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arXiv 2023年

作者： Wang, Chunzhuo Kumar, T. Sunil De Raedt, Walter Camps, Guido Hallez, Hans Vanrumste, Bart E-Media Research Lab Belgium ESAT-STADIUS Division KU Leuven Leuven3000 Belgium University of Gävle Gävle801 76 Sweden Life Science Department IMEC Heverlee3001 Belgium Division of Human Nutrition and Health Department of Agrotechnology and Food Sciences Wageningen University and Research Wageningen6700EA Netherlands One-Planet Research Center Wageningen6708WE Netherlands M-Group DistriNet Department of Computer Science KU Leuven Sint-Michiels8200 Belgium

Eating speed is an important indicator that has been widely investigated in nutritional studies. The relationship between eating speed and several intake-related problems such as obesity, diabetes, and oral health has received increased attention from researchers. However, existing studies mainly use self-reported questionnaires to obtain participants’ eating speed, where they choose options from slow, medium, and fast. Such a non-quantitative method is highly subjective and coarse at the individual level. This study integrates two classical tasks in automated food intake monitoring domain: bite detection and eating episode detection, to advance eating speed measurement in near-free-living environments automatically and objectively. Specifically, a temporal convolutional network combined with a multi-head attention module (TCN-MHA) is developed to detect bites (including eating and drinking gestures) from IMU data. The predicted bite sequences are then clustered into eating episodes. Eating speed is calculated by using the time taken to finish the eating episode to divide the number of bites. To validate the proposed approach on eating speed measurement, a 7-fold cross validation is applied to the self-collected fine-annotated full-day-I (FD-I) dataset, and a holdout experiment is conducted on the full-day-II (FD-II) dataset. The two datasets are collected from 61 participants with a total duration of 513 h, which are publicly available. Experimental results show that the proposed approach achieves a mean absolute percentage error (MAPE) of 0.110 and 0.146 in the FD-I and FD-II datasets, respectively, showcasing the feasibility of automated eating speed measurement in near-free-living environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Nutrition

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A mobile visual analytics approach for instant trend analysis in mobile contexts 5

A mobile visual analytics approach for instant trend analysi...

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5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019

作者： Burkhardt, Dirk Nazemi, Kawa Kuijper, Arjan Ginters, Egils Human-Computer Interaction and Visual Analytics Research Group Darmstadt University of Applied Sciences Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Riga Technical University Riga Latvia

ISBN: (纸本)9788885741423

The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design. © 5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019. All rights reserved.

关键词： Decision support systems

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Left inferior parietal lobe and auditory cortex jointly contribute to sound knowledge retrieval

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Brain Stimulation 2025年第4期18卷 1037-1047页

作者： Kuhnke, Philipp Numssen, Ole Voeller, Johannah Cheung, Vincent K.M. Weise, Konstantin Kiefer, Markus Hartwigsen, Gesa Wilhelm Wundt Institute for Psychology Leipzig University Germany Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany Methods and Development Group Brain Networks MPI CBS Leipzig Germany TMS Group Department of Neuroscience and Biomedical Engineering Aalto University Finland TMS Lab Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston United States Department of Experimental Psychology University of Granada Spain Mind Brain and Behavior Research Center (CIMCYC) University of Granada Spain Sony Computer Science Laboratories Tokyo Japan Leipzig University of Applied Sciences HTWK Leipzig Germany Department of Psychiatry Ulm University Germany

Background: Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing relies on the joint contribution of modality-specific perceptual-motor and multimodal brain regions. In particular, the multimodal left inferior parietal lobe (IPL) couples with auditory association cortex during sound knowledge retrieval and with somatomotor cortex during action feature retrieval. However, it remains unknown whether the interaction between modality-specific and multimodal cortices is causally relevant for conceptually-guided behavior. Methods: To tackle this issue, we applied offline transcranial magnetic stimulation (TMS) over modality-specific cortex (somatomotor, auditory, or sham), before 24 healthy participants received online TMS over multimodal cortex (IPL, or sham) during action and sound knowledge tasks on written words. For the first time in a TMS study on higher cognition, TMS targeting and dosing were optimized using individual a priori computational electric field simulations. Results: We found that combined stimulation of auditory association cortex and multimodal IPL selectively impaired sound judgments, but not action judgments, on low sound–low action words (BF10 = 5.316). Unifocal TMS over auditory cortex or IPL alone did not disrupt behavior, suggesting adaptive compensatory plasticity. Conclusions: These results indicate that the joint contribution of multimodal IPL and auditory cortex is causally relevant for sound knowledge retrieval: The functional relevance of left IPL depends on the integrity of the auditory cortex, and vice versa, as single perturbation of either region did not disrupt performance. Our findings support “hybrid theories” of conceptual processing, which propose that task-dependent functional interactions between modality-specific and multimodal regions are crucial. © 2025 The Authors

关键词： Compensation Computational modeling Concepts Plasticity Semantics TMS

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MACHINE-LEARNING-BASED DIFFERENTIATION BETWEEN SUICIDE ATTEMPT AND SUICIDE IDEATION IN PATIENTS WITH MAJOR DEPRESSIVE DISORDER USING CORTICAL FUNCTIONAL NETWORK

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IBRO Neuroscience Reports 2023年 15卷 S784-S784页

作者： Kuk-In Jang Sungkean Kim Jihoon Kim Chany Lee Se-Hoon Shim Ji Sun Kim Korea Brain Research Institute Cognitive Science Research Group Daegu Korea Republic of Hanyang University Human-computer Interaction Ansan Korea Republic of Soonchunhyang University Cheonan Hospital Department Of Psychiatry Cheonan Korea Republic of

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A data-driven validation of mobile-based care (mCARE) project for children with ASD in LMICs

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Smart Health 2022年 26卷

作者： Rabbani, Masud Haque, Munirul M. Dipal, Dipranjan Das Zarif, Md Ishrak Islam Iqbal, Anik Schwichtenberg, Amy Bansal, Naveen Soron, Tanjir Rashid Ahmed, Syed Ishtiaque Ahamed, Sheikh Iqbal Ubicomp Lab Department of Computer Science Marquette University Milwaukee WI United States R.B. Annis School of Engineering University of Indianapolis Indianapolis IN United States College of Health and Human Sciences Purdue University West Lafayette IN United States Department of Mathematical and Statistical Sciences Marquette University Milwaukee WI United States Telepsychiatry Research and Innovation Network Ltd Dhaka Bangladesh Department of Computer Science University of Toronto Toronto ON Canada

In this study, we have validated the aims of the NIH project mCARE (mobile-based care for monitoring the milestone and behavioral parameters for children with ASD) by data-driven techniques. This project was developed and deployed for remote monitoring of the data on the development of children with ASD in the remote setting in the Low-and-Middle-Income-Countries (LMICS). mCARE was deployed for two years among 300 children with ASD in Bangladesh's four specialist autism care centers. Initially, mCARE's had three aims to monitor the development of children (with ASD) development: (a) develop and deployment the mCARE app, (b) develop and deployment the mCARE data management platform, and (c) increase the ASD knowledge among the caregiver of the children with ASD. In this paper, we will evaluate and describe these three aims based on the two years of data from the mCARE project. After analyzing the data, we can conclude that this mCARE project could apply both the LMICs and developed countries for remote monitoring the children with ASD and increasing collective knowledge among the caregivers. © 2022 Elsevier Inc.

关键词： Autism spectrum disorder Caregiver satisfaction Healthcare data processing Low-and-Middle-Income-Countries mHealth Mobile-based care National Institute of Health (NIH) Public health and home monitoring

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Designing with Genes in Early Childhood: An exploratory user study of the tangible CRISPEE technology

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International Journal of Child-computer interaction 2020年 26卷

作者： Strawhacker, Amanda Verish, Clarissa Shaer, Orit Bers, Marina Umaschi Department of Child Study and Human Development Tufts University 105 College Ave Medford 02155 MA United States Department of Computer Science Human-Computer Interaction Lab 106 Central Street Wellesley 02481 MA United States

Biodesign, a speculative and creative offshoot from the field of bioengineering, is an area of STEM that is growing in popularity in education settings, primarily because of its unique interdisciplinary lens that connects STEM disciplinary knowledge and creative design practices. Although this trend is currently limited to middle school, high school, and higher education, prior research suggests that children 5 years and older, may yield long-term gains from exploring developmentally-appropriate concepts from novel STEM fields. Although there is little research on educational technologies or resources to support young children's curiosity and learning in this novel domain, some research suggests that young children may already be forming preconceptions about genetics and biology (e.g., from popular media). Tangible technologies, which provide children qualitatively new, developmentally appropriate ways to engage with ideas and techniques, have been shown to support children's engagement with foundational ideas relevant to biodesign, including the engineering design process. By applying developmentally appropriate constraints to our technology development (e.g., through frameworks such as the Positive Technological Development), the research team developed and evaluated a novel tangible technology called CRISPEE to introduce young children to concepts of biology and engineering. This article describes an experimental pilot study to investigate (1) how young children interact with the CRISPEE technological prototype, and (2) what prior knowledge the average child might bring to an educational biodesign activity. Implications for ongoing technology development and developmentally appropriate learning goals are discussed. © 2020 Elsevier B.V.

