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6th International Congress on Sport Sciences Research and technology Support, icSPORTS 2018

作者： Horvat, Tomislav Job, Josip Medved, Vladimir Department of Electrical Engineering University North 104. brigade 3 Varaždin Croatia Chair of Visual Computing Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia Department of General and Applied Kinesiology Faculty of Kinesiology University of Zagreb Zagreb Croatia

ISBN: (纸本)9789897583254

Machine learning becomes one of the top fields in world of ICT (information and communications technology). In the last few decades machine learning has taken a big boost in sports. Sport fans are using machine learning in sport betting and sport managers are using it for player selection, performance evaluation and even in outcome prediction. This paper gives basketball outcome prediction algorithms using k-nearest neighbours (k-nn). Few methods of preparing data calculated with different k and time period for k-nn algorithm will be presented and closely explained. Feature selection will also be discussed and results based on feature selection will be presented. Copyright © 2018 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved

关键词： Sports

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Cover Feature: Direct Near Infrared Light–Activatable Phthalocyanine Catalysts (Chem. Eur. J. 2/2022)

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Chemistry – A European Journal 2021年第2期28卷

作者： Yoshino Katsurayama Prof. Yasuhiro Ikabata Prof. Hajime Maeda Prof. Masahito Segi Prof. Hiromi Nakai Prof. Taniyuki Furuyama Graduate School of Natural Science and Technology Kanazawa University Kakuma-machi Kanazawa 920-1192 Japan Waseda Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo 169-8555 Japan Information and Media Center Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan Department of Chemistry and Biochemistry School of Advanced Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo 169-8555 Japan Elements Strategy Initiative for Catalysts and Batteries (ESICB) Kyoto University Katsura Kyoto 615-8520 Japan Japan Science and Technology Agency (JST)-PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Ensemble super-resolution with a reference dataset

arXiv

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arXiv 2019年

作者： Jiang, Junjun Yu, Yi Wang, Zheng Tang, Suhua Hu, Ruimin Ma, Jiayi School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Peng Cheng Laboratory Shenzhen China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Communication Engineering and Informatics University of Electro-Communications Tokyo182-8585 Japan National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan430072 China Electronic Information School Wuhan University Wuhan430072 China

By developing sophisticated image priors or designing deep(er) architectures, a variety of image Super-Resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component superresolvers). Based on the assumption that better component superresolver should have larger ensemble weight when performing SR reconstruction, we present a Maximum A Posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Specially, we introduce a reference dataset, which is composed of High-Resolution (HR) and Low-Resolution (LR) image pairs, to measure the super-resolution abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR image should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art non-deep learning based methods, four latest deep learning based methods and one ensemble learning based method, and prove its effectiveness and superiority on three public datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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Collaborative learning for extremely low bit asymmetric hashing

arXiv

引用

arXiv 2018年

作者： Luo, Yadan Li, Yang Shen, Fumin Yang, Yang Cui, Peng Huang, Zi School of Information Technology and Electrical Engineering University of Queensland Brisbane4072 Australia Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Extremely low bit (e.g., 4-bit) hashing is in high demand for retrieval and network compression, yet it could hardly guarantee a manageable convergence or performance due to its severe information loss and shrink of discrete solution space. In this paper, we propose a novel Collaborative Learning strategy for high-quality low-bit deep hashing. The core idea is to distill bit-specific representations for low-bit codes with a group of hashing learners, where hash codes of various length actively interact by sharing and accumulating knowledge. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Multiple views from different embedding heads provide supplementary guidance as well as regularization for extremely low bit hashing, hence making convergence faster and more stable. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and show that 8-bit hash codes generated by MAH achieve 94:4% of MAP score, which significantly surpasses the performance of 48-bit codes by the state-of-the-arts for image retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Hash functions

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Fuzzy Logic Controller for Classroom Air Conditioner

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Journal of Physics: Conference Series 2020年第1期1484卷

作者： Safa Riyadh Waheed Myasar Mundher Adnan Norhaida Mohd Suaib Mohd Shafry Mohd Rahim Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculity of information Technology Imam Jaafar Al-sadiq University Baghdad Iraq Islamic University Najaf Iraq UTM Big Data Center Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia UTM-IRDA Digital Media Institute of Human-Centred Engineering University Technology of Malaysia Johar Bahru Malaysia

In the modernized era, the air conditioners are an integrated part of comfort living especially in hot climates. They are used to control the interior spatial temperature, relative humidity, degree of cleanliness, and speed of air streaming. The automatic controllers are the key elements of the modern air conditioning systems that ensure the reliable operation, improved quality, low operation cost, and better security. Thus, the realization, design, and application of the controller systems require the exact specifications of the involved processes. In this regard, controllers based on the fuzzy logic (FL) are prospective for air conditioners due to the easy accessibility of different output levels. Furthermore, using the FL it is possible to scale and control the users' air processing demand depending on the temperature and relative humidity of the space. Based on these factors, this paper reports the design and performance evaluation of a FL based controller useful for air conditioners when implemented in the classroom setting. This FL based control system can reduce the complexity of programming thereby can be executed on general microcontrollers utilized in the control panels of classroom air conditioner. The results revealed the outperforming nature of the FL based controllers over other traditional controllers used to adjust the indoor temperature and relative humidity by air conditioners.

