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作者： Luo, Qiuming Liu, Senhong Huang, Jinke Li, Jinrong College of Computer Science and Software Engineering Shenzhen University Shenzhen China SKT Group Guangdong Province Key Laboratory of Popular High-Performance Computers Shenzhen China SKT Group Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

This article designs and implements a runtime library for general dataflow programming, DFCPP (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), and builds upon it to design and implement a multi-machine C++ dataflow library, M-DFCPP. In comparison to existing dataflow programming environments, DFCPP features a user-friendly interface and richer expressive capabilities (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), enabling the representation of various types of dataflow actor tasks (static, dynamic and conditional task). Besides that, DFCPP addresses the memory management and task scheduling for non-uniform memory access architectures, while other dataflow libraries lack attention to these issues. M-DFCPP extends the capability of current dataflow runtime libraries (DFCPP, taskflow, openstream, etc.) and capable of multi-machine computing, while maintains the API compatible with DFCPP. M-DFCPP adopts the concepts of master and follower (Dean J, Ghemawat S. Commun ACM. 2008;51(1):107-113;Ghemawat S, Gobioff H, Leung ST. ACM SIGOPS Operating systems Review. ACM;2003:29-43.), which form a worksharing framework as many multi-machine system. To shift to the M-DFCPP framework, a filtering layer is inserted to the original DFCPP, transforming it into followers that can cooperate with each other. The master is made of modules for scheduling, data processing, graph partition, state management and so forth. In benchmark tests with workload with directed acyclic graph topology of binary trees and random graphs, DFCPP demonstrated performance improvements of 20% and 8%, respectively, compared to the second fastest library. M-DFCPP consistently exhibits outstanding performance across varying levels of concurrency and task workloads, achieving a maximum speedup of more than 20 over DFCPP, when the task parallelism e

关键词： Libraries

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FedCrowd: Towards Promoting the Concurrency of the Server in Asynchronous Federated Learning 2

FedCrowd: Towards Promoting the Concurrency of the Server in...

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2nd International Symposium on Electrical, Electronics and Information engineering, ISEEIE 2022

作者： Xi, Wen Liao, Yuying Zhou, Bin Wang, Haiyang Gao, Liqun College of Computer Science and Technology National University of Defence Technology Changsha China National University of Defence Technology Key Lab. of Software Engineering for Complex System Changsha China

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

Federated learning is a privacy-preserving machine learning paradigm where a global model is trained collaboratively across resource-limited devices such as mobile phones, wearable devices, and sensors while the training data remains on these devices. Recently proposed asynchronous federated learning framework FedAsync has addressed the problems occurred in synchronous federated learning such as wasting time waiting for all nodes to finish uploading or losing parts of the contribution of nodes due to some unexpected reasons. However, on the server-side, it is not suitable for concurrency due to the data race on the global model. We proposed a method named FedCrowd, where the server's concurrency is improved by elaborately coordinating the working threads to relieve the data race on the global model. Several experiments are performed to show that our method could effectively increase concurrency of the server and reduce the training time. © 2022 IEEE.

关键词： Concurrent computing Training Wearable computers Training data Machine learning Collaborative work Data models

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Inspection Area Allocation for Forest Surveillance Based on Different Starting Points of Multi-UAVs 7th

Inspection Area Allocation for Forest Surveillance Based on ...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Zhou, Zhengjun Mu, Lingxia Ning, Kaikai Zhang, Youmin Xue, Xianghong Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819733392

An inspection allocation algorithm based on different starting points and endurance capabilities of unmanned aerial vehicles (UAVs) is proposed for the multi-UAVs forest inspection process. Firstly, the area map to be inspected is rasterized, and the distances between the starting points of each UAV and individual grid cells are computed, which will be used for area decomposition. Subsequently, an evaluation function is employed to allocate individual grid cells reasonably to each UAV. Then, the boundary lines of the allocated sub-regions are generated, by which the raster map is converted into an inspection map with continuous boundaries. This ultimately yields the sub-regions for inspection by each UAV. Simulation results demonstrate that this method effectively allocates the forest inspection area to the various UAVs in a rational manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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An online drilling rate of penetration(ROP) optimization method 41

An online drilling rate of penetration(ROP) optimization met...

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第41届中国控制会议

作者： Tuo Wang Chao Gan Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Drilling rate of penetration(ROP) optimization is crucial due to its vital role in increasing the drilling efficiency and decreasing the non-productive *** existing methods seldom pay attention to updating model parameter during drilling,which is actually important for ROP optimization under dynamical and uncertain downhole *** this paper,an online drilling ROP optimization method is proposed via three stages(drilling data pre-processing,online ROP modelling and online optimal operational parameters optimization).In the first stage,drilling data is pre-processed by resampling and Savitzky-Golay *** this,support vector regression and moving-window strategy are introduced together to establish an online ROP model in the next ***,particle swarm optimization algorithm is applied in the online ROP optimization framework to obtain the optimal operational parameters in the last *** results indicated the effectiveness of the proposed method.

