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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Corrigendum to “Improved Combinatorial Benders Decomposition for a Scheduling Problem with Unrelated Parallel Machines”

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Journal of Applied Mathematics 2017年第1期2017卷

作者： Francisco Regis Abreu Gomes Geraldo Robson Mateus Graduate Program in Production Engineering Federal University of Minas Gerais and Federal Institute of Education Science and Technology of Ceará Belo Horizonte MG Brazilufmg.br Computer Science Department Federal University of Minas Gerais Belo Horizonte MG Brazilufmg.br

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Retraction: LUC Algorithm in Visual Cryptography (J. Phys.: Conf. Ser. 1114 012090)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Ratnadewi Christian Dio Alpha G Darmawan Napitupulu Heri Nurdiyanto Nuning Kurniasih Abdurrozaq Hasibuan GS Achmad Daengs Rosida Tiurma Manurung Indrya Mulyaningsih Robbi Rahim Castaka Agus Sugianto Dahlan Abdullah Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STMIK Dharma Wacana Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Universitas Islam Sumatera Utara Indonesia University of 45 Surabaya Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Department of Education Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Politeknik TEDC Bandung Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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An easy-to-hard learning paradigm for multiple classes and multiple labels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Weiwei Liu Ivor W. Tsang Klaus-Robert Müller School of Computer Science and Engineering The University of New South Wales Sydney NSW Australia and Centre for Artificial Intelligence FEIT University of Technology Sydney NSW Australia Centre for Artificial Intelligence FEIT University of Technology Sydney NSW Australia Machine Learning Group Computer Science Berlin Institute of Technology Berlin Germany and Max Planck Institute for Informatics Saarbrcken and Department of Brain and Cognitive Engineering Korea University Seoul Korea

Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classification due to its simplicity and excellent performance. However, many confusing classes in such applications will degrade its results. For example, hand clap and boxing are two confusing actions. Hand clap is easily misclassified as boxing, and vice versa. Therefore, precisely classifying confusing classes remains a challenging task. To obtain better performance for multiclass classifications that have confusing classes, we first develop a classifier chain model for multiclass classification (CCMC) to transfer class information between classifiers. Then, based on an analysis of our proposed model, we propose an easy-to-hard learning paradigm for multiclass classification to automatically identify easy and hard classes and then use the predictions from simpler classes to help solve harder classes. Similar to CCMC, the classifier chain (CC) model is also proposed by Read et al. (2009) to capture the label dependency for multi-label classification. However, CC does not consider the order of di_culty of the labels and achieves degenerated performance when there are many confusing labels. Therefore, it is non-trivial to learn the appropriate label order for CC. Motivated by our analysis for CCMC, we also propose the easy-to-hard learning paradigm for multi-label classification to automatically identify easy and hard labels, and then use the predictions from simpler labels to help solve harder labels. We also demonstrate that our proposed strategy can be successfully applied to a wide range of applications, such as ordinal classification and relationship prediction. Extensive empirical studies validate our analysis and the efiectiveness of our proposed easy-to-hard learning strategies.

关键词： classifier Chain easy-to-hard learning paradigm multi-label classification multiclass classification

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LUC Algorithm in Visual Cryptography

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Journal of Physics: Conference Series 2018年第1期1114卷

Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero.

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Retraction: The Effect of Mixed Pertamax with Tuak Nias (Tuonifaro) on Fuel Consumption at 125CC Machine and Its Dissemination Using Website (J. Phys.: Conf. Ser. 1114 012075)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Kristian Tarigan Erianto Ongko H Hartono Dahlan Abdullah Cut Ita Erliana Nurmawati Sulfikar Sallu S Sriadhi Akbar Iskandar Nuning Kurniasih GS Achmad Daengs Ricardo Freedom Nanuru A A Saleh Muhammad Ikhsan Setiawan Wiwien Hadikurniawati Edy Winarno Department of Mechanical Engineering Universitas Darma Agung Medan Indonesia Department of Informatics Akademi Teknologi Industri ImmanuelIndonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Faculty Of Information Technology Universitas Sembilan Belas November Kolaka Sulawesi Tenggara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Management Universitas 45 Surabaya Indonesia Universitas Kristen Indonesia Maluku. Ambon Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

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Entrainment of overground human walking to mechanical perturbations at the ankle joint

Entrainment of overground human walking to mechanical pertur...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Julieth Ochoa Dagmar Sternad Neville Hogan Department of Mechanical Engineering Massachusetts Institute of Technology (MIT) Cambridge MA USA Departments of Biology Electrical and Computer Engineering and Physics Northeastern University (NEU) Boston MA USA Departments of Mechanical Engineering and Brain and Cognitive Science Massachusetts Institute of Technology (MIT) Cambridge MA USA

