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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Minh Nhat Vu Jongwoo Lee Yonghwan Oh Department of Human-Computer Interaction and Robotics University of Science and Technology (UST) Korea Center for Robotics Research Korea Institute of Science and Technology (KIST) Seoul Korea

In this paper, we present a new control strategy for the Bipedal Trunk Spring Loaded Inverted Pendulum (BTSLIP) model, consisting of springy legs and rigid trunk. Our walking controller is inspired from the Virtual Pendulum (VP) concept. In this concept, by intersecting the ground reaction forces (GRF) to a virtual pivot point (VPP), the trunk is provided the restoring moment to keep its balance and to behave like a virtual hanging pendulum. The simple hip torque control was presented with the name the VPP method. However, this approach is not reliable in terms of rejecting disturbances. Thus, we propose a new control strategy to achieve the robustness and stabilize the walking gait. Herein, the control is introduced with the combination of two strategies, 1) updating the position of the VPP by using Discrete Linear Quadratic Regulator controller and 2) controlling the stiffness of springy legs by feedback linearization controller to track both reference vertical position and velocity of the Center of Mass (CoM). We present the dynamic simulation of the BTSLIP model under the presence of external disturbances. The performance of the proposed control strategy shows stable, robust walking which is compared with the VPP method.

关键词： Legged locomotion Hip Force Trajectory Springs Torso Load modeling

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Analyzing hand therapy success in a web-based therapy system 22

Analyzing hand therapy success in a web-based therapy system

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22nd International Workshop on Intelligent and Personalized human-computer interaction, ABIS 2016

作者： Elnaggar, Ahmed Reichardt, Dirk Intelligent Interaction Lab Computer Science Department DHBW Stuttgart Germany

After an injury, hand therapy should help regaining the full functionality of the hand, wrist and fingers. The application we developed helps a therapist in tracking the success of the therapy. Using the Leap Motion device as a basis, we calculate the individual finger joint angles. A second subsystem measures the angles between the fingers. This data is used to derive a report on the fingers‘adduction and abduction during the therapy. The third subsystem detects the wrist rotation angles. Three algorithms were designed to detect the patient hand data, which the system feeds it to the three subsystems, the final algorithm max average error is 5.6 degrees, while the average max error is 9.3 degrees. The web-based system is part of a therapy support system, which allows patients to do their exercise at home and record their individual success. © 2016 Proceedings ABIS 2016 - 22nd International Workshop on Intelligent and Personalized human-computer interaction. All rights reserved.

关键词： Patient treatment

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An ensemble model with ranking for social dialogue

arXiv

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arXiv 2017年

作者： Papaioannou, Ioannis Curry, Amanda Cercas Part Igor Shalyminov, Jose L. Xu, Xinnuo Dušek, Yanchao Yu Ondřej Rieser, Verena Lemon, Oliver Interaction Lab Department of Computer Science Heriot-Watt University EdinburghEH14 4AS United Kingdom

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition. Copyright © 2017, The Authors. All rights reserved.

关键词： Real time systems

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Exploring interaction modalities and task allocation for household robotic arms

Exploring interaction modalities and task allocation for hou...

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34th Annual CHI Conference on human Factors in Computing Systems, CHI EA 2016

作者： Herr, Sascha Gross, Tom Gradmann, Michael Henrich, Dominik Human-Computer Interaction Group University of Bamberg Germany Department for Applied Computer Science III University of Bayreuth Germany

ISBN: (纸本)9781450340823

human-robot interaction in private households demands easy and natural interfaces, as well as an understanding of areas of application. Two fundamental issues are multimodal interaction (i.e., multifarious ways of communication with the robot) and task allocation (i.e., division of labour between the user and the robot). We report on an explorative interview study that gathered users' requirements for multimodal interaction and gained understanding for task allocation in private households. © 2016 Authors.

关键词： human robot interaction

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The role of artificial intelligence in achieving the Sustainable Development Goals

arXiv

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arXiv 2019年

作者： Vinuesa, Ricardo Azizpour, Hossein Leite, Iolanda Balaam, Madeline Dignum, Virginia Domisch, Sami Felländer, Anna Langhans, Simone Tegmark, Max Nerini, Francesco Fuso Linné FLOW Centre KTH Mechanics StockholmSE-100 44 Sweden School of Electrical Engineering and Computer Science KTH Royal Institute Of Technology Stockholm Sweden Division of Robotics Perception and Learning School of EECS KTH Royal Institute Of Technology Stockholm Sweden Division of Media Technology and Interaction Design KTH Royal Institute of Technology Lindstedtsvägen 3 Stockholm Sweden Responsible AI Lab Umeå University UmeåSE-90358 Sweden Leibniz-Institute of Freshwater Ecology and Inland Fisheries Müggelseedamm 310 Berlin12587 Germany Division of Media Technology and Interaction Design KTH Royal Institute of Technology Lindstedtsvägen 3 Stockholm Sweden Leioa Spain Department of Zoology University of Otago 340 Great King Street Dunedin9016 New Zealand Center for Brains Minds & Machines Massachusetts Institute of Technology CambridgeMA02139 United States KTH Royal Institute of Technology Brinellvagen 68 StockholmSE-100 44 Sweden

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors across the society requires an assessment of its effect on sustainable development. Here we analyze published evidence of positive or negative impacts of AI on the achievement of each of the 17 goals and 169 targets of the 2030 Agenda for Sustainable Development. We find that AI can support the achievement of 128 targets across all SDGs, but it may also inhibit 58 targets. Notably, AI enables new technologies that improve efficiency and productivity, but it may also lead to increased inequalities among and within countries, thus hindering the achievement of the 2030 Agenda. The fast development of AI needs to be supported by appropriate policy and regulation. Otherwise, it would lead to gaps in transparency, accountability, safety and ethical standards of AI-based technology, which could be detrimental towards the development and sustainable use of AI. Finally, there is a lack of research assessing the medium- and long-term impacts of AI. It is therefore essential to reinforce the global debate regarding the use of AI and to develop the necessary regulatory insight and oversight for AI-based technologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Planning

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Sequence matters, but how exactly? A method for evaluating activity sequences from data 9

Sequence matters, but how exactly? A method for evaluating a...

