Future Generation Computer Systems-The International Journal of Escience最新文献_第9页

Click-level supervision for online action detection extended from SCOAD 从SCOAD扩展到在线动作检测的点击级监督

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-19 DOI: 10.1016/j.future.2024.107668

Xing Zhang , Yuhan Mei , Ye Na , Xia Ling Lin , Genqing Bian , Qingsen Yan , Ghulam Mohi-ud-din , Chen Ai , Zhou Li , Wei Dong

{"title":"Click-level supervision for online action detection extended from SCOAD","authors":"Xing Zhang , Yuhan Mei , Ye Na , Xia Ling Lin , Genqing Bian , Qingsen Yan , Ghulam Mohi-ud-din , Chen Ai , Zhou Li , Wei Dong","doi":"10.1016/j.future.2024.107668","DOIUrl":"10.1016/j.future.2024.107668","url":null,"abstract":"<div><div>Data-driven fully-supervised online action detection algorithms heavily rely on manual annotations, which are challenging to obtain in real-world applications. Current research efforts aim to address this issue by introducing weakly supervised online action detection (WOAD) methods that utilize video-level annotations. However, these approaches frequently face challenges with blurred temporal boundaries, stemming from the lack of explicit temporal information. In this work, we revisit WOAD and propose an algorithm for weakly supervised online action detection using click-level annotations, which we call Single-frame Click Supervision for Online Action Detection (SCOAD). SCOAD stands out by significantly improving prediction accuracy without substantially increasing the annotation cost. This improvement is achieved through a set of well-engineered loss functions that leverage the limited temporal information provided by click labels. Additionally, we present an enhanced version of SCOAD called SCOAD++. It introduces a novel mechanism that enhances the model’s ability to utilize historical information and significantly refines detail differentiation, addressing the limitations of traditional fully connected frameworks that neglect temporal variations. Furthermore, to explore the issue of accuracy variation caused by inherent randomness in click-level annotation, we have constructed a human fitness video dataset for this study. On the other hand, we also reveal the limitations of video-level labels in the field of action detection with this well-constructed dataset. We perform extensive experiments on numerous benchmark datasets and demonstrate that our approach outperforms state-of-the-art methods.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107668"},"PeriodicalIF":6.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensuring the federation correctness: Formal verification of Federated Learning in industrial cyber-physical systems 确保联邦正确性：工业网络物理系统中联邦学习的形式化验证

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-19 DOI: 10.1016/j.future.2024.107675

Badra Souhila Guendouzi , Samir Ouchani , Hiba Al Assaad , Madeleine El Zaher

{"title":"Ensuring the federation correctness: Formal verification of Federated Learning in industrial cyber-physical systems","authors":"Badra Souhila Guendouzi , Samir Ouchani , Hiba Al Assaad , Madeleine El Zaher","doi":"10.1016/j.future.2024.107675","DOIUrl":"10.1016/j.future.2024.107675","url":null,"abstract":"<div><div>In industry 4.0, Industrial Cyber–Physical Systems (<span>ICPS</span>) integrate industrial machines with computer control and data analysis. Federated Learning (FL) improves this by enabling collaborative machine learning and improvement while maintaining data privacy. This method improves the security, and intelligence of industrial processes. FL-based frameworks proposed in the literature do not perform rigorous validation of collaborators’ behaviors, especially with regard to reliability and operational correctness. In contrast, non-FL-based cyber–physical systems have already been verified in the literature using formal methods. Therefore, there is a significant gap in the application of these verification techniques to FL-based systems. To fill this gap, we explore the possibility of introducing formal verification into FL-based cyber–physical systems, starting with our <span><strong>FedGA-Meta</strong></span> published framework. Thus, our research focuses on expanding our <span><strong>FedGA-Meta</strong></span> framework in the context of Industry 4.0, this paper delves into a comprehensive validation of the framework’s operational reliability and correctness within <span>ICPS</span> based on FL. To achieve this, we employ Timed Computation Tree Logic (TCTL) for the precise specification of system requirements, coupled with Labeled Transition Systems (LTS) to construct the <span>ICPS</span> semantic in detail. Through the usage of Uppaal for both simulation and model-checking purposes, we rigorously test the framework under a variety of operational scenarios. This approach allows us to confirm the system’s reliability and correctness, ensuring that the <span><strong>FedGA-Meta</strong></span> framework operates effectively and as intended within the demanding environments of Industry 4.0.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107675"},"PeriodicalIF":6.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preface of Special Issue on Highlights from the Joint-Laboratory on Extreme Scale Computing

