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Challenges and Decisions in WOBCompute Design, a P2P computing System Architecture 15th

Challenges and Decisions in WOBCompute Design, a P2P Computi...

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15th international conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Filep, Levente Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania

ISBN: (纸本)9783030700058;9783030700065

In the field of large-scale computing, Cloud computing Services gained popularity due to their low cost and availability. Alternatives exist in the form of distributed computing systems, most of which combine existing, cheap, commodity hardware into clusters of computing and storage resources. Most of these implementations are Client-Server model-based. Decentralized solutions employ a form of Peer-to-Peer (P2P) design, however, without major benefits besides the decentralized task coordination, and due to increased design complexity compared to the Client-Server models, these implementations haven't gained widespread popularity. In this paper, the architecture of WOBCompute is presented, which is P2P based, and features decentralized task coordination, the possibility of workload transfer via checkpoints, and task tracking and location queries with the possibility of messaging between parallel branches of a distributed application. Architecture design considerations and choices are also presented. The overlay of the P2P system is a super-peer driven clusters organized into an extended star topology. Using backup super-peers, and distributing the cluster members between them, the size and stability of the clusters are improved;query and lookup messages are limited to only the super-peers and the topology employed reduces the longest message path. The complexity of the system mandates the use of a middleware, which hides these and provides a simplified interface for a distributed application to take advantage of the computing resources, being a combination of in-house computing devices, personal or volunteer donated resources, as well as Cloud-based VMs.

关键词： Peer-to-peer networks Super-peer topology distributed computing Middleware P2P computing architecture

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yizhou Li Yijie Wang Hongzuo Xu Xiaohui Zhou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors.

关键词： Time series analysis Semantics Neural networks Training data Manuals Signal processing Data models Speech processing Anomaly detection Testing

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A Scalable Blockchain-based High-Definition Map Update Management System

A Scalable Blockchain-based High-Definition Map Update Manag...

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IEEE international Smart Cities conference (ISC2)

作者： Zhong, Lei Onishi, Ryokichi Wang, Ling Ruan, Leifeng Tan, Soo Jin Toyota Motor Co Ltd Tokyo Japan Iallchain Technol Co Ltd Beijing Peoples R China Intel Corp Santa Clara CA 95051 USA

ISBN: (纸本)9781665449199

High-Definition (HD) map service requires more frequent update and much larger data volume than traditional map services, so that it is very difficult to handle HD map update by existing offline data collection such as survey fleets, and centralized cloud-based map services. To solve this problem, vehicle crowd-sourcing and edge computing infrastructure are proposed to timely capture the change of the physical world and distributedly process the huge amount of raw data close to the vehicles. In such a decentralized environment, distributed Ledger Technology (DLT), such as blockchain, is very promising to meet some of the important requirements of data integrity and traceability of map update, but it is challenging to meet the requirement of frequent map update and scale to a large-scale system. In this work, we propose a salable III) map update management system by leveraging multiple blockchain networks in parallel and dynamically assign the generated map update to the corresponding blockchains according to the location and update frequency requirements. To verify the proposed system, we prototyped it based on the Hyperledger Fabric, an opensource implementation of consortium blockchain, and tested it in the lab environment.

关键词： distributed ledger Smart cities Data integrity conferences Data collection Fabrics Blockchains

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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A Comparative Study on Improving Word Embeddings Beyond Word2Vec and GloVe

A Comparative Study on Improving Word Embeddings Beyond Word...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Rajdeep Biswas Suman De BITS Pilani & Data Scientist CXS APM SAP Labs India Pvt. Ltd. Bangalore India CCDS IIT Kharagpur & S4 Product Insights SAP Labs India Pvt. Ltd. Bangalore India

ISBN: (纸本)9781665454025

NLP or Natural Language Processing in Machine Learning forms a subarea with linguistic roots that has applications in analyzing and predicting natural language data, namely speech and text. Deep neural networks are used in cutting-edge NLP, and the process includes several steps: data collection, preprocessing, language modeling, parsing, and prediction. This research will focus on the aspect of language modeling where words must be represented using embeddings. The study emphasizes improving post-embedding model performance, particularly for unsupervised models, although the whole word embeddings used by these models have remained relatively unchanged. Word2Vec, for example, employs an unsupervised shallow 2-layered network that has suited its function thus far, but making this network denser and adding supervised elements appears to be a viable means of increasing spatial-semantic embedding. A better representation early in the model pipeline will significantly help any computer understand the semantic similarity of the data it is examining, which will considerably enhance the performance of any model that employs the stated representation.

