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30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Chariskou, Chrysanthi Bazinas, Christos Daniels, Andries J. Opara, Umezuruike L. Nieuwoudt, Helene H. Kaburlasos, Vassilis G. Human-Machines Interaction Lab International Hellenic University Department of Computer Science Kavala Greece University of Stellenbosch Department of Viticulture and Oenology Stellenbosch South Africa University of Stellenbosch Faculty of AgriSciences Department of Horticultural Sciences Postharvest Technology Research Stellenbosch South Africa Institute for Wine Biotechnology University of Stellenbosch Department of Viticulture and Oenology Stellenbosch South Africa

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

Wavenumbers of high absolute value of correlation coefficient to Total Soluble Solids (TSS), pH, or Titratable Acidity (TA) were selected from reflection Fourier transform near infrared (FT -NIR) spectra of intact grape berries of the white variety Thompson Seedless. Multiple linear regression (MLR) and partial least squares (PLS) regression were applied to the spectra to construct trained regression models able to predict TSS, pH, and TA. Square Pearson's correlation coefficient (R2) and the Mean Square Error (MSE) were used to evaluate the precision of prediction. TSS content was predicted with R2 score of 0.972 and MSE 0.094 using MLR and with R2 0.926 and MSE 0.223 using PLS regression. The pH prediction scores were R2 0.812 and MSE 0.002 with MLR. With PLS regression the values were R2 0.485 and MSE 0.004. TA can be predicted only from the second derivatives of the spectra. MLR produced R2 for prediction 0.745 and MSE 0.076, while the scores using PLS regression were R2 0.648 and MSE 0.114. It was concluded that variable selection could greatly improve the prediction accuracy. The appropriateness of the two regression methods depends on the structure of the spectra dataset and on the characteristics whose prediction is sought. © 2022 University of Split, FESB.

关键词： Multiple linear regression

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ToP-ToM: Trust-aware Robot Policy with Theory of Mind

ToP-ToM: Trust-aware Robot Policy with Theory of Mind

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chuang Yu Baris Serhan Angelo Cangelosi Computer Science Department UCL Interaction Centre University College London London United Kingdom Cognitive Robotics Lab Manchester Centre for Robotics and AI The University of Manchester Manchester United Kingdom

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communication and team collaboration. In this paper, we investigated trust-aware robot policy with the theory of mind in a multi-agent setting where a human collaborates with a robot against another human opponent. We show that by only focusing on team performance, the robot may resort to the reverse psychology trick, which poses a significant threat to trust maintenance. The human’s trust in the robot will collapse when they discover deceptive behavior by the robot. To mitigate this problem, we adopt the robot theory of mind model to infer the human’s trust beliefs, including true belief and false belief (an essential element of ToM). We designed a dynamic trust-aware reward function based on different trust beliefs to guide the robot policy learning, which aims to balance between avoiding human trust collapse due to robot reverse psychology and leveraging its potential to boost team performance. The experimental results demonstrate the importance of the ToM-based robot policy for human-robot trust and the effectiveness of our robot ToM-based robot policy in multiagent interaction settings.

关键词： Psychology Focusing Collaboration Maintenance Robots

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immersiTea: Exploring Multisensory Virtual Reality Environments to Enrich Bubble Tea Drinking Experiences

immersiTea: Exploring Multisensory Virtual Reality Environme...

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

作者： Zheng, Yuchen Deng, Jialin Wang, Hongyue Mueller, Florian Pan, Xueni Gillies, Marco Fyfe Pietro Department of Computing Goldsmiths University of London London United Kingdom School of Computer Science University of Bristol Bristol United Kingdom Exertion Games Lab. Department of Human-Centred Computing Monash University Melbourne VIC Australia SEEVR Lab. Department of Computing Goldsmiths University of London London United Kingdom

