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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Rosenthal, Stephanie Selvaraj, Sai P. Veloso, Manuela Software Engineering Institute United States Robotics Institute United States Computer Science Department Carnegie Mellon University Pittsburgh United States

Autonomous mobile robots navigate in our spaces by planning and executing routes to destinations. When a mobile robot appears at a location, there is no clear way to understand what navigational path the robot planned and experienced just by looking at it. In this work, we address the generation of narrations of autonomous mobile robot navigation experiences. We contribute the concept of verbalization as a parallel to the well-studied concept of visualization. Through verbalizations, robots can describe through language what they experience, in particular in their paths. For every executed path, we consider many possible verbalizations that could be generated. We introduce the verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans. We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. We believe our verbalization space and algorithm are applicable to different narrative aspects for many mobile robots, including autonomous cars.

关键词： Mobile robots

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On the quantitative analysis of sparse RBMs 17th

On the quantitative analysis of sparse RBMs

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Zhang, Yanxia Yang, Lu Meng, Binghao Cheng, Hong Zhang, Yong Wang, Qian Zhu, Jiadan School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu611731 China Department of Mathematics and Information Engineering Chongqing University of Education Chongqing400067 China School of Automation Engineering Center for Robotics University of Electronic Science and Technology of China Chengdu611731 China Ricoh Software Research Center of Beijing Beijing100044 China

ISBN: (纸本)9783319488899

With the development of deep neural networks, the model of restricted Boltzmann machine(RBM) has gradually become one of the essential aspects in deep learning researches. Because of the presence of the partition function, it is intractable to get the model selection, control the complexity, and learn an exact maximum likelihood in RBM model. A kind of effective measure is approximate inference that adopts annealing importance sampling(AIS) scheme only to evaluate a RBM’s performance. At present, there is little quantitative analysis on discrepancies generated by different RBM models. So we focus on the innovation research on some quantitative evaluation of the generalization performance of all kinds of sparse RBM models, including the classical sparse RBM(SpRBM) and the log sum sparse RBM(LogSumRBM). We discuss the influence and efficiency of the AIS strategy for these sparse RBMs’ estimations. Particularly, we confirm that the LogSumRBM is the optimal model in RBM and sparse RBMs for its smaller deviations in the assessment results regardless of the training MNIST data and the test, which provides a guarantee on some theories and experience in the choice of the deep learning models in the future. © Springer International Publishing AG 2016.

关键词： Importance sampling

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Enhancing Human Understanding of a Mobile Robot's State and Actions using Expressive Lights

Enhancing Human Understanding of a Mobile Robot's State and ...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： Kim Baraka Stephanie Rosenthal Manuela Veloso Robotics Institute Carnegie Mellon University Pittsburgh PA USA Software Engineering Institute Carnegie Mellon University United States Computer Science Department Carnegie Mellon University United States

ISBN: (纸本)9781509039302

In order to be successfully integrated into human-populated environments, mobile robots need to express relevant information about their state to the outside world. In particular, animated lights are a promising way to express hidden robot state information such that it is visible at a distance. In this work, we present an online study to evaluate the effect of robot communication through expressive lights on people's understanding of the robot's state and actions. In our study, we use the CoBot mobile service robot with our light interface, designed to express relevant robot information to humans. We evaluate three designed light animations on three corresponding scenarios for each, for a total of nine scenarios. Our results suggest that expressive lights can play a significant role in helping people accurately hypothesize about a mobile robot's state and actions from afar when minimal contextual clues are present. We conclude that lights could be generally used as an effective non-verbal communication modality for mobile robots in the absence of, or as a complement to, other modalities.

关键词： Robots Animation Color Videos Navigation Mobile communication Speech Robot Mobile robots Animation Light mobile communication Videotapes navigation Speech Action Afar

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Large bridge cable tension estimation with Cyber vibration Sensing System

Large bridge cable tension estimation with Cyber vibration S...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Shaodong Zhan Meng Chen Zhi Li Winston Sun Wen J. Li Guanglie Zhang Shenzhen Academy of Robotics Institute of Intelligent Cyber Sensing Systems China Department of Mechanical and Biomedical Engineering City University of Hong Kong Hong Kong SAR College of Computer Science & Software Engineering Shenzhen University China

ISBN: (纸本)9781509027347

We present in this paper our development of a cyber physical sensing platform for monitoring the structural tension of large bridge cables using MEMS vibration sensors and proprietary wireless technology. In order to implement the online tension analysis of the bridge cables, we developed an improved technology for the general identification of cable tension using the Gauss-Newton method to determine the eigenfrequency order and bending stiffness. The improved identification method was applied to vertical suspender cables, i.e., more commonly known as “hangers”, of the south section of the Huangpu Bridge. The result shows that the proposed method can effectively estimate the cable tension of the bridge's cables of different lengths.

