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Context-dependent multimodal communication in human-robot collaboration

Procedia CIRP 2018年 72卷 15-20页

作者： Csaba Kardos Zsolt Kemény András Kovács Balázs E. Pataki József Váncza EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control Hungarian Academy of Sciences Department of Manufacturing Science and Technology Budapest University of Technology and Economics Department of Distributed Systems Institute for Computer Science and Control Hungarian Academy of Sciences

In the dynamic environment of human-robot collaboration, a key for boosting the efficiency of human workers is supporting them with context-dependent work instructions, delivered via communication modalities that suit the actual context. Workers, in turn, should be supported in controlling the robot or other components of the production system by using the most convenient modality, thus lifting the limitations of traditional interfaces as push buttons installed at fixed locations. We introduce a workflow for context-dependent multimodal communication in a collaborative work environment and present its implementation in a Human-Machine Interface Controller system.

关键词： human-robot collaboration worker information systems multi-modal communication

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Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

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Archives of Computational Methods in Engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

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The Future of Cloud Computing: Opportunities, Challenges and Research Trends

The Future of Cloud Computing: Opportunities, Challenges and...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Amanpreet Kaur V.P. Singh Sukhpal Singh Gill Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala Punjab India Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (纸本)9781538614433;9781538614426

In a cloud computing environment, datacenter consists of number of servers, cooling and power delivery equipment's that require enormous measure of computational energy to drive complex frameworks. Due to the rising demand of the computation power, datacenter has become the hub for significant increase in the power consumption, heat dissipation and rise in temperature of the servers. Cloud datacenter's energy consumption has increased tremendously due to increase in the computation requirements of the user workload. Thus, saving energy has become an important concern to address. Researchers proposed different techniques to optimize the energy consumption. In this paper, we focus on different aspects of cloud computing for holistic management of cloud resources in an energy-efficient, reliable and sustainable manner. We recognized different opportunities, identified research challenges and propose possible future research directions for cloud computing.

关键词： Servers Cloud computing Energy efficiency Energy consumption Data centers Processor scheduling Dynamic scheduling

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2nd Context-aware, Autonomous and Smart Architectures International Workshop (CASA@ECSA 2018) 12

2nd Context-aware, Autonomous and Smart Architectures Intern...

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12th European Conference on Software Architecture, ECSA 2018

作者： Raibulet, C. Bures, T. Drira, K. Fugini, M.G. Rodrigues, G. Nunes Pelliccione, P. Università degli Studi di Milano- Bicocca DISCo-Dipartimento di Informatica Sistemistica e Comunicazione Viale Sarca Milan336 Italy Charles University Department of Distributed and Dependable Systems Malostranske namesti 25 Praha 1 Czech Republic LAAS-CNRS Université de Toulouse CNRS ToulouseF-31400 France Politecnico di Milano Department of Electronics Information and Bioengineering Leonardo da Vinci 32 Milan Italy University of Brasilia Computer Science Department Brazil Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering Gothenburg Sweden

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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SPECTRE: Supporting consumption policies in window-based parallel complex event processing

arXiv

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arXiv 2017年

作者： Mayer, Ruben Slo, Ahmad Tariq, Muhammad Adnan Rothermel, Kurt Gräber, Manuel Ramachandran, Umakishore Institute of Parallel and Distributed Systems University of Stuttgart Department of Computer Science Fast - National University of Computer and Emerging Sciences Islamabad Pakistan College of Computing Georgia Institute of Technology United States

distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of complex situations in the surrounding world from basic events like sensor readings. In doing so, DCEP operators detect event patterns on their incoming event streams. To yield high operator throughput, data parallelization frameworks divide the incoming event streams of an operator into overlapping windows that are processed in parallel by a number of operator instances. In doing so, the basic assumption is that the different windows can be processed independently from each other. However, consumption policies enforce that events can only be part of one pattern instance;then, they are consumed, i.e., removed from further pattern detection. That implies that the constituent events of a pattern instance detected in one window are excluded from all other windows as well, which breaks the data parallelism between different windows. In this paper, we tackle this problem by means of speculation: Based on the likelihood of an event's consumption in a window, subsequent windows may speculatively suppress that event. We propose the SPECTRE framework for speculative processing of multiple dependent windows in parallel. Our evaluations show an up to linear scalability of SPECTRE with the number of CPU cores. Copyright © 2017, The Authors. All rights reserved.

