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Dangerous HRI: Testing Real-World Robots has Real-World Consequences 14

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Dangerous HRI: Testing Real-World Robots has Real-World Cons...

14th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2019

作者： Robinette, Paul Novitzky, Michael Duncan, Brittany Jeon, Myounghoon Wagner, Alan Park, Chung Hyuk Dept. of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA United States Dept. of Computer Science and Engineering University of Nebraska Lincoln LincolnNE United States Dept. of Industrial and Systems Engineering Virginia Tech BlacksburgVA United States Dept. of Aerospace Engineering Pennsylvania State University State CollegePA United States Dept. of Biomedical Engineering George Washington University WashingtonDC United States

ISBN: (纸本)9781538685556

Robotic rescuers digging through rubble, fire-fighting drones flying over populated areas, robotic servers pouring hot coffee for you, and a nursing robot checking your vitals are all examples of current or near-future situations where humans and robots are expected to interact in a dangerous situation. Dangerous HRI is an as-yet understudied area of the field. We define dangerous HRI as situations where humans experience some amount of risk of bodily harm while interacting with robots. This interaction could take many forms, such as a bystander (e.g. when an autonomous car waits at a crossing for a pedestrian), as a recipient of robotic assistance (rescue robots), or as a teammate (like an autonomous robot working with a SWAT team). To facilitate better study of this area, the Dangerous HRI workshop brings together researchers who perform experiments with some risk of bodily harm to participants and discuss strategies for mitigating this risk while still maintaining validity of the experiment. This workshop does not aim to tackle the general problem of human safety around robots, but instead focused on guidelines for and experience from experimenters. © 2019 IEEE.

关键词： Human robot interaction

Combining Photometric Features and Relative Position to Detect and Track Target Person

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Combining Photometric Features and Relative Position to Dete...

International Conference on computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Bima Sena Bayu Dewantara Jun Miura Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Computer Science and Engineering Toyohashi University of Technology Toyohashi Japan

ISBN: (纸本)9781728129662

Tracking a target person is a vital job in human-robot interaction. The robot must always notice a particular person as the interaction target partner. However, it is sometimes very hard to distinguish the target person because there are many other persons around the target. In this paper, we propose a target person detection and tracking system by combining person's frontal photometric features such as face and clothing color, and coordinate of the person's location in the real world. We apply an illumination invariant face recognition method named OptiFuzz. Hue-Saturation histogram (HS-histogram) is used to obtain the clothing color feature, and a location of the person is acquired from a calibrated single camera view. All these features are then fed into an algorithm of Naive Bayes to discriminate between the target person and others. Our experimental results indicate a successful outcome as it is always possible to detect and track the target person.

关键词： human-robot interaction target partner illumination invariant face recognition HS-histogram Naive Bayes Persons Human machine interaction Targets Photomechanics relative positioning

Location histogram privacy by sensitive location hiding and target histogram avoidance/resemblance (extended version)

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arXiv 2019年

作者： Loukides, Grigorios Theodorakopoulos, George Dept. of Informatics King’s College London United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom

A location histogram is comprised of the number of times a user has visited locations as they move in an area of interest, and it is often obtained from the user in the context of applications such as recommendation and advertising. However, a location histogram that leaves a user’s computer or device may threaten privacy when it contains visits to locations that the user does not want to disclose (sensitive locations), or when it can be used to profile the user in a way that leads to price discrimination and unsolicited advertising (e.g. as "wealthy" or "minority member"). Our work introduces two privacy notions to protect a location histogram from these threats: sensitive location hiding, which aims at concealing all visits to sensitive locations, and target avoidance/resemblance, which aims at concealing the similarity/dissimilarity of the user’s histogram to a target histogram that corresponds to an undesired/desired profile. We formulate an optimization problem around each notion: Sensitive Location Hiding (SLH), which seeks to construct a histogram that is as similar as possible to the user’s histogram but associates all visits with nonsensitive locations, and Target Avoidance/Resemblance (TA/TR), which seeks to construct a histogram that is as dissimilar/similar as possible to a given target histogram but remains useful for getting a good response from the application that analyzes the histogram. We develop an optimal algorithm for each notion, which operates on a notion-specific search space graph and finds a shortest or longest path in the graph that corresponds to a solution histogram. In addition, we develop a greedy heuristic for the TA/TR problem, which operates directly on a user’s histogram. Our experiments demonstrate that all algorithms are effective at preserving the distribution of locations in a histogram and the quality of location recommendation. They also demonstrate that the heuristic produces near-optimal solutions while being orders of magn

关键词： Location based services

Generating a Gray code for prefix normal words in amortized polylogarithmic time per word

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arXiv 2020年

作者： Burcsi, Péter Fici, Gabriele Lipták, Zsuzsanna Raman, Rajeev Sawada, Joe Dept. of Computer Algebra Eötvös Loránd University Budapest Hungary Dip. di Matematica e Informatica Università degli Studi di Palermo Italy Dip. di Informatica Università degli Studi di Verona Italy Dept. of Informatics University of Leicester United Kingdom School of Computer Science University of Guelph Canada

A prefix normal word is a binary word with the property that no substring has more 1s than the prefix of the same length. By proving that the set of prefix normal words is a bubble language, we can exhaustively list all prefix normal words of length n as a combinatorial Gray code, where successive strings differ by at most two swaps or bit flips. This Gray code can be generated in O(log2 n) amortized time per word, while the best generation algorithm hitherto has O(n) running time per word. We also present a membership tester for prefix normal words, as well as a novel characterization of bubble languages. Copyright © 2020, The Authors. All rights reserved.

