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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Sava Stanišić Milan Vesković Olga Ristić Borislav Đorđević Department of Computer and Software Engineering Faculty of Technical Sciences Čačak Republic of Serbia IMP Computer Systems Mihajlo Pupin Institute Belgrade Republic of Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

Security is one of the key challenges in container orchestration, especially in complex environments. This paper explores the security aspects of implementing containerized applications using Docker within a Kubernetes cluster. The first part of the paper describes Docker, Kubernetes, and various ways of applying them within DevOps methodology. It then presents potential vulnerabilities during the implementation of these technologies, as well as vulnerabilities specific to Docker and Kubernetes. Subsequently, some solutions for securing a Kubernetes environment are described.

关键词： DevOps Scalability Organizations Containers Stability analysis Malware Complexity theory Security Object recognition Resilience

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HAMI: Hybrid Adaptive AutoML for IoT Intrusion Detection

HAMI: Hybrid Adaptive AutoML for IoT Intrusion Detection

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IEEE Communication Strategies in Digital Society Workshop (ComSDS)

作者： Mohamed Saber Natalia Gusarova Vladimir Bogatyrev Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

ISBN: (数字)9798331536121

ISBN: (纸本)9798331536138

the growth of IoT devices presents significant security challenges due to their diverse and dynamic environments. To address this, we developed HAMI, a novel intrusion detection system. Our method leverages the GIFS algorithm, which combines feature rankings from ensemble models with genetic algorithms to uncover subtle inter-feature correlations, enhancing detection accuracy. Additionally, we introduce GridSearchSMOTE, a technique that optimizes data balancing by evaluating various SMOTE variants through a meticulous grid search, ensuring optimal performance for each dataset. Empirical evaluations of HAMI show superior accuracy, achieving a 99% detection rate and reduced false positives, outperforming existing systems. These results underscore HAMI’s potential to significantly improve IoT network security by exploring its scalability and real-time detection capabilities.

关键词： Adaptation models Adaptive learning Accuracy Biological system modeling Intrusion detection Feature extraction Automated machine learning Data models Real-time systems Internet of Things

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Research on AI-Powered Employee Feedback Tools

Research on AI-Powered Employee Feedback Tools

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Konstantin Babushkin Nikolai Timofeev Ivan Filianin Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper explores methods for employee feedback (EF) collection and introduces a new framework to improve the process. Current feedback collection process often suffers from slow workflow, bias, and poor Artificial Intelligence (AI) integration. Large Language Models (LLM) combined with human oversight may provide a more agile, objective, and user-friendly way for collecting and analyzing feedback. Existing EF tools were evaluated based on criteria such as AI capabilities, usability, and costs. Building on these findings, an open-source and self-hosted EF tool was developed. The tool integrates AI at every stage of the process and offers real-time AI assistance in writing, summarizing, and interpreting feedback. Pilot testing in two tech companies demonstrated user satisfaction.

关键词： Surveys Performance evaluation Industries Costs Large language models Companies Writing Real-time systems Usability Testing

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Architecting Digital Twins for Intelligent Transportation systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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Architecting Digital Twins for Intelligent Transportation systems

arXiv

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arXiv 2025年

作者： Bhatt, Hiya Sahil Vaidhyanathan, Karthik Biju, Rahul Gangadharan, Deepak Trestian, Ramona Shah, Purav Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London United Kingdom

关键词： Adaptive control systems

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What Drives the Use of Generative Artificial Intelligence to Promote Educational Sustainability? Evidence from SEM-ANN Approach

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TechTrends 2025年 1-15页

作者： Arpaci, Ibrahim Al-Emran, Mostafa Al-Qaysi, Noor Al-Sharafi, Mohammed A. Management Information Systems College of Business Administration Gulf University for Science and Technology Mishref Kuwait Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emirates Engineering Technical College Al-Bayan University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq IRC for Finance and Digital Economy KFUPM Business School King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education. © Association for Educational Communications & Technology 2025.

