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arXiv 2019年

作者： Chen, Bo Zhang, Jing Tang, Xiaobin Chen, Hong Li, Cuiping Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education Renmin University of China Information School Renmin University of China

Cross-lingual knowledge alignment suffers from the attribute heterogeneity when leveraging the attributes and also suffers from the conflicts when combing the results inferred from attributes and relationships. This paper proposes an interaction-based attribute model to capture the attribute-level interactions for estimating entity similarities, eliminating the negative impact of the dissimilar attributes. A matrix-based strategy is adopted in the model to accelerate the similarity estimation. We further propose a co-training framework together with three merge strategies to combine the alignments inferred by the attribute model and the relationship model. The whole framework can effectively and efficiently infer the aligned entities, relationships, attributes, and values simultaneously. Experimental results on several cross-lingual knowledge datasets show that our model significantly outperforms state-of-the-art comparison methods (improving 2.35-51.57% in terms of Hit Ratio@1). Copyright © 2019, The Authors. All rights reserved.

关键词： Alignment

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Contrasting the Indian and western North Pacific summer monsoons in terms of their intensity of interannual variability and biennial relationship with ENSO

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Atmospheric and Oceanic Science Letters 2020年第5期13卷 462-469页

作者： CHEN Baiyang WANG Lei WU Minmin Laboratory for Coastal Ocean Variation and Disaster Prediction College of Ocean and MeteorologyGuangdong Ocean UniversityZhanjiangChina Key Laboratory of Climate Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong ProvinceGuangdong Ocean UniversityZhanjiangChina Marine Resources Big Data Center of South China Sea Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)ZhanjiangChina

The intensity of interannual variability(IIV)of the monsoon and monsoon–ENSO biennial relationship(MEBR)were examined and compared for both the Indian summer monsoon(ISM)and western North Pacific summer monsoon(WNPSM)during 1958–*** of the IIV and MEBR were identified for the two *** the MEBR was strong(weak),the IIV of the monsoon was observed to be large(small).This rule applied to both the ISM and ***-ofphase relationships were found between the ISM and the *** the IIV and MEBR of the ISM were strong(weak),those of the WNPSM tended to be weak(strong).During the period with a stronger(weaker)ENSO–Atlantic coupling after(before)the mid-1980 s,the IIV and MEBR of the WNPSM(ISM)were observed to be *** increasing influences from the tropical Atlantic sea surface temperature(SST)may trigger the observed seesaw pattern of the ISM and WNPSM in terms of the IIV and MEBR multidecadal *** results imply that tropical Atlantic SST may need to be given more attention and consideration when predicting future monsoon variability of the ISM and WNPSM.

关键词： Indian summer monsoon western North Pacific summer monsoon ENSO intensity of interannual variability monsoon–ENSO biennial relationship

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Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport Systems

Integrating ARIMA and Bidirectional LSTM to Predict ETA in M...

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Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Lechen Wang Xuechun Li Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao'an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight's destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal feature

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R2-Net: Relation of relation learning network for sentence semantic matching

arXiv

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arXiv 2020年

作者： Zhang, Kun Wu, Le Lv, Guangyi Wang, Meng Chen, Enhong Ruan, Shulan School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite their effectiveness, most of these models treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about relations. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches. © 2020, CC BY.

关键词： Natural language processing systems

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Research on the motivation to contribution and influencing factors of university students——a semi-structured interview based on qualitative research

Research on the motivation to contribution and influencing f...

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第二届先进电子材料、计算机与材料工程国际学术会议（AEMCME 2019）

作者： Yueyao Li Sanhong Deng Yue Zhang School of Information Management Nanjing University Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University

Due to the lack of theoretical knowledge and practical experience, the university students have many deficiencies in contribution. We conducted semi-structured interviews with 28 undergraduate and postgraduate students of Nanjing Agricultural University. At the same time, we used descriptive statistics and cross-analysis methods to analyse the motivations of college students' initial contribution and the influencing factor of their choice. Based on this, this paper puts forward some suggestions for the initial submission of college students, and the journals are also provided for reference in the selection of manuscripts.

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Erratum to: Incremental algorithms for the maximum internal spanning tree problem

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Science China Information Sciences 2022年第10期65卷 1-1页

作者： Zhu, Xianbin Li, Wenjun Yang, Yongjie Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Chair of Economic Theory Saarland University Saarbrücken Germany

In the original paper [1], there are two errors on page 5. The rules in the algorithm are not sufficient since we omitted one rule during publication. The following rule should be added to the paper. Rule 3: If P1 = (u, v) and P2 = (x, y) are two maximal leaf-paths both with two vertices in T, where u and x are leaves in T and (v, y) ∈ E(G), we remove (y′, y) and then add (v, y) to T where y′ is the non-leaf neighbor of y in T. The expression of Algorithm 2 is not accurate. It should be modified as follows. Line 3: Exhaustively apply Rules 1–3, and for i ∈ {2, 3} only apply Rule i when none of Rules j where 1 ⩽ j < i is applicable. Line 8: Determine the longest path P among all paths whose endpoints have nonempty neighbors in T \ V(T′).

