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MPLNet: Industrial Anomaly Detection with Memory Bank and Prompt Learning 22

MPLNet: Industrial Anomaly Detection with Memory Bank and Pr...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Zhou, Hao Guo, Xuanru Ji, Mingcheng Jiang, Jielin Xiang, Haolong Xue, Shengjun University of Science and Technology Beijing Beijin China Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China Silicon Lake College Suzhou China

ISBN: (纸本)9798331509712

Anomaly detection is a critical aspect of industrial production processes. Most of the self-supervised training system utilise Convolutional Neural Networks (CNNs) for feature extraction. However, these methods often exhibit poor detection performance on small structural anomalies due to the limited receptive field of CNN. The prompt learning method has been demonstrated to be an effective means of enhancing the global information extraction abilities of the model. However, this approach necessitates the input of a significant number of manual prompts. To address these issues, we propose a Memory And Prompt Learning Based Network (MPLNet) for anomaly detection. MPLNet obtains the differences information between normal and detected images by comparing their features and then uses these differences to generate prompts. By automatically generating prompts, it reduces the workload of manually entering prompts in prompt learning. At the same time, it effectively enhances the model's ability to extract and use global information. Extensive experiments have shown that the proposed MPLNet achieves state-of-the-art anomaly detection performance on the widely used and challenging MVTec AD dataset and MVTec AD-3D dataset. Index Terms - Anomaly detection, U-Net, Transformer, Prompt, artificial intelligence System © 2024 IEEE.

关键词： Convolutional neural networks

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Toward Multi-Agent Coordination in IoT via Prompt Pool-based Continual Reinforcement Learning 22

Toward Multi-Agent Coordination in IoT via Prompt Pool-based...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Xu, Chenhang Wang, Jia Zhu, Xiaohui Yue, Yong Qi, Jun Ma, Jieming Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China Xi'an Jiaotong-Liverpool University Department of Intelligent Science Suzhou China

ISBN: (纸本)9798331509712

The Internet of Things (IoT) represents a complex, dynamic environment where edge devices continuously optimize their policies to address a continual stream of tasks. Previous studies have typically relied on a rehearsal buffer containing data from past tasks or a known task identity to mitigate catastrophic forgetting. Our research, Prompt Pool-based Continual Reinforcement Learning (PPCRL), aims to create a more efficient memory system by expanding a single prompt into a prompt pool, allowing agents to automatically select a set of relevant prompts without needing task identity knowledge. Similar to prompt-based learning techniques, our approach utilizes a small trainable prompt pool to guide pre-trained models through sequential task learning systematically. This allows us to optimize prompts for guiding model predictions and effectively manage both shared and task-specific knowledge while maintaining model generalization. We conducted experiments on two multi-agent benchmarks where traditional methods suffer from significant performance degradation. In contrast, PPCRL demonstrates the capability to outperform baselines and exhibits high generalization ability. © 2024 IEEE.

关键词： Contrastive Learning

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QDL: Quantitative Dynamic Latency Model in Permissioned Blockchain System 22

QDL: Quantitative Dynamic Latency Model in Permissioned Bloc...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Wei, Jiacheng Wang, Qingmei Wang, Zheng Beihang University School of Artificial Intelligence Beijing China Beihang University Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China University of Science and Technology Beijing National Center for Materials Service Safety Beijing China Shanghai Jiao Tong University Institute of Cyber Science and Technology China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security China

ISBN: (纸本)9798331509712

Latency, as a critical attribute influencing scalability and consistency in blockchain systems, has attracted significant research attention. This paper addresses the issue of latency evaluation by presenting a novel framework. We thoroughly analyze the factors affecting latency in blockchain system, including concurrency, consistency, and contention. To accurately model the correlation between latency and the quantity of nodes in permissioned blockchain setup, we introduce the Latency Regression Loss (LRL) function. By utilizing Newton's interpolation and least squares methods, we determine the hyperparameters. Subsequently, we devise the framework for the Quantitative Dynamic Latency (QDL) evaluation model, which accounts for both the growth rate and magnitude of latency. Utilizing the parameters derived from the LRL function facilitated the calculation of latency growth rate, allowing for a comprehensive evaluation of latency. Finally, we validated the accuracy of the LRL function through extensive experiments and simulated the adaptability of the QDL model in latency assessment. © 2024 IEEE.