ISBN: (纸本)9781509032884

Unlike upper-extremity robotic therapy, robotic therapy of lower extremities has not matched the effectiveness of human-administered approaches. We hypothesize that this may stem from inadvertent interference with natural movement control and investigated the oscillatory dynamics of human locomotion. Specifically, we assessed gait entrainment to periodic mechanical perturbations. Because the treadmills used in most studies necessarily interact with the dynamics of natural walking, we compared our experimental intervention during gait entrainment in treadmill and overground walking. Fourteen healthy subjects walked overground and on a treadmill while wearing an exoskeletal ankle robot which exerted short plantarflexion torque pulses at periods 50 ms shorter or longer than the subjects' preferred stride period. Entrainment to the periodic perturbation occurred in all conditions, however more readily in overground walking. In all cases, the stride period phase-locked with the torque pulse at `push-off' such that it assisted propulsion. This entrainment of the stride period and its sensitivity to context indicate the subtlety and adaptability of human walking. Our observations suggest new avenues for gait rehabilitation and implications for exoskeleton design and legged locomotion research.

关键词： Legged locomotion Torque Knee Medical treatment Oscillators Convergence

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The Effect of Mixed Pertamax with Tuak Nias (Tuonifaro) on Fuel Consumption at 125CC Machine and Its Dissemination Using Website

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Journal of Physics: Conference Series 2018年第1期1114卷

The purpose of this research is to know how influence of mixture of pertamax with Tuak Nias (Tuonifaro) to fuel consumption at 125 cc engine To prove that Tuak Nias or Tuonifaro can be used as fuel mixture on motorcycle and examine how big influence of mix between Tuak Nias or Tuonifaro with Pertamax, conducting research by literature study method or literature study. Many authors use various supporting / input factors and supporting books related to the titles discussed. because Tuak Nias or tuonifaro contain low 4-5% ethanonic elements and contain 90% alcoholic elements so that the fuel easily burns easily in the combustion chamber and produces great power. The results of this research will be disseminated using the website

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Study of cybercrime and security in medical devices

Study of cybercrime and security in medical devices

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Biomedical engineering International Conference (BMEiCON)

作者： S. Noimanee K. Noimanee S. Krisanachinda W. Senavongse Department of Computer Engineering Srinakharinwirot University Ongkharak Thailand Department of Computer Engineering Rajamangala University of Technology Lanna Chiang Mai Chiang Mai Thailand Forensic Science Program Kasetsart University Nakhon Phatom Thailand Department of Biomedical Engineering Srinakharinwirot University Nakhon Nayok Thailand

ISBN: (纸本)9781509039418

This paper presents a cybercrime in medical devices such as all of devices feed in RJ-45. The reason of the physician can monitoring the patient's data from medical devices in hospital and how to protect them using technique of anti-crime, we study behavior of hacker to fine history of his/her crime and understanding cybercrime method. Finally we use as forensic evidence by analyzing signal data from a database of DSI database crime comparison with cybercrime investigation. The result was that there was no security in medical devices procedure bad effects.

关键词： computer crime Biomedical monitoring Blood pressure Hospitals Forensics

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Publisher Correction: Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

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Scientific reports 2019年第1期9卷 5528页

作者： Sergey D Stavisky Jonathan C Kao Paul Nuyujukian Chethan Pandarinath Christine Blabe Stephen I Ryu Leigh R Hochberg Jaimie M Henderson Krishna V Shenoy Neurosurgery Department Stanford University Stanford CA USA. sergey.stavisky@stanford.edu. Electrical Engineering Department Stanford University Stanford CA USA. sergey.stavisky@stanford.edu. Electrical Engineering Department Stanford University Stanford CA USA. Electrical and Computer Engineering Department University of California at Los Angeles Los Angeles CA USA. Neurosurgery Department Stanford University Stanford CA USA. Bioengineering Department Stanford University Stanford CA USA. Stanford Wu Tsai Neurosciences Institute Stanford University Stanford CA USA. Bio-X Program Stanford University Stanford CA USA. Neurosurgery Department Palo Alto Medical Foundation Palo Alto CA USA. Center for Neurorestoration and Neurotechnology Rehabilitation R&D Service VA Medical Center Providence RI USA. School of Engineering and Carney Institute for Brain Science Brown University Providence RI USA. Department of Neurology Harvard Medical School Boston MA USA. Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Boston MA USA. Neurobiology Department Stanford University Stanford CA USA. Howard Hughes Medical Institute at Stanford University Stanford CA USA.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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