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A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions

A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ye Yuan Mingxing Yang Ruoting Xiong Yani Sun Xianchao Zhang Wei Ren HuBei Land Resources Vocational College China Gemological Institute China University of Geosciences Wuhan China School of Computing Science University of East Anglia UK Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang University Kunming China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Beijing China Ministry of Education Key Laboratory of Data Protection and Intelligent Management (Sichuan University) China School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens; but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient.

关键词： distributed processing Data privacy Manufacturing processes Information processing Handover Throughput Data structures Blockchains Delays Periodic structures

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PSO Optimized Design of Error Balanced Weight Stationary Systolic Array Architecture for CNN

PSO Optimized Design of Error Balanced Weight Stationary Sys...

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IEEE international symposium on Quality Electronic Design

作者： Dantu Nandini Devi Gandi Ajay Kumar Bindu G Gowda Madhav Rao IIIT-Bangalore Bangalore India

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

The utilization of hardware-designed approximate computing in Convolutional Neural Networks (CNNs) offers notable advantages, including accelerated performance, enhanced power efficiency, and a compact design footprint. Systolic Array (SA) architectures, optimized for matrix multiplication and convolution operations, have been extensively studied in the context of stand-alone image processing applications. However, their potential for CNN workloads has not been thoroughly assessed. SAs consist of an array of Processing Elements (PEs) structured to perform product operations and accumulations. Incorporating inexact computing units in the SA introduces deviations from precise results, posing a challenge for sustaining hardware accelerator designs in CNN workloads. This paper presents a strategy for the optimal placement of both positive and negative error-distributed multipliers as PE elements to create an error-diluted SA structure. The proposed strategy to structure SA is evaluated for prewitt filter and three other filters extracted from first layer of *** paper introduces an optimization framework for selecting the most suitable PEs from a pool of positive and negative error-distributed multipliers, aiming to achieve a balance between hardware efficiency and image quality metrics. Furthermore, the framework and hardware design files are made available for further usage to the designers’ and researchers community.

关键词： Costs Computer architecture parallel processing Filtering algorithms Hardware Systolic arrays Delays

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Dynamic Task Scheduling for Coordinated Truck-Drone Parcel Delivery: A Hybrid Genetic Tabu Algorithm Approach

Dynamic Task Scheduling for Coordinated Truck-Drone Parcel D...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ruitai Li Quan Yuan Tongxin Liao Lijun Luo Menglan Hu Pan Lai Xiao Zhang College of Computer Science South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission School of Electronic Information and Communications Huazhong University of Science and Technology Hubei Key Laboratory of Smart Internet Technology

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid growth of the on-demand economy, logistics companies and merchants increasingly struggle to meet customer demands in dynamic and uncertain conditions. This paper studies the coordinated delivery of parcels by trucks and drones under such demands, proposing a Dynamic Task Scheduling Algorithm based on Hybrid Genetic Tabu algorithm (DTSAGT) for route optimization. Simulating dynamic customer demands with a Poisson distribution and statistical methods, the algorithm addresses timeliness issues due to variations in customer needs. It optimizes drone path planning and task allocation considering drone endurance and payload limits to minimize total delivery time. The algorithm includes three steps: initial solution construction, iterative optimization, and dynamic operations. Experimental results show that DTSAGT reduces the total service time by 15.05%, 34.13%, and 35.71% on average compared to the baseline algorithms. This paper’s contribution is the combination of hybrid genetic and tabu search algorithms applied to dynamic task scheduling in truck-drone delivery, enhancing logistics efficiency.

关键词： Scheduling algorithms Statistical analysis Heuristic algorithms Transportation Dynamic scheduling Genetics Vehicle dynamics Optimization Drones Logistics

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Learning-based Variable Neighborhood Search Algorithm for Cloud Service Deployment Problem with Time Windows

Learning-based Variable Neighborhood Search Algorithm for Cl...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Fanchao Meng Yang Lu Weigong Lv Dianhui Chu School of Computer Science and Technology Harbin Institute of Technology at Weihai Weihai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

