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2021 China Automation Congress, CAC 2021

作者： Wang, Lijun Xu, Pinghai Su, Wei Li, Yang Chen, Xianzhong School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School of University of Science and Technology Beijing Foshan China School of Science University of Jiaotong Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781665426473

Aiming at the problem of low voting enthusiasm of nodes in the Delegated Proof of Stake (DPOS) consensus mechanism in the blockchain, an incentive mechanism based on a clustering algorithm is proposed. By establishing a node positivity model, the nodes are classified by a clustering algorithm, and rewards are distributed to different nodes according to the classification results, so as to improve the enthusiasm of nodes in voting. The traditional K-means clustering algorithm has problems such as the need to predict the number of clusters K and the inability to deal with outliers, so it is not suitable for the incentive mechanism of node classification. Aiming at the problem of K-means clustering algorithm, an Hegselmann-Krause (HK) opinion dynamics clustering algorithm is introduced, which can meet the needs of the reward distribution mechanism in this paper. Through simulation and comparison with the existing schemes, the effectiveness of the improved algorithm is verified. © 2021 IEEE

关键词： Blockchain

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A Multi-Agent Deep Reinforcement Learning Method for Fully Noisy Observations

SSRN

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SSRN 2023年

作者： Wang, Kaiyu Zhang, Menglin Qu, Bohao Wang, Xianchang College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Chengdu Kestrel Artificial Intelligence Institute Chengdu611730 China

Multi-agent reinforcement learning (MARL) algorithms have achieved great breakthroughs in many aspects. The MARL algorithms can learn effective policies in ideal simulation environments. But different from the ideal simulation environments, noise is unavoidable in the real world. MARL algorithms need to learn effective policies in unavoidable fully noisy environments. In this paper, we consider a challenging multi-agent reinforcement learning problem: All agents cannot observe any noiseless observations from environments during the whole training process and MARL algorithms cannot learn effective policies in these fully noisy observation environments. To solve this problem, we propose a method called Robust Policy Learning under Fully Noisy Observation viA DeNoising REpresentation NeTwork (PLANET), which enables MARL algorithms learning effective policies in fully noisy observation environments. The PLANET method learns the effective policy through two steps. (1) Extracting the noise characteristics and motion laws information from fully noisy observation histories. (2) Making MARL algorithms extract information from the noise characteristics and motion laws information and learn effective policies. The results of a series of exhaustive experiments show that our method can eliminate the effects of noise and learn effective policies in fully noisy observation environments. © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization

arXiv

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arXiv 2023年

作者： Wei, Feng-Feng Chen, Wei-Neng Guo, Xiao-Qi Zhao, Bowen Jeon, Sang-Woon Zhang, Jun The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The State Key Laboratory of Subtropical Building Science With The Guangdong-Hong Kong Joint Innovative Platform of Big Data and Computational Intelligence South China University of Technology Guangzhou510006 China The Pazhou Lab Guangzhou510006 China Guangzhou Institute of Technology Xidian University Guangzhou510006 China The Department of Electronics Engineering and Communication Engineering Hanyang University Ansan15588 Korea Republic of

Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, it is also a great demand for the crowd to conduct optimization. Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization. EC is helpful for optimization tasks of crowdsourcing and in turn, crowdsourcing can break the spatial limitation of EC for large-scale distributed optimization. Therefore, this paper firstly introduces the paradigm of crowdsourcing-based distributed optimization. Then, CEC is elaborated. CEC performs optimization based on a server and a group of workers, in which the server dispatches a large task to workers. Workers search for promising solutions through EC optimizers and cooperate with connected neighbors. To eliminate uncertainties brought by the heterogeneity of worker behaviors and devices, the server adopts the competitive ranking and uncertainty detection strategy to guide the cooperation of workers. To illustrate the satisfactory performance of CEC, a crowdsourcing-based swarm optimizer is implemented as an example for extensive experiments. Comparison results on benchmark functions and a distributed clustering optimization problem demonstrate the potential applications of CEC. Copyright © 2023, The Authors. All rights reserved.

