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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

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CSA4Rec: Collaborative Signals Augmentation Model Based on GCN for Recommendation 25th

CSA4Rec: Collaborative Signals Augmentation Model Based on ...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Liu, Haibo Yu, Lianjie Si, Yali Liu, Jinglian School of Cyber Security and Computer Hebei University Baoding Baoding China School of Computer Science and Engineering Changshu Institute of Technology Suzhou China

ISBN: (纸本)9789819605699

Graph Convolution Network (GCN) is a potent deep learning methodology and GCN-based graph data augmentation methods excel in recommender systems. the augmented data, as the higher-order collaborative signals, is generated by pre-training the GCN model on raw data, before making the final prediction. Despite their success, three problems have always existed. First, during the pre-training phase, over-smooth and data sparsity have a serious impact on the quality of augmented graph data. Second, the influence degree of higher-order collaborative signals are difficult to control, resulting in unstable embeddings. Last, the output of the pre-trained model is only used to augment the data and is not utilized in subsequent training phases, leading to performance penalty. In this study, we propose an efficient Collaborative Signals Augmentation model based on GCN for Recommendation (CSA4Rec). In the pre-training phase, graph contrastive learning is utilized to generate distinct embeddings to address over-smooth and data sparsity. To manipulate the higher-order collaborative signals effectively, the signals are generated from raw data in the modeling phase and aggregated independently by GCN in the training phase. the Residual-like connection is employed to leverage the output of the pre-trained model to improve the convergence speed in the next training phase. Extensive experiments demonstrate the effectiveness and rationality of CSA4Rec. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-Based learning 25th

Cross-Domain Sequential Recommendation with Temporal Encodi...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Chen, Lvying Zhang, Ji Zhang, Yuxi Yu, Sujie Li, Bohan Nanjing University of Aeronautics and Astronautics Nanjing China University of Southern Queensland QLD Australia Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing China Key Laboratory of Intelligent Decision and Digital Operations Ministry of Industrial and Information Technology Beijing China

ISBN: (纸本)9789819605699

Cross-domain sequential recommendation (CDSR) aims to predict user-item interactions from historical sequences across domains. Current CDSR approaches mainly focus on leveraging intrinsic connections among items to capture the dependencies across domains for representation learning. However, these approaches still exist major limitations, including: (1) Extensive CDSR methods overlook temporal dynamics, failing to utilize evolving sequential patterns of user-item interactions. (2) Irrelevant features from source domain to target domain lead to negative transfer of user preferences. To overcome these challenges, we propose an innovative Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-based learning (TP-CDSR). It features a temporal encoding module that captures the evolving sequences of user interactions by considering the temporal effects as kernels. the projection mechanism learns domain-specific matrices to map the user and item representations across domains, which can reduce migration of redundant features. Comprehensive experiments on two real datasets confirm that TP-CDSR achieves superior results compared to various state-of-the-art recommendation algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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A Cross-Lingual Meta-learning Method Based on Domain Adaptation for Speech Emotion Recognition 25th

A Cross-Lingual Meta-Learning Method Based on Domain Adapta...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Ion, David-Gabriel Smădu, Răzvan-Alexandru Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Bucharest Romania Academy of Romanian Scientists Bucharest Romania Paris 1 Panthéon-Sorbonne University Paris France University of Bucharest Bucharest Romania

ISBN: (纸本)9789819605781

Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models challenging. this shortage of data is even more prevalent in speech emotion recognition. Our work explores the model’s performance in limited data, specifically for speech emotion recognition. Meta-learning specializes in improving the few-shot learning. As a result, we employ meta-learning techniques on speech emotion recognition tasks, accent recognition, and person identification. To this end, we propose a series of improvements over the multistage meta-learning method. Unlike other works focusing on smaller models due to the high computational cost of meta-learning algorithms, we take a more practical approach. We incorporate a large pre-trained backbone and a prototypical network, making our methods more feasible and applicable. Our most notable contribution is an improved fine-tuning technique during meta-testing that significantly boosts the performance on out-of-distribution datasets. this result, together with incremental improvements from several other works, helped us achieve accuracy scores of 83.78% and 56.30% for Greek and Romanian speech emotion recognition datasets not included in the training or validation splits in the context of 4-way 5-shot learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Privacy Enhanced P2P Power Trading Using DID-Based Identity Verification in a Microgrid 25th

Privacy Enhanced P2P Power Trading Using DID-Based Identity ...

