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RsDiff: Rational score based knowledge graph diffusion for recommendation

Information Sciences 2025年 717卷

作者： Cui, Mengmeng Wu, Siyu Chen, Hao Zhang, Xiangnan School of Software Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Nanjing China National Research Facility For Phenotypic and Genotypic Analysis of Model Animals China Agricultural University Beijing China

The Knowledge graph (KG) provides auxiliary information to improve the recommendation system performance. However, the knowledge graph includes a large number of triplets that have nothing to do with the recommendation task, leading to suboptimal results. To address this challenge, we propose a knowledge graph diffusion model based on rationality score for recommendation, called RsDiff. Firstly, we design a rational scoring mechanism for the knowledge graph triplets. Then, we propose a knowledge graph diffusion model based on rational scores to mitigate the impact of noise. Finally, we employ cross-view contrastive learning to align collaborative signals across different graphs. Experiments show that our proposed RsDiff outperforms the most advanced recommendation models in terms of NDCG@20 and Recall@20 indicators in the Last-FM, Alibaba-iFashion, and MIND datasets. © 2025

关键词： Graph neural networks

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PA-Rank: A GAN and Reinforcement Learning Powered Framework for Multi-Metric Anomaly Detection and Causal Diagnosis

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IEEE Internet of Things Journal 2025年

作者： Wang, Zhaowen Zhou, Yong Zhang, YangSiyu Cong, Fengyu Zhou, Dongdong An, Zhijian School of Software Engineering Dalian University of Technology Dalian116024 China Dalian University of Technology School of Software Dalian116024 China Dalian University of Technology School of Biomedical Engineering Faculty of Medicine Dalian116024 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland Dalian University of Technology School of Computer Science and Technology Dalian116024 China Faculty of Information Technology University of Jyvaskyla Jyvaskyla40014 Finland

The increasing scale and complexity of modern IT systems necessitate advanced solutions for monitoring and managing performance anomalies. Artificial intelligence for IT operations (AIOps) has emerged as a promising approach to enhance the efficiency and effectiveness of IT operations. However, existing methods struggle with effectively detecting anomalies in multi-dimensional performance data and accurately identifying their root causes in complex interdependent systems. This paper proposes a novel framework, PA-Rank, that combines generative adversarial networks (GANs), reinforcement learning, and graph-based methods to address these challenges comprehensively. For anomaly detection, an unsupervised GAN-based model is developed to identify anomalous time periods and assign weighted scores to metrics, facilitating precise anomaly identification. For root cause localization, a Causal Graph Construction Model (CGCM) has been developed, utilizing a reinforcement learning-based causal discovery method that is integrated with graph attention networks (GAT) to construct a causal graph representing the relationships between metrics. A random walk algorithm further ranks metric importance during anomalies, enabling effective root cause localization. Extensive experiments on real-world datasets, including SMD, ASD, and DAMADICS, demonstrate the superiority of PA-Rank over traditional statistical and state-of-the-art machine learning methods. On the SMD dataset, the proposed framework achieved an F1 score of 0.9542 for anomaly detection and consistently identified root causes among top-ranked candidates on the Pymicro and RMS datasets with the highest PR@Avg scores. These results underscore PA-Rank’s efficacy in diagnosing performance anomalies and supporting efficient system maintenance. © 2014 IEEE.

关键词： Reinforcement learning

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Tip the Scales: Achieving Balance in Adversarial Examples Across Modalities

Tip the Scales: Achieving Balance in Adversarial Examples Ac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenbo Shi Zhidong Yu Yuxuan Zhang Shuchang Wang Xiaoman Liu Wei Yang Liusheng Huang School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Software Engineering University of Science and Technology of China Suzhou China Hefei National Laboratory University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of multimodal learning, controlling the training of unimodal encoders from different perspectives is a primary approach to addressing Training Imbalance. However, the inherent capacity limitations of the modality affect the model’s capability. Therefore, generating adversarial examples that can achieve balanced transferability remains a challenging and perplexing problem. In this paper, we propose the InterModality Balanced Attack (MOBA) to address this problem. MOBA leverages Aggregated Modality Perturbation (AMP), which exploits the unbalanced effects of text and image perturbations to maximize the impact on the victim model. AMP capitalizes on the intrinsic feature connections between modalities during the optimization process, adjusting perturbations through Cross-Modality Discrepancy Loss to enhance attack success rates. Additionally, we devise the Transferability-Enhanced Evolution (TEE) to overcome the issue of diminished attack transferability due to model capacity limitations. TEE employs Transfer-Driven Optimization Loss to alleviate overfitting in single models, thereby enhancing the generalization ability.

关键词： Training Adaptation models Perturbation methods Architecture Buildings Signal processing Acoustics Speech processing Optimization Overfitting

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Chebyshev Polynomial Based Emergency Conditions with Authentication Scheme for 5G-Assisted Vehicular Fog computing

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Al-Shareeda, Mahmood A. Gaber, Tarek Alqarni, Mohammed A. Alkinani, Monagi H. Almazroey, Alaa Atallah Almazroi, Abdulwahab Ali Southern Technical University Department of Electronic Technologies Basra Technical Institute Basra61001 Iraq Engineering and Environment University Salford School of Science Manchester United Kingdom University of Jeddah College of Computer Science and Engineering of Software Engineering Jeddah Saudi Arabia University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah Saudi Arabia Modern Aljazeera Company Jeddah Saudi Arabia University of Jeddah College of Computing and Information Technology at Khulais Department of Information Technology Jeddah Saudi Arabia

Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle-infrastructure communication paths lack security. Modern authentication systems used to close security vulnerabilities require a lot of computing and storage power from the vehicle's OBU. Thus, a 5G automobile network emergency situations Chebyshev polynomial using fog computing and authentication is proposed. The Chebyshev chaotic mapping algorithm uses a chaotic map, rotation, and XOR to generate a one-way hash and eliminate modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme has four stages: installation system, initialization, enrollment, mutual authentication, and emergency request. Critical to emergency services, the suggested protocol works better in resource-limited settings like car systems. The security analysis reveals that the suggested approach guarantees message authenticity and integrity, non-repudiation, traceability, unlinkability, identity privacy, certificate independence, and emergency response. The proposed method cannot execute forgery, impersonation, or replay attacks. Proposed method has lower computational, communication, and storage overhead than earlier efforts. © 2004-2012 IEEE.

关键词： Fog computing

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Optimizing Cloud Load Balancing with a Hybrid Bio-Inspired Approach: Enhancing Performance and Scalability

Optimizing Cloud Load Balancing with a Hybrid Bio-Inspired A...

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

作者： Salam Al-E'mari Yousef Sanjalawe Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Software Engineering Department College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Managing resources efficiently is critical in modern cloud computing environments due to the growing complexity and increasing demand for cloud services. Load balancing, a key aspect of cloud resource management, ensures optimal distribution of workloads across servers to improve system performance, minimize response times, and increase resource utilization. However, many existing load-balancing algorithms suffer from challenges such as premature convergence, high computational complexity, and inefficiency in handling dynamic and heterogeneous workloads. To address these challenges, this research proposes a Hybrid Bio-Inspired Approach that combines the Salp Swarm Algorithm (SSA) and Elephant Herding Optimization (EHO). The motivation behind this research is to develop a scalable and adaptable load-balancing mechanism capable of improving performance and handling fluctuating workloads effectively. The main objective of this research is to enhance cloud load balancing by leveraging the unique strengths of SSA and EHO, aiming for better exploration and exploitation balance, improved resource utilization, and minimized response times. The proposed hybrid approach integrates SSA's efficient search capabilities with EHO's natural clustering and resource optimization features, creating a robust framework for load balancing in cloud systems. The methodology is implemented using the CloudSim simulator, a widely used platform for modeling and simulating cloud environments. The performance of the proposed hybrid algorithm is evaluated against traditional and other bio-inspired techniques. Results from the simulations demonstrate that the hybrid SSA-EHO approach outperforms existing algorithms in terms of load distribution, response time, scalability, and resource utilization, highlighting its potential as an efficient solution for cloud resource management.

关键词： Cloud computing Energy consumption Heuristic algorithms Scalability Clustering algorithms Load management Resource management Time factors Optimization Load modeling

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Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning 25

Mixed Blessing: Class-Wise Embedding guided Instance-Depende...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Fuchao Yang Jianhong Cheng Hui Liu Yongqiang Dong Yuheng Jia Junhui Hou College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Yam Pak Charitable Foundation School of Computing and Information Sciences Saint Francis University Hong Kong China School of Computer Science and Engineering Southeast University Nanjing China and Saint Francis University Hong Kong China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (纸本)9798400712456

In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On the other side, it brings high label ambiguity as the noisy labels are quite undistinguishable from the ground-truth label. To leverage the nuances of IDPLL effectively, for the first time we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels, i.e., the class-wise embeddings in the candidate label set should have high similarity, while the class-wise embeddings between the candidate label set and the non-candidate label set should have high dissimilarity. Moreover, to reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information to disambiguate the candidate label set. Extensive experimental comparisons with twelve methods on six benchmark data sets, including four fine-grained data sets, demonstrate the effectiveness of the proposed method. The code implementation is publicly available at https://***/Yangfc-ML/CEL.

关键词： instance-dependent partial label learning

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Implementing an NFT by Developing a Smart Contract in OpenSea Marketplace 6th

Implementing an NFT by Developing a Smart Contract in OpenSe...

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6th International Conference on Advanced Informatics for computing Research, ICAICR 2023

作者： Shanmugam, Udhayakumar Krisha, Kotte Mohan Nancy John Mari Vienni, Silviya Nandhini, D. Uma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Software Analyst Virtusa Corporation Chennai India School of Information and Communications Technology Saskatchewan Polytechnic College SaskatoonSK Canada Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031840586

Blockchain-based security applications have tremendously increased in the recent past with a decentralized network being the best way to implement a secure environment. Blockchain is one such implementation methodology that is considered the biggest disruptive technology. Smart contract-based transactions and assets have a high value and work because of a consensus mechanism without centralized trusted authority. Developing a smart contract for non-fungible tokens has various scopes and our proposed work uses various tools and libraries to achieve it. Brownie is used to implement the smart contract and Infura is used for the Ethereum node. Opensea marketplace is used for rendering the transaction. An Interplanetary distributed file system is used to store the NFTs. The work proposes the coordination of multiple tools to create a blockchain that is reliable and efficient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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