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Saudi Arabia Smart Grid Conference (SASG)

作者： Yiannis Kamarianakis Yannis Pantazis Evangelia Kalligiannaki Theodoros D. Katsaounis Konstantinos Kotsovos Issam Gereige Marwan Abdullah Aqil Jamal Athanasios Tzavaras Data Science Group Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Greece Dept. of Mathematics and Applied Mathematics University of Crete Greece Carbon Management Division Saudi Aramco R&D Center Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.

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SPOCKMIP: Segmentation of Vessels in MRAs with Enhanced Continuity using Maximum Intensity Projection as Loss

arXiv

引用

arXiv 2024年

作者： Radhakrishna, Chethan Chintalapati, Karthikesh Varma Kumar, Sri Chandana Hudukula Ram Sutrave, Raviteja Mattern, Hendrik Speck, Oliver Nürnberger, Andreas Chatterjee, Soumick Faculty of Computer Science Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy

Identification of vessel structures of different sizes in biomedical images is crucial in the diagnosis of many neurodegenerative diseases. However, the sparsity of good-quality annotations of such images makes the task of vessel segmentation challenging. Deep learning offers an efficient way to segment vessels of different sizes by learning their high-level feature representations and the spatial continuity of such features across dimensions. Semi-supervised patch-based approaches have been effective in identifying small vessels of one to two voxels in diameter. This study focuses on improving the segmentation quality by considering the spatial correlation of the features using the Maximum Intensity Projection (MIP) as an additional loss criterion. Two methods are proposed with the incorporation of MIPs of label segmentation on the single (z-axis) and multiple perceivable axes of the 3D volume. The proposed MIP-based methods produce segmentations with improved vessel continuity, which is evident in visual examinations of ROIs. In this study, a UNet MSS with ReLU activation replaced by LeakyReLU is trained on the Study Forrest dataset. Patch-based training is improved by introducing an additional loss term, MIP loss, to penalise the predicted discontinuity of vessels. A training set of 14 volumes is selected from the StudyForrest dataset comprising of 18 7-Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) images. Then it is used to perform a five-fold cross-validation. The generalisation performance of the method is evaluated using the other unseen volumes in the dataset. It is observed that the proposed method with multi-axes MIP loss produces better quality segmentations with a median Dice of 80.245 ± 0.129. Also, the method with single-axis MIP loss produces segmentations with a median Dice of 79.749 ± 0.109. Furthermore, a visual comparison of the ROIs in the predicted segmentation reveals a significant improvement in the continuity of the vesse

关键词： Neurodegenerative diseases

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Guest Editors

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Journal of Discrete Mathematical Sciences and Cryptography 2021年第8期24卷 ii页

作者： Poonia, Ramesh Chandra Paulose, Joy Dass, Pranav Singh, Vijander Saudagar, Abdul Khader Jilani Diván, Ing. Mario José Department of Computer Science CHRIST (Deemed to be University) Karnataka Bangalore India Department of Computer Science CHRIST (Deemed to be University) Karnataka Bangalore India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Computer Science and Engineering Manipal University Jaipur India Information Systems Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Data Science Research Group Economy School Universidad Nacional de La Pampa Coronel Gil 353La Pampa Primer Piso - Santa Rosa CP 6300 Argentina

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Understanding and comparing scalable Gaussian process regression for big data

arXiv

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arXiv 2018年

作者： Liua, Haitao Cai, Jianfei Ong, Yew-Soon Wang, Yi Rolls-Royce@NTU Corporate Lab Nanyang Technological University Singapore637460 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Data Science and Artificial Intelligence Research Center Nanyang Technological University Singapore639798 Singapore Central Technology Group Rolls-Royce Singapore 1 Seletar Aerospace Crescent Singapore797565 Singapore

As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the era of big data. Hence, various scalable GPs have been developed in the literature in order to improve the scalability while retaining desirable prediction accuracy. This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness. The numerical experiments on two toy examples and five real-world datasets with up to 250K points offer the following findings. In terms of scalability, most of the scalable GPs own a time complexity that is linear to the training size. In terms of capability, the sparse approximations capture the long-term spatial correlations, the local aggregations capture the local patterns but suffer from over-fitting in some scenarios. In terms of controllability, we could improve the performance of sparse approximations by simply increasing the inducing size. But this is not the case for local aggregations. In terms of robustness, local aggregations are robust to various initializations of hyperparameters due to the local attention mechanism. Finally, we highlight that the proper hybrid of global and local scalable GPs may be a promising way to improve both the model capability and scalability for big data. Copyright © 2018, The Authors. All rights reserved.