When services are deployed in a cloud environment, the selection of virtual machines mostly relies on the historical experience of service providers, which leads to a high cost of cloud resource. Additionally, such problem rarely considers the impact of service time on the cost of virtual machines. In this paper, we introduce and model the cloud service deployment problem with time windows (CSDPTW), wherein the cost of virtual machines is influenced by the usage time windows. To solve this problem, we propose a learning-based variable neighborhood search (L-VNS) algorithm. The algorithm first employs a hierarchical clustering algorithm to generate the initial solution. Then, a Q-learning-based reinforcement learning algorithm is used to optimize the selection of operators, which includes two neighborhood operators and three local search operators. To evaluate the performance of our proposed L-VNS, genetic algorithm (GA), variable neighborhood search (VNS), iterated local search (ILS) and the commercial solver CPLEX are applied to CSDPTW. Computational experiments on 20 test instances demonstrate that the proposed L-VNS algorithm can find better solutions than GA, VNS, ILS and CPLEX.

关键词： Cloud computing Costs Clustering algorithms Reinforcement learning Quality of service Search problems Virtual machines Stability analysis Thermal stability Genetic algorithms

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A semi-supervised GCN-based community detection algorithm

A semi-supervised GCN-based community detection algorithm

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Shu-Han Shi Lei-Lei Shi Lu Liu Zi-Xuan Han Liang Jiang Fu-Xiang Chen School of Computer Science and Communication Engineering Jiangsu University China Department of Computer Science University of Exeter UK School of Ocean Jiangsu University of Science and Technology China School of Computing and Mathematical Sciences University of Leicester UK

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Community detection reveals the unique characteristics and relationships of in-network members, differentiated from out-of-community members and plays a pivotal role in network analysis. In recent years, deep learning techniques have made great strides in the application of community detection, especially on label sampling models, which train graph convolutional networks by constructing a balanced training set through structural centre localization and neighbourhood node expansion. However, such algorithms are limited on centre selection in community structure. To address this, this paper introduces a novel community detection algorithm based on peak density adaptive iterative segmentation, or LDACN in short. First, labels are assigned by adaptively selecting the centre node of the community structure, which results in a more even distribution of labels in the network. Subsequently, more accurate community segmentation is achieved by taking advantage of GCN's combined ability in capturing the connectivity relationships between the nodes and their intrinsic characteristics. Experimental results on synthetic and real-world network datasets show that our algorithm improves the effectiveness of community segmentation compared to the state-of-the-art algorithms.

关键词： Training Representation learning Measurement Location awareness Accuracy Graph convolutional networks Network analyzers Prediction algorithms Reliability Detection algorithms

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A Task Transmission Path Selection Algorithm Based Object Identification and Intent-Driven in Uncertain Environments

A Task Transmission Path Selection Algorithm Based Object Id...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Lei Hu Yang Zhang Shukui Zhang Wubing Pan Yingying Wang School of Computer Science and Technology Soochow University Suzhou China School of Information & Software Suzhou Global Institute Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In environments lacking infrastructure, such as the battlefield, a diverse array of intelligent devices continues to evolve. These devices face challenges in flexibly addressing resource allocation and information transmission within their communication range, necessitating exploration of business awareness and resource identification technologies. Addressing these issues hinges on solving object identification problems related to address, identity, content, service, location, and resource. To tackle these challenges, we propose a Mult-IP object identification mechanism that serves as the foundation for information exchange, task allocation, and service provisioning among heterogeneous communication entities. Additionally, we design a multi-dimensional resource and service intelligence matching mechanism based on object identification. Building upon this framework is the intent-driven control algorithm AntEdge which dynamically senses task states using information encapsulated in Mult-IP packets and optimizes task packet transmission according to business intentions. Finally, leveraging the Mult-IP object identification mechanism enables task offloading to achieve timely completion of tasks within limited timeframes. Simulation experiments demonstrate that our proposed algorithm effectively enhances task completion ratios.

关键词： Heuristic algorithms Fasteners Routing Routing protocols Object recognition Resource management Information exchange Mobile ad hoc networks Edge computing Business

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Incremental Checkpointing of Large State Simulation Models with Write-Intensive Events via Memory Update Correlation on Buddy Pages

Incremental Checkpointing of Large State Simulation Models w...

