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Smart World Congress (SWC), IEEE

作者： Ke Liu Jiankai Tang Zhang Jiang Yuntao Wang Xiaojing Liu Dong Li Yuanchun Shi Department of Computer Technology and Applications Qinghai University Qinghai National Key Laboratory of Human Factors Engineering Beijing Department of Computer Science and Technology Tsinghua University Beijing

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO 2 predictions by $\mathbf{7. 6 \%}$ and $\mathbf{1 0. 6 \%}$ compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and $\mathbf{2. 5 \%}$ for SpO 2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation by $\mathbf{1 7. 8 \%}$.

关键词： Training Heart rate Fingers Estimation Photoplethysmography Synchronization Biomedical monitoring Monitoring Faces Blood

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Can I Trust You? Exploring the Impact of Misleading AI Suggestions on User's Trust

Can I Trust You? Exploring the Impact of Misleading AI Sugge...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Daniele Pretolesi Olivia Zechner Helmut Schrom-Feiertag Manfred Tscheligi Center for Technology Experience Austrian Institute of Technology Vienna Austria Department of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Department of Artificial Intelligence and Human Interfaces Human-Computer Interaction Division University of Salzburg Salzburg Austria

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

Extended Reality (XR) offers immersive and interactive training experiences across fields like policing and medicine. However, tailoring training difficulty to individual needs remains a challenge. AI-driven training assistants could be a promising solution to personalize XR training by proposing adjustments based on user data. Yet, the effectiveness of such suggestions and the user's ability to discern accurate recommendations are not fully understood. This study investigates the effectiveness of suggestions within an AI-driven interface for proposing scenario changes in an XR training environment. We employ a $2\times 2$ experimental design, manipulating both the presence of stress during training (stressful versus neutral scenarios) and the biosignal information provided by the trainees during training (congruent versus incongruent). This work aims to determine whether trainers can reject misleading suggestions made by the system and how this affects their trust in the system. A sample of N=17 medical first responder trainers was recruited from three European countries. Our results show that the majority of the participants did not reject incorrect suggestions and the expected decrease in trust and perceived transparency towards the misleading interface was not observed. These results underscore the complex interplay between user perception, trust, and technological interface design, highlighting avenues for further research and refinement in the development of AI-driven suggestion systems.

关键词： Training Extended reality Decision making Neural engineering Metrology Reliability Artificial intelligence Usability Stress Optimization

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Investigating fairness in machine learning-based audio sentiment analysis

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AI and Ethics 2024年第2期5卷 1099-1108页

作者： Luitel, Sophina Liu, Yang Anwar, Mohd Human-Centered AI (HCAI) Lab Department of Computer Science North Carolina Agricultural and Technical State University Greensboro USA Quantum Computing Research Center <QC|RC> Department of Computer Science North Carolina Agricultural and Technical State University Greensboro USA

Audio sentiment analysis is a growing area of research, however little attention has been paid to the fairness of machine learning models in this field. Whilst the current literature covers research on machine learning models’ reliability and fairness in various demographic groups, fairness in audio sentiment analysis with respect to gender is still an uninvestigated field. To fill this knowledge gap, we conducted experiments aimed at assessing the fairness of machine learning algorithms concerning gender within the context of audio sentiment analysis. In this research, we used 442 audio files of happiness and sadness—representing equal samples of male and female subjects—and generated spectrograms for each file. Then we performed feature extraction using bag-of-visual-words method followed by building classifiers using Random Forest, Support Vector Machines, and K-nearest Neighbors algorithms. We investigated whether the machine learning models for audio sentiment analysis are fair across female and male genders. We found the need for gender-specific models for audio sentiment analysis instead of a gender-agnostic-model. Our results provided three pieces of evidence to back up our claim that gender-specific models demonstrate bias in terms of overall accuracy equality when tested using audio samples representing the other gender, as well as combination of both genders. Furthermore, gender-agnostic-model performs poorly in comparison to gender-specific models in classifying sentiments of both male and female audio samples. These findings emphasize the importance of employing an appropriate gender-specific model for an audio sentiment analysis task to ensure fairness and accuracy. The best performance is achieved when using a female-model (78% accuracy) and a male-model (74% accuracy), significantly outperforming the 66% accuracy of the gender-agnostic model.

