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Anomalous Behavior Identification with Visual Federated Learning in Multi-UAVs systems 29

Anomalous Behavior Identification with Visual Federated Lear...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Wang, Pengfei Yu, Xinrui Ye, Yefei Qi, Heng Yu, Shuo Yang, Leyou Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian China Ministry of Education Dalian116024 China Northeastern University School of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350330717

Anomaly detection aims to identify data or behav-iors that are different from the usual patterns. In traditional anomaly detection settings, edge devices collect the data and send it to a centralized server for model training, which faces two critical issues: (1) it risks data exposure during transmission;(2) it demands a large amount of network bandwidth for data transfer. To tackle these problems, we propose a Visual Federated Learning algorithm (VFLA) for anomalous behavior identification in the multi-UAVs system. To the best of our knowledge, we are the first to merge federated learning with video-based anomaly detection. VFLA consists of two phases: The initial phase is training a pseudo-label generator. UAVs collect a dataset and manually annotate it. This labeled data is then used to train the pseudo-label generator on the server, which is subsequently distributed back to the UAVs. The second phase is the federated learning-based anomaly detection model training. UAVs leverage the pseudo-label generator to automatically annotate the collected video footage. These annotated videos are fed into an anomaly detection network for training. Once the local training is completed, UAVs upload their local models to a server for federated aggregation. The global model is then redistributed to the UAVs for additional training rounds, until reach the target accuracy. Finally, we simulate the federated learning anomaly detection algorithm on the Shanghai-tech dataset, it demonstrates an average accuracy boost of 5.6% compared to baselines. © 2023 IEEE.

关键词： Anomaly detection distributed training Federated learning UAVs

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parallel Optimization of Super Pixel Algorithm SLIC 3

Parallel Optimization of Super Pixel Algorithm SLIC

引用

3rd international conference on Information Science, parallel and distributed systems, ISPDS 2022

作者： Luo, Xiaoqi Xing, Yuanjie Xu, Senhai Southwest Petroleum University Chengdu China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Super pixel algorithm SLIC uses K-means mean clustering method to effectively generate super pixels. Compared with other super pixel algorithms, it is more efficient and improves the segmentation performance. In order to further improve its performance, the program is optimized from five major directions: compilation optimization, data structure optimization, loop vectorization, OpenMP parallel optimization and algorithm optimization. © 2022 IEEE.

关键词： K-means clustering

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PANDA: Performance Prediction for parallel ANd Dynamic StreAm Processing 22

PANDA: Performance Prediction for Parallel ANd Dynamic StreA...

引用

16th ACM international conference on distributed and Event-Based systems, DEBS 2022

作者： Agnihotri, Pratyush Koldehofe, Boris Binnig, Carsten Luthra, Manisha Technical University of Darmstadt Germany University of Groningen Netherlands

ISBN: (纸本)9781450393089

distributed Stream Processing (DSP) systems highly rely on parallelism mechanisms to deliver high performance in terms of latency and throughput. Yet the development of such parallel systems altogether comes with numerous challenges. In this paper, we focus on how to select appropriate resources for parallel stream processing under the presence of highly dynamic and unseen workloads. We present PANDA that provides a novel learned approach for highly efficient and parallel DSP systems. The main idea is to provide accurate resource estimates and hence optimal parallelism degree using zero-shot cost models to ensure the performance demands. © 2022 ACM.

关键词： Cost benefit analysis

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Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU parallel

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale usin...

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2024 Workshops of the international conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Maheshwari, Ketan Arndt, William Karimi, Ahmad Maroof Yin, Junqi Suter, Frederic Johnson, Seth Da Silva, Rafael Ferreira Oak Ridge National Laboratory Oak RidgeTN United States Lawrence Berkeley National Laboratory BerkeleyCA United States

ISBN: (纸本)9798350355543

GNU parallel is a versatile and powerful tool for process parallelization widely used in scientific computing. This paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. Through real-world examples, we highlight GNU parallel's performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF's Frontier and NERSC's Perlmutter, showcase GNU parallel's rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU parallel's application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU parallel can be employed in conjunction with other workflow systems as a "last-mile"parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries. © 2024 IEEE.

