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arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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“You Are Doing so Great!” – The Effect of a Robot’s Interaction Style on Self-Efficacy in HRI

“You Are Doing so Great!” – The Effect of a Robot’s Inte...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Setareh Zafari Isabel Schwaninger Matthias Hirschmanner Christina Schmidbauer Astrid Weiss Sabine T. Koeszegi Institute of Management Science TU Wien Vienna Austria Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Institute of Automation and Control TU Wien Vienna Austria

ISBN: (纸本)9781728126234

People form mental models about robots' behavior and intention as they interact with them. The aim of this paper is to evaluate the effect of different interaction styles on self-efficacy in human-robot interaction (HRI), people's perception of the robot, and task engagement. We conducted a user study in which a social robot assists people verbally while building a house of cards. Data from our experimental study revealed that people engaged longer in the task while interacting with a robot that provides person related feedback than with a robot that gives no person or task related feedback. Moreover, people interacting with a robot with a person-oriented interaction style reported a higher self-efficacy in HRI, perceived higher agreeableness of the robot and found the interaction less frustrating, as compared to a robot with a task-oriented interaction style. This suggests that a robot's interaction style can be considered as a key factor for increasing people's perceived self-efficacy in HRI, which is essential for establishing trust and enabling human-robot collaboration.

关键词： Robot EIF2AK1 gene Persons Self Efficacy RXFP2 gene Use Studies Models, Mental

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Optical music recognition in mensural notation with region-based convolutional neural networks 19

Optical music recognition in mensural notation with region-b...

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19th International Society for Music Information Retrieval Conference, ISMIR 2018

作者： Pacha, Alexander Calvo-Zaragoza, Jorge Institute of Visual Computing and Human-Centered Technology TU Wien Austria PRHLT Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9782954035123

In this work, we present an approach for the task of optical music recognition (OMR) using deep neural networks. Our intention is to simultaneously detect and categorize musical symbols in handwritten scores, written in mensural notation. We propose the use of region-based convolutional neural networks, which are trained in an end-to-end fashion for that purpose. Additionally, we make use of a convolutional neural network that predicts the relative position of a detected symbol within the staff, so that we cover the entire image-processing part of the OMR pipeline. This strategy is evaluated over a set of 60 ancient scores in mensural notation, with more than 15000 annotated symbols belonging to 32 different classes. The results reflect the feasibility and capability of this approach, with a weighted mean average precision of around 76% for symbol detection, and over 98% accuracy for predicting the position. © Alexander Pacha, Jorge Calvo-Zaragoza.

关键词： Deep neural networks

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Partial Face Images Classification Using Geometrical Features 21st

Partial Face Images Classification Using Geometrical Feature...

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21st International Multi-Conference on Advanced Computer Systems, ACS 2018

作者： Milczarski, Piotr Stawska, Zofia Dowdall, Shane Faculty of Physics and Applied Informatics University of Lodz Pomorska Str. 149/153 Lodz Poland Department of Visual and Human Centred Computing Dundalk Institute of Technology Dundalk Co. Louth Ireland

ISBN: (纸本)9783030033132

In the paper, we have focused on the problem of choosing the best set of features in the task of gender classification/recognition. Choosing a minimum set of features, that can give satisfactory results, is important in the case where only a part of the face is visible. Then, the minimum set of features can simplify the classification process to make it useful in video analysis, surveillance video analysis as well as for IoT and mobile applications. We propose four partial view areas and show that the classification accuracy is lower by maximum 5% than in using full view ones and we compare the results using 5 different classifiers (SVM, 3NN, C4.5, NN, Random Forrest) and 2 test sets of images. That is why the proposed areas might be used while classifying or recognizing veiled or partially hidden faces. © 2019, Springer Nature Switzerland AG.

关键词： Support vector machines

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EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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Transdisciplinary reflections on social robotics in academia and beyond 3

Transdisciplinary reflections on social robotics in academia...

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3rd International Conference on Robophilosophy 2018 / Transor 2018: Envisioning Robots in Society - Politics, Power, and Public Space

作者： Hannibal, Glenda Lindner, Felix Vienna University of Technology Institute of Visual Computing and Human-Centered Technology Research Division of Human Computer Interaction Argentinierstraße 8 Vienna1040 Austria Department of Computer Science University of Freiburg Germany

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A hybrid assessment approach for medical device software development companies

A hybrid assessment approach for medical device software dev...

