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检索条件"任意字段=21st International Conference on Distributed Computing and Intelligent Technology"

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Research on paper submission management system by using automatic text categorization

Research on paper submission management system by using auto...

引用

AHFE 2017 international conference on Human Factors, Software, and Systems Engineering, 2017

作者： Luo, Qi College of Sports Engineering and Information Technology Wuhan Sports University Wuhan430079 China School of Educational Information Technology Central China Normal University Wuhan430079 China Flat 5 La Medidionale Gorg Borg Olivier Street St. Julian’s1017 Malta

ISBN: (纸本)9783319600109

The submission management system is a software system, also known as submission processing that streamlines and eases out the collection, tracking and management of paper submissions. Paper information can be received, authenticated, tracked, stored, and distributed electronically. The submission system of transaction and conference need sort thousands of papers into hundreds of reviewers to examine and comment. It is not good that these papers are sort into hundreds of reviewers to examine and comment in a short time, because the matching precision is low. Aiming at solving the above questions, the paper submission management system by using automatic text categorization is proposed in the paper. The system can automatically sort the submission papers and the published papers of the reviewer into the right subject, so we can judge the subject of the submission papers and the research direction of the reviewer. © Springer international Publishing AG 2018.

关键词： Support vector machines

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A Trustworthy Data Aggregation Model Based on Context and Data Density Correlation Degree 18

A Trustworthy Data Aggregation Model Based on Context and Da...

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21st ACM international conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)

作者： Gao, Yunquan Li, Xiaoyong Li, Jirui Gao, Yali Beijing Univ Posts & Telecomm Key Lab Trustworthy Distributed Comp & Serv BUPT Minist Educ Beijing Peoples R China

ISBN: (纸本)9781450359603

Data aggregation is widely used in wireless sensor networks (WSNs) due to the resource constraints of computational capability, energy and bandwidth. Because WSNs are often deployed in an unattended hostile environment, WSNs are prone to various attacks. The traditional security technologies such as privacy protection and encryption technology can not address the attacks from the internal nodes of network. Therefore, the trust management mechanism for data aggregation has become a hot research topic, and an efficient trust management mechanism plays an important role in data aggregation. In this paper, we propose an efficient trust model based on context and data density correlation degree. Our proposed trust model consists of three major contexts, sensing trust, link trust, node trust. We take into full account data aggregating characteristic and different impacts of node trust, link trust and sensing trust on the secure of data aggregation. We also take into account data correlation degree in computing sensing trust, which leads to more accurate trust result. The experiment results show that compared to the existing trust models our proposed trust model provides more accurate sensing trust and improves the throughput and robustness against malicious attacks. Our proposed trust model is more suitable for data aggregation than conventional trust models.

关键词： WSNs data aggregation trust management

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Siting and Sizing of distributed Generation and Electric Vehicle Charging station Under Active Management Mode 1st

Siting and Sizing of Distributed Generation and Electric Veh...

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1st international conference on intelligent Manufacturing and Internet of Things (IMIOT) / 5th international conference on computing for Sustainable Energy and Environment (ICSEE)

作者： Shan, Weilu Li, Xue Du, Dajun Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

ISBN: (纸本)9789811323812;9789811323805

This paper is concerned with bi-level joint planning model of distributed generation (DG) and electric vehicle charging station (EVCS) siting and sizing under active management (AM) mode. Firstly, the joint planning model including DG and EVCS is established by taking the lowest annual comprehensive cost as the upper level objective. Then, the lower level model optimizes DG output curtailment by taking the uncertainty of load, EVCS charging load, and the intermittent DG output of wind farm and photovoltaic generator into account. According to the bidirectional interaction of upper and lower levels, the bi-level planning model is optimized by biogeography-based optimization (BBO) and primal-dual interior point method (PDIPM) respectively. Finally, simulation is operated on the revised IEEE-33 nodes distribution network, and simulation results show that the joint planning of DG and EVCS can obtain better planning scheme.