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Episodic Future Thinking as Digital Micro-interventions 25th

Episodic Future Thinking as Digital Micro-interventions

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25th International Conference on human-computer interaction, HCII 2023

作者： Persson, Dan Roland Yoo, Soojeong Bardram, Jakob E. Skinner, Timothy C. Bækgaard, Per Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark University College London Interaction Centre London United Kingdom Department of Health Technology Technical University of Denmark Kongens Lyngby Denmark Institute of Psychology University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031480379

Micro-interventions are quick focused behavioural interventions aimed at matching users’ current capacity for engagement. Mobile devices are powerful, interactive, and sensor-rich platforms for delivering micro-interventions and for determining when and where to do so. This paper presents a novel smart-phone based approach to micro-interventions based on established ‘Episodic Future Thinking (EFT)’ research. The paper both presents the background for EFT-based micro-interventions and the design of an ‘EFT’ smartphone application implementing this. The approach and technology were evaluated in a feasibility study including 14 participants using the system for 14 days. Results demonstrate the feasibility of implementing EFT as micro-interventions, with participants willing to use the application, providing positive feedback and constructive suggestions. The paper concludes with a thorough discussion of the implications for smartphone-based EFT delivered as micro-interventions and how these can be used as part of a larger behaviour change and health improvement initiatives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： mHealth

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Exploring Assumptions about Sustainability: Towards a Constructive Framework for Action in Sustainable HCI 25

Exploring Assumptions about Sustainability: Towards a Constr...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Laurell Thorslund, Minna Leifler, Ola Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Department of Computer and Information Science Linköping University Linköping Sweden

ISBN: (纸本)9798400713941

The global environmental crises continue to get worse, fast approaching various irreversible thresholds. While a vast array of approaches to solving sustainability problems are found under the umbrella of Sustainable HCI, their contributions are sometimes hard to compare. In this essay, we describe a set of assumptions that influence what is considered meaningful and important areas of sustainability research, along four dimensions of sustainability: 1) the depth and nature of the sustainability challenges;2) the role of technological innovation in sustainability;3) what gets defined as "externalities"to a design or system;and 4) the time perspective used to consider sustainability. We argue that what one assumes within each of these dimensions directly influences what one means by the term "sustainability", which is then reflected in the questions that are asked, the methods chosen, the proposed solutions and the developed systems. By describing these assumptions and some of their commensurate actions, we offer a framework that may enable members of the SHCI community to reflect on and better position their own work and that of others in the field. Our intention is for the framework to lead to better transparency and more constructive conversations about where we might collectively direct our efforts moving forward. © 2025 Copyright held by the owner/author(s).

关键词： climate change predicament sustainability theories of change

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Semantic-Preserving Adversarial Code Comprehension 29

Semantic-Preserving Adversarial Code Comprehension

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Yiyang Wu, Hongqiu Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Based on the tremendous success of pre-trained language models (PrLMs) for source code comprehension tasks, current literature studies either ways to further improve the performance (generalization) of PrLMs, or their robustness against adversarial attacks. However, they have to compromise on the trade-off between the two aspects and none of them consider improving both sides in an effective and practical way. To fill this gap, we propose Semantic-Preserving Adversarial Code Embeddings (SPACE) to find the worst-case semantic-preserving attacks while forcing the model to predict the correct labels under these worst cases. Experiments and analysis demonstrate that SPACE can stay robust against state-of-the-art attacks while boosting the performance of PrLMs for code © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Poster: Extracting and Annotating Mental Health Forum Corpus: A Comprehensive Validation Pipeline

Poster: Extracting and Annotating Mental Health Forum Corpus...

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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Rohith Sundar Jonnalagadda Abm Adnan Azmee Dinesh Attota Md Abdullah Al Hafiz Khan Yong Pei Monica Nandan Department of Computer Science Data Science and Analytics Social Work and Human Services Kennesaw State University Marietta USA

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

This research emphasizes the essential role of mental health forums as vital online communities providing solace, support, and resources for individuals grappling with mental health issues, especially among young people. Acknowledging the presence of severe content in some posts, indicative of acute distress and potential self-harm risk, the study draws on prior research highlighting the forums' critical role in fulfilling lower-level support needs for young individuals. By employing advanced classification and summarization techniques, namely Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM) and BERT, the project aims to enhance the efficiency of these forums through systematic categorization and summarization of user posts. Preliminary results show promising outcomes, with improved post-classification accuracy ranging from 40% to 83.33% and average Rouge F1 scores ranging from 43% to 54%. This research contributes to fortifying the role of mental health forums in providing essential support to young individuals in distress.

