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Cloud Computing, Big Data Applications and Software engineering (CBASE), International Conference on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Xiaoqi Lu Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers.

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MTD: Multi-Timestep Detector for Delayed Streaming Perception

arXiv

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arXiv 2023年

作者： Huang, Yihui Chen, Ningjiang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Education Department of Guangxi Zhuang Autonomous Region Nanning China

Autonomous driving systems require real-time environmental perception to ensure user safety and experience. Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. In real-world applications, factors such as hardware limitations and high temperatures inevitably cause delays in autonomous driving systems, resulting in the offset between the model output and the world state. In order to solve this problem, this paper propose the Multi-Timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction, giving model the ability to resist delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the existing delay sensing method, continuously monitoring the model inference stack and calculating the delay trend. Moreover, a novel Timestep Branch Module (TBM) is constructed, which includes static flow and adaptive flow to adaptively predict specific timesteps according to the delay trend. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration 6

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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6th IEEE International Conference on computer and Communications, ICCC 2020

作者： Zhang, Zhe Liu, Jie Chi, Lihua Chen, Xinhai Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728186351

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%. © 2020 IEEE.

关键词： Recurrent neural networks

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Knowledge Distillation with Source-free Unsupervised Domain Adaptation for BERT Model Compression

Knowledge Distillation with Source-free Unsupervised Domain ...

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International Conference on computer Supported Cooperative Work in Design

作者： Jing Tian Juan Chen Ning Jiang Chen Lin Bai Suqun Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task.

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EDDNet: An Efficient and Accurate Defect Detection Network for the Industrial Edge Environment

EDDNet: An Efficient and Accurate Defect Detection Network f...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Runbing Qin Ningjiang Chen Yihui Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Guangxi Colleges and Universities Key Laboratory of Parallel and Distributed Computing Nanning China

Defect detection aims to locate the accurate position of defects in images, which is of great significance to quality inspection in the industrial product manufacturing. Currently, many defect detection methods rely on deep neural networks to extract features. Although the accuracy of these methods is relatively high, it is computationally intensive, making the methods difficult to deploy in resource-limited edge devices. In order to solve these problems, a lightweight defect detection model for the industrial edge environment is proposed, termed the efficient defect detection network (EDDNet). EfficientNet-B0 is used as the feature extraction backbone, extracting feature maps from feature layers of different depths of the network and fusing multilevel features by multilevel feature fusion (MFF). To obtain more information, we redesign the attention mechanism in MBConv blocks, taking the encoding space (ES) attention mechanism as a new module, which solves the problem that the defective image spatial information is ignored. The experimental results on the NEU-DET and DAGM2007 datasets and PCB defect datasets demonstrate the effectiveness of the proposed EDDNet and its possibility for application in industrial edge device.

关键词： Image edge detection Computational modeling Neural networks Software quality Object detection Feature extraction Transformers

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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Neutronic calculations of the China dual-functional lithium–lead test blanket module with the parallel discrete ordinates code Hydra

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Nuclear Science and Techniques 2020年第8期31卷 1-12页

作者： Guang-Chun Zhang Jie Liu Liang-Zhi Cao Hong-Chun Wu Xian-Bao Yuan Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China College of Mechanical and Power Engineering China Three Gorges UniversityYichang 443002China

The China dual-functional lithium–lead test blanket module(DFLL-TBM) is a liquid Li Pb blanket concept developed by the Institute of Nuclear Energy Safety Technology of the Chinese Academy of Sciences for testing in ITER to validate relevant tritium breeding and shielding technologies. In this study, neutronic calculations of DFLL-TBM were carried out using a massively parallel three-dimensional transport code, Hydra, with the Fusion Evaluated Nuclear Data Library/MG. Hydra was developed by the Nuclear engineering Computational Physics Lab based on the discrete ordinates method and has been devoted to neutronic analysis and shielding evaluation for nuclear facilities. An in-house Monte Carlo code(MCX) was employed to verify the discretized calculation model used by Hydra for the DFLL-TBM calculations. The results showed two key aspects:(1) In most material zones,Hydra solutions are in good agreement with the reference MCX results within 1%, and the maximal relative difference of the neutron flux is merely 3%, demonstrating the correctness of the calculation model;(2) while the current DFLL-TBM design meets the operation shielding requirement of ITER for 4 years, it does not satisfy the tritium self-sufficiency requirement. Compared to the two-step approach, Hydra produces higher accuracies as it does not rely on the homogenization technique during the calculation process. The parallel efficiency tests of Hydra using the DFLL-TBM model also showed that this code maintains a high parallel efficiency on O(100) processors and, as a result, is able to significantly improve computing performance through parallelization. Parameter studies have been carried out by varying the thickness of the beryllium armor layer and the tritium breeding zone to understand the influence of the beryllium layer and breeding zone thickness on tritium breeding performance. This establishes a foundation for further improvement in the tritium production performance of DFLL-TBM.

关键词： Discrete ordinates method DFLL-TBM Neutronic analysis Tritium breeding performance

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MilKBQA: A Complex Knowledge Base Question Answering Dataset on Chinese Military Field

MilKBQA: A Complex Knowledge Base Question Answering Dataset...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Pike Xu Tiejun Li Zhen Huang Min Guo Jiezhong He Ding Sun Institute of Computer National University of Defense Technology Changsha China Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665418164

Facts in military field tend to involve elements of time, space, quantity, status, and so on. Existing methods of representing knowledge in the form of triples fail to adequately express these facts, and also cause obstacles to knowledge storage and updating. Furthermore, question answering on these facts introduces new complexity dimension, which are complicated to be supported by existing corpus. Thus, we construct a Chinese knowledge base for military field covering entities and events centric knowledge, referred as MilKB. It consists of 965 entities and 3,017 facts. Moreover, we classify the natural questions into 26 types and construct a complex question answering dataset derived from MilKB, referred as MilKBQA. It consists of 2,829 question-answer pairs, in which 600 are event-centric ones. Experiments with three recent strong baseline models demonstrate that MilKBQA requires further research.

关键词： Triples (Data structure) Conferences Knowledge based systems Cyberspace Knowledge representation Data science Cognition

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