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International Conference on Systems and Informatics (ICSAI)

作者： Runping Xi Tianyi Han Yaqing Zhang Qianqian Han School of Computer Science Northwestern Polytechnical University National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Xi'an China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

Aerial video stitching has been widely used in many fields such as large-scale videos surveillance, disaster monitoring and unmanned aerial vehicle (UAV) navigation. Ghosting and parallax are challenging problems when the video streams are captured by UAV. This paper presents a novel method based on hybrid projective model which combines local warp alignment and global warp projection for better registration accuracy. The geometric alignment model is used for global registration of images and the content-preserving alignment model is used for local refinement. Simultaneously, reference frame is updated adaptively according to the shift of scenery, which is used to eliminate the cumulative error appearing in long video streams. The proposed approach has been tested under multiple datasets in different scenarios, which shows good performance.

关键词： Streaming media Feature extraction Adaptation models Cameras Image fusion Distortion Unmanned aerial vehicles

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Geographic information science in the era of geospatial big data: A cyberspace perspective

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The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

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FedQL: A framework for federated queries processing on RDF stream and relational data 23rd

FedQL: A framework for federated queries processing on RDF s...

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23rd International Conference on Database Systems for Advanced applications, DASFAA 2018

作者： Rao, Guozheng Zhao, Bo Zhang, Xiaowang Feng, Zhiyong School of Computer Science and Technology Tianjin University Tianjin300350 China School of Computer Software Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

ISBN: (纸本)9783319914541

In this paper, we present a framework (FedQL) for processing RDF stream and relational data in a federal way. Firstly, we introduce a formalization of our federated query language by conjunction of continuous queries and SQL queries. Secondly, we present a white-box-based framework to separate query processing from query executing. The framework mainly includes three modules, namely, Query processor, Data transformer, and SPARQL query execution. Finally, we implement FedQL built on C-SPARQL and MySQL by employing three centralized SPARQL engines (e.g. Jena, RDF-3X, and gStore) and one distributed SPARQL engine (e.g. TriAD) in an adaptive way and evaluate FedQL on a real-world dataset. The experimental results show that FedQL is efficient and effective in processing RDF stream and relational data in a federal way. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Engines

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A Hybrid Framework for Query Processing and Data Analytics on Spark

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Chen, Haokun Zhang, Xiaowang Zhang, Jiahui Feng, Zhiyong School of Computer Science and Technology Tianjin University Tianjin300350 China School of Computer Software Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

ISBN: (纸本)9783030012977

In this paper, we propose a hybrid framework for query processing and data analytics over large-scale data on Spark, to support multi-paradigm process (incl. SQL, OLAP, data mining, machine learning etc.) in distributed environments. The framework features a three-layer data process module and a work flow module which controls the former. We will demonstrate the strength of our framework properly applying traffic scenarios in a real world. © 2018, Springer Nature Switzerland AG.

关键词： Data Analytics

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A new runoff calculation model based on multi-source radar remote sensing technology 6

A new runoff calculation model based on multi-source radar r...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Zhao, Jianhui Wang, Ning Wu, Lin Min, Lin College of Computer and Information Engineering Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

River runoff changing will directly affect the safety of the surrounding areas. In theory, river runoff can be calculated from water depth, river width and flow velocity. But in the actual monitoring, the observation of water depth, river width and flow velocity is complex. Radar remote sensing can observe target all-time and all-weather, and has the ability to penetrate clouds, rain and fog. The common Radar remote sensing runoff calculation model is depth-runoff relationship model, which is constructed by fitting water depth and in-situ runoff. The river depth is derived from Radar Altimetry (RA) data. But this model has only one input parameter, the accuracy of this model inverted runoff is not high. In view of the above problems, this paper proposed the Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM). Firstly, river width and relative water depth were derived from Synthetic Aperture Radar (SAR) images and RA data. Secondly, based on the Manning's equation, the flow velocity were described from river width and relative water depth. Thirdly, river width relative water depth, and in-situ runoff were fitted to construct MRRS-RCM. Experiments show the accuracy of MRRS-RCM estimated runoff is better than depth-runoff relationship model, the RRMSE values of MRRS-RCM reach 13%. © 2019 IEEE.

