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2023 Annual Intermountain Engineering, Technology and Computing, IETC 2023

作者： Anu, Vaibhav Hagiwara, Sumi Herbert, Katherine Sultana, Kazi Zakia Shin, Minsun Goldstein, Rebecca Virella, Patricia Montclair State University Department of Computer Science Montclair United States Montclair State University Department of Teaching and Learning Montclair United States Montclair State University Department of Educational Leadership Montclair United States

ISBN: (纸本)9798350335903

Supported by a state grant, our team of researchers (consisting of both computer science faculty and Teacher Education faculty) is offering a series of Professional Development sessions to K-8 teachers. These Professional Development (or PD) sessions are meant to help K-8 teachers develop their understanding of core computing concepts (such as algorithms, programming, data analysis, and networks) and thereby develop strong computer science programs for their students. In order to offer effective and meaningful Professional Development (PD) sessions, our research team first intended to understand the perceptions and experiences of K-8 teachers about computer science (CS) and Computational Thinking (CT) education. This paper presents the results of a K-8 teacher survey that we conducted as a pre-cursor to our PD series. The results of this survey provided valuable insights about elementary and middle-school teachers' perceptions of computer science education, self-perception of their ability to teach and learn CS, and understanding of CS discipline and those who typically engage in CS activities. The impact of teachers' perceptions impact how leaders in education and the CS industry can meet the needs of teachers, who in turn can meet the growing demand of CS education in K-8 schools. © 2023 IEEE.

关键词： Professional aspects

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Current Trends in Artificial Intelligence for Educational Advancements

Current Trends in Artificial Intelligence for Educational Ad...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Abdallah M. A. Al-Tarawneh Reem AbdElkareem AlOmoush Tanveer ul Islam Jamshaid Iqbal Janjua Tahir Abbas Anaum Ihsan Clinical Psychology Al-Ahliyya Amman University Amman Jordan Department of Curricula and Methods of Teaching Faculty of Educational Sciences Yarmouk University Jordan Department of Computer Science TIMES Institute Multan Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The focus of this paper is the integration of artificial intelligence (AI) within the educational field including but not limited to, adaptive learning, teaching evaluation, online learning environments, and individualized instruction. The authors investigate the positive role of AI in enhancing teaching performance and the quality of students' learning experiences. The study aims to demonstrate what role AI may play in the course of education development and practice improvement. It also sheds light on the limitations AI will have in the realm of education, such as accessibility, data ownership, and ethical issues. The paper does not contain only the limitations but the opportunities for this technology as well and describes how AI could be effectively incorporated into education, subsequently providing insights into the evolution of AI-based educational transformation.

关键词： Ethics Adaptive learning Education Learning (artificial intelligence) Market research Artificial intelligence

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Visualising Bibliographic Metadata Using CAQDAS in the Research on the Gender Gap in STEM Studies in Higher Education 7th

Visualising Bibliographic Metadata Using CAQDAS in the Resea...

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7th World Conference on Qualitative Research, WCQR 2023

作者： Verdugo-Castro, Sonia Sánchez-Gómez, Mª Cruz García-Holgado, Alicia García-Peñalvo, Francisco José Costa, António Pedro GRIAL Research Group Department of Didactics Organization and Research Methods Universidad de Salamanca Salamanca Spain GRIAL Research Group Computer Science Department Universidad de Salamanca Salamanca Spain University of Aveiro Aveiro Portugal

ISBN: (纸本)9783031313455

Besides providing the consulted publication’s findings, the literature review can offer information through metadata. computer-Assisted Qualitative Data Analysis Software (CAQDAS) can support visualising bibliometric metadata through RIS files. The usefulness is to provide an interactive image of the reality and actuality of scientific production. This article presents a case study to exemplify how metadata can be analysed and visualised using CAQDAS. The topic for the case study is the gender gap in STEM studies in higher education. The study aims to identify the value and usefulness of data visualisation in representing bibliometric data to support literature review processes. The phenomenon of the gender gap in the STEM education sector is used as a case study. The research questions addressed by the study are: (1) What does CAQDAS contribute to the results obtained?;(2) What are the possible causes of the gender gap? The analysis concludes that the cultural, social, educational, family, and peer group environment generates positive and negative force fields when deciding which studies to pursue;some people follow the patterns expected of them according to their gender. Finally, data visualisation helps understand the scientific evolution of a phenomenon and supports the research on a particular topic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metadata

