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AdaptTrack: Multi-Object Tracking by Adaptive Correlation

AdaptTrack: Multi-Object Tracking by Adaptive Correlation

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Kunpeng Li Lei Wang Hongjun Ren Yangjie Cao School of Cyber Science and Engineering Zhengzhou University Zhengzhou China Hanwei Electronics GroupCorporation Zhengzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The main idea of Multi-Object tracking is to accurately identify and track the position and motion status of multiple objects in a video sequence in real time. The current mainstream tracking algorithm is to associate the detection boxes with the trajectory in a comprehensive and violent way, as a way to accurately determine and maintain the unique identity mark of the moving object. However, implementing a unified association process for detection boxes with different confidence levels, this suffers from non-negligible low information utilization, long association time, and high resource consumption. To solve this problem, we propose a faster, simple and effective association algorithm ADAPT, dividing the queue of detection boxes and trajectories by confidence level to achieve adaptive association of detection boxes and trajectories in the queue, so as to realize tracking, shorten the association time, and improve tracking effect. When applied to six different trackers, our method achieved significant improvements in both MOTA and IDF1 scores. In order to improve the MOT performance, we designed a simple and powerful tracker, AdaptTrack, and evaluated it on 3090GPU for VisDrone-MOT datasets, achieving 41.3 MOTA, 53.6 IDF1, and 42.9 HOTA. AdaptTrack on UAVDT datasets also shows the same excellent results of 69.8 MOTA, 78.9 IDF1, 66.3 HOTA, and achieves a tracking speed of 18.2 FPS.

关键词： distributed processing Correlation Accuracy Video sequences Robustness Real-time systems Trajectory Object tracking Object recognition

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Efficient Scaling on GPU for Federated Learning in Kubernetes: A Reinforcement Learning Approach

Efficient Scaling on GPU for Federated Learning in Kubernete...

引用

international Technical Conference o Circuits/systems, Computers and Communications (ITC-CSCC)

作者： Charn-Doh Bak Seung-Jae Han Department of Computer Science & Engineering Yonsei University Seoul Korea

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

distributed learning enables efficient training with large-scale data, allowing processing to occur in multiple locations without centralization. However, the “straggler problem”, refer-ring to delayed update due to performance heterogeneity, hinders efficient learning. GPUs are crucial for enhancing learning speed by efficiently processing large-scale data through parallel computing capabilities. Efficient GPU utilization, considering resource costs, is essential for reducing training time and costs. Meanwhile, Kubernetes offers scalability, management ease, and resource provisioning for efficient operations. In this paper, we propose a Kubernetes-based vertical scaling scheme to address FL's straggler problem. For GPU scaling, we formulate an opti-mization problem that considers both resource cost and learning speed. Then, we propose a DRL-based approach to address this problem. We also leverage various Kubernetes features as well as CUDA Multi-Process Service (MPS) for execution of vertical scaling. We validate the proposed scheme's performance through various evaluations on a real testbed.

关键词： Training Costs Federated learning Distance learning Scalability Graphics processing units distributed databases

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Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines

Sequre: a high-performance framework for rapid development o...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Haris Smajlović Ariya Shajii Bonnie Berger Hyunghoon Cho Ibrahim Numanagić University of Victoria BC Canada MIT Massachusetts USA Broad Institute of MIT and Harvard Massachusetts USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed indefinitely, and affects the owner's family members as well. The recent large-scale genomic data collections [1], [2] render the traditional privacy protection mechanisms, like the Health Insurance Portability and Accountability Act (HIPAA), inadequate for protection against the novel security attacks [3]. On the other hand, data access restrictions hinder important clinical research that requires large datasets to operate [4]. These concerns can be naturally addressed by the employment of privacy-enhancing technologies, such as a secure multiparty computation (MPC) [5]–[10]. Secure MPC enables computation on data without disclosing the data itself by dividing the data and computation between multiple computing parties in a distributed manner to prevent individual computing parties from accessing raw data. MPC systems are being increasingly adopted in fields that operate on sensitive datasets [11]–[13], such as computational genomics and biomedical research [14]–[22].

