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Large-scale video compression： recent advances and challenges

Frontiers of Computer science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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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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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Astronomical Detection of the Interstellar Anion C10H – towards TMC-1 from the GOTHAM Large Program on the GBT

arXiv

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arXiv 2023年

作者： Remijan, Anthony Scolati, Haley N. Burkhardt, Andrew M. Changala, P. Bryan Charnley, Steven B. Cooke, Ilsa R. Cordiner, Martin A. Gupta, Harshal Herbst, Eric Lee, Kin Long Kelvin Loomis, Ryan Shingledecker, Christopher N. Siebert, Mark A. Xue, Ci McCarthy, Michael C. McGuire, Brett A. National Radio Astronomy Observatory CharlottesvilleVA22903 United States Department of Chemistry University of Virginia CharlottesvilleVA22904 United States Department of Physics Wellesley College WellesleyMA02481 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Astrochemistry Laboratory The Goddard Center for Astrobiology NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Chemistry University of British Columbia 2036 Main Mall VancouverBCV6T 1Z1 Canada Institute for Astrophysics and Computational Sciences The Catholic University of America WashingtonDC20064 United States National Science Foundation AlexandriaVA22314 United States Department of Astronomy University of Virginia CharlottesvilleVA22904 United States Accelerated Computing Systems and Graphics Group Intel Corporation 2111 NE 25th Ave HillsboroOR97124 United States Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Benedictine College AtchisonKS66002 United States

Using data from the GOTHAM (GBT Observations of TMC-1: Hunting for Aromatic Molecules) survey, we report the first astronomical detection of the C10H– anion. The astronomical observations also provided the necessary data to refine the spectroscopic parameters of C10H– . From the velocity stacked data and the matched filter response, C10H– is detected at >9σ confidence level at a column density of 4.04−+102.2367 × 1011 cm−2. A dedicated search for the C10H radical was also conducted towards TMC-1. In this case, the stacked molecular emission of C10H was detected at a ∼3.2σ confidence interval at a column density of 2.02−+20.8268 ×1011 cm−2. However, since the determined confidence level is currently – /C10H column density ratio is ∼2.0+5−1-.96the highest value measured between an anion and neutral species to date. Such a high ratio is at odds with current theories for interstellar anion chemistry. For the radical species, both models can reproduce the measured abundances found from the survey;however, the machine learning analysis matches the detected anion abundances much better than the gas/grain chemical model, suggesting that the current understanding of the formation chemistry of molecular anions is still highly *** Codes 85-11 Copyright © 2023, The Authors. All rights reserved.

关键词： Matched filters

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Vector Symbolic Architectures as a computing Framework for Emerging Hardware

arXiv

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arXiv 2021年

作者： Kleyko, Denis Davies, Mike Frady, E. Paxon Kanerva, Pentti Kent, Spencer J. Olshausen, Bruno A. Osipov, Evgeny Rabaey, Jan M. Rachkovskij, Dmitri A. Rahimi, Abbas Sommer, Friedrich T. The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden The Neuromorphic Computing Laboratory Intel Labs Santa ClaraCA95054 United States The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden The Department of Electrical Engineering and Computer Sciences The University of California BerkeleyCA94720 United States International Research and Training Center for Information Technologies and Systems Kyiv03680 Ukraine IBM Research – Zurich Rüschlikon8803 Switzerland

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing. Copyright © 2021, The Authors. All rights reserved.

关键词： Vectors

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A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 934-945页

作者： Liang Qi Mengchu Zhou Wenjing Luan IEEE Department of Computer Scicnce and Technology Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102USA Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghai Electronic Transactions and Information Service Collaborative Innovation CenterDepartment of Com-puter ScienceTongji UniversityShanghai 201804China

Advanced information and communication technolo-gies can be used to facilitate traffic incident *** an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this ***-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well *** results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is *** results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

关键词： Cell transmission model(CTM) intelligent transportation systems(ITS) traffic incident management(TIM) urban traffic congestion

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Proposal of IoT based Learning Material and its Management System for Primary/Secondary Education 19

Proposal of IoT based Learning Material and its Management S...

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Proceedings of the 3rd International Conference on Digital technology in Education

作者： Takafumi Todoriki Mizue Kayama Nobuyuki Tachi Takashi Nagai Takao Futagami Takehiko Asuke Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Shinshu University Nagano Nagano Japan Nagoya University Center for Embedded Computing Systems Furotyou Nagoya Aichi Japan Institute of Technologists Department of Mechanical and Production Engineering Maeya Gyoda Saitama Japan TOYO Corporation Chuo-ku Tokyo Japan Ina City Takato Junior Highschool Takato Machi Higashi Ina Nagano Japan

ISBN: (纸本)9781450372206

The purpose of this study was lesson support by building a system which IoT based learning material and using it. This system is assumed to use the regular curriculum of primary and secondary education in Japan. We previously examined that apply IoT technology to learning material. That learning material use when experiment with a measurement. And, we designed its management system. Here we describe the outline of IoT based learning material and its management system. And we report about concrete IoT based learning material.

关键词： Learning material STEM Design of System IoT

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Forecasting of Forex Time Series Data Based on Deep Learning

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Procedia Computer science 2019年 147卷 647-652页

作者： Lina Ni Yujie Li Xiao Wang Jinquan Zhang Jiguo Yu Chengming Qi College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China Beijing Union University Beijing 100101 China

This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.

关键词： Deep learning Recurrent neural network Convolutional neural network Foreign Exchange Rate Time series analysis

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Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

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Procedia Computer science 2019年 147卷 643-646页

作者： Jinquan Zhang Bowen Zhao Guochao Song Lina Ni Jiguo Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China

Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.

关键词： Privacy protection Data publishing Social networks (α, L)-diversity K-anonymity Clustering feature (CF) tree

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Corrigendum to "Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients" [J. Adv. Res. 37 (2022) 147-168]

引用

Journal of advanced research 2023年 45卷 193页

作者： O S Albahri A A Zaidan A S Albahri H A Alsattar Rawia Mohammed Uwe Aickelin Gang Kou F M Jumaah Mahmood M Salih A H Alamoodi B B Zaidan Mamoun Alazab Alhamzah Alnoor Jameel R Al-Obaidie Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. Electronic address: aws.alaa@fskik.upsi.edu.my. Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. School of Computing and Information Systems University of Melbourne 700 Swanston Street Victoria 3010 Australia. School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu 611130 China. Department of Advanced Applications and Embedded Systems Intel Corporation Plot 6 Bayan Lepas Technoplex 11900 Georgetown Pulau Pinang Malaysia. Department of Computer Science Computer Science and Mathematics College Tikrit University Tikrit Iraq. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia. Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan ROC. College of Engineering IT and Environment Charles Darwin University NT Australia. School of Management Universiti Sains Malaysia Pulau Pinang Malaysia. Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia.

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