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DEEP GENERATIVE MODELING ON LIMITED DATA WITH REGULARIZATION BY NONTRANSFERABLE PRE-TRAINED MODELS 11

DEEP GENERATIVE MODELING ON LIMITED DATA WITH REGULARIZATION...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhong, Yong Liu, Hongtao Liu, Xiaodong Bao, Fan Shen, Weiran Li, Chongxuan Gaoling School of AI Renmin University of China Beijing China Beijing Key Lab of Big Data Management and Analysis Methods Beijing China Department of Computer Science Technology Tsinghua University Beijing China

Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the expectation of an energy function, where the divergence is between the data and the model distributions, and the energy function is defined by the pre-trained model w.r.t. the model distribution. We analyze a simple yet representative Gaussian-fitting case to demonstrate how the weighting hyperparameter trades off the bias and the variance. Theoretically, we characterize the existence and the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and prove its convergence with neural networks trained by gradient-based methods. Empirically, with various pre-trained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data and achieves competitive results to the state-of-the-art methods. Our implementation is available at https://***/ML-GSAI/Reg-ADA-APA. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Covid severity prediction: Who cares about the data quality?

Covid severity prediction: Who cares about the data quality?

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Nae, Teodora Krabbe, Johannes Bukhsh, Faiza Allah Jayasinghe Arachchige, Jeewanie Ahmed, Faizan University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands Medische Spectrum Twente Enschede Netherlands University of Ruhuna Department of Computer Science Matara Sri Lanka University of Twente Formal Methods and Tools Group Enschede Netherlands

ISBN: (纸本)9798350345933

COVID-19 is an ongoing pandemic disrupting daily life and overwhelming the healthcare infrastructure. Since the outburst of the pandemic, researchers have used various techniques to predict many aspects of the disease, including mortality rate and severity. The reproducibility of this research is challenging due to varying methodologies used to collect data, data quality, vague description of methodological approach to training prediction models, over-relying on data imputation, and over-fitting. This paper focuses on these challenges and provides a short yet comprehensive review of research on COVID mortality and severity prediction. The emphasis is on the reproducibility of the results and data quality issues. To further elaborate on the issue, we report the development of severity prediction models using two data sets. CRISP-DM is used as a methodological approach. We analyze and criticize the quality of the used data sets and how they affect the performance and limitations of the trained models. We conclude this paper with comments on data quality issues, the importance of reproducibility, and suggestions to improve reproducibility. © 2022 IEEE.

关键词： COVID-19

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Understanding Inflicted Injuries in Young Children: Toward an Ontology Based Approach 24th

Understanding Inflicted Injuries in Young Children: Toward ...

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24th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2024

作者： Maikore, Fatima Mazumdar, Suvodeep Offiah, Amaka Hughes, Anthony Roychowdhury, Sneha Hocking, Katie Lanfranchi, Vitaveska Department of Computer Science University of Sheffield Sheffield United Kingdom Information School University of Sheffield Sheffield United Kingdom School of Medicine and Population Health Sheffield United Kingdom Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom

ISBN: (纸本)9783031777912

Investigations of children experiencing inflicted injuries is often initiated once admitted into the emergency department for injuries, and involves understanding the complex interactions between a range of different factors such as health conditions, fracture biomechanics, family history, carer accounts and so on. In this position paper, we propose the use of ontologies for capturing case details of such radiology investigations in order to create an initial knowledge base of retrospective cases, to be further expanded in the future. We discuss how we developed our ELECTRICA (ELEctronic knowledge base for Clinicians, Trainers and Researchers in Child Abuse) ontology and the different components of the ontology. We are currently using this ontology to create a knowledge base, to be used as a vision demonstrator to access larger datasets. In the longer term, we would like to use the knowledge base to support clinicians and radiologists in making decisions on current cases by offering a mechanism for searching historical cases, identifying similar cases and offering insights on risk factors that may have been missed during investigations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： child abuse inflicted injury non-accidental injury ontology development

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Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

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computers, Materials & Continua 2023年第5期75卷 4137-4154页

作者： Manar Ahmed Hamza Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali Alsaif Badriyya B.Al-onazi Heba Mohsen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Deanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

关键词： Computational linguistics natural language processing deep learning irony detection social media

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Verification of Genuineness of Educational Internet Resources Using Machine Learning methods

Verification of Genuineness of Educational Internet Resource...

