检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Taniguchi, Tadahiro Ugur, Emre Hoffmann, Matej Jamone, Lorenzo Nagai, Takayuki Rosman, Benjamin Matsuka, Toshihiko Iwahashi, Naoto Oztop, Erhan Piater, Justus Wörgötter, Florentin College of Information Science and Engineering Ritsumeikan University 1-1-1 Noji Higashi Shiga Kusatsu525-8577 Japan Dept. of Computer Engineering Bogazici University Istanbul Turkey Dept. Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Faculty of Informatics and Engineering The University of Electro-communications Japan Mobile Intelligent Autonomous Systems The Council for Scientific and Industrial Research The School of Computer Science and Applied Mathematics The University of the Witwatersrand South Africa Department of Cognitive and Information Science Chiba University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan Dept. of Computer Science Ozyegin University Istanbul Turkey The Dept. of Computer Science Universität Innsbruck Austria The Department for Computational Neuroscience The Bernstein Center of the University of Göttingen Inst. F. Physics Iii Germany

Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems.like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems.we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems. the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems.in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems.in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems. showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems.that can be part of symbol emergence systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Cognitive systems

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems. the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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An Iterative Spanning Forest Framework for Superpixel Segmentation

arXiv

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arXiv 2018年

作者： Vargas-Muñoz, John E. Chowdhury, Ananda S. Alexandre, Eduardo B. Galvão, Felipe L. Vechiatto Miranda, Paulo A. Falcão, Alexandre X. IEEE IEEE Dept. of Information Systems Institute of Computing University of Campinas Campinas Brazil Dept. of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Dept. of Computer Science Institute of Mathematics and Statistics University of São Paulo São Paulo Brazil

Superpixel segmentation has become an important research problem in image processing. In this paper, we propose an Iterative Spanning Forest (ISF) framework, based on sequences of Image Foresting Transforms, where one can choose i) a seed sampling strategy, ii) a connectivity function, iii) an adjacency relation, and iv) a seed pixel recomputation procedure to generate improved sets of connected superpixels (supervoxels in 3D) per iteration. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF methods to illustrate different choices of its components. These methods are compared with approaches from the state-of-the-art in effectiveness and efficiency. The experiments involve 2D and 3D datasets with distinct characteristics, and a high level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show that some of its methods are competitive with or superior to the best baselines in effectiveness and efficiency. Copyright © 2018, The Authors. All rights reserved.

关键词： Image segmentation

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An Energy Efficient Target Specific Code Dissemination Scheme with Forwarder Selection Algorithm in WSNs

An Energy Efficient Target Specific Code Dissemination Schem...

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International Symposium on Personal, Indoor, and Mobile Radio Communications

作者： Hiromu Asahina Kentaroh Toyoda Iwao Sasase P. Takis Mathiopoulos Hisao Yamamoto Dept. of Information and Computer Science Keio University 3-14-1 Hiyoshi Kohoku Yokohama 223-8522 Japan Dept. of Informatics and Telecommunications University of Athens 15784 Athens Greece Dept. of Systems Information Engineering Tokyo City University 1-28-1 Tamazutsumi Setagaya Tokyo 158-8557 Japan

ISBN: (纸本)9781538660102

In order to fix a bug or install a new application, disseminating a code to specific target sensors is an important management task in wireless sensor networks. In order to disseminate a code to specific target sensors in an energy efficient manner, it is required to select the minimum required number of forwarders with a small number of control messages. In this paper, we propose an energy efficient target specific code dissemination scheme with a forwarder selection algorithm. The proposed scheme consists of two steps, (i) a forwarder selection algorithm with a control message suppression, (ii) unnecessary forwarders reduction with a sleep mechanism. In order to select the minimum required forwarders with a small number of control messages, we propose an algorithm to select a forwarder where more target sensors are reachable, through a single control message by leveraging overhearing of control messages. In addition, in order to reduce the number of forwarders, we propose a sleep mechanism to put unnecessary forwarders to sleep where any target sensor is not reachable. A performance analysis has shown that the number of control messages of the proposed scheme is always theoretically smaller than that of a previously published forwarder selection scheme under the same condition. In addition, we have shown that the proposed scheme reduces the energy consumption compared with the previous scheme.

关键词： control messages select minimum required forwarders

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Leveraging STEM education via UMI technologies 2017

Leveraging STEM education via UMI technologies

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16th World Conference on Mobile and Contextual Learning, mLearn 2017

作者： Goumopoulos, Christos Xanos, Nikolaos Kameas, Achilles Computer Technology Institute and Press Diophantus and Information Communication Systems Engineering Dept. University of the Aegean Greece School of Science and Technology Hellenic Open University Greece

ISBN: (纸本)9781450352550

The use of Ubiquitous Computing, Mobile Computing and Internet of Things (UMI) technologies can open new opportunities for STEM education1. UMI-Sci-Ed, a Horizon 2020 project, explores the new possibilities and through a multidisciplinary approach proposes a new integrated framework for engaging all stakeholders (students, teachers, domain and technology experts, entrepreneurs) in order to promote positive attitudes towards innovation and a knowledge-based society. In this paper we present preliminary results on the methodology followed to leverage STEM education via UMI technologies. In particular, we discuss key components of this methodology, an example educational scenario and the corresponding UMI application developed and a preliminary evaluation of the approach in the context of a local school. © 2017 Association for Computing Machinery.