关键词： Rate of penetration Drilling optimization Online optimization Moving-window strategy

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There are More Fish in the Sea: Automated Vulnerability Repair via Binary Templates

arXiv

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arXiv 2024年

作者： Lin, Bo Wang, Shangwen Chen, Liqian Mao, Xiaoguang Key Laboratory of Software Engineering for Complex Systems College of Computer Science and Technology National University of Defense Technology Changsha China

As software vulnerabilities increase in both volume and complexity, vendors often struggle to repair them promptly. Automated vulnerability repair has emerged as a promising solution to reduce the burden of manual debugging and fixing activities. However, existing techniques exclusively focus on repairing the vulnerabilities at the source code level, which has various limitations. For example, they are not applicable to those (e.g., users or security analysts) who do not have access to the source code. Consequently, this restricts the practical application of these techniques, especially in cases where vendors are unable to provide timely patches. In this paper, we aim to address the above limitations by performing vulnerability repair at binary code level, and accordingly propose a template-based automated vulnerability repair approach for Java binaries. Built on top of the literature, we collect fix templates from both existing template-based automated program repair approaches and vulnerability-specific analyses, which are then implemented for the Java binaries. Our systematic application of these templates effectively mitigates vulnerabilities: experiments on the Vul4J dataset demonstrate that TemVUR successfully repairs 11 vulnerabilities, marking a notable 57.1% improvement over current repair techniques. Moreover, TemVUR securely fixes 66.7% more vulnerabilities compared to leading techniques (15 vs. 9), underscoring its effectiveness in mitigating the risks posed by these vulnerabilities. To assess the generalizability of TemVUR, we curate the ManyVuls4J dataset, which goes beyond Vul4J to encompass a wider diversity of vulnerabilities. With 30% more vulnerabilities than its predecessor (increasing from 79 to 103), ManyVuls4J serves as a robust benchmark for evaluating the performance of TemVUR. The evaluation on ManyVuls4J reaffirms TemVUR’s effectiveness and generalizability across a diverse set of real-world vulnerabilities. Copyright © 2024, The Authors.

关键词： Program debugging

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A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise

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Defence Technology（防务技术） 2022年第2期18卷 183-193页

作者： Yu-xing Li Shang-bin Jiao Bo Geng Qing Zhang You-min Zhang School of Automation and Information Engineering Xi’an University of TechnologyXi’an710048China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an710048China Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversityMontrealQuebec H3G 1M8Canada

Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault *** this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed *** results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and *** is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.

关键词： Nonlinear dynamic Refined composite multi-scale dispersion entropy(RCMDE) Multi-scale dispersion entropy(MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy(MPE) Classification of ship-radiated noise

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Exploring Problems and Solutions About Launch Files in ROS from Q&A Community 3

Exploring Problems and Solutions About Launch Files in ROS f...

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3rd International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2022

作者： Jiang, Guangjun Mao, Xinjun College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Robot Operating system (ROS) is a widely used software architecture for robotic systems, which provides hardware abstractions and common functions in robotics. Launch files, as one of the configuration files in the actual program development of ROS, provide a method to start multiple nodes and set global parameters on the local or multiple machines at the same time. For launch files with complex and complex content, we need to get the questions we encounter in writing and using launch files from the Q&A community. We crawled 854 Q&A posts about Launch files from SO and ROS Answers respectively, and conducted in-depth empirical research. Based on the content of the Q&A posts, we roughly divided them into five topics, including syntax specification, function implementation, running error, dependency related and environment configuration. We used topic modelling LDA to categorize Q&A posts by topics, and segmented them by combining the five topics already obtained. Through the results, we found that parameter configuration and syntax errors were the main problems, and then we made a rough analysis of the answer content of the post, and found that the quality of the ROS-related Q&A posts was not high. The above findings inspired us to develop a template tool to simplify the writing of Launch flies and use the node information in the Launch files to recommend ROS nodes to lay a good foundation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Robot Operating system

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A Co-regularization Facial Emotion Recognition Based on Multi-Task Facial Action Unit Recognition 41

A Co-regularization Facial Emotion Recognition Based on Mult...

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第41届中国控制会议

作者： Chinonso Paschal Udeh Luefeng Chen Sheng Du Min Li Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Facial emotion recognition helps feed the growth of the future artificial intelligence with the development of emotion recognition,learning,and analysis of different angles of a human face and head *** worlds recent pandemic gave rise to the rapid installment of facial recognition for fewer applications,while emotion recognition is still within the experimental *** current challenges encountered with facial emotion recognition(FER) are the difference between background *** todays world shows us that humans soon need robotics in the most significant role of human perception,attention,memory,decision-making,and human-robot interaction(HRI) needs *** merging the head pose as a combination towards the FER to boost the robustness in understanding emotions using the convolutional neural networks(CNN).The stochastic gradient descent with a comprehensive model is adopted by applying multi-task learning capable of implicit parallelism,inherent and better global optimizer in finding better network *** executing a multi-task learning model using two independent datasets,the experiment with the FER and head pose learning multi-views co-regularization frameworks were subsequently merged with validation accuracy.

关键词： head pose facial expression convolution neural network deep learning emotion recognition

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5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Peng, Yafei Yang, Fande Aerospace Engineering University Complex Electronic System Simulation Key Laboratory Beijing China

ISBN: (纸本)9781665415989

In order to improve the decision-making ability of aerospace commanders, aerospace command decision-making theory was studied from the concepts and characteristics of space command decision-making, and various situation information involved in aerospace command decision-making was analyzed, a general process for aerospace command decision-making was established. and the auxiliary technical means of aerospace command decision-making was summarized. It has laid a foundation for aerospace command and decision-making related theories, which is convenient for commanders to clarify the aerospace command and decision-making process, obtain situational knowledge required for decision-making, and improve the timeliness of decision-making. © 2021 IEEE.

关键词： Decision making

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A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification

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Chinese Journal of Electronics 2025年第4期23卷 706-711页

作者： Jiarui Zhou Zexuan Zhu Zhen Ji College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China

A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.

关键词： Memetics Metabolomics Metaheuristics Urban areas Liver Prediction algorithms Search problems Physiology Vectors Classification algorithms

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