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Transforming health systems with design health literacy: Presenting the 40-20-40 model for digital development

Computer Methods and Programs in Biomedicine Update 2023年 4卷

作者： Münter, Lars Drachmann, Danielle Ghanem, Mouna Prinzellner, Yvonne Smits, Carolien Werner, Katharina Bulsink, Vera Schwaninger, Isabel Van Velsen, Lex Faber, Nicolaj Holm The Danish Committee for Health Education Copenhagen Denmark Ketotic Hypoglycemia International Skanderborg Denmark Women for Peace Spain St. P.ltenlten University for Applied Science Austria Pharos Dutch Centre of Expertise on Health Disparities Utrecht Netherlands TU Wien (Vienna University of Technology) Institute of Visual Computing and Human-Centered Technology Vienna Austria Medich Spectrum Twente Twente Netherlands Digital Medicine Group Luxembourg Centre for Systems Biomedicine University of Luxembourg Luxembourg Luxembourg Nedap Healthcare Groenlo Netherlands Steno Diabetes Center Zealand Holbaek Denmark

Intro: Digital tools and services are becoming the standard for delivery of health care, especially hastened by the restrictions and needs during the COVID-19 pandemic. While early experiences with telemedicine have been a foundation for modern day digital tool development, the use of co-creation, user meta dialogue, and follow up services are often short and few. This represents a powerful potential for designing upcoming services for a multi-level platform. This requires, however, equity in digital health literacy, which is often not the case. Rather than seeing effect or impact as the outcome of the service itself, the value of including and referencing user expectations before and after the session holds an even stronger value;therefore we've explored and created a new co-design approach to digital development we call the 40-20-40 model. Results: Using the 40-20-40 approach we focus on early user communication and input as a part of the specific session or service design, a prologue-phase, that gathers vital input to align expectations. After the specific intervention-phase, we utilise the epilogue-phase as an extension of the intervention itself, an echo of the prologue, and a gathering of user outcomes. We believe the pro- and epilogue phases represent a total of 80% of the overall impact of our services. We also argue that digital developers and public health service providers would benefit from a stronger use of this design model to improve the quality of care and the use and impact of care services, in particular for patients with limited digital health literacy. © 2023 The Authors

关键词： Co-creation Digital health literacy Empowerment Health literacy Self-management

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AVTENet: Audio-visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection

arXiv

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arXiv 2023年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan The Department of Computer Science National Chengchi University Taipei11605 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Forged content shared widely on social media platforms is a major social problem that requires increased regulation and poses new challenges to the research community. The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilizes visual modality or audio modality. While there are some methods in the literature that exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multi-modal datasets of deepfake videos involving acoustic and visual manipulations. Moreover, these existing methods are mostly based on CNN and suffer from low detection accuracy. Inspired by the recent success of Transformer in various fields, to address the challenges posed by deepfake technology, in this paper, we propose an Audio-visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation to achieve effective video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multi-modal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that our best model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. © 2023, CC BY.

关键词： Statistical tests

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AI exposure predicts unemployment risk

arXiv

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arXiv 2023年

作者： Frank, Morgan R. Ahn, Yong-Yeol Moro, Esteban Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15216 United States Digital Economy Lab Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Connection Science Massachusetts Institute of Technology CambridgeMA United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Indiana University Network Science Institute Indiana University BloomingtonIN United States Department of Mathematics & GISC Universidad Carlos III de Madrid Leganes28911 Spain Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure. Interactive visualizations for this study are available at https://***/~mrfrank/uiRiskDemo/. © 2023, CC BY.

关键词： visualization

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Explaining Machine Learning Models for Age Classification in human Gait Analysis

arXiv

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arXiv 2022年

作者： Slijepcevic, Djordje Horst, Fabian Simak, Marvin Lapuschkin, Sebastian Raberger, Anna-Maria Samek, Wojciech Breiteneder, Christian Schöllhorn, Wolfgang Immanuel Zeppelzauer, Matthias Horsak, Brian Institute of Creative Media Technologies Department of Media & Digital Technologies St. Pölten University of Applied Sciences St. Pölten Austria Department of Training and Movement Science Institute of Sport Science Johannes Gutenberg-University Mainz Mainz Germany Department of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Berlin Germany Center for Digital Health & Social Innovation Department of Health Sciences St. Pölten University of Applied Sciences St. Pölten Austria Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria

Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing human understandable explanations for their predictions. This "black-box" behavior impedes the understanding of which input features the model predictions are based on. We investigated an Explainable Artificial Intelligence method, i.e., Layer-wise Relevance Propagation (LRP), for gait analysis data. The research question was: Which input features are used by ML models to classify age-related differences in walking patterns? We utilized a subset of the AIST Gait Database 2019 containing five bilateral ground reaction force (GRF) recordings per person during barefoot walking of healthy participants. Each input signal was min-max normalized before concatenation and fed into a Convolutional Neural Network (CNN). Participants were divided into three age groups: young (20-39 years), middle-aged (40-64 years), and older (65-79 years) adults. The classification accuracy and relevance scores (derived using LRP) were averaged over a stratified ten-fold cross-validation. The mean classification accuracy of 60.1% was clearly higher than the zero-rule baseline of 37.3%. The confusion matrix shows that the CNN distinguished younger and older adults well, but had difficulty modeling the middle-aged adults. © 2022, CC BY-NC-SA.

