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A Federated Learning Aggregation that Integrates Clustering and Momentum Correction Based on Historical high-Quality Gradients 21

A Federated Learning Aggregation that Integrates Clustering ...

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21st IEEE international symposium on Parallel and Distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Jian Guo, Bing Chen, Fei Shen, Yan Dai, Shengxin Dai, Cheng Sichuan University School of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China

ISBN: (纸本)9798350329223

Nowadays, federated learning emerges as a new technique to preserve data privacy in distributed machine learning by limiting local data to the client and only aggregating model parameters from multiple parties on the server side. However, it also faces consequent threats from malicious clients, and some methods have been proposed without sufficient defense capability and efficient aggregation. To address these problems, we propose a novel aggregation algorithm, which incorporates PCA operation and clustering to select the excellent client in each round;and brings in a momentum-accelerated historical quality gradient-based approach to increase the weight when the performances of the global model are consistently degraded, which can achieve faster convergence on the basis of ensuring that the update direction of the global model is not manipulated by malicious nodes. Experimental results on the MNIst datasets show that our algorithm is only 0.93% lower than FedAvg under a no-attack scenario, and exhibits faster convergence compared to Krum, Multi-Krum, and Trimmed Mean Aggregation, and the accuracy improves 10.68%,35.83%,19.65% under Black-Box Edge Attack, Sign Flipping Attack, and Same Value Attack. © 2023 IEEE.

关键词： Momentum

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Tensor Ring Decomposition Based Collaborative Filtering Recommendation with Differential Privacy 25

Tensor Ring Decomposition Based Collaborative Filtering Reco...

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25th IEEE international Conferences on high performance computing and Communications, 9th international Conference on Data Science and Systems, 21st IEEE international Conference on Smart City and 9th IEEE international Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhou, Fang Fu, Xuemei Lu, Zhixing School of Computer Science and Technology Hainan University Haikou China School of Information and Communication Engineering Hainan University Haikou China

ISBN: (纸本)9798350330014

Recommendation systems offer a solution to problems such as information overload and lack of relevant information, thereby enhancing sales and improving users satisfaction. Collaborative filtering (CF) uses the preferences of a known user group to predict the preferences of unknown users. At present, many related models have been proposed, with matrix and CP factorizations being particularly popular. But in practical recommendation applications, matrix decomposition is difficult to deal with high-dimensional data, and CP decomposition is limited by its few parameters. To optimize the efficacy of recommendations while still retaining a low level of complexity, we propose employing a tensor ring (TR) decomposition framework for CF recommendation. In this paper, TR decomposition the original tensor to obtain core tensors and a rank vector. To minimize the rating error, a loss function is constructed, and stochastic gradient descent is used to repeatedly update each parameter. In addition, to protect user sensitive information, we employ Laplace random noise to disturbe ratings. By controlling the value of the privacy budget, a balance is achieved between the preservation of data privacy and the utility of data. Extensive experiments show that our approach not only improves recommendations but also protects users privacy by using this perturbation technique. © 2023 IEEE.

关键词： Collaborative filtering

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Exploring Three-Dimensional Locomotion Techniques in Virtual Reality 21

Exploring Three-Dimensional Locomotion Techniques in Virtual...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Lim, Donghae Shirai, Shizuka Orlosky, Jason Ratsamee, Photchara Uranishi, Yuki Takemura, Haruo Osaka Univ Suita Osaka Japan Augusta Univ Augusta GA USA Osaka Inst Technol Osaka Japan

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

Most existing Virtual Reality (VR) locomotion interfaces are designed for ground-based locomotion, limiting the degrees of freedom (DoF) during movement as they would in the real world. However, an optimized three-dimensional travel technique is required for certain virtual scenes such as flying and diving. This study evaluated three different interface types: Slider, Teleport, and Point-Tug, that were created based on existing ground-based locomotion methods in order to investigate the features required for three-dimensional (3D) movement. We conducted a within-subjects study with 3D navigation tasks and evaluated the performance when navigating a virtual environment using three different 3D locomotion interfaces. The results show that Slider, which allows users to move continuously, has an advantage over the others, and interfaces such as teleportation, which are widely used in ground-based locomotion, are likely to be perceived differently in 3D locomotion.

