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Journal of Network Intelligence 2020年第2期5卷 34-43页

作者： Zhang, Jing Nian, Hongrui Ye, Xiucai Ji, Xiaorong He, Yao College of Information Science and Engineering Institute of Artificial Intelligence Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33th Xueyuan Road University Town Minhou FuzhouFujian350118 China College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Department of Computer Science University of Tsukuba Tsukuba3058573 Japan

Wireless Sensor Networks are formed by a mass of various sensors, they are adopted in lots of applications. Because of the existence of spatial correlation in WSNs, the information from some geographic independence sensors are more significant than that from high correlation ones. Thus, it is unnecessary to transmit information from every sensor to the sink node. This paper focus on the coverage area for data monitoring in WSNs. To construct the virtual backbone network, we propose an efficient connected dominating set construction method based on clique, by taking advantage of the high spatial correlation characteristics of clique construction. Furthermore, by minimizing the number of sensors to activate, we propose a Spatial Correlation based partial Coverage Scheduling Scheme (SCCS) to prolong the network lifetime. The corresponding experiment indicate the efficiency and the feasibility of the proposed SCCS scheme. Moreover, this algorithm is able to guarantee better performance by comparing to the existing schemes. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Scheduling

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Fast One-Pass Approximation for Matrix-Based Rényi's Entropy

SSRN

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SSRN 2024年

作者： Gong, Tieliang Dong, Yuxin Gao, Zeyu Chen, Hong Zhang, Weizhan Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China Department of Oncology University of Cambridge CambridgeCB2 1TN United Kingdom Department of Mathematics and Statistics Huazhong Agriculture University Hubei Wuhan430070 China

This paper is concerned with improving the computational efficiency of the matrix-based Rényi's α-order entropy, which enables directly being accessed from the given data without knowing the probability density function of the underlying data. However, leveraging either trace estimation or the eigenvalue-based methods will cost O(n3) in time complexity, where n is the number of observed samples. The intensive computational demands impede its utility in modern large-scale machine learning tasks. Recently, the Hutch++ based entropy approximation was developed, by converting the original entropy estimation problem into stochastic trace estimation. It reduces the time complexity to O(n2s) (s © 2024, The Authors. All rights reserved.

关键词： Distillation

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A Dynamic Parallel Harris Hawks Optimization Based WSN Node Localization Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第4期6卷 688-703页

作者： He, Xiankang Chu, Shu-Chuan Liu, Shi-Jian Pan, Jeng-Shyang Yan, Lijun College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Shenzhen Institute of Information Technology Shenzhen518000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Science and Engineering Flinders University Adelaide Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

The localization of wireless sensor network (WSN) is an increasingly promi-nent problem. The goal of this problem is to use the anchor nodes in WSN to esti-mate the geographical location of the unknown nodes. This paper proposes a novel al-gorithm, named dynamic parallel Harris Hawks optimization (DPHHO). It contains the dynamic control strategy of the escaping energy and the parallel communication mecha-nism. DPHHO algorithm significantly improves the global search ability of the original Harris Hawks optimization (HHO) algorithm. Also, a novel localization algorithm based on hop distance correction and DPHHO is proposed. The proposed DPHHO algorithm was tested on the 23 classical test functions and the DPHHO-DV-Hop algorithm is applied to the localization of WSN. The experimental results show that, compared with HHO and other optimization algorithms, the proposed DPHHO algorithm is more effective and efficient. Compared with DV-Hop and other localization algorithms, the proposed DPHHO-DV-Hop is an effective algorithm for the localization of WSN. © 2021, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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The impact of the change of trade pattern on China’s energy industry——Based on the measurement of the upstreamness of production of the value chains

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Procedia Computer Science 2018年 139卷 248-255页

作者： Xinran Wang Xiaoxian Guo Yan Song Shasha Cao Tian Wu Xin Tian Zhenping Li Shanghai Pudong Development Bank Shnaghai 200000 China China Petroleum Technology and Development Corporation Beijing 100028 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Information Beijing Wuzi University Beijing 101149 China

The natural monopoly of China’s upstream industry and the vertical structure of industrial chain’s upstream and downstream competition is an important feature of China’s industrial economic structure, and the impact of the change of trade pattern on the energy industry, which located at the upstream of the China’s value chain, has become an important issue in the industrial economy. Since China has entered into the WTO in 2001, we use it for event analysis based on 1987-2007 China input-output data. The study calculates the upstreamness of production of the value chain in various industrial sectors of China’s energy industry every five years, and make correlation analysis of upstreamness of production and other economic indicators to check the characteristics of periodic inspection of China’s industrial structure with the development of industrial economy and the change of trade pattern.

