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SELL:A Method for Low-Light Image Enhancement by Predicting Semantic Priors

IEEE Signal Processing Letters 2025年 32卷 1785-1789页

作者： Xiao, Quanquan Jin, Haiyan Su, Haonan Yan, Ruixia Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an710000 China Guizhou Minzu University Guiyang550000 China

In recent years, low-light image enhancement techniques have made significant progress in generating reasonable visual details. However, current methods have not yet fully utilized the full semantic prior of visual elements in low-light environments. Therefore, images generated by these low-light image enhancement methods often suffer from degraded visual quality and may even be distorted. To address this problem, we propose a method to guide low-light image enhancement by predicting semantic priors. Specifically, we train a semantic prior predictor under standard lighting conditions, which is made to learn and predict semantic prior features for low-light images by knowledge distillation on high-quality standard images. Subsequently, we utilize a semantic-aware module that enables the model to adaptively integrate these learned semantic priors, thus ensuring semantic consistency of the enhanced images. Experiments show that the method outperforms several current state-of-the-art methods in terms of visual performance on the LOL-v2 and SICE benchmark datasets. © 1994-2012 IEEE.

关键词： Economic and social effects

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EduGym Post-Mortem: Open Educational Resource for Educational Game

EduGym Post-Mortem: Open Educational Resource for Educationa...

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Tarigan, Jos Timanta Zamzami, Elviawaty Muisa Zendrato, Niskarto Nababan, Anandhini Medianty Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331513320

This paper aims to delve into the development of EduGym, an educational microlearning game specifically designed for mobile platforms. The discussion highlights the implementation and integration of Open Educational Resources (OER) and examines their significant impact on the development process. The project strategically used OER as the primary content source to ensure seamless future content expansion without requiring changes to the core application. This approach also empowers other developers to repurpose and adapt the content for various educational applications. Furthermore, the paper addresses key challenges encountered during the development process from a software engineering standpoint, including maintaining flexibility, ensuring scalability, and managing resource allocation. The study outlines the overall architecture of EduGym, detailing the development workflow and the ways in which OER integration influenced both development and resource management. The aim is to offer valuable insights and practical guidance for developers and educators who are exploring similar methodologies, informed by the lessons learned and experiences gained through the creation of EduGym. © 2025 IEEE.

关键词： Game design

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Exploration and Exploitation Mechanism in Pairwise Test Case Generation: A Systematic Literature Review

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IEEE Access 2025年 13卷 82342-82377页

作者： Yahaya, Muhammad Sabo Hashim, Ahmad Sobri B. Oluwagbemiga Balogun, Abdullateef Aminu Muazu, Aminu Sabo Usman, Fatima Adamu Aliyu, Dahiru Uwaisu Muhammad, Abdullahi Universiti Teknologi PETRONAS Faculty of Science Management and Computing Department of Computing Perak32610 Malaysia Faculty of Engineering and Technology Federal Polytechnic Computer Engineering Technology Department Katsina Daura824101 Nigeria Umaru Musa Yar’adua University Faculty of Natural and Applied Sciences Computer Science Department Katsina820102 Nigeria Gombe State University Faculty of Science Computer Science Department Gombe760101 Nigeria Federal University Dutse Faculty of Computing Computer Science Department Jigawa 720223 Nigeria

Software systems are essential in modern life and require rigorous testing to ensure reliability. Pairwise testing, a combinatorial testing methodology, optimizes test case generation by minimizing redundancy while maintaining defect detection effectiveness. This review systematically examines the role of metaheuristic algorithms in pairwise test case generation, focusing on their exploration and exploitation mechanisms to address local optima challenges. Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. These methods consistently outperform traditional techniques by reducing test suite sizes and enhancing fault detection capabilities. The findings underscore the growing adoption of the one-test-at-a-time (OTAT) approach over the one-parameter-at-a-time (OPAT) approach and identify key challenges in scalability, hyperparameter tuning, and constraint handling, highlighting the need for hybrid strategies and adaptive optimization techniques. This review offers valuable insights into advancing Software testing methodologies and presents recommendations for future research to improve efficiency, scalability, and fault detection in pairwise testing. © 2013 IEEE.

