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Design, Automation and Test in Europe Conference and Exhibition

作者： Anika Christmann Robin Hapka Rolf Ernst Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

The design of autonomous systems, such as for automated driving and avionics, is challenging due to high performance requirements combined with high criticality. Complex applications demand the full performance of commercial high performance multi-core systems of-the-shelf (COTS), with or without accelerators. While these systems are optimized for performance, hard real-time requirements and deterministic timing behavior are major constraints for safety-critical systems. Unfortunately, infrequent timing outliers caused by interleaved hardware-software effects of COTS systems complicate traditional worst-case design. This conflict often prohibits deploying COTS hardware and consequently prevents sophisticated applications, too. Recently, an approach called Timing Diversity was introduced, which proposes to exploit existing dual modular redundant hardware platforms to mask deadline violations. This paper puts Timing Diversity on a theoretical foundation and provides specification for different implementations. It demonstrates that Timing Diversity needs fast recovery to be effective, proposes a recovery strategy and provides a mathematical model for the reliability of the resulting system. Using experimental data in a Linux based system, it shows that fast recovery is useful, making Timing Diversity a realistic option for compute demanding hard real-time applications.

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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VN-GT: Optimizing Virtual network Deployment via Game Theory

VN-GT: Optimizing Virtual Network Deployment via Game Theory

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Weijie Wang, Yan Xu, Guokun Chen, Zuxin Li, Siyuan Yu, Min Huang, Weiqing Sun, Degang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

The static and homogeneous nature of traditional networks presents a significant challenge for our defense efforts. These characteristics enable an experienced attacker to quickly determine our network topology and gather detailed information about the internal hosts through systematic scanning techniques. Implementing a virtual network view can mitigate this by simulating a virtual topology, thereby consuming the attacker's resources and time. However, deploying a virtual network view reduces network throughput and increase latency. Additionally, an improperly configured virtual network view can waste resources and degrade Quality of Service (QoS). Most existing studies have focused solely on the defender's perspective, resulting in overly idealistic solutions that are ineffective in real-world scenarios. To address this, we propose VN-GT, a game-theoretic based model that optimizes virtual network deployment by considering both attackers and defenders. We provide a detailed example scenario, analyze the game's equilibrium, and validate the effectiveness of our method through a real attack and defense experiment. © 2025 IEEE.

关键词： Game Theory network Deception Software Defined networks Virtual network

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Evaluating Recent 2D Human Pose Estimators for 2D-3D Pose Lifting

Evaluating Recent 2D Human Pose Estimators for 2D-3D Pose Li...

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International Conference on Automatic Face and Gesture Recognition

作者： Soroush Mehraban Yiqian Qin Babak Taati KITE Research Institute University Health Network Institute of Biomedical Engineering University of Toronto Department of Electrical and Computer Engineering University of Toronto Department of Computer Science University of Toronto

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Monocular 3D human pose estimation involves predicting the 3D pixel coordinates of key body joints from a 2D image or video. Typically, a 2D estimation model is employed to initially determine joint locations in an image, followed by training a separate model to lift these positions to 3D coordinates. In this paper, we evaluate the performance of recently proposed 2D human pose estimation models as different inputs for training and evaluation of 2D-3D lifting models. In addition, we propose four simple merging strategies to combine the outputs of these 2D human pose estimators and generate less noisy 2D inputs. To evaluate, four recent 2D pose estimators—ViTPose, PCT, MogaNet, and TransPose—are selected, and their corresponding 2D outputs are generated on the Human3.6M dataset. Subsequently, MotionAGFormer and PoseFormerV2 are trained and evaluated using each created 2D input and its corresponding 3D motion-capture ground truth. ViTPose stands out as the top-performing 2D estimator, and employing all merging strategies proves beneficial in generating a less noisy 2D input. Code and data are available at https://***/TaatiTeam/2DEstimatorEval.

关键词： Training Solid modeling Three-dimensional displays Codes Face recognition Merging Pose estimation

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Poster: Chameleon: Automatic and Adaptive Tuning for DCQCN Parameters in RDMA networks 23

Poster: Chameleon: Automatic and Adaptive Tuning for DCQCN P...

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ACM SIGCOMM 2023 Conference

作者： Chen, Ziteng Zhang, Menghao Li, Guanyu Xu, Mingwei School of Cyber Science and Engineering Southeast University Nanjing China Kuaishou Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Quan Cheng Laboratory Jinan China Department of Computer Science and Technology Institute for Network Sciences and Cyberspace Tsinghua University Beijing China

ISBN: (纸本)9798400702365

Datacenter Quantized Congestion Notification (DCQCN) [12] is the default congestion control algorithm for Mellanox RDMA (Remote Direct Memory Access) NICs [2] in RoCEv2 (RDMA over Converged Ethernet v2) networks, one of the most widely used NICs in leading industry companies [4, 5, 7, 9]. In DCQCN, firstly switches mark packets with ECN (Explicit Congestion Notification) when the queue length exceeds ECN thresholds, then receivers respond to ECN-marked packets with CNPs (Congestion Notification Packets), and finally senders reduce transmission rate when receiving CNPs. DCQCN has 10+ parameters at both NICs and switches, including Alpha Update, Rate Increase & Decrease, Notification Point and ECN thresholds [3], and these parameters have a non-negligible impact on the network performance. Our experiments also verify the network performance of common AI (Artificial Intelligence) training workloads in RoCEv2 networks (e.g., all-to-all collective communication) is greatly influenced by different DCQCN parameter settings (§3). Therefore, when deploying applications in practice, the DCQCN parameters need to be carefully tested and tuned to improve the network performance. © 2023 Owner/Author(s).

关键词： network performance

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Innovations and Challenges in Green Cloud Computing via LEED, Ant Colony Optimization 3

Innovations and Challenges in Green Cloud Computing via LEED...

