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arXiv 2023年

作者： Shao, Shuai Guan, Yu Zhai, Bing Missier, Paolo Plötz, Thomas Department of Computer Science University of Warwick Coventry United Kingdom Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Newcastle University Newcastle upon Tyne United Kingdom School of Interactive Computing Georgia Institute of Technology Atlanta United States

Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response to such challenges, we propose ConvBoost – a novel, three-layer, structured model architecture and boosting framework for convolutional network based HAR. Our framework generates additional training data from three different perspectives for improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three conceptual layers–Sampling Layer, Data Augmentation Layer, and Resilient Layer–we develop three "boosters"–R-Frame, Mix-up, and C-Drop–to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively. These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project, and the code can be found at https://***/sshao2013/ConvBoost © 2023, CC BY.

关键词： Deep learning

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ARXIVEDITS: Understanding the Human Revision Process in Scientific Writing

arXiv

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arXiv 2022年

作者： Jiang, Chao Xu, Wei Stevens, Samuel School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science and Engineering Ohio State University United States

Scientific publications are the primary means to communicate research discoveries, where the writing quality is of crucial importance. However, prior work studying the human editing process in this domain mainly focused on the abstract or introduction sections, resulting in an incomplete picture. In this work, we provide a complete computational framework for studying text revision in scientific writing. We first introduce ARXIVEDITS, a new annotated corpus of 751 full papers from arXiv with gold sentence alignment across their multiple versions of revision, as well as fine-grained span-level edits and their underlying intentions for 1,000 sentence pairs. It supports our data-driven analysis to unveil the common strategies practiced by researchers for revising their papers. To scale up the analysis, we also develop automatic methods to extract revision at document-, sentence-, and word-levels. A neural CRF sentence alignment model trained on our corpus achieves 93.8 F1, enabling the reliable matching of sentences between different versions. We formulate the edit extraction task as a span alignment problem, and our proposed method extracts more fine-grained and explainable edits, compared to the commonly used diff algorithm. An intention classifier trained on our dataset achieves 78.9 F1 on the fine-grained intent classification task. Our data and system are released at ***/arxivedits. © 2022, CC BY-NC-SA.

关键词： Classification (of information)

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Enhancing Predictive Imaging Biomarker Discovery Through Treatment Effect Analysis

Enhancing Predictive Imaging Biomarker Discovery Through Tre...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Xiao, Shuhan Klein, Lukas Petersen, Jens Vollmuth, Philipp Jaeger, Paul F. Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Germany Heidelberg University Faculty of Mathematics and Computer Science Germany Dkfz Heidelberg Interactive Machine Learning Group Germany Institute for Machine Learning Eth Zürich Switzerland Dkfz Heidelberg Helmholtz Imaging Germany Germany University of Bonn Medical Faculty Bonn Germany Heidelberg University Hospital Pattern Analysis and Learning Group Department of Radiation Oncology Germany

ISBN: (纸本)9798331510831

Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomarkers, are extracted from pretreatment data, often within randomized controlled trials, and should be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on discovering predictive imaging biomarkers, specific image features, by leveraging pretreatment images to uncover new causal relationships. Unlike laborintensive approaches relying on handcrafted features prone to bias, we present a novel task of directly learning predictive features from images. We propose an evaluation protocol to assess a model's ability to identify predictive imaging biomarkers and differentiate them from purely prognostic ones by employing statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally developed for estimating the conditional average treatment effect (CATE) for this task, which have been assessed primarily for their precision of CATE estimation while overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates the feasibility and potential of our approach in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. Our code is available at https://***/MIC-DKFZ/predictive_image_biomarker_analysis. © 2025 IEEE.

关键词： Deep learning

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Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding

arXiv

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arXiv 2022年

作者： Singh, Janvijay Bai, Fan Wang, Zhen School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science and Engineering The Ohio State University United States

Cross-task knowledge transfer via multitask learning has recently made remarkable progress in general NLP tasks. However, entity tracking on the procedural text has not benefited from such knowledge transfer because of its distinct formulation, i.e., tracking the event flow while following structural constraints. State-of-the-art entity tracking approaches either design complicated model architectures or rely on task-specific pretraining to achieve good results. To this end, we propose MEET, a Multi-task learning-enabled entity Tracking approach, which utilizes knowledge gained from general domain tasks to improve entity tracking. Specifically, MEET first fine-tunes T5, a pre-trained multitask learning model, with entity tracking-specialized QA formats, and then employs our customized decoding strategy to satisfy the structural constraints. MEET achieves state-of-the-art performances on two popular entity tracking datasets, even though it does not require any task-specific architecture design or pre-training. © 2022, CC BY.

关键词： Decoding

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An Audit on the Perspectives and Challenges of Hallucinations in NLP

arXiv

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arXiv 2024年

作者： Venkit, Pranav Narayanan Chakravorti, Tatiana Gupta, Vipul Biggs, Heidi Srinath, Mukund Goswami, Koustava Rajtmajer, Sarah Wilson, Shomir College of Information Sciences and Technology Pennsylvania State University United States Adobe Research United States School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science & Engineering Pennsylvania State University United States

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Through the examination of the literature, we identify a lack of agreement with the term ‘hallucination’ in the field of NLP. Additionally, to compliment our audit, we conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on hallucination. Our analysis calls for the necessity of explicit definitions and frameworks outlining hallucination within NLP, highlighting potential challenges, and our survey inputs provide a thematic understanding of the influence and ramifications of hallucination in society. © 2024, CC BY-NC-SA.