关键词： Data handling

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Author Correction: The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics

npj biodiversity

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npj biodiversity 2024年第1期3卷 31页

作者： Ann M Mc Cartney Giulio Formenti Alice Mouton Diego De Panis Luísa S Marins Henrique G Leitão Genevieve Diedericks Joseph Kirangwa Marco Morselli Judit Salces-Ortiz Nuria Escudero Alessio Iannucci Chiara Natali Hannes Svardal Rosa Fernández Tim De Pooter Geert Joris Mojca Strazisar Jonathan M D Wood Katie E Herron Ole Seehausen Phillip C Watts Felix Shaw Robert P Davey Alice Minotto José M Fernández Astrid Böhne Carla Alegria Tyler Alioto Paulo C Alves Isabel R Amorim Jean-Marc Aury Niclas Backstrom Petr Baldrian Laima Baltrunaite Endre Barta Bertrand BedHom Caroline Belser Johannes Bergsten Laurie Bertrand Helena Bilandija Mahesh Binzer-Panchal Iliana Bista Mark Blaxter Paulo A V Borges Guilherme Borges Dias Mirte Bosse Tom Brown Rémy Bruggmann Elena Buena-Atienza Josephine Burgin Elena Buzan Alessia Cariani Nicolas Casadei Matteo Chiara Sergio Chozas Fedor Čiampor Angelica Crottini Corinne Cruaud Fernando Cruz Love Dalen Alessio De Biase Javier Del Campo Teo Delic Alice B Dennis Martijn F L Derks Maria Angela Diroma Mihajla Djan Simone Duprat Klara Eleftheriadi Philine G D Feulner Jean-François Flot Giobbe Forni Bruno Fosso Pascal Fournier Christine Fournier-Chambrillon Toni Gabaldon Shilpa Garg Carmela Gissi Luca Giupponi Jessica Gomez-Garrido Josefa González Miguel L Grilo Björn Grüning Thomas Guerin Nadege Guiglielmoni Marta Gut Marcel P Haesler Christoph Hahn Balint Halpern Peter W Harrison Julia Heintz Maris Hindrikson Jacob Höglund Kerstin Howe Graham M Hughes Benjamin Istace Mark J Cock Franc Janžekovič Zophonias O Jonsson Sagane Joye-Dind Janne J Koskimäki Boris Krystufek Justyna Kubacka Heiner Kuhl Szilvia Kusza Karine Labadie Meri Lähteenaro Henrik Lantz Anton Lavrinienko Lucas Leclère Ricardo Jorge Lopes Ole Madsen Ghislaine Magdelenat Giulia Magoga Tereza Manousaki Tapio Mappes Joao Pedro Marques Gemma I Martinez Redondo Florian Maumus Shane A McCarthy Hendrik-Jan Megens Jose Melo-Ferreira Sofia L Mendes Matteo Montagna Joao Moreno Mai-Britt Mosbech Mónica Moura Zuzana Musilova Eugene Myers Will J N Genomics Institute University of California Santa Cruz CA USA. anmmccar@ucsc.edu. The Vertebrate Genome Laboratory The Rockefeller University New York NY USA. Department of Biology University of Florence Sesto Fiorentino Italy. InBios-Conservation Genetics Laboratory University of Liege Liege Belgium. Leibniz Institut für Zoo und Wildtierforschung Berlin Germany. Berlin Center for Genomics in Biodiversity Research Berlin Germany. Department of Biology University of Antwerp Antwerp Belgium. Institute of Zoology University of Cologne Cologne Germany. Department of Chemistry Life Sciences and Environmental Sustainability University of Parma Parma Italy. Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra) Barcelona Spain. Naturalis Biodiversity Center Leiden The Netherlands. Neuromics Support Facility VIB Center for Molecular Neurology VIB Antwerp Belgium. Neuromics Support Facility Department of Biomedical Sciences University of Antwerp Antwerp Belgium. Tree of Life Wellcome Sanger Institute Hinxton Cambridge UK. School of Biology and Environmental Science University College Dublin Belfield Ireland. Aquatic Ecology & Evolution Institute of Ecology & Evolution University of Bern Bern Switzerland. Department of Fish Ecology & Evolution Eawag Kastanienbaum Switzerland. Department of Biological and Environmental Science University of Jyvaskyla Jyvaskyla Finland. The Earlham Institute Norwich Research Park Norwich UK. Digital Science London UK. Barcelona Supercomputing Center Spanish National Bioinformatics Institute ELIXIR Spain Getafe Spain. Leibniz Institute for the Analysis of Biodiversity Change Museum Koenig Bonn Bonn Germany. CE3C-Centre for Ecology Evolution and Environmental Changes & CHANGE-Global Change and Sustainability Institute Faculdade de Ciências Universidade de Lisboa Campo Grande Lisboa Portugal. Centro Nacional de Análisis Genómico (CNAG) Barcelona Spain. Universitat de Barcelona (UB) Barcelona Spain. CIBIO C

