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2023 IEEE International Conference on Agents, ICA 2023

作者： Goh, Michelle Jeong, Harim Yoo, Joo Hun Han, Oakyoung Sungkyunkwan University Department of Interaction Science Seoul Korea Republic of Sungkyunkwan University Department of Artificial Intelligence Suwon Korea Republic of Sungkyunkwan University Department of Computer Education Seoul Korea Republic of

ISBN: (纸本)9798350312782

In the growing digital healthcare field, enhancing user engagement is crucial for the sustained usage of e-intervention treatments. This study focuses on improving user engagement by investigating how users interact with agents in a conversational context. To achieve this, we examined communication techniques for building relationships in counseling and selected self-disclosure (SD) as our approach to assess its effectiveness. We created conversational scripts based on iCBT, a prevalent psychotherapy method in digital mental healthcare, and conducted an empirical study with 40 participants. Participants were randomly assigned to two groups: one that contained SD and one without. Our analysis focused on the user experience and its impact on engagement. The findings indicate that employing SD enhances user engagement, agent perception, and intention to use. Conversely, conversations without SD are perceived as task-focused, resulting in reduced engagement and intention to use. This study serves as a foundation for understanding the potential of SD and underscores the need for further exploration in diverse digital healthcare contexts. © 2023 IEEE.

关键词： human computer interaction

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TouchPose: Hand Pose Prediction, Depth Estimation, and Touch Classification from Capacitive Images 21

TouchPose: Hand Pose Prediction, Depth Estimation, and Touch...

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34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021

作者： Ahuja, Karan Streli, Paul Holz, Christian Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science ETH Zürich Switzerland

ISBN: (纸本)9781450386357

Today's touchscreen devices commonly detect the coordinates of user input through capacitive sensing. Yet, these coordinates are the mere 2D manifestations of the more complex 3D configuration of the whole hand - a sensation that touchscreen devices so far remain oblivious to. In this work, we introduce the problem of reconstructing a 3D hand skeleton from capacitive images, which encode the sparse observations captured by touch sensors. These low-resolution images represent intensity mappings that are proportional to the distance to the user's fingers and hands. We present the first dataset of capacitive images with corresponding depth maps and 3D hand pose coordinates, comprising 65,374 aligned records from 10 participants. We introduce our supervised method TouchPose, which learns a 3D hand model and a corresponding depth map using a cross-modal trained embedding from capacitive images in our dataset. We quantitatively evaluate TouchPose's accuracy in touch classification, depth estimation, and 3D joint reconstruction, showing that our model generalizes to hand poses it has never seen during training and can infer joints that lie outside the touch sensor's volume. Enabled by TouchPose, we demonstrate a series of interactive apps and novel interactions on multitouch devices. These applications show TouchPose's versatile capability to serve as a general-purpose model, operating independent of use-case, and establishing 3D hand pose as an integral part of the input dictionary for application designers and developers. We also release our dataset, code, and model to enable future work in this domain. © 2021 Owner/Author.

关键词： Tactile sensors

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In Time and Space: Towards Usable Adaptive Control for Assistive Robotic Arms

arXiv

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arXiv 2023年

作者： Pascher, Max Kronhardt, Kirill Goldau, Felix Ferdinand Frese, Udo Gerken, Jens The Westphalian University of Applied Sciences Human-Computer Interaction Gelsenkirchen45897 Germany The University of Duisburg-Essen Human-Computer Interaction Essen45127 Germany The University of Bremen Mathematics & Computer Science Bremen28359 Germany

