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Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender Systems

IEEE Transactions on Information Forensics and Security 2025年 20卷 5054-5068页

作者： Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia

In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also having the ability to overcome cold start problems. The Differential Trust Mechanism (DTM) introduced in this paper is such an approach. The DTM provides a unique use of Gaussian distributions in modeling trust relationships within data, offering a novel way to balance recommendation accuracy with user privacy. This mechanism innovatively applies differential privacy principles, using Gaussian noise addition to protect individual user data from inference attacks, while maintaining the integrity and utility of the overall dataset. Unlike traditional anonymization techniques that often compromise data utility or vulnerability to reverse engineering, DTM provides a robust solution by dynamically adjusting privacy levels based on the trustworthiness of data requests. By combining DTM with existing mainstream recommendation algorithms, the prediction accuracy of MAE and RMSE increases by at least 6.60% and 2.69%, respectively. This dual benefit positions DTM as a significant advancement in secure data processing, especially relevant for online businesses and platforms where personalized recommendations are crucial yet privacy concerns are paramount. © 2005-2012 IEEE.

关键词： Sensitive data

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A Lightweight Authentication Protocol for RFID-Assisted Supply Chain Management System

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IEEE Internet of Things Journal 2025年第9期12卷 12845-12852页

作者： Tariq, Tayyaba Kuo, Wen-Chung Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J. F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency. © 2014 IEEE.

关键词： Authentication Protocol

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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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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Optimizing Heart-Kidney Interaction for Cancer Detection Through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Othman, Zalinda Yahya, Yazrina Zakree Ahmad Nazri, Mohd Leng Goh, Say Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran96351-14578 Iran Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Kebangsaan Malaysia Center for Software Technology and Management Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Malaysia Sabah Kampus Antarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States RayanMiniFab Tehran University Science and Technology Park Tehran*** Iran

A decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization by analyzing the available data. The heart-kidney (HK) model proposed in this paper, as a DSS, simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA’s local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm’s effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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A graph attention network integrated with gated spiking neural P systems for session-based recommendation

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Expert Systems with Applications 2025年 286卷

作者： Bai, Xinzhu Peng, Hong Huang, Yanping Wang, Jun Yang, Qian Ramírez-De-Arellano, Antonio School of Computer and Software Engineering Xihua University Chengdu610039 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Department of Computer Science and Artificial Intelligence Research Group of Natural Computing University of Seville Sevilla41012 Spain

With the development of deep learning technology, the recommender system began to use deep neural networks for modeling, which enables the recommendation system to better capture the complex patterns and features in the data. Session-based recommendation has also gradually received attention, especially for scenarios which deal with user behavior sequences and real-time requirements in a session. However, existing session-based recommendation methods tend to ignore users’ long-term interest changes, and there are still challenges for context understanding. In addition, session-based recommendation also has drift problems (i.e., the short-term behavior of users does not necessarily represent their real interests), which will affect the accuracy of recommendations. Therefore, this paper proposes a novel graph attention network, termed GAN-GSNP. This network combines a gated spiking neural P system with an extra balancing effect (EB-GSNP) to alleviate the drift problem in session-based recommendation by processing both global and local embeddings simultaneously. In addition, the GAN-GSNP model also proposes a multi-branch feature extraction strategy, which uses Self-Attention and feedforward networks in parallel to extract the high-order features embedded globally by users, so as to capture the global dependencies within the session or between different items, and make full use of the user's contextual information. Experimental results on two real datasets demonstrate the effectiveness and accuracy of the proposed GAN-GSNP model. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Educational data mining: a 10-year review

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Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh 786004 India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå 931 87 Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter United Kingdom Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi 626005 India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo 11754 Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir 10200 Turkey Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda 92294 Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh 786004 India

This systematic review comprehensively examines the application and impacts of Educational data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations. © The Author(s) 2025.

关键词： Artificial intelligence in education Education data mining Explainability in education Multimodal learning analytics

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Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories 34

Adaptive Activation Steering: A Tuning-Free LLM Truthfulness...

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34th ACM Web Conference, WWW 2025

作者： Wang, Tianlong Jiao, Xianfeng Zhu, Yinghao Chen, Zhongzhi He, Yifan Chu, Xu Gao, Junyi Wang, Yasha Ma, Liantao School of Software and Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Beihang University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Centre for Medical Informatics University of Edinburgh Edinburgh United Kingdom Health Data Research UK London United Kingdom Key Laboratory of High Confidence Software Technologies Ministry of Education National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400712746

Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false statements. This gap between "knowing" and "telling" poses a challenge for ensuring the truthfulness of generated content. Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM’s activations in the "truthful" direction during inference. ACT addresses diverse categories of hallucinations by utilizing diverse truthfulness-related steering vectors and adjusting the steering intensity adaptively. Applied as an add-on across various models, ACT significantly improves truthfulness in LLaMA (↑ 142%), LLaMA2 (↑ 24%), Alpaca (↑ 36%), Vicuna (↑ 28%), LLaMA2-Chat (↑ 19%), and LLaMA3(↑ 34%). Furthermore, we verify ACT’s scalability across larger models (13B, 33B, 65B), underscoring the adaptability of ACT to large-scale language models. © 2025 Copyright held by the owner/author(s).

关键词： hallucination large language model tuning-free

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Direct Estimation of Pediatric Heart Rate Variability from BOLD-fMRI: A Machine Learning Approach Using Dynamic Connectivity

arXiv

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arXiv 2025年

作者： Addeh, Abdoljalil Ardila, Karen Williams, Rebecca J. Pike, G. Bruce MacDonald, M. Ethan Department of Biomedical Engineering Schulich School of Engineering University of Calgary Canada Department of Electrical & Software Engineering Schulich School of Engineering University of Calgary Canada Department of Radiology Cumming School of Medicine University of Calgary Canada Department of Clinical Neurosciences Cumming School of Medicine University of Calgary Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Canada Brain-Behaviour Research Group University of New England Australia

In many pediatric fMRI studies, cardiac signals are often missing or of poor quality. A tool to extract Heart Rate Variation (HRV) waveforms directly from fMRI data, without the need for peripheral recording devices, would be highly beneficial. We developed a machine learning framework to accurately reconstruct HRV for pediatric applications. A hybrid model combining one-dimensional Convolutional Neural Networks (1D-CNN) and Gated Recurrent Units (GRU) analyzed BOLD signals from 628 ROIs, integrating past and future data. The model achieved an 8% improvement in HRV accuracy, as evidenced by enhanced performance metrics. This approach eliminates the need for peripheral photoplethysmography devices, reduces costs, and simplifies procedures in pediatric fMRI. Additionally, it improves the robustness of pediatric fMRI studies, which are more sensitive to physiological and developmental variations than those in *** Codes 68T07 (Primary), 92C55, 62P10 (Secondary) © 2025, CC BY.

关键词： Convolutional neural networks

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization software development management

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