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16th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2023

作者： Hu, Wenjia Jin, Zhifei Carley, Kathleen M. Human-Computer Interaction Institute PittsburghPA15213 United States Software and Societal Systems Department Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783031431289

Studying the expression of vulnerability – the psychological state that arises in moments of uncertainty, risk, and emotional exposure – is key to understanding how individuals cope during time of crisis. In this study, we synthesize past theoretical and qualitative work on vulnerability and present a psycholinguistic dictionary featuring seven different lexicons commonly associated with vulnerability language: accusation, incompetence, lack of trust, riot, out-group speech, agency, and helplessness. To validate our dictionary, we apply the identified lexicons to three different datasets and then generate samples for evaluation by two independent human labelers. The comparison between the human-labeled and machine-generated results demonstrates the dictionary’s robustness, achieving a Cohen’s Kappa of 0.66 in inter-rater reliability among two labelers and an overall accuracy of 0.74 when treating human label as the ground truth. With the dictionary, we aim to provide a nuanced understanding and detection of vulnerability language on social media content and provide a foundation for text feature extraction for traditional machine learning methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes 10

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at H...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Ke Tang, Jiankai Jiang, Zhang Wang, Yuntao Liu, Xiaojing Li, Dong Shi, Yuanchun Qinghai University Department of Computer Technology and Applications Qinghai810016 China National Key Laboratory of Human Factors Engineering Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

ISBN: (纸本)9798331520861

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO 2 predictions by 7. 6 % and 1 0. 6 % compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2. 5 % for SpO 2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation by 1 7. 8 %. © 2024 IEEE.

关键词： Photoplethysmography

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CL-MIL: Multi-Instance Learning on Whole-Slide Images with Self-Supervised Contrastive Learning 10

CL-MIL: Multi-Instance Learning on Whole-Slide Images with S...

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10th International Conference on computer and Communications, ICCC 2024

作者： Xiu, Pengfei Liu, Dongying Wang, Sheng Wang, Hongliang Wang, Wei Li, Shun Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shengjing Hospital of China Medical University Department of Gastroenterology Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

Histopathological examination of tissue slides is considered the gold standard for clinical cancer diagnosis. However, it heavily relies on pathologists' subjective judgment and involves substantial workload. Existing convolutional neural network (CNN)-based whole slide image (WSI) diagnostic methods suffer from high annotation costs and complex data acquisition processes. Therefore, this study proposes a weakly supervised multiple instance learning method based on self-supervised contrastive learning (CL-MIL) for WSI classification. By integrating self-supervised contrastive learning strategies, leveraging data augmentation through H&E stain separation, and introducing causal intervention via backdoor adjustment to mitigate dataset bias, our approach significantly enhances feature representation and classification performance in pathological images. Experimental results on the Camelyon16 and TCGA lung cancer datasets demonstrate that our method outperforms other comparable WSI classification models across various evaluation metrics, achieving higher diagnostic efficiency and accuracy. Additionally, our method exhibits good interpretability, enabling precise localization of pathological regions and providing robust technical support for clinical applications. © 2024 IEEE.

关键词： Self-supervised learning

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AMSFuse:Adaptive Multi-Scale Feature Fusion Network for Diabetic Retinopathy Classification

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computers, Materials & Continua 2025年第3期82卷 5153-5167页

作者： Chengzhang Zhu Ahmed Alasri Tao Xu Yalong Xiao Abdulrahman Noman Raeed Alsabri Xuanchu Duan Monir Abdullah School of Computer Science and Engineering Central South UniversityChangsha410083China School of Humanities Central South UniversityChangsha410083China Department of Human Anatomy and Histology & Embryology Basic Medical SciencesChangsha Health Vocational CollegeChangsha410100China Glaucoma Institute Changsha Aier Eye HospitalChangsha410000China Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia

Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people *** widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective *** learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and ***,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic *** the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological *** address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual ***,our model incorporates global features for extracting high-level contextual information from retinal ***,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR *** global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR *** model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.

关键词： Diabetic retinopathy multi-scale feature fusion global features local features integrated attention mechanism retinal images

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Personalization Trade-offs in Designing a Dialogue-based Information System for Support-Seeking of Sexual Violence Survivors 22

Personalization Trade-offs in Designing a Dialogue-based Inf...

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2022 CHI Conference on human Factors in Computing Systems, CHI 2022

作者： Kim, Hyeok Hwang, Youjin Lee, Jieun Kwon, Youngjin Park, Yujin Lee, Joonhwan Computer Science Northwestern University EvanstonIL United States Human-Computer Interaction+Design Lab. Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781450391573

The lack of reliable, personalized information often complicates sexual violence survivors' support-seeking. Recently, there is an emerging approach to conversational information systems for support-seeking of sexual violence survivors, featuring personalization with wide availability and anonymity. However, a single best solution might not exist as sexual violence survivors have different needs and purposes in seeking support channels. To better envision conversational support-seeking systems for sexual violence survivors, we explore personalization trade-offs in designing such information systems. We implement a high-fidelity prototype dialogue-based information system through four design workshop sessions with three professional caregivers and interviewed with four self-identified survivors using our prototype. We then identify two forms of personalization trade-offs for conversational support-seeking systems: (1) specificity and sensitivity in understanding users and (2) relevancy and inclusiveness in providing information. To handle these trade-offs, we propose a reversed approach that starts from designing information and inclusive tailoring that considers unspecified needs, respectively. © 2022 ACM.

关键词： Information systems

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MODEL AGNOSTIC INTERPRETABILITY FOR MULTIPLE INSTANCE LEARNING 10

MODEL AGNOSTIC INTERPRETABILITY FOR MULTIPLE INSTANCE LEARNI...