关键词： Human-centered computing Human computer interaction (HCI) Interaction paradigms Virtual reality

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Shelf schedules for independent moldable tasks to minimize the energy consumption 33

Shelf schedules for independent moldable tasks to minimize t...

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33rd IEEE international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Benoit, Anne Canon, Louis-Claude Elghazi, Redouane Heam, Pierre-Cyrille ENS Lyon LIP Lyon France Univ Bourgogne Franche Comte FEMTO ST Inst CNRS Besancon France

ISBN: (纸本)9781665443012

Scheduling independent tasks on a parallel platform is a widely-studied problem, in particular when the goal is to minimize the total execution time, or makespan (P parallel to C-max problem in Graham's notations). Also, many applications do not consist of sequential tasks, but rather parallel moldable tasks that can decide their degree of parallelism at execution (i.e., on how many processors they are executed). Furthermore, since the energy consumption of data centers is a growing concern, both from an environmental and economical point of view, minimizing the energy consumption of a schedule is a main challenge to be addressed. One can then decide, for each task, on how many processors it is executed, and at which speed the processors are operated, with the goal to minimize the total energy consumption. We further focus on co-schedules, where tasks are partitioned into shelves, and we prove that the problem of minimizing the energy consumption remains NP-complete when static energy is consumed during the whole duration of the application. We are however able to provide an optimal algorithm for the schedule within one shelf, i.e., for a set of tasks that start at the same time. Several approximation results are derived, and simulations are performed to show the performance of the proposed algorithms.

关键词： Energy consumption Schedules Program processors Costs high performance computing Parallel processing Approximation algorithms

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Towards Efficient HW Acceleration in Edge-Cloud Infrastructures: The SERRANO Approach Invited Paper 21st

Towards Efficient HW Acceleration in Edge-Cloud Infrastructu...

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21st international Conference on Embedded computer Systems - architectures, Modeling, and Simulation (SAMOS)

作者： Ferikoglou, Aggelos Oroutzoglou, Ioannis Kokkinis, Argyris Danopoulos, Dimitrios Masouros, Dimosthenis Chondrogiannis, Efthymios Gomez, Aitor Fernandez Kretsis, Aristotelis Kokkinos, Panagiotis Varvarigos, Emmanouel Siozios, Kostas Aristotle Univ Thessaloniki Thessaloniki Greece Innovat Acts Ltd Nicosia Cyprus IDEKO ICT & AUTOMAT Res Grp Elgoibar Basque Country Spain Natl Tech Univ Athens Athens Greece

ISBN: (纸本)9783031045806;9783031045790

Nowadays, we witness an ever-increased number of applications deployed over Edge, Cloud and HPC infrastructures. This rapid explosion of computing devices across the computing continuum poses new challenges in terms of providing a power-efficient, secure and automatic way for deployment of different applications in such heterogeneous environments. Moreover, the need for performance efficient deployments within such environments, has introduced the presence of hardware accelerators over the entire computing stack. In this paper, we present SERRANO's approach for providing efficient HW accelerated deployments over edge-cloud infrastructures. First, we give a brief overview of the SERRANO project, describing its goals and objectives, providing a high-level overview of SERRANO's platform architecture and presenting the use-cases involved. Then, we describe SERRANO's approach for providing efficient HW accelerators by identifying trade-offs between performance, accuracy and power consumption and also demonstrate how SERRANO aims to automate the optimization process through machine learning models in order to construct a generic optimization heuristic to fine-tune programs for both GPU and FPGA accelerators. Through some illustrative examples, we showcase that by applying approximation and optimization techniques, we are able to achieve an average decrease of 28% in power consumption for FPGA devices and trade-off between performance and power usage for GPUs, achieving up to x1.21 speedups and 8% power improvement.

