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Correction: Privacy preservation in the internet of vehicles using local differential privacy and IOTA ledger

Cluster Computing 2023年第6期26卷 3379-3379页

作者： Iftikhar, Zainab Anjum, Adeel Khan, Abid Shah, Munam Ali Jeon, Gwanggil Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Institute of Information Technology Quaid i Azam University Islamabad Pakistan College of Science and Engineering University of Derby Derby UK Department of Embedded Systems Engineering Incheon National University Incheon Republic of Korea Energy Excellence & Smart City Lab Incheon National University Incheon Republic of Korea

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Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

arXiv

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arXiv 2020年

作者： Löffler, Felicitas Wesp, Valentin König-Ries, Birgitta Klan, Friederike Heinz-Nixdorf Chair for Distributed Information Systems Department of Mathematics and Computer Science Friedrich Schiller University Jena Jena Germany Michael-Stifel-Center for Data-Driven and Simulation Science Jena Germany Halle-Jena-Leipzig Germany Citizen Science Group DLR-Institute of Data Science German Aerospace Center Jena Germany

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. We analyze the primary source in dataset search - metadata - and determine if they reflect scholarly search interests. We examine if metadata standards provide elements corresponding to search interests, we inspect if selected data repositories use metadata standards representing scholarly interests, and we determine how many fields of the metadata standards used are filled. To determine search interests in biodiversity research, we gathered 169 questions that researchers aimed to answer with the help of retrieved data, identified biological entities and grouped them into 13 categories. The categories were evaluated with nine biodiversity scholars who assigned one of the types to pre-labeled biological entities in the questions. Our findings indicate that environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important search interests in biodiversity research. The comparison with existing metadata standards shows that domain-specific standards cover search interests quite well, whereas general standards do not explicitly contain elements that reflect search interests. We inspect metadata from five large data repositories. Our results confirm that metadata currently poorly reflect search interests in biodiversity research. From these findings, we derive recommendations for researchers and data repositories how to bridge the gap between search interest and metadata provided.

关键词： Semantic Web

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Correction to: Network Functions Virtualization for Mobile Core and Heterogeneous Cellular Networks

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Wireless Personal Communications 2021年第3期122卷 2561-2561页

作者： Qureshi, Kashif Naseer Ahmad , Ejaz Anwar, Muhammad Ghafoor, Kayhan Zrar Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer Research Faisalabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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An intelligent fuzzy-induced recommender system for cloud-based cultural communities

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International Journal of Web Based Communities 2019年第3期15卷 271-288页

作者： Ravi, Logesh Devarajan, Malathi Jeon, Gwanggil Bayat, Oguz Subramaniyaswamy, V. Department of Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai Tamilnadu India School of Computing SASTRA Deemed University Thanjavur India Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of School of Electronic Engineering Xidian University Xi'an China Department of Electrical Engineering Altinbas University Istanbul Turkey

The rapid development of communication technologies and web-based services generate a large amount of information. In recent years, recommender systems (RS) emerge as an effective mechanism to tackle the information overloading problems. By exploiting the cloud computing paradigm, RS discovers interesting new cultural items based on user preferences and interests. Recent investigations on RS reveal that employing social network data can yield enhanced personalised recommendations with better prediction accuracy. Since users tend to visit only conventional monuments, and many charming cultural items are hidden from them due to lack of awareness about the cultural sites. This article proposes a personalised recommendation model in the field of cultural heritage (CH) with the help of the cloud computing environment. The experimental results obtained demonstrate the improved performance of developed RS in the area of cultural heritage tourism services. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Recommender systems

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Engineering of an Effective Automatic Dynamic Assertion Mining Platform

Engineering of an Effective Automatic Dynamic Assertion Mini...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Tara Ghasempouri Jan Malburg Alessandro Danese Graziano Pravadelli Goerschwin Fey Jaan Raik Department of Computer System Tallinn University of Technology Estonia Avionics Systems German Aerospace Center Institute of Space Systems Germany Department of Computer Science University of Verona Italy Institute of Embedded Systems Hamburg University of Technology Germany

