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Interdisciplinary perspectives on privacy awareness in lifelogging technology development

Journal of Ambient Intelligence and humanized Computing 2023年第3期14卷 2291-2312页

作者： Wilkowska, Wiktoria Offermann, Julia Colonna, Liane Florez-Revuelta, Francisco Climent-Pérez, Pau Mihailidis, Alex Poli, Angelica Spinsante, Susanna Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Aachen Germany Swedish Law and Informatics Research Institute Stockholm University Stockholm Sweden Department of Computer Technology University of Alicante Alicante Spain Department of Occupational Science and Occupational Therapy University of Toronto Toronto Canada Department of Information Engineering Marche Polytechnic University Ancona Italy

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL (‘Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People’), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies. © 2022, The Author(s).

关键词： human computer interaction

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks 12

Real-Time Facial Expression Recognition using Facial Landmar...

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12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

作者： Haghpanah, Mohammad A. Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Lab School of Electrical and Computer Engineering Tehran Iran University of Nevada Department of Computer Science and Engineering Reno United States

ISBN: (纸本)9781665412162

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. © 2022 IEEE.

关键词： Emotion Recognition

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Investigating the Role of Metaphors in human-AI Conversation and Engagement 24

Investigating the Role of Metaphors in Human-AI Conversation...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Maheshwary, Pragati Liu, Qiawen Mao, Rui Carnegie Mellon University Human-Computer Interaction Institute United States University of Wisconsin-Madison Department of Psychology United States Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331530631

Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages. © 2024 IEEE.

关键词： Modeling languages

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Towards Context-Aware Adaptation in Extended Reality: A Design Space for XR Interfaces and an Adaptive Placement Strategy

arXiv

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arXiv 2024年

作者： Davari, Shakiba Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States

By converting the entire 3D space around the user into a screen, Extended Reality (XR) can ameliorate traditional displays’ space limitations and facilitate the consumption of multiple pieces of information at a time. However, if designed inappropriately, these XR interfaces can overwhelm the user and complicate information access. In this work, we explored the design dimensions that can be adapted to enable suitable presentation and interaction within an XR interface. To investigate a specific use case of context-aware adaptations within our proposed design space, we concentrated on the spatial layout of the XR content and investigated non-adaptive and adaptive placement strategies. In this paper, we (1) present a comprehensive design space for XR interfaces, (2) propose Environment-referenced, an adaptive placement strategy that uses a relevant intermediary from the environment within a Hybrid Frame of Reference (FoR) for each XR object, and (3) evaluate the effectiveness of this adaptive placement strategy and a non-adaptive Body-Fixed placement strategy in four contextual scenarios varying in terms of social setting and user mobility in the environment. The performance of these placement strategies from our within-subjects user study emphasized the importance of intermediaries’ relevance to the user’s focus. These findings underscore the importance of context-aware interfaces, indicating that the appropriate use of an adaptive content placement strategy in a context can significantly improve task efficiency, accuracy, and usability. © 2024, CC BY-NC-ND.

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Pseudo-Haptics for Weight Perception in VR: Controller vs. Bare Hand interactions with Tracking Delay and Vertical Offset

Pseudo-Haptics for Weight Perception in VR: Controller vs. B...

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Virtual Reality (ICVR), IEEE International Conference on

作者： Chiwoong Hwang Tiare Feuchtner Kaj Grønbæk Department of Computer Science Aarhus University Denmark Human-Computer Interaction Group Konstanz University Germany