关键词： Edge computing Cloud continuum Hardware accelerators GPU FPGA Heterogeneous

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Minimizing Age of Information for Mobile Edge computing Systems: A Nested Index Approach 21

Minimizing Age of Information for Mobile Edge Computing Syst...

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21st international symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Chen, Shuo Yang, Ning Zhang, Meng Wang, Jun Institute of Automation Chinese Academy of Sciences Beijing100190 China The ZJU-UIUC Institute Zhejiang University Zhejiang314499 China The Department of Computer Science University College London WC1E 6BT United Kingdom

ISBN: (纸本)9783903176553

Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, by offloading tasks from mobile devices to edge nodes. We use the metric Age-of-Information (AoI) to evaluate information freshness. An efficient solution to minimize the AoI for the MEC system with multiple users is non-trivial to obtain due to the random computing time. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB) problem and establish a hierarchical Markov Decision Process (MDP) to characterize the updating of AoI for the MEC system. Based on the hierarchical MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. Finally, the closed form of the nested index is obtained, which enables the performance tradeoffs between computation complexity and accuracy. Our algorithm leads to an optimality gap reduction of up to 40%, compared to benchmarks. Our algorithm asymptotically approximates the lower bound as the system scalar gets large enough. © 2023 IFIP.

关键词： Markov processes

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Effects of Avatar Face Level of Detail Control on Social Presence in Augmented Reality Remote Collaboration 21

Effects of Avatar Face Level of Detail Control on Social Pre...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Kang, Seoyoung Yoon, Boram Kim, Bowon Woo, Woontack Korea Adv Inst Sci & Technol UVR Lab Daejeon South Korea Korea Adv Inst Sci & Technol VCL Daejeon South Korea Korea Adv Inst Sci & Technol KI ITC ARRC Daejeon South Korea

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

This study explored how avatar face Level of Detail (LOD) and task types affect users' social presence and interpersonal communication in Augmented Reality remote collaboration. In AR remote collaboration, users are represented as avatars, and an actual user's appearance inevitably changes. While certain situations require an avatar that resembles a user, it is challenging to reconstruct everything realistically due to its high cost. Since facial parts show relative significance compared to body parts, we proposed controlling face LOD as an approach to lowering the cost while maintaining social presence in AR environments. Our results showed that while Mid-LOD maintains social presence but reduces costs compared to high-LOD, Low-LOD negatively impacts social presence mainly due to emotional understanding. Based on our findings, we highlight the importance of 1) considering facial expressions and emotional understanding in managing face LOD, 2) figuring out the collaboration context with different effects of tasks and communication behaviors, and 3) providing additional communication cues for maintaining social presence while lowering the cost.

关键词： Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User studies

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Creation of a high-Precision, Single-Board computer-Based Intelligent Passenger Counting System 19

Creation of a High-Precision, Single-Board Computer-Based In...

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19th international Conference on Future Networks and Communications, FNC 2024 / 21st international Conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Rakhymova, Aktumar Mussina, Aigerim Aubakirov, Sanzhar Trigo, Paulo Astana IT University 55/11 Mangilik-el Avenue Astana010000 Kazakhstan Al-Farabi Kazakh National University 71 al-Farabi Avenue Almaty050040 Kazakhstan Instituto Superior de Engenharia de Lisboa 1 Rua Conselheiro Emídio Navarro Lisbon1959-007 Portugal