关键词： Bridges Vibrations Sensors Force Bridge circuits Monitoring Acceleration

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Anti‐Adhesive Coatings: Lubricin Antiadhesive Coatings Exhibit Size‐Selective Transport Properties that Inhibit Biofouling of Electrode Surfaces with Minimal Loss in Electrochemical Activity (Adv. Mater. Interfaces 7/2018)

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Advanced Materials Interfaces 2018年第7期5卷

作者： George W. Greene Vanessa Ortiz Cristina Pozo‐Gonzalo Simon E. Moulton Xiaoen Wang Lisandra L. Martin Agnes Michalczky Patrick C. Howlett Institute for Frontier Materials and ARC Centre of Excellence for Electromaterials Science Deakin University 75 Pigdons Road Geelong VIC 3216 Australia Institut Charles Gerhardt Montpellier UMR 5253 CNRS UM Université Montpellier Place E. Bataillon 34 095 Montpellier France School of Software and Electrical Engineering and Department of Telecommunications Electrical Robotics and Biomedical Engineering Swinburne University John St Hawthorn VIC 3122 Australia School of Chemistry Monash University Wellington Rd Clayton 3800 Australia School of Life and Environmental Science Deakin University 221 Burwood Highway Burwood 3125 Australia

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Comparing models for modeling subjective and objective measures for two task types

Comparing models for modeling subjective and objective measu...

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IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)

作者： Stephanie Lackey Brandon Sollins Lauren Reinerman-Jones Service Robotics Department Institut Supérieur d'Electronique et du Numérique (ISEN) Lille France Computer and Software Eng. Department Politehnica University of Timisoara Romania E&A (Energy & Automation) Faculty of Engineering Technology Gent Belgium

Adaptive automation (AA) has emerged as a viable solution to improving human performance in complex environments. However, understanding when to prompt, pause, and terminate AA remains unclear. Augmenting the user with physiological sensors offers new insight into the user's state, and thus, offers insight into when and how to implement AA. The research presented investigates the efficacy of prediction algorithms for modeling physiological and subjective data in AA environments. A comparison of traditional and emerging modeling methods results in recommendations for algorithm selection, generalizability, and risks of over fitting data are provided.

关键词： Classification algorithms Prediction algorithms Biomedical monitoring Logistics Linear regression Algorithm design and analysis Static VAr compensators

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New Hierarchical Approach for Microaneurysms Detection with Matched Filter and Machine Learning

New Hierarchical Approach for Microaneurysms Detection with ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Jiayi Wu Jingmin Xin Lai Hong Jane You Nanning Zheng the Institute of Artificial Intelligence and Robotics Xi'an Jiaotong UniversityXi'an 710049 China the School of Software Engineering Shenzhen Institute of Information Technology Shenzhen GuangDong China the Department of Computing The Hong Kong Polytechnic University KLN Hong Kong China

ISBN: (纸本)9781424492695

Microaneurysms are regarded as the first signs of diabetic retinopathy (DR), but the microaneurysms are not clear in the color retinal images, and many researches were studied to detect and locate these lesions. In this paper, a new hierarchical computing-aided diagnosis approach is proposed for the microaneurysms detection by using the multi-scale and multi-orientation sum of matched filter (MMMF) and machine learning, where 37 dimensional features are extracted from each candidate. Furthermore, several classifiers such as the k-nearest neighbor (kNN), local linear discrimination analysis (LLDA) and support vector machine (SVM) are modified to distinguish the true microaneurysms from the false ones, which is a typical unbalanced classification problem. The effectiveness of the proposed method is verified through the training set of a publicly available database, and the experiment results show that the proposed method has better detection performance including the receiver operating characteristic (ROC) curve and the free-response receiver operating characteristic (FROC) curve. Moreover, the proposed method with 37 dimensional features outperforms that with other features and has a sensitivity from 1/8 to 8 with the average of all seven points being 0.286 tested on the same database.

关键词： DR MMMF FROC Charcot-Bouchard Aneurysm matched filters DR Support Vector Network Artificial Intelligence Machine Learning multi-scale

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1st International Workshop on software engineering for Smart Cyber-Physical Systems (SEsCPS 2015) 15

1st International Workshop on Software Engineering for Smart...

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International Conference on software engineering (ICSE)

作者： Tomas Bures Denny Weyns Mark Klein Rodolfo E. Haber Department of Distributed and Dependable Systems Charles University Prague Czech Republic Software Engineering Institute Carnegie Mellon University Pittsburgh USA AdaptWise Lineaus University Växjö Sweden Center for Automation and Robotics (CAR) UPM-CSIC Madrid Spain

Cyber-physical system (CPS) have been recognized as a top-priority in research and development. The innovations sought for CPS demand them to deal effectively with dynamicity of their environment, to be scalable, adaptive, tolerant to threats, etc. -- i.e. they have to be smart. Although approaches in software engineering (SE) exist that individually meet these demands, their synergy to address the challenges of smart CPS (sCPS) in a holistic manner remains an open challenge. The workshop focuses on software engineering challenges for sCPS. The goals are to increase the understanding of problems of SE for sCPS, study foundational principles for engineering sCPS, and identify promising SE solutions for sCPS. Based on these goals, the workshop aims to formulate a research agenda for SE of sCPS.

关键词： Conferences software engineering Cyber-physical systems software Adaptation models Committees Technological innovation

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