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13th International Conference on computer Supported Collaborative Learning - A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, CSCL 2019

作者： Sharma, Kshitij Olsen, Jennifer Department of Computer Science Norwegian University of Science and Technology Norway Computer Human Interaction Lab in Learning and Instruction École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781732467231

To provide beneficial feedback to students during their collaboration, it is important to identify behaviors that are indicative of good collaboration. However, in a collaborative learning session, students engage in a range of behaviors and it can be difficult to indicate which of those behaviors correlate with higher outcomes. In this paper, we propose using Extreme Value Theory (EVT), a method that considers the data points in the tail (upper or lower) of the distribution, to analyse the relationship between collaborative process variables and outcome measures through insights derived from high impact, low-frequency events. Specifically, in this paper, we analyse the relationship between dual gaze patterns and outcome measures across two different datasets. In both datasets we found that students with lower outcomes had lower focus during the collaborative session. This paper provides a contribution by both introducing EVT as a viable method for analysing CSCL data as well as demonstrating the effectiveness of eyetracking as a collaborative indicator to use to adapt to in real-time. © ISLS.

关键词： Students

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Speech AI for All: Promoting Accessibility, Fairness, Inclusivity, and Equity

Speech AI for All: Promoting Accessibility, Fairness, Inclus...

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

作者： Wu, Shaomei Wenzel, Kimi Li, Jingjin Li, Qisheng Pradhan, Alisha Kushalnagar, Raja Lea, Colin Koenecke, Allison Vogler, Christian Hasegawa-Johnson, Mark Su, Norman Makoto Ratner, Nan Bernstein *** Mountain View CA United States Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States *** Seattle WA United States Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology Newark NJ United States Gallaudet University Washington DC United States Apple Cupertino CA United States Cornell University Ithaca NY United States Technology Access Program Gallaudet University Washington DC United States The Grainger College of Engineering University of Illinois Urbana-Champaign Urbana-Champaign IL United States Department of Computational Media University of California Santa Cruz Santa Cruz CA United States Department of Hearing and Speech Sciences University of Maryland College Park College Park MD United States Neuroscience and Cognitive Science University of Maryland College Park College Park MD United States

ISBN: (纸本)9798400713958

Trained and optimized for typical and fluent speech, speech AI works poorly for people with speech diversities, often interrupting them and misinterpreting their speech. The increasing deployment of speech AI in automated phone menus, AI-conducted job interviews, and everyday devices poses tangible risks to people with speech diversities. To mitigate these risks, this workshop aims to build a multidisciplinary coalition and set the research agenda for fair and accessible speech AI. Bringing together a broad group of academics and practitioners with diverse perspectives, including HCI, AI, and other relevant fields such as disability studies, speech language pathology, and law, this workshop will establish a shared understanding of the technical challenges for fair and accessible speech AI, as well as its ramifications in design, user experience, policy, and society. In addition, the workshop will invite and highlight first-person accounts from people with speech diversities, facilitating direct dialogues and collaboration between speech AI developers and the impacted communities. The key outcomes of this workshop include a summary paper that synthesizes our learnings and outlines the roadmap for improving speech AI for people with speech diversities, as well as a community of scholars, practitioners, activists, and policy makers interested in driving progress in this domain. © 2025 Copyright held by the owner/author(s).

关键词： accessibility AI FATE automatic speech recognition disability speech diversity speech technology

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Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI

arXiv

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arXiv 2023年

作者： Zhang, Xiatan Zheng, Sisi Shum, Hubert P.H. Zhang, Haozheng Song, Nan Song, Mingkang Jia, Hongxiao Beijing Key Laboratory of Mental Disorders National Clinical Research Center for Mental Disorders National Center for Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Advanced Innovation Center for Human Brain Protection Beijing Anding Hospital Capital Medical University Beijing China Department of Computer Science Durham University Durham United Kingdom

Resting-state fMRI (rs-fMRI) functional connectivity (FC) analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design efforts to predict treatment response from rs-fMRI remain limited due to difficulties in understanding the current brain state and the underlying mechanisms driving the observed patterns, which limited the clinical application of rs-fMRI. To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss. This approach results in a more expressive framework that captures brain dynamic features at different scales and enables more accurate prediction of treatment response. Our experiments on the chronic pain and depersonalization disorder datasets demonstrate that our proposed method outperforms current methods in different scenarios. To the best of our knowledge, we are the first to explore the integration of distance-based and correlation-based neural similarity into graph learning for treatment response prediction. © 2023, CC BY.

