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Efficient Node Selection for Coding-based Timely Computation over Heterogeneous systems 21

Efficient Node Selection for Coding-based Timely Computation...

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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： Lin, Yuxuan Tang, Bin Zhou, Siyuan Xie, Zaipeng Ye, Baoliu Hohai University Key Laboratory of Water Big Data Technology Ministry of Water Resources Nanjing211100 China Hohai University School of Computer and Information Nanjing211100 China Nanjing University National Key Laboratory for Novel Software Technology Nanjing210023 China

ISBN: (纸本)9798350329223

In large-scale distributed systems, computational nodes often experience random slowdown which can degrade the performance of timely computation tasks significantly. Recently, coded computing has emerged as a promising approach where coded subtasks are generated for parallel execution over different nodes. In this paper, we focus on exploring the tradeoff between the performance of coding-based timely computation tasks and the computation cost in distributed systems with heterogeneous nodes. We introduce a node selection problem (NSP), which aims at minimizing the task failure probability by selecting proper participating nodes under a total cost constraint. We show that NSP is NP-hard due to the total cost constraint, and is more challenging since the task failure probability is a complicated multivariate polynomial. By approximating the task failure probability using central limit theorem and establishing relationships with certain integer linear programming problems, we then propose a linear programming relaxation based algorithm with performance guarantee, as well as an efficient Lagrangian relaxation inspired heuristic algorithm. The effectiveness of the proposed node selection algorithms is further demonstrated via both trace-driven and model-driven simulations. © 2023 IEEE.

关键词： Heuristic algorithms

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Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain systems 21

Transaction Data Management Optimization Based on Multi-Part...

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作者： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. first, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 IEEE.

关键词： information management

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proceedings of the 2023 IEEE international conference on Computer, information, and Telecommunication systems, CITS 2023

Proceedings of the 2023 IEEE International Conference on Com...

引用

2023 IEEE international conference on Computer, information, and Telecommunication systems, CITS 2023

ISBN: (纸本)9798350336092

The proceedings contain 33 papers. The topics discussed include: deep learning polar convolutional parallel concatenated (DL-PCPC) channel decoding for 6G communications;cyber deception system based on Monte Carlo simulation in the mobile edge computing (MEC);lightweight hash function based on Fermat number transform (FNT);automatic recognition for LPI radar waveform using sequence-based deep learning model;a transfer learning based CSI indoor localization using GASF image construction;profit aware resource allocation in fog computing: a Stackelberg game approach;proposal for a security model applying artificial intelligence for administrative management in a higher education institution;on implementation of sparse finite impulse response secondary path for active noise control;high layer coding for video transmission;deep neural network for user attribute recognition in metaverse hand images;lifetime estimation and reparameterization of GPS devices for tracking applications;a reparameterization and monitoring platform for energy efficient GPS trackers in behavioral analysis of wildlife;and scheduling delay-sensitive bag-of-task jobs with imprecise computations on distributed resources.

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Robust Traffic Light Recognition Pipeline Based on YOLOv8 for Autonomous Driving systems 29

Robust Traffic Light Recognition Pipeline Based on YOLOv8 fo...

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

作者： Zhang, Yuwei Wu, Yan Zhao, Junqiao Hu, Yinghao He, Yufei Wang, Jijun Tongji University College of Electronic and Information Engineering Shanghai China