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25th international conference on Information Security Applications, WISA 2024

作者： Lee, Taeyang Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of

ISBN: (纸本)9789819616237

Smart meters play a crucial role in accurately measuring power usage and distribution data in microgrids. However, fine-grained data collection raises significant privacy concerns. To address this, it is essential to develop a distributed power trading system that is both trustworthy and privacy-preserving. In this paper, we propose a peer-to-peer power trading mechanism that ensures privacy through identity verification based on decentralized identifiers, zero-knowledge proofs, and pseudonyms. Besides, we introduce an efficient claims verification scheme with Boneh-Lynn-Shacham aggregate signature. We also validate the security of our proposed mechanism through the Real-or-Random model. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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DURLLCON: Deep Reinforcement learning for URLLC Optimization in Multi-edge Networks

DURLLCON: Deep Reinforcement Learning for URLLC Optimizatio...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems engineering, WISE 2024

作者： Dawoud, Heba Ansari, Shuja Mohamed, Amr Imran, Muhammad Popoola, Olaoluwa University of Glasgow GlasgowG12 8QQ United Kingdom Qatar University Doha Qatar

ISBN: (纸本)9789819614820

Ultra-Reliable and Low Latency Communications (URLLC) is crucial for enabling next-generation applications, particularly in areas that require stringent delay constraints, such as healthcare and vehicular networks. Multi-access Edge Computing (MEC) enhances URLLC applications by bringing computation closer to end users, maximizing resource utilization and minimizing latency. this paper introduces Deep Reinforcement learning for URLLC Optimization in Multi-Edge Networks (DURLLCON), a Deep Reinforcement learning (DRL)-based framework for efficient task offloading in a hybrid MEC environment, specifically targeting both URLLC and non-URLLC applications. By leveraging DRL and Long Short Term Memory (LSTM) networks, the proposed method dynamically adjusts the offloading decisions based on user energy levels, task priority, and network congestion. the simulation results show that the proposed DURLLCON framework achieves up to 30% improvement in energy efficiency and 20% reduction in average delay compared to existing methods, significantly extending user device battery life while maintaining high Quality of Service (QoS). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Advancing Evasion: Distributed Backdoor Attacks in Federated learning 25th

Advancing Evasion: Distributed Backdoor Attacks in Federate...

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25th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Wang, Jian Shen, Hong Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia School of Software Technology Guangzhou Institute of Software Guangzhou China

ISBN: (纸本)9789819642069

Federated learning (FL) is vulnerable to backdoor attacks through data poisoning if the data is not scrutinized, as malicious participants can inject backdoor triggers in normal samples, leading to poisoned updates. Distributed backdoor attacks pose a greater threat than centralized ones, as they often use fixed pixel blocks as triggers, increasing the risk of detection. this paper presents a novel distributed backdoor attack strategy that leverages edge structure poisoning to circumvent existing defense mechanisms, employing a distributed poisoning strategy to evade current defense mechanisms, thereby enhancing the stealth of the attack. Experimental results on multiple benchmark datasets demonstrate that this method is more effective and stealthy compared to other backdoor attack methods. Furthermore, this paper also proposes targeted defense strategies based on the experimental results, offering a new perspective on the security of FL systems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Safe-Path: A Perspective on Next-Generation Road Safety Recommendations 25th

Safe-Path: A Perspective on Next-Generation Road Safety Rec...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Ibtissem, Khedher Noura, Faci Sami, Faiz National School of Computer Sciences University of Manouba Manouba Tunisia National Institute of Applied Sciences and Technology LTSIRS - LR20ES06 Geomatics Team University of Carthage Ariana Tunis Tunisia Université Claude Bernard Lyon 1 Computer Science Department CNRS LIRIS Villeurbanne France Higher Institute of Multimedia Arts of Manouba Geomatics Team LTSIRS University of Manouba Manouba Tunisia

ISBN: (纸本)9789819605729

Recommending safe routes has become a fundamental necessity due to the increasing number of accidents. this heavily relies on how to evaluate the road severity. Existing recommendation systems are based on user feedback, either positive or negative. However, this type of evaluation overlooks many aspects of road severity like accident history and volunteered geographic information on road conditions. To fill this gap, we elaborate a comprehensive and predictive road risk analysis, relying on objective and subjective data. To recommend safe roads, we propose an algorithm called Safe-Path based on accurate and reliable risk values. To validate our approach, we conduct some experiments to benchmark various machine and deep learning models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Selectivity Estimation for Spatial Filters Using Optimizer Feedback: A Machine learning Perspective 25th

