptive statistics and complemented with qualitative coding of interviews with participants.ResultsThe results demonstrate that social and psychological aspects of gender are important for understanding the outcomes and perceptions of gender in a CS hackathon. The analysis further suggests that collective self‐esteem can be used as a key variable to assess gender differences in CSCL studies, providing explanatory benefits. More broadly, results gave reason to believe that CSCL in the CS domain currently severely fails to account for gender representation. Interviewed participants raised substantial concerns about the underlying gender stereotypes prevalent in communication, team roles, and work division. We provide recommendations for practitioners seeking to create gender‐inclusive and counter‐stereotypical CSCL and wider, critical proposals for how we, as researchers, can assess gender with appropriate methodologies and interventions in computer science education.

Analysing soliton dynamics and a comparative study of fractional derivatives in the nonlinear fractional Kudryashov’s equation

In this scholarly exploration, we employ new mapping method to unveil new soliton solutions to the nonlinear fractional Kudryashov’s equation, using β-derivative and M-Truncated fractional derivatives. Soliton phenomena, invaluable for enhancing computational capabilities in computer systems, find particular utility in tasks like data analysis, image processing, and simulations across various computer science domains. Our research reveals diverse solution forms, encompassing dark, periodic, kink, singular, dark-bright, and bright solitons. These versatile soliton types offer adaptable tools for both modelling and simulation. To enhance understanding, we provide 3D and 2D graphical representations, which facilitate a comparative analysis of solutions derived from the two fractional derivatives. Additionally we compare the effects of different fractional derivative operators (β-derivative and M-Truncated derivative) involve in the nonlinear fractional Kudryashov’s equation. The incorporation of both derivatives allows for a broader exploration of solution forms and enhance the understanding of the equation’s behaviour and its solution. Our work bridges soliton physics and computer science, underscoring the significance of soliton phenomena in advancing computational modelling and simulations. This research contributes to the intersection of theoretical mathematics and practical computer science, highlighting the vital role of solitons in scientific fields.

Detecting and visualizing research trends of blended learning: A bibliometric analysis of studies from 2013-2022

Blended learning (BL), an innovative, technology-supported pedagogical approach, has been extensively adopted in schools and universities. The learning effectiveness of BL has been investigated in multiple domains of education, computer science, nursing, engineering, and psychology. To uncover the major trends of BL research, this study embarked on a bibliometric analysis of a total of 719 studies published in the recent 10 years (2013-2022) and indexed in the Web of Science core collection. Adopting a quantitative approach and the visual analytical tool of CiteSpace, the review study identified the development trends, the influential researchers and research institutions, and pivotal studies and topics of the field and informed its future progression. The findings revealed a growing trend in BL research in the past decade as reflected in the exponential growth in the number of publications and citations. Charles R. Graham, Chang Zhu, Robert A. Ellis, and Feifei Han were the most prolific, influential researchers in the field, and the Griffith University, the University of Hong Kong, the Vrije Universiteit Brussel, the Monash University, and the National Taiwan Normal University were the prominent research institutions, which engaged in frequent collaborations with others. The United States, China, and Australia were the top-3 contributors to BL research measured by the number of publications, and the studies conducted and reported by researchers in the USA, Turkey, Taiwan, and Spain were cited the most often. Document co-citation analysis unveiled the pivotal studies and topics of the research field, including blended course designs, institutional adoption, achievement, higher education, active BL, flipped classroom, and communication skills.

Model Counting Meets Distinct Elements

Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment ( F 0 ) of a data stream. Our investigations lead us to observe striking similarity in the core techniques employed in the algorithmic frameworks that have evolved separately for model counting and distinct elements computation. We design a recipe for the translation of algorithms developed for distinct elements estimation to that of model counting, resulting in new algorithms for model counting. We then observe that algorithms in the context of distributed streaming can be transformed into distributed algorithms for model counting. We next turn our attention to viewing streaming from the lens of counting and show that framing distinct elements estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of streaming problems, which had been subjected to case-specific analysis in prior works.