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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...

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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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Viroinformatics-based investigation of SARS-CoV-2 core proteins for potential therapeutic targets

arXiv

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arXiv 2020年

作者： Agrawal, Lokesh Poullikkas, Thanasis Eisenhower, Scott Monsanto, Carlo Bakku, Ranjith Kumar Universidad Integral del Caribe y América Latina Kaminda Cas Grandi #79 Curaçao United States Graduate School of Comprehensive Human Sciences University of Tsukuba 1-1-1 Tennodai Tsukuba305-8577 Japan Molecular Neuroscience Unit Okinawa Institute of Science and Technology Graduate University Kunigami-gun Okinawa904-0412 Japan Human Biology School of Integrative and Global Majors University of Tsukuba 1-1-1 Tennodai Tsukuba305-0006 Japan Department of Experimental Pathology Faculty of Medicine University of Tsukuba 2-1-1 Tennodai Tsukuba305-8576 Japan Department of Infection Biology Faculty of Medicine University of Tsukuba 1-1-1 Tennodai Tsukuba305-8575 Japan Research Workgroup Ronin Institute 127 Haddon Place MontclairNJ07043-2314 United States Faculty of Engineering Information and Systems Dept. of Computer Science University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8572 Japan University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8572 Japan

Due to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) being a novel virus, there are currently no known effective antiviral drugs capable of slowing its progress. To accelerate the discovery of potential drug candidates, bioinformatics based in silico drug discovery can be applied as a very robust tool. In the present study, more than 60 antiviral drugs already available on the market, were chosen after literature survey. These can be used in clinical trials for the treatment of COVID-19. In this study, these candidate drugs were ranked based on their potential to interact with the Spike protein and RdRp (RNA-dependent RNA polymerase) of SARS-CoV-2. Additionally, the mechanism of their action as well as how the virus infection can utilize Hemoglobin to decrease the oxygen level in blood is explained. Moreover, multiple sequence alignments of the Spike protein with 75 sequences of different viruses from the Orthocoronavirinae subfamily were performed. This gives insight into the evolutionarily conserved domains that can be targeted using drug or antibody treatment. This multidimensional study opens a new layer of understanding about the most effective drug-targetable sites on the Spike protein of SARS-CoV-2. Copyright © 2020, The Authors. All rights reserved.

关键词： Viruses

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Baggage detection and classification using human body parameter & boosting technique

Baggage detection and classification using human body parame...

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Conference on Human System Interactions, HSI

作者： Tahmina Khanam Kaushik Deb Kang-Hyun Jo Dept. of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of EE and Information Systems University of Ulsan Ulsan Korea

ISBN: (纸本)9781509056132

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime in public places has increased in the twenty first century. As a new branch of AVSS, baggage detection and classification has a broad area of security applications. Some of them are, detecting carriage of illegal materials into baggage, detecting unclaimed baggage in public space that can be placed by terrorists for violence, detecting baggage in baggage restricted super shop etc. However, in this paper, a detection & classification framework of baggage is proposed using dynamic human body parameter with boosting strategy. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with uneven illumination condition. Then, to overcome the shadow effect a model is introduced. Extraction of rotational signal descriptor (RSD_HOG) from Region of Interest (ROI) added efficiency in HOG. Finally, dynamic approach in human body parameter setting enabled the system to detect & classify single or multiple carried baggages although some portions of human are absent. In baggage detection, boosting of similarity measure based cascade multilayer SVMs into HOG based SVM generated a strong classifier. This scheme has used to deal with various texture patterns of baggages. Experimental results discovered the system satisfactorily accurate and faster comparative to other alternatives.

关键词： Feature extraction Boosting Support vector machines Lighting Biological system modeling Nonhomogeneous media information filtering

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An Architecture for the Development of Mutation Operators

An Architecture for the Development of Mutation Operators

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Macario Polo Usaola Gonzalo Rojas Isyed Rodríguez Suilen Hernández Dept. of Information Systems and Technologies University of Castilla-La Mancha Ciudad Real Spain Dept. of Computer Science University of Concepción Concepción Chile

ISBN: (纸本)9781509066773

This paper introduces an abstract specification of mutation operators that (1) we have used to create traditional operators and, (2) we are currently using to define and implement mutation operators for context-aware, mobile applications that comeproceed from a list of common errors reported by three companies. This specification describes the structure and behavior of mutation operators at a high abstraction level, thus supporting the specification of new mutation operators according to the evolving state of the art of context-awareness. Facing The paper also gives some notes about BacterioWeb, a web-based mutant tool with an execution engine for Android applications.

关键词： Sensors Testing computer architecture Androids Humanoid robots Concrete Java

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Model transformations via XSLT 17

Model transformations via XSLT

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17th Annual International Workshop on Databases, Texts, Specifications, and Objects, DATESO 2017

作者： Pavlát, Jakub Richta, Karel Richta, Tomáš Janoušek, Vladimír Czech Technical University in Prague Faculty of Electrical Engineering Dept. of Computer Science Karlovo nám. 13 Praha 2121 35 Czech Republic Brno Institute of Technology Faculty of Information Technology Dept. of Intelligent Systems Božetěchova 2 Brno612 66 Czech Republic

ISBN: (纸本)9788001061381

Many present systems.are conceived as a set of autonomous agents that communicate together in solving problems. Petri nets are commonly used for a specification of parallel systems. An interesting question is whether we can implement arbitrary system by a set of Petri nets agents. These agents could be specified by Petri nets, but such a description is not sufficiently user-friendly. Our idea is to define these agents by classical workflow models and then transform them into a set of Petri nets. Such transformation would support development of software systems. whose specification is based on classical workflow models, but the implementation is based on Petri nets. Each net of the resulting system is translated into a specific target representation called Petri Nets Byte Code (PNBC). These codes are interpreted by the special Petri Nets Virtual Machines (PNVM), which are installed on all nodes of the system, under the Petri Nets Operating System (PNOS). This paper deals with the one step of this process, which is an implementation of required transformations from workflow model into Petri nets with the help of XSLT. © K. Richta, P. Moravec (Eds.): Dateso 2017.

关键词： Petri nets

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