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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

IEEE Transactions on Affective Computing 2024年第2期16卷 969-985页

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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InterLink: Linking Text with Code and Output in Computational Notebooks 25

InterLink: Linking Text with Code and Output in Computationa...

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

作者： Lin, Yanna Yang, Leni Li, Haotian Qu, Huamin Moritz, Dominik Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Microsoft Research Asia Beijing China

ISBN: (纸本)9798400713941

Computational notebooks, widely used for ad-hoc analysis and often shared with others, can be difficult to understand because the standard linear layout is not optimized for reading. In particular, related text, code, and outputs may be spread across the UI making it difficult to draw connections. In response, we introduce InterLink, a plugin designed to present the relationships between text, code, and outputs, thereby making notebooks easier to understand. In a formative study, we identify pain points and derive design requirements for identifying and navigating relationships among various pieces of information within notebooks. Based on these requirements, InterLink features a new layout that separates text from code and outputs into two columns. It uses visual links to signal relationships between text and associated code and outputs and offers interactions for navigating related pieces of information. In a user study with 12 participants, those using InterLink were 13.6% more accurate at finding and integrating information from complex analyses in computational notebooks. These results show the potential of notebook layouts that make them easier to understand. © 2025 Copyright held by the owner/author(s).

关键词： Interactive computer systems

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Identity and Community Matter(s): Exploring Socio-cultural Dimensions of Functional Service Robots Acceptance in Public Spaces

Identity and Community Matter(s): Exploring Socio-cultural D...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Anna Dobrosovestnova Ralf Vetter Astrid Weiss Human-Computer Interaction Group TU Wien Austria Department for Artificial Intelligence and Human Interfaces Human-Computer Interaction Division University of Salzburg Austria

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

As autonomous functional robots proliferate in public spaces, understanding their acceptance among diverse populations becomes crucial. Traditional models of technology acceptance focus on behavioral and cognitive factors, overlooking the socio-cultural and temporal scaffolding of human-robot interaction. To address this gap, we present two themes (Identity Matters, Community Matters) resulting from a qualitative analysis of ethnographic interviews (n = 20) with passersby and vendors who have experienced Starship delivery robots deployed in their communities. Situating the outcomes of our analysis in relation to existing technology acceptance models, we offer five lessons contributing to the efforts in understanding existence and usage-related acceptance of autonomous functional robots in public spaces.

关键词： Technology acceptance model Service robots human-robot interaction Interviews Robots

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CSCW – past, present and future

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i-com 2024年第1期23卷 1-5页

作者： Richter, Alexander Koch, Michael Prilla, Michael Computer Science Department Universität der Bundeswehr München München Germany Wellington School of Business and Government Victoria University of Wellington Wellington New Zealand Interactive Systems Department for Human Centered Computing and Cognitive Science Faculty for Computer Science University of Duisburg-Essen Duisburg Germany

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How are ML-Based Online Content Moderation Systems Actually Used? Studying Community Size, Local Activity, and Disparate Treatment 22

How are ML-Based Online Content Moderation Systems Actually ...

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5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Wang, Leijie Zhu, Haiyi Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450393522

Machine learning-based predictive systems are increasingly used to assist online groups and communities in various content moderation tasks. However, there are limited quantitative understandings of whether and how different groups and communities use such predictive systems differently according to their community characteristics. In this research, we conducted a field evaluation of how content moderation systems are used in 17 Wikipedia language communities. We found that 1) larger communities tend to use predictive systems to identify the most damaging edits, while smaller communities tend to use them to identify any edit that could be damaging;2) predictive systems are used less in content areas where there are more local editing activities;3) predictive systems have mixed effects on reducing disparate treatment between anonymous and registered editors across communities of different characteristics. Finally, we discuss the theoretical and practical implications for future human-centered moderation algorithms. © 2022 Owner/Author.

关键词： Online systems

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VisiMark: Characterizing and Augmenting Landmarks for People with Low Vision in Augmented Reality to Support Indoor Navigation 25

VisiMark: Characterizing and Augmenting Landmarks for People...

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

作者： Chen, Ruijia Jiang, Junru Maheshwary, Pragati Cochran, Brianna R Zhao, Yuhang Department of Computer Sciences University of Wisconsin-Madison MadisonWI United States Human Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713941

Landmarks are critical in navigation, supporting self-orientation and mental model development. Similar to sighted people, people with low vision (PLV) frequently look for landmarks via visual cues but face difficulties identifying some important landmarks due to vision loss. We first conducted a formative study with six PLV to characterize their challenges and strategies in landmark selection, identifying their unique landmark categories (e.g., area silhouettes, accessibility-related objects) and preferred landmark augmentations. We then designed VisiMark, an AR interface that supports landmark perception for PLV by providing both overviews of space structures and in-situ landmark augmentations. We evaluated VisiMark with 16 PLV and found that VisiMark enabled PLV to perceive landmarks they preferred but could not easily perceive before, and changed PLV's landmark selection from only visually-salient objects to cognitive landmarks that are more important and meaningful. We further derive design considerations for AR-based landmark augmentation systems for PLV. © 2025 Copyright held by the owner/author(s).