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Technology Enhanced Learning in Higher Education (TELE), International Conference on

作者： Aleksandr N. Privalov Vadim A. Smirnov Institute of Advanced Information Technologies Tula State Lev Tolstoy Pedagogical University Tula Russia Department of mathematics computer science and teaching methods Ivanovo State University Shuya Russia

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

The analysis of various sources has been carried out, confirming the demand for protective solutions against information threats from fake websites of organizations. The relevance of the research topic is confirmed by the presence of addresses similar in spelling to popular educational Internet resources in the list of domain names of the RU zone registered over the past year. When developing security solutions, it is proposed to use a site verifying the genuineness method based on the use of machine learning algorithms. A review of research by various authors has been conducted in order to determine the most frequently used features for verifying the genuineness of Internet resources in the machine learning models. The authors propose their own verifying the genuineness features that can be used in a situation where data about an Internet resource is extracted from the domain name registry. The proposed features differ from the known ones in that they contain signs of micro-markup on the site and storage of site settings files for search engines on servers. The developed features were tested using a naive Bayesian classifier as a machine learning algorithm. The proposed features can be used in the implementation of a software tool to counteraction threats from fake educational sites.

关键词： Machine learning algorithms Reviews Machine learning Organizations Search engines Feature extraction Internet

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Augmented Node Placement Model in t-WSN Through Multiobjective Approach

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computers, Materials & Continua 2021年第12期69卷 3629-3644页

作者： Kalaipriyan Thirugnansambandam Debnath Bhattacharyya Jaroslav Frnda Dinesh Kumar Anguraj Jan Nedoma School of Computer Science and Engineering VIT UniversityChennai CampusTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturIndia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of Ostrava70833 Ostrava-PorubaCzech Republic

In Wireless Sensor Network(WSN),coverage and connectivity are the vital challenges in the target-based *** linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement *** this paper,a multiobjective problem on target-based WSN(t-WSN)is derived,which minimizes the number of deployed nodes,and maximizes the cost of coverage and sensing *** Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)is incorporated to tackle this multiobjective problem *** problems are intended to solve different objectives of a problem ***-inspired algorithms address the NP-hard problem most effectively in recent *** NSGA-II,the Non-Dominated sorting preserves the better solution in different objectives simultaneously using dominance *** the diversity maintenance phase,density estimation and crowd comparison are the two components that balance the exploration and exploitation phase of the *** of NSGA-II on this multiobjective problem is evaluated in terms of performance indicators Overall Non-dominated Vector Generation(ONGV)and Spacing(SP).The simulation results show the proposed method performs outperforms the existing algorithms in different aspects of the model.

关键词： Focused wireless sensor network m-coverage k-connectivity problem non-dominated sorting NSGA-II

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An Authentication Model Using Brainwave Panic Region Classifications from Electroencephalography 7th

An Authentication Model Using Brainwave Panic Region Classif...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Abiodun, Opeyemi Anuoluwa Oyinloye, Oghenerukevwe E. Thompson, Aderonke F. Olowoyo, Paul Imoize, Agbotiname Lucky Sur, Samarendra Nath Computer Science Department The Federal Polytechnic Ekiti State Ado-Ekiti360211 Nigeria Computer Science Department Ekiti State University Ekiti State Ado-Ekiti360211 Nigeria Cyber Security Department Federal University of Technology Ondo State Akure350106 Nigeria Federal Teaching Hospital Ekiti State Ido-Ekiti Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

ISBN: (纸本)9789819764648

Biometric authentication scheme has been widely adopted for authentication purpose due to its uniqueness, universality, and distinctiveness. However, research has shown that these schemes are not necessarily more secured, especially with issues of coercion proliferating the cyberspace. Also, available techniques on repudiation of biometric features for authentication have not adequately explored this exciting topic. Integrating emotional state into the authentication scheme helps to mitigate coercion. This paper presents a framework for emotion as a way of biometric authentication scheme. An emotion classification model was developed by training an emotion classifier brainwave signal from eight subjects under normal state and duress using KNN machine learning algorithm. The authentication scheme grants access to users who pass both verification phases. The emotion classification model achieved an accuracy of 93.6% on the full feature set, while the reduced-feature set, as a result of feature selection, produced an improved accuracy of 94.15%. Hence, the emotion classification model can be integrated into existing biometric authentication system for improved security of critical user information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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