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arXiv 2021年

作者： Wu, Julia Sivaraman, Venkatesh Kumar, Dheekshita Banda, Juan M. Sontag, David Dept of Electrical Engineering and Computer Science Massachusetts Institute of Technology Human-Computer Interaction Institute Carnegie Mellon University Department of Computer Science Georgia State University

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research. © 2021, CC BY.

关键词： Data mining

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

arXiv

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arXiv 2024年

作者： Vedove, Matteo Dalle Lamon, Edoardo Fontanelli, Daniele Palopoli, Luigi Saveriano, Matteo 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

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator. © 2024, CC BY-SA.

关键词： Intelligent robots

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On the potential for human-centered, cognitively inspired AI to bridge the gap between optimism and reality for autonomous robotics in healthcare: a respectful critique

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Proceedings of the International Symposium on human Factors and Ergonomics in Health Care 2024年第1期13卷 106 - 112页

作者： Stephanie Tulk Jesso Christopher Greene Shiqi Zhang Amy Booth Michael DiFabio Grace Babalola Adeola Adegbemijo Sumantra Sarkar 1Department of Systems Science and Industrial Engineering SUNY Binghamton2Human-Centered Mindful Technologies Lab SUNY Binghamton 1Department of Systems Science and Industrial Engineering SUNY Binghamton 3Department of Computer Science SUNY Binghamton 4New York United Health Services 5School of Management SUNY Binghamton

Historically, the fields of computer science, cognitive science, and neuroscience have been tightly linked. To date, this collaboration has yielded major advances in how the brain and mind are understood, as well as the ways in which artificial minds can be constructed to serve as new collaborators to humans. Yet there are still significant gaps between the capabilities of state-of-the-art autonomous robots and the expectations developed by real users who are now encountering autonomous robots on the job. We present our views as well as a case study of our evaluation of two autonomous robots intended to aid nurses within hospital settings: Moxi and TUG. Both cobots were originally considered for procurement by our collaborating healthcare system,UHS, at which time our team began the process of trying to systematically vet each option to aid in the decision making process. What we found was a lack of evidence on either platform in academic literature, which led us to analyze user comments on social media. In order to improve the fit of autonomous robots into human environments, research must be conducted and evidence must be shared, and the human Factors community can aid in this effort

关键词： Cognitively Plausible AI Autonomous Robots Cobots Healthcare Nursing 4E Cognition

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EventBERT: Incorporating Event-Based Semantics for Natural Language Understanding 21

EventBERT: Incorporating Event-Based Semantics for Natural L...

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21st China National Conference on Computational Linguistics, CCL 2022

作者： Zou, Anni Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031183140

Natural language understanding tasks require a comprehensive understanding of natural language and further reasoning about it, on the basis of holistic information at different levels to gain comprehensive knowledge. In recent years, pre-trained language models (PrLMs) have shown impressive performance in natural language understanding. However, they rely mainly on extracting context-sensitive statistical patterns without explicitly modeling linguistic information, such as semantic relationships entailed in natural language. In this work, we propose EventBERT, an event-based semantic representation model that takes BERT as the backbone and refines with event-based structural semantics in terms of graph convolution networks. EventBERT benefits simultaneously from rich event-based structures embodied in the graph and contextual semantics learned in pre-trained model BERT. Experimental results on the GLUE benchmark show that the proposed model consistently outperforms the baseline model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Design lessons from AI’s two grand goals: human emulation and useful applications

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2020年第2期1卷 73-82页

作者： Shneiderman, Ben Department of Computer Science University of Maryland at College Park College ParkMD20742 United States Human–Computer Interaction Laboratory University of Maryland at College Park College ParkMD20742 United States

Researchers’ goals shape the questions they raise, collaborators they choose, methods they use, and outcomes of their work. This article offers a fresh vision of artificial intelligence (AI) research by suggesting a simplification to two goals: 1) emulation to understand human abilities to build systems that perform tasks as well as or better than humans and 2) application of AI methods to build widely used products and services. Researchers and developers for each goal can fruitfully work along their desired paths, but this article is intended to limit the problems that arise when assumptions from one goal are used to drive work on the other goal. For example, autonomous humanoid robots are prominent with emulation researchers, but application developers avoid them, in favor of tool-like appliances or teleoperated devices for widely used commercial products and services. This article covers four such mismatches in goals that affect AI-guided application development: 1) intelligent agent or powerful tool;2) simulated teammate or teleoperated device;3) autonomous system or supervisory control;and 4) humanoid robot or mechanoid appliance. This article clarifies these mismatches to facilitate the discovery of workable compromise designs that will accelerate human-centered AI applications research. A greater emphasis on human-centered AI could reduce AI’s existential threats and increase benefits for users and society, such as in business, education, healthcare, environmental preservation, and community safety. © 2020 IEEE. All right reserved.

关键词： Anthropomorphic robots

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Incremental Feature Selection for Dynamic Incomplete Data Using Sub-Tolerance Relation

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Shallow Hierarchical CNN-LSTM for Activity Recognition to Integrate Postural Transition States

Shallow Hierarchical CNN-LSTM for Activity Recognition to In...

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IEEE SENSORS

作者： Douglas Tilley Uriel Martinez-Hernandez Department of Computer Science University of Bath Bath UK EEE Department ART-AI Multimodal Interaction and Robot Active Perception (inte-R-action) Lab University of Bath UK Department of Electronic and Electrical Engineering University of Bath Bath UK

This paper presents an approach to enhance classification accuracy of human Activity Recognition (HAR) datasets using a shallow hierarchical method, comprised of 2 Convolutional layers and 1 LSTM Layer, to identify transition tasks. The designed model was tested on the human Activity and Postural Transition (HAPT) dataset from the University of California, Irvine, achieving an accuracy of 93 %, demonstrating it's efficacy in correctly identifying a variety of human activities, including the postural transition states. An F1 score of 85 % was attained, highlighting a reliable method of reducing false positives on an unbalanced dataset. The performance on these metrics showcases the model's reliability and proficiency in classifying activities within unbalanced datasets.

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

arXiv

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arXiv 2024年

作者： Schieber, Hannah Young, Jacob Langlotz, Tobias Zollmann, Stefanie Roth, Daniel Erlangen Germany Department of Computer Science University of Otago Dunedin New Zealand Technical University of Munich Human-Centered Computing and Extended Reality Lab TUM University Hospital Orthopedics and Sports Orthopedics Germany

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 scenes. We propose Semantics-Controlled GS (SCGS), a segmentationdriven 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. © 2024, CC BY.

关键词： Virtual environments

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans 10

Implementation of a Hip Fracture Classification Network Base...

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10th International Conference on computer and Communications, ICCC 2024

作者： Zhao, Kui Yang, Meng Zhang, Ti Hu, Die Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China University of Chinese Academy of Science Shenyang China Peking University Third Hospital Department of Orthopedics Beijing China

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition. © 2024 IEEE.

关键词： Classification ConvNeXt Hip Fracture Segmentation V-Net

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