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Will Health Experts Adopt a Clinical Decision Support System for Game-Based Digital Biomarkers? Investigating the Impact of Different Explanations on Perceived Ease-of-Use, Perceived Usefulness, and Trust 25

Will Health Experts Adopt a Clinical Decision Support System...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Chen, Yu Verbert, Katrien Gerling, Kathrin Vanden Abeele, Marie-Elena Vanden Abeele, Vero Department of Computer Sciences Ku Leuven Leuven Belgium Human-Computer Interaction and Accessibility Karlsruhe Institute of Technology Karlsruhe Germany Department of Geriatrics Jessa Hospital Hasselt Belgium

ISBN: (纸本)9798400713064

This paper explores the adoption of a clinical decision support system (cDSS) utilizing game-based digital biomarkers for diagnosing mild cognitive impairment (MCI). Specifically, it investigates how different explanation methods, with a focus on data-centric explanations, impact perceived ease-of-use, perceived usefulness, and trust among healthcare professionals (HCPs). Through a qualitative study with 12 HCPs, we assess their interactions with an explainable AI (XAI)-enriched cDSS. The findings indicate that HCPs are open to adopting XAI-enriched cDSS to communicate the outcomes of game-based digital biomarkers. HCPs preferred to receive key diagnostic information in an easily digestible format. Both local explanations of intra-personal evolutionary data and global overview of normative data were found to be valuable for interpreting digital biomarkers. HCPs tended to trust the machine learning algorithms as a black box, but they considered the dataset used for training the model and the outcome prediction to be crucial. Therefore, presenting the uncertainty alongside the prediction was deemed important. These insights underscore the importance of designing cDSS tools that foster trust through clear, actionable explanations, paving the way for improved decision-making in clinical contexts. © 2025 Copyright held by the owner/author(s).

关键词： Electronic health record

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

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Tsinghua science and Technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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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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Unlocking the potential of public opinions for niche development using acceptance mining

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Methodological Innovations 2023年第1期16卷 78-90页

作者： Digmayer, Claas Jakobs, Eva-Maria Department of Textlinguistics and Technical Communication Human-Computer Interaction Center RWTH Aachen University Aachen Germany

This paper proposes a combination of text mining and acceptance research as a methodological approach (“acceptance mining”) to facilitate the development of innovative technologies in niches. At the example of recharging infrastructures for electric vehicles, the approach is explored on a sample of over 20,000 user comments collected from social media and discussion forums. The results indicate that acceptance mining provides valuable insights into the demands of the public during all phases of niche development which allow shaping niches in socially acceptable ways. In particular, the method reveals requirements and risks of niche-development measures such as shielding, nurturing, and empowering niches as perceived by the public. The results are discussed regarding the potentials of acceptance mining for developing innovations in niches as well as its limitations and possibilities for optimization. Acceptance mining and its application to a case study are especially of interest for researchers and practitioners involved in niche development;the study results provide valuable insights for professionals engaged in developing recharging infrastructures for electric vehicles. © The Author(s) 2023.

关键词： Acceptance mining combination of quantitative and qualitative methods development of technological innovations in niches social acceptance of niche development technology development text mining

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Dense 3D Gaussian Splatting Initialization for Sparse Image Data

Dense 3D Gaussian Splatting Initialization for Sparse Image ...

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45th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2024

作者： Seibt, S. Chang, T. von Rymon Lipinski, B. Latoschik, M.E. Game Tech Lab. Faculty of Computer Science Nuremberg Institute of Technology Nuremberg Germany Human-Computer Interaction Group Institute of Computer Science University of Wuerzburg Wuerzburg Germany

ISBN: (纸本)9783038682394

This paper presents advancements in novel-view synthesis with 3D Gaussian Splatting (3DGS) using a dense and accurate SfM point cloud initialization approach. We address the challenge of achieving photorealistic renderings from sparse image data, where basic 3DGS training may result in suboptimal convergence, thus leading to visual artifacts. The proposed method enhances precision and density of initially reconstructed point clouds by refining 3D positions and extrapolating additional points, even for difficult image regions, e.g. with repeating patterns and suboptimal visual coverage. Our contributions focus on improving "Dense Feature Matching for Structure-from-Motion" (DFM4SfM) based on a homographic decomposition of the image space to support 3DGS training: First, a grid-based feature detection method is introduced for DFM4SfM to ensure a well-distributed 3D Gaussian initialization uniformly over all depth planes. Second, the SfM feature matching is complemented by a geometric plausibility check, priming the homography estimation and thereby improving the initial placement of 3D Gaussians. Experimental results on the NeRF-LLFF dataset demonstrate that this approach achieves superior qualitative and quantitative results, even for fewer views, and the potential for a significantly accelerated 3DGS training with faster convergence. © 2024 The Authors.