关键词： Crowdsourcing

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Interference-Aware Opportunistic Job Placement for Shared Distributed Deep Learning Clusters

SSRN

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SSRN 2023年

作者： Li, Hongliang Zhao, Hairui Sun, Ting Li, Xiang Xu, Haixiao Li, Keqin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Changchun China High Performance Computing Center Jilin University Changchun China Department of Computer Science State University of New York New PaltzNY12561 United States

Distributed deep learning frameworks facilitate large deep learning workloads. These frameworks support sharing one GPU device among multiple jobs to improve resource utilization. Modern deep learning training jobs consume a large amount of GPU memory. Despite that, sharing GPU memory among jobs is still possible because a training job has iterative steps that its memory usage fluctuates over time. However, resource sharing also introduces the risk of job performance degradation. Colocated jobs sharing a GPU device may suffer from different levels of interference, mainly caused by memory oversharing. How to improve resource utilization while maintaining good job performance is a novel challenge for job placement strategies. This paper studies the job placement problem. We propose an opportunistic memory sharing model to describe the time-varying job memory requirements. Based on this model, we introduce an Opportunistic Job Placement Problem (OJPP) for shared GPU clusters that seek job placement configurations using a minimum number of GPU devices and guarantee user-defined performance requirements at the same time. We propose a greedy algorithm and a heuristic algorithm with computational complexities of O(n log n) and O(n2 log n), respectively, to solve the problem. We also propose an online adjustment algorithm with the computational complexity of O(n log n) to perform updates to job placement configurations in runtime. A machine-learning-based interference prediction method is used to prepare accurate interference estimations. Extensive experiments are conducted on a GPU cluster to verify the correctness and effectiveness of our algorithms. Compared with standalone training jobs on dedicated clusters, the proposed approach reduces resource consumption by 46% in a shared cluster, while guaranteeing over 92.97% of the job performance, in terms of average job completion time. © 2023, The Authors. All rights reserved.

关键词： Computational complexity

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Developing Global Digital Economy after COVID-19: Preface for ITQM 2020 & 2021

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Procedia Computer Science 2021年 199卷 1-9页

作者： Liu, Yubin Shi, Yong Wang, Yong Ergu, Daji Berg, Daniel Tien, James Li, Jianping Tian, Yingjie Southwest Minzu University Sichuan Chengdu610041 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Engineering University of Miami Coral GablesFL33124 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Welcome to the Eighth International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021), July 9-11, Chengdu, China. The theme of ITQM 2020 & 2021 is “Developing Global Digital Economy after COVID-19”. ITQM 2020 & 2021 is organized by International Academy of Information Technology and Quantitative Management (IAITQM), Southwest Minzu University, Chinese Academy of Sciences and University of Nebraska at Omaha. IAITQM was formally inaugurated on June 3, 2012 with more than 50 founding members from China, United States, Australia, Japan, Lithuania, Poland, Romania, Spain, Singapore, South Korea, The Netherlands, Turkey and other countries. The International Conference on Information Technology and Quantitative Management (ITQM), established by IAITQM, is a global forum for exchanging research findings and case studies that bridge the latest information technology and quantitative management techniques. It explores how the use of information technology techniques to improve quantitative management and how the development of management tools can reshape the development of information technology. The First International Conference on Information Technology and Quantitative Management (ITQM 2013) took place in Suzhou, China. The Second International Conference on Information Technology and Quantitative Management (ITQM 2014) was held in Moscow, Russia. The Third International Conference on Information Technology and Quantitative Management (ITQM 2015) was held at Rio de Janeiro, Brazil. The Forth International Conference on Information Technology and Quantitative Management (ITQM 2016) was held at Asan, Korea. The Fifth International Conference on Information Technology and Quantitative Management (ITQM 2017) was held at New Delhi, India. The Sixth International Conference on Information Technology and Quantitative Management (ITQM 2018) was hosted at Omaha, USA. The Seventh International Conference on Information Technology and Quantitative

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ETO Meets Scheduling: Learning key knowledge from Single-Objective Problems to Multi-Objective Problem

ETO Meets Scheduling: Learning Key Knowledge from Single-Obj...