关键词： Symbiosis Computational modeling Semantics Pipelines Neural networks Predictive models Linguistics

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32nd international Workshop on Languages and Compilers for parallel computing, LCPC 2019

32nd International Workshop on Languages and Compilers for P...

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32nd international Workshop on Languages and Compilers for parallel computing, LCPC 2019

ISBN: (纸本)9783030727888

The proceedings contain 11 papers. The special focus in this conference is on Languages and Compilers for parallel computing. The topics include: CubeGen: Code Generation for Accelerated GEMM-Based Convolution with Tiling;PostSLP: Cross-Region Vectorization of Fully or Partially Vectorized Code;FLARE: Flexibly Sharing Commodity GPUs to Enforce QoS and Improve Utilization;foundations of Consistency Types for a Higher-Order distributed Language;Common Subexpression Convergence: A New Code Optimization for SIMT Processors;Using Performance Event Profiles to Deduce an Execution Model of MATLAB with Just-In-Time Compilation;CLAM: Compiler Leasing of Accelerator Memory;abstractions for Polyhedral Topology-Aware Tasking [Position Paper];SWIRL ++ : Evaluating Performance Models to Guide Code Transformation in Convolutional Neural networks.

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Research on Dynamic Fuzzy Testing in Securing Cloud Infrastructure based on Deep Learning

Research on Dynamic Fuzzy Testing in Securing Cloud Infrastr...

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Cloud computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Ningyun Dan Lun Zhao Wenjin Pan Yan Cai College of Physics and Information Engineering Zhaotong University Zhaotong China Yunnan Electric Test & Research Institute Group Co. Ltd. China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

As cloud infrastructure assumes an increasingly pivotal role in contemporary computing environments, ensuring its security becomes a matter of paramount importance. As a prevalent vulnerability detection technology, traditional fuzz testing encounters the challenge of scalability in the context of the intricate and evolving nature of cloud computing systems. Our approach employs deep neural networks (DNNs) to analyze the behavior of diverse software components within cloud systems. It incorporates multi-dimensional data, including system logs, network traffic, and resource utilization in virtualized environments. The neural network is capable of identifying potential vulnerability points through an automated process. To generate effective test cases, we introduce a reinforcement learning framework based on a policy gradient algorithm. This framework enables an intelligent agent to interact with the system in order to obtain feedback and subsequently update the policy in order to progressively generate more aggressive test cases. The framework is designed with an adaptive reward mechanism that not only encourages triggering crashes but also focuses on edge use cases that reveal system bottlenecks, thereby improving vulnerability coverage. Furthermore, in consideration of the dynamic nature of the cloud environment, we have developed a distributed test architecture that enables the parallel deployment of tasks in virtual machines or containers, simulates multi-tenancy and dynamic load change scenarios, and introduces a vulnerability detection mechanism at the virtualization layer. The proposed model is capable of adaptively generating highly targeted test cases and optimizing the test strategy through continuous learning, thereby markedly enhancing the detection capability of complex vulnerabilities in cloud infrastructure.

关键词： Deep learning Cloud computing security Software algorithms Reinforcement learning Telecommunication traffic Fuzzing Virtual machines Security Testing Software engineering

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Implementing an Intelligent Irrigation System in Oman Using Machine Learning Technique

Implementing an Intelligent Irrigation System in Oman Using ...