关键词： distributed generation (DG) Electric vehicle charging station (EVCS) Biogeography-based optimization (BBO) Joint bi-level planning

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5th international conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI international 2018

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5th international conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI international 2018

ISBN: (纸本)9783319917429

The proceedings contain 76 papers. The special focus in this conference is on Learning and Collaboration Technologies. The topics include: Learning in Marketing Simulation;classroom Gamification: Merging Game Design Theory and Behavior Analysis for Increased Engagement;Slayers vs Slackers: An Examination of Users’ Competitive Differences in Gamified IT Platforms Based on Hedonic Motivation System Model;an e-Learning System Using Gamification to Support Preliminary Learning for Job Hunting;the Use of Digital Game Based Learning in Manufacturing Education – A Case study;gamification as Learning Scenario in Programming Course of Higher Education;gamification with Lego in Higher Education: Experience in the Course of Logistic Distribution;learning in Doing: A Model of Design and Assessment for Using New Interaction in Educational Game;warm Robot Classroom_Using Wearable technology as a Gateway to Culturally Responsive Teaching;Exploring New Ways to Increase Engagement in Full-Path MOOC Programs;go/No Go Criteria in Formative E-Rubrics;exploring Sociocultural Theory Application in Online Language Courses;Applications of distributed Socio-technical Synthetic intelligent (SI) Systems Using Hybrid “Compensating Controls” Architectures;an Online Environment to Compare students’ and Expert Solutions to Ill-structured Problems;An Analytical Investigation on Ecological strategy Based on “Edu+AR” Education Cloud Platform;a Framework for Interactive Exploratory Learning Analytics;quiz-R-Us – Re-Conceptualizing Quizzes to Enrich Blended Learning in Occupational Therapy study Lines;evaluation of Flip-Flop Learning Methodology;applying studio-Based Learning Methodology in Computer Science Education to Improve 21st Century Skills;uniqueness Profile of Mobile Applications for Learning;study on the Attitude of Young People Towards technology.

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5th international conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI international 2018

5th International Conference on Learning and Collaboration T...

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5th international conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI international 2018

ISBN: (纸本)9783319911519

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Towards Dynamic Fog Resource Provisioning for Smart City Applications 14

Towards Dynamic Fog Resource Provisioning for Smart City App...

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14th international conference on Network and Service Management, CNSM 2018 and Workshops, 1st international Workshop on High-Precision Networks Operations and Control, HiPNet 2018 and 1st Workshop on Segment Routing and Service Function Chaining, SR+SFC 2018

作者： Santos, Jose Wauters, Tim Volckaert, Bruno Turck, Filip De IDLab Department of Information Technology Technologiepark-Zwijnaarde Ghent University - Imec Gent15 9052 Belgium

ISBN: (纸本)9783903176140

Over the past few years, the Internet of Things (IoT) has transformed the Smart City concept into an attractive and relevant opportunity. Smart Cities aim to connect billions of objects of everyday life to the Internet to improve sustainability and citizen welfare. Fog computing has been introduced to provide scalable and low latency services for the future IoT use cases by placing cloud resources at the edges of the network. Nevertheless, crucial challenges still remain in the Fog computing domain. One of them is providing proper resource provisioning while reducing allocation costs, maximizing energy efficiency and minimizing latency. Therefore, in this paper, a novel Resource Discovery Service based on Peer-to-Peer (P2P) distributed Hash Tables (DHT) is presented to enable automated resource discovery functionalities for IoT services. The proposed approach provides a flexible way of exchanging resource allocation information between the Fog and the Cloud Layer. Evaluations have been conducted to demonstrate the performance of implementing information dissemination systems based on DHTs. Performance ratios higher than 99%, including latencies under 0.7 seconds for a static context have been obtained. Results show that DHTs provide scalable discovery solutions showing the full applicability of the proposed approach in the Smart City environment. © 2018 IFIP.

关键词： Fog computing

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Multi-sensor multi-object tracking with different fields-of-view using the LMB filter

Multi-sensor multi-object tracking with different fields-of-...

