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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

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Domain-Adversarial Graph Neural Networks for Text Classification

Domain-Adversarial Graph Neural Networks for Text Classifica...

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IEEE International Conference on Data Mining (ICDM)

作者： Man Wu Shirui Pan Xingquan Zhu Chuan Zhou Lei Pan Dept. of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton USA Faculty of Information Technology Monash University Melbourne Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Information Technology Deakin University Geelong VIC Australia

Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to improve the learning on the target domain; on the other hand, domain variance and hierarchical structure of documents from words, key phrases, sentences, paragraphs, etc. make it difficult to align domains for effective learning. To date, existing cross-domain text classification methods mainly strive to minimize feature distribution differences between domains, and they typically suffer from three major limitations - (1) difficult to capture semantics in non-consecutive phrases and long-distance word dependency because of treating texts as word sequences, (2) neglect of hierarchical coarse-grained structures of document for feature learning, and (3) narrow focus of the domains at instance levels, without using domains as supervisions to improve text classification. This paper proposes an end-to-end, domain-adversarial graph neural networks (DAGNN), for cross-domain text classification. Our motivation is to model documents as graphs and use a domain-adversarial training principle to lean features from each graph (as well as learning the separation of domains) for effective text classification. At the instance level, DAGNN uses a graph to model each document, so that it can capture non-consecutive and long-distance semantics. At the feature level, DAGNN uses graphs from different domains to jointly train hierarchical graph neural networks in order to learn good features. At the learning level, DAGNN proposes a domain-adversarial principle such that the learned features not only optimally classify documents but also separates domains. Experiments on benchmark datasets demonstrate the effectiveness of our method in cross-domain classification tasks.

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A de-centralized framework for data sharing, ontology matching and distributed analytics 1

A de-centralized framework for data sharing, ontology matchi...

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1st International Workshop on Semantic Web Technologies for Health Data Management, SWH 2018

作者： Pezoulas, Vasileios C. Kourou, Konstantina D. Exarchos, Themis P. An-dronikou, Vassiliki Varvarigou, Theodora Tzioufas, Athanasios G. De Vita, Salvatore Fotiadis, Dimitrios I. Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece Dept. of Informatics Ionian University CorfuGR 49100 Greece Division of Communication Electronic and Information Engineering School of Electrical and Computer Engineering National Technical University of Athens AthensGR 15780 Greece Dept. of Pathophysiology Faculty of Medicine National and Kapodistrian University of Athens AthensGR 15772 Greece Clinic of Rheumatology Dept. of Medical and Biological Sciences Udine University UdineIT 33100 Italy

In this paper we present the fundamental concept, as well as, the objectives and the architecture of HarmonicSS EU project. The proposed architecture envisages to address (i) the lack of patient stratification in large patient cohorts, (ii) the dataset heterogeneity across these cohorts, and (iii) the incomplete understanding of disease pathogenesis. In order to do so, ontology matching mechanisms are recruited for harmonizing the clinical data, distributed data analytics services have been designed for the de-centralized analysis of patient data, and different health policies can be assessed, all of them with respect to the data protection guidelines posed by the upcoming General Data Protection Regulation (GDPR). The HarmonicSS architecture has been employed towards the development of a data sharing, harmonization and distributed analytics framework for the recruitment and analysis of heterogeneous regional, national and international longitudinal cohorts of patients with primary Sjögren’s syndrome (pSS). The outcomes can be used by researchers, clinicians, patients, and health policy makers. © 2018 CEUR-WS. All rights reserved.

关键词： Large dataset

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APPINITE: A Multi-Modal Interface for Specifying Data Descriptions in Programming by Demonstration Using Natural Language Instructions

APPINITE: A Multi-Modal Interface for Specifying Data Descri...

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IEEE Symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Toby Jia-Jun Li Igor Labutov Xiaohan Nancy Li Xiaoyi Zhang Wenze Shi Wanling Ding Tom M. Mitchell Brad A. Myers HCI Institute Machine Learning Dept. Computer Science Dept. Computer Science &Engineering University of Washington Seattle WA USA Information Systems Dept. Carnegie Mellon University Pittsburgh PA USA

A key challenge for generalizing programming-by-demonstration (PBD) scripts is the data description problem - when a user demonstrates performing an action, the system needs to determine features for describing this action and the target object in a way that can reflect the user's intention for the action. However, prior approaches for creating data descriptions in PBD systems have problems with usability, applicability, feasibility, transparency and/or user control. Our APPINITE system introduces a multimodal interface with which users can specify data descriptions verbally using natural language instructions. APPINITE guides users to describe their intentions for the demonstrated actions through mixed-initiative conversations. APPINITE constructs data descriptions for these actions from the natural language instructions. Our evaluation showed that APPINITE is easy-to-use and effective in creating scripts for tasks that would otherwise be difficult to create with prior PBD systems, due to ambiguous data descriptions in demonstrations on GUIs.

