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International Conference on New Trends in Computing sciences (ICTCS)

作者： Seif Hshem Alazzam Mohammad Al-oudat Department of Software Engineering Philadelphia University of Jordan Amman Jordan Department of Computer Science Philadelphia University of Jordan Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Students enrolled in university experience academic along with mental stressors, which negatively impact their academic results. The research implements artificial intelligence methods for classifying and forecasting the difficulties which higher education students encounter within Jordanian universities. An electronic questionnaire was designed through structured procedures and received validation from eight academics before being distributed to 1020 students. Statistical analysis through Statistical Package for the Social sciences (SPSS) validated the questionnaire data while testing the reliability of its findings. Students were classified into four categories—Academic Difficulties and Academic and Psychological Challenges and Psychological Distress alongside Normal—through the utilization of the GPT-4o mini API as a Large Language Model (LLM). Machine learning algorithms were applied to evaluate classification performance. Support Vector Machine (SVM) demonstrated the best result among classification models with an accuracy rate of 88.2% while Logistic Regression came second with 87.7% accuracy. A significant number of 54.7% students faced academic challenges while 60.8% of students reported psychological issues. The generated results will assist educational institutions by guiding their early prevention programs along with choosing appropriate assistance methods to enhance educational outcomes.

关键词： Support vector machines Accuracy Statistical analysis Large language models Computational modeling Education Psychology Nearest neighbor methods Reliability Testing

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Two-stage object detection in low-light environments using deep learning image enhancement

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PeerJ computer science 2025年 11卷 e2799-e2799页

作者： Al-refai, Ghaith Elmoaqet, Hisham Al-Refai, Abdullah Alzu’bi, Ahmad Al-Hadhrami, Tawfik Alkhateeb, Abedalrhman Department of Mechatronics Engineering German Jordanian University Amman Jordan Software Engineering Department King Hussein School of Computing Science Princess Sumaya University College for Technology Amman Jordan Department of Computer Science Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Computer Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom Department of Computer Science Lakehead University Thunder Bay Canada

This study presents a two-stage object detection system specifically tailored for low-light conditions. In the initial stage, supervised deep learning image enhancement techniques are utilized to improve image quality and enhance features. The second stage employs a computer vision algorithm for object detection. Three image enhancement algorithms—ZeroDCE++, Gladnet, and two-branch exposurefusion network for low-light image enhancement (TBEFN)—were assessed in the first stage to enhance image quality. YOLOv7 was utilized in the object detection phase. The ExDark dataset, recognized for its extensive collection of low-light images, served as the basis for training and evaluation. No-reference image quality evaluators were applied to measure improvements in image quality, while object detection performance was assessed using metrics such as recall and mean average precision (mAP). The results indicated that the two-stage system incorporating TBEFN significantly improved detection performance, achieving a mAP of 0.574, compared to 0.49 for YOLOv7 without the enhancement stage. Furthermore, this study investigated the relationship between object detection performance and image quality evaluation metrics, revealing that the image quality evaluator NIQE exhibited a strong correlation with mAP for object detection. This correlation aids in identifying the features that influence computer vision performance, thereby facilitating its enhancement. © 2025 Al-refai et al.

关键词： Image quality

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Revisiting kernel attention with correlated Gaussian process representation 24

Revisiting kernel attention with correlated Gaussian process...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Long Minh Bui Tho Tran Huu Duy Dinh Tan Minh Nguyen Trong Nghia Hoang FPT Software AI Center and School of Electrical Engineering and Computer Science Washington State University FPT Software AI Center and Department of Mathematics National University of Singapore FPT Software AI Center Department of Mathematics National University of Singapore School of Electrical Engineering and Computer Science Washington State University

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP's kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks.

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Recursive Broadcasting Approach 33

Recursive Broadcasting Approach

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Concordia University Department of Computer Science and Software Engineering MontrealQCH3G 1M8 Canada Institute for Informatics and Automation Problems NAS RA Yerevan0014 Armenia

ISBN: (纸本)9798331524937

Broadcasting is one of the fundamental information dissemination primitives in interconnection networks, where a message is passed from one node (called originator) to all other nodes in the network. Following the increasing interest in interconnection networks, extensive research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this study, we will mainly focus on determining the broadcast time and nearoptimal broadcasting schemes in networks. Determination of the broadcast time of any node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few network topologies. There also exist various heuristic and approximation algorithms for different network topologies. In this study, we consider the broadcast time problem on graphs that comprise some recursive structures. We initiate a novel direction to designing broadcasting algorithms on recursively defined graphs. We provide a theoretical foundation for future broadcasting studies, as well as discuss several practical applications of the approach we introduce. © 2025 IEEE.