Several approaches exist for specification mining of hardware designs, both at the RTL and system levels (e.g, TLM). These approaches mine assertions that specify the behavior of the design. Some of the techniques require the source code itself while others can extract assertions directly from simulation traces. The performance of some approaches is highly dependent on the number of simulation traces/use cases while there exist approaches which can extract assertions from a limited number of simulation traces. Apart from this aspect, the core of each assertion miner is different from the other ones. Some use expression templates to define assertions while some are based on the static analysis or information flow analysis. Unfortunately, it has been rarely considered which of the current approaches are more effective in describing functionality of particular types of designs. Thus, in this work, we analyze assertion miners which are template based and dynamic dependency graph based, respectively. We generate assertions from both approaches. The evaluation considers fault analysis on both assertion sets of extracted assertions. Moreover, both sets are combined and fault analysis has been applied on them. Experimental results show that each set approximately detects the same number of faults while when the two sets are combined the number of detected faults increases. Finally, a new, more efficient architecture for an effective assertion miner has been developed based on the study in this work.

关键词： Hardware design languages computer architecture Data mining Tools Hardware Manuals Documentation

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Correction to: Partially Overlapped Channel Assignment for Cloud-Based Heterogeneous Cellular and Mesh Networks

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Wireless Personal Communications 2021年第3期122卷 2583-2583页

作者： Iqbal, Saleem Qureshi, Kashif Naseer Majeed, Saqib Ghafoor, Kayhan Zrar Jeon, Gwanggil University Institute of Information Technology PMS UAAR Rawalpindi Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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Learning to Prevent Monocular SLAM Failure using Reinforcement Learning 18

Learning to Prevent Monocular SLAM Failure using Reinforceme...

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Proceedings of the 11th Indian Conference on computer Vision, Graphics and Image Processing

作者： Vignesh Prasad Karmesh Yadav Rohitashva Singh Saurabh Swapnil Daga Nahas Pareekutty K. Madhava Krishna Balaraman Ravindran Brojeshwar Bhowmick Embedded Systems & Robotics TCS Research & Innovation Kolkata India Robotics Institute Carnegie Mellon University Pittsburgh USA Dept. of Robotics Engineering John Hopkins University Baltimore USA Robotics Research Center KCIS IIIT Hyderabad India Dept. of Computer Science Indian Institute of Technology Madras India

ISBN: (纸本)9781450366151

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

关键词： Monocular SLAM

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Adversarial bandit approach for stand alone RIS operation

TechRxiv

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TechRxiv 2021年

作者： Ahmed Ouameur, Messaoud Lê, Dương Tun Anh Jeon, Gwanggil de Figueiredo, Felipe A.P. Massicotte, Daniel The Université du Québec à Trois-Rivières Department of Electrical and Computer Eng. 3351 Boul. des Forges Trois-RivièresQCG9A 5H7 Canada Faculty of Information Technology VNU-HCM University of Science Viet Nam Department of Embedded Systems Engineering College of Information Technology Incheon National University Incheon Korea Republic of National Institute of Telecommunications Minas Gerais Santa Rita do Sapucaí Brazil

Even though, reconfigurable intelligent surfaces (RISs) are adopted in various scenarios to enable the implementation of a smart radio environment, there are still challenging issues for its real-time operation due to the need for a costly full dimensional channel estimation with offline exhaustive search or online exhaustive beam-training. The application of the deep learning (DL) tools is favored to enable feasible solutions. In this work, we propose two low training overhead and energy efficient adversarial bandit-based schemes with outstanding performance gains compared to reference DL based reflection beamforming methods. The resulting deep learning models are also discussed using state-of-the art model quality prediction trends. © 2021, CC BY.

关键词： Beamforming

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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