关键词： Scalability

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Container-Aware Service Function Chains Placement and Optimization in Vehicular Edge computing 22

Container-Aware Service Function Chains Placement and Optimi...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Zhang, Kaixin Hu, Shihong Qu, Zhihao Ye, Baoliu Hohai University School of Computer Science and Software Engineering China Nanjing University National Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798331509712

In the domain of Vehicular Edge computing (VEC), this paper addresses the complex problem of Service Function Chain (SFC) placement, which is crucial for the efficient deployment of cloud applications in vehicular environments. Our approach leverages Virtual Network Function (VNF) technology, transitioning network services from traditional hardware to more flexible, container-based edge computing frameworks. The primary objective is to organize these VNFs into functional SFCs, thereby minimizing service delays in VEC systems. SFC placement faces two significant challenges. The first is the sequential dependency among VNFs within an SFC, which adds substantial complexity to container deployment. The second challenge is the cold start delay of VNF containers, a critical issue in scenarios requiring swift response times, which negatively impacts service quality in VEC applications. To address these challenges, we propose a comprehensive model for SFC placement that considers the deployment status of VNF containers, acknowledging the complexity of this NP-hard problem. The core of our contribution is the development of the Single SFC Placement Algorithm (SSPA), a sophisticated, greedy-based approach designed for the effective placement of individual SFCs. We enhance this algorithm by incorporating a Particle Swarm Optimization (PSO) technique, making it capable of efficiently handling the placement of multiple SFCs. Our solution aims to improve edge resource utilization and mitigate startup delays associated with the initial activation of containers, thereby reducing service delays. Extensive experimental evaluations demonstrate that our algorithm achieves a 34.3% average reduction in service delay compared to four baselines and a 5.8% average reduction compared to other container-aware algorithms. © 2024 IEEE.

关键词： service function chain SFC placement vehicle edge computing virtual network function

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Collecting Clustering Coefficient of distributed Graph Data with Shuffled Differential Privacy 22

Collecting Clustering Coefficient of Distributed Graph Data ...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Ding, Hongfa Fu, Peiwang Luo, Yingxuan Jiang, Heling Liu, Hai Guizhou University of Fiance and Economics School of Information Guiyang China Co. Ltd Guiyang China Jiangxi Datang International Fuzhou Power Generation Co. Ltd Fuzhou China

ISBN: (纸本)9798331509712

The intricate properties and relevance of graph data make it difficult to collect graph statistics privately via differential privacy (DP). Traditional centralized or local DP on graph data, face challenges like third-party threats and low data utility when collecting the clustering coefficient. In this regard, we introduce GCC-SDP, a scheme for collecting distributed Graph Clustering Coefficient with Shuffled DP (SDP). GCC-SDP gathers the local wedge lists of all edges and adjacency bit vectors through SDP and random response for calculating the noisy local triangle counts. It then collects the local degree values of all users by using Laplace mechanism, followed by estimating the global clustering coefficient of the global graph data by data collector. We provide specific steps of GCC-SDP and demonstrate through theoretical analysis that GCC-SDP conforms to various DPs, with unbiased results. Empirical experiments show that GCC-SDP performs better than existing local DP-based techniques across most accuracy metrics. © 2024 IEEE.

关键词： Data collection

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Proceedings - 23rd IEEE/ACM international symposium on Cluster, Cloud and Internet computing, CCGrid 2023

Proceedings - 23rd IEEE/ACM International Symposium on Clust...

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23rd IEEE/ACM international symposium on Cluster, Cloud and Internet computing, CCGrid 2023

ISBN: (纸本)9798350301199

The proceedings contain 59 papers. The topics discussed include: rethinking design paradigm of graph processing system with a CXL-like memory semantic fabric;a case study of data management challenges presented in large-scale machine learning workflows;an asynchronous dataflow-driven execution model for distributed accelerator computing;an empirical study of container image configurations and their impact on start times;how workflow engines should talk to resource managers: a proposal for a common workflow scheduling interface;Layercake: efficient inference serving with cloud and mobile resources;optimal sizing of a globally distributed low carbon cloud federation;predicting the performance-cost trade-off of applications across multiple systems;a deep learning pipeline parallel optimization method;HDFL: a heterogeneity and client dropout-aware federated learning framework;heterogeneous federated learning using dynamic model pruning and adaptive gradient;and measuring the impact of gradient accumulation on cloud-based distributed training.

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CuGraph C++ primitives: Vertex/edge-centric building blocks for parallel graph computing

CuGraph C++ primitives: Vertex/edge-centric building blocks ...