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-18 DOI: 10.1016/j.future.2024.107688

Franck Cappello , Ruth Partzsch , Daniel S. Katz

引用次数: 0

Enhancing E-business in industry 4.0: Integrating fog/edge computing with Data LakeHouse for IIoT 加强工业4.0中的电子商务：将雾/边缘计算与工业物联网的数据湖集成

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-14 DOI: 10.1016/j.future.2024.107653

Hayat Routaib , Soukaina Seddik , Abdelali Elmounadi , Anass El Haddadi

引用次数: 0

KDRSFL: A knowledge distillation resistance transfer framework for defending model inversion attacks in split federated learning KDRSFL：一种用于分离联邦学习中防御模型反转攻击的知识蒸馏抵抗转移框架

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-14 DOI: 10.1016/j.future.2024.107637

Renlong Chen , Hui Xia , Kai Wang , Shuo Xu , Rui Zhang

{"title":"KDRSFL: A knowledge distillation resistance transfer framework for defending model inversion attacks in split federated learning","authors":"Renlong Chen , Hui Xia , Kai Wang , Shuo Xu , Rui Zhang","doi":"10.1016/j.future.2024.107637","DOIUrl":"10.1016/j.future.2024.107637","url":null,"abstract":"<div><div>Split Federated Learning (SFL) enables organizations such as healthcare to collaborate to improve model performance without sharing private data. However, SFL is currently susceptible to model inversion (MI) attacks, which create a serious problem of risk for private data leakage and loss of accuracy. Therefore, this paper proposes an innovative framework called Knowledge Distillation Resistance Transfer for Split Federated Learning (KDRSFL). The KDRSFL framework combines one-shot distillation techniques with adjustment strategies optimized for attackers, aiming to achieve knowledge distillation-based resistance transfer. KDRSFL enhances the classification accuracy of feature extractors and strengthens their resistance to adversarial attacks. First, a teacher model with strong resistance to MI attacks is constructed, and then this capability is transferred to the client models through knowledge distillation. Second, the defense of the client models is further strengthened through attacker-aware training. Finally, the client models achieve effective defense against MI through local training. Detailed experimental validation shows that KDRSFL performs well against MI attacks on the CIFAR100 dataset. KDRSFL achieved a reconstruction mean squared error (MSE) of 0.058 while maintaining a model accuracy of 67.4% for the VGG11 model. KDRSFL represents a 16% improvement in MI attack error rate over ResSFL, with only 0.1% accuracy loss.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107637"},"PeriodicalIF":6.2,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Straggler mitigation via hierarchical scheduling in elastic stream computing systems 弹性流计算系统中基于分层调度的掉队者缓解

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-14 DOI: 10.1016/j.future.2024.107673

Minghui Wu , Dawei Sun , Shang Gao , Rajkumar Buyya

{"title":"Straggler mitigation via hierarchical scheduling in elastic stream computing systems","authors":"Minghui Wu , Dawei Sun , Shang Gao , Rajkumar Buyya","doi":"10.1016/j.future.2024.107673","DOIUrl":"10.1016/j.future.2024.107673","url":null,"abstract":"<div><div>Skewed data distribution leads to certain tasks or nodes handling much more data than others, thereby slowing down their execution speed and classifying them as stragglers. Existing solutions attempt to establish a well-balanced workload to mitigate stragglers by using either data stream grouping or task scheduling. This “one size fits all” approach only considers single-level requirements and fails to address the diverse needs of the system across multiple levels, ultimately limiting its performance. To address these issues and mitigate stragglers effectively, we propose a hierarchical collaborative strategy called Ms-Stream. It aims to balance the data stream workloads among tasks and maintain load difference among compute nodes within an acceptable range. This paper discusses this strategy from the following aspects: (1) Ms-Stream constructs models for topology, grouping, and resource, along with the formalization of problems, including data stream grouping, task subgraph partitioning, and task deployment. (2) Ms-Stream employs a lightweight two-level grouping method to support dynamic workload assignment for stateful tasks, selectively offloading resources from task stragglers to others. (3) Ms-Stream allocates communication-intensive tasks to the same group through the directed acyclic graph representations of streaming applications, concurrently ensuring the equitable distribution of computation-intensive tasks across groups. (4) Ms-Stream deploys task groups to compute nodes with varying resource capacities following the descending maximum padding priority rule for a balanced workload. Performance metrics such as system throughput and latency are evaluated with real-world streaming applications. Experimental results demonstrate the significant improvements made by Ms-Stream, reducing maximum system latency by 61% and increasing maximum throughput by more than 2x compared to existing state-of-the-art works.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107673"},"PeriodicalIF":6.2,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing mobile blockchain networks: A game theoretical approach to cooperative multi-terminal computation 移动区块链网络优化：一种多终端协同计算的博弈方法

