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14th IFIP WG 2.13 International Conference on Open Source Systems, OSS 2018

作者： Vafopoulos, Michalis Rallis, Stylianos Anagnostopoulos, Ioannis Peristeras, Vassilios Negkas, Dimitrios Skaros, Ilias Tzani, Aggelos Software and Knowledge Engineering Laboratory IIT NCSR-"Demokritos" Athens Greece School of Engineering Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece School of Sciences Department of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece School of Science and Technology International Hellenic University Thessaloniki Greece

ISBN: (纸本)9783319923741

In this paper, we propose a model conceptualization (Linked Open Economy - LOE) capable of exploiting the massive amount and variety of open economic data that are gradually becoming available by governments and open source communities. The main aim is to unleash the power of open data and open source systems and create a common ground to serve as a catalyst in providing more efficient answers in important economic activities. © 2018, IFIP International Federation for Information Processing.

关键词： Open Data

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Work in Progress – Establishing a Master Program in Cyber Physical Systems: Basic Findings and Future Perspectives

Work in Progress – Establishing a Master Program in Cyber P...

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International Conference on Promising Electronic Technologies (ICPET)

作者： Isam Ishaq Rashid Jayousi Salaheddin Odeh Ezzaldeen Edwan Asmaa Shaheen Mohamed Elnaggar Ahmed Elagha Samy Salamah Christian Weber Hasan Abu Rasheed Raimund Kirner Martina Doolan Hamidreza Ahmadian Roman Obermaisser Dhiah el Diehn I. Abou-Tair Ala Khalifeh Sahel Alouneh Zaid Alhalhouli Khalid Alemerien Faiez Gargouri Bassem Bouaziz Nadia Aloui Mohammad Saleh Computer Engineering and Science Departments Al-Quds University Jerusalem Palestine Engineering Program and Computer Department Palestine Technical College - Deir El-Balah Deir El-Balah Palestine Institute of Knowledge Based Systems & Knowledge Management University of Siegen Siegen Germany University of Siegen Siegen Germany School of Engineering and Computer Science University of Hertfordshire Hatfield UK Chair of Embedded Systems University of Siegen Siegen Germany German Jordanian University Amman Jordan School of Electrical Engineering and Information Technology German Jordanian University Amman Jordan Computer and Information Technology Department Tafila Technical University Tafila Jordan Higher Institute of Computer Science and Multimedia University of Sfax Sfax Tunisia Higher Institute of Applied Sciences and Technology of Mateur University of Carthage Mateure Tunisia KTH Royal Institute of Technology Stockholm Sweden

This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of science on Cyber Physical Systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is presented with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes.

关键词： computer science Internet of Things Industries knowledge engineering Embedded systems Europe Collaboration

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computer and network security

arXiv

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arXiv 2020年

作者： Sen, Jaydip Mehtab, Sidra Sone, Michael Ekonde Jyothsna, Veeramreddy Prasad, Koneti Munivara Singh, Rajeev Sharma, Teek Parval Noskov, Anton Velásquez, Ignacio Caro, Angélica Rodríguez, Alfonso Fatayer, Tamer S.A. Mulani, Altaf O. Mane, Pradeep B. Chitrakar, Roshan Bhusal, Roshan Maharjan, Prajwol School of Computing and Analytics NSHM Knowledge Campus Kolkata India College of Technology University of Buea Buea Cameroon Sree Vidyanikethan Engineering College Tirupati India Chadalawada Ramanamma Engineering College Tirupati India G.B. Pant University of Agriculture and Technology PantnagarUttarakhand India National Institute of Technology HamirpurHimachal Pradesh India Yaroslavl State Technical University Yaroslavl Russia Computer Science and Information Technologies Department University of Bío-Bío Chillán Chile Computer Science and Information Technology Al-Aqsa University Gaza Palestine AISSMS Institute of Information Technology PuneMaharashtra India Nepal Open University Nepal Nepal College of Information Technology Nepal

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Performance Analysis of Machine Learning Algorithms for Gender Classification

Performance Analysis of Machine Learning Algorithms for Gend...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Laxmi Narayana Pondhu Govardhani Kummari Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Basar India Department of Computer Science and Engineering Kodada Institute of Technology and Science for Women Kodada India

We have various machine algorithms for gender classification but choosing best one is important task. For selecting best algorithm we conducted experimental study on machine learning algorithms for gender classification. In this experimental study of machine learning algorithms, we analyzed performance of various algorithms for gender classification using voice dataset. From this study we concluded that SVM and ANN are giving best results. After tuning parameters ANN outperforms SVM giving accuracy 99.87% on test data.

