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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Jun Shang Hanwen Zhang Weixiong Rao Yiguang Hong Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Sci-ence and Technology Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

关键词： Technological innovation Automation Wireless networks Encryption Numerical models Kalman filters State estimation Robots Intelligent sensors

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

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arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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CLDG: Contrastive Learning on Dynamic Graphs

CLDG: Contrastive Learning on Dynamic Graphs

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International Conference on Data engineering

作者： Yiming Xu Bin Shi Teng Ma Bo Dong Haoyi Zhou Qinghua Zheng Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

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On the Move to Meaningful Internet Systems: OTM 2014 Conferences 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Robert Meersman Hervé Panetto Tharam Dillon Michele Missikoff Lin Liu Oscar Pastor Alfredo Cuzzocrea Timos Sellis

ISBN: (数字)9783662455630

ISBN: (纸本)9783662455623

This volume constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2014, and Ontologies, Databases, and Applications of Semantics, ODBASE 2014, held as part of OTM 2014 in October 2014 in Amantea, Italy. The 39 full papers presented together with 12 short papers and 5 keynotes were carefully reviewed and selected from a total of 115 submissions. The OTM program covers subjects as follows: process designing and modeling, process enactment, monitoring and quality assessment, managing similarity, software services, improving alignment, collaboration systems and applications, ontology querying methodologies and paradigms, ontology support for web, XML, and RDF data processing and retrieval, knowledge bases querying and retrieval, social network and collaborative methodologies, ontology-assisted event and stream processing, ontology-assisted warehousing approaches, ontology-based data representation, and management in emerging domains.

关键词： Information Systems Applications (incl. Internet) Information Storage and Retrieval software engineering Artificial Intelligence Systems and Data Security Computer Appl. in Administrative Data Processing

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Mandari: Multi-Modal Temporal knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

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Linking Data Sovereignty and Data Economy: Arising Areas of Tension 17

Linking Data Sovereignty and Data Economy: Arising Areas of ...

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17th International Conference on Wirtschaftsinformatik, WI 2022

作者： Lauf, Florian Scheider, Simon Bartsch, Jan Herrmann, Philipp Radic, Marija Rebbert, Marcel Nemat, André T. Schlueter-Langdon, Christoph Konrad, Ralf Sunyaev, Ali Meister, Sven Fraunhofer Institute for Software and Systems Engineering ISST Healthcare Dortmund Germany TU Dortmund Chair of Industrial Information Management Dortmund Germany Department of Economics and Management Karlsruhe Germany Fraunhofer Center for International Management and Knowledge Economy IMW Corporate Development in International Competition Division Leipzig Germany Witten/Herdecke University Institute for Digital Transformation in Healthcare GmbH Witten Germany T-Systems International GmbH Digital Solutions - Data Intelligence Hub Frankfurt Germany Claremont Graduate University Drucker School of Management Claremont United States KASTEL Security Research Labs Karlsruhe Germany Witten/Herdecke University Faculty of Health School of Medicine Witten Germany

In the emerging information economy, data evolves as an essential asset and personal data in particular is used for data-driven business models. However, companies frequently leverage personal data without considering individuals’ data sovereignty. Therefore, we strive to strengthen individuals’ position in data ecosystems by combining concepts of data sovereignty and data economy. Our research design comprises an approach to design thinking iteratively generating, validating, and refining such concepts. As a result, we identified ten areas of tension that arise when linking data sovereignty and data economy. Subsequently, we propose initial solutions to resolve these tensions and thus contribute to knowledge about the development of fair data ecosystems benefiting both individuals’ sovereignty and companies’ access to data. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Ecosystems

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Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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Collaborative tools and processes to support software engineering shift work

British Telecom technology journal

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British Telecom technology journal 1997年第3期15卷 189-198页

作者： Gorton, I. Hawryszkiewycz, I. Ragoonaden, K. Sheffield Hallam University United Kingdom CSIRO Australia Software Engineering Research Group CSIRO Transarc Corporation Sydney NSW Australia Department of Computing Sciences University of Technology Sydney NSW Australia Computer Supported Cooperative Work Ger. Natl. Computer Research Center Res. Inst. of Appl. Knowledge Proc. University of Ulm Germany University of Technology Sydney NSW Australia University of Canberra Australia University of Maryland College Park MD United States Telecom Australia Australia MIT United States University of Adelaide Australia UMIST United Kingdom City University London United Kingdom BT Laboratories United Kingdom Software Technology Division BT Asia Pacific Regional MIS Unit Sydney NSW Australia GWSE

This paper describes the construction of and early experiences with a software engineering support environment for projects using globally distributed teams. The goals of the project are twofold. Firstly, it aims to construct a pragmatic solution to the problems experienced by widely geographically dispersed groups which collaborate on software development projects. Secondly, it aims to experiment with processes which facilitate software shift work through exploitation of time differences between collaborating groups. The construction of the support environment, known as GWSE (Global Working in software engineering) system, is presented, including its architecture and integration with existing workflow, document and project management tools. The use of the GWSE system in a trial development project is then described, including an initial quantitative analysis of the collaboration overheads experienced.