ISBN: (纸本)9798400713958

HCI is increasingly interested in multisensory experiences;however, multisensory drinking seems to be underexplored. We introduce "immersiTea", a novel multisensory virtual reality (VR) system that aims to enrich the experience of drinking low-sugar bubble tea. Leveraging crossmodal correspondences between visual, auditory, and flavor stimuli, immersiTea integrates immersive VR environments with physical drinking actions to enhance sensory and emotional engagement. Through a mixed-method evaluation (n = 31), we found that the VR environments can elevate participants’ enjoyment and sweetness perception while reducing reliance on sugar. Participants highlighted the role of playful interactivity and sensory congruence in drinking, making it a more engaging experience. These findings contribute to the growing field of human-Food interaction by showcasing the potential of immersive technologies to promote healthier and more enjoyable consumption practices. This work demonstrates the potential of immersiTea to not only enrich the drinking experience but also inspire future advancements in multisensory beverage design. © 2025 Copyright held by the owner/author(s).

关键词： Cross-modal Correspondences Food science human-food interaction Multisensory Experience Design Virtual Reality

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Minion: A Technology Probe for Resolving Value Conflicts through Expert-Driven and User-Driven Strategies in AI Companion Applications

arXiv

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arXiv 2024年

作者： Fan, Xianzhe Xiao, Qing Zhou, Xuhui Su, Yuran Lu, Zhicong Sap, Maarten Shen, Hong Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science City University of Hong Kong Hong Kong SAR China Language Technologies Institute Carnegie Mellon University PittsburghPA United States

AI companions based on large language models can role-play and converse very naturally. When value conflicts arise between the AI companion and the user, it may offend or upset the user. Yet, little research has examined such conflicts. We first conducted a formative study that analyzed 151 user complaints about conflicts with AI companions, providing design implications for our study. Based on these, we created Minion, a technology probe to help users resolve human-AI value conflicts. Minion applies a user-empowerment intervention method that provides suggestions by combining expert-driven and user-driven conflict resolution strategies. We conducted a technology probe study, creating 40 value conflict scenarios on *** and Talkie. 22 participants completed 274 tasks and successfully resolved conflicts 94.16% of the time. We summarize user responses, preferences, and needs in resolving value conflicts, and propose design implications to reduce conflicts and empower users to resolve them more effectively. Copyright © 2024, The Authors. All rights reserved.

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Tensor Embedding: A Supervised Framework for human Behavioral Data Mining and Prediction 11

Tensor Embedding: A Supervised Framework for Human Behaviora...

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Hosseinmardi, Homa Ghasemian, Amir Lerman, Kristina Narayanan, Shrikanth Ferrara, Emilio University of Pennsylvania Department of Computer and Information Science PhiladelphiaPA United States Yale University Human Nature Lab New HavenCT United States University of Southern California Information Sciences Institute Marina Del ReyCA United States

ISBN: (纸本)9798350302639

Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data, providing temporal characterization of individual behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual and insightful assessments of individual behavior? Such data is noisy, incomplete, and collected from multiple sources, each with a different temporal resolution and dimensionality. In addition, longitudinal studies tend to examine a number of aspects of human behavior, such as well being, performance, or personality, which poses a challenge for handcrafted features engineering. To address these challenges, we propose supervised tensor embedding (STE), an algorithm for the joint decomposition of input and target variables in high-dimensional multimodal data. Latent features obtained from STE can be fed into any regression model for the estimation of the target variable(s). Following the embedding of higher-order data, features from different sources can be coupled. Additionally, we demonstrate that feature selection on higher-order data can enhance performance. The efficiency of our method is tested on two real-world datasets consisting of 29 target variables. Compared to the state of the art, we find that (i) our method outperforms others in 21 prediction tasks, and (ii) there are a few aspects of human behavior that are unpredictable regardless of the method, which can be the limitation of multilinear methods or lack of proper independent variables. © 2023 IEEE.