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-13 DOI: 10.1016/j.future.2024.107669

Lin Pan , Fengrui Chen , Yan Ding , Yunan Zhai , Liyuan Zhang , Jia Zhao

{"title":"Optimizing mobile blockchain networks: A game theoretical approach to cooperative multi-terminal computation","authors":"Lin Pan , Fengrui Chen , Yan Ding , Yunan Zhai , Liyuan Zhang , Jia Zhao","doi":"10.1016/j.future.2024.107669","DOIUrl":"10.1016/j.future.2024.107669","url":null,"abstract":"<div><div>Facing the computational challenges in mobile devices within blockchain networks, particularly the scarcity and underutilization of computational resources, this paper introduces the CAGE Framework: a novel architecture based on cooperative game theory within alliance blockchains. Designed to optimize computational resource allocation across multiple mobile terminals, CAGE Framework leverages a tri-layer structure – comprising the Blockchain Network Layer, User Network Layer, and Distributed Collaborative Computing Layer – to facilitate efficient resource sharing and task scheduling. Through intelligent contracts, the framework automatically aggregates user demands, utilizing the InterPlanetary File System (IPFS) for data storage, thereby enhancing privacy protection and blockchain data throughput. Validated on the Hyperledger Fabric platform and benchmarked against state-of-the-art approaches, CAGE demonstrates superior transaction throughput, reduced latency, and enhanced resource efficiency. The core strategy, dubbed CAGE, is predicated on cooperative gaming, aiming to maximize user satisfaction by balancing energy consumption, computational load, and resource allocation multi-objectively. Experiments reveal a notable improvement in system load balancing (by 51%) and a significant reduction in energy consumption (by 62%), affirming the framework’s efficacy in addressing computational resource deficiencies both within and outside the alliance under low energy and balanced load conditions. The CAGE Framework not only charts a new path for computational resource optimization in mobile blockchain networks but also lays a theoretical and practical foundation for the furtherance of blockchain technology application and optimization.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107669"},"PeriodicalIF":6.2,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Devising an actor-based middleware support to federated learning experiments and systems 设计一个基于参与者的中间件来支持联邦学习实验和系统

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-11 DOI: 10.1016/j.future.2024.107646

Alessio Bechini, José Luis Corcuera Bárcena

{"title":"Devising an actor-based middleware support to federated learning experiments and systems","authors":"Alessio Bechini, José Luis Corcuera Bárcena","doi":"10.1016/j.future.2024.107646","DOIUrl":"10.1016/j.future.2024.107646","url":null,"abstract":"<div><div>Federated Learning (FL) recently emerged as a practical privacy-preserving paradigm to exploit data distributed over separated repositories for Machine Learning purposes, with no need to migrate data. FL algorithms entail concerted activities of multiple distributed players: a dedicated supporting system aims to relieve programmers from dealing with the intricate implementation details of communication and synchronization activities required along the distributed model learning, and the necessary information exchange during operation. Such support plays a crucial role in the experimentation of FL algorithms and their eventual field operation, so its architecture must be carefully designed. In this work, we propose a novel architecture where the pivotal role is assigned to a runtime system based on actors, working at the middleware level. The distinctive points of this approach are portability across diverse platforms, location transparency for the involved nodes, opportunity to choose diverse languages for implementing the core parts of custom software systems. Moreover, with the proposed solution, scalability requirements can be easily met. The implementation of FL algorithms is made easier by APIs to programmatically access the middleware functionalities. Another benefit is that the same code can be used in both simulated and Fed-lang, the reference implementation of the proposed architecture, has been used to quantitatively compare the characteristics of our approach with other existing FL frameworks, showing its ability to address the challenges posed by various operating conditions and settings. The described architecture has shown to be adequate to deliver the functionalities required for the effective development of FL systems.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107646"},"PeriodicalIF":6.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VLCQ: Post-training quantization for deep neural networks using variable length coding 使用变长编码的深度神经网络的训练后量化

