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Posters and Demos Track of the 14th International Conference on Semantic systems, SEMPDS 2018

作者： Kleedorfer, Florian Suda, Fabian Stolze, Max Huemer, Christian Research Studio Smart Agent Technologies Thurngasse 8/16 Vienna1090 Austria Institute for Software Technology and Interactive Systems Vienna University of Technology Favoritenstraße 9-11 Vienna1040 Austria

Matchmaking and cooperation are interactions widely available within internet-based platforms. However, to start using such functionality, one must first become a member of the platform. In an effort to push matchmaking and cooperation into the protocol stack of the Web, we demonstrate how an existing taxi service is integrated with a decentralized Web based infrastructure, the Web of Needs (WoN), using a chatbot that can be programmed declaratively to enter into certain types of agreements with users. The bot mediates between an existing Web service and users of the RDF-based WoN network. © 2018 CEUR-WS. All Rights Reserved.

关键词： Web services

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Avoiding undertrust and overtrust

Avoiding undertrust and overtrust

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2019 Joint of International Conference on Requirements Engineering: Foundation for software Quality Workshops, Doctoral Symposium, Live Studies Track, and Poster Track, REFSQ-JP 2019

作者： Kaindl, Hermann Svetinovic, Davor Institute of Computer Technology TU Wien Vienna Austria Center on Cyber-Physical Systems Department of Computer Science Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

An important question with regard to new systems is whether they can be trusted by human users, especially when they are safety-critical. This problem should already be addressed in the course of requirements engineering for such systems. However, trust is actually a complex psychological and sociological concept. Thus, it cannot simply be taken into account as a desired or needed property of a system. We propose to learn from the Human Factors discipline on trust in technical systems. In particular, we argue that both undertrust and overtrust need to be avoided. The challenge is to determine system properties and activities in the course of requirements engineering for achieving that. We conjecture that both actual properties like safety and subjective assessment like perceived safety will be important, and how they will have to be balanced for avoiding undertrust and overtrust. Copyright © 2019 by the paper’s authors.

关键词： Requirements engineering

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A survey of fast recovery mechanisms in the data plane

TechRxiv

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TechRxiv 2020年

作者： Chiesa, Marco Kamisiński, Andrzej Rak, Jacek Rétvári, Gábor Schmid, Stefan KTH Royal Institute of Technology Sweden AGH University of Science and Technology Kraków Poland Gdansk ´ University of Technology Poland MTA–BME Information Systems Research Group Hungary Faculty of Computer Science University of Vienna Austria

In order to meet their stringent dependability requirements, most modern communication networks support fast-recovery mechanisms in the data plane. While reactions to failures in the data plane can be significantly faster compared to control plane mechanisms, implementing fast recovery in the data plane is challenging, and has recently received much attention in the literature. This survey presents a systematic, tutorial-like overview of packet-based fast-recovery mechanisms in the data plane, focusing on concepts but structured around different networking technologies, from traditional link-layer and IP-based mechanisms, over BGP and MPLS to emerging software-defined networks and programmable data planes. We examine the evolution of fast-recovery standards and mechanisms over time, and identify and discuss the fundamental principles and algorithms underlying different mechanisms. We then present a taxonomy of the state of the art and compile open research questions. © 2020, CC BY-NC-SA.

关键词： Surveys

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Quantum experiments and graphs. III. High-dimensional and multiparticle entanglement

引用

Physical Review A 2019年第3期99卷 032338-032338页

作者： Xuemei Gu Lijun Chen Anton Zeilinger Mario Krenn State Key Laboratory for Novel Software Technology Nanjing University 163 Xianlin Avenue Qixia District 210023 Nanjing City China Institute for Quantum Optics and Quantum Information (IQOQI) Austrian Academy of Sciences Boltzmanngasse 3 1090 Vienna Austria Vienna Center for Quantum Science & Technology (VCQ) Faculty of Physics University of Vienna Boltzmanngasse 5 1090 Vienna Austria

Quantum entanglement plays an important role in quantum information processes, such as quantum computation and quantum communication. Experiments in laboratories are unquestionably crucial to increase our understanding of quantum systems and inspire new insights into future applications. However, there are no general recipes for the creation of arbitrary quantum states with many particles entangled in high dimensions. Here we exploit a recent connection between quantum experiments and graph theory and answer this question for a plethora of classes of entangled states. We find experimental setups for Greenberger-Horne-Zeilinger states, W states, general Dicke states, and asymmetrically high-dimensional multipartite entangled states. This result sheds light on the producibility of arbitrary quantum states using photonic technology with probabilistic pair sources and allows us to understand the underlying technological and fundamental properties of entanglement.