关键词： Pattern matching

Efficient reflection string analysis via graph coloring 32

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Efficient reflection string analysis via graph coloring

32nd European Conference on Object-Oriented Programming, ECOOP 2018

作者： Grech, Neville Kastrinis, George Smaragdakis, Yannis Dept. of Informatics and Telecommunications University of Athens Greece Dept. of Computer Science University of Malta Malta Malta

ISBN: (纸本)9783959770798

Static analyses for reflection and other dynamic language features have recently increased in number and advanced in sophistication. Most such analyses rely on a whole-program model of the flow of strings, through the stack and heap. We show that this global modeling of strings remains a major bottleneck of static analyses and propose a compact encoding, in order to battle unnecessary complexity. In our encoding, strings are maximally merged if they can never serve to differentiate class members in reflection operations. We formulate the problem as an instance of graph coloring and propose a fast polynomial-time algorithm that exploits the unique features of the setting (esp. large cliques, leading to hundreds of colors for realistic programs). The encoding is applied to two different frameworks for string-guided Java reflection analysis from past literature and leads to significant optimization (e.g., a ∼ 2x reduction in the number of string-flow inferences), for a whole-program points-to analysis that uses strings. © Neville Grech, George Kastrinis, and Yannis Smaragdakis.

关键词： Static analysis

Interaction laws of monads and comonads

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arXiv 2019年

作者： Katsumata, Shinya Rivas, Exequiel Uustalu, Tarmo National Institute of Informatics Tokyo Japan Inria Paris France Dept. of Computer Science Reykjavik University Dept. of Software Science Tallinn University of Technology

We introduce and study functor-functor and monad-comonad interaction laws as mathematical objects to describe interaction of effectful computations with behaviors of effect-performing machines. Monad-comonad interaction laws are monoid objects of the monoidal category of functorfunctor interaction laws. We show that, for suitable generalizations of the concepts of dual and Sweedler dual, the greatest functor resp. monad interacting with a given functor or comonad is its dual while the greatest comonad interacting with a given monad is its Sweedler dual. We relate monad-comonad interaction laws to stateful runners. We show that functor-functor interaction laws are Chu spaces over the category of endofunctors taken with the Day convolution monoidal structure. Hasegawa's glueing endows the category of these Chu spaces with a monoidal structure whose monoid objects are monad-comonad interaction *** Codes 18C50, 68Q55 Copyright © 2019, The Authors. All rights reserved.

关键词： Algebra

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

Applications of deep learning techniques for automated multiple Sclerosis detection using magnetic resonance imaging: A review

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arXiv 2021年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Moridian, Parisa Rezaei, Mitra Alizadehsani, Roohallah Khozeimeh, Fahime Gorriz, Juan Manuel Heras, Jónathan Panahiazar, Maryam Nahavandi, Saeid Acharya, U. Rajendra K. N. Toosi University of Technology Tehran Iran Faculty of Engineering Mashhad Branch Islamic Azad University Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Electrical and Computer Engineering Dept. Tarbiat Modares University Tehran Iran Deakin University Geelong Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom Department of Mathematics and Computer Science University of La Rioja La Rioja Spain University of California San Francisco San FranciscoCA United States Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Dept. of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far;among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error;on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

2020 CATARACTS Semantic Segmentation Challenge

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arXiv 2021年

作者： Luengo, Imanol Grammatikopoulou, Maria Mohammadi, Rahim Walsh, Chris Nwoye, Chinedu Innocent Alapatt, Deepak Padoy, Nicolas Ni, Zhen-Liang Fan, Chen-Chen Bian, Gui-Bin Hou, Zeng-Guang Ha, Heonjin Wang, Jiacheng Wang, Haojie Guo, Dong Wang, Lu Wang, Guotai Islam, Mobarakol Giddwani, Bharat Hongliang, Ren Pissas, Theodoros Ravasio, Claudio Huber, Martin Birch, Jeremy Nunez Do Rio, Joan M. da Cruz, Lyndon Bergeles, Christos Chen, Hongyu Jia, Fucang Tomar, Nikhil Kumar Jha, Debesh Riegler, Michael A. Halvorsen, Pal Bano, Sophia Vaghela, Uddhav Hong, Jianyuan Ye, Haili Huang, Feihong Wang, Da-Han Stoyanov, Danail Digital Surgery Ltd Medtronic Ireland ICube University of Strasbourg CNRS IHU Strasbourg France Institute of Automation Chinese Academy of Sciences China Hutom Seoul Korea Republic of Department of Computer Science Xiamen University Xiamen China University of Electronic Science and Technology of China Chengdu China Dept. of Biomedical Engineering National University of Singapore Singapore Dept. of Electronics and Telecoms. Engineering National Institute of Technology Raipur India School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom University College London London United Kingdom Institute of Ophthalmology University College London London United Kingdom Moorfields Eye Hospital London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academyof Sciences China SimulaMet UiT The Arctic University of Norway Oslo Metropolitan University Norway Department of Computer Science University College London United Kingdom School of Computer and Information Engineering Xiamen University of Technology China

Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmentation Challenge, which was a sub-challenge of the 2020 MICCAI Endoscopic Vision (EndoVis) Challenge, presented three sub-tasks to assess participating solutions on anatomical structure and instrument segmentation. Their performance was assessed on a hidden test set of 531 images from 10 videos of the CATARACTS test set. © 2021, CC BY-NC-ND.

关键词： Semantics