关键词： Scalability

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Verification of adaptive systems

Verification of adaptive systems

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AIAA Infotech at Aerospace Conference and Exhibit 2012

作者： Pullum, Laura Cui, Xiaohui Vassev, Emil Hinchey, Mike Rouff, Christopher Buskens, Richard Computational Data Analytics Group Oak Ridge National Laboratory P.O. Box 2008 MS-6085 Oak Ridge TN 37830 United States Computer Science Department New York Institute of Technology 1855 Broadway Manhattan NY 10023 United States Lero - The Irish Software Engineering Research Centre University of Limerick Limerick Ireland Lockheed Martin Advanced Technology Laboratories Informatics Laboratory 3 Executive Campus Suite 600 Cherry Hill NJ 08002 United States Lockheed Martin Advanced Technology Laboratories Informatics Laboratory Applied Science Laboratory 3 Executive Campus Cherry Hill NJ 08002 United States

ISBN: (数字)9781600869396

ISBN: (纸本)9781600869396

Adaptive systems are critical for future space and other unmanned and intelligent systems. Verification of these systems is also critical for their use in systems with potential harm to human life or with large financial investments. Due to their nondeterministic nature and extremely large state space, current methods for verification of software systems are not adequate to provide a high level of assurance for them. The combination of stabilization science, high performance computing simulations, compositional verification and traditional verification techniques, plus operational monitors, provides a complete approach to verification and deployment of adaptive systems that has not been used before. This paper gives an overview of this approach. © 2012 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Adaptive systems

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Process Support and Knowledge Representation in Health Care 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Richard Lenz Silvia Miksch Mor Peleg Manfred Reichert David Riaño Annette Teije

ISBN: (数字)9783642364389

ISBN: (纸本)9783642364372

This book constitutes thoroughly refereed revised selected papers from the BPM 2012 Joint Workshop on Process-Oriented Information Systems and Knowledge Representation in Health Care, ProHealth 2012/KR4HC 2012, held in Tallinn, Estonia, in September 2012. The 9 papers presented were carefully reviewed and selected from 19 submissions. In addition the book contains 1 keynote paper and 2 invited contributions. The papers are organized in topical sections named: guidelines and summarization; archetypes and cooperation; and process mining and temporal analysis.

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Information Storage and Retrieval database Management User Interfaces and Human computer Interaction computer Communication Networks

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Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients

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JMIR Formative research 2025年 9卷 e68782页

作者： Lumsdon, Jack Wilson, Cameron Alcock, Lisa Becker, Clemens Benvenuti, Francesco Bonci, Tecla van den Brande, Koen Brittain, Gavin Brown, Philip Buckley, Ellen Caruso, Marco Caulfield, Brian Cereatti, Andrea Delgado-Ortiz, Laura Del Din, Silvia Evers, Jordi Garcia-Aymerich, Judith Gaßner, Heiko Gur Arieh, Tova Hansen, Clint Hausdorff, Jeffrey M. Hiden, Hugo Hume, Emily Kirk, Cameron Maetzler, Walter Megaritis, Dimitrios Rochester, Lynn Scott, Kirsty Sharrack, Basil Sutton, Norman Vereijken, Beatrix Vogiatzis, Ioannis Yarnall, Alison Keogh, Alison Cantu, Alma Population Health Sciences Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom School of Clinical Medicine Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom NIHR Newcastle Biomedical Research Centre Newcastle University The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne United Kingdom Robert Bosch Society for Medical Research Stuttgart Germany Digital Geriatrics Unit Medical Centre University of Heidelberg Heidelberg Germany Mobilise-D Patient and Public Advisory Group Newcastle Upon Tyne United Kingdom School of Mechanical Aerospace and Civil Engineering University of Sheffield Sheffield United Kingdom Insigneo Institute for Silico Medicine University of Sheffield Sheffield United Kingdom Department of Neuroscience Sheffield NIHR Translational Neuroscience BRC Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom Sheffield Institute for Translational Neuroscience University of Sheffield Sheffield United Kingdom The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom Division of Clinical Medicine University of Sheffield Sheffield United Kingdom Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Insights Centre Data Analytics University College Dublin Dublin Ireland School of Public Health Physiotherapy and Sports Science University of College Dublin Dublin Ireland Barcelona Institute for Global Health Barcelona Spain Department of Medicine and Life Sciences Pompeu Fabra University Barcelona Spain Center for Biomedical Research Madrid Spain McRoberts BV The Hague Netherlands Department of Molecular Neurology University Hospital Erlangen Erlangen Germany Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Departmen