关键词： distributed Computing GNU parallel High-Throughput Computing Scientific Workflows

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Algorithm for Digital Processing of Seismic Signals in distributed systems 14th

Algorithm for Digital Processing of Seismic Signals in Distr...

引用

14th international conference on Intelligent Human Computer Interaction, IHCI 2022

作者： Mallaev, Oybek Azimov, Bunyodbek Muslimjon, Kuchkarov Kamola, Ahmadova Department of Artificial Intelligence Tashkent University of Information Technologies named after Muhammad al Khwarizmi Tashkent Uzbekistan Department of Information Security Tashkent University of Information Technologies named after Muhammad al Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9783031271984

The problem of misallocation of cache lines in processors is studied based on the specific features of memory organization in multi-core and multi-processor systems of distributed systems. Incorrect allocation of cache lines leads to sequential execution of tasks that should be executed in parallel on several cores. It is difficult to determine the problem, and its presence can cause a sharp decrease in the performance of the entire system. In many seismic studies, scientists’ efforts are aimed at finding reliable signs of seismic hazard. Abrupt changes in one or another parameter are called abrupt effects, emissions or anomalies, through which the principle of prediction can be implemented - this means predicting the place, strength and time of a future seismic event. The use of distributed systems in the implementation of predictions with the help of machine learning, digital processing of large volumes of digital signals, ensures fast and high-quality determination of results. To increase the speed, it is necessary to develop parallel algorithms. Synchronization of common parallel streams is necessary to solve parallel computing problems and to ignore cache memory in parallel programming. This causes cache lines to be misallocated. Acceleration of parallel algorithms for digital processing of seismic signals and the dependence of the efficiency of using the system’s computing resources on the number of parallel flows were analyzed, a parallel algorithm for determining the epicenter using cubic splines and a software tool was created based on this algorithm, and the proposed spline method was used to determine the characteristics of P and S waves. Made it possible to determine the detection time 2.5 times faster than usual. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital signal processing

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Towards Optimizing Cost and Performance for parallel Workloads in Cloud Computing 15

Towards Optimizing Cost and Performance for Parallel Workloa...

引用

15th international conference on Cloud Computing and Services Science, CLOSER 2025

作者： Maas, William Rossi, Fábio Diniz Luizelli, Marcelo C. Navaux, Philippe O.A. Lorenzon, Arthur F. Institute of Informatics Federal University of Rio Grande do Sul Brazil Federal Institute Farroupilha Campus Alegrete Brazil Federal University of Pampa Campus Alegrete Brazil

ISBN: (纸本)9789897587474

The growing popularity of data-intensive applications in cloud computing necessitates a cost-effective approach to harnessing distributed processing capabilities. However, the wide variety of instance types and configurations available can lead to substantial costs if not selected based on the parallel workload requirements, such as CPU and memory usage and thread scalability. This situation underscores the need for scalable and economical infrastructure that effectively balances parallel workloads’ performance and expenses. To tackle this issue, this paper comprehensively analyzes performance, costs, and trade-offs across 18 parallel workloads utilizing 52 high-performance computing (HPC) optimized instances from three leading cloud providers. Our findings reveal that no single instance type can simultaneously offer the best performance and the lowest costs across all workloads. Instances that excel in performance do not always provide the best cost efficiency, while the most affordable options often struggle to deliver adequate performance. Moreover, we demonstrate that by customizing instance selection to meet the specific needs of each workload, users can achieve up to 81.2% higher performance and reduce costs by 95.5% compared to using a single instance type for every workload. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： parallel processing systems

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Enriching Big Data Intrusion Detection and Service Through Mapping and parallel Computation

Enriching Big Data Intrusion Detection and Service Through M...

引用

international conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Roy, Koyel Deshmukh, Rushali JSPM Rajarshi Shahu Coll Engn Pune Maharashtra India