ISBN: (纸本)9798350330717

Traffic Light Recognition (TLR) aims at detecting Traffic Lights (TLs) and then classifying the status of light signals, being an essential constituent of autonomous driving perception systems. However, it's challenging for existing TLR methods to accurately distinguish both color and shape status of TLs due to small object sizes, illumination variations, close resemblance with other objects and varying weather conditions. Existing public datasets for TLR have three main drawbacks:(i) poor diversity, (ii) sample imbalance, and (iii) insufficient category labels, greatly hindering the development of TLR. To overcome the aforementioned problems, we propose a Robust Traffic Light Recognition Pipeline based on YOLOv8 (RTLRP-YOLO) that can recognize TLs accurately with strong robustness based on adaptively generated high-quality images. Specifically, we develop a Self-Adaptive Preprocessing Module (SAPM) which is designed to adaptively generate high-quality images under hostile conditions, followed by a Two-stage Traffic Light Recognition Model based on YOLOv8 (TTRM) to obtain both the location and status information of TLs. Moreover, We also provide our self-made Tongji Small Traffic Light Dataset (TSTLD), covering a variety of weather conditions, regions, light intensities and shooting angles. To the best of our knowledge, our proposed method is the first one to be able of simultaneously identifying three colors (i.e., red, yellow and green) and four shapes (i.e., circle, left arrow, right arrow and up arrow) of TLs, achieving 95.43% accuracy on TSTLD with the inference time of 26 ms for per image. © 2023 IEEE.

关键词： Model Robustness Traffic Light Dataset Traffic Light Recognition YOLOv8

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TransFlowLog: Log Anomaly Detection Based on Transformer Encoder and Interflow Decoder 29

TransFlowLog: Log Anomaly Detection Based on Transformer Enc...

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

作者： Lin, Zaichao Lu, Siyang Han, Ningning Wang, Dongdong Wei, Xiang Wang, Mingquan Beijing Jiaotong University School of Computer and Information Technology Beijing China University of Central Florida Department of Civil Environmental and Construction Engineering OrlandoFL United States Beijing Jiaotong University School of Software Engineering Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9798350330717

Logs are valuable resources that record the health status of systems. Analyzing logs to uncover and investigate abnormal behaviors has become an essential approach of ensuring system security. However, some potential anomalies may be missed when performing log anomaly detection, and even a seemingly insignificant abnormal behavior can lead to a series of severe anomalies or continuous negative impact on the performance of systems. Therefore, the reliability and security of systems are facing significant challenges. To address this issue, in this study, we propose TransFlowLog, an Encoder-Decoder architecture-based approach for log anomaly detection. It utilizes the Transformer Encoder, a state-of-the-art sequence modeling technique, to comprehensively understand contextual relationships using self-attention mechanism. Moreover, we introduce the Interflow Decoder, which considers information exchange between channels in embedded log sequences. The Interflow Decoder enhances the features encoded by the Transformer Encoder in both sequence and channel dimensions, thereby capturing interdependence between different channels. Comparative experiments conducted on three real-world datasets demonstrate the effectiveness of the proposed method, as it achieves higher F1-score and reduces the number of false negatives. © 2023 IEEE.

关键词： anomaly detection Encoder-Decoder architecture self-attention systems reliability

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Connecting Domain Experts and Data: Enriching User-Centric Data Analysis with Neural Network-Aided Data Source Suggestion 25th

Connecting Domain Experts and Data: Enriching User-Centric D...

引用

25th international conference on Enterprise information systems (ICEIS)

作者： Behringer, Michael Treder-Tschechlov, Dennis Voggesberger, Julius HirmePD, Pascal Mitschang, Bernhard Univ Stuttgart Inst Parallel & Distributed Syst Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9783031647475;9783031647482

Nowadays, data analysis is widely used in numerous areas to identify new trends, opportunities, or risks and to improve decision-making. In many cases, however, data analysis is only possible by incorporating specific domain knowledge, which is why domain experts need to be involved. To this end, data mashups are a popular tool for modeling tailored analyses. Yet, with today's data volumes from heterogeneous source systems, it is very difficult to identify beneficial data sources, in particular for explorative data analysis. In this paper, we first define requirements aiming for user-centric analytics, followed by the introduction of SDRank, a deep-learning-based approach to identify beneficial data sources. In an extensive evaluation with three scenarios, we show that this approach offers high robustness concerning the training data used and can reliably identify beneficial data sources, even for previously unknown domains, i.e., transfer learning.

关键词： User-centric data analysis Data mashups

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Challenges in work procedures in distributed crisis management 20

Challenges in work procedures in distributed crisis manageme...