关键词： distributed processing Data privacy Pipelines Employment distributed databases Genomics Insurance

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Hardware Accelerator for Transformer based End-to-End Automatic Speech Recognition System

Hardware Accelerator for Transformer based End-to-End Automa...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Shaarada D. Yamini Ganesh S. Mirishkar Anil Kumar Vuppala Suresh Purini International Institute of Information Technology Hyderabad India

Hardware accelerators are being designed to offload compute-intensive tasks such as deep neural networks from the CPU to improve the overall performance of an application, specifically on the performance-per-watt metric. Encoder-decoder-based sequence-to-sequence models such as the Transformer model have demonstrated state-of-the-art results in end-to-end automatic speech recognition systems (ASRs). The Transformer model being intensive on memory and computation poses a challenge for an FPGA implementation. This paper proposes an end-to-end architecture to accelerate a Transformer for an ASR system. The host CPU orchestrates the computations from different encoder and decoder stages of the Transformer architecture on the designed hardware accelerator with no necessity for intervening FPGA reconfiguration. The communication latency is hidden by prefetching the weights of the next encoder/decoder block while the current block is being processed. The computation is split across both the Super Logic Regions (SLRs) of the FPGA, mitigating the inter-SLR communication. The proposed design presents an optimal latency, exploiting the available resources. The accelerator design is realized using high-level synthesis tools and evaluated on an Alveo U-50 FPGA card. The design demonstrates an average speed-up of $32 \times$ compared to an Intel Xeon E5-2640 CPU and $8.8 \times$ compared to NVIDIA GeForce RTX 3080 Ti Graphics card for a 32-bit floating point single precision model.

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Revisiting Consistent Hashing with Bounded Loads 35

Revisiting Consistent Hashing with Bounded Loads

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35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th symposium on Educational Advances in Artificial Intelligence

作者： Chen, John Coleman, Benjamin Shrivastava, Anshumali Rice Univ Dept Comp Sci Houston TX 77005 USA

ISBN: (纸本)9781577358664

Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. Load balancing is critical to many areas including cloud systems, distributed databases, and distributed and data-parallel machine learning. A popular and widely adopted solution to dynamic load balancing is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.

关键词： distributed database systems

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parallel Virtual Impedance Based Fluctuation Power Delivery Strategy For MMC Based SST

Parallel Virtual Impedance Based Fluctuation Power Delivery ...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Jaiyi Li Jianqiao Zhou Jianwen Zhang Gang Shi Xu Cai Yong Wang Xiaofeng Dong Wei Bao College of Information Science and Technology Donghua University Shanghai China Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Jiao Tong University Shanghai China HVDC Transmission and Power Electronics Business Unit Beijing Sifang Automation CO. Ltd. Beijing China Facilities Management Suzhou Power Supply Company Suzhou China Shanghai Electrical Apparatus Research Institute Group CO. Ltd. State Grid Shanghai Municipal Electric Power Company Shanghai China

Modular multilevel converter (MMC) based solid state transformer (MMC-SST) shows promise in hybrid AC/DC distribution grid. This paper introduces a topology consisting of cells made of directly coupled MMC sub-modules (SMs) and isolated SAB modules forming a multi-port SST for grid interconnection. Due to the large SM capacitors for low frequency fluctuation power handling, the device power density is low, which will limit its application. This paper proposes an MMC-SST SM capacitor optimization method based on fluctuation power delivery. It reduces the equivalent low frequency impedance of the SAB by parallel virtual impedance. Low frequency fluctuation power will be delivered into SAB circuit and naturally eliminated in low-voltage DC side due to three-phase symmetrical MMC-SST structure. Therefore, the SM capacitance could be optimized where the power fluctuation flowing into it are reduced. The feasibility of the proposed method is verified by a simulation model.

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parallel Simulation of Stochastic Reward Nets using Theatre 25

Parallel Simulation of Stochastic Reward Nets using Theatre

引用

25th IEEE/ACM international symposium on distributed Simulation and Real Time Applications (DS-RT)

作者： Cicirelli, Franco Nigro, Libero CNR Inst High Performance Comp & Networking ICAR Natl Res Council Italy I-87036 Arcavacata Di Rende CS Italy Univ Calabria DIMES Dept Informat Modelling Elect & Syst Sci I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781665433266

This work aims at the development of tools for supporting modelling and analysis of timed systems by Stochastic Reward Nets (SRN). In a first approach it was proposed and experimented a formal reduction of SRN over Timed Automata (TA) in the context of the Uppaal popular toolbox. The reduction has the merit to allow both exhaustive model checking of an SRN model, useful for the assessment of qualitative properties (e.g., absence of deadlocks, occurrence of particular event sequences etc.), and quantitative analysis through the statistical model checker, which is based on simulations. However, although Uppaal enabled formal reasoning on the semantics of SRN, its practical usage suffers of scalability problems, that is it can introduce severe limitations in time and space when studying complex models. To cope with this problem, this paper describes a Java implementation of the SRN operational core engine, using the lock-free and efficient Theatre actor system which permits the parallel simulation of large models. The realization can be used for functional property checking on an untimed version of a source SRN model, and quantitative estimation of measurables through simulations. The paper discusses the design and implementation of the core engine of SRN on top of Theatre, together with supported intuitive configuration process of an SRN model, and reports some experimental results using a scalable grid computing model. The experiments confirm Theatre/SRN are capable of exploiting the potential of modern multi-core machines and can deliver good execution performances on large models.