引用

Chinese Automation Congress (CAC)

作者： Wendi Xu Xianpeng Wang College of Information Science and Engineering Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9781665426480

Evolutionary transfer optimization(ETO) serves as "a new frontier in evolutionary computation research", which will avoid zero reuse of experience and knowledge from solved problems in traditional evolutionary computation. In schedule-ing applications via ETO, a highly competitive "meeting" framework between them could be constituted towards both intelligent scheduling and green scheduling, especially for carbon neutrality within the context of *** the best of our knowledge, our study on scheduling here, is the 1 st work of ETO for complex optimization when multi-objective problem "meets" single-objective problems in combinatorial case (not multitasking optimization). More specifically, key knowledge like positional building blocks clustered, could be learned and transferred for permutation flow shop scheduling problem (PFSP). Empirical studies on well-studied benchmarks validate relatively firm effectiveness and great potential of our proposed ETO-PFSP framework.

关键词： Job shop scheduling Automation Processor scheduling Green buildings Evolutionary computation Benchmark testing Multitasking

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Violence Detection In Videos via Motion-Guided Global and Local Views

Violence Detection In Videos via Motion-Guided Global and Lo...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Ning Su Lijuan Sun Yutong Gao Jingchen Wu Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. This lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. Therefore, in this paper, we design a novel Global-and-Local Cross-Modal Network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, The enhanced features are simultaneously fed into two parallel branches of the network. The global branch fuses the visual and audio features into holistic representations. The local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset.

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MANN-TYPE INERTIAL SUBGRADIENT EXTRAGRADIENT METHODS FOR BILEVEL EQUILIBRIUM PROBLEMS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第4期84卷 19-32页

作者： Ceng, Lu-Chuan Zhu, Li-Jun Yao, Zhangsong Department of Mathematics Shanghai Normal University Shanghai200234 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China

In this paper, we introduce and analyze two Mann-type implicit inertial sub-gradient extragradient algorithms for solving the monotone bilevel equilibrium problem with a general system of variational inclusions and a common fixed-point problem of a finite family of strict pseudocontraction mappings and an asymptotically nonexpansive mapping constraints. Some strong convergence theorems for the proposed algorithms are established under the suitable assumptions. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Variational techniques

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Big data Technology in Intelligent Distribution Network: Demand and Applications 6th

Big Data Technology in Intelligent Distribution Network: De...

引用

6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Ye, Zhi-Peng Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fuzhou University No. 33 Xuefu South Road New District FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

Nowadays, with the development and maturity of big data technology, big data analysis technology is more and more widely used in practice, and more and more data are gradually applied to the smart grid of our country. Since the data in the smart grid meets the 4V characteristics of big data (large quantity, fast speed, many types, low value density), the use of big data technology can provide more accurate and cheaper data for power information generation Economic value and significance. Through the analysis of the development process of Chinas power industry, the development of distribution network in China obviously lags behind the development of power generation and transmission network. At present, more than 95% of the blackouts are caused by the distribution network, and half of the power loss occurs in the distribution network, so the automation of the distribution network system urgently needs the support of new technologies. This paper first enumerates several key points of big data technology, including big data collection, storage and analysis, and then expounds several methods of big data analysis. On this basis, big data technology is applied to the field of intelligent distribution network. Especially in the application of distribution forecasting, it can provide more powerful technical support for the operation of smart distribution network, continuously improve the technical level of Chinas smart distribution network, and promote the optimization and upgrading of smart grid system. Finally, an optimized prediction model is proposed, and the application of the new technology (5G technology) developed at the present stage is prospected, and its contribution to the data acquisition and application of big data technology is analyzed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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