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20th Global information systems for Crisis Response and Management conference, ISCRAM 2023

作者： Erik, A.M. Borglund Granholm, Martina Mid Sweden University Sweden

ISBN: (纸本)9798218217495

This is a work in progress paper on work and IT usage in distributed crisis management. The data presented in this paper has been collected at a one-day tabletop exercise with four Swedish municipalities. Four members of the four municipalities’ crisis organizations were invited to the exercise, which was designed as one scenario divided into two cases. At the start of each case of the exercise, each municipality was split into two separate rooms, to simulate a distributed crisis management. During the first case they could communicate using phone, TETRA radio, and the Internet. During case two in the scenario, there was no Internet connection. The study indicates that all the municipalities managed to organize and solve the given tasks using primarily voice communication, in case one using phone or, e.g., Teams, and in case two using TETRA radio. information sharing using IT was non-existing. © 2023 information systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： information use

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GraphUnet: Graph Make Strong Encoders for Remote Sensing Segmentation 29

GraphUnet: Graph Make Strong Encoders for Remote Sensing Seg...

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

作者： Shou, Yuntao Ai, Wei Meng, Tao Zhang, Fuchen Li, Keqin Central South University of Forestry and Technology School of Computer and Information Engineering ChangSha China State University of New York Department of Computer Science New PaltzNY12561 United States

ISBN: (纸本)9798350330717

Remote sensing segmentation are widely applied in environmental protection, and urban change detection, etc. Despite the success of deep learning-based remote sensing segmentation methods (e.g., CNN and Transformer), they are not flexible enough to model irregular objects. To tackle the above problems, this paper treats images as graph structures and introduces a simple vision GNN (GraphUNet) architecture for remote sensing segmentation. Specifically, we construct a graph view and utilize GNN to extract the visual feature. To extract the global contextual location information in the image, we introduce a multi-head attention mechanism for global information extraction. Furthermore, we introduce a feed-forward network for each node to perform feature transformation on node features to encourage information diversity. Extensive experiments on three real datasets demonstrate that our model GraphUNet outperforms SOTA remote sensing image segmentation methods. © 2023 IEEE.

关键词： Graph Learning Multi-head attention Remote Sensing Segentation U-shaped structure

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A modeling method for cyber-physical attacks on distributed photovoltaic systems based on probabilistic colored Petri nets 10

A modeling method for cyber-physical attacks on distributed ...

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10th international conference on Energy Materials and Electrical Engineering, ICEMEE 2024

作者： Di, Li Zhang, Weijian Chen, Cen Xi, Qingyu State Grid Henan Electric Power Company Electric Power Science Research Institute Henan Zhengzhou450000 China Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing210044 China

ISBN: (纸本)9781510686243

As distributed photovoltaic systems (DPS) are widely adopted in power grids, their cyber-physical security has become an increasingly pressing concern. This paper proposes a modeling method based on Probabilistic Colored Logical Petri Nets (PCLPN) to describe and analyze cyber-physical attacks in DPS. first, we introduce the basic concepts and formal definition of PCLPN, detailing its advantages in handling the complexities of randomness, colored tokens, and logical conditions in such systems. We then construct a basic cyber-physical attack model. By introducing attack nodes, we simulate various cyber-physical attack scenarios, such as cyber-attacks and physical disruptions. In the model building process, we use dependency relationship matrices and logical conditions to describe the dynamic changes of attack behaviors, leverage colored tokens to differentiate attack types, and employ a mixed-strategy game to calculate the probabilities of nodes being attacked, thereby quantifying the propagation probabilities of attacks along different paths. Finally, through simulation analysis, we verify the effectiveness of the proposed method in capturing the complex dynamic behaviors of DPS and evaluating system vulnerabilities. This research provides new ideas and tools for the cyber-physical security assessment and protection strategy development of DPS. © 2024 SPIE.

关键词： Cyber attacks

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