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international conference on Information Fusion

作者： Suqi Li Giorgio Battistelli Luigi Chisci Wei Yi Bailu Wang Lingjiang Kong University of Electronic Science and Technology of China Chengdu China Dipartimento di Ingegneria dell'Informazione Universita degli Studi di Firenze Firenze Italy

A key issue in multi-sensor surveillance is the capability to surveil a much larger region than the field-of-view (FoV) of any individual sensor by exploiting cooperation among sensor nodes. Whenever a centralized or distributed information fusion approach is undertaken, this goal cannot be achieved unless a suitable fusion approach is devised. This paper proposes a novel approach for dealing with different FoVs within the context of Generalized Covariance Intersection (GCI) fusion. The approach can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic tracking scenarios demonstrate the effectiveness of the proposed solution.

关键词： Peer-to-peer computing Robot sensing systems Radio frequency Surveillance Correlation Electronic mail

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Knowledge engineering for distributed case-based reasoning systems 1st

Knowledge engineering for distributed case-based reasoning s...

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1st Workshop on Knowledge Engineering and Software Engineering, KESE 2005, Co-Located with 28th German conference on Artificial Intelligence, KI-2005

作者： Bach, Kerstin Department of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783319641607

This chapter describes how to identify and collect human knowledge and transform it into machine readable and actionable knowledge. We will focus on the knowledge acquisition for distributed case-based reasoning systems. Case-based reasoning (CBR) is a well-known methodology for implementing knowledge-intensive decision support systems (Aamodt, Plaza, Artif Intell Commun, 7(1):39–59, 1994) [1] and has been applied in a broad range of applications. It captures experiences in the form of problem and solution pairs, which are recalled when similar problems reoccur. In order to create a CBR system the initial knowledge has to be identified and captured. In this chapter, we will summarise the knowledge acquisition method presented by Bach, Knowledge acquisition for case-based reasoning systems. Ph.D. thesis, University of Hildesheim, München (2012) [2] and give an running example within the travel medicine domain utilising the open source tool for developing CBR systems, myCBR. © 2018, Springer international Publishing AG.

关键词： Case based reasoning

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Teaching–Learning-Based Optimization on Hadoop 1st

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1st international conference on Soft computing: Theories and Applications, SoCTA 2016

作者： Jagdeo, S.M. Umbarkar, A.J. Sheth, P.D. Department of Information Technology Walchand College of Engineering SangliMS India Department of MCA Government College of Engineering KaradMS India

ISBN: (纸本)9789811056864

The challenges of evolutionary algorithms (EA) are high dimensionality of problems, curse of dimensionality, premature convergence, and controlling parameters. There are various advancements that happen to the EAs over a period of time. Various kinds of EAs are the GA, PSO, TLBO, GSA, etc. Teaching–learning-based algorithm (TLBO) is the recent algorithm introduced in 2011. Since its introduction, many researchers have modified TLBO and applied in various real-world problems. Various modifications in TLBO have been done to enhance and get the original version better. There is no clear information about how much the TLBO algorithm has evolved and the research has been done. Hence, this paper presents modifications of TLBO done in chronological way. Advancements in technologies also happened based on processing of data. Various new technologies like Hadoop based on distributed computing are introduced to process data quickly when compared to sequential code. TLBO can be fused with Hadoop for enhancement in processing speed thus a novel model of Hybrid TLBO is presented. Lastly, new approaches towards EAs are discusses. © 2018, Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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A survey of machine learning-based resource scheduling algorithms in cloud computing environment 4th

A survey of machine learning-based resource scheduling algor...

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4th international conference on Cloud computing and Security, ICCCS 2018

作者： Liu, Qi Jiang, Yinghang Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9783030000059

As a new type of computing resource, cloud computing attracts more and more users because it is convenient and quick service. The cloud server is used by a large number of users, which brings about the problem of how to reasonably schedule resources to ensure the load balance of the cloud environment. With the development of research, scholars have found that the simple job scheduling of physical resources cannot meet the utilization of resources. Connecting the characteristic of resource scheduling in cloud environment and machine learning, researchers gradually abstract a resource scheduling problem into a mathematical problem, and then combine machine learning with group algorithm to put forward the intelligent algorithm which can optimize the resource structure and the improve the resource utilization. In this survey, we discuss several algorithms that use machine learning to solve resource scheduling problems in a cloud environment. Experiments show that machine learning can assist the cloud environment to achieve load balancing. © Springer Nature Switzerland AG 2018.

关键词： Cloud computing

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