关键词： Entanglement production Quantum entanglement Graph theory

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Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Network and Systems Medicine

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Network and systems Medicine 2020年第1期3卷 36-56页

作者： Stalidzans, Egils Zanin, Massimiliano Tieri, Paolo Castiglione, Filippo Polster, Annikka Scheiner, Stefan Pahle, Jürgen Stres, Blaž List, Markus Baumbach, Jan Lautizi, Manuela Van Steen, Kristel Schmidt, Harald H.H.W. Computational Systems Biology Group University of Latvia Riga Latvia Latvian Biomedical Reasearch and Study Centre Riga Latvia Centro de Tecnología Biomédica Universidad Politécnica de Madrid Pozuelo de Alarcón Spain CNR National Research Council IAC Institute for Applied Computing Rome Italy Oslo Norway Institute for Mechanics of Materials and Structures Vienna University of Technology Vienna Austria BioQuant Heidelberg University Heidelberg Germany Department of Animal Science University of Ljubljana Ljubljana Slovenia Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Big Data in BioMedicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Computational Systems Medicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany BIO-Systems Genetics GIGA-R University of Liège Liège Belgium BIO3 - Systems Medicine Department of Human Genetics KU Leuven Leuven Belgium Department of Pharmacology and Personalised Medicine Faculty of Health Medicine and Life Science Maastricht University Maastricht Netherlands

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body. © Egils Stalidzans et al. 2020 Published b

关键词： Iterative methods

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"Computer science for all": Concepts to engage teenagers and non-CS students in technology

arXiv

引用

arXiv 2019年

作者： Spieler, Bernadette Grandl, Maria Ebner, Martin Slany, Wolfgang Graz University of Technology Institute of Software Technology Graz Austria Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria Graz University of Technology Department Educational Technology Graz Austria

Knowledge in Computer Science (CS) is essential, and companies have increased their demands for CS professionals. Despite this, many jobs remain unfilled. Furthermore, employees with computational thinking (CT) skills are required, even if they are not actual technicians. Moreover, the gender disparity in technology related fields is a serious problem. Even if companies want to hire women in technology, the number of women who enter these fields is remarkably low. In high schools, most teenagers acquire only low-level skills in CS. Thus, they may never understand the fundamental concepts of CS, have unrealistic expectations or preconceptions, and are influenced by stereotype-based expectations. Consequently, many teenagers exclude computing as a career path. In this research study, we present two promising concepts to overcome these challenges. First, we consider alternative paths to enter the field of CS. In 2018, a voluntary lecture "Design your own app" at the university of Graz for students of all degree programs was introduced. In total, 202 students participated. We applied a Game Development-Based Learning (GDBL) approach with the visual coding tool Pocket Code, a mobile app developed at Graz university of technology. The students were supposed to create simple games directly on smartphones. The course received positive evaluations and led to our second concept;In January 2019, we started to design a MOOC (Massive Open Online Course) with the title "Get FIT in Computer Science". The MOOC will be launched in August 2019 on the platform *** and will provide a general introduction to the field of CS. For exercises and the final submission, the students need to apply game design strategies by using Pocket Code. The MOOC has several target groups. First, this course can be used to encourage young women who have little to no previous knowledge in CS. Second, it should help all teenagers to get a more realistic picture of CS to its basic concepts. Third, teache

关键词： Students

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GESTALT INTEREST POINTS WITH A NEURAL NETWORK FOR MAKEUP-ROBUST FACE RECOGNITION

GESTALT INTEREST POINTS WITH A NEURAL NETWORK FOR MAKEUP-ROB...

引用

IEEE International Conference on Image Processing

作者： Markus Horhan Horst Eidenberger Vienna University of Technology Institute of Software Technology and Interactive System Vienna Austria

In this paper, we propose a novel approach for the domain of makeup-robust face recognition. Most face recognition schemes usually fail to generalize well on these data where there is a large difference between the training and testing sets, e.g., makeup changes. Our method focuses on the problem of determining whether face images before and after makeup refer to the same identity. The work on this fundamental research topic benefits various real-world applications, for example automated passport control, security in general, and surveillance. Experiments show that our method is highly effective in comparison to state-of-the-art methods.

关键词： Feature extraction Face recognition Face Training Visual perception Neural networks Semantics

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Truck circuits diagnosis for railway lines equipped with an automatic block signalling system

Truck circuits diagnosis for railway lines equipped with an ...

引用

International Conference on Applied Sciences 2017, ICAS 2017

作者： Spunei, E. Piroi, I. Muscai, C. Rǎduca, E. Piroi, F. Eftimie Murgu University of Resita Department of Electrical Engineering and Informatics Traian Vuia str. no. 1-4 Resita320085 Romania Vienna University of Technology Institute of Software Technology and Interactive Systems Viena Austria

This work presents a diagnosis method for detecting track circuits failures on a railway traffic line equipped with an Automatic Block Signalling installation. The diagnosis method uses the installation's electrical schemas, based on which a series of diagnosis charts have been created. Further, the diagnosis charts were used to develop a software package, CDCBla, which substantially contributes to reducing the diagnosis time and human error during failure remedies. The proposed method can also be used as a training package for the maintenance staff. Since the diagnosis method here does not need signal or measurement inputs, using it does not necessitate additional IT knowledge and can be deployed on a mobile computing device (tablet, smart phone). © Published under licence by IOP Publishing Ltd.

关键词： Timing circuits

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