Robotic solutions, in particular robotic arms, are becoming more frequently deployed for close collaboration with humans, for example in manufacturing or domestic care environments. These robotic arms require the user to control several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving grasping and manipulating objects. Standard input devices predominantly have two DoFs, requiring time-consuming and cognitively demanding mode switches to select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have shown to decrease the necessary number of mode switches but were up to now not able to significantly reduce the perceived workload. Users still bear the mental workload of incorporating abstract mode switching into their workflow. We address this by providing feed-forward multimodal feedback using updated recommendations of ADMC, allowing users to visually compare the current and the suggested mapping in real-time. We contrast the effectiveness of two new approaches that a) continuously recommend updated DoF combinations or b) use discrete thresholds between current robot movements and new recommendations. Both are compared in a Virtual Reality (VR) in-person study against a classic control method. Significant results for lowered task completion time, fewer mode switches, and reduced perceived workload conclusively establish that in combination with feedforward, ADMC methods can indeed outperform classic mode switching. A lack of apparent quantitative differences between Continuous and Threshold reveals the importance of user-centered customization options. Including these implications in the development process will improve usability, which is essential for successfully implementing robotic technologies with high user acceptance. © 2023, CC BY.

关键词： Mapping

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Co-Creating Reassurance Journey Maps to Foster Engagement in Remote Patient Monitoring for Post-Operative Cancer Care 25

Co-Creating Reassurance Journey Maps to Foster Engagement in...

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

作者： Timinis, Constantinos Opie, Jeremy Watt, Simon Rogers, Yvonne Drobnjak, Ivana Department of Computer Science University College London London United Kingdom UCL Hawkes Institute University College London London United Kingdom UCL Interaction Centre University College London London United Kingdom

ISBN: (纸本)9798400713941

Remote patient monitoring can significantly enhance post-operative home recovery for cancer patients, yet its effectiveness is often hindered by low patient engagement. Reassurance has been identified as a key factor in improving engagement. Our study explored how cancer patients seek reassurance through a Patient Public Involvement workshop with former patients. This involved developing personas for participants to navigate reassurance scenarios and share their post-operative experiences. Based on this, we co-created a reassurance journey map to illustrate when reassurance is needed, the behaviours patients use to seek it, and how it can be effectively provided. Our findings highlight three key design principles: the limitations of digital technology in offering reassurance, the personalised nature of reassurance, and the need for holistic integration. These are intended to inform the design of reassurance-focused RPM systems that better support cancer patients during home recovery. Practical design recommendations are also provided for developers and clinicians. © 2025 Copyright held by the owner/author(s).

关键词： Remote patient monitoring

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Developable metamaterials: Mass-fabricable metamaterials by laser-cutting elastic structures 21

Developable metamaterials: Mass-fabricable metamaterials by ...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Signer, Madlaina Signer, Alexandra Sorkine-Hornung, Olga Department of Computer Science Eth Zurich Switzerland Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9781450380966

We propose a novel design of engineered, structured materials that leverages fast fabrication technologies, pushing them towards mass-fabrication. Specifically, our metamaterial is designed to be laser cut, to approximate the volumetric shape and allow for locally varying compliance. Traditional mechanical metamaterials consist of intricate cells arranged on a 3-dimensional grid, limiting them to 3D printing-which is slow. Our metamaterial is designed for laser cutting, which is drastically faster. Our structures are best described as ruffled strips of thin sheet material, such as paper, plastics, metals, etc. Users can interactively define the ruffles' anisotropic stiffness directions and local density. Our computational design tool assists users by automatically optimizing the ruffle to fill the shape's volume, and exporting the flat ruffle design ready for cutting. We demonstrate how such ruffled metamaterials can be utilized for, e.g., custom toys with locally varying compliance, custom packaging material, or lightweight formwork for architectural shells. © 2021 ACM.

关键词： Fabrication

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Intelligent Detection System for Multi-Step Cyber-Attack Based on Machine Learning 15

Intelligent Detection System for Multi-Step Cyber-Attack Bas...

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15th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Jasim, Omar Hammad Al-Dosary, Duaa Ahmed, Hamsa M. Al-Ani, Muzhir Shaban University of Anbar Computer Networking Systems Department Ramadi Iraq Al Baha University Faculty of Computer Science and Information Technology Al Baha Saudi Arabia Human Resources Department Ramadi Iraq College of Computer Sciences and Information Technology University of Anbar Anbar Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah Iraq

ISBN: (纸本)9798350335149

Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms. © 2023 IEEE.

关键词： Network security

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3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems

3DG: A Framework for Using Generative AI for Handling Sparse...

