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arXiv 2024年

作者： Zhu, Mengna Xu, Zijie Zeng, Kaisheng Xiao, Kaiming Wang, Mao Ke, Wenjun Huang, Hongbin Laboratory for Big Data and Decision National University of Defense Technology China School of Computer Science and Engineering Southeast University Bangladesh Computer Science and Technology Tsinghua University China

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://***/Mzzzhu/CMNEE. Copyright © 2024, The Authors. All rights reserved.

关键词： data mining

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Disjoint-Path Routing Mechanism in Mobile Opportunistic Networks

Disjoint-Path Routing Mechanism in Mobile Opportunistic Netw...

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2020国际计算机前沿大会

作者： Peiyan Yuan Hao Zhang Xiaoyan Huang School of Computer and Information Engineering Henan Normal University Engineering Laboratory of Intellectual Business and Internet of Things Technologies Big Data Engineering Laboratory for Teaching Resources & Assessment of Education Quality

The prevalent multi-copy routing algorithms in mobile opportunistic networks(MONs) easily cause network congestion. This paper introduces a disjoint-path(DP) routing algorithm, where each node can only transmit packets once except the source node, to effectively control the number of packet copies in the network. The discrete continuous time Markov chain(CTMC) was utilized to analyze the state transition between nodes, and the copy numbers of packets with the DP routing algorithm were calculated. Simulation results indicate that DP has a great improvement in terms of packet delivery ratio, average delivery delay,average network overhead, energy and average hop count.

关键词： Multi-copy Disjoint-path Mobileopportunisticnetworks Routing ContinuoustimeMarkovchain

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Generating summarized preview for education resource based on exploring and comparing GUIs 28

Generating summarized preview for education resource based o...

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28th International Conference on Software engineering and Knowledge engineering, SEKE 2016

作者： Ma, Chao Chen, Xiangping Rao, Yongsheng Lin, Mouguang School of Data and Computer Science Sun Yat-sen University Guangzhou China Research Institute Sun Yat-sen University in Shenzhen Shenzhen China Institute of Advanced Technology Sun Yat-sen University Guangzhou China School of Computer Science and Educational Software Guangzhou University Guangzhou China Institute of Advanced Technology National Engineering Research Center of Digital Life Sun Yat-sen University Guangzhou China

ISBN: (纸本)189170639X

Compared to traditional education resource, Interactive Education Resource (IER) includes dynamic content to interact with users in order to enhance comprehension. The contents of IERs are relatively complex because of its dynamic feature. Exploring IERs and understanding its content during resource search become time-consuming. In this paper, we propose a preview generation approach based on automated software testing and image processing. Our approach first analyzes IERs to collect interactive elements and the event list. Based on the interaction related information, our approach simulates user actions and record snapshots step by step. Our approach compares snapshots to generate an image including the basic content, interaction region and dynamic contents. The image is used as the overall preview and the animation of snapshots collected during simulation is used as detail preview of the IER. In the experiment, we evaluate the completeness, correctness, and comprehension of the preview. The experiment result shows that the preview can be used to provide most information in the resource correctly and easy to understand. Our approach can save time for users in understanding the IERs. Copyright © 2016 by KSI Research Inc. and Knowledge Systems Institute Graduate school.

关键词： Image processing

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Foreground Detection Algorithm via Fusing Confidence by Weight and Visual Attention

Foreground Detection Algorithm via Fusing Confidence by Weig...

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Internet of Things and Intelligent Applications (ITIA), International Conference on

作者： Cheng Keyang Wu Jinxia Liu Qing Sun Shuang School of Computer Science and Telecommunications Engineering Jiangsu University Zhenjiang China National Engineering Laboratory for Public Safety Risk Perception Control by Big Data Zhenjiang China

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

In the foreground detection of video, the result of pixel-level background subtraction is clear and flexible. However, the pixel-level classification method based on sample consistency can't make full use of the pixel information effectively and get full foreground mask when meeting the complex situation of color camouflage and static object. In order to solve this problem effectively, we proposed an algorithm based on fusing confidences with weight and visual attention. Firstly, the foreground is determined by the sum of fusing with color confidence and texture confidence based on weight, then the confidence and weight of the samples are updated adaptively; the static foreground is determined by constructing the visual attention mechanism in the subsequence, and finally the background model is dynamically updated with the strategy of updating the minimum confidence samples. The method proposed in this paper is tested on the open dataset. It is found that the proposed algorithm is superior to the contrast algorithms, and get the accuracy and recall rate of foreground detection in the complex situation of color camouflage and still objects, and get better detection effects.