关键词： Task analysis Natural languages Programming Graphical user interfaces Semantics Automation Visualization

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Game of drones - Detecting streamed POI from encrypted FPV channel

arXiv

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arXiv 2018年

作者： Nassi, Ben Ben-Netanel, Raz Shamir, Adi Elovici, Yuval Dept. of Software and Information Systems Eng. BGU Beer-Sheva Israel Dept. of Communication Systems Eng. BGU Beer-Sheva Israel Computer Science Department Weizmann Institute of Science Rehovot Israel

Drones have created a new threat to people's privacy. We are now in an era in which anyone with a drone equipped with a video camera can use it to invade a subject's privacy by streaming the subject in his/her private space over an encrypted first person view (FPV) channel. Although many methods have been suggested to detect nearby drones, they all suffer from the same shortcoming: they cannot identify exactly what is being captured, and therefore they fail to distinguish between the legitimate use of a drone (for example, to use a drone to film a selfie from the air) and illegitimate use that invades someone's privacy (when the same operator uses the drone to stream the view into the window of his neighbor's apartment), a distinction that in some cases depends on the orientation of the drone's video camera rather than on the drone's location. In this paper we shatter the commonly held belief that the use of encryption to secure an FPV channel prevents an interceptor from extracting the POI that is being streamed. We show methods that leverage physical stimuli to detect whether the drone's camera is directed towards a target in real time. We investigate the influence of changing pixels on the FPV channel (in a lab setup). Based on our observations we demonstrate how an interceptor can perform a side-channel attack to detect whether a target is being streamed by analyzing the encrypted FPV channel that is transmitted from a real drone (DJI Mavic) in two use cases: when the target is a private house and when the target is a subject. Copyright © 2018, The Authors. All rights reserved.

关键词： Video cameras

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SAR-Based Indoor Localization of UHF-RFID Tags via Mobile Robot

SAR-Based Indoor Localization of UHF-RFID Tags via Mobile Ro...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Andrea Motroni Paolo Nepa Valerio Magnago Alice Buffi Bernardo Tellini Daniele Fontanelli David Macii Dept. Information Engineering University of Pisa Pisa Italy Dept. Information Engineering and Computer Science University of Trento Trento Italy Dept. Energy Systems Territory and Constructions Engineering University of Pisa Pisa Italy Dept. Industrial Engineering University of Trento Trento Italy

This paper presents a localization method exploiting a mobile robot equipped with an Ultra High Frequency Radio Frequency IDentification (UHF-RFID) reader to locate stationary tags in warehouse scenarios. The measurement method is based on the Synthetic Aperture Radar (SAR) approach and the robot trajectory knowledge is achieved through a calibrated vision-based system suitable for indoor environments. The technique capability is demonstrated through an experimental analysis employing commercial UHF-RFID hardware and a wheeled robot. Localization accuracy is evaluated on the field by using a calibrated vision system, used to both locate the robotic vehicle and the tags when detected.

关键词： Radiofrequency identification Antennas Antenna measurements Trajectory Mobile robots Position measurement

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Alert system for timely medication administration

Alert system for timely medication administration

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2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in computer science, computer engineering and Applied Computing, CSCE 2018

作者： Puente, C. Sobrino, A. Villa-Monte, A. Olivas, J.A. Advanced Technical Faculty of Engineering ICAI Pontificia Comillas University Madrid Spain Faculty of Philosophy University of Santiago de Compostela La Coruna Spain Institute of Research in Computer Science LIDI Faculty of Computer Science National University of La Plata Buenos Aires Argentina Information Technologies and Systems Dept. University of Castilla-La Mancha Ciudad Real Spain