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9th International Conference on Educational Data Mining, EDM 2016

作者： Doroudi, Shayan Holstein, Kenneth Aleven, Vincent Brunskill, Emma Computer Science Department Carnegie Mellon University United States Human-Computer Interaction Institute Carnegie Mellon University United States

How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint Violation Analysis (SCOVA): a general method for evaluating alternative activity sequences using existing data. SCOVA can be used to explore many complex sequencing constraints, such as prerequisite relationships, blocking, interleaving, and spiraling. We demonstrate SCOVA on data collected from a fractions intelligent tutoring system (ITS). Some of our findings challenge our initial hypotheses regarding sequencing, illustrating the utility and versatility of the method. The method can also be applied to other learning environments, as long as the available data has substantial variability in students’ activity sequences. © 2016 International Educational Data Mining Society. All rights reserved.

关键词： Students

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Sequence matters, but how do I discover how? Towards a workflow for evaluating activity sequences from data

Sequence matters, but how do I discover how? Towards a workf...

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2016 EDM Workshops and Tutorials, WT-EDM 2016

How should a wide variety of educational activities be sequenced in order to maximize student learning? We recently proposed the Sequencing Constraint Violation Analysis (SCOVA) method to help address this question. In this paper, we propose how SCOVA could be transformed into a workflow in LearnSphere so that other researchers and practitioners can find answers to the aforementioned question in their own datasets. We hope that such a workflow will lead to more and better research into this important question, as well as interesting new findings for both the educational data mining and learning sciences communities. © 2016, CEUR-WS. All rights reserved.

关键词： Data mining

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences

arXiv

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arXiv 2025年

作者： Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernández-Díaz, Raúl Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C.E. Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece 1Department of Biomedical Sciences University of Padova Padova35121 Italy Department of Biomedical Sciences University of Padova Via Ugo Bassi 58/B Padova35131 Italy Athena Research & Innovation Center Aigialias & Chalepa Marousi15125 Greece VIB Data Core VIB.AI Center for AI & Computational Biology Technologiepark-Zwijnaarde 75 Ghent9052 Belgium ELIXIR Europe Hub EMBL-EBI South Building Wellcome Genome HinxtonCB10 1SD United Kingdom Plaça Eusebi Güell 1-3 Barcelona08034 Spain Department of Pharmacy and Biotechnology University of Bologna Bologna40126 Italy Computational Genomics Platform IRCCS University Hospital of Bologna Bologna40138 Italy ZB MED Information Centre for Life Sciences Gleueler Straße 60 Cologne50931 Germany SequenceAnalysis.co.uk United Kingdom University of East Anglia Research Park NorwichNR4 7TJ United Kingdom Department of Computational Biology University of Lausanne Genopode 2024.3 Lausanne1015 Switzerland Swiss Institute of Bioinformatics Amphipôle Quartier UNIL-Sorge Lausanne1015 Switzerland Institute for Fundamental Biomedical Science Biomedical Sciences Research Center Alexander Fleming Vari Greece Department of Informatics & Telematics School of Digital Technology Harokopio University Athens Greece School of Medicine University College Dublin Belfield Dublin 4 Ireland Conway Institute of Biomolecular and Biomedical Research University College Dublin Belfield Dublin 4 Ireland IBM Research Dublin DublinD15 HN66 Ireland Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki57001 Greece Department of Biology National & Kapodistrian University of Athens Athens15772 Greece Department of Computer Science University of Manchester Oxford Road ManchesterM19 3PL United Kingdom Luxembourg National Data Service 6 avenue des Hauts-Fourneaux Esch-sur-AlzetteL-4362 Luxembourg Insti

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI *** Codes 92 © 2025, CC BY-NC-ND.

关键词： Reusability

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Confidence measures for CTC-based phone synchronous decoding

Confidence measures for CTC-based phone synchronous decoding

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhehuai Chen Yimeng Zhuang Kai Yu Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781509041183

Connectionist Temporal Classification (CTC) model has achieved state-of-the-art LVCSR performance. However, due to the introduction of the blank symbol, word-level confidence measures (CM) based on CTC model can not be easily calculated by directly using the traditional phone posterior normalization or confusion network (CN) approaches. Recently, a phone synchronous decoding (PSD) framework has been proposed for efficient decoding with CTC model. By automatically ignoring blank frames, PSD decoding not only achieves significant speed-up, but also yields highly compact and precise CTC phone lattices. In this work, two CM generation approaches on top of the PSD CTC lattice are proposed. Detailed investigation is also carried out to demonstrate the effectiveness of PSD CTC lattice. Experiments on an English switchboard LVCSR task showed that the performance of the proposed PSD CTC lattice based CM can significantly outperform the CM based on traditional frame synchronous decoding with CTC or HMM models.

关键词： CTC Confidence measure PSD CTC lattice Power spectral density PSD gene PREVENTION OF SIGNIFICANT DETERIORATION confidence measure Meltdown confidence Telephone DECODING Blanks crystal lattices

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