关键词： Economic and social effects

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OffRIPP: Offline RL-based Informative Path Planning

arXiv

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arXiv 2024年

作者： Gadipudi, Srikar Babu Deolasee, Srujan Kailas, Siva Luo, Wenhao Sycara, Katia Kim, Woojun The Department of Electrical Engineering Indian Institute of Technology Madras India The Robotics Institute Carnegie Mellon University United States The School of Interactive Computing Georgia Institute of Technology United States The Department of Computer Science University of North Carolina at Charlotte United States

Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution—key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Distributed Multi-robot Source Seeking in Unknown Environments with Unknown Number of Sources

arXiv

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arXiv 2025年

作者： Chen, Lingpeng Kailas, Siva Deolasee, Srujan Luo, Wenhao Sycara, Katia Kim, Woojun Shenzhen Institute of Artificial Intelligence and Robotics for Society Chinese University of Hong Kong Shenzhen China Robotics Institute Carnegie Mellon University United States School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science University of Illinois Chicago United States

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function. Copyright © 2025, The Authors. All rights reserved.

关键词： Multipurpose robots

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Translating HCI Research to Broader Audiences: Motivation, Inspiration, and Critical Factors on Alternative Research Outcomes 25

Translating HCI Research to Broader Audiences: Motivation, I...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Yoo, MinYoung Ppali, Sophia Odom, William Zhuang, Yumeng Kritika, Kritika Olson, Wyatt Wieczorek, Catherine Biggs, Heidi Berger, Arne Desjardins, Audrey Wakkary, Ron Ringland, Kathryn E. School of Interactive Arts and Technology Simon Fraser University Surrey BC Canada Hci Research Group Cyens Centre of Excellence Nicosia Cyprus Computational Media Department University of California Santa Cruz Santa Cruz United States School of Art + Art History + Design University of Washington Seattle WA United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States School of Literature Media and Communication Georgia Institute of Technology Atlanta GA United States Computer Science and Languages Anhalt University of Applied Sciences Koethen Germany

ISBN: (纸本)9798400713941

Alternative Research Outcomes (AROs) go beyond traditional academic publications, taking diverse forms such as documentaries, DIY tutorials, or exhibitions. With growing recognition of the need for more inclusive and contextually appropriate research dissemination, AROs are particularly relevant in HCI and design research. Yet, little has been discussed on why it is important to work on AROs. What are key qualities of AROs? How can the HCI community benefit from learning more about creating AROs? By analyzing six case studies, we propose four qualities of AROs and demonstrate how they emerge in the timeline of a research project. We argue AROs can be adapted to diverse audience needs and share research insights that may extend beyond the original research goals. Our work contributes to a deeper understanding of how AROs can support inclusive research dissemination practices, enabling HCI researchers to engage broader audiences and extend the relevance of their work. © 2025 Copyright held by the owner/author(s).

关键词： Alternative Forms Art Installation Audio/Video Documentary Digital Media Content DIY Tutorial Knowledge Production Research Communication Research Outcomes Zine

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Technology Mediated Caregiving For Older Adults Aging in Place

Technology Mediated Caregiving For Older Adults Aging in Pla...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Mynatt, Elizabeth D. Kobayashi, Masatomo Pradhan, Alisha Mathur, Niharika Vines, John Seaborn, Katie Buehler, Erin Waycott, Jenny Rudnik, John Zubatiy, Tamara Rozga, Agata Khoury College of Computer Sciences Northeastern University Boston MA United States IBM Research Tokyo Japan Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology Newark NJ United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States University of Edinburgh Edinburgh United Kingdom Institute of Science Tokyo Japan Google Mountain View CA United States School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Information University of Michigan Ann Arbor MI United States Northeastern University Boston MA United States

ISBN: (纸本)9798400713958

The caregiving environment for an older adult aging in place includes a network of caregivers working with the older adult to support their needs and maintain independence. As older adults experience cognitive and functional changes, their caregiving network expands to include spouses or siblings (who are often older adults themselves), children, friends, neighbors and community members—each bringing unique values, expectations, and goals. In this network of care, technology-enabled support offers the potential to mediate care responsibilities, such as coordinating activities and assisting with everyday tasks. However, designing these systems requires addressing value tensions among caregivers, cultural norms around aging, participatory research practices and balancing autonomy with safety concerns for older adults in later life. This workshop brings together researchers and practitioners to discuss (1) opportunities and challenges for designing technological systems for caregiving for older adults;(2) longitudinal interactions with these systems as older adults progress through stages of functional and cognitive changes;(3) potential for such systems to support caregivers while centering older adults’ privacy and autonomy needs;and (4) the influence of cultural norms on caregiving and technology use. © 2025 Copyright held by the owner/author(s).

关键词： Assistive Technology Caregiving Older Adults

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Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition

arXiv

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arXiv 2022年

作者： Shao, Shuai Guan, Yu Guan, Xin Missier, Paolo Ploetz, Thomas Department of Computer Science University of Warwick Coventry United Kingdom School of Computing Newcastle University Newcastle upon Tyne United Kingdom School of Interactive Computing Georgia Institute of Technology Atlanta United States

Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic/static activities (e.g, walking, lying, cycling, etc.) has become a well-studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non-periodic, and occur less frequently when compared with the often large amount of irrelevant "background" activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling non-periodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Specifically, we proposed two simple yet effective LSTM variants, namely delay model and inverse model, for two SAR scenarios (with and without time-critical requirement). For time-critical SAR, the delay model can effectively exploit pre-defined delay intervals (within tolerance) in form of contextual information for improved performance. For regular SAR task, the second proposed, inverse model can learn patterns from the time-series in an inverse manner, which can be complementary to the forward model (i.e., LSTM), and combining both can boost the performance. These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two SAR and one non-SAR datasets, and the promising results demonstrated the effectiveness of our approaches in HAR applications. © 2022, CC BY.

关键词： Long short-term memory

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