ISBN: (数字)9798350364231

ISBN: (纸本)9798350364248

Training in virtual reality (VR) offers advantages such as a safe learning environment, good scalability, and cost saving. However, state-of-the-art simulations are limited in faithfully conveying real-world physical object properties such as weight without complex technologies and instrumentation of the user or environment, which may negatively impact learning. A potential solution is the application of pseudo-haptics: illusory haptic sensations induced by a mismatch between real and virtual hand positions. Researchers have proposed various pseudo-haptic techniques to alter the perception of weight in virtual environments, but their effectiveness with hand tracking compared to VR controllers is underexplored. Hence, we investigate impacts of the interaction method for two different types of pseudo-haptic feedback based on Tracking Delay and Vertical Offset. Our results indicate that pseudo-haptics can effectively facilitate the sensation of weight both with controllers and bare hands, whereby each has benefits: hand tracking can enhance body ownership, while controllers may improve perceived responsiveness of virtual environments. Also, pseudo-haptics with Tracking Delay may be preferable for repetitive lifting tasks, as Vertical Offset can be prone to higher physical demand. The current study provides insights into the design of pseudo-haptic feedback and interaction technologies for weight perception in VR.

关键词： Hands Training Manufacturing industries Solid modeling Scalability Instruments Virtual environments User experience Delays Haptic interfaces

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Towards Intelligent Augmented Reality (iAR): A Taxonomy of Context, an Architecture for iAR, and an Empirical Study

arXiv

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arXiv 2024年

作者： Davari, Shakiba Stover, Daniel Giovannelli, Alexander Ilo, Cory Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States

Recent advancements in Augmented Reality (AR) research have highlighted the critical role of context awareness in enhancing interface effectiveness and user experience. This underscores the need for intelligent AR (iAR) interfaces that dynamically adapt across various contexts to provide optimal experiences. In this paper, we (a) propose a comprehensive framework for contextaware inference and adaptation in iAR, (b) introduce a taxonomy that describes context through quantifiable input data, and (c) present an architecture that outlines the implementation of our proposed framework and taxonomy within iAR. Additionally, we present an empirical AR experiment to observe user behavior and record user performance, context, and user-specified adaptations to the AR interfaces within a context-switching scenario. We (d) explore the nuanced relationships between context and user adaptations in this scenario and discuss the significance of our framework in identifying these patterns. This experiment emphasizes the significance of context-awareness in iAR and provides a preliminary training dataset for this specific Scenario. © 2024, CC BY-NC-ND.

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Toward a Secure Educational Metaverse: A Tale of Blockchain Design for Educational Environments 49

Toward a Secure Educational Metaverse: A Tale of Blockchain ...

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49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023

作者： Di Dario, Dario Bilott, Umberto Sibilio, Maurizio Gravino, Carmine Palomba, Fabio University of Salerno SeSa Lab Department of Computer Science Salerno Italy University of Salerno Lab H Department of Human Science Salerno Italy

ISBN: (纸本)9798350342352

In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persistent, virtual, three-dimensional environment that is supposed to address most of the limitations of 2D information technologies. Nonetheless, there are still software engineering challenges to face to enable such a metaverse, especially when turning to software security and privacy. In this paper, we aim at performing the first steps toward an improved understanding of the security perspective of educational metaverse, by analyzing how blockchain can be employed within educational environments and how applications may be designed. Our ultimate goal is to provide insights into how blockchain can be further tailored in the context of educational metaverse. We conduct a systematic literature review, which targets 20 primary studies. The key findings of the study showcase the use of blockchain in 3 educational tasks, other than describing the blockchain design approaches, which protocol they commonly use and the associated limitations. We conclude by developing a conceptualization of a blockchain-based educational metaverse. © 2023 IEEE.

关键词： Blockchain

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Rethinking (human) Bodies in HRI: Embodied interactions Every Day 6

Rethinking (Human) Bodies in HRI: Embodied Interactions Ever...

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6th Social Robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Fantasia, Valentina Winkle, Katie Harrison, Katherine Ziemke, Tom Lisy, Dominika Dobrosovestnova, Anna Department of Philosophy and Cognitive Science Lund University Sweden Department of Information Technology Uppsala University Sweden Department of Thematic Studies Linköping University Sweden Department of Cognitive Science Linköping University Sweden Human Computer Interaction Group TU Wien Austria

