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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Future Self Agents to Enhance Young Adults’ Career Exploration

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arXiv 2025年

作者： Jeon, Hayeon Yoon, Suhwoo Lee, Keyeun Kim, Seo Hyeong Kim, Esther Hehsun Cho, Seonghye Ko, Yena Yang, Soeun Dabbish, Laura Zimmerman, John Kim, Eun-Mee Lim, Hajin hci+d lab. Seoul National University Korea Republic of Department of Communication Seoul National University Korea Republic of Human-Computer Interaction Institute Carnegie Mellon University United States HCI Institute Carnegie Mellon University United States

Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants’ future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants’ engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults’ career exploration. Copyright © 2025, The Authors. All rights reserved.

关键词： Chatbots

Trans4Trans: Efficient transformer for transparent object and semantic scene segmentation in real-world navigation assistance

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arXiv 2021年

作者： Zhang, Jiaming Yang, Kailun Constantinescu, Angela Peng, Kunyu Müller, Karin Stiefelhagen, Rainer Computer Vision for Human-Computer Interaction Lab Center for Digital Accessibility and Assistive Technology Karlsruhe Institute of Technology Germany

Transparent objects, such as glass walls and doors, constitute architectural obstacles hindering the mobility of people with low vision or blindness. For instance, the open space behind glass doors is inaccessible, unless it is correctly perceived and interacted with. However, traditional assistive technologies rarely cover the segmentation of these safety-critical transparent objects. In this paper, we build a wearable system with a novel dualhead Transformer for Transparency (Trans4Trans) perception model, which can segment general- and transparent objects. The two dense segmentation results are further combined with depth information in the system to help users navigate safely and assist them to negotiate transparent *** propose a lightweight Transformer Parsing Module (TPM) to perform multi-scale feature interpretation in the transformer-based decoder. Benefiting from TPM, the double decoders can perform joint learning from corresponding datasets to pursue robustness, meanwhile maintain efficiency on a portable GPU, with negligible calculation increase. The entire Trans4Trans model is constructed in a symmetrical encoder-decoder architecture, which outperforms state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2 datasets, obtaining mIoU of 45:13% and 75:14%, respectively. Through a user study and various pre-tests conducted in indoor and outdoor scenes, the usability and reliability of our assistive system have been extensively verified. Meanwhile, the Tran4Trans model has outstanding performances on driving scene datasets. On Cityscapes, ACDC, and DADA-seg datasets corresponding to common environments, adverse weather, and traffic accident scenarios, mIoU scores of 81:5%, 76:3%, and 39:2% are obtained, demonstrating its high efficiency and robustness for real-world transportation applications. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

Communicative Grounding of Analogical Explanations in Dialogue: A Corpus Study of Conversational Management Acts and Statistical Sequence Models for Tutoring through Analogy

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Communicative Grounding of Analogical Explanations in Dialog...

2021 Conference on Reasoning and interaction, ReInAct 2021

作者： Del-Bosque-Trevino, Jorge Hough, Julian Purver, Matthew Education Lab. Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Human Interaction Lab. Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Jožef Stefan Institute Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

ISBN: (纸本)9781955917070

We present a conversational management act (CMA) annotation schema for one-to-one tutorial dialogue sessions where a tutor uses an analogy to teach a student a concept. CMAs are more fine-grained sub-utterance acts compared to traditional dialogue act markup. The schema achieves an inter-annotator agreement (IAA) Cohen Kappa score of at least 0.66 across all 10 classes. We annotate a corpus of analogical episodes with the schema and develop statistical sequence models from the corpus which predict tutor content related decisions, in terms of the selection of the analogical component (AC) and tutor conversational management act (TCMA) to deploy at the current utterance, given the student’s behaviour. CRF sequence classifiers perform well on AC selection and robustly on TCMA selection, achieving respective accuracies of 61.9% and 56.3% on a cross-validation experiment over the corpus. © 2021 Association for Computational Linguistics.

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

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arXiv 2024年

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China Zhidayuan AI Lab. Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Person image synthesis with controllab.e body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal encoding

Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration 25

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Letters from Future Self: Augmenting the Letter-Exchange Exe...

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

作者： Hayeon Jeon Suhwoo Yoon Keyeun Lee Seo Hyeong Kim Esther Hehsun Kim Seonghye Cho Yena Ko Soeun Yang Laura Dabbish John Zimmerman Eun-mee Kim Hajin Lim hci+d lab. Seoul National University Seoul Republic of Korea Department of Communication Seoul National University Seoul Republic of Korea Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA HCI Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

From Lenses to Living Rooms: A Policy Brief on Eye Tracking in XR Before the Impending Boom

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From Lenses to Living Rooms: A Policy Brief on Eye Tracking ...

IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Sean Anthony Byrne Nora Castner Efe Bozkir Diederick C. Niehorster Enkelejda Kasneci MoMiLab IMT School for Advanced Studies Lucca Lucca Italy Carl Zeiss Vision International GmbH Tübingen Germany Human-Centered Technologies for Learning Technical University of Munich Munich Germany Human-Computer Interaction University of Tübingen Tübingen Germany Dept. of Psychology Lund University Humanities Lab & Lund University Lund Sweden

As tech giants such as Apple and Meta invest heavily in Virtual and Augmented Reality (VR/AR) technologies, often collectively termed Extended Reality (XR) devices, a significant societal concern emerges: The use of eye-tracking technology within these devices. Gaze data holds immense value, revealing insights into user attention, health, and cognitive states. This raises substantial concerns over privacy and fairness, with potential risks of targeted ads, unauthorized surveillance, and data re-purposing. As the impact of eye tracking is due to transition from the lab.to the broader public, this paper underscores these pivotal issues in a digestible manner to the general audience. To this end, we first outline the eye-tracking data collection process and its potential for user insights. Second, we introduce perspectives from the domain of privacy, emphasizing its significance as a pivotal measure to guard against the improper use of eye-tracking data. Third, we provide a set of guidelines created by researchers actively working within this space. These recommendations are designed to guide policymakers and the general public toward establishing informed, equitable, and privacy-centric standards surrounding these devices.

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Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

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arXiv 2023年

作者： Marinov, Zdravko Jäger, Paul F. Egger, Jan Kleesiek, Jens Stiefelhagen, Rainer The Computer Vision for Human-Computer Interaction Lab Department of Informatics Karlsruhe Institute of Technology Adenauerring 10 Karlsruhe76131 Germany Girardetstraße 2 Essen45131 Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 223 Heidelberg69120 Germany The Helmholtz Imaging DKFZ Im Neuenheimer Feld 223 Heidelberg69120 Germany

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Appendixes are here. © 2023, CC BY.

关键词： Medical imaging

Linguistic Features to Consider When Applying Persona of the Real Person to the Text-based Agent 20

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Linguistic Features to Consider When Applying Persona of the...

22nd International Conference on human-computer interaction with Mobile Devices and Services

作者： Youjin Hwang Seokwoo Song Donghoon Shin Joonhwan Lee Human-Computer Interaction & Design Lab. Seoul National University Korea Republic of Samsung Research Korea Republic of

ISBN: (纸本)9781450380522

As artificial intelligence (AI) technologies advance, the possibility of developing virtual agents capable of mimicking human beings is increasing. Furthermore, AI techniques applicable to mimicking certain features of a specific person (e.g., facial expression, voice, motion) are becoming more sophisticated. Although the HCI community has explored how to design or develop AI agents mimicking a real person, limited studies on mimicking someone’s text-based behavior shown in the instant messaging exist. This study investigates the features that make users perceive text-based agents as people they know in reality. On top of the previous efforts of designing human-like virtual agents, our work suggests design guidelines for applying the persona of the real person (PRP) to text-based agents.

关键词： authorship attribution personality chat analysis chatbot

Semantic Visual Simultaneous Localization and Mapping: A Survey

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arXiv 2022年

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Institute of Computer Vision College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Human-Robot Interfaces and Interaction Lab Istituto Italiano di Tecnologia Genoa Italy National University of Defense Technology China Università di Genova Genoa Italy School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Institute of Computer Vision School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM cannot achieve good localization in dynamic and complex environments. Numerous publications have reported that, by combining with the semantic information with vSLAM, the semantic vSLAM systems have the capability of solving the above problems in recent years. Nevertheless, there is no comprehensive survey about semantic vSLAM. To fill the gap, this paper first reviews the development of semantic vSLAM, explicitly focusing on its strengths and differences. Secondly, we explore three main issues of semantic vSLAM: the extraction and association of semantic information, the application of semantic information, and the advantages of semantic vSLAM. Then, we collect and analyze the current state-of-the-art SLAM datasets which have been widely used in semantic vSLAM systems. Finally, we discuss future directions that will provide a blueprint for the future development of semantic vSLAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping