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arXiv 2023年

作者： Blanco, Raquel Trinidad, Manuel Suárez-Cabal, María José Calderón, Alejandro Ruiz, Mercedes Tuya, Javier Software Engineering Research Group University of Oviedo Department of Computer Science Gijón Spain Software Process Improvement and Formal Methods Research Group University of Cadiz Department of Computer Science and Engineering Cádiz Spain

Software testing is an essential knowledge area required by industry for software engineers. However, software engineering students often consider testing less appealing than designing or coding. Consequently, it is difficult to engage students to create effective tests. To encourage students, we explored the use of gamification and investigated whether this technique can help to improve the engagement and performance of software testing students. We conducted a controlled experiment to compare the engagement and performance of two groups of students that took an undergraduate software testing course in different academic years. The experimental group is formed by 135 students from the gamified course whereas the control group is formed by 100 students from the non-gamified course. The data collected were statistically analyzed to answer the research questions of this study. The results show that the students that participated in the gamification experience were more engaged and achieved a better performance. As an additional finding, the analysis of the results reveals that a key aspect to succeed is the gamification experience design. It is important to distribute the motivating stimulus provided by the gamification throughout the whole experience to engage students until the end. Given these results, we plan to readjust the gamification experience design to increase student engagement in the last stage of the experience, as well as to conduct a longitudinal study to evaluate the effects of gamification. © 2023, CC BY-NC-ND.

关键词： Software testing

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Enhancing Online Learning in LCM Moodle through Interactive Adaptive Learning 13

Enhancing Online Learning in LCM Moodle through Interactive ...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Habrusiev, Valerii Tereshchuk, Hryhorii Panchenko, Valerii Martyniuk, Serhii Sysoiev, Oleksii Henseruk, Halyna Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Science and Teaching Methods Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Theory and Practice of Translation Ternopi Ukraine Kyiv International University Licensing and Accreditation Department Kyiv Ukraine

The article considers the practical experience of introducing interactive adaptive learning in the e-learning environment. The basis of the learning environment is the LCM Moodle learning resource management system. One of the ways to individualize and personalize the learning process is to attract interactive and adaptive technologies for teaching students. The LCM Moodle's interactive module of activity 'Lesson'' is a series of HTML pages linked by transitions. The main difference between 'Lesson'' and other LCM Moodle modules is that there are elements of adaptability. Using this tool, each student's choice can be accompanied by appropriate comments from the teacher and the ability to go to different pages of the course depending on the correctness/incorrectness and completeness of the answers. With such planning, 'Lesson'' can provide theoretical material and control tasks to check its mastery for each student automatically, without additional action by the teacher. As a result of completing the tasks, the corresponding score appears in the student's register of marks. In general, this module of activity is similar to a classroom lesson, when the teacher, presenting new material, from time to time interviews students in order to identify the level of its mastery. © 2023 IEEE.

关键词： Students

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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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Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?

Are Existing Out-Of-Distribution Techniques Suitable for Net...

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IEEE Conference on Communications and Network Security (CNS)

作者： Andrea Corsini Shanchieh Jay Yang Department of Science and Methods for Engineering University of Modena and Reggio Emilia Modena Italy Department of Computer Engineering Rochester Institute of Technology Rochester USA

Machine learning (ML) has become increasingly popular in network intrusion detection. However, ML-based solutions always respond regardless of whether the input data reflects known patterns, a common issue across safety-critical applications. While several proposals exist for detecting Out-Of-Distribution (OOD) in other fields, it remains unclear whether these approaches can effectively identify new forms of intrusions for network security. New attacks, not necessarily affecting overall distributions, are not guaranteed to be clearly OOD as instead, images depicting new classes are in computer vision. In this work, we investigate whether existing OOD detectors from other fields allow the identification of unknown malicious traffic. We also explore whether more discriminative and semantically richer embedding spaces within models, such as those created with contrastive learning and multi-class tasks, benefit detection. Our investigation covers a set of six OOD techniques that employ different detection strategies. These techniques are applied to models trained in various ways and subsequently exposed to unknown malicious traffic from the same and different datasets (network environments). Our findings suggest that existing detectors can identify a consistent portion of new malicious traffic, and that improved embedding spaces enhance detection. We also demonstrate that simple combinations of certain detectors can identify almost 100% of malicious traffic in our tested scenarios.

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System Methodology of Data Analysis and Preprocessing for Solving Classification Problems

System Methodology of Data Analysis and Preprocessing for So...