关键词： Computer aided software engineering

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Quantifying patterns of research interest evolution

arXiv

引用

arXiv 2017年

作者： Jia, Tao Wang, Dashun Szymanski, Boleslaw K. College of Computer and Information Science Southwest University Chongqing400715 China Laboratory for Software and Knowledge Engineering Southwest University Chongqing400715 China Kellogg School of Management Northwestern University EvanstonIL60208 United States Northwestern University EvanstonIL60208 United States McCormick School of Enginnering and Applied Sciences Northwestern University EvanstonIL60208 United States Social Cognitive Networks Academic Research Center Rensselaer Polytechnic Institute TroyNY12180 United States Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Spo leczna Akademia Nauk Lòdz90-113 Poland

Our quantitative understanding of how scientists choose and shift their research focus over time is highly consequential, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded [1-9]. Despite extensive investigations of various factors that influence a scientist's choice of research topics [8-21], quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research interest evolution of individual scientists remain limited. Here we perform a large-scale analy- sis of publication records, finding that research interest change follows a reproducible pattern characterized by an exponential distribution. We identify three fundamen- tal features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research interest evolution [5, 22]. We develop a random walk based model, allowing us to accurately reproduce the empirical observations. This work presents a quantitative analysis of macroscopic patterns governing research interest change, discovering a high degree of regularity underlying scientific research and individual careers. Copyright © 2017, The Authors. All rights reserved.

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Verification of a numerical model of the offshore wind turbine from the alpha ventus wind farm within OC5 phase III 37

Verification of a numerical model of the offshore wind turbi...

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ASME 2018 37th International Conference on Ocean, Offshore and Arctic engineering, OMAE 2018

作者： Popko, Wojciech Huhn, Matthias L. Robertson, Amy Jonkman, Jason Wendt, Fabian Muller, Kolja Kretschmer, Matthias Vorpahl, Fabian Hagen, Torbjørn Ruud Galinos, Christos Dreff, Jean-Baptiste Le Gilbert, Philippe Auriac, Bertrand Villora, Francisco Navarro Schunemann, Paul Bayati, Ilmas Belloli, Marco Oh, Sho Totsuka, Yoshitaka Qvist, Jacob Bachynski, Erin Sørum, Stian Høegh Thomassen, Paul E. Shin, Hyunkyoung Vittori, Felipe Galvan, Josean Molins, Climent Bonnet, Paul Van Der Zee, Tjeerd Bergua, Roger Wang, Kai Fu, Pengcheng Cai, Jifeng Fraunhofer Institute for Wind Energy Systems IWES Division Wind Turbine and System Technology Bremerhaven27572 Germany National Renewable Energy Laboratory GoldenCO United States University of Stuttgart Stuttgart Wind Energy Stuttgart Germany Senvion Osnabruck Germany OWEC Tower AS Oslo Norway Technical University of Denmark Department of Wind Energy Roskilde Denmark Electricite de France Recherche et Developpement Palaiseau France IFP Energies Nouvelles Solaize France Principia La Ciotat France Siemens Gamesa Renewable Energy Hamburg Germany University of Rostock Endowed Department of Wind Energy Technology Rostock Germany Politecnico di Milano Department of Mechanical Engineering Milan Italy Tokyo Japan Wind Energy Institute of Tokyo Inc. Tokyo Japan Subsea Hvalstad Norway Norwegian University of Science and Technology Department of Marine Technology Trondheim Norway Simis AS Trondheim Norway University of Ulsan School of Naval Architecture and Ocean Engineering Ulsan Korea Republic of Sarriguren Spain TECNALIA Derio Spain Polytechnic University of Catalonia Barcelona Spain Siemens Industry Software Cornella de Llobregat Spain Knowledge Centre WMC Wieringerwerf Netherlands Envision Energy Limited Shanghai China China General Certification Center Beijing China

ISBN: (纸本)9780791851319

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project, is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project analyzes the Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. This paper shows results of the verification of the OWT models (code-to-code comparison). A subsequent publication will focus on their validation (comparison of simulated results to measured physical system response data). Based on the available data, the participants of Phase III set up numerical models of the OWT in their simulation tools. It was necessary to verify and to tune these models. The verification and tuning were performed against an OWT model available at the University of Stuttgart - Stuttgart Wind Energy (SWE) and documentation provided by Senvion and OWEC Tower. A very good match was achieved between the results from the reference SWE model and models set up by OC5 Phase III participants. © Copyright 2018 ASME.

关键词： Numerical models

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