关键词： Artificial intelligence Educational sustainability SEM-ANN UTAUT-2

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michał Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Paweł Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： acoustics mel-frequency cepstral coefficient mfcc sound spectrogram vehicle type detection vehicle type recognition

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An intelligent framework for sarcasm detection in Arabic tweets using deep learning with Al-Biruni earth radius optimization algorithm

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Alexandria engineering Journal 2025年 127卷 562-572页

作者： AL-anazi, Reema G. A. Alzaidi, Muhammad Swaileh Eltahir, Majdy M. Almukadi, Wafa Sulaiman Alajmani, Samah Hazzaa Darem, Abdulbasit A. Alshahrani, Mohammed Alhebaishi, Nawaf Department of Arabic Language and Literature College of Humanities and Social Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Software Engineering College of Engineering and Computer Science University of Jeddah Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Computer Department Applied College Najran University Najran66462 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Social media networks play a major role in expressing people's feelings, reviews, and thoughts. One of the popular linguistic patterns to express or criticize one's ideas with ridicule is sarcasm, where they have unintended and intended meanings in written text. The sarcastic text is used to reverse the polarity of the sentiment. As a result, sarcasm detection in the text positively affects the sentimental analysis (SA) tasks and improves accuracy. The sarcasm recognition of Arabic content is constrained even though Arabic is the most popular language for web content sharing, and yet still naive owing to various challenges, such as the insufficient data sources, the multiple dialects, and the morphological structure of the Arabic language. This study presents an Artificial Intelligence Driven Sarcasm Detection using Optimal Deep Learning (AIDSD-ODL) technique on Arabic tweets. In the AIDSD-ODL technique, a hyperparameter-tuned DL model is utilized to identify sarcastic or non-sarcastic Arabic tweets. At the initial stage, the AIDSD-ODL method undergoes data pre-processing to convert the input tweets into a compatible format. In the next phase, the Glove word embedding process is used in the AIDSD-ODL technique. The self-attention bidirectional long short-term memory (SA-BiLSTM) model is utilized for sarcasm detection. The Al-Biruni Earth Radius (BER) model is implemented for the hyperparameter selection process to enhance the performance of the SA-BiLSTM network. The experimental results of the AIDSD-ODL method are examined under the Arabic tweets dataset. The comparison study of the AIDSD-ODL method portrayed a superior accuracy value of 94.20 % over existing models. © 2025 The Authors

关键词： Deep learning

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Research and Development of Kubernetes Operator for Lightweight Multi-Access Edge Computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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systems of Signals Generating and Processing in the Field of on Board Communications

作者： Ivan Filianin Nikita Borisov Vladimir Bogatyrev Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it.

关键词： computer languages Multi-access edge computing Codes Computational modeling Machine learning computer architecture Complex systems Research and development

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AI-based Adaptive Programming Education for Socially Disadvantaged Students: Bridging the Digital Divide

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TechTrends 2025年 1-18页

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

In the context of the digital economy, programming proficiency is an essential competency that promotes upward socio-economic mobility and expands career opportunities. However, students from socially disadvantaged backgrounds often face significant barriers to acquiring these skills, such as limited access to technology and educational resources. This study explores the impact of AI-based adaptive programming education on socially disadvantaged students' learning outcomes and engagement levels. The 122 participants in the research were divided into an experimental group (EG) that received AI-driven adaptive instruction, and a control group (CG) taught through the traditional curriculum during the 13-week experimental period. In the research, the combined pre-test/post-test assessments and self-report engagement questionnaires were used to focus on behavioural, emotional, and cognitive engagement. The findings showed that the EG demonstrated higher levels in programming knowledge and full-scale engagement across behavioural, emotional, and cognitive dimensions compared to the CG. This difference was confirmed through ANOVA, while ANCOVA, which controlled for students' socio-economic factors, further confirmed that the AI system had a positive impact on students’ results regardless of their socio-economic background. Indeed, current research findings suggest that AI-based adaptive learning environments can hold a promising opportunity to narrow educational gaps, boost engagement, and contribute toward better learning outcomes for socially disadvantaged students. © The Author(s) 2025.

关键词： Adaptive learning AI-based education Disadvantaged students Programming education Student engagement

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