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Adaptive Deployment of UAV-Aided Networks Based on Hybrid Deep Reinforcement Learning

Adaptive Deployment of UAV-Aided Networks Based on Hybrid De...

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IEEE Conference on Vehicular Technology (VTC)

作者： Xiaoyong Ma Shuting Hu Danyang Zhou Yi Zhou Ning Lu School of Computer and Information Engineering Henan University Kaifeng China International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Henan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China Queen’s University Kingston Ontario Canada

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

Unmanned aerial vehicles (UAVs) can be used as air base stations to provide fast wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforemetioned constraints. In this paper, we propose an adaptive deployment strategy for UAV-aided networks based on hybrid deep reinforcement learning, where a UAV can adjust its movement direction and distance to serve users who move randomly in the target area. Through hybrid deep reinforcement learning, UAVs can be trained offline to obtain the global state information and learn a completely distributed control strategy, with which each UAV only needs to take actions based on its observed state in the real deployment to be fully adaptive. Moreover, in order to improve the speed and effect of learning, we improve hybrid reinforcement learning, by adding genetic algorithms and TD-error-based resampling optimization mechanism. Simulation results show that the hybrid deep reinforcement learning algorithm has better efficiency and robustness in multi-UAV control, and has better performance in terms of coverage, energy consumption and average throughput, by which average throughput can be increased by 20% to 60%.

关键词： Energy consumption Adaptive systems Simulation Reinforcement learning Throughput Unmanned aerial vehicles Optimization

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Records systems and information systems: Connecting in organizations 8th

Records systems and information systems: Connecting in organ...

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8th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2016

作者： Xie, Sherry L. Fan, Guanyan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing100872 China Center for Electronic Records Management Research Beijing100872 China School of Information Resource Management Renmin University of China 59 Zhongguancun Ave Haidian District Beijing100872 China

ISBN: (纸本)9783319997001

The field of Information Systems (ISs) has long been recognized, so has Enterprise Information Systems (EISs), a field close to it. Long existing also in organizations or enterprises is the field of records management, now predominantly digital records management (DRM), which shares the many goals of ISs and EISs in supporting the operation and success of organizations. While the DRM field recognized rather early in its battle to digital records challenges that the need to establish formal collaborations with the ISs and EISs professions, it is still rare to spot today discussions regarding such collaborations in the general information and communication technology (ICT) literature. To help bridge the gap, this chapter introduces to the ISs and EISs professions one of the major developments of the international DRM field, that is, the records systems elaborated by the InterPARES (International Research on Permanent and Authentic Records in Electronic Systems) project, for the purpose of invoking further discussions. © Springer Nature Switzerland AG 2019.

关键词： Information systems

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Collusion-Proof Result Inference in Crowdsourcing

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Journal of Computer Science & Technology 2018年第2期33卷 351-365页

作者： Peng-Peng Chen Hai-Long Sun Yi-Li Fang Jin-Peng Huai State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China

In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. To pursue more rewards with little effort, some workers may collude to provide repeated answers, which will damage the quality of the aggregated results. Nonetheless, there are few efforts considering the negative impact of collusion on result inference in crowdsourcing. In this paper, we are specially concerned with the Collusion-Proof result inference problem for general crowdsourcing tasks in public platforms. To that end, we design a metric, the worker performance change rate, to identify the colluded answers by computing the difference of the mean worker performance before and after removing the repeated answers. Then we incorporate the collusion detection result into existing result inference methods to guarantee the quality of the aggregated results even with the occurrence of collusion behaviors. With real-world and synthetic datasets, we conducted an extensive set of evaluations of our approach. The experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.

关键词： crowdsourcing quality control collusion collaborative crowdsourcing result inference

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On the security of a three factor remote user authentication scheme using fuzzy extractor 14th

On the security of a three factor remote user authentication...

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14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Chen, Chien-Ming Huang, Yanyu Deng, Xiaoting Wu, Tsu-Yang Shenzhen China College of Computer Science and Engineering Shandong University of Technology Shandong China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou 350118 China Fujian University of Technology Fuzhou 350118 China

ISBN: (纸本)9783030037444

A secure authenticated key exchange protocol is an important key to establish a secure wireless communication. Various research have been conducted to study the efficiency and security of these authenticated key exchange protocol. A recent work by Giri et al. proposed a three factor remote user authentication scheme using Fuzzy Extractor for single server environment. However we found that their protocol is still vulnerable against an online password guessing attack. We also found that their protocol does not provide the perfect forward secrecy. To solve such problems, we propose a simple but effective improvement. © Springer Nature Switzerland AG 2019.

关键词： Authentication

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