Background: Recent technological advances in wearable devices offer new potential for measuring mobility in real-world contexts. Mobilise-D has validated digital mobility outcomes to provide novel outcomes and end points in clinical research of 4 different long-term health conditions (Parkinson disease, multiple sclerosis, chronic obstructive pulmonary disease, and proximal femoral fracture). These outcomes also provide unique information that is important to patients;however, there is limited literature that explores the optimal methods to achieve this, such as the best way to visualize patients’ data. Objective: This study aimed to identify meaningful outcomes for each condition and how to best visualize them from the perspective of end users. Methods: Using a Delphi-type protocol with patients as subject matter experts, we gathered iterative feedback on the cocreation of visualizations through 3 rounds of questionnaires. An open-ended questionnaire was used in round 1 to understand what aspects of mobility were most influenced by their health condition. These responses were mapped onto relevant digital mobility outcomes and walking experiences and then prioritized for visualization. Using patient responses, we worked alongside researchers, clinicians, and a patient advisory group to develop visualizations that depicted a week of mobility data. During rounds 2 and 3, participants rated usefulness and ease of understanding on a 5-point Likert scale and provided unstructured feedback in comment boxes for each visualization. Visualizations were refined using the feedback from round 2 before receiving further feedback in round 3. Results: Participation varied across rounds 1 to 3 (n=48, n=79, and n=78, respectively). Round 1 identified important outcomes and contexts for each health condition, such as walking speed and stride length for people with Parkinson disease or multiple sclerosis and number of steps for people with chronic obstructive pulmonary disease or prox

关键词： cocreation data visualization digital mobility outcomes mobility wearable devices

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Towards the Trust-Enhancements of Single Sign-On Services

Towards the Trust-Enhancements of Single Sign-On Services

引用

IEEE Conference on Dependable and Secure Computing

作者： Xuhua Bao Xiaokun Zhang Jingqiang Lin Dawei Chu Qiongxiao Wang Fengjun Li Legendsec Information Technology (Beijing) Inc. CHINA Joint Laboratory of Institute of Information Engineering Chinese Academy of Sciences and QiAnXin Group Academy of Opto-Electronics Chinese Academy of Sciences State Key Laboratory of Information Security Chinese Academy of Sciences Data Assurance and Communication Security Research Center Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Department of Electrical Engineering and Computer Science University of Kansas USA

Single sign-on (SSO) becomes popular as the identity management and authentication infrastructure in the Internet. A user receives an SSO ticket after being authenticated by the identity provider (IdP), and this IdP-issued ticket enables him to sign onto the relying party (RP). However, there are vulnerabilities (e.g., Golden SAML) that allow attackers to arbitrarily issue SSO tickets and then sign onto any RP on behalf of any user. Meanwhile, several incidents of certification authorities (CAs) also indicate that the trusted third party of security services is not so trustworthy as expected, and fraudulent TLS server certificates are signed by compromised or deceived CAs to launch TLS man-in-the-middle attacks. Various approaches are then proposed to tame the absolute authority of (compromised) CAs, to detect or prevent fraudulent TLS server certificates in the TLS handshakes. The trust model of SSO services is similar to that of certificate services. So this paper investigates the defense strategies of these trust-enhancements of certificate services, and attempts to apply these strategies to SSO to derive the trust-enhancements applicable in the SSO services. Our analysis derives (a) some security designs which have been commonly-used in the SSO services or non-SSO authentication services, and (b) two schemes effectively improving the trustworthiness of SSO services, which are not widely discussed or adopted.

关键词： Servers Public key Browsers Authentication Authorization Pins

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Innovative informatics methods for process mining in health care

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Journal of Biomedical Informatics 2020年 109卷 103551-103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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Anti-Collision System for Accident Prevention in Underground Mines using computer Vision 22

Anti-Collision System for Accident Prevention in Underground...

引用

Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamed Imam Karim Baïna Youness Tabii Intissar Benzakour Youssef Adlaoui El Mostafa Ressami El Hassan Abdelwahed Alqualsadi Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat ENSIAS Morocco and Digitalization & Microelectronic Smart Devices - MAScIR Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Morocco Alqualsadi Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat ENSIAS Morocco Information Retrieval and Data Analytics (IRDA) Research Team Rabat IT Center ENSIAS ENSIAS Morocco Reminex Research and Engineering center MANAGEM Group REMINEX Morocco Digitalization & Microelectronic Smart Devices - MAScIR Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Morocco Laboratory of Computer Systems Engineering (LISI) FSSM Cadi Ayyad University FSSM Morocco

ISBN: (纸本)9781450396943

Underground prospecting operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. This can result in vehicle-to-vehicle collisions, as well as vehicle-to-pedestrian or structural-element collisions, resulting in accidents. In this article, we discuss an anti-collision system for pedestrian identification in deep mines under the premise that we are looking to prevent collisions with moving machinery. This study presents the findings from testing an image processing module and sensory system based on deep learnig in the context of "smart connected mine" project.

关键词： Yolov5 Pedestrian detection Deep Learning Underground mining

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