引用

2023 IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2023

作者： Kang, Seunghwa Hastings, Chuck Eaton, Joe Rees, Brad NVIDIA United States

ISBN: (纸本)9798350311990

Software development of high-performance graph algorithms is difficult on modern parallel computers. To simplify this task, we have designed and implemented a collection of C++ graph primitives, basic building blocks, within cuGraph to assist graph analytics software developers on parallel computers, ranging from desktops to large clusters. This graph primitives API provides a vertex/edge-centric C++ Standard Template Library (STL)-like interface, allowing users to pick a primitive algorithm, and specify desired operations on vertices and edges and how to reduce the output of such operations through C++ functors. The API implementation is responsible for executing these functors on the underlying hardware. In this case, the graph primitives are implemented to run on NVIDIA GPU systems, from a single-GPU to multi-GPUs in a distributed cluster. RAPIDS cuGraph is NVIDIA's graph analytics solution for data scientists and software integrators. Algorithms in cuGraph are either implemented using the cuGraph C++ primitives API or being migrated over to using the primitives API. The Louvain and PageRank algorithms have been tested on clusters with over 1000 GPUs. © 2023 IEEE.

关键词： C++ (programming language)

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Learning to Scale the Summit: AI for Science on a Leadership Supercomputer 36

Learning to Scale the Summit: AI for Science on a Leadership...

引用

36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Joubert, Wayne Messer, Bronson Roth, Philip C. Georgiadou, Antigoni Lietz, Justin Eisenbach, Markus Yin, Junqi Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN 37830 USA

ISBN: (纸本)9781665497473

The Summit system at Oak Ridge National Laboratory (ORNL) has been the world's top AI for science supercomputer for several years, ranked world's fastest computer at its 2018 launch and currently top system in the US and #2 on the TOP500 list. Summit's purposeful design to handle both conventional modeling and simulation science and emerging AI workloads has made it a leading destination for AI-powered computational science. We report here on AI for science usage on Summit near the midpoint of its lifespan. We review AI usage across the many science projects that have used Summit. We then examine in detail a set of applications scaling AI to full system as well as projects implementing AI-coordinated science discovery workflows on Summit. Finally, we offer some observations regarding the future of advancing scientific knowledge and understanding via AI, especially in the context of leadership-class scientific computing.

关键词： HPC high performance computing AI artificial intelligence machine learning

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Multi-Class Task Offloading Optimization in Mobile Edge computing 22

Multi-Class Task Offloading Optimization in Mobile Edge Comp...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Ma, Songkang He, Zhenli Feng, Libo Zhai, Xiaolong Tong, Yiyan Yunnan University Yunnan Key Laboratory of Software Engineering Kunming China Yunnan University School of Software Kunming650504 China

ISBN: (纸本)9798331509712

The proliferation of mobile devices and the increasing demand for portable services have led to a surge in computationally intensive and time-sensitive applications, necessitating efficient computation offloading in Mobile Edge computing. Existing research often overlooks the distinct response time requirements of different applications and their mutual interference during queuing. This paper addresses these gaps by optimizing offloading strategies with multiple response time constraints, focusing on the queuing impact of various tasks. We model edge servers as M/M/1 queuing systems and develop a set of mathematical models. Using algorithms based on Karush-Kuhn-Tucker conditions, we derive optimal offloading strategies to minimize system power consumption. Our approach significantly reduces system power consumption in practical applications, enhancing resource utilization for service providers. Our work introduces a comprehensive and realistic model of task offloading, considering multiple task types, their distinct average response time requirements, and their mutual interference during queuing. This advancement ensures efficient and effective offloading strategies, addressing the complexity of multiple tasks and varying response time constraints. © 2024 IEEE.

关键词： Computation offloading

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Nonlinear Control of Crane Systems Based on Intelligence computing of Disturbance Observer Under Mismatched Disturbance 22

Nonlinear Control of Crane Systems Based on Intelligence Com...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Wang, Tianlei Wang, Yiyan Xu, Meng Tian, Jiajie Zhang, Jingling Hong, Zhiyong Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Kejie Technology Co. Ltd Jiangmen529020 China

ISBN: (纸本)9798331509712

A control method based on disturbance observer is proposed to address the positioning drift issue of the gantry crane under unmatched disturbances, aiming at precise positioning of the trolley and effective payload swing angle attenuation in a variable-length two-dimensional lifting system. The control scheme comprises a combination of a proportional-derivative controller and a disturbance observer. Firstly, the design of the coupling function in this paper is based on the observation of the natural characteristics of the dynamical model. It cleverly designs the coupling function, which not only helps to effectively reduce the swing angle but also prevents the problem of positioning drift under non-matching disturbances, subsequently, design a controller by integrating intelligent computing with control theory. Secondly, the designed disturbance observer observes and compensates for the system output to achieve disturbance suppression. Thirdly, a thorough stability analysis has been conducted to demonstrate that the proposed controller satisfies the desired conditions. Finally, simulation results illustrate that the pro-posed method success-fully addresses the limitations of existing methods and exhibits superior performance and robustness. © 2024 IEEE.

关键词： Gantry cranes

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