关键词： Forecasting

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Experience with the use of a digital sleep diary in symptom management by individuals with insomnia -a pilot mixed method study

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Sleep Medicine: X 2023年 6卷 100093页

作者： Thorshov, Thea Christine Øverby, Caroline Tonje Hansen, Diana Dobran Bong, Way Kiat Skifjeld, Knut Hurlen, Petter Dammen, Toril Moen, Anne Hrubos-Strøm, Harald Division of Surgery Department of Otorhinolaryngology Akershus University Hospital Lørenskog Norway Faculty of Technology Art and Design Department of Computer Science Human-Computer Interaction and Universal Design of ICT Oslo Metropolitan University Oslo Norway Norsk e-helse Oslo Norway Division of Clinical Informatics Department of Diagnostics and Technology Akershus University Hospital Lørenskog Norway Faculty of Medicine Institute of Clinical Medicine Campus Ahus University of Oslo Norway Faculty of Medicine Institute of Health and Society Department of Nursing Science University of Oslo Norway Faculty of Medicine Institute of Clinical Medicine University of Oslo Norway Department of Research and Innovation Division of Mental Health and Addiction Oslo University Hospital Oslo Norway

Background: Insomnia is the most common sleep disorder. The recommended treatment is cognitive behavioural therapy for insomnia (CBTi). A sleep diary is a core tool in CBTi. We have developed a digital sleep diary with a standardised feedback function. Aim: To study feasibility of the digital sleep diary in participants of the Akershus Sleep Apnea (ASAP) cohorts with difficulties falling asleep or maintaining sleep. To describe sleep diary engagement and explore experiences with the digital sleep diary with potential influences in insomnia symptom management. Material and methods: Twenty participants were recruited from the ASAP. All filled out a digital sleep diary up to 12 weeks. Treatment options provided were a self-help book (N = 11) or electroencephalography neurofeedback (N = 9) in addition to the sleep diary standardised feedback function. We collected quantitative data from the sleep diary reports and we sub-divided insomnia by sleep onset insomnia and non-sleep onset insomnia. Finally, we performed qualitative interviews. Results: The median number of entries to the sleep diary was 81 (25th quartile: 26, 75th quartile 84). In the qualitative analysis, we identified two main themes;“structure and overview” and “usability and digital features”. Conclusion: The sleep diary was found to be feasible when distributed in combination with a self-help book or electroencephalography neurofeedback. The qualitative results emphasised the importance of a timely graphical overview and visualisations of self-recorded sleep. © 2023 The Authors

关键词： Digital sleep diary Engagement Feasibility Insomnia Motivation Patient-reported outcomes Self-management

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Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

Image-guided Breast Biopsy of MRI-visible Lesions with a Han...

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International Symposium on Medical Robotics (ISMR)

作者： Marta Lagomarsino Vincent Groenhuis Maura Casadio Marcel K. Welleweerd Françoise J. Siepel Stefano Stramigioli Robotics and Mechatronics (RAM) Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands University of Genoa Genoa Italy Human-Robot Interfaces and Physical Interaction (HRII) Laboratory Istituto Italiano di Tecnologia Genoa Italy Politecnico di Milano Milan Italy

ISBN: (纸本)9781665406239

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient’s skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.

关键词： Target tracking Biopsy Phantoms Breast Needles Real-time systems Skin

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WEDeepT3: predicting type Ⅲ secreted effectors based on word embedding and deep learning

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Quantitative Biology 2019年第4期7卷 293-301页

作者： Xiaofeng Fu Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tbng Universityand Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive EngineeringShanghai 200240China

Background:The type Ⅲ secreted effectors(T3SEs)are one of the indispensable proteins in the growth and reproduction of Gram-negative *** particular,the pathogenesis of Gram-negative bacteria depends on the type Ⅲ secreted effectors,and by injecting T3SEs into a host cell,the host cell's immunity can be *** high diversity of T3SE sequences and the lack of defined secretion signals make it difficult to identify and ***,the related study of the pathological system associated with T3SE remains a hot topic in *** computational tools have been developed to meet the growing demand for the recognition of T3SEs and the studies of type Ⅲ secretion systems(T3SS).Although these tools can help biological experiments in certain procedures,there is still room for improvement,even for the current best model,as the existing methods adopt handdesigned feature and traditional machine learning ***:In this study,we propose a powerful predictor based on deep learning methods,called *** work consists mainly of three key ***,we train word embedding vectors for protein sequences in a large-scale amino acid sequence ***,we combine the word vectors with traditional features extracted from protein sequences,like PSSM,to construct a more comprehensive feature ***,we construct a deep neural network model in the prediction of type Ⅲ secreted ***:The feature representation of WEDeepT3 consists of both word embedding and position-specific *** together with convolutional neural networks,the new model achieves superior performance to the state-ofthe-art methods,demonstrating the effectiveness of the new feature representation and the powerful learning ability of deep ***:WEDeepT3 exploits both semantic information of Ar-mer fragments and evolutional information of protein sequences to accurately difYerentiate between T3SEs and ***3 is availa