关键词： Synthetic aperture radar

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Expressivity issues in SPARQL: monotonicity and two-versus three-valued semantics

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Science China(Information Sciences) 2018年第12期61卷 187-189页

作者： Xiaowang ZHANG Chenhong MENG Lei ZOU School of Computer Science and Technology Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Institute of Computer Science and Technology Peking University

Dear editor,In 2008, the SPARQL query language was the official world wide web consortium (W3C) recommendation for a resource description framework(RDF) query language [1], which is a database query language that can retrieve and manipulate data stored in the RDF format. SPARQL allows a query to have triple patterns, conjunctions (AND),disjunctions (UNION), optional patterns (OPT)and built-in conditions (constraints) that can be filtered (FILTER).

关键词： Expressivity issues in SPARQL monotonicity and two-versus three-valued semantics

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Exploiting cognitive structure for adaptive learning

arXiv

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arXiv 2019年

作者： Liu, Qi Tong, Shiwei Liu, Chuanren Zhao, Hongke Chen, Enhong Ma, Haiping Wang, Shijin Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology School of Data Science University of Science and Technology of China Business Analytics and Statistics University of Tennessee College of Management and Economics Tianjin University IFLYTEK Research IFLYTEK CO. LTD. State Key Laboratory of Cognitive Intelligence

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes

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Remote real-time wastewater surveillance by the optical sensor supported by machine learning

Sensors and Actuators Reports

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Sensors and Actuators Reports 2025年 10卷

作者： Małgorzata Szczerska Kacper Cierpiak Monika Kosowska Paweł Wityk Sebastián García-Galán Patryk Sokołowski Sylwia Fudala-Książek Michał T. Tomczak Bei Ye Aneta Łuczkiewicz Department of Metrology and Optoelectronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Faculty of Telecommunications Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Al. Prof. S. Kaliskiego 7 85-796 Bydgoszcz Poland Department of Biotechnology and Microbiology Faculty of Chemistry Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Department of Biopharmaceutics and Pharmacodynamics Medical University of Gdańsk Hallera 107 Pharmacy Faculty Gdańsk Poland Telecommunications Engineering Department Linares Higher Polytechnic School Jaén University Avenida de la Universidad S/N 23700 Linares Jaén Spain Department of Water and Wastewater Technology Faculty of Civil and Environmental Engineering Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Faculty of Management and Economics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Environmental Protection Key Laboratory of Microorganism Application and Risk Control Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University Shenzhen 518055 PR China

In this study, a remote monitoring sewage system based on optical method is presented for the first time. The built-in wastewater surveillance system can perform autonomous monitoring with no requirement of sample collection and its transport to the laboratory. The measurement results can be obtained in real time, the work operation allows continuous or on-demand mode. The study includes design, development and application of a biofunctionalized fiber-optic sensor, as well as engaging machine learning algorithms for measured signals classification. The validated SARS-CoV-2 antibodies sensor has a measurement range of 10 −12 mg/mL to 10 −1 mg/mL. Known concentrations of Immunoglobulin G (IgG) from 10 −6 mg/mL to 10 −1 mg/mL were added to the tested wastewater samples and then detected by the prepared optical probe. The data obtained were then processed and classified by traditional method and selected machine learning algorithms; the results obtained for the KNeighbors algorithm are Balanced Accuracy of 92.97 % and F1-score of 94.19 %. This study is of significance to establish a system that can effectively monitor the outbreak of potential infections in society.

关键词： Remote wastewater surveillance Real-time monitoring SARS-CoV-2 Optical sensors Fiber optic sensor Machine learning

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Li, An, and Morozov Reply:

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Physical Review Letters 2019年第11期123卷 119602-119602页

作者： Guodong Li Qi An Sergey I. Morozov Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Science Wuhan University of Technology Wuhan 430070 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan 430070 China Department of Chemical and Materials Engineering University of Nevada Reno Reno Nevada 89557 USA Department of Computer Simulation and Nanotechnology South Ural State University Chelyabinsk 454080 Russia

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A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

A Diverse Biases Non-negative Latent Factorization of Tensor...

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IEEE International Conference on Networking, Sensing and Control

作者： Xuke Wu Hang Gou Hao Wu Juan Wang Minzhi Chen Siyu Lai Computer School China West Normal University Nanchong China Computer School of China West Normal University Nanchong Sichuan China University of Chinese Academy of Sciences Beijing China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China North Sichuan Medical College Nanchong China Medical College Nanchong Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy.

关键词： Tensors Conferences Predictive models Data models Sensors Data mining

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