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On cognitive preferences and the plausibility of rule-based models

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arXiv 2018年

作者： Fürnkranz, Johannes Kliegr, Tomáš Paulheim, Heiko TU Darmstadt Department of Computer Science Hochschulstraße 10 DarmstadtD-64289 Germany University of Economics Prague Department of Information and Knowledge Engineering Nám Winstona Churchilla 4 Prague13067 Czech Republic University of Mannheim Institut für Informatik und Wirtschaftsinformatik MannheimD-68159 Germany

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that-all other things being equal-longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowdsourcing study based on about 3,000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility. Copyright © 2018, The Authors. All rights reserved.

关键词： Data mining

Human Action Recognition with Skeleton Data Using Extreme Learning Machine

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Human Action Recognition with Skeleton Data Using Extreme Le...

2017中国智能自动化会议(CIAC 2017)

作者： Ying Li Xiong Luo Weiping Wang Wenbing Zhao School of Computer and Communication Engineering University of Science and Technology Beijing(USTB) Beijing Key Laboratory of Knowledge Engineering for Materials Science Department of Electrical Engineering and Computer Science Cleveland State University

1 Introduction Action recognition is a popular research topic all the time due to its wide range of *** with 2D image data,the human skeleton data is robust to sophisticated environment and provides more meaningful skeletal *** the superiority of skeleton data,in this paper the recognition algorithm is studied through the use of the 3D coordinates of skeleton joints.

关键词： Action frame Human Action Recognition with Skeleton Data Using Extreme Learning Machine

Mist Data: Leveraging Mist Computing for Secure and Scalable Architecture for Smart and Connected Health

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Procedia computer science 2018年 125卷 647-653页

作者： Rabindra K. Barik Amaresh Chandra Dubey Ankita Tripathi T. Pratik Sapna Sasane Rakesh K. Lenka Harishchandra Dubey Kunal Mankodiya Vinay Kumar School of Computer Applications KIIT University Odisha India CDAC Knowledge Park Bengaluru Karnataka 560038 India John Deere India Pvt. Ltd. Pune 411013 India Department of Computer Science and Engineering IIIT Bhubaneswar India Department of Geography Savitribai Phule Pune University India Center for Robust Speech Systems University of Texas at Dallas Richardson TX-75080 USA Wearable Biosensing Lab University of Rhode Island Kingston RI-02881 USA Department of ECE Visvesvaraya National Institute of Technology Nagpur India

The smart health paradigms employ Internet-connected wearables for tele-monitoring, diagnosis providing inexpensive healthcare solutions. Mist computing reduces latency and increases throughput by processing data near the edge of the network. In the present paper, we proposed a secure mist Computing architecture that is validated on recently released public geospatial health dataset. Results and discussion support the efficacy of proposed architecture for smart geospatial health applications. The present research paper proposed SoA-Mist i.e. a three-tier secure framework for efficient management of geospatial health data with the use of mist devices. It proposed the security aspects in client layer, mist layer, fog layer and cloud layer. It has defined the prototype development by using win-win spiral model with use case and sequence diagram. Overlay analysis has been performed with the developed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014 in mobile clients as test case. Finally, It concludes with the comparison analysis of cloud based framework and proposed SoA-Mist framework.

关键词： Mist Computing Fog Computing Secure Architecture Smart Health Big Data Analytics

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Abstracts

JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

Surgical data science-from concepts toward clinical translation

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arXiv 2020年

作者： Maier-Hein, Lena Eisenmann, Matthias Sarikaya, Duygu März, Keno Collins, Toby Malpani, Anand Fallert, Johannes Feussner, Hubertus Giannarou, Stamatia Mascagni, Pietro Nakawala, Hirenkumar Park, Adrian Pugh, Carla Stoyanov, Danail Vedula, Swaroop S. Cleary, Kevin Fichtinger, Gabor Forestier, Germain Gibaud, Bernard Grantcharov, Teodor Hashizume, Makoto Heckmann-Nötzel, Doreen Kenngott, Hannes G. Kikinis, Ron Mündermann, Lars Navab, Nassir Onogur, Sinan Sznitman, Raphael Taylor, Russell H. Tizabi, Minu D. Wagner, Martin Hager, Gregory D. Neumuth, Thomas Padoy, Nicolas Collins, Justin Gockel, Ines Goedeke, Jan Hashimoto, Daniel A. Joyeux, Luc Lam, Kyle Leff, Daniel R. Madani, Amin Marcus, Hani J. Meireles, Ozanan Seitel, Alexander Teber, Dogu Ückert, Frank Müller-Stich, Beat P. Jannin, Pierre Speidel, Stefanie Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey LTSI Inserm UMR 1099 University of Rennes 1 Rennes France IRCAD Strasbourg France The Malone Center for Engineering in Healthcare The Johns Hopkins University BaltimoreMD United States KARL STORZ SE and Co. KG Tuttlingen Germany Department of Surgery Klinikum Rechts der Isar Technical University of Munich Munich Germany The Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom ICube University of Strasbourg CNRS France IHU Strasbourg Strasbourg France Altair Robotics Lab University of Verona Verona Italy Department of Surgery Anne Arundel Health System AnnapolisMD United States Johns Hopkins University School of Medicine BaltimoreMD United States Department of Surgery Stanford University School of Medicine StanfordCA United States Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany The Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital WashingtonDC United States The Perk Lab Queen's University KingstonON Canada University of Haute-Alsace Mulhouse France Faculty of Information Technology Monash University ClaytonVIC Australia University of Toronto TorontoON Canada The Li Ka Shing Knowledge Institute of St. Michael's Hospital TorontoON Canada Kyushu University Fukuoka Japan Kitakyushu Koga Hospital Fukuoka Japan Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Computer Science The Johns Hopkins University Baltim

