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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Preface

Studies in Big Data

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Studies in Big Data 2021年 73卷 v-vii页

作者： Chakraborty, Chinmay Banerjee, Amit Kolekar, Maheshkumar H. Garg, Lalit Chakraborty, Basabi Department of Electronics and Communication Engineering Birla Institute of Technology Ranchi Mesra India Department of Electrical Engineering Indian Institute of Technology Patna Patna India Faculty of Software and Information Science Iwate Prefectural University Takizawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta Msida Malta

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Online Intelligent Resource Management for Power-Delay Tradeoff in Backhaul-Limited Mobile Edge Computing systems

Online Intelligent Resource Management for Power-Delay Trade...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yuan Sun Gang Yang Xing-She Zhou Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of China Electronics Technology Group Corporation Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Computer Science and Engineering Northwestern Polytechnical University Xi???an China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mobile edge computing (MEC) has emerged as a promising paradigm to enhance the quality of experience for mobile device users, as it can offload computation intensive tasks from mobile devices to edge clouds. Nevertheless, task offloading also incurs additional overhead in both power consumption and execution delay. In essence, energy consumption and task delay cannot be optimized simultaneously as these two objectives contradict each other. Although many studies have been conducted to make tradeoff between them, the backhaul limitation of MEC-enabled wireless networks has not been tackled. In this paper, we study the power-delay tradeoff for MEC in backhaul-limited wireless networks. An average weighted sum power consumption minimization problem with task buffer stability constraints is formulated to investigate the problem, and a Lyapunouv Optimization based online intelligent computation and communication resource management algorithm is also proposed to solve it. In particular, at each time slot, optimal CPU-cycle frequencies, transmit power and backhaul capacity allocation are efficiently determined. Performance analysis indicates that under the proposed algorithm an [O(1/V), O(V)] tradeoff exists between the power consumption and execution delay, where V is a control parameter. Finally, simulations are conducted to validate the analytic results and demonstrate the changes of the system performance with various algorithm parameters.

关键词： Task analysis Mobile handsets Power demand Resource management Delays Optimization Wireless communication

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Effect of number of versions on receiving high citations

TechRxiv

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TechRxiv 2020年

作者： Ale Ebrahim, Nader Salehi, Hadi Mohammadjafari, Marjan Embi, Mohamed Amin Bakhtiyari, Kaveh Danaee, Mahmoud Shakiba, Masoud Shahbazi-Moghadam, Masoomeh Zavvari, Azam Department of Industrial Engineering Faculty of Engineering Kerman Branch Islamic Azad University Kerman Iran Faculty of Literature and Humanities Najafabad Branch Islamic Azad University Isfahan Najafabad Iran Bangi 43600 Malaysia Department of Computer & Cognitive Science Facultyof Engineering University of Duisburg-Essen Duisburg Germany Department of Electrical Electronics and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Malaysia University of Malaya Malaysia Faculty of Agriculture Roudehen Branch Islamic Azad University Roudehen Iran Center for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia UKM Bangi Selangor 43600 Malaysia Perdana School of Science Technology and Innovation Policy Universiti Teknologi Malaysia Malaysia

Nowadays, the h-index is an index that attempts to measure both the productivity and impact of the published work of a scientist or scholar. The index is based upon the set of the scientist's most cited papers and the number of citations that they have received in other publications. Besides, the most commonly used measure of journal quality is Impact Factor. This is a number which attempts to measure the impact of a journal in terms of the average number of citations to recent articles published in the journal. So, receiving more citation is very important for authors and journals to get high h-index and impact factor. In this paper, we tried to analyses the effect of the number of available version from the web on receive more citations. We analyzed 10162 papers which are published in Scopus database in year 2010. Then we developed a software to collect the number of citations and versions of each paper from Google Scholar automatically. The results show that … © 2020, CC BY.

关键词： Indexing (of information)

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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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A proposal of an example and experiments repository to foster industrial adoption of formal methods 8th

A proposal of an example and experiments repository to foste...