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10th International Conference on Learning Representations, ICLR 2022

作者： Early, Joseph Evers, Christine Ramchurn, Sarvapali Agents Interaction and Complexity Group Department of Electronics and Computer Science University of Southampton United Kingdom

In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Classification (of information)

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Technical Feasibility, Financial Viability, and Clinician Acceptance: On the Many Challenges to AI in Clinical Practice

Technical Feasibility, Financial Viability, and Clinician Ac...

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AAAI 2021 Fall Symposium on human Partnership with Medical AI: Design, Operationalization, and Ethics, AAAI-human 2021

作者： Yildirim, Nur Zimmerman, John Preum, Sarah Human-Computer Interaction Institute Carnegie Mellon University United States Computer Science Department Dartmouth College

Artificial intelligence (AI) applications in healthcare offer the promise of improved decision making for clinicians, and better healthcare outcomes for patients. While technical AI advances in healthcare showcase impressive performances in lab settings, they seem to fail when moving to clinical practice. In this position paper, we reflect on our experiences of designing for AI acceptance and discuss three interrelated challenges to AI in clinical practice: technical feasibility, financial viability, and clinician acceptance. We discuss each challenge and their implications for future research in clinical AI. We encourage the research community to take on these lenses in collaboratively tackling the challenges of moving AI systems into real-world healthcare applications. Copyright © 2021, for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Decision making

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TransCell:In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning

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Genomics, Proteomics & Bioinformatics 2024年第2期22卷 157-170页

作者： Shan-Ju Yeh Shreya Paithankar Ruoqiao Chen Jing Xing Mengying Sun Ke Liu Jiayu Zhou Bin Chen Department of Pediatrics and Human Development Michigan State UniversityGrand RapidsMI 49503USA Department of Pharmacology and Toxicology Michigan State UniversityGrand RapidsMI 49503USA Department of Computer Science and Engineering Michigan State UniversityEast LansingMI 48824USA

Gene expression profiling of new or modified cell lines becomes routine today;however,obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines,including those derived from underrepresented groups,is not trivial when resources are *** gene expression to predict other measurements has been actively explored;however,systematic investigation of its predictive power in various measurements has not been well ***,we evaluated commonly used machine learning methods and presented TransCell,a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and *** these models,TransCell had the best performance in predicting metabolite,gene effect score(or genetic dependency),and drug sensitivity,and had comparable performance in predicting mutation,copy number variation,and protein ***,TransCell improved the performance by over 50%in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score ***,predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines,and predicted gene effect scores reflected BRAF resistance in melanoma cell ***,we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal(http://***/transcell/)that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

关键词： Genomics Transcriptomics Cancer dependency Drug repurposing Transfer learning

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GestureSock: Exploring toe gestures as alternative input method

GestureSock: Exploring toe gestures as alternative input met...

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

作者： van Rheden, Vincent Abewickrema, Sasindu Mueller, Florian Elvitigala, Don Samitha Human Computer Interaction Division University of Salzburg Salzburg Austria Exertion Games Lab. Monash University Melbourne Australia Exertion Games Lab. Department of Human-Centred Computing Monash University MelbourneVIC Australia Department of Human Centred Computing Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9798400713958

In everyday life we often find ourselves with both hands occupied, e.g. while holding a child in one hand and a dog leash in the other or riding a bicycle. These situations limit our ability to interact with our devices, leading to HCI investigating "hands-free" interactions such as voice commands. However, they are limited in noise environments and certain social situations. A recent lab study using a motion capture system has shown that toe movements might be a useful way of interacting. In this work we advance this research by exploring toe interactions through integrating pressure sensors in a sock. We address practical issues of implementation and applicability of an interactive sock capable of capturing toe interactions. We show that combining pressure with movement sensing can increase the interaction possibilities, allowing for a broad interaction vocabulary. © 2025 Copyright held by the owner/author(s).

关键词： Augmented Foot Foot interaction Gestures Touchless Interfaces

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Higher-Order DeepTrails: Unified Approach to *Trails

Higher-Order DeepTrails: Unified Approach to *Trails

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2023 Learning. Knowledge. Data. Analytics, LWDA 2023

作者： Koopmann, Tobias Pfister, Jan Markus, André Carolus, Astrid Wienrich, Carolin Hotho, Andreas University of Würzburg Department of Computer Science CAIDAS Germany University of Würzburg Institute Human-Computer-Media Media Psychology Germany University of Würzburg Institute Human-Computer-Media Psychology of Intelligent Interactive Systems Germany

Analyzing, understanding, and describing human behavior is advantageous in different settings, such as web browsing or traffic navigation. Understanding human behavior naturally helps to improve and optimize the underlying infrastructure or user interfaces. Typically, human navigation is represented by sequences of transitions between states. Previous work suggests to use hypotheses, representing different intuitions about the navigation to analyze these transitions. To mathematically grasp this setting, first-order Markov chains are used to capture the behavior, consequently allowing to apply different kinds of graph comparisons, but comes with the inherent drawback of losing information about higher-order dependencies within the sequences. To this end, we propose to analyze entire sequences using autoregressive language models, as they are traditionally used to model higher-order dependencies in sequences. We show that our approach can be easily adapted to model different settings introduced in previous work, namely HypTrails, MixedTrails and even SubTrails, while at the same time bringing unique advantages: 1. Modeling higher-order dependencies between state transitions, while 2. being able to identify short comings in proposed hypotheses, and 3. naturally introducing a unified approach to model all settings. To show the expressiveness of our approach, we evaluate our approach on different synthetic datasets and conclude with an exemplary analysis of a real-world dataset, examining the behavior of users who interact with voice assistants. © 2023 CEUR-WS. All rights reserved.

关键词： User interfaces

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