关键词： Frequency measurement Support vector machines Machine learning algorithms Artificial neural networks Acoustics Machine learning Classification algorithms

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Collective fast delivery by energy-efficient agents

arXiv

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arXiv 2018年

作者： Bärtschi, Andreas Graf, Daniel Mihalák, Matúš ETH Zürich Department of Computer Science Switzerland Department of Data Science and Knowledge Engineering Maastricht University Netherlands

We consider k mobile agents initially located at distinct nodes of an undirected graph (on n nodes, with edge lengths). The agents have to deliver a single item from a given source node s to a given target node t. The agents can move along the edges of the graph, starting at time 0, with respect to the following: Each agent i has a weight ωithat defines the rate of energy consumption while travelling a distance in the graph, and a velocity νiwith which it can move. We are interested in schedules (operating the k agents) that result in a small delivery time Τ (time when the item arrives at t), and small total energy consumption ϵ. Concretely, we ask for a schedule that: either (i) Minimizes Τ, (ii) Minimizes lexicographically (Τ, ϵ) (prioritizing fast delivery), or (iii) Minimizes ϵ · Τ + (1 - ϵ) · ϵ, for a given ϵ ∈ (0, 1). We show that (i) is solvable in polynomial time, and show that (ii) is polynomial-time solvable for uniform velocities and solvable in time O(n + k log k) for arbitrary velocities on paths, but in general is NP-hard even on planar graphs. As a corollary of our hardness result, (iii) is NP-hard, too. We show that there is a 3-approximation algorithm for (iii) using a single agent. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile agents

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A study of lexical ambiguity in large forum discussions for multidisciplinary knowledge engineering 13

A study of lexical ambiguity in large forum discussions for ...

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13th International Conference on knowledge, Information and Creativity Support Systems, KICSS 2018

作者： Takhom, Akkharawoot Boonkwan, Prachya Ulrich Hoppe, H. Ikeda, Mitsuru Usanavasin, Sasiporn Supnithi, Thepchai Japan Advanced Institute of Science and Technology School of Knowledge Science Nomi Japan School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Computer Science and Applied Cognitive Science Faculty of Engineering University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781728101613

Lexical ambiguity is a challenging issue in multidisciplinary knowledge engineering due to the tendency that lexical terms can be used among different domains with different specific meanings. Particularly in large forum discussions, such ambiguous cross-disciplinary terms hard to be identified and detected by the discussion participants because domain expertise from several relevant fields is required to detect those terms and discover the actual divergence of interpretation. Having many ambiguous terms in the discussion context will result in gradual misunderstanding and delayed knowledge construction. We studied the effects of data sizes and morphological analysis in discovering ambiguous cross-disciplinary terms in large forum discussions. Our findings are twofold. First, it is more likely to discover cross-disciplinary terms as forum discussions deepen. This correlates with domain experts' tendency to use general terms in metaphorically describing domain-specific concepts, therefore causing lexical ambiguity. Second, we found that lemmatization outperforms stemming in forming more understandable key terms. This is because lemmatization eliminates only inflectional affixes and keeps derivational affixes. On the other hand, stemming eliminates both types of affixes, causing semantic bleaching. © 2018 IEEE.