关键词： Image color analysis Adaptation models Visualization Color Detection algorithms Telecommunications computer science

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Quality of Work-Life During Pandemic 1

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丛书名： Studies in Big data

1000年

作者： Gitanjali Rahul Shinde Soumi Majumder Haribhau R. Bhapkar Parikshit N. Mahalle

ISBN: (数字)9789811675232

ISBN: (纸本)9789811675225;9789811675256

This book is focused on the impact of the COVID-19 pandemic on different sectors, i.e., education, real estate, health, and agriculture. The lockdown has been announced to control the spread of COVID-19 infections, however people/industries/organizations were not ready for lockdown and it has greatly affected their growth. The front workers in the healthcare sector suffered a lot as major responsibilities they needed to carry on. The education sector is also hampered due to the pandemic as schools, colleges were closed and teaching, examinations were carried out on online platforms. These platforms were new to teachers as well as students. The real estate sector faced tremendous loss in this pandemic as people were scared and no one ready to invest their money in such an uncertain time. The agriculture filed is also suffered as raw materials required for agriculture were not available readily due to pandemic.

关键词： Computational Intelligence Simulation and Modeling Mathematical Models of Cognitive Processes and Neural Networks Statistics, general

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A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model

A Semi-supervised Bilingual Lexicon Induction Method for Dis...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Wenwu Zhi Yuhong Zhang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs.

关键词： Training Geometry Dictionaries Conferences Bidirectional control Task analysis

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SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RARE EVENTS: A STUDY IN PRECIPITATION EXTREMES

SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RA...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Debasish Das Auroop Ganguly Snigdhansu Chatterjee Vipin Kumar Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Civil and Environmental Engineering Northeastern University School of Statistics University of Minnesota Computer Science and Engineering University of Minnesota

ISBN: (纸本)9781467311601

Discovery of dependence structure between precipitation extremes and other climate variables (covariates) within a smaller spatial and temporal neighborhood is an important step in better understanding the drivers of this complex phenomenon as well as short-term prediction of extremes occurrence. Apart from the inherent spatio-temporal variability of the dependence, it is further complicated by the availability of the covariates at different vertical levels. The above problem can be split into three different sub-problems. Firstly, a spatio-temporal neighborhood of influence has to be discovered, which can be different for different locations. Secondly, the dependence structure between the precipitation extremes and the covariates has to be discovered within this neighborhood and thirdly, it has to be investigated whether this dependence structure can be exploited for any predictive power. Climate scientists have already discovered some physics-based relations between some of the covariates (e.g. temperature, relative humidity, precipitable water etc.) and precipitation extremes. We are exploring data-dependent alternatives for these problems and any possibility of incorporating the physics-based relations into the resulting data model. In particular, we used elastic net-based sparse optimization technique which solves all three problems of neighborhood discovery, covariate dependence discovery and predictive modeling and at the same time maintains the interpretability of the resulting model. Preliminary results look promising and show potential for some interesting knowledge discovery. We are currently exploring non-linear correlations and the alternatives to combine the physics-based relationships into the data model.

关键词： One Two Three Four Five data models precipitation Chemical Precipitation precipitation (meteorology) extremum values Neighborhood DEPENDENCE data mining interpretability

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Parallel Multi-Strategy Evolutionary Algorithm Using Massage Passing Interface for Many-Objective Optimization

Parallel Multi-Strategy Evolutionary Algorithm Using Massage...