This paper introduces research aimed at developing and implementing an intelligent passenger counting system in public transportation. The goal is to enhance fare evasion control and reduce financial losses for transit operators by automatically monitoring the entering and exiting of passengers. Utilizing the advanced capabilities of YOLO and DeepSORT algorithms, the system demonstrates high accuracy in detecting and tracking passengers, even in congested scenarios. Practical experiments have highlighted several challenges, including real-time frame processing rates, single-board computer performance, and the accuracy of passenger counts. It was discovered that the selection and positioning of USB cameras significantly influence system efficiency. Notably, changing the IP cameras to USB cameras significantly improves the processing speeds. A dataset containing images was created to improve the YOLO model's accuracy in detecting passengers. This dataset was particularly relevant considering the winter season, where identifying passengers could be challenging due to their bulky outerwear. To upgrade the performance of the DeepSORT system, the YOLO model was also improved with the pre-designed dataset to focus solely on the heads. This trained model achieved a recognition accuracy of 91% during peak hours. During periods of lighter traffic, when there were fewer passengers, the accuracy increased further due to reduced interference and higher precision. To enhance real-time processing efficiency, this research incorporates the status of public transport doors as a key parameter. By optimizing the number of frames forwarded to the YOLO algorithm for processing, we ensure that the system maintains accuracy without compromising on speed. Future development will focus on incorporating depth-sensing cameras and developing advanced data analysis algorithms for real-time processing on single-board computers. This will further enhance the precision of passenger counts during h

关键词： Mass transportation

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FL-HSDP: Client-Side Defense for Federated Learning via Hessian Optimization and Shuffled Differential Privacy 25

FL-HSDP: Client-Side Defense for Federated Learning via Hess...

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作者： Li, Yuting Wang, Guojun Wang, Tian Jia, Weijia School of Computer Science and Cyber Engineer Guangzhou University Guangzhou China Institute of Ai and Future Networks Beijing Normal University and UIC China Zhuhai China

ISBN: (纸本)9798350330014

Federated learning (FL) is a prominent paradigm for distributed training that can decrease data leakage by not explicitly sharing private information. Lots of recent research has revealed, however, that malicious clients can easily launch model poisoning attacks and the private information of benign clients can still be divulged in the setting of federated learning. As the existing works are either incapable of providing quantitative privacy for user data or vulnerable to attacks that result in aggregated model misclassifications, we present a framework, FL-HSDP, that uses hessian matrix optimization to creatively involve perturbation into the process of updating gradients for local iterations. Instead of using conventional noise adding method for high-dimensional parameters, we improve the system's performance by employing differential privacy to the regularization term of the Hessian matrix during each local training epoch. In order to further enhance the security of our system, we introduce the privacy amplification effect brought by the shuffler between the clients and server to bolster the confidentiality while minimizing any adverse impact on algorithm performance. The extensive experimental results on FashionMNIst and CIFAR10 datasets demonstrate the effectiveness and efficiency of our approach, almost maintaining the accuracy and effectively mitigating tar-geted model poisoning attack by up to 73.7%. Both the similarity scores and the visualization of the reconstructed images have been demonstrated to be effective against the state-of-the-art weight inversion attacks. © 2023 IEEE.

关键词： Matrix algebra

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ChatGPT: A Threat to Spam Filtering Systems 25

ChatGPT: A Threat to Spam Filtering Systems

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作者： Utaliyeva, Assem Pratiwi, Millati Park, HyeGyoung Choi, Yoon-Ho School of Computer Science and Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9798350330014

ChatGPT gathered the attention of millions of users shortly after its release, leading to the popularization of generative AI technologies. This research aims to emphasize one of the potential vulnerabilities associated with commonly used generative AI models. Even though it follows strict ethical and security policies, proper prompt engineering enables malicious misuse of ChatG PT such as spam email generation. In this paper, we present various scenarios of malicious prompt engineering to encourage the chatbot for spam email generation and rewriting the existing email. Also, we present the adversarial prompt engineering examples intended to evade the detection by spam filters by means of rewriting the given email while circumventing common spam characteristics. We experimentally evaluate the practical feasibility of prompt engineering on ChatGPT by assessing the performance of six common ML-based spam filters with emails modified by ChatG PT. From the experimental results, we show that adversarial prompt engineering decreases the performance of common ML-based spam filters, while NLP-based filter is robust to such modification. We also demonstrate that including ChatG PT rewritten emails in the training set leads to more robust ML-based spam filters, while the use of available AI-text detectors does not guarantee high detection rates of emails modified by the chatbot. © 2023 IEEE.

关键词： Electronic mail

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