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-11 DOI: 10.1016/j.future.2024.107654

Reem Abdel-Salam, Ahmed H. Abdel-Gawad, Amr G. Wassal

{"title":"VLCQ: Post-training quantization for deep neural networks using variable length coding","authors":"Reem Abdel-Salam, Ahmed H. Abdel-Gawad, Amr G. Wassal","doi":"10.1016/j.future.2024.107654","DOIUrl":"10.1016/j.future.2024.107654","url":null,"abstract":"<div><div>Quantization plays a crucial role in efficiently deploying deep learning models on resources constraint devices. Post-training quantization does not require either access to the original dataset or retraining the full model. Current methods that achieve high performance (near baseline results) require INT8 fixed-point integers. However, to achieve high model compression by achieving lower bit-width, significant degradation to the performance becomes the challenge. In this paper, we propose VLCQ, which relaxes the constraint of fixed-point encoding which limits the quantization techniques from better quantizing the weights. Therefore, this work utilizes variable-length encoding which allows for exploring the whole space of quantization techniques. Thus, achieving much better results (close to or even better than the baseline results) while achieving lower bit-widths without the need to access any training data or to fine-tune the model. Extensive experiments were carried out on various deep-learning models for the image classification and segmentation, and object detection tasks. When compared to state-of-the-art post-training quantization approaches, experimental results reveal that our suggested method offers improved performance with better model compression (lower bit-rate). For per-channel quantization, our method surpassed the FP32 accuracy and Piece-Wise Linear Quantization (PWLQ) method in most models while achieving up-to 6X model compression ratio compared to the FP32 and up-to 1.7X compared to PWLQ. If the model compression is the concern with little effect on performance, our method achieves up-to 12.25X compression ratio compared to FP32 within 4% performance loss. For per-tensor, our method is competitive with Data-Free Quantization scheme (DFQ) in achieving the best performance. However, our method is more flexible in getting lower bit rates than DFQ across the different tasks and models.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107654"},"PeriodicalIF":6.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flow timeout matters: Investigating the impact of active and idle timeouts on the performance of machine learning models in detecting security threats

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-11 DOI: 10.1016/j.future.2024.107641

Meryem Janati Idrissi , Hamza Alami , Abdelkader El Mahdaouy , Abdelhak Bouayad , Zakaria Yartaoui , Ismail Berrada

{"title":"Flow timeout matters: Investigating the impact of active and idle timeouts on the performance of machine learning models in detecting security threats","authors":"Meryem Janati Idrissi , Hamza Alami , Abdelkader El Mahdaouy , Abdelhak Bouayad , Zakaria Yartaoui , Ismail Berrada","doi":"10.1016/j.future.2024.107641","DOIUrl":"10.1016/j.future.2024.107641","url":null,"abstract":"<div><div>In the era of high-speed networks and massive data, several network security technologies are shifting focus from payload-based to flow-based methods. This has led to the incorporation of Machine Learning (ML) models in network security systems, where high-quality network flow features are of paramount importance. However, limited attention has been dedicated to studying the impact of the flow metering hyperparameters, specifically idle and active timeouts, on ML models’ performance. This paper, therefore aims to address this gap by designing a series of experiments related to flow features and learning models in the case of Network Intrusion Detection Systems (NIDS). Our experiments investigate the impact idle and active timeouts have on the quality of the extracted features from network data and their subsequent impact on the performance of ML models. For this end, we consider three flow exporters for feature extraction (NFStream, Zeek, and Argus), three ML models, and different feature sets. We conducted extensive experiments with public datasets including, USTC-TFC2016, CICIDS2017, UNSW-NB15, and CUPID. The results show that the difference between best and worst timeout combinations may reach up to 8.77% in terms of macro F1-score. They also unveil varying sensitivity to changes in timeouts among different models and feature sets. Finally, we propose a distributed learning approach based on federated learning. The latter showcased potential in handling multiple NIDS with different timeout configurations. The code is available at <span><span>https://github.com/meryemJanatiIdrissi/Flow-timeout-matters</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107641"},"PeriodicalIF":6.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0