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2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024

作者： Zhang, Liang Lin, Jionghao Borchers, Conrad Cao, Meng Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Memphis MemphisTN38152 United States

Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring Systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN’s superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology. © 2024 CEUR-WS. All rights reserved.

关键词： Generative adversarial networks

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Framing Theory on Music Streaming Platforms: How Vocabulary Influences Music Playlist Decision-Making and Expectations

Framing Theory on Music Streaming Platforms: How Vocabulary ...

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2024 ACM International Conference on Intelligent User Interfaces Workshops, IUI-WS 2024

作者： van Bree, Lotte Graus, Mark Ferwerda, Bruce Department of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden Human-Centric SARL Paris France

To improve decision-making and prevent information overload, music streaming services try to offer users personalized content. Vocabulary can be an important component in achieving personalization, in addition to recommender systems that ensure users are given access to their musical preferences. One way to apply personalization through vocabulary is to use personal pronouns. However, it is still unclear how the use of vocabulary personalization affects users’ decisions and expectations when it comes to music streaming services. This study aims to explore how vocabulary framing in playlist titles influences decision-making and the content expectations of the music playlist. Despite challenges in making informed vocabulary choices, our results show a substantial impact of personalized vocabulary in playlist titles on user choice and overall expectations. Users distinctly favor playlists with personalized vocabulary, emphasizing their crucial role in enhancing the music streaming experience. © 2024 Copyright for this paper by its authors.

关键词： Decision making

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Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval

Zero-shot Persuasive Chatbots with LLM-Generated Strategies ...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Furumai, Kazuaki Legaspi, Roberto Vizcarra, Julio Yamazaki, Yudai Nishimura, Yasutaka Semnani, Sina J. Ikeda, Kazushi Shi, Weiyan Lam, Monica S. Human-Centered AI Laboratories KDDI Research Inc. Saitama Japan Computer Science Department Stanford University StanfordCA United States

ISBN: (纸本)9798891761681

Persuasion plays a pivotal role in a wide range of applications from health intervention to the promotion of social good. Persuasive chatbots employed responsibly for social good can be an enabler of positive individual and social change. Existing methods rely on fine-tuning persuasive chatbots with task-specific training data which is costly, if not infeasible, to collect. Furthermore, they employ only a handful of pre-defined persuasion strategies. We propose PersuaBot, a zero-shot chatbot based on Large Language Models (LLMs) that is factual and more persuasive by leveraging many more nuanced strategies. PersuaBot uses an LLM to first generate natural responses, from which the strategies used are extracted. To combat hallucination of LLMs, Persuabot replace any unsubstantiated claims in the response with retrieved facts supporting the extracted strategies. We applied our chatbot, PersuaBot, to three significantly different domains needing persuasion skills: donation solicitation, recommendations, and health intervention. Our experiments on simulated and human conversations show that our zero-shot approach is more persuasive than prior work, while achieving factual accuracy surpassing state-of-the-art knowledge-oriented chatbots. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Interactive Data Fusion of Neural Radiance Fields for Industrial Facility Inspection in Virtual Reality

Interactive Data Fusion of Neural Radiance Fields for Indust...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Ke Li Susanne Schmidt Tim Rolff Reinhard Bacher Wim Leemans Frank Steinicke Deutsches Elektronen-Synchrotron DESY Germany Department of Informatics Human-Computer Interaction Universität Hamburg

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Real-world industrial facilities often present complex equipment that requires extensive inspection and maintenance for their operations. In this work, we present a virtual reality (VR) framework that supports virtual facility inspection and maintenance tasks by using neural radiance fields (NeRF) models to replicate, store, and visualize the appearance of complex industrial facilities. To overcome the performance bottleneck of VR NeRF rendering, we present two novel interactive data fusion techniques that can merge a NeRF model with its' corresponding CAD model through a mixed reality tunneling effect and contextual 3D NeRF drawing interaction. Technical benchmarking results and preliminary expert feedback are presented for the initial evaluation of our framework.

关键词： Solid modeling Three-dimensional displays Data integration Virtual reality Inspection Rendering (computer graphics) Industrial facilities

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