Selectivity Estimation for Spatial Filters Using Optimizer ...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Guermoudi, Nadir Matallah, Houcine Mesmoudi, Amin Benkabou, Seif-Eddine Hadjali, Allel LRIT Science Faculty University Abu Bekr Belkaid Tlemcen Algeria ISAE-ENSMA LIAS Université de Poitiers Poitiers France

ISBN: (纸本)9789819605729

In query optimization, the precision of selectivity estimates for query predicates is foundational for selecting efficient execution plans. Spatial selectivity estimation, which assesses the count of relevant objects meeting specific spatial criteria, poses a significant challenge due to its multi-dimensional and complex nature. Moreover, it is essential that these estimation methods be both fast and minimize memory usage. In this paper, we leverage optimizer feedback to tackle the challenging task of estimating selectivity for multi-dimensional spatial predicates. We redefine spatial selectivity estimation as a regression problem and investigate the application of three types of machine learning (ML) models: neural networks, tree-based models, and instance-based models to address this challenge. We compare these ML approaches against baseline methods that rely on Minimum Bounding Rectangles (MBRs), encompassing both RTree-based and histogram-based estimations. through extensive empirical evaluations using a real dataset, our study guides the choice of ML models in accordance with the data collected by the optimizer. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Improving Malware Detection Accuracy in Volatile Memory using Contrastive learning and CNN-based Classification 5

Improving Malware Detection Accuracy in Volatile Memory usin...

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5th international conference on Emerging Systems and intelligent Computing, ESIC 2025

作者： Pradhan, Sasmita Dash, Subhasis Acharya, Arup Abhinna Vashishtha, Lalit Kumar Kiit School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

Cyberattack is getting sophisticated with each passing day. thus, the forensic techniques need to evolve accordingly. While traditional techniques rely on persistent storage, it is unable to catch hold of critical evidences, as these (critical evidences) reside in the volatile memory. In this scenario, the role of analyzing system RAM in Memory Forensics is pivotal, where transient data is captured. this approach offers insight into system states, running processes and malware activity that would otherwise be lost. the aim of this study is to investigate novel methods for improving memory forensics through the use of machine learning-based classification techniques. the proposed method intends to increase the accuracy of forensic analysis, speed up investigations, and automate the detection of malicious patterns by classifying memory artefacts. the study is based on analysis of binary and multi-class AI models for malware detection in memory forensics. Experimental evaluations were conducted using VGG16, Xception, Inception V3, CNN and Contrastive-Malware-CNN-based classifiers on two image-based datasets capable of extending classifications to 25 distinct families or classes. As indicated in the results, the proposed frameworks achieve better performance, with a classification accuracy of 94.7%, compared to the 91.7% accuracy achieved in previously studied model. this research helps development of an effective and efficient memory forensics framework to fight modern malware threats. © 2025 IEEE.

关键词： Contrastive learning

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Deep Reinforcement learning-Based Dependent Task Offloading for QoS Optimization in Satellite Edge Computing 25th

Deep Reinforcement Learning-Based Dependent Task Offloading ...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Tian, Di Chen, Juan Chen, Peng Zhong, Jie Li, Xi Xiong, Ling School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9789819605699

Satellite edge computing (SatEC) is an emerging computing paradigm for remote and disaster-affected areas with scarce computing resources. It is crucial to make the appropriate offloading strategy to maximize quality of service (QoS) due to the scarcity of satellite computing resources. However, existing researches mostly focus on the offloading of independent tasks without considering tasks with dependencies. To address this problem, we first design a satellite-aerial task offloading (SATO) framework. In the SATO, unmanned aerial vehicles (UAVs) collect ground tasks and upload them to high-altitude platform (HAP), while HAP provides local computing, and low earth orbit (LEO) satellites provide edge computing. Furthermore, we propose SatEC directed acyclic graph (DAG) task offloading (SECDTO) algorithm to investigate the task offloading problem. Specifically, we model tasks with dependencies as a DAG, then we propose an encoder-decoder model with attention based on deep reinforcement learning (DRL) to translate graph vector information into offloading strategies. In order to maximize QoS, we use proximal policy optimization (PPO) to train the model. Simulation results show that our method converges quickly and stably during the training process and outperforms several baseline algorithms in terms of QoS@. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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