关键词： Systematics Accuracy Pipelines Mental health Bidirectional control Distance measurement Encoding

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GFG - Gender-Fair Generation: A CALAMITA Challenge 10

GFG - Gender-Fair Generation: A CALAMITA Challenge

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10th Italian Conference on Computational Linguistics, CLiC-it 2024

作者： Frenda, Simona Piergentili, Andrea Savoldi, Beatrice Madeddu, Marco Rosola, Martina Casola, Silvia Ferrando, Chiara Patti, Viviana Negri, Matteo Bentivogli, Luisa Interaction Lab Heriot-Watt University Edinburgh United Kingdom Aequa-Tech Turin Italy Fondazione Bruno Kessler Trento Italy University of Trento Trento Italy Computer Science Department University of Turin Turin Italy Universitat de Barcelona Barcelona Spain MaiNLP MCML LMU Munich Germany

Gender-fair language aims at promoting gender equality by using terms and expressions that include all identities and avoid reinforcing gender stereotypes. Implementing gender-fair strategies is particularly challenging in heavily gender-marked languages, such as Italian. To address this, the Gender-Fair Generation challenge intends to help shift toward gender-fair language in written communication. The challenge, designed to assess and monitor the recognition and generation of gender-fair language in both mono- and cross-lingual scenarios, includes three tasks: (1) the detection of gendered expressions in Italian sentences, (2) the reformulation of gendered expressions into gender-fair alternatives, and (3) the generation of gender-fair language in automatic translation from English to Italian. The challenge relies on three different annotated datasets: the GFL-it corpus, which contains Italian texts extracted from administrative documents provided by the University of Brescia;GeNTE, a bilingual test set for gender-neutral rewriting and translation built upon a subset of the Europarl dataset;and Neo-GATE, a bilingual test set designed to assess the use of non-binary neomorphemes in Italian for both fair formulation and translation tasks. Finally, each task is evaluated with specific metrics: average of F1-score obtained by means of BERTScore computed on each entry of the datasets for task 1, an accuracy measured with a gender-neutral classifier, and a coverage-weighted accuracy for tasks 2 and 3. © 2024 CEUR-WS. All rights reserved.

关键词： Gender-fair language Generation Inclusive language Machine translation Neomorphemes Unfairness detection

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ArT: All-round Thinker for Unsupervised Commonsense Question Answering 29

ArT: All-round Thinker for Unsupervised Commonsense Question...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wang, Jiawei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Without labeled question-answer pairs for necessary training, unsupervised commonsense question-answering (QA) appears to be extremely challenging due to its indispensable unique prerequisite on commonsense source like knowledge bases (KBs), which are usually highly resource consuming in construction. Recently pre-trained language models (PLMs) show effectiveness as an alternative for commonsense clues when they play a role of knowledge generator. However, existing work either relies on large-scale in-domain or out-of-domain labeled data, or fails to generate knowledge of high quality in a general way. Motivated by human thinking experience, we propose an approach of All-round Thinker (ArT) by fully taking association during knowledge generating. In detail, our model first focuses on key parts in the given context, and then generates highly related knowledge on such a basis in an association way like human thinking. Besides, for causal reasoning, a reverse thinking mechanism is especially added to further enhance bidirectional inferring between cause and effect. ArT is totally unsupervised and KBs-free. We evaluate it on three commonsense QA benchmarks: COPA, SocialIQA and SCT. On all scales of PLM backbones, ArT shows its brilliant performance and outperforms previous advanced unsupervised models. Our code is available at https://***/WangJW424/ commonsenseQA-ArT. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Computational linguistics

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In Vivo Bioelectronic Nose Based on a Bioengineered Rat for the Explosive Odors with Benzene Ring Detection and Identification 24

In Vivo Bioelectronic Nose Based on a Bioengineered Rat for ...

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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Ma, Jing Wang, Yimeng Yang, Jun Sun, Biao Yang, Qing Zheng, Chenguang Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China College of Precision Instruments & Optoelectronics Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Accidents caused by explosives occur frequently, which seriously threaten people's lives and health, so it is urgent to develop new technologies for sensitively and rapidly detecting trace explosives. In this study, a bioengineered rat with the olfactory receptor (OR3) overexpression in the olfactory epithelium (OE) was successfully developed, and the unique features of three representative explosives (NT, DNT, and TNT) were conducted by recording electrophysiological-responsive signals from the olfactory bulb (OB) with implanted multichannel microelectrodes during awake-state-independent sniffing behavior induced by explosives and non-explosives. These three explosives were regarded as goal odors, while citral and air were no-goal odors. The analysis of local field potential (LFP) power changes revealed that, goal, and no-goals could be distinguished in both the vector group and the OR3 group at concentrations as low as 10-12 mol/L, and the classification performance of the OR3 group was superior to that of the vector group overall. Our findings suggest that OR3 bioengineered rats can reliably and accurately identify trace explosives, which provides a novel approach for detecting and identifying explosives with promising applications in odor discrimination in complex environments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic nose

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