关键词： typeⅢsecreted effectors word2vector PSSM feature representation

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Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

arXiv

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arXiv 2022年

作者： Li, Junlong Wang, Jinyuan Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China SJTU-Paris Elite Institute of Technology Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. This task becomes notably challenging in a zeroshot setting where no data is available to train tailored retrieval-reader models. While recent Large Language Models (LLMs) like GPT-3 have demonstrated their effectiveness in zeroshot ODQA using direct prompting methods, these methods still fall short of fully harnessing the potential of LLMs when implicitly invoked. In this paper, we propose a Self- Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo QA pairs with background passages and explanations entirely from scratch. These generated elements are then utilized for in-context learning. Experimental results show that our method significantly surpasses previous stateof- the-art zero-shot methods on three widelyused ODQA datasets and even achieves comparable performance with various customized fine-tuned models on full training data. Our code is available at https://***/ lockon-n/self-prompting. © 2022, CC BY-NC-SA.

关键词： Computational linguistics

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Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication

arXiv

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arXiv 2023年

作者： Xie, Haihui Xia, Minghua Wu, Peiran Wang, Shuai Poor, H. Vincent The School of Electronics and Information Technology Sun Yat-Sen University Guangzhou510006 China The Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China The Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms. © 2023, CC BY-NC-SA.

关键词： Internet of things

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EEG Source Localization of μ-Rhythm Event-Related Desynchronization during Tactile Imagery

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Moscow University Biological sciences Bulletin 2024年第Suppl 1期79卷 S100-S106页

作者： Yakovlev, L. Syrov, N. Miroshnikov, A. Morozova, M. Berkmush-Antipova, A. Petrova, D. Kaplan, A. Laboratory for Neurophysiology and Neuro-Computer Interfaces Department of Human and Animal Physiology Faculty of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation Vladimir Zelman Center for Neurobiology and Brain Rehabilitation Skolkovo Institute of Science and Technology Moscow 121205 Russian Federation Baltic Center for Neurotechnology and Artificial Intelligence Immanuel Kant Baltic Federal University Kaliningrad 236041 Russian Federation

Abstract: Tactile imagery remains a relatively understudied phenomenon of mental imagery research. However, this technique holds promise as an effective tool for sensorimotor rehabilitation following stroke and spinal cord injuries alongside motor imagery. In this study, conducted with 22 healthy volunteers, the authors investigated the source localization of μ-rhythm event-related desynchronization (ERD) using multichannel electroencephalogram recordings and subsequent inverse problem solution with the sLORETA. All participants exhibited ERD during tactile imagery task and under vibrotactile stimulation. It was demonstrated that μ-rhythm ERD during both vibrotactile stimulation and tactile imagery was localized in the sensorimotor areas of the contralateral hemisphere. Within the source space, ERD in the postcentral gyrus was significantly stronger compared to the precentral gyrus. These findings indicate that tactile imagery, akin to the perception of real tactile stimuli, leads to prominent activation of sensorimotor cortical areas, consistent with the general understanding of the shared neural substrate during mental imagery and perception. © Allerton Press, Inc. 2024. ISSN 0096-3925, Moscow University Biological sciences Bulletin, 2024, Vol. 79, Suppl. 1, pp. S100–S106. Allerton Press, Inc., 2024. Russian Text The Author(s), 2024, published in Vestnik Moskovskogo Universiteta, Seriya 16: Biologiya, 2024, Vol. 79, No. 2 (Suppl.), pp. 105–112.

关键词： event-related desynchronization inverse task sensorimotor rhythm source localization tactile imagery μ-rhythm

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Bipartite flat-graph network for nested named entity recognition 58

Bipartite flat-graph network for nested named entity recogni...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

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

ISBN: (纸本)9781952148255

In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models. © 2020 Association for Computational Linguistics

关键词： Long short-term memory

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