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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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Korean Language NLP Model Based Emotional Analysis of LGBTQ Social Media Communities 17

Korean Language NLP Model Based Emotional Analysis of LGBTQ ...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Chi, Younghyun Kim, Jang Hyun Sun, Seungjong College of Computing and Informatics Sungkyunkwan University Department of Computer Science Suwon Korea Republic of Intelligence Interaction Sungkyunkwan University Department of Interaction Science Department of Human-Artificial Seoul Korea Republic of

ISBN: (纸本)9781665453486

Sexual minorities are increasingly gaining social visibility and legal rights guarantees at the constitutional level across much of the world, from South America, the United States, and Europe to Japan, Taiwan, and Thailand. At the same time, the COVID-19 pandemic has brought on significant mental health challenges for the public due to accompanying social and economic impact and measures, most of them adverse. Given pre-existing studies highlighting the minority demographic's vulnerability to depression and other mental health symptoms, and the increasing availability of accessible NLP tools, datasets, and models, this paper uses an emotional classification model to analyze emotional trends in queer communities on social media. Using KoBERT with a pre-labelled dataset containing some forty thousand scraped social media posts labelled with emotions, patterns of emotional expression on Twitter in the queer community is revealed. Resulting data provided a validation of the viability of this method of analyzing trends in negative and positive emotional expression as well as the impact COVID-19 had on online queer communities in early 2020 but revealed limitations. © 2023 IEEE.

关键词： Natural language processing systems

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Folk Tales of IoT: Understanding the Impact of Stories on Users' Positive and Negative Perceptions of Smart Home IoT Devices 25

Folk Tales of IoT: Understanding the Impact of Stories on Us...

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

作者： Zhang-Kennedy, Leah Valiquette, Michaela Chen, An Bella Hadan, Hilda Suh, Sangho Stratford School of Interaction Design and Business University of Waterloo Waterloo ON Canada Human-Computer Interaction Carleton University Ottawa ON Canada Cheriton School of Computer Science University of Waterloo Waterloo ON Canada Department of Computer Science University of Toronto Toronto ON Canada

ISBN: (纸本)9798400713941

This study examines how anecdotal stories from friends, peers, and online sources influence non-experts' perceptions and behaviors toward smart home IoT devices. We surveyed 263 participants, collecting narratives that either positively or negatively influenced their perception of IoT devices, which they retold in text and comic formats to encourage deeper reflection. Thematic analysis of the narratives, combined with quantitative survey data, reveals that stories significantly impact trust and willingness to use and adopt IoT devices. Negative stories, particularly those concerning security, privacy, and device unreliability, reduced trust and usage, while positive stories about home safety through monitoring and improved quality of life increased interest in IoT devices. Perceptions of different IoT devices varied based on the themes associated with the stories. The findings highlight the powerful role of storytelling in driving consumer acceptance of technology. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Folk Models Internet of Things Mental Models Privacy and Security Smart Home Stories Technology Acceptance

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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

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Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 969-985页

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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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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Chameleon: Unobtrusive Substitution of Real-World Obstacles in VR with Risk-Level-Aware Adaptation

Chameleon: Unobtrusive Substitution of Real-World Obstacles ...

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

作者： Yu, Yichen Jin, Qiao Department of Computer Science University of Rochester Rochester NY United States Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States

ISBN: (纸本)9798400713958

In VR environments, free movement in real space enhances immersion but increases the risk of collisions with real-world obstacles. Prior solutions investigated using substitute obstacles with context-related digital objects in VR but often treat all obstacles uniformly without considering their varying levels of risk. This oversight might result in reduced awareness for high-risk obstacles and a missed opportunity to utilize low-risk objects to enhance haptic feedback and interactivity in VR. In this study, we propose Chameleon, a system that classifies real-world obstacles by their varying risk levels and substitutes them with context-related virtual objects in VR. The substitutions are designed to align with the obstacles’ real-world risk levels to ensure both safety and immersion. A preliminary heuristic evaluation assessed the usability of using visual textures to implicitly represent obstacle risk levels. © 2025 Copyright held by the owner/author(s).

关键词： Cross-reality System Obstacle Avoidance Safety Virtual Reality

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Virtual Displays for Knowledge Work: Extending or Replacing Physical Monitors for More Flexibility and Screen Space

Virtual Displays for Knowledge Work: Extending or Replacing ...

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

作者： Leonardo Pavanatto Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

In this paper, we explore the potential of virtual displays in sup-porting knowledge work, focusing on scenarios where conventional physical monitors fall short. Our research investigates the feasibility and productivity costs of extending or replacing physical monitors with augmented and virtual reality displays. We present three com-pelling use cases, illustrating how virtual displays enhance flexibility, adaptability, and customization in remote and diverse work settings. Lessons learned from user studies highlight hardware and interface design challenges, emphasizing the need for larger resolution and field of view in head-worn displays (HWD). We conclude the paper with research opportunities and a call to address the evolving demands of knowledge work interfaces.

关键词： Productivity Three-dimensional displays Head-mounted displays Focusing Virtual reality User interfaces Hardware

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Enhancing Impulse Response-Based SSVEP Decoding Method by Incorporating Temporal Local Correlation 7

Enhancing Impulse Response-Based SSVEP Decoding Method by In...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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Planet Purifiers: A Collaborative Immersive Experience Proposing New Modifications to HOMER and Fishing Reel interaction Techniques

Planet Purifiers: A Collaborative Immersive Experience Propo...

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

作者： Alexander Giovannelli Fionn Murphy Trey Davis Chaerin Lee Rehema Abulikemu Matthew Gallagher Sahil Sharma Lee Lisle Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents our solution to the 2025 3DUI Contest challenge. We aimed to develop a collaborative, immersive experience that raises awareness about trash pollution in natural landscapes while enhancing traditional interaction techniques in virtual environments. To achieve these objectives, we created an engaging multiplayer game where one user collects harmful pollutants while the other user provides medication to impacted wildlife using enhancements to traditional interaction techniques: HOMER and Fishing Reel. We enhanced HOMER to use a cone volume to reduce the precise aiming required by a selection raycast to provide a more efficient means to collect pollutants at large distances, coined as FLOW-MATCH. To improve the animal feed distribution to wildlife far away from the user with Fishing Reel, we created RAWR-XD, an asymmetric bi-manual technique to more conveniently adjust the reeling speed using the non-selecting wrist rotation of the user.

关键词： Wrist Three-dimensional displays Pollution Planets Wildlife Collaboration Virtual environments Immersive experience Games Food products

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