关键词： Gait analysis

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Investigation of a Single-Channel Frequency-Domain Speech Enhancement Network to Improve End-to-End Bengali Automatic Speech Recognition Under Unseen Noisy Conditions

Investigation of a Single-Channel Frequency-Domain Speech En...

引用

Oriental COCOSDA International Conference on Speech Database and Assessments

作者： Md Mahbub E Noor Yen-Ju Lu Syu-Siang Wang Supratip Ghose Chia-Yu Chang Ryandhimas E. Zezario Shafique Ahmed Wei-Ho Chung Yu Tsao Hsin-Min Wang Taiwan International Graduate Program in Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taiwan National Tsing Hua University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan University of Information Technology & Sciences Bangladesh

ISBN: (纸本)9781665408714

Due to the presence of distortion, most of the single-channel frequency-domain speech enhancement (SE) approaches are still challenging for downstream automatic speech recognition (ASR) tasks, even with satisfactory improvements in enhancing speech quality and intelligibility. Recently, transformer-based models have shown better performance in speech processing tasks. Therefore, we intend to explore a transformer-based SE model, which is fine-tuned through a two-stage training scheme. Pre-training is performed using a feature-level optimization criterion through SE loss, and then a pre-trained end-to-end ASR model is used to fine-tune the SE model using an ASR-oriented optimization criterion through SE and ASR losses. We evaluate the proposed approach on low-resourced Bengali language, which has not received as much attention as resource-rich English or Mandarin languages in both SE and ASR fields. Experimental results show that it can improve the performance of SE and ASR under severe unseen noisy conditions and its performance is reasonably good compared with other state-of-the-art SE methods.

关键词： Training Frequency-domain analysis Training data Speech enhancement Transformers Robustness Noise measurement

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LOCI: A Mobile QA System with Multimodal Motivation Scheme for Local Intent Questions in Dynamic Social Networks 91

LOCI: A Mobile QA System with Multimodal Motivation Scheme f...

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91st IEEE Vehicular technology Conference, VTC Spring 2020

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

ISBN: (纸本)9781728152073

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. © 2020 IEEE.

关键词： Motivation

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Deep learning-based similarity retrieval in clinical 3D gait analysis

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Gait & Posture 2021年 90卷 127-128页

作者： P. Krondorfer D. Slijepčević F. Unglaube A. Kranzl C. Breiteneder M. Zeppelzauer B. Horsak University of Applied Sciences Technikum Wien Institute of Life Science Engineering Vienna Austria St. Pölten University of Applied Sciences Institute of Creative∖Media/Technologies St. Pölten Austria Orthopaedic Hospital Vienna-Speising Laboratory of Gait and Movement Analysis Vienna Austria TU Wien Institute of Visual Computing and Human-Centered Technology Vienna Austria St. Pölten University of Applied Sciences Institute of Health Sciences St. Pölten Austria

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Using cultural probes in the sensitive research setting of informal caregiving. A case study

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i-com 2018年第2期17卷 103-117页

作者： Hensely-Schinkinger, Susanne Schorch, Martin Tellioglu, Hilda Multidisciplinary Design and User Research Institute of Visual Computing and Human-Centered Technology Vienna University of Technology Vienna Austria University of Siegen Siegen Germany

This case study report covers our experiences in using Cultural Probes during the frst phase of our European and interdisciplinary research project TOPIC (The Online Platform for Informal Caregivers). In that stage of our research, we focused on two major issues: frst, describing and analyzing the characteristics of the care and coordination work of elderly informal caregivers, and second, on frst implications for design for the feld of informal care. Although our general methodological approach was qualitative (ethnographic) with participant observation and interviewing, we included Cultural Probes as an additional method to ethnography for gaining insight information about the care practices by the means of selfobservation of and reflection by the informal caregivers. The paper describes our adaptation of the Cultural Probes approach, the similarities and differences to Gaver et al. [1999. Interactions. 6(1): 21-29], the items of our TOPIC Cultural Probes Kit in detail, and re-constructs the design process of one of the items (the actimoClock). Based on the experiences of our participants with the probes kit and our analyses of that use, we also present lessons learned, pros and cons for including that method in the sensitive setting of informal caregiving. © 2011-2019 by Walter de Gruyter GmbH.

关键词： Probes

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An investigation of optimal light source setups for photometric stereo reconstruction of historical coins

An investigation of optimal light source setups for photomet...

引用

2018 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2018

作者： Brenner, S. Zambanini, S. Sablatnig, R. Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Wien Austria

ISBN: (纸本)9783038680574

In this paper, we address the 3D reconstruction of historical coins by means of Photometric Stereo. We investigate the influence of the number and arrangement of lights to the reconstruction quality by comparing mean angular errors on 22 historical coin models. Our results demonstrate that 6 lights circularly placed at an optimal elevation angle do not show a significant loss of reconstruction quality compared to a full semispherical dome setup with 54 lights. This represents a considerable saving of acquisition time and system complexity when it comes to the mass digitization of historical coins. © 2018 GCH 2018 - Eurographics Workshop on Graphics and Cultural Heritage. All rights reserved.

关键词： Photometry

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Preface

Cognitive Technologies

引用

Cognitive Technologies 2021年 v页

作者： Hofkirchner, Wolfgang Kreowski, Hans-Jörg Vienna Austria Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Department of Computer Science University of Bremen Bremen Germany

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