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3rd IEEE International Conference on Transdisciplinary AI, TransAI 2021

作者： Raval, Aaditya Anwar, Mohd Human-Centered AI Lab Computer Science Department North Carolina Agricultural and Technical State University GreensboroNC United States

ISBN: (纸本)9781665434126

A unique digital identity, user ID, is essential for everyday online activities in the Internet era. These user IDs represent a user in a digital environment using stored credentials on a system called authentication system. It is possible to capture unique patterns of user movements from smartphone sensor data. This paper presents a framework for passive user identification and authentication using onboard sensors of an Android smartphone. Using this framework, we propose a data preprocessing scheme that uses the absolute difference of consecutive repeated measurements of 7 onboard sensors. We developed 5 models for user identification and authentication using various machine learning and deep learning methods. The experimental results and performance assessment conclude that the Sequential Neural Network (SNN) model performs best with 98.24% accuracy for authenticating users (binary classification) and 84.41% accuracy during multi-class classification for user identification. Furthermore, all the models developed for this research, namely MLP, SNN, CNN, SVM, and Bi-LSTM, provide 100% precision during binary classification for passive user authentication. Our models were trained on 556,746 sensor data samples, each having 20 features. These features include the proximity sensor data, light sensor data, triaxial measurements from accelerometers, gravity sensors, gyroscopes, magnetometers, and rotational vector sensors. We study the possible influence of absolute difference samples for user identification and authentication. © 2021 IEEE.

关键词： Smartphones

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Semantics-Controlled Gaussian Splatting for Outdoor Scene Reconstruction and Rendering in Virtual Reality

Semantics-Controlled Gaussian Splatting for Outdoor Scene Re...

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IEEE Annual International Symposium Virtual Reality

作者： Hannah Schieber Jacob Young Tobias Langlotz Stefanie Zollmann Daniel Roth Technical University of Munich Human-Centered Computing and Extended Reality Lab TUM University Hospital Clinic for Orthopedics and Sports Orthopedics Munich Germany Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Computer Science University of Otago Dunedin New Zealand Department of Computer Science Aarhus University Aarhus Denmark

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Advancements in 3D rendering like Gaussian Splatting (GS) allow novel view synthesis and real-time rendering in virtual reality (VR). However, GS-created 3D environments are often difficult to edit. For scene enhancement or to incorporate 3D assets, segmenting Gaussians by class is essential. Existing segmentation approaches are typically limited to certain types of scenes, e.g., "circular" scenes, to determine clear object boundaries. However, this method is ineffective when removing large objects in non-"circling" scenes such as large outdoor *** propose Semantics-Controlled GS (SCGS), a segmentation-driven GS approach, enabling the separation of large scene parts in uncontrolled, natural environments. SCGS allows scene editing and the extraction of scene parts for VR. Additionally, we introduce a challenging outdoor dataset, overcoming the "circling" setup. We outperform the state-of-the-art in visual quality on our dataset and in segmentation quality on the 3D-OVS dataset. We conducted an exploratory user study, comparing a 360-video, plain GS, and SCGS in VR with a fixed viewpoint. In our subsequent main study, users were allowed to move freely, evaluating plain GS and SCGS. Our main study results show that participants clearly prefer SCGS over plain GS. We overall present an innovative approach that surpasses the state-of-the-art both technically and in user experience.

关键词： Visualization Three-dimensional displays Semantics Virtual reality User interfaces Rendering (computer graphics) User experience Real-time systems

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User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions 25

User-Driven Value Alignment: Understanding Users' Perception...

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

作者： Xianzhe Fan Qing Xiao Xuhui Zhou Jiaxin Pei Maarten Sap Zhicong Lu Hong Shen Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Language Technologies Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Stanford University Stanford California USA Language Technologies Institute Carnegie Mellon Unviersity Pittsburgh Pennsylvania USA Department of Computer Science George Mason University Fairfax Virginia USA

ISBN: (纸本)9798400713941

Content Warning: This paper presents textual examples that may be offensive or *** language model-based AI companions are increasingly viewed by users as friends or romantic partners, leading to deep emotional bonds. However, they can generate biased, discriminatory, and harmful outputs. Recently, users are taking the initiative to address these harms and re-align AI companions. We introduce the concept of user-driven value alignment, where users actively identify, challenge, and attempt to correct AI outputs they perceive as harmful, aiming to guide the AI to better align with their values. We analyzed 77 social media posts about discriminatory AI statements and conducted semi-structured interviews with 20 experienced users. Our analysis revealed six common types of discriminatory statements perceived by users, how users make sense of those AI behaviors, and seven user-driven alignment strategies, such as gentle persuasion and anger expression. We discuss implications for supporting user-driven value alignment in future AI systems, where users and their communities have greater agency. © 2025 Copyright held by the owner/author(s).

关键词： User-Driven Value Alignment

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design 25

Vision-Based Multimodal Interfaces: A Survey and Taxonomy fo...

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

作者： Yongquan ‘Owen’ Hu Jingyu Tang Xinya Gong Zhongyi Zhou Shuning Zhang Don Samitha Elvitigala Florian ‘Floyd’ Mueller Wen Hu Aaron J. Quigley School of Computer Science and Engineering University of New South Wales Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The University of Tokyo Tokyo Japan Tsinghua University Beijing China Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Computer Science and Engineering University of New South Wales Syndey Australia CSIRO's Data61 Sydney Australia and University of New South Wales Sydney Australia

ISBN: (纸本)9798400713941

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： survey system context aware computer vision vision based interface visual data multimodal artificial intelligence

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Roopkotha: A Companion Robot for Enhancing Interactive Storytelling with Natural interaction

Roopkotha: A Companion Robot for Enhancing Interactive Story...

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Image Processing and Robotics (ICIP), International Conference on

作者： Kazi Mayesha Mehzabin Md. Zahidul Islam Md. Ashaduzzaman Nur Mohammad Shidujaman Hooman Samani Haipeng Mi RIoT Center and Department of Computer Science & Engineering Independent University Bangladesh Dhaka Bangladesh Creative Computing Institute University of the Arts London London United Kingdom Media and Interaction Lab Academy of Arts and Design Tsinghua University Beijing China

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

Roopkotha is a storytelling robot that seamlessly combines traditional storytelling methods and technology, creating a captivating robot storyteller. We are creating a special prototype in the world of robots that tells stories in a way that is easy to understand and enjoy. In this era of technological advancement, Roopkotha combines voice recognition with Bangla Language processing, emotion recognition, human behavior detection. Roopkotha aims to revolutionize the way stories are told and engage with users on a deep emotional level. Furthermore, Roopkotha is equipped with advanced facial expressions and emotion recognition technology. The emotion recognition feature helps the robot to have a profound connection with the users.

关键词： Emotion recognition Image recognition human-robot interaction Prototypes Speech recognition Linguistics Feature extraction

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

arXiv

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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 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 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

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Surface defect identification using Bayesian filtering on a 3D mesh

arXiv

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arXiv 2025年

作者： Vedove, Matteo Dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025, CC BY-SA.

关键词： Mesh generation

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Surface defect identification using Bayesian filtering on a 3D mesh

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Measurement: Sensors 2025年 38卷

作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

关键词： Mesh generation

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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