ISBN: (纸本)9781643685670

Significant amounts of HRI research effort are spent on the design/evaluation of robot bodies. Critical discussion and debates concerning the way bodies are treated and considered within HRI have tended to focus on the implications of designing/interacting with highly anthropomorphic robots, yet drawing attention away from the human body in HRI, particularly in the context of everyday interactions with "unexpectedly" social robots, i.e. those non-humanoid robots that human users interact with in a somewhat "social" way. These kind of robots crystallise particular aspects of (and tensions between) the different ways in which bodies are understood across disciplines. At the same time, a fundamental, and largely neglected tool for HRI research is the researchers' own embodied experience of the intersubjective role. This workshop has invited speakers and audience to engage in cross-disciplinary conversations on embodiment in HRI, highlighting the different ways in which "the body" is or can be variously conceptualised and researched. © 2025 IOS Press. All rights reserved.

关键词： Social robots

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Artificial Intelligence Driven Predictive Analysis of Acoustic and Linguistic Behaviors for ASD Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5709-5719页

作者： Ashwini, B. Deeptanshu Gulati, Sheffali Shukla, Jainendra Human-Machine Interaction Laboratory Delhi New Delhi110020 India All India Institute of Medical Sciences Department of Pediatrics Child Neurology Division Centre of Excellence and Advanced Research for Childhood Neurodevelopmental Disorders New Delhi110029 India Department of Computer Science and Engineering Delhi New Delhi110020 India

The identification of autism spectrum disorder (ASD) faces challenges due to the lack of reliable biomarkers and the subjectivity in diagnostic procedures, necessitating improved tools for objectivity and efficiency. Being a key characteristic of autism, language impairments are regarded as potential markers for identifying ASD. However, current research predominantly focuses on analyzing language characteristics in English, overlooking linguistic and contextual specificities in other resource-constrained languages. Motivated by these, we developed an artificial intelligence (AI)-based system to detect ASD, utilizing a range of acoustic and linguistic features extracted from dyadic conversations between a child and their communication partner. Validating our model on 76 English-speaking children [35 ASD and 41 typically developing (TD)] and 33 Hindi-speaking children (15 ASD and 18 TD), our extensive analysis of a diverse and comprehensive set of acoustic and linguistic speech attributes, including lexical, syntactic, semantic, and pragmatic elements revealed reliable speech attributes as predictors of ASD. This comprehensive analysis achieved a remarkable macro F1-score of approximately ∼91.30%. We further addressed the influence of linguistic diversity on speech-based ASD assessment by examining speech behaviors in both English and the low-resource language, Hindi. Specific features such as adverbs and distinct roots contributed significantly to ASD classification in English, while the proportion of unintelligible utterances and adposition use held greater importance in Hindi. This study underscores the reliability of speech-based biomarkers in ASD assessment, emphasizing their effectiveness across diverse linguistic backgrounds and highlighting the need for language-specific research in this domain. © 2024 IEEE.

关键词： Semantics

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What Lies Beneath? Exploring the Impact of Underlying AI Model Updates in AI-Infused Systems 25

What Lies Beneath? Exploring the Impact of Underlying AI Mod...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Vikram Mohanty Jude Lim Kurt Luther Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Independent Researcher Arlington Virginia USA Department of Computer Science & Center for Human-Computer Interaction Virginia Tech Alexandria Virginia USA

ISBN: (纸本)9798400713941

AI models are constantly evolving, with new versions released frequently. human-AI interaction guidelines encourage notifying users about changes in model capabilities, ideally supported by thorough benchmarking. However, as AI systems integrate into domain-specific workflows, exhaustive benchmarking can become impractical, often resulting in silent or minimally communicated updates. This raises critical questions: Can users notice these updates? What cues do they rely on to distinguish between models? How do such changes affect their behavior and task performance? We address these questions through two studies in the context of facial recognition for historical photo identification: an online experiment examining users' ability to detect model updates, followed by a diary study exploring perceptions in a real-world deployment. Our findings highlight challenges in noticing AI model updates, their impact on downstream user behavior and performance, and how they lead users to develop divergent folk theories. Drawing on these insights, we discuss strategies for effectively communicating model updates in AI-infused systems. © 2025 Copyright held by the owner/author(s).

关键词： AI Model Updates

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