关键词： Biodesign Child–computer interaction Early childhood Novel technology STEM education

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Smoothing dialogue states for open conversational machine reading

arXiv

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arXiv 2021年

作者： Zhang, Zhuosheng Ouyang, Siru Zhao, Hai Utiyama, Masao Sumita, Eiichiro Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Kyoto Japan

Conversational machine reading (CMR) requires machines to communicate with humans through multi-turn interactions between two salient dialogue states of decision making and question generation processes. In open CMR settings, as the more realistic scenario, the retrieved background knowledge would be noisy, which results in severe challenges in the information transmission. Existing studies commonly train independent or pipeline systems for the two subtasks. However, those methods are trivial by using hard-label decisions to activate question generation, which eventually hinders the model performance. In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference. Experiments on the OR-ShARC dataset show the effectiveness of our method, which achieves new state-of-the-art results. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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Span fine-tuning for pre-trained language models

arXiv

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arXiv 2021年

作者： Bao, Rongzhou Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China

Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words. Previous works have shown that incorporating span-level information over consecutive words in pre-training could further improve the performance of PrLMs. However, given that span-level clues are introduced and fixed in pre-training, previous methods are time-consuming and lack of flexibility. To alleviate the inconvenience, this paper presents a novel span fine-tuning method for PrLMs, which facilitates the span setting to be adaptively determined by specific downstream tasks during the fine-tuning phase. In detail, any sentences processed by the PrLM will be segmented into multiple spans according to a pre-sampled dictionary. Then the segmentation information will be sent through a hierarchical CNN module together with the representation outputs of the PrLM and ultimately generate a span-enhanced representation. Experiments on GLUE benchmark show that the proposed span fine-tuning method significantly enhances the PrLM, and at the same time, offer more flexibility in an efficient way. The code is available at https://***/BAORONGZHOU/spanfine-tuning. © 2021, CC BY.

关键词： Computational linguistics

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Dual-Consistency semi-supervised learning with uncertainty quantification for covid-19 lesion segmentation from CT images

arXiv

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arXiv 2021年

作者： Li, Yanwen Luo, Luyang Lin, Huangjing Chen, Hao Heng, Pheng-Ann Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide. Automatically segmenting lesions from chest Computed Tomography (CT) is a promising way to assist doctors in COVID-19 screening, treatment planning, and follow-up monitoring. However, voxel-wise annotations are extremely expert-demanding and scarce, especially when it comes to novel diseases, while an abundance of unlabeled data could be available. To tackle the challenge of limited annotations, in this paper, we propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Specifically, we present a dual-consistency learning scheme that simultaneously imposes image transformation equivalence and feature perturbation invariance to effectively harness the knowledge from unlabeled data. We then quantify the segmentation uncertainty in two forms and employ them together to guide the consistency regularization for more reliable unsupervised learning. Extensive experiments showed that our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins, demonstrating high potential in real-world clinical practice. 2 © 2021, CC BY-NC-ND.

关键词： Coronavirus

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OXnet: Deep omni-supervised thoracic disease detection from chest X-rays

arXiv

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arXiv 2021年

作者： Luo, Luyang Chen, Hao Zhou, Yanning Lin, Huangjing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Imsight AI Research Lab Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Chest X-ray (CXR) is the most typical diagnostic X-ray examination for screening various thoracic diseases. Automatically localizing lesions from CXR is promising for alleviating radiologists' reading burden. However, CXR datasets are often with massive image-level annotations and scarce lesion-level annotations, and more often, without annotations. Thus far, unifying different supervision granularities to develop thoracic disease detection algorithms has not been comprehensively addressed. In this paper, we present OXnet, the first deep omni-supervised thoracic disease detection network to our best knowledge that uses as much available supervision as possible for CXR diagnosis. We first introduce supervised learning via a one-stage detection model. Then, we inject a global classification head to the detection model and propose dual attention alignment to guide the global gradient to the local detection branch, which enables learning lesion detection from image-level annotations. We also impose intra-class compactness and inter-class separability with global prototype alignment to further enhance the global information learning. Moreover, we leverage a soft focal loss to distill the soft pseudo-labels of unlabeled data generated by a teacher model. Extensive experiments on a large-scale chest X-ray dataset show the proposed OXnet outperforms competitive methods with significant margins. Further, we investigate omni-supervision under various annotation granularities and corroborate OXnet is a promising choice to mitigate the plight of annotation shortage for medical image diagnosis.2 © 2021, CC BY-NC-ND.

关键词： Deep learning

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