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A Geospatial Orchestration Framework on Cloud for Processing User Queries

A Geospatial Orchestration Framework on Cloud for Processing...

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IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

作者： Jaydeep Das Arindam Dasgupta Soumya K. Ghosh Rajkumar Buyya Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) The University of Melbourne Australia

ISBN: (纸本)9781509045747

The demand for computing resources to process the geographical information (GI) queries has been increased drastically. The query helps the users to get the variety of information to serve their needs. Resolving the spatial queries, huge number of heterogeneous data sources along with different computing services are involved. Getting appropriate results within a specific time bound, orchestration among those data sources and web services are required. These services are available on the web and require different resource specifications in order to resolve a geospatial query. A cloud infrastructure has been utilized for scalable resource allocation. An orchestration engine has been developed to access the geospatial resources according to query requirement. In this paper, we have proposed and developed geographical data query processing framework which orchestrates spatial services according to user query in cloud environment. The empirical experimentation shows the efficiency of the proposed framework to resolve spatial queries in timely manner.

关键词： Geospatial analysis Cloud computing Engines Spatial databases Roads Spatial resolution

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Assessing the benefits of search-based approaches when designing self-adaptive systems: a controlled experiment

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Journal of Software Engineering Research and Development 2015年第1期3卷 1-27页

作者： Sandro S Andrade Raimundo J de A Macêdo Distributed Systems Laboratory (LaSiD) Federal University of Bahia (UFBa) Institute of Mathematics Department of Computer Science Salvador-BA Brazil GSORT Distributed Systems Group Federal Institute of Education Science and Technology of Bahia (IFBa) Department of Computer Science Salvador-BA Brazil

The well-orchestrated use of distilled experience, domain-specific knowledge, and well-informed trade-off decisions is imperative if we are to design effective architectures for complex software-intensive systems. In particular, designing modern self-adaptive systems requires intricate decision-making over a remarkably complex problem space and a vast array of solution mechanisms. Nowadays, a large number of approaches tackle the issue of endowing software systems with self-adaptive behavior from different perspectives and under diverse assumptions, making it harder for architects to make judicious decisions about design alternatives and quality attributes trade-offs. It has currently been claimed that search-based software design approaches may improve the quality of resulting artifacts and the productivity of design processes, as a consequence of promoting a more comprehensive and systematic representation of design knowledge and preventing design bias and false intuition. To the best of our knowledge, no empirical studies have been performed to provide sound evidence of such claim in the self-adaptive systems domain. This paper reports the results of a quasi-experiment performed with 24 students of a graduate program in distributed and Ubiquitous Computing. The experiment evaluated the design of self-adaptive systems using a search-based approach proposed by us, in contrast to the use of a non-automated approach based on architectural styles catalogs. The goal was to investigate to which extent the adoption of search-based design approaches impacts on the effectiveness and complexity of resulting architectures. In addition, we also analyzed the approach’s potential for leveraging the acquisition of distilled design knowledge. Our findings show that search-based approaches can improve the effectiveness of resulting self-adaptive systems architectures and reduce their design complexity. We found no evidence regarding the approach’s potential for leveraging the acqu

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Two-dimensional euler PCA for face recognition 21

Two-dimensional euler PCA for face recognition

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Tan, Huibin Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional PCA suffers from following deficiencies: 1) it spends much computational costs to handle high-dimensional data, and 2) it cannot reveal the nonlinear relationship among different features of data. To overcome these deficiencies, this paper proposes an efficient two-dimensional Euler PCA (2D-ePCA) algorithm. Particularly, 2D-ePCA learns projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. Since such 2D complex representation induces much smaller kernel matrix and principal subspaces, 2D-ePCA costs much less computational overheads than Euler PCA on large-scale dataset. Experimental results on popular face datasets show that 2D-ePCA outperforms the representative algorithms in terms of accuracy, computational overhead, and robustness. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

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