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Vimal Kumar Stephen Sanjiv Sharma Zaina Rashid Salim Al-Jufaili Senthil Jayapal College of Computing and Information Sciences University of Technology And Applied Sciences Ibra Oman

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

In this study, machine-learning technique is used to collect environmental data in real time and measures other aspects like soil moisture, temperature, water level, humidity. Water use efficiency has recently increased because to new agricultural techniques, sophisticated sensors as Micro Controller, and Wireless Sensor Network (WSN). Irrigation Fault Detection method based on XGBoost (IFD-XGBoost) Algorithm used as proposed method as robust to missing values imputing them naturally during tree splitting. The Suggested method is optimized for speed, using parallel and distributed computing to make predictions faster compare to other Regression Algorithm and avoid over fitting. Using Min-Max Normalization, features with different sizes and units can affect the efficacy of several machine learning algorithms. By warping the statistical relationship between features, removal of noisy data and error detection can reduce the machine learning models' efficiency. Lasso Regression is a linear model that combines regularization and feature selection. Additionally, less data will be kept in the centralized cloud as a result. Using the IFD-XGBoost Algorithm's ability to handle missing data, learn from past errors, and adjust parameters, our proposed approach would be the best model to implement. It outperform from other algorithm in Predictive accuracy, making it reliable for calculating water needs and forecasting weather. Investigating the implementation of several machine learning models that can offer the best real-world irrigation decision management is the main goal of this article. The comparative analysis to evaluate the IFD-XGBoost method yielded a minimum RMSE of 0.3524, MAE of 0.1832, MAPE of 4.24 with the highest accuracy of 99.2%, precision of 96.2% and AUC-ROC of 95%, indicates when combined, these metrics provide insightful data about each model's ability to anticipate future developments in irrigation systems.

关键词： Temperature sensors Temperature measurement Irrigation Wireless sensor networks Machine learning algorithms Soil moisture Machine learning Humidity Prediction algorithms Data models

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation

A Drug Box Recognition System Based on Deep Learning and Clo...

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international conference on parallel and distributed Systems (ICPADS)

作者： Honglei Zhu Anming Dong Jiguo Yu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

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17th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2020

17th IFIP WG 10.3 International Conference on Network and Pa...

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17th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2020

ISBN: (纸本)9783030794774

The proceedings contain 39 papers. The special focus in this conference is on Network and parallel computing. The topics include: LCache: Machine Learning-Enabled Cache Management in Near-Data Processing-Based Solid-State Disks;security Situation Prediction of Network Based on Lstm Neural Network;Dynamic GMMU Bypass for Address Translation in Multi-GPU Systems;parallel Fast DOA Estimation Algorithm Based on SML and Membrane computing;segmented Merge: A New Primitive for parallel Sparse Matrix Computations;a Hierarchical Model of Control Logic for Simplifying Complex networks Protocol Design;FPGA-Based Multi-precision Architecture for Accelerating Large-Scale Floating-Point Matrix computing;a Configurable Hardware Architecture for Runtime Application of Network Calculus;FEB3D : An Efficient FPGA-Accelerated Compression Framework for Microscopy Images;A Dynamic Mapping Model for General CNN Accelerator Based on FPGA;NUMA-Aware Optimization of Sparse Matrix-Vector Multiplication on ARMv8-Based Many-Core Architectures;CARAM: A Content-Aware Hybrid PCM/DRAM Main Memory System Framework;Optimization of RDMA-Based HDFS Data Distribution Mechanism;reducing the Time of Live Container Migration in a Workflow;RDMA-Based Apache Storm for High-Performance Stream Data Processing;payment Behavior Prediction and Statistical Analysis for Shared Parking Lots;location-Based Service Recommendation for Cold-Start in Mobile Edge computing;an Adaptive Delay-Limited Offloading Scheme Based on Multi-round Auction Model for Mobile Edge computing;an Efficient Data Transmission Strategy for Edge-computing-Based Opportunistic Social networks;shadow Data: A Method to Optimize Incremental Synchronization in Data Center;A Dynamic Protection Mechanism for GPU Memory Overflow;DROAllocator: A Dynamic Resource-Aware Operator Allocation Framework in distributed Streaming Processing;a Medical Support System for Prostate Cancer Based on Ensemble Method in Developing Countries.

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