关键词： Tensors

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

DRDM: A Disentangled Representations Diffusion Model for Syn...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Enbo Huang Yuan Zhang Faliang Huang Guangyu Zhang Yang Liu Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University 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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Person image synthesis with controllable 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 photorealistic 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. The associated project can be found at https://***/lovemusiceb/DRDM

关键词： Limbs Image synthesis Shape Clothing Semantics Production Feature extraction Diffusion models Sampling methods Speech processing

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RJoules: An Energy Measurement Tool for R

RJoules: An Energy Measurement Tool for R

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Rajrupa Chattaraj Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Systems and Human Analytics Lab Indian Institute of Technology Tirupati India

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

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DBJoules: An Energy Measurement Tool for Database Management Systems

arXiv

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arXiv 2023年

作者： Lella, Hemasri Sai Chattaraj, Rajrupa Manasa, Kurra Chimalakonda, Sridhar Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

In the rapidly evolving landscape of modern data-driven technologies, software relies on large datasets and constant data center operations using various database systems to support computation-intensive tasks. As energy consumption in software systems becomes a growing concern, selecting the right database from energy-efficiency perspective is also critical. To address this, we introduce DBJoules, a tool that measures the energy consumption of activities in database systems. DBJoules supports energy measurement of CRUD operations for four popular databases. Through evaluations on two widely-used datasets, we identify disparities of 7% to 38% in the energy consumption of these databases. Hence, the goal is to raise developer awareness about the effect of running queries in different databases from an energy consumption perspective, enabling them to select appropriate database for sustainable usage. The tool’s demonstration is available at https://***/D1MTZum0jok and related artifacts at https://***/DBJoules/. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscle Stimulation for Social Play

Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscl...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Patibanda, Rakesh Overdevest, Nathalie Nisal, Shreyas Saini, Aryan Elvitigala, Don Samitha Knibbe, Jarrod van den Hoven, Elise Mueller, Florian Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Electrical Engineering and Computer Science Faculty of Engineering The University of Queensland St LuciaQLD Australia Materialising Memories & Visualisation Institute University of Technology Sydney Sydney Australia Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400705830

Traditional games like "Tag" rely on shared control via inter-body interactions (IBIs) – touching, pushing, and pulling – that foster emotional and social connection. Digital games largely limit IBIs, with players using their bodies as input to control virtual avatars instead. Our "Shared Bodily Fusion" approach addresses this by fusing players’ bodies through a mediating computer, creating a shared input and output system. We demonstrate this approach with "Hidden Touch", a game where a novel social electrical muscle stimulation system transforms touch (input) into muscle actuations (output), facilitating IBIs. Through a study (n=27), we identifed three player experience themes. Informed by these fndings and our design process, we mapped their trajectories across our three experiential spaces – threshold, tolerance, and precision – which collectively form our design framework. This framework facilitates the creation of future digital games where IBIs are intrinsic, ultimately promoting the many benefts of social play. © 2024 Copyright held by the owner/author(s).

关键词： Muscle

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Assessment and Quantification of Virtual Reality Induced Sickness in Relation to Age and Gender: A Multi-Modal Approach

Assessment and Quantification of Virtual Reality Induced Sic...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Abhiram Reddy Jin Ryong Kim Multimodal Interactions Lab University of Texas at Dallas Allen High School Allen Texas Department of Computer Science and Director of the Multimodal Interaction Lab University of Texas at Dallas Dallas Texas

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

This research paper examines the prevalence and severity of Virtual Reality (VR) induced sickness across various genders and age groups. Utilizing a novel multimodal methodology, we integrated objective physiological data with subjective self-reporting to capture a comprehensive understanding of VR sickness in relation to age and gender. Our study recruited 54 participants from the general population, consisting of an equal number of men and women distributed among four age groups. We recorded heart rate variability (HRV) and Electrodermal Activity (EDA) before and after exposure to a standardized 3-minute VR clip. Additionally, participants completed a questionnaire to self-report symptoms of VR sickness. By analyzing both sets of data, our research reveals that both age and gender impact the likelihood of experiencing VR sickness, with older individuals and females generally experiencing higher levels of discomfort induced by VR.

关键词： Visualization Correlation Sociology Virtual reality Physiology Heart rate variability

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