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8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018

作者： Schlick, Rupert Felderer, Michael Majzik, Istvan Nardone, Roberto Raschke, Alexander Snook, Colin Vittorini, Valeria Center for Digital Safety and Security AIT Austrian Institute of Technology GmbH Vienna Austria Department of Computer Science University of Innsbruck Innsbruck Austria Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Department of Measurement and Information Systems Budapest University of Technology and Economics Budapest Hungary University of Naples Federico II Naples Italy Institute of Software Engineering and Programming Languages Ulm University Ulm Germany Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9783030034269

Formal methods (in a broad sense) have been around almost since the beginning of computer science. Nonetheless, there is a perception in the formal methods community that take-up by industry is low considering the potential benefits. We take a look at possible reasons and give candidate explanations for this effect. To address the issue, we propose a repository of industry-relevant example problems with an accompanying open data storage for experiment results in order to document, disseminate and compare exemplary solutions from formal model based methods. This would allow potential users from industry to better understand the available solutions and to more easily select and adopt a formal method that fits their needs. At the same time, it would foster the adoption of open data and good scientific practice in this research field. © Springer Nature Switzerland AG 2018.

关键词： Formal methods

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Radiation Assurance of CubeSat Payloads Using Bayesian Networks and Fault Models

Radiation Assurance of CubeSat Payloads Using Bayesian Netwo...

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2018 Annual Reliability and Maintainability Symposium, RAMS 2018

作者： Austin, Rebekah A. Mahadevan, Nagabhushan Witulski, Arthur F. Evans, John Witulseki, Arthur F. Vanderbilt University NashvilleTN37235 United States Institute for Software Integrated Systems Department of Electrical Engineering and Computer Science Vanderbilt University 1025 16th Av. South Suite 102 NashvilleTN37212 United States Institute of Space and Defense Electronics Department of Electrical Engineering and Computer Science Vanderbilt University 1025 16th Avenue South Suite 200 NashvilleTN37212 United States Office of Safety and Mission Assurance NASA 300 E St SW WashingtonDC20546 United States

ISBN: (纸本)9781538628706

Commercial off-the-shelf (COTS) electronics are used in satellites that span the size, cost, and acceptable risk spectrum. At one extreme the CubeSat, a platform originally created for university-based spacecraft design, is often constructed only from COTS electronics. At the other extreme, even high-budget NASA spacecraft use COTS electronics in non-mission critical circuits, especially when no radiation-hardened alternatives exist or the radiation-hardened parts do not meet the system requirements. There is significant uncertainty in the radiation test results of COTS electronics;hence additional means of assessing risk and radiation hardness are needed. In this work we construct a fault-propagation model of a CubeSat payload in a SysML platform, and then develop a Bayesian network from the fault model of the system. The sensitivity of the system to variations in component radiation response can be evaluated quantitatively, within the limits of the assumptions and available data. This sensitivity study then informs the structure and content of the overall radiation-reliability assurance case. © 2018 IEEE.

关键词： Bayesian networks

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Inferring Network Connectivity from Event Timing Patterns

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Physical Review Letters 2018年第5期121卷 054101-054101页

作者： Jose Casadiego Dimitra Maoutsa Marc Timme Chair for Network Dynamics Institute of Theoretical Physics and Center for Advancing Electronics Dresden (cfaed) Technical University of Dresden 01062 Dresden Germany Network Dynamics Max Planck Institute for Dynamics and Self-Organization (MPIDS) 37077 Göttingen Germany Artificial Intelligence Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin 10587 Berlin Germany Bernstein Center for Computational Neuroscience (BCCN) 37077 Göttingen Germany Max Planck Institute for the Physics of Complex Systems 01069 Dresden Germany

Reconstructing network connectivity from the collective dynamics of a system typically requires access to its complete continuous-time evolution, although these are often experimentally inaccessible. Here we propose a theory for revealing physical connectivity of networked systems only from the event time series their intrinsic collective dynamics generate. Representing the patterns of event timings in an event space spanned by interevent and cross-event intervals, we reveal which other units directly influence the interevent times of any given unit. For illustration, we linearize an event-space mapping constructed from the spiking patterns in model neural circuits to reveal the presence or absence of synapses between any pair of neurons, as well as whether the coupling acts in an inhibiting or activating (excitatory) manner. The proposed model-independent reconstruction theory is scalable to larger networks and may thus play an important role in the reconstruction of networks from biology to social science and engineering.

关键词： Collective behavior in networks Collective dynamics Network structure Neural information Neuronal dynamics Neuronal networks Patterns in complex systems Synapses Topological tools

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