关键词： Gaussian distribution

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InterLink: Linking Text with Code and Output in Computational Notebooks 25

InterLink: Linking Text with Code and Output in Computationa...

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

作者： Lin, Yanna Yang, Leni Li, Haotian Qu, Huamin Moritz, Dominik Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Microsoft Research Asia Beijing China

ISBN: (纸本)9798400713941

Computational notebooks, widely used for ad-hoc analysis and often shared with others, can be difficult to understand because the standard linear layout is not optimized for reading. In particular, related text, code, and outputs may be spread across the UI making it difficult to draw connections. In response, we introduce InterLink, a plugin designed to present the relationships between text, code, and outputs, thereby making notebooks easier to understand. In a formative study, we identify pain points and derive design requirements for identifying and navigating relationships among various pieces of information within notebooks. Based on these requirements, InterLink features a new layout that separates text from code and outputs into two columns. It uses visual links to signal relationships between text and associated code and outputs and offers interactions for navigating related pieces of information. In a user study with 12 participants, those using InterLink were 13.6% more accurate at finding and integrating information from complex analyses in computational notebooks. These results show the potential of notebook layouts that make them easier to understand. © 2025 Copyright held by the owner/author(s).

关键词： Interactive computer systems

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VisiMark: Characterizing and Augmenting Landmarks for People with Low Vision in Augmented Reality to Support Indoor Navigation 25

VisiMark: Characterizing and Augmenting Landmarks for People...

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

作者： Chen, Ruijia Jiang, Junru Maheshwary, Pragati Cochran, Brianna R Zhao, Yuhang Department of Computer Sciences University of Wisconsin-Madison MadisonWI United States Human Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713941

Landmarks are critical in navigation, supporting self-orientation and mental model development. Similar to sighted people, people with low vision (PLV) frequently look for landmarks via visual cues but face difficulties identifying some important landmarks due to vision loss. We first conducted a formative study with six PLV to characterize their challenges and strategies in landmark selection, identifying their unique landmark categories (e.g., area silhouettes, accessibility-related objects) and preferred landmark augmentations. We then designed VisiMark, an AR interface that supports landmark perception for PLV by providing both overviews of space structures and in-situ landmark augmentations. We evaluated VisiMark with 16 PLV and found that VisiMark enabled PLV to perceive landmarks they preferred but could not easily perceive before, and changed PLV's landmark selection from only visually-salient objects to cognitive landmarks that are more important and meaningful. We further derive design considerations for AR-based landmark augmentation systems for PLV. © 2025 Copyright held by the owner/author(s).

关键词： Disabled persons

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. ©2024 IEEE.

关键词： Optical flows

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DFM4SFM - Dense Feature Matching for Structure from Motion

DFM4SFM - Dense Feature Matching for Structure from Motion

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2023 IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023

作者： Seibt, Simon Von Rymon Lipinski, Bartosz Chang, Thomas Latoschik, Marc Erich Nuremberg Institute of Technology Game Tech Lab Faculty of Computer Science Nuremberg Germany Institute of Computer Science University of Wuerzburg Human-Computer Interaction Group Wuerzburg Germany

ISBN: (纸本)9798350302585

Structure from motion (SfM) is a fundamental task in computer vision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in the early stages of SfM. So in this work, we propose a novel method for computing image correspondences based on dense feature matching (DFM) using homographic decomposition: The underlying pipeline provides refinement of existing matches through iterative rematching, detection of occlusions and extrapolation of additional matches in critical image areas between image pairs. Our main contributions are improvements of DFM specifically for SfM, resulting in global refinement and global extrapolation of image correspondences between related views. Furthermore, we propose an iterative version of the Delaunay-triangulation-based outlier detection algorithm for robust processing of repeated image patterns. Through experiments, we demonstrate that the proposed method significantlv improves the reconstruction accuracy. © 2023 IEEE.

关键词： computer vision

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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