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3rd International Conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Rohit, V. Robin Saravanan, S.K. Lenin, J. Nisha, F. Sivakumar, S. Rajmohan, M. Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Saveetha School of Engineering Simats Department of BigData and Network Security Tamil Nadu Chennai India Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Karnataka Bengaluru India School of Engineering and Technology Computer Science & Engineering Cmr University Karnataka Bengaluru India Department of Computer Science Tamil Nadu Perambalur India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

This paper investigates how Green Cloud Computing may be made more efficient by combining LEED standards with Ant Colony Optimization (ACO), which is an approach to cloud computing that takes environmental considerations into account. The goal is to maximize resource allocation in data centers while minimizing energy use and carbon impact. The innovative combination of ACO and LEED offers a fresh perspective on achieving a balance between computational performance and environmental sustainability. The goal is to design a system that uses ACO-driven algorithms to optimize energy consumption in cloud infrastructure so it meets LEED standards. Innovative solutions for large-scale cloud settings are needed to address the challenges of scalability and convergence speed that ACO faces. Further, in order to widen the scope, Multi-Objective Optimization (MOO) and Particle Swarm Optimization (PSO) are investigated. While MOO has it tough dealing with competing goals, PSO has trouble striking a balance between the two. We can learn more about how to optimize cloud operations while reducing environmental effect thanks to innovations in these fields. By solving problems in ACO, PSO, and MOO, this study paves the way for Green Cloud Computing to become more efficient and less harmful to the environment. © 2023 IEEE.

关键词： Ant Colony Optimization Challenges Energy Environmental Design Green Cloud Computing Innovations Leadership Resource Allocation Sustain ability Technology

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Enhancing students' learning achievements, self-efficacy, and motivation using mobile augmented reality

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Journal of computer Assisted Learning 2024年第4期40卷 1823-1837页

作者： Aldeeb, Fatimah H. Sallabi, Omar M. Elaish, Monther M. Hwang, Gwo-Jen Computer Network College of Computer Technology Benghazi Libya Computer Science The Libyan Academy Benghazi Libya Software Engineering Information Technology Faculty University of Benghazi Benghazi Libya Computer Science Information Technology Faculty University of Benghazi Benghazi Libya Graduate Institute of Educational Information and Measurement National Taichung University of Education Taichung City Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei City Taiwan Yuan Ze University Taoyuan City Taiwan

Background: This paper examines the use of augmented reality (AR) as a concept-association tool in schools, with the aim of enhancing primary school students' learning outcomes and engagement. Conflicting findings exist in previous studies regarding the cognitive load of AR-enriched learning, with some reporting reduced load and others indicating increased demand and poorer performance. Understanding these implications is essential for effectively leveraging AR in education. The study offers a fresh perspective on the potential of AR technology in improving educational experiences. Objectives: The primary goal of this research was to evaluate the effectiveness of an AR-assisted concept-association strategy for improving essential knowledge acquisition and skills outcomes, increasing cognitive load, and increasing self-efficacy and learning motivation among primary school students. Methods: A quasi-experimental design with a control group was employed to investigate the effectiveness of the intervention. Results and Conclusions: The results demonstrate that the implementation of the AR-assisted concept-association strategy effectively enhanced essential knowledge acquisition and skills outcomes, increased cognitive load, and increased self-efficacy and learning motivation among primary school students. These findings highlight the potential of AR technology to improve the learning experience and engagement of primary school students. The study contributes to the existing literature on the effectiveness of AR technology in education, suggesting future research directions. Ultimately, it offers a practical solution for improving the learning experience by presenting a new approach for teaching using AR technology. © 2024 John Wiley & Sons Ltd.

关键词： augmented reality biology education cognitive load learning motivation mobile learning self-efficacy

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Cucumber Disease Identification Using Multiple Machine Learning Classifiers with a Pre-Trained VGG16 Model 12

Cucumber Disease Identification Using Multiple Machine Learn...

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12th International Electrical engineering Congress, iEECON 2024

作者： Sutacha, Chalermkiat Yodrot, Teerapon Orachon, Teerapong Orkweha, Khwanjit Ubon Ratchathani Rajabhat University Faculty Of Industrial Technology Department Of Computer Network Engineering Ubon Ratchathani Thailand Rajamangala University Of Technology Rattanakosin Faculty Of Industry And Technology Department Of Electrical Engineering Technology Prachuap Khiri Khan Thailand Kosen Of King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Engineering Bangkok Thailand School Of Telecommunication Engineering Suranaree University Of Technology Nakhon Ratchasima Thailand

ISBN: (纸本)9798350383591

Agriculture is a foundational element of global food security and economic stability, yet it faces the perennial challenge of crop diseases, which can significantly impact yield and quality. Traditional disease detection methods, reliant on manual inspection, are labor-intensive and can lack precision. Addressing this, we propose a hybrid model that combines the strengths of Convolutional Neural networks (CNNs) and machine learning classifiers for the automated detection of diseases in cucumber plants. Specifically, we employ the VGG16 network, renowned for its feature extraction capabilities, coupled with Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN) classifiers. Our model is trained on a dataset consisting of high-resolution cucumber images, with 80% used for training and 20% for validation. Performance metrics such as accuracy, precision, recall, and F1-score are utilized for evaluation, with a particular emphasis on the F1-score for its balance between precision and recall. The SVM classifier emerges as the most effective, followed by RF, KNN, and DT. The results, analyzed through confusion matrices, validate the SVM's superior performance in accurately classifying the health status of cucumber plants. This study not only enhances disease detection accuracy but also suggests a direction for future agricultural practices, highlighting the potential integration of such models into real-time crop management systems. © 2024 IEEE.

关键词： Decision trees

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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