ISBN: (纸本)9789819984756;9789819984763

Large-scale analysis of data requires a significant amount of computing power and resources, so the vast and flexible interpreting, preservation, and remote servers that cloud computing companies provide are very attractive. MongoDB is a widely used database management system for big data that is known for its fast performance and ability to execute activities within the MapReduce framework. The notion of big data relies on the conceptualization of resource collaboration, where applications and related infrastructure are frequently distributed among multiple users or organizations through isolation techniques such as virtualization. Big data is vulnerable to various types of assaults owing to its collaborative assets and extensive system software. The lack of efficacy and utility in improving the efficiency and privacy of big data is a significant challenge. The mission of securing big data is of utmost importance, as any discrepancies in this regard can have significant detrimental effects. The complex and voluminous characteristics of big data pose significant challenges in the application of traditional security methodologies, thereby leading to potential issues. Therefore, this research approach utilizes effective big data query classification and partitioning along with mapping and parallel computation for improving efficiency, as well as a bilinear pairing, avalanche effect, and tamper detection for forensic analysis and report generation. The approach has been effectively tested and compared with conventional approaches to achieve improved outcomes.

关键词： Data mapping Avalanche effect Bilinear pairing parallel computation Big data

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Delays in Computing with parallel Metaheuristics on HPC Infrastructure 16th

Delays in Computing with Parallel Metaheuristics on HPC Infr...

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16th international conference on Computational Collective Intelligence (ICCCI)

作者： Bielaszek, Sylwia Nowak, Adam Gadek, Krzysztof Byrski, Aleksander AGH Univ Sci & Technol Fac Comp Sci Inst Comp Sci Krakow Poland

ISBN: (纸本)9783031708183;9783031708190

Due to their structure, metaheuristics such as parallel evolutionary algorithms (PEA) are well suited to be run on parallel and distributed infrastructure, e.g. supercomputers. However, there are still many issues that are not well researched in this context, e.g. existence of delays in HPC-grade implementations of metaheuristics and how they affect the computation itself. The lack of this knowledge may expose the fact, that the power of supercomputers in this context may be not properly used. We want to focus our research on examining such white spots. In the paper we focus on giving the evidence for the existence of delays, showing the differences among them in different island topologies, try to explain their nature and prepare to propose dedicated migration operators considering these observations.

关键词： high performance computing delays PEA island model genetic algorithm migrations

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A Robust Reactive Power Sharing Solution For Meshed Multi-PCC Microgrids 17

A Robust Reactive Power Sharing Solution For Meshed Multi-PC...

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17th international conference on Engineering of Modern Electric systems, EMES 2023

作者： Ait Ben Hassi, Youssef Amine Hennane, Youssef Berdai, Abdelmajid Tytiuk, Valerii National School of Electricity and Mechanics Ensem Hassan Ii University of Casablanca Energy and Electrical Systems Laboratory Casablanca Morocco University of Loraine Lemta Laboratory Nancy France Kryvyi Rih National University Department of Electromechanics Kryvyi Rih Ukraine

ISBN: (纸本)9798350310634

In light of the great transition toward renewable energy generation, islanded microgrids are offering a wide opportunity to reach this aim. Hence, power sharing strategies of parallel distributed generation sources are becoming an interesting research point. Traditional droop control is the fundamental decentralized method to reach proper sharing of active and reactive power. Yet, in case of unequal values of the interconnecting line impedances, reactive power sharing errors become remarkable. To resolve this issue, a decentralized power sharing method is suggested in this paper. The chosen microgrid is a multi-Point of Common Coupling (PCC), unlike the majority of research works, which consider mono PCC microgrids. In this paper, synchronization, load variation, communication delay and communication interruption are considered and proved by simulation using MATLAB/Simulink. © 2023 IEEE.

关键词： distributed power generation

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Cutting a Wire with Non-Maximally Entangled States

Cutting a Wire with Non-Maximally Entangled States

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Bechtold, Marvin Batten, Johanna Leyrnann, Frank Mandl, Alexander Univ Stuttgart Inst Architecture Applicat Syst Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9798350364613;9798350364606

distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations via classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of circuit executions required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in circuit executions. Nevertheless, each teleportation requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. Addressing this, our work explores utilizing non maximally entangled qubit pairs in wire cutting, a specific form of circuit cutting, to mitigate the associated costs. The cost of this cutting procedure reduces with the increasing degree of entanglement in the pre-shared qubit pairs. We derive the optimal sampling overhead in this context and present a wire cutting technique employing pure non-maximally entangled slates that achieves this optimal sampling overhead. Hence, this offers a continuum between existing wire cutting and quantum teleportation.

关键词： distributed Quantum Computing Circuit Cutting Quantum Teleportation Entanglement

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