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29th International Conference on Information Technology, IT 2025

作者： Rakic, Branka Dejanovic, Igor University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Serbia

ISBN: (纸本)9798331517649

Advancements in deep learning and computer vision have greatly enhanced object detection, playing a vital role in applications such as autonomous driving and surveillance. This study explores the trade-offs between two distinct YOLO model architectures: the vision-only models, referred to as pre-YOLO-World, and a vision-language model known as YOLO-World. While the pre-YOLO-World models are renowned for their speed and accuracy, the YOLO-World model integrates large language models to enhance contextual comprehension, enabling open-vocabulary object detection. This paper provides a qualitative analysis of their strengths and limitations, focusing on speed, accuracy, adaptability, computational requirements, and application scenarios. By contrasting these architectures, we aim to offer insights into their potential roles in evolving object detection paradigms and to guide practitioners in selecting models suited to specific use cases. © 2025 IEEE.

关键词： Machine vision

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Position Paper: Enhancing the Learning and Mastery of Academic Writing in the Serbian Language Through an AI Tool with Adaptive Scaffolding

Position Paper: Enhancing the Learning and Mastery of Academ...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Francišković, Teodor Sakal Gajić, Dušan Luburić, Nikola Slivka, Jelena Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9789897587467

Academic writing is a significant challenge for many learners striving for proficiency. Adaptive scaffolding techniques and AI tools in education have proven effective in addressing this challenge and supporting learners in improving their academic writing skills when used correctly. This position paper proposes combining adaptive scaffolding techniques with AI tools in a public university's final year academic writing course to enhance the learning experience and mastery of academic writing skills in Serbian. The proposed plan outlines the course structure and details how the AI-driven adaptive scaffolding techniques will be integrated to support the learning experience, focusing on summative and formative feedback from the AI tool. The proposed plan is a work in progress. It will be implemented in the next iteration of the course for evaluation, taking into account potential counter-arguments and their impact on the tool's development and the student's learning experience and outcomes. This study will analyse our plan's effectiveness in enhancing the learning experience and outcomes. The expected outcome is to assist students in their learning while contributing to the development of AI in education and the Serbian language. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Qualitative Comparison of Vision and Vision-Language Models in Object Detection: Capabilities and Challenges

Qualitative Comparison of Vision and Vision-Language Models ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Branka Rakic Igor Dejanovic Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Serbia

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

关键词： YOLO Adaptation models Accuracy Computational modeling Surveillance Large language models Focusing Computer architecture Information technology Context modeling

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System Optimal Routing and Distribution of Benefits on National Road Networks

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Transportation Research Procedia 2025年 82卷 2532-2545页

作者： Alexander Roocroft Giuliano Punzo Muhamad Azfar Ramli Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Institute of High Performance Computing A*STAR Singapore

Rerouting drivers from selfish route choices to system-optimal traffic patterns has the potential to improve the performance of existing infrastructure. Previous research has looked into assessing the potential of rerouting through the empirical price of anarchy, a measure of network efficiency. However, studies using real-world measurements have been limited by methodological accuracy and network size. Also, they have lacked understanding of the spatial distribution of benefits from rerouting and the relationship with marginal external cost road charges that can be used for implementation. In this article, we create an accurate data-driven traffic assignment model of England's Strategic Road Network. We use it to calculate the national price of anarchy, which is found to be almost 1 implying gains from rerouting at the national scale are minimal and smaller than in other studies. The results show the distribution of rerouting benefits varies strongly with different network zones and demand profiles. This did not match the distribution of marginal external cost charges. Some zones have noticeable benefits from rerouting although the overall network benefit is small, however, these zones do not coincide with where the largest road charges have to be applied for system-optimal rerouting. These results have implications for rerouting implementation.

关键词： Strategic Road Network Empirical Price of Anarchy National Traffic Assignment

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Improving graph collaborative filtering with network motifs

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Neural computing and Applications 2025年 1-20页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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Distill & Contrast: A New Graph Self-Supervised Method With Approximating Nature Data Relationships

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3284-3297页

作者： He, Dongxiao Zhao, Jitao Guo, Rui Feng, Zhiyong Huo, Cuiying Jin, Di Pedrycz, Witold Zhang, Weixiong Tianjin University College of Intelligence and Computing Tianjin300350 China Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Hong Kong Polytechnic University Department of Data Science and Ai Hong Kong

Contrastive Learning (CL) has emerged as a popular self-supervised representation learning paradigm that has been shown in many applications to perform similarly to traditional supervised learning methods. A key component of CL is mining the latent discriminative relationships between positive and negative samples and using them as self-supervised labels. We argue that this discriminative contrastive task is, in essence, similar to a classification task, and the "either positive or negative"hard label sampling strategies are arbitrary. To solve this problem, we explore ideas from data distillation, which considers probabilistic logit vectors as soft labels to transfer model knowledge. We attempt to abandon the classical hard sampling labels in CL and instead explore self-supervised soft labels. We adopt soft sampling labels that are extracted, without supervision, from the inherent relationships in data pairs to retain more information. We propose a new self-supervised graph learning method, Distill and Contrast (D&C), for learning representations that closely approximate natural data relationships. D&C extracts node similarities from the features and structures to derive soft sampling labels, which also eliminate noise in the data to increase robustness. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method. © 2025 IEEE.

关键词： Self-supervised learning

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

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Modification of the PSO Algorithm: Variable Population Size and Hybridization with the Nelder-Mead Method

Modification of the PSO Algorithm: Variable Population Size ...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Topalov, Stefan Radović, Mirna University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

This paper presents a modified Particle Swarm Optimization (PSO) algorithm designed to enhance computational efficiency without compromising solution quality. Two approaches are proposed: the first emphasizes the advantages of incorporating a variable population size to optimize both computational efficiency and solution quality. The second integrates this adaptive population strategy with the Nelder-Mead method to refine local search capabilities. By combining these techniques, the hybrid algorithm achieves a balance between global exploration and local exploitation, while also improving the algorithm's speed without sacrificing solution quality. The performance of both approaches is evaluated through numerical experiments, demonstrating their ability to deliver high-quality solutions with reduced computational resources. © 2025 IEEE.

关键词： Local search (optimization)

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Design of adaptive controllers by means of PPO algorithm using MATLAB

Design of adaptive controllers by means of PPO algorithm usi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Veljko Radojičić Milan R. Rapaić Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper investigates reinforcement learning (RL) as a practical framework for achieving optimal adaptive control across several simple dynamical system models. All experiments were conducted using the Proximal Policy Optimization (PPO) algorithm, implemented within the MATLAB Reinforcement Learning Toolbox. The primary focus of this study is to explore how the learning process can be empirically designed to sufficiently excite the system dynamics and obtain a sufficiently robust controller.

关键词： Adaptation models System dynamics Heuristic algorithms Process control Reinforcement learning MATLAB Dynamical systems Adaptive control Optimization

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