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International computer sciences and Information Technologies (CSIT)

作者： Irina Kalinina Aleksandr Gozhyj Victoria Vysotska Eugene Malakhov Victor Gozhyj Iryna Tregubova Intelligent Information Systems Department Petro Mohyla Black Sea National University Mykolayiv Ukraine Department of Information Systems and Networks Institute of Computer Science and Information Technologies National University “Lviv Polytechnic” Lviv Ukraine Department of Mathematical Methods of Computer Systems of Odessa National Mechnikov University Odesa Ukraine Department of Computer Science State University of Intelligent Technologies and Telecommunications Odesa Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article describes and investigates the systematic methodology of data analysis and preprocessing for solving classification problems. The methodology combines the following groups of methods on the basis of a systemic approach: methods of processing data gaps, methods of processing anomalous values, methods of feature generation, methods of identifying nonlinearities and non-stationarity, and normalization methods. methods of character generation were studied. methods of feature selection and generation are divided into three main groups: filtering methods, wrapping methods, and embedded methods. Since feature selection plays a crucial role in machine learning, increasing model performance and reducing computational costs, the paper proposes a combined feature selection method that includes the step-by-step use of both filtering and wrapping methods. The method consists of five steps to efficiently select the most relevant features of a data set. It offers a better approach to feature selection. This results in improved model performance with fewer features and reduced computational cost. The creation of a red wine classification system was considered for the experimental verification of the system methodology of analysis and pre-processing of data. The Red Wine Quality dataset was used. The purpose of the classification is to identify factors associated with the risk of untimely delivery of previous orders to customers and information about recipients of goods. To solve the task of wine quality classification, simulations were carried out using various algorithms. The effectiveness of the system approach to solving problems of analysis and preprocessing of data to solve problems of classification was proved.

关键词： Data analysis Systematics Filtering Computational modeling Machine learning Feature extraction Computational efficiency Classification algorithms Wrapping Information technology

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Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communities

arXiv

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arXiv 2024年

作者： Roytburg, Dani Olorunisola, Deborah Soni, Sandeep Klein, Lauren Emory University Department of Quantitative Theory & Methods United States Emory University Department of Computer Science United States Yale University Department of Data Science and Statistics United States Emory University Department of English United States

In this project we describe a method of modeling semantic leadership across a set of communities associated with the #BlackLivesMatter movement, which has been informed by qualitative research on the structure of social media and Black Twitter in particular. We describe our bespoke approaches to time-binning, community clustering, and connecting communities over time, as well as our adaptation of state-of-the-art approaches to semantic change detection and semantic leadership induction. We find substantial evidence of the leadership role of BLM activists and progressives, as well as Black celebrities. We also find evidence of the sustained engagement of the conservative community with this discourse, suggesting an alternative explanation for how we arrived at the present moment, in which "anti-woke" and "anti-CRT" bills are being enacted nation-wide. © 2024, CC BY-NC-SA.

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Machine Learning for Energy Load Prediction and its Interpretation 11

Machine Learning for Energy Load Prediction and its Interpre...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Charytanowicz, Malgorzata Olwert, Anna Radziszewska, Weronika Jarnicka, Jolanta Gajowniczek, Krzysztof Zabkowski, Tomasz Brozyna, Jacek Mentel, Grzegorz Matejko, Grzegorz Research Institute Pas Center for Methods of Data Analysis Systems Warsaw Poland Research Institute Pas Department of Computer Science Systems Warsaw Poland Research Institute Pas Center for Computer Modelling Systems Warsaw Poland Warsaw University of Life Sciences Institute of Information Technology Warsaw Poland Rzeszow University of Technology Department of Quantitative Methods Rzeszów Poland Polskie Towarzystwo Cyfrowe Lublin Poland

ISBN: (纸本)9781665456562

This paper refers to real-life data on energy consumption from three companies, connected to a medium voltage grid. The companies use single-, two-, or three-zone tariffs and the general characteristic of their activity is comparable. The input dataset contains hourly electricity consumption recorded from January 2020 to December 2020. The independent variables used for the study include weather, time-dependent factors, and aggregated energy factors. We perform the prediction of consumption using three methods: naive, multiple regression, and extreme gradient boosting regression. The performance of the methods is compared based on the MSE, RMSE, MAE, and MAPE metrics. The study shows that the scores achieved for the extreme gradient boosting method are higher than for the multiple regression. To gain the interpretability of the energy load forecasting and feature analysis the Shapley additive explanations method is used for the best model. As a result, a ranking of the most important factors is created. It shows the importance of aggregated energy factors and weather conditions while placing daylight and season at the bottom of the ranking. © 2022 IEEE.

关键词： Energy utilization

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Multyperson Health Monitoring Wireless Network 30

Multyperson Health Monitoring Wireless Network

引用

30th Telecommunications Forum, TELFOR 2022

作者： Shevchuk, Bohdan Ivakhiv, Orest Kochan, Roman National Academy of Sciences of Ukraine Glushkov Institute of Cybernetics Department of Numerical Methods Optimization Kyiv03187 Ukraine Lviv Polytechnic National University Computer Technology Automation and Metrology Institute Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine University of Bielsko-Biala Department of Computer Science and Automatics Bielsko-Biala Poland

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Methodological and algorithmic bases of the local-regional and global wireless networks operation for functional states of people long-term monitoring, processing, encoding, encrypting and transmitting samples of monitoring signals and video data frames using a signaling approach are proposed. The technology is focused on evidence-based monitoring of the condition of operators of various human-machine complexes, athletes and healthy people in order to prevent various diseases. © 2022 IEEE.

关键词： Wireless networks

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis 27

Exploring the Impact of Random Distribution Choices on Parti...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?

arXiv

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arXiv 2023年

作者： Corsini, Andrea Yang, Shanchieh Jay Department of Science and Methods for Engineering University of Modena and Reggio Emilia Modena Italy Department of Computer Engineering Rochester Institute of Technology Rochester United States

关键词： Embeddings

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