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作者： Özcan-Top, Özden McCaffery, Fergal Department of Visual and Human-Centred Computing Dundalk Institute of Technology Dundalk Ireland

Medical device software development organizations are bound by regulatory requirements and constraints to ensure that developed medical devices will not harm patients. Medical devices have to be treated as complete systems and be evaluated in this manner. Instead of manufacturers having to ensure compliance to various regulatory standards individually, the authors previously developed a medical device software process assessment framework called MDevSPICE® that integrates the regulatory requirements from all the relevant medical device software standards. The MDevSPICE® was developed in a manner that suits plan-driven software development. To improve the usability of MDevSPICE® in agile settings, we extended the assessment approach. The hybrid assessment approach described here combines the MDevSPICE®-based process assessment method with steps for prioritization of improvement needs through value stream mapping and enabling process improvement through the use of KATA technique. This approach integrates agile methods into the medical device software development process while adhering to the requirements of the regulatory standards. This paper describes the implementation of the approach within 4 organizations that develop software in line with medical device regulations. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Software design

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Know Your Enemy: Identifying Quality Problems of Time Series Data

Know Your Enemy: Identifying Quality Problems of Time Series...

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Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Theresia Gschwandtner Oliver Erhart Institute of Visual Computing and Human-Centered Technology TU Wien

Sensible data analysis requires data quality control. An essential part of this is data profiling, which is the identification and assessment of data quality problems as a prerequisite for adequately handling these problems. Differentiating between actual quality problems and unusual, but valid data values requires the "human-in-the-loop" through the use of visual analytics. Unfortunately, existing approaches for data profiling do not adequately support the special characteristics of time, which is imperative to identify quality problems in time series data - a data type prevalent in a multitude of disciplines. In this design study paper, we outline the design, implementation, and evaluation of "Know Your Enemy" (KYE) - a visual analytics approach to assess the quality of time series data. KYE supports the task of data profiling with (1) predefined data quality checks, (2) user-definable, customized quality checks, (3) interactive visualization to explore and reason about automatically detected problems, and (4) the visual identification of hidden quality problems.

关键词： Data integrity Time series analysis Data visualization Task analysis Prototypes Bars Taxonomy

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CRED: Credibility-Enabled Social Network Based Q&A System for Assessing Answers Correctness

CRED: Credibility-Enabled Social Network Based Q&A System fo...

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IEEE Conference on Wireless Communications and Networking

作者： Imad Ali Ronald Y. Chang Cheng-Hsin Hsu Taiwan International Graduate Program in Social Networks and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

In a question & answer (Q& A) system, credible users provide answers of higher correctness. However, in a distributed social network based Q& A (SNQ& A) system, an asker does not know a k-hop answerer's credibility, thus making it difficult for the asker to assess the answer correctness. Therefore, a credibility-enabled distributed SNQ& A system is crucial for determining the correctness of the answers. To this end, we propose CRED, a credibility-enabled distributed SNQ& A system, which facilitates each user to assess the correctness of the provided answers. CRED utilizes subjective logic to build interestwise friend-to-friend credibility opinions under uncertainties. The developed opinions are then accumulated by CRED to get each user's aggregated credibility opinion, which may reflect the user's real credibility. CRED forwards a question to users with highest credibility beliefs in the question interest category. Our evaluation results show that, on average, CRED accomplishes higher success ratio, higher answer correctness, and lower answer uncertainty by 12.1%, 16.4%, and 22.2%, respectively, as compared to the best-performing baseline systems.

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LOCI: A Mobile Q&A System with Multimodal Motivation Scheme for Local Intent Questions in Dynamic Social Networks

LOCI: A Mobile Q&A System with Multimodal Motivation Scheme ...

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IEEE Conference on Vehicular technology (VTC)

作者： Imad Ali Ronald Y. Chang Cheng-Hsin Hsu Chi-Han Lee Taiwan International Graduate Program in Social Networks and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Recent research has shown that people are frequently searching for information with local intent. The existing systems fail to answer this class of questions as they are not mainly designed to handle it. In this paper, we propose LOCI, a mobile question & answer system with a multimodal motivation scheme for local intent questions where users provide timely and accurate answers in a dynamic social network. LOCI mines users' shared and contextual information to identify the most relevant users for each question. The proposed multimodal motivation scheme exploits users' social ties and monetary rewards to raise their basic motivation levels. LOCI assigns questions to the best answerers via our three proposed algorithms: (i) LOCI-RT, which immediately assigns newly arrived questions/users to available users/questions, (ii) LOCI-LO, which uses short-time batches for locally optimal questions-users assignment, and (iii) LOCI-AD, which assigns short- and long-deadline questions immediately and via batching, respectively. We conduct: (i) a survey to support our assumptions regarding the proposed multimodal motivation scheme, and (ii) trace-driven experiments to evaluate the performance of the three proposed LOCI's algorithms.

关键词： Social networking (online) Resource management Heuristic algorithms Real-time systems Computer science Search engines Servers

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