关键词： Disabled persons

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Road Sign Classification Using Transfer Learning and Pre-trained CNN Models 1

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1st International Conference on Artificial Intelligence and Smart Vehicles, ICAISV 2023

作者： Hosseini, Seyed Hossein Ghaderi, Foad Moshiri, Behzad Norouzi, Mojtaba Human Computer Interaction Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran School of ECE University of Science and Technology Tehran Iran

ISBN: (数字)9783031437632

ISBN: (纸本)9783031437625

In this paper, we propose a transfer learning-based approach for road sign classification using pre-trained CNN models. We evaluate the performance of our fine-tuned VGG-16, VGG-19, ResNet50 and EfficientNetB0 models on the German Traffic Sign Recognition Benchmark (GTSRB) test dataset. Our work makes several contributions, including the utilization of transfer learning with pre-trained CNN models, the integration of augmentation techniques, and a comprehensive evaluation of multiple state-of-the-art architectures on the road sign classification task. Our experimental results show that the proposed approach achieves an average accuracy of 99.2% on the GTSRB test dataset, outperforming existing state-of-the-art approaches. The results confirm that transfer learning and pre-trained models can significantly improve the accuracy of road sign classification, even with a relatively small dataset. Our study contributes to the field of intelligent transportation systems by providing an efficient and accurate method for road sign classification. We believe that our approach can be easily extended to other computer vision tasks and applied to real-world scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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A Neural Architecture Search Method with Multi-Dimensional Correlation Representation for Fault Diagnosis 9

A Neural Architecture Search Method with Multi-Dimensional C...

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9th IEEE Smart World Congress, SWC 2023

作者： Liu, Yumeng Wang, Tingqi Zheng, Xu Tian, Ling Wang, Hongan Institute of Software Chinese Academy of Sciences Beijing Key Laboratory of Human-Computer Interaction Beijing China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350319804

As deep learning has become more widely used for fault diagnosis, the shortcomings of model transferability and human model design costs are growing increasingly evident. The current work has tackled each of these two issues. Domain adaptation methods can provide transportability by mapping data to a common domain. For the consumption of manually constructed networks, Neural Architecture Search (NAS) is an intelligent solution that automatically select the ideal network structure. Unfortunately, there is little work to connect these two methodologies in fault diagnosis. Furthermore, these approaches are incapable in extracting internal correlations from instrumental signal data. In this paper, we propose a transferrable NAS fault diagnosis method, which maximizes savings in manual costs. We also integrate graph neural networks (GNN) in a stepwise manner into the NAS and domain adaptation to provide a multi-dimension correlation representation. Firstly, we structure the signal data into graph based on temporal relationships. Then, NAS designs automatically for selecting the optimal network structure and useful features. In NAS, graph encoder of GNN is used to establish internal relationships. Finally, domain adaptation provides transferability. At the same time, GNN's weights in domain adaptation help to further mine data internal associations. We tested our method on two publicly available datasets and performed comparisons. The experimental results show the proposed method has superior performance. © 2023 IEEE.

关键词： Domain Adaptation Fault Diagnosis Graph Neural Network Neural Architecture Search

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Delivering Script Therapy for people with aphasia in EVA Park: Two single case treatment studies

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Advances in Communication and Swallowing 2024年第1期27卷 29-40页

作者： Marshall, Jane Devane, Niamh Berraondo, Jude Talbot, Richard Temponera, Panagiota Clegg, Katie Wilson, Stephanie Division of Language and Communication Science City University of London London United Kingdom Linguistic Resolutions Pinner United Kingdom Centre for Human-Computer Interaction Design City University of London London United Kingdom

BACKGROUND: There are few applications of virtual reality (VR) in aphasia rehabilitation. EVA Park is an online VR platform developed with and for people with aphasia. Our research is testing its potential to host aphasia therapies. OBJECTIVE: Two case studies evaluated if delivery of Script Therapy in EVA Park is feasible and acceptable to participants, whether it improved production of trained scripts and promoted generalisation to untrained scripts, narrative speech and functional communication. METHODS: Two participants with aphasia received 20 hours of Script Therapy in EVA Park. Feasibility was assessed through session attendance, completion of practice and ratings of treatment fidelity. Acceptability was explored via post therapy interviews. The impact of therapy on script production, narrative production and functional communication was assessed through measures administered twice before therapy, immediately post therapy and at 5 weeks follow up. RESULTS: Participants attended at least 85% of sessions. Compliance with practice was good for one, but not the other. Fidelity ratings indicated that over 80% of core treatment components were fully present in recorded sessions. Participants expressed positive views about the intervention. Therapy significantly improved the production of words in trained scripts, with maintenance for one participant. Neither participant improved in the production of untrained scripts or personal narratives. One improved on the assessment of functional communication, but the margin of change was small. CONCLUSIONS: The study adds to the evidence that EVA Park can host a range of interventions and that this platform is acceptable to its intended user group. © 2024 – IOS Press. All rights reserved.

关键词： Aphasia Script Therapy tele-rehabilitation virtual reality

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Enhancing Impulse Response-Based SSVEP Decoding Method by Incorporating Temporal Local Correlation 7

Enhancing Impulse Response-Based SSVEP Decoding Method by In...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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