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IEEE Symposium Series on Computational Intelligence

作者： Zi-Jia Wang Zhi-Hui Zhan Jun Zhang the School of Computer Science and Engineering South China University of Technology 510006 Guangzhou P. R. China the School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China

ISBN: (纸本)9781509042418

Evolutionary multi-objective optimization (EMO) algorithms have become prevalent and obtained a great success for solving two- or three-objective problems. However, with the number of objectives increases, most of the algorithms cannot perform well due to the expansion of the objective space. Therefore, there is an urgent need for improving EMO algorithms to handle many-objective (four or more objectives) optimization problems (MaOPs). To this end, this paper proposes a parallel multi-strategy evolutionary algorithm (PMEA) to make full use of the advantages of different selection strategies. Specially, PMEA maintains three populations in parallel to select individuals based on three strategies as decomposition-based approach, indicator-based approach, and shift-based density estimation approach. PMEA uses message passing interface (MPI) to share the information after the selection of the three strategies, so that the advantages of diverse approaches can be utilized. In this way, PMEA can explore the objective space more thoroughly and thus achieve more promising performance. We evaluated PMEA on two frequently used MaOP suites and compared the results with several state-of-the-art many-objective peer algorithms. Numerical results demonstrate that PMEA can achieve a statistically superior performance, or at least highly competitive performance on most of the problems instances.

关键词： Parallel Multi-Strategy Message Passing Interface (MPI) PMEA Many-Objective Optimization

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Tensor Extended Kalman Filter and its Application to Traffic Prediction

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 13813-13829页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan32003 Taiwan National Yang Ming Chiao Tung University Information Technology Service Center Hsinchu30010 Taiwan

Traffic prediction is a very important mechanism in intelligent transportation systems for applications including routing planning and traffic control. In order to infer multifarious traffic information, one/two-relational traffic data in the vector/matrix form need to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent nonlinear dynamics which are often encountered in realistic time-series analysis. Although the extended Kalman filter (EKF) has been proven to be quite promising in inferring nonlinear dynamics from time series, but the current EKF approach still suffers, unfortunately, from a serious drawback that state variables have to be represented in vector form. In fact, the characteristics of multi-relational states in practice can never been manifested accurately in practice and the performance of an EKF would be greatly restricted thereby. In this work, we introduce a new tensor extended Kalman filter (TEKF) approach to accommodate arbitrary input, output, and state variables all in arbitrary tensor forms. We also propose a new tensor-based expectation-maximization (EM) algorithm to estimate the nonlinear state-transition and observation-model mappings. The computational and memory complexities of the proposed TEKF approach are also studied in this paper. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of the proposed new TEKF approach over the simulated and realworld traffic datasets in comparison with three other existing deep-learning prediction methods. © 2000-2011 IEEE.

关键词： Tensors

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Joint Scheduling and Beamforming Design in Traffic-Aware RIS Aided MEC Network

Joint Scheduling and Beamforming Design in Traffic-Aware RIS...

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IEEE Globecom Workshops

作者： Aichen Li Yang Liu Qingqing Wu Qingjiang Shi Jun Zhao School of Information and Communication Engineering Dalian University of Technology Department of Electrical and Computer Engineering University of Macau Macau China School of Software Eng. Tongji Univ. Shanghai Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Nanyang Technological University

ISBN: (纸本)9781665459761

This paper considers exploiting reconfigurable intelligent surface (RIS) in a multi-user multi-access mobile computing (MEC) system. To precisely model the traffic behaviors of the tasks, we consider queuing procedures at both mobile users and the cloud server. This consideration leads to concatenation of queues and makes our problem much more difficult than existing literature since analytic expression of the objective is impossible. Our goal is to jointly optimize the task scheduling, RIS configuration and computation resource allocation to improve the MEC network’s overall computation rate in the long run while maintaining the stability of the queues. To tackle this highly challenging stochastic problem, we develop a Lyapunov guided deep reinforcement learning (DRL) solution, which borrows the Lyapunov optimization theory to stabilize queues and at the same time can effectively optimize the intractable objective in an online manner. Numerical results will be presented to verify the effectiveness of our solution and the performance boost due to the deployment of RIS.

关键词： Processor scheduling Stochastic processes Reinforcement learning Reconfigurable intelligent surfaces Stability analysis Servers Resource management

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