关键词： Software reliability

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On-device diagnostic recommendation with heterogeneous federated BlockNets

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science China(Information sciences) 2025年第4期68卷 33-49页

作者： Minh Hieu NGUYEN Thanh Trung HUYNH Thanh Toan NGUYEN Phi Le NGUYEN Hien Thu PHAM Jun JO Thanh Tam NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Information Technology HUTECH University School of Information and Communication Technology Hanoi University of Science and Technology Commonwealth Scientific and Industrial Research Organization (CSIRO)

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to ***, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users' reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support(such as biomedical image diagnostics), is computed at the client *** leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device's computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenario

关键词： intelligent recommendation federated learning heterogeneous devices E-health diagnostics

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Harnessing generative AI in exercise and sports science education: enhancing real-world learning and overcoming traditional barriers in data analysis

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Advances in Physiology Education 2025年第2期49卷 496-502页

作者： Fazackerley, L.A. Perrin, D. Minett, G.M. School of Exercise and Nutrition Sciences Faculty of Health Queensland University of Technology Brisbane Australia School of Computer Science Faculty of Science Queensland University of Technology Brisbane Australia Centre for Data Science Queensland University of Technology Brisbane Australia

Generative AI (GenAI) offers transformative potential for exercise and sports science education, addressing traditional data analysis and visualization barriers while promoting real-world learning. This Perspectives article explores how integrating GenAI into exercise and sports science degrees can enhance students’ ability to interpret complex physiological data, improve their analytical skills, and foster creativity in problem-solving. By automating routine technical tasks such as data cleaning and visual ization, GenAI allows students to focus on critical interpretation, inquiry-based learning, and evidence-based application. An example lesson plan is provided, incorporating GenAI tools to simulate real-world data analysis tasks, helping students develop hands-on data interpretation and decision-making skills. Additionally, the article discusses strategies for responsible implementa tion, ensuring that ethical considerations and foundational learning are prioritized. © (2025), (American Physiological Society). All rights reserved.

关键词： artificial intelligence ChatGPT higher education lesson plan teaching

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Deep Learning for Sentiment Analysis 4

Deep Learning for Sentiment Analysis

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rokade, Gayatri Ughade, Radhe Gaurshettiwar, Palash Faculty of Engineering and Technology Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331523923

This paper represents that one of the critical subfields of NLP, SA applies DL techniques to analyze the feelings expressed in text, image, and voice context. As new technology of Web 2.0 emerges, there are many opportunities and challenges because of the large amount of content generated from blogs, forums, social networking sites, etc. This work explores the development of DL-based SA and notes that enhancements have been made primarily on the document, sentence, phrase, as well as aspect levels. Ambiguity, sarcasm, and negation are also discussed, as well as specific domain and multimodal aspects. The work shows that it might be possible to transition to semi-supervised, and unsupervised learning to overcome real-world data limitations and offers emergent prospects in the real-time analysis, domain fine-tuning, and data enhancement. The inspiration for future research is offered by the multimodal sentiment analysis and the enhancement of the DL architectures. © 2025 IEEE.

关键词： Contrastive Learning

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Assessment of ChatGPT in extracting Arabic morphology: active and passive participles

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International Journal of Speech technology 2025年第1期28卷 85-97页

作者： Alian, Marwah Hussein, Ibtisam Al-Khazaleh, Maisa Computer Science Department Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan Basic Sciences Department Faculty of Science The Hashemite University Zarqa Jordan Arabic Language Department Faculty of Arts Yarmouk University Irbid Jordan

ChatGPT is a language model that has been developed by Open AI. It has the ability to answer any question raised by a participant in a form of conservation. ChatGPT takes advantages of the effective transformers architecture to generate text discussions that are relevant and perceptive. Although ChatGPT was initially trained on English text data, it has also been fine-tuned on other languages including Spanish, French, Chinese, German and Arabic. ChatGPT has been employed in different NLP domains for English language, but specifically it is still slightly limited in languages other than English. In this research, we assess ChatGPT in extracting active participle (ism al-fa’il) and passive participle (ism al-maf’uul) from Arabic verbs of base forms (alfilu alsahih) and verbs with vowels (alfilu almutal) to help the non-native speakers who find it difficult to extract these participles and try to use ChaTGPT as a learning tool and get inaccurate responses. We experiment ChatGPT’s performance in zero-shot learning then with a wide range of prompt templates as well as few-shot learning. The results show a greet enhancement in the ChatGPT performance when tuning the rule-based prompts with k-shot examples especially for passive participle extraction. ChatGPT performed well in extracting participles from verbs without vowels, but after training with k-shot samples, it improved by 6% for active participles and 25% for passive participles. Additionally, extracting passive participles for verbs with vowels significantly improves performance compared to extracting active participles, especially the case when the vowels exist at two positions, which results in a 100% improvement. The performance of ChatGPT improved in extracting active participle for verbs with no vowels by 6% and passive participles by 25%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Zero-shot learning

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Blockchain-Based Trust Model for Inter-Domain Routing

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computers, Materials & Continua 2025年第3期82卷 4821-4839页

作者： Qiong Yang Li Ma Sami Ullah Shanshan Tu Hisham Alasmary Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia School of Computing andMathematical Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerthWA 6027Australia

Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.

关键词： Inter-domain routing BGP security blockchain trust model trust mechanisms trust evaluation

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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