关键词： Stochastic Reward Nets performability analysis actors high-performance computing Theatre Java

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Research on Rolling Bearing Fault Diagnosis Based on distributed Fuzzy Broad Learning System Under Imbalanced Data

Research on Rolling Bearing Fault Diagnosis Based on Distrib...

引用

Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), international Conference on

作者： Haiyang Pan Haoran Liu Jinde Zheng Jinyu Tong Jian Cheng School of Mechanical Engineering Anhui University of Technology Ma'anshan PR China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

As a novel fast and effective intelligent algorithm, Broad Learning System (BLS) is popularly used in data classification task. However, the real data are often imbalanced, which makes BLS have limited performance in dealing with imbalanced data and cannot achieve good classification results. In view of this, a method based on distributed Fuzzy Broad Learning System (DFBLS) under imbalanced data is proposed in this paper. The model assigns a fuzzy affiliation value to each training point, reducing the effect of imbalanced samples on the model, and DFBLS executes fuzzy function in parallel, thus improving computational efficiency and processing speed. Moreover, DFBLS learns the features in the input data by mapping features and enhancement features, which solves the deficiency of fuzzification that leads to the loss or confusion of data samples. Finally, it is validated by two rolling bearing experimental datasets, and the research results show that this technology has demonstrated excellent performance in various evaluation indexes and provides an efficient solution to the fault diagnosis problem in imbalanced data sets.

关键词： Learning systems Fault diagnosis Training Computational modeling distributed databases Rolling bearings Data models Indexes Kernel Optimization

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AURA: Adaptive Unified Real-Time Analytics for IoT Intrusion Detection

AURA: Adaptive Unified Real-Time Analytics for IoT Intrusion...

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international symposium on Advanced Networks and Telecommunication systems (ANTS)

作者： Sanket Mishra V Aravindan Rajkanwar Singh Vikash Kumar Singh School of Computer Science & Engineering VIT-AP University Amaravati India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In high-volume, high-velocity contexts, threat identification requires effective real-time data stream analysis. This study offers a novel architecture—real-time processing of high-speed data streams—that is critical for effective threat identification in dynamic contexts. By using a publish-subscribe approach with Apache Kafka, the system is able to manage differences in data volume between many nodes. Experiments on the CICIoV24 and the CICEVSE2024 datasets indicate that the XGBoost model performs better, with high accuracy and robustness against adversarial attacks. Its performance degrades during the HopSkipJump attack, however, defence training can help with it. Moreover, our analysis shows that RandomForest and ExtraTrees perform better in noisy data from the CICIoV24 and XGBoost perform better in noisy data from the CICEVSE24 dataset, emphasizing the importance of selecting algorithms based on performance indicators. The architecture utilizes PyFlink’s distributed computation framework to improve computational efficiency for real-time processing and solves idea drift to ensure flexibility in changing data attributes.

关键词： Training Intrusion detection distributed databases Publish-subscribe Computer architecture Real-time systems Robustness Computational efficiency Telecommunications Noise measurement

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Stability Analysis of Circulating Current in Multi-parallel Grid-Connected Inverter System

Stability Analysis of Circulating Current in Multi-Parallel ...

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Power Electronics and Application symposium (PEAS), IEEE international

作者： Mingliang Li Xinbo Ruan Mingming Shi Xian Zheng College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd. Nanjing China

Multi-parallel grid-connected inverter system is increasingly applied in distributed power generation systems. Due to the existence of grid impedance, the output current of the grid-connected inverter cannot be fed to the grid totally, and a part of it will circulate to other inverters connected in parallel under weak grid. And the stability of circulating current also affects the safety and reliability of the system. Also, the large circulating current will bring more power loss to system. This paper uses the average model of the grid-connected inverter to establish the Norton equivalent circuit of the multi-parallel grid-connected inverter system. On this basis, the generation mechanism of the circulating current is explained by the current separateness, and the relationship between the stability of the circulating current and the injected grid current is indicated. Finally, the accuracy of the proposed theory is verified by simulation.

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