ISBN: (纸本)1601324804

Time has an extraordinary relevance in medicine. The diagnoses of diseases or prescription of medicines frequently presents temporary references. The etiology of a disease frequently depends on when such a symptom appeared and the prescription of some drugs is severely constrained to certain temporary annotations. Every year, the health of the patients suffers due to the bad administration of medicines and an appreciable amount of money is lost by that bad practice. In this paper we want to contribute to mend this problem. In that vein, we approach a release software for controlling the medicine doses administration with an intuitive traffic-light code in order to help the patient to consume the drugs according to the temporary restrictions indicated by the doctor. That software involves three steps. The first one encodes into a database the guidelines of the IHS BCMA for timely medication administration. The second part is in charge to locate the timely restrictions associated to a given illness. By last, the software display to the user the time alarms associated to aprescribed treatment. CSREA Press ©.

关键词： Artificial intelligence

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Composite risk modeling for automated threat mitigation in medical devices 4

Composite risk modeling for automated threat mitigation in m...

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4th Modeling and Simulation in Medicine Symposium, MSM 2017, Part of the 2017 Spring Simulation Multi-Conference, SpringSim 2017

作者： Rao, Aakarsh Rozenblit, Jerzy Lysecky, Roman Sametinger, Johannes Dept. of Electrical and Computer Engineering University of Arizona TucsonAZ United States Dept. of Information Systems - Software Engineering Johannes Kepler University Linz Austria

ISBN: (纸本)9781510838253

Medical device security is a growing concern with increasing incorporation of complex software and hardware. Security threats exploiting vulnerabilities in medical devices may directly impact patient safety. Standardization and federal organizations are hence, actively involved in setting up new paradigms for guidance and regulation of security throughout the lifecycle. To protect medical devices against threats a risk-based framework that continually manages and assesses security risks along with their proactive addressing is highly recommended. In this paper, we model a multi-modal design approach for risk assessment in medical devices and propose an adaptive remediation scheme to mitigate security threats. Our multi-modal approach is integrated into the hardware-software development with a middleware for interaction between the modes. This provides an effective premarket risk management while the adaptive remediation scheme pro-actively mitigate risk during postmarket deployment. We model our approaches in detail and demonstrate them in a pacemaker design model and deployment scenario. ©2017 Society for Modeling & Simulation International (SCS).

关键词： Risk management

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FCLAB: An EEGLAB module for performing functional connectivity analysis on single-subject EEG data

FCLAB: An EEGLAB module for performing functional connectivi...

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

作者： Pezoulas, Vasileios C. Athanasiou, Alkinoos Nolte, Guido Zervakis, Michalis Fratini, Antonio Fotiadis, Dimitrios I. Klados, Manousos A. Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina Ioannina45112 Greece Lab of Medical Physics Faculty of Medicine School of Health Sciences Aristotle University of Thessaloniki Thessaloniki54124 Greece Dept. of Neurophysiology and Pathophysiology University Medical Center Hamburg-Eppendorf Hamburg Germany School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece Dept. of Biomedical Engineering School of Life and Health Sciences Aston University Aston United Kingdom

ISBN: (纸本)9781538624050

Functional connectivity (FC) analysis constitutes a fundamental neuroscientific approach that has been extensively used for the investigation of brain's connectivity and activation patterns. To that end, several software tools have been developed. This paper presents FCLAB, the only EEGLAB-based plugin, which is able to work with EEG signals in order to estimate and visualize brain functional connectivity networks based on a variety of similarity measures as well as run a complete graph analysis procedure followed by a detailed visualization of the ensuing local and global measures distribution. FCLAB entails optimization procedures for the implementation of the connectivity structures and is the result of long-term research in EEG functional connectivity. The computed functional connectivity measures have been carefully selected to reflect the state-of-art in the field. Future work focuses on extending the platform for multi-subject analysis in order to enable the implementation of statistical analysis tools. © 2018 IEEE.

关键词： Neurophysiology

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PRESGen: A fully automatic equivalence checker for validating optimizing and parallelizing transformations

PRESGen: A fully automatic equivalence checker for validatin...

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2017 Workshop on software engineering Methods for Parallel and High Performance Applications, SEM4HPC 2017

作者： Bandyopadhyay, Soumyadip Banerjee, Kunal Dept. of Computer Science and Information Systems Birla Institute of Technology and Science K K Birla Goa Campus Pilani India Indian Institute of Technology Dept. of Computer Science and Engineering India

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