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Naive gabor networks for hyperspectral image classification

arXiv

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arXiv 2019年

作者： Liu, Chenying Li, Jun He, Lin Plaza, Antonio Li, Shutao Li, Bo Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Electrical and Information Engineering Hunan University Changsha410082 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura CáceresE-10071 Spain Beijing Key Laboratory of Digital Media School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large amount of training samples in order to avoid overfitting. Additionally, it is a typical non-convex problem affected by many local minima and flat regions. To address these problems, in this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space, and hence improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and thus yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2020年 12260 LNCS卷 vi-ix页

作者： Arampatzis, Avi Kanoulas, Evangelos Tsikrika, Theodora Vrochidis, Stefanos Joho, Hideo Lioma, Christina Névéol, Aurélie Eickhoff, Carsten Cappellato, Linda Ferro, Nicola Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece Faculty of Library Information and Media Science University of Tsukuba Ibaraki Japan Brown University ProvidenceRI United States Department of Information Engineering University of Padova Padua Italy University of Amsterdam Amsterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark LIMSI-CNRS Orsay France University Paris-Saclay Saint-Aubin France

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Conversational Agents as Group-Teacher Interaction mediators in MOOCs 5

Conversational Agents as Group-Teacher Interaction Mediators...

引用

5th International Conference on Learning with MOOCs, LWMOOCS 2018

作者： Demetriadis, Stavros Tegos, Stergios Psathas, Georgios Tsiatsos, Thrasyvoulos Weinberger, Armin Caballe, Santi Dimitriadis, Yannis Sanchez, Eduardo Gomez Papadopoulos, Pantelis M. Karakostas, Anastasios Aristotle University School of Informatics Thessaloniki Greece Department of Educational Technology Saarland University Saarbriicken Germany Faculty of Computer Science Multimedia and Telecommunications Universitat Oberta de Catalunya Barcelona Spain Universidad de Valladolid School of Telecommunications Engineering Valladolid Spain Aarhus University Centre for Teaching Development and Digital Media Aarhus Denmark Center for Research and Technology - Hellas Information Technologies Institute Thessaloniki Greece

ISBN: (纸本)9781538665336

This paper presents the colMOOC project perspective for integrating a conversational agent service in Massive Open Online Courses (MOOCs) to facilitate peer dialogue during chat-based activities. Furthermore, it provides high level system architecture to highlight key aspects of the technology infrastructure and the functionality of the software system. The rationale of the approach follows the tenets of several converging theoretical perspectives emphasizing the value of learners' dialogue and constructive argumentation as knowledge building processes during collaborative activities. The MOOC teacher (domain expert) is expected to play a key role by entering in the system conceptual links that guide the agent to make relevant questioning interventions. Thus, the agent tool is mediating group-teacher interactions in a MOOC environment aiming to increase the transactive quality of peer dialogue. The paper discusses also the 'Academically Productive Talk' model that provides the basis for the agent interventions and the major software components of the system, including the colMOOC Editor and the colMOOC Player. © 2018 IEEE.

关键词： computer aided instruction

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IT-based Architecture for Power Market Oriented Optimization at Multiple Levels in Production Processes

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Procedia CIRP 2019年 81卷 618-623页

作者： Philipp Seitz Eberhard Abele Lukas Bank Thomas Bauernhansl Eduardo Colangelo Gilbert Fridgen Johannes Schilp Paul Schott Johannes Sedlmeir Nina Strobel Thomas Weber Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstrasse 12 70569 Stuttgart Germany Institute of Production Management Technology and Machine Tools Technische Universität Darmstadt Otto-Berndt-Straße 2 64287 Darmstadt Germany Fraunhofer Research Institution for Casting Composite and Processing Technology IGCV Beim Glaspalast 5 86153 Augsburg Germany Fraunhofer Institute for Applied Information Technology FIT Wittelsbacherring 10 95444 Bayreuth Germany University of Augsburg Chair of Digital Manufacturing Department of Applied Computer Science Universitätsstraße 2 86159 Augsburg Germany FIM Research Center University of Bayreuth Wittelsbacherring 10 95444 Bayreuth Germany

Given the increasingly volatile prices on the power markets, it becomes economically more and more important for companies to develop and realize flexible strategies for energy consumption. A steady adaption of production processes which considers current power prices can take place on several levels of the automation pyramid, where each level has its own characteristics and requirements. In this paper, we present an optimization architecture based on an IT-platform which meets the challenges of complex multilayered production processes. We introduce layer-specific optimization strategies as well as an associated information flow, which facilitates creating holistic and well-coordinated optimizations.

关键词： Energy flexibility Demand Response Production process optimization

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