关键词： knowledge engineering

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Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

arXiv

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arXiv 2020年

作者： Chatterjee, Soumick Saad, Fatima Sarasaen, Chompunuch Ghosh, Suhita Krug, Valerie Khatun, Rupali Mishra, Rahul Desai, Nirja Radeva, Petia Rose, Georg Stober, Sebastian Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Artificial Intelligence Lab Otto von Guericke University Magdeburg Germany Department of Mathematics and Computer Science University of Barcelona Barcelona Spain Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Apollo Hospitals Bilaspur India HCG Cancer Centre Vadodara India Computer Vision Centre Cerdanyola Barcelona Spain Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected patients. Medical imaging, such as X-ray and Computed Tomography (CT), combined with the potential of Artificial Intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 and DenseNet161) and their ensemble, using majority voting have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods - occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT, and using a global technique - neuron activation profiles. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

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Publisher Correction: Increasing the presence of BIPOC researchers in computational science

Nature computational science

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Nature computational science 2024年第10期4卷 798页

作者： Christine Yifeng Chen Alan Christoffels Roger Dube Kamuela Enos Juan E Gilbert Sanmi Koyejo Jason Leigh Carlo Liquido Amy McKee Kari Noe Tai-Quan Peng Karaitiana Taiuru Cosmochemical and Isotopic Signatures Group Nuclear and Chemical Sciences Division Lawrence Livermore National Laboratory Livermore CA USA. chen127@llnl.gov. South African MRC (SAMRC) Bioinformatics Unit South African National Bioinformatics Institute University of the Western Cape Cape Town South Africa. alan@sanbi.ac.za. Africa Centers for Disease Control and Prevention African Union Addis Ababa Ethiopia. alan@sanbi.ac.za. College of Science Rochester Institute of Technology Rochester NY USA. rrdube@***. Office of Indigenous Knowledge and Innovation University of Hawai'i Honolulu HI USA. kenos@hawaii.edu. Computer & Information Science & Engineering Department University of Florida Gainesville FL USA. juan@ufl.edu. Department of Computer Science Stanford University Stanford CA USA. sanmi@cs.stanford.edu. Department of Information and Computer Sciences University of Hawai'i at Mānoa Honolulu HI USA. leighj@hawaii.edu. Pi'ikū Co. Kailua HI USA. carlo.h.liquido@***. Department of Educational Foundations University of Hawai'i at Mānoa Honolulu HI USA. amymckee@hawaii.edu. 'ĀinaQuest Honolulu HI USA. amymckee@hawaii.edu. Office of Indigenous Knowledge and Innovation University of Hawai'i Honolulu HI USA. karinoe@hawaii.edu. Department of Communication Michigan State University East Lansing MI USA. pengtaiq@msu.edu. Taiuru & Associates Limited Auckland New Zealand. karaitiana@taiuru.maori.nz. Ngāi Tahu Ngāti Kahungunu Ngāti Toa Auckland New Zealand. karaitiana@taiuru.maori.nz.

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Chinese text-line detection from web videos with fully convolutional networks

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Big Data Analytics 2018年第1期3卷 1-11页

作者： Yang, Chun Pei, Wei-Yi Wu, Long-Huang Yin, Xu-Cheng Department of Computer Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Materials Science Knowledge Engineering University of Science and Technology Beijing Beijing China

In recent years, video becomes the dominant resource of information on the Web, where the text within video usually carries significant semantic information. Video text extraction and recognition plays an essential role in web multimedia understanding and retrieval for big visual data analytics and applications. To deal with challenging backgrounds and embedding noises, most conventional approaches usually tend to design sophisticated pre-processing and post-progressing steps before and after text detection. In this paper, we present a simple yet powerful pipeline that directly and uniformly detects Chinese text lines for embedded captions from web videos. In this Chinese text-line detection system, a fully convolutional network with local context is adopted to localize via an end-to-end learning way. The produced caption predictions are with the word level that could be directly fed into the character classifier. Text-line construction is then performed by heuristic strategies. A variety of experiments are conducted on several real-world web video datasets and demonstrated the effectiveness and efficiency of our proposed method. The proposed system can directly detect the English word and Chinese characters in the caption text-lines without word or character segmentation with the high performance on real-world web video datasets.

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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