关键词： Energy industry industrial economic structure upstreamness of production trade pattern

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Innovative Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Sung, Tien-Wen Lee, Chao-Yang Gaber, Tarek Nassar, Hamed Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan School of Science Engineering & Environment University of Salford Manchester United Kingdom Department of Computer Science Suez Canal University Ismailia Egypt

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Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks

Block-Structured Optimization for Anomalous Pattern Detectio...

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IEEE International Conference on data mining (ICDM)

作者： Fei Jie Chunpai Wang Feng Chen Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University at Albany – SUNY Albany NY USA Erik Jonsson School of Engineering & Computer Science The University of Texas at Dallas Dallas TX USA Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Knowledge Science Hefei University of Technology Hefei China

We propose a generalized optimization framework for detecting anomalous patterns (subgraphs that are interesting or unexpected) in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. We frame the problem as a non-convex optimization that has a general nonlinear score function and a set of block-structured and non-convex constraints. We develop an effective, efficient, and parallelizable projection-based algorithm, namely Graph Block-structured Gradient Projection (GBGP), to solve the problem. It is proved that our algorithm 1) runs in nearly-linear time on the network size, and 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications, and we conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.

关键词：

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VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection

arXiv

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arXiv 2024年

作者： Song, Ziying Zhang, Guoxin Xie, Jun Liu, Lin Jia, Caiyan Xu, Shaoqing Wang, Zhepeng School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang050018 China Lenovo Research Beijing100085 China State Key Laboratory of Internet of Things for Smart City Department of Electrome chanical Engineering University of Macau 999078 China

LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, resulting in the loss of the advantages of images, including semantic and continuity information, leading to sub-optimal detection performance, especially at long distances. In this paper, we present VoxelNextFusion, a multi-modal 3D object detection framework specifically designed for voxel-based methods, which effectively bridges the gap between sparse point clouds and dense images. In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features. These features are then fused using a self-attention to obtain a combined representation. Moreover, to address the issue of background features present in patches, we propose a feature importance module that effectively distinguishes between foreground and background features, thus minimizing the impact of the background features. Extensive experiments were conducted on the widely used KITTI and nuScenes 3D object detection benchmarks. Notably, our VoxelNextFusion achieved around +3.20% in AP@0.7 improvement for car detection in hard level compared to the Voxel R-CNN baseline on the KITTI test dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Block-Structured Optimization for Subgraph Detection in Interdependent Networks

arXiv

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arXiv 2022年

作者： Jie, Fei Wang, Chunpai Chen, Feng Li, Lei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University at Albany – SUNY AlbanyNY United States Erik Jonsson School of Engineering & Computer Science The University of Texas at Dallas DallasTX United States Mininglamp Academy of Sciences Mininglamp Technologies Beijing China Institute of Big Knowledge Science Hefei University of Technology Hefei China

We propose a generalized framework for block-structured nonconvex optimization, which can be applied to structured subgraph detection in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. Specifically, we design an effective, efficient, and parallelizable projection algorithm, namely Graph Block-structured Gradient Projection (GBGP), to optimize a general non-linear function subject to graph-structured constraints. We prove that our algorithm: 1) runs in nearly-linear time on the network size;2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications and conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Functions

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An Empirical Study on Information Extraction using Large Language Models

arXiv

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arXiv 2023年

作者： Han, Ridong Yang, Chaohao Peng, Tao Tiwari, Prayag Wan, Xiang Liu, Lu Wang, Benyou College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Information Technology Halmstad University Sweden

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI’s GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs’ information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs’ human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods’ effectiveness and some of their remaining issues in improving GPT-4’s information extraction ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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Ramp Loss Linear Programming Nonparallel Support Vector Machine

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Procedia Computer Science 2016年 80卷 1745-1754页

作者： Dalian Liu Dandan Chen Yong Shi Yingjie Tian School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Basic Course Teaching Beijing Union University Beijing 100101 China College of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Motivated by the fact that the l 1 -penalty is piecewise linear, we proposed a ramp loss linear programming nonparallel support vector machine (ramp-LPNPSVM), in which the l 1 -penalty is applied for the RNPSVM, for binary classification. Since the ramp loss has the piecewise linearity as well, ramp- LPNPSVM is a piecewise linear minimization problem and a local minimum can be effectively found by the Concave Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm. Moreover, the l 1 -penalty can enhance the sparsity.

关键词： support vector machine nonparallel CCCP linear programming ramp loss

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