关键词： Interconnection networks (circuit switching)

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On Transfer-based Universal Attacks in Pure Black-box Setting

arXiv

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arXiv 2025年

作者： Jalwana, Mohammad A.A.K. Akhtar, Naveed Mian, Ajmal Rahnavard, Nazanin Shah, Mubarak Department of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia Department of Computer Science University of Central Florida OrlandoFL32816 United States

—Despite their impressive performance, deep visual models are susceptible to transferable black-box adversarial attacks. Principally, these attacks craft perturbations in a target model-agnostic manner. However, surprisingly, we find that existing methods in this domain inadvertently take help from various priors that violate the black-box assumption such as the availability of the dataset used to train the target model, and the knowledge of the number of classes in the target model. Consequently, the literature fails to articulate the true potency of transferable black-box attacks. We provide an empirical study of these biases and propose a framework that aids in a prior-free transparent study of this paradigm. Using our framework, we analyze the role of prior knowledge of the target model data and number of classes in attack performance. We also provide several interesting insights based on our analysis, and demonstrate that priors cause overestimation in transferability scores. Finally, we extend our framework to query-based attacks. This extension inspires a novel image-blending technique to prepare data for effective surrogate model training. © 2025, CC BY.

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Experimental Investigation of Artificial Intelligence Models for Recommender Systems

Experimental Investigation of Artificial Intelligence Models...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Onur Özbey Fatih Kiliç Department of Computer Engineering Adana Alparslan Turkes Science and Technology University Adana Turkey Department of Software Engineering Adana Alparslan Turkes Science and Technology University Adana Turkey

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The increasing amount of information and content on the Internet has made it more difficult for users to access items that may interest them. Recommender systems have emerged to determine items that may affect users by filtering information obtained from users' behaviors through certain filters and presenting them to the user. This study presents the performance of widely preferred recommendation system models such as artificial neural network, XGBoost, KNNRegressor, and LightGBM models in the related literature. The well-known datasets, namely MovieLens-100k and MovieLens-32M, are used to evaluate these models. According to the presented results, it was observed that the artificial neural network model was ahead of other models in terms of performance data.

关键词： Training Adaptation models Social networking (online) Artificial neural networks Data models Graph neural networks Reliability Recommender systems Robots Optimization

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection Systems Considering Domain-Specific Constraints 25th

Investigating the Transferability of Evasion Attacks in N...

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25th International Conference on Information Security Applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Blockchain-Based Forensic Investigation Metamodel (BBFIM) for Investigating IoT Cybercrimes

Blockchain-Based Forensic Investigation Metamodel (BBFIM) fo...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Daifallah Zaid Alotaibe Department of Software Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin Kingdom of Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

As smart devices are rapidly being developed, and people trust data transfer mechanisms such as Wi-Fi and the way they use them, as well as the fact that they rely heavily on data transfer mechanics, there has been an increase in cyber-attacks. Therefore, examining IoT safety threats and potential countermeasures can support investigators in determining how to deal with a range of stressful scenarios encountered when conducting cyber-crime studies to develop acceptable methods for dealing with them. The goal of this paper is to develop Blockchain-Based Forensic Investigation Metamodel for Investigating IoT Cybercrimes called (BBFIM). The purpose of the developed BBFIM is to improve the productivity and accuracy of examinations by providing a safe and decentralized environment where digital evidence, related to IoT-related crimes, can be stored, and analyzed to improve the productivity and accuracy of examinations. Through the integration of blockchain technologies the developed BBFIM ensures that the reliability of the evidence is retained throughout the investigation. It also provides transparency throughout the process. This study utilizes design science methods for development and validation purposes. Therefore, 889 IoT forensic models and frameworks have been reviewed in this study, and 15 models have been selected to develop the BBFIM. The developed BBFIM consists of three main IoT forensic practices: Forensic investigation phases, Forensic Tools and Techniques, and Blockchain Infrastructure. Compared to the existing IoT forensics models the developed BBFIM is fully decentralized which can guarantee the authority, consistency, and privacy of the data.

关键词： Productivity Accuracy Forensics Design methodology Data transfer Blockchains Safety Smart devices Cyberattack Wireless fidelity

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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Adaptive Parameter Tuning in QRDPSO Using Fuzzy Logic for Enhanced Swarm Robot Navigation

Adaptive Parameter Tuning in QRDPSO Using Fuzzy Logic for En...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Duaa Mehiar Hamed Elsimary Zati Hakim Azizul Huthaifa Abuhammad Fady Alnajjar Mohammad Alhawarat Department of Computer Science Middle East University Amman Jordan Department of Computer Science Cairo Higher Institute for Engineering & Management Cairo Egypt Department of Artificial Intelligence University of Malaya Kuala Lumpur Malaysia Department of Computer Science and Software Engineering UAE University Al Ain UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Particle Swarm Optimizer (PSO) is a general optimization tool. It may, however, get stuck in local optima when solving multimodal, non-separable complex problems. PSO is a class of optimization algorithms that model a set of solutions as swarm robots distributed in the search space. In this paper a quantum multi-dimensional moving particle of Robotic Darwinian POS (RDPSO) and quantum potential well model of RDPSO are presented. The parameters for Quantum Robot Darwinian PSO method QRDPSO were then optimized. Experimental results show that the QRDPSO algorithm performs better and is more stable than other optimization algorithms, reaching a global best solution of zero. For regulating robot behavior context-based evaluation metrics are presented in this paper. A fuzzy system with such metrics as input improves Quantum Robot Darwinian PSO parameters. The steady state adaptation of the algorithm permits higher convergence speed, obstacle avoidance agility, and communication limits compliance. An adapted QRDPSO was challenged by mobile robotics simulation evaluation.

关键词： Measurement Fuzzy logic Quantum computing Swarm robotics Steady-state Robots Optimization Tuning Standards Convergence

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