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process. © 2020, CC BY.

关键词： Deep learning

Hybrid tourism recommendation system based on functionality/accessibility levels 15th

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Hybrid tourism recommendation system based on functionality/...

15th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2017

作者： Santos, Filipe Almeida, Ana Martins, Constantino de Oliveira, Paulo Moura Gonçalves, Ramiro Computer Science Department Institute of Engineering Polytechnic of Porto Porto Portugal GECAD Knowledge and Decision Support Research Group Porto Portugal INESC TEC Trás-os-Montes e Alto Douro University Vila Real Portugal

ISBN: (纸本)9783319615776

This paper describes a proposal to develop a Tourism Recommendation System based in Users and Points-of-Interest (POI) functionality/accessibility levels. The focus is to evaluate if user’s physical and psychological functionality levels can perform an important role in recommendation results accuracy. This work also aims to show the importance of POI classification (accessibility levels are related with each POI ability to receive tourists with certain levels of physical and psychological issues), through the definition of a different model regarding their accessibility and other characteristics. © Springer International Publishing AG 2018.

关键词： Recommender systems

It’s Time to Consider ‘Time’ when Evaluating Recommender-System Algorithms [Proposal]

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arXiv 2017年

作者： Beel, Joeran Trinity College Dublin Department of Computer Science and Statistics Knowledge and Data Engineering Group ADAPT Centre Ireland

In this position paper, we question the current practice of calculating evaluation metrics for recommender systems as single numbers (e.g. precision p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express only average effectiveness over usually a rather long period (e.g. a year or even longer). This practice provides only a vague and static view of the data. We propose that recommender-system researchers should instead calculate metrics for time-series such as weeks or months, and plot the results in e.g. a line chart. This way, results show how algorithms’ effectiveness develops over time, and hence the results allow drawing more meaningful conclusions about how an algorithm will perform in the future. In this paper, we explain our reasoning, provide an example to illustrate our reasoning and present suggestions for what the community should do next. Copyright © 2017, The Authors. All rights reserved.

关键词： Recommender systems

Temporal Pattern Mining from Evolving Networks

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arXiv 2017年

作者： Impedovo, Angelo Loglisci, Corrado Ceci, Michelangelo University of Bari Aldo Moro Department of Computer Science Knowledge Discovery and Data Engineering Laboratory Bari70125 Italy

A Web Based Decision Support System for Intermodal Port and Dry Port Facilities

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Journal of Transportation Technologies 2017年第1期7卷 36-49页

作者： Chrysostomos D. Stylios George Georgoulas Petros Karvelis Branislav Opacic Fabrizio Simeoni Srecko Maksimovic Walter Gerreto Labratory of Knowledge and Intelligent Computing Department of Computer Engineering Technological Educational Insti-tute of Epirus Arta Greece Teorema Engineering Srl. Area Science Park Basovizza Trieste Italy

This paper presents a Web-based Decision Support System (Web-DSS) that was designed and developed to support and provide suggestions on the procedures taking place between a port and a dry port, which have to collaborate, work concurrently and optimize their joint operation. The system operates at the highest hierarchical level supervising a number of different components dealing with three different time scale horizons so as to provide assistance at operational, tactical and strategic level. The Web-based DSS coordinates and integrates the subsystems operating at lower levels and it interfaces with all the involved actors: customers, suppliers, relevant authorities so as to receive all the necessary information to come up with “optimal” suggestions and decisions. In this paper, the overall architecture is presented and the individual modules are described.

关键词： Decision Support System Modeling Simulation Optimization Intermodal Systems Operational Research