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Uncertainty-oriented ensemble data visualization and exploration using variable spatial spreading

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arXiv 2020年

作者： Zhang, Mingdong Chen, Li Li, Quan Yuan, Xiaoru Yong, Junhai School of Software BNRist Tsinghua University Beijing China AI Group WeBank ShenzhenGuangdong China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China

As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

An Improved Honey Badger Algorithm for Electric Vehicle Charge Orderly Planning

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Journal of Network Intelligence

Journal of Network Intelligence 2022年第2期7卷 332-346页

作者： Chen, Ran-Fan Luo, Hao Huang, Kuan-Chun Nguyen, Trong-The Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China School of Aeronautics and Astronautics Zhejiang University HangZhou310000 China Department of Industrial and Systems Engineering Chung Yuan University Taoyuan320-33 Taiwan Multimedia Communications Lab VNUHCM-University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Multiple electric vehicles simultaneously connected to a distribution load network will significantly impact the stability of the power grid. This paper proposes an improved Honey-badger algorithm (IHBA) through elite reverse learning, spiral update, and wild dog survival strategies to optimize the orderly charging of electric vehicles (EV). Objective functions of EV charge orderly planning are taken by using load fluctuation satisfaction, user cost satisfaction, and user convenience satisfaction to achieve the high efficiency of orderly charging. The benchmark test function and the electric vehicle se-quential charging problem were employed to evaluate the proposed IHBA approach. The obtained results are compared with the other algorithms in the literature, which indicate that the IHBA algorithm does not only provides more accurate outcome but better con-vergence speed than the other competitors. © 2022;Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Food products

Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

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Generating High-quality Movie Tags from Social Reviews: A Le...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

An Experimental Evaluation of Large Scale GBDT Systems

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arXiv 2019年

作者： Fu, Fangcheng Jiang, Jiawei Shao, Yingxia Cui, Bin Peking University Center for Data Science & National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing University of Posts and Telecommunications Tencent Inc

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we find it surprising that the existing systems manage the training dataset in different ways, but none of them have studied the impact of data management. To that end, this paper aims to study the pros and cons of different data management methods regarding the performance of distributed GBDT. We first introduce a quadrant categorization of data management policies based on data partitioning and data storage. Then we conduct an in-depth systematic analysis and summarize the advantageous scenarios of the quadrants. Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases. To validate our analysis empirically, we implement different quadrants in the same code base and compare them under extensive workloads, and finally compare Vero with other state-of-the-art systems over a wide range of datasets. Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

Research on Intelligent Frequency Control of Permanent Magnet Synchronous Motor

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Research on Intelligent Frequency Control of Permanent Magne...

2020 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2020

作者： Chang, Kuo-Chi Hu, Zhongxin Chu, Kai-Chun Chang, Fu-Hsiang Wang, Hsiao-Chuan Hsu, Tsui-Lien Lin, Yuh-Chung Zhou, Yuwen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China Department of Business Administration North Borneo University College Sabah Malaysia Fujian University of Technology Department of Business Management Fuzhou China Shih-Hsin University Department of Tourism Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

ISBN: (纸本)9781728191461

The development of PMSM is inseparable from the continuous improvement of the performance-to-price ratio of permanent magnet materials and the development of power electronic equipment. This is exactly the study of PMSM. Entered a new stage, PMSM plays an important role in military, industry and daily life. Dating vector control theory to the field of PMSM speed regulation is also a major breakthrough. Researchers combined the mathematical model of PMSM with the vector control model. It is based on this that the PMSM vector control double closed-loop model was established. Simulate in MATLAB software. There are current module, speed module and PMSM main module in the simulation diagram, and then analyze the simulation results. © 2020 IEEE.

关键词： MATLAB

DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search

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arXiv 2020年

作者： Guan, Yushuo Liu, Ning Zhao, Pengyu Che, Zhengping Bian, Kaigui Wang, Yanzhi Tang, Jian The School of Computer Science Peking University Beijing100871 China Midea Group Beijing100102 China The School of Computer Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China The Department of Electrical & Computer Engineering Northeastern University BostonMA02115 United States

The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Channel pruning is usually applied to reduce the model redundancy while preserving the network structure, such that the pruned network can be easily deployed in practice. However, existing channel pruning methods require hand-crafted rules, which can result in a degraded model performance with respect to the tremendous potential pruning space given large neural networks. In this paper, we introduce Differentiable Annealing Indicator Search (DAIS) that leverages the strength of neural architecture search in the channel pruning and automatically searches for the effective pruned model with given constraints on computation overhead. Specifically, DAIS relaxes the binarized channel indicators to be continuous and then jointly learns both indicators and model parameters via bi-level optimization. To bridge the non-negligible discrepancy between the continuous model and the target binarized model, DAIS proposes an annealing-based procedure to steer the indicator convergence towards binarized states. Moreover, DAIS designs various regularizations based on a priori structural knowledge to control the pruning sparsity and to improve model performance. Experimental results show that DAIS outperforms state-of-the-art pruning methods on CIFAR-10, CIFAR-100, and ImageNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Annealing

How Does Knowledge Graph Embedding Extrapolate to Unseen data: A Semantic Evidence View

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arXiv 2021年

作者： Li, Ren Cao, Yanan Zhu, Qiannan Bi, Guanqun Fang, Fang Liu, Yi Li, Qian Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China National Computer Network Emergency Response Technical Team Coordination Center of China China University of Technology Sydney Australia

Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r,?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability? For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods. For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural Network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation. Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://***/renli1024/SE-GNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

An efficient aggregate signature scheme for healthcare wireless sensor networks

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Journal of Network Intelligence

Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

QCluster: Clustering Packets for Flow Scheduling

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arXiv 2020年

作者： Yang, Tong Wang, Hao Li, Jizhou Jiang, Jie Zhao, Yikai Zhang, Yinda Yang, Kaicheng Zhang, Nicholas School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Huawei Theory Lab China

Flow scheduling is crucial in data centers, as it directly influences user experience of applications. According to different assumptions and design goals, there are four typical flow scheduling problems/solutions: SRPT, LAS, Fair Queueing, and Deadline-Aware scheduling. When implementing these solutions in commodity switches with limited number of queues, they need to set static parameters by measuring traffic in advance, while optimal parameters vary across time and space. This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. The key idea of QCluster is to cluster packets with similar weights/properties into the same queue. QCluster is implemented in Tofino switches, and can cluster packets at a speed of 3.2 Tbps. To the best of our knowledge, QCluster is the fastest clustering algorithm. Experimental results in testbed with programmable switches and ns-2 show that QCluster reduces the average flow completion time (FCT) for short flows up to 56.6%, and reduces the overall average FCT up to 21.7% over state-of-the-art. All the source code in ns-2 is available in Github [45]. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory