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2. Transforming HCI Research Cycles using Generative AI and “Large Whatever Models” (LWMs)

Author

Elagroudy, Passant, primary, Li, Jie, additional, Väänänen, Kaisa, additional, Lukowicz, Paul, additional, Ishii, Hiroshi, additional, Mackay, Wendy E., additional, Churchill, Elizabeth F, additional, Peters, Anicia, additional, Oulasvirta, Antti, additional, Prada, Rui, additional, Diening, Alexandra, additional, Barbareschi, Giulia, additional, Gruenerbl, Agnes, additional, Kawaguchi, Midori, additional, El Ali, Abdallah, additional, Draxler, Fiona, additional, Welsch, Robin, additional, and Schmidt, Albrecht, additional

Published
2024
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14. Reflecting on Hybrid Events: Learning from a Year of Hybrid Experiences

Author

Ansah, Alberta A, primary, Vivacqua, Adriana S, additional, Zhong, Sailin, additional, Boll, Susanne, additional, Constantinides, Marios, additional, Verma, Himanshu, additional, El Ali, Abdallah, additional, Lushnikova, Alina, additional, Alavi, Hamed, additional, Rintel, Sean, additional, Kun, Andrew L, additional, Shaer, Orit, additional, Cox, Anna L, additional, Gerling, Kathrin, additional, Muller, Michael, additional, Rusnak, Vit, additional, Machado, Leticia Santos, additional, Kosch, Thomas, additional, Collective, Chiwork, additional, and Executive Committee, Sigchi, additional

Published
2023
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15. Group Synchrony for Emotion Recognition Using Physiological Signals.

Author

Bota, Patricia, Zhang, Tianyi, El Ali, Abdallah, Fred, Ana, da Silva, Hugo Placido, and Cesar, Pablo

Abstract

During group interactions, we react and modulate our emotions and behaviour to the group through phenomena including emotion contagion and physiological synchrony. Previous work on emotion recognition through video/image has shown that group context information improves the classification performance. However, when using physiological data, literature mostly focuses on intrapersonal models that leave-out group information, while interpersonal models are unexplored. This paper introduces a new interpersonal Weighted Group Synchrony approach, which relies on Electrodermal Activity (EDA) and Heart-Rate Variability (HRV). We perform an analysis of synchrony metrics applied across diverse data representations (EDA and HRV morphology and features, recurrence plot, spectrogram), to identify which metrics and modalities better characterise physiological synchrony for emotion recognition. We explored two datasets (AMIGOS and K-EmoCon), covering different group sizes (4 vs dyad) and group-based activities (video-watching vs conversation). The experimental results show that integrating group information improves arousal and valence classification, across all datasets, with the exception of K-EmoCon on valence. The proposed method was able to attain mean M-F1 of $\approx$ ≈ 72.15% arousal and 81.16% valence for AMIGOS, and M-F1 of $\approx$ ≈ 52.63% arousal, 65.09% valence for K-EmoCon, surpassing previous work results for K-EmoCon on arousal, and providing a new baseline on AMIGOS for long-videos. [ABSTRACT FROM AUTHOR]

Published
2023
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17. Weakly-Supervised Learning for Fine-Grained Emotion Recognition Using Physiological Signals.

Author

Zhang, Tianyi, El Ali, Abdallah, Wang, Chen, Hanjalic, Alan, and Cesar, Pablo

Abstract

Instead of predicting just one emotion for one activity (e.g., video watching), fine-grained emotion recognition enables more temporally precise recognition. Previous works on fine-grained emotion recognition require segment-by-segment, fine-grained emotion labels to train the recognition algorithm. However, experiments to collect these labels are costly and time-consuming compared with only collecting one emotion label after the user watched that stimulus (i.e., the post-stimuli emotion labels). To recognize emotions at a finer granularity level when trained with only post-stimuli labels, we propose an emotion recognition algorithm based on Deep Multiple Instance Learning (EDMIL) using physiological signals. EDMIL recognizes fine-grained valence and arousal (V-A) labels by identifying which instances represent the post-stimuli V-A annotated by users after watching the videos. Instead of fully-supervised training, the instances are weakly-supervised by the post-stimuli labels in the training stage. The V-A of instances are estimated by the instance gains, which indicate the probability of instances to predict the post-stimuli labels. We tested EDMIL on three different datasets, CASE, MERCA and CEAP-360VR, collected in three different environments: desktop, mobile and HMD-based Virtual Reality, respectively. Recognition results validated with the fine-grained V-A self-reports show that for subject-independent 3-class classification (high/neutral/low), EDMIL obtains promising recognition accuracies: 75.63% and 79.73% for V-A on CASE, 70.51% and 67.62% for V-A on MERCA and 65.04% and 67.05% for V-A on CEAP-360VR. Our ablation study shows that all components of EDMIL contribute to both the classification and regression tasks. Our experiments also show that (1) compared with fully-supervised learning, weakly-supervised learning can reduce the problem of overfitting caused by the temporal mismatch between fine-grained annotations and physiological signals, (2) instance segment lengths between 1-2 s result in the highest recognition accuracies and (3) EDMIL performs best if post-stimuli annotations consist of less than 30% or more than 60% of the entire video watching. [ABSTRACT FROM AUTHOR]

Published
2023
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21. A Story to Go, Please

Author

Nack, Frank, El Ali, Abdallah, van Kemenade, Philo, Overgoor, Jan, van der Weij, Bastiaan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Aylett, Ruth, editor, Lim, Mei Yii, editor, Louchart, Sandy, editor, Petta, Paolo, editor, and Riedl, Mark, editor

Published
2010
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22. Social Virtual Reality Avatar Biosignal Animations as Availability Status Indicators

Author

El Ali, Abdallah, Lee, Sueyoon, and César Garcia, Pablo Santiago

Subjects
availability status, avatars, social virtual reality, Biosignals
Abstract

In this position paper, we outline our research challenges in Affective Interactive Systems, and present recent work on visualizing avatar biosignals for social VR entertainment. We highlight considerations for how biosignals animations in social VR spaces can (falsely) indicate users’ availability status.

Published
2022

23. Understanding and Designing Avatar Biosignal Visualizations for Social Virtual Reality Entertainment

Author

Lee, Sueyoon, El Ali, Abdallah, Wijntjes, M.W.A., Cesar, Pablo, Lampe, Cliff, and Barbarossa, Simona

Subjects
entertainment, social VR, design, ComputingMilieux_PERSONALCOMPUTING, virtual reality, Biosignals, perception, visualization
Abstract

Visualizing biosignals can be important for social Virtual Reality (VR), where avatar non-verbal cues are missing. While several biosignal representations exist, designing effective visualizations and understanding user perceptions within social VR entertainment remains unclear. We adopt a mixed-methods approach to design biosignals for social VR entertainment. Using survey (N=54), context-mapping (N=6), and co-design (N=6) methods, we derive four visualizations. We then ran a within-subjects study (N=32) in a virtual jazz-bar to investigate how heart rate (HR) and breathing rate (BR) visualizations, and signal rate, influence perceived avatar arousal, user distraction, and preferences. Findings show that skeuomorphic visualizations for both biosignals allow differentiable arousal inference; skeuomorphic and particles were least distracting for HR, whereas all were similarly distracting for BR; biosignal perceptions often depend on avatar relations, entertainment type, and emotion inference of avatars versus spaces. We contribute HR and BR visualizations, and considerations for designing social VR entertainment biosignal visualizations.

Published
2022

24. Exploring Emotion Responses toward Pedestrian Crossing Actions for Designing In-vehicle Empathic Interfaces

Author

Ghosh, Surjya, Pons Rodriguez, Gerard, Rao, Shruti, El Ali, Abdallah, César Garcia, Pablo Santiago, and Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Subjects
in-vehicle interface, actions, emotion, pedestrians, drivers, empathic
Abstract

While affective non-verbal communication between pedestrians and drivers has been shown to improve on-road safety and driving experiences, it remains a challenge to design driver assistance systems that can automatically capture these affective cues. In this early work, we identify users' emotional self-report responses towards commonly occurring pedestrian actions while crossing a road. We conducted a crowd-sourced web-based survey (N=91), where respondents with prior driving experience viewed videos of 25 pedestrian interaction scenarios selected from the JAAD (Joint Attention for Autonomous Driving) dataset, and thereafter provided valence and arousal self-reports. We found participants' emotion self-reports (especially valence) are strongly influenced by actions including hand waving, nodding, impolite hand gestures, and inattentive pedestrian(s) crossing while engaged with a phone. Our findings provide a first step towards designing in-vehicle empathic interfaces that can assist in driver emotion regulation during on-road interactions, where the identified pedestrian actions serve as future driver emotion induction stimuli.

Published
2022

31. Empathic Interactions in Automated Vehicles #EmpathicCHI

Author

Daher, Karl, primary, Capallera, Marine, additional, Lucifora, Chiara, additional, Casas, Jacky, additional, Meteier, Quentin, additional, El Kamali, Mira, additional, El Ali, Abdallah, additional, Grosso, Giorgio Mario, additional, Chollet, Gérard, additional, Abou Khaled, Omar, additional, and Mugellini, Elena, additional

Published
2021
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35. A Story to Go, Please

Author

Nack, Frank, primary, El Ali, Abdallah, additional, van Kemenade, Philo, additional, Overgoor, Jan, additional, and van der Weij, Bastiaan, additional

Published
2010
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42. NaviBike

Author

Matviienko, Andrii, primary, Ananthanarayan, Swamy, additional, El Ali, Abdallah, additional, Heuten, Wilko, additional, and Boll, Susanne, additional

Published
2019
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46. Technology literacy in poor infrastructure environments

Author

El Ali, Abdallah, Bachour, Khaled, Heuten, Wilko, and Boll, Susanne

Subjects
G400 Computer Science
Abstract

While HCI for development (HCI4D) research has typically focused on technological practices of poor and low-literate communities, little research has addressed how technology literate individuals living in a poor infrastructure environment use technology. Our work fills this gap by focusing on Lebanon, a country with longstanding political instability, and the wayfinding issues there stemming from missing street signs and names, a poor road infrastructure, and a non-standardized addressing system. We examine the relationship between technology literate individuals' navigation and direction giving strategies and their usage of current digital navigation aids. Drawing on an interview study (N=12) and a web survey (N=85), our findings show that while these individuals rely on mapping services and WhatsApp's share location feature to aid wayfinding, many technical and cultural problems persist that are currently resolved through social querying. We discuss our results in light of problems that any map user encounters in poor infrastructure environments.

Published
2016

47. Face2Emoji

Author

El Ali, Abdallah, primary, Wallbaum, Torben, additional, Wasmann, Merlin, additional, Heuten, Wilko, additional, and Boll, Susanne CJ, additional

Published
2017
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48. All about Acceptability?

Author

Koelle, Marion, primary, El Ali, Abdallah, additional, Cobus, Vanessa, additional, Heuten, Wilko, additional, and Boll, Susanne CJ, additional

Published
2017
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49. NaviLight

Author

Matviienko, Andrii, primary, Löcken, Andreas, additional, El Ali, Abdallah, additional, Heuten, Wilko, additional, and Boll, Susanne, additional

Published
2016
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