关键词： Ubiquitous computing

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Common Limitations of Image Processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Unsupervised detection of epileptic seizures from EEG signals: A channel-specific analysis of long-term recordings

Unsupervised detection of epileptic seizures from EEG signal...

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2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018

作者： Tsiouris, Kostas M. Konitsiotis, Spiros Markoula, Sofia Rigas, Georgios Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens AthensGR15773 Greece Department of Neurology Medical School University of Ioannina IoanninaGR45110 Greece University Hospital of Ioannina IoanninaGR45110 Greece Unit of Medical Technology and Intelligent Information Systems Dept. of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH University of Ioannina IoanninaGR45110 Greece

ISBN: (纸本)9781538624050

As long-term EEG recordings are becoming more and more frequent in clinical practice, the volume of EEG data that require annotation by clinical experts is growing exponentially, highlighting the need for automated epileptic seizure detection systems. Supervised methodologies based on machine learning techniques have been proposed as a solution, but they require annotated EEG data from multiple patients for training, while unsupervised methodologies, which do not have such limitations, are scarce in the literature. Thus, an unsupervised seizure detection methodology is developed to offer high seizure detection performance with substantial reduction in the time and effort required to inspect large volumes of EEG signals. The ictal rhythmical activity is detected by analyzing the spectral information of each EEG channel independently in order to find intensive accumulation of signal energy over the fundamental delta, theta and alpha frequency bands. The ictal rhythmical activity that is expressed during a seizure is detected using a set of four simple seizure detection conditions. The proposed methodology is validated using the public CHB-MIT EEG database, and the results suggest that an average sensitivity of 95.1% can be obtained with a false detection rate of 10.13 FP/h, while 95% of the each patient's EEG recordings is automatically rejected as non-ictal. © 2018 IEEE.

关键词： Neurophysiology

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A joint network/control design for cooperative automatic driving

A joint network/control design for cooperative automatic dri...

引用

2017 IEEE Vehicular Networking Conference, VNC 2017

作者： Giordano, Giulia Segata, Michele Blanchini, Franco Cigno, Renato Lo Delft Center for Systems and Control Delft University of Technology Netherlands Dipartimento di Matematica e Informatica University of Udine Italy Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (纸本)9781538609866

Cooperative automatic driving, or platooning, is a promising solution to improve traffic safety, while reducing congestion and pollution. The design of a control system for this application is a challenging, multi-disciplinary problem, as cooperation between vehicles is obtained through wireless communication. So far, control and network issues of platooning have been investigated separately. In this work we design a cooperative driving system from a joint network and control perspective, determining worst-case upper bounds on the safety distance subject to network losses, so the actual inter-vehicle gap can be tuned depending on vehicle or network performance. By means of simulation, we show that the system is very robust to packet losses and that the derived bounds are never violated. © 2017 IEEE.

关键词： Traffic congestion

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Link before you share: Managing privacy policies through blockchain

arXiv

引用

arXiv 2017年

作者： Banerjee, Agniva Joshi, Karuna Pande Dept. of Computer Science and Electrical Engineering Umbc Information Systems Department Umbc

With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. Users consent to providers to gather and share their Personally Identifiable information (PII). We have developed a novel framework to automatically track details about how a user's PII data is stored, used and shared by the provider. We have integrated our Data Privacy ontology with the properties of blockchain, to develop an automated access-control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by Big Data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders. Copyright © 2017, The Authors. All rights reserved.

关键词： Ontology

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From retweet to believability: Utilizing trust to identify rumor spreaders on twitter 9

From retweet to believability: Utilizing trust to identify r...

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9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

作者： Rath, Bhavtosh Gao, Wei Ma, Jing Srivastava, Jaideep Dept. Of Computer Science and Engineering University of Minnesota Twin CitiesMN United States School of Information Management Victoria University of Wellington Wellington New Zealand Dept. Of Systems Engineering and Engineering Management Chinese University of Hong Kong Hong Kong Qatar Computing Research Institute Doha Qatar

ISBN: (纸本)9781450349932

Ubiquitous use of social media such as microblog-ging platforms brings about ample opportunities for the false information to diffuse online. It is very important not just to determine the veracity of information but also the authenticity of the users who spread the information, especially in time-critical situations like real-world emergencies, where urgent measures have to be taken for stopping the spread of fake information. In this work, we propose a novel machine learning based approach for automatic identification of the users spreading rumorous information by leveraging the concept of believability, i.e., the extent to which the propagated information is likely to be perceived as truthful, based on the trust measures of users in Twitter’s retweet network. We hypothesize that the believability between two users is proportional to the trustingness of the retweeter and the trustworthiness of the tweeter, which are two complementary measures of user trust and can be inferred from retweeting behaviors using a variant of HITS algorithm. With the retweet network edge-weighted by believability scores, we use network representation learning to generate user embeddings, which are then leveraged to classify users into as rumor spreaders or not. Based on experiments on a very large real-world rumor dataset collected from Twitter, we demonstrate that our method can effectively identify rumor spreaders and outperform four strong baselines with large margin. © 2017 Association for Computing Machinery.

关键词： Spreaders

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