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Romolo Marotta Federica Montesano Alessandro Pellegrini Francesco Quaglia DICII Tor Vergata University of Rome Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing

Checkpointing techniques for speculative parallel simulation of discrete event models have been widely studied in the literature. However, there has been a very marginal attempt to exploit operating system page-protection services, which have instead been largely exploited in the context of checkpointing for fault tolerance. In this article, we discuss how these services can effectively manage simulation models with large states and write-intensive events in zones of the state layout. In particular, we present a solution where the correlation of write operations on buddy pages in the state layout can be exploited to achieve effective incremental checkpointing support, which allows scaling down the costs of operating system services. Our solution does not require any instrumentation of the simulation application code and is usable on any Posix-compliant operating system. We also discuss its integration within the USE (Ultimate-Share-Everything) open-source speculative simulation package and report some experimental data for its assessment.

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Enhanced Log Anomaly Detection with Contrastive Learning and BERT Embeddings

Enhanced Log Anomaly Detection with Contrastive Learning and...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Mengmeng Cui Tong Gao Haolong Xiang Xiaolong Xu Changyan Lu Junqun Xiong Shengjun Xue Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China China Automotive Innovation Corporation China Silicon Lake College Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In modern software systems, log anomaly detection is a key technology to ensure system stability and reliability. The existing log-based anomaly detection methods have make significant development, but they fail to provide high detection accuracy when processing semantic information, especially in terms of semantic noise and the need for task-specific semantic embeddings. To address these issues, we propose a novel anomaly detection method called Contrast-Enhanced Log Anomaly Detection (CELA). The CELA model first enriches the content of log events using large language models and further optimizes the pretrained BERT model through contrastive learning techniques, ensuring that the event representations extracted from logs more accurately reflect potential anomalous patterns. On this basis, we have developed an anomaly detection model based on LSTM, which can effectively learn and identify anomalies from rich event representations. Evaluated on two public log datasets, the CELA model demonstrated superior performance compared to existing methods.

关键词： Adaptation models Accuracy Semantics Noise Contrastive learning Software systems Data models Stability analysis Anomaly detection Long short term memory

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Introducing parallel computing in a Second CS Course

Introducing Parallel Computing in a Second CS Course

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

作者： Tia Newhall Kevin C. Webb Vasanta Chaganti Andrew Danner Computer Science Department Swarthmore College Swarthmore PA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The ubiquity of multicore processors, cloud computing, and hardware accelerators have elevated parallel and distributed computing (PDC) topics into fundamental building blocks of the undergraduate CS curriculum. Therefore, it is increasingly important for students to learn a common core of introductory PDC topics and develop parallel thinking skills early in their CS studies. We present the curricular design, pedagogy, and goals of an introductory-level course on com-puter systems that introduces parallel computing to students who have only a CS1 background. Our course focuses on three curricular goals that serve to integrate the ACM-IEEE TCPP guidelines throughout: a vertical slice through the computer of how it runs a program; evaluating system costs associated with running a program; and taking advantage of the power of parallel computing. We elaborate on the goals and details of our course's key modules, and we discuss our pedagogical approach that includes active-learning techniques. We find that the PDC foundation gained through early exposure in this course helps students gain confidence in their ability to expand and apply their understanding of PDC concepts throughout their CS education.

关键词： distributed processing Costs Multicore processing Instruction sets Education Focusing parallel processing

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computing Challenges for High Energy Physics 21

Computing Challenges for High Energy Physics

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30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Girone, Maria CERN Openlab Geneva Switzerland

ISBN: (纸本)9781450382175

High-energy physics faces unprecedented computing challenges in preparation for the 'high-luminosity' phase of the Large Hadron Collider, which will be known as the HL-LHC. The complexity of particle-collision events will increase, together with the data collection rate, substantially outstripping the gains expected from technology evolution. The LHC experiments, through the Worldwide LHC computing Grid (WLCG), operate a distributed computing infrastructure at about 170 sites over more than 40 countries. This infrastructure has successfully exploited the exabyte of data collected and processed during the first 10 years of the program. During the HL-LHC regime, each experiment will collect an exabyte of data annually and additional computing resources will be needed. The efficient use of HPC facilities may be an important opportunity to address the anticipated resource gap. In this talk, I will discuss the future computing needs in high-energy physics and how these can be met combining our dedicated distributed computing infrastructure with large-scale HPC sites. As a community, we have identified common challenges for integrating these large facilities into our computing ecosystem. I will also discuss the current progress in addressing those challenges, focusing on software development for heterogeneous architectures, data management at scale, supporting services and opportunities for collaboration.

关键词： High-Energy Physics Data Analytics Big-Data

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