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Designing a Pseudo-Haptics Study for Virtual Anesthesia Skills Development

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Designing a Pseudo-Haptics Study for Virtual Anesthesia Skil...

IEEE Games Media Entertainment (GEM)

作者： Bill Kapralos Alvaro Quevedo KC Collins Celina Da Silva Eva Peisachovich Kamen Kanev Michael Jenkin Adam Dubrowski Fahad Alam maxSIMhealth. Ontario Tech University Oshawa Ontario Canada Software and Informatics Research Centre. Ontario Tech University Oshawa Ontario Canada Collaborative Human Immersive Interaction Lab Toronto Canada School of Information Technology Carleton University Ottawa Ontario Canada Department of Health York University Toronto Ontario Canada Research Institute of Electronics Shizuoka University Hamamatsu Japan Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Canada Faculty of Health Sciences Ontario Tech University Oshawa Ontario Canada Department of Anesthesia Sunnybrook Health Science Centre Toronto Ontario Canada

ISBN: (纸本)9781665461399

Pseudo-haptics refers to the simulation of haptic sensations without the use of haptic interfaces, using, for example, audiovisual feedback and kinesthetic cues. Given the COVID-19 pandemic and the shift to online learning, there has been a recent interest in pseudo-haptics as it can help facilitate psychomotor skills development away from simulation centers and laboratories. Here we present work-in-progress that describes the study design of a pseudo-haptics for virtual anesthesia skills development. We anticipate this work will provide greater insight to pseudo-haptics and its application to anesthesia-based training.

关键词： Training COVID-19 Pandemics laboratories Entertainment industry Games Media

vieCap4H-VLSP 2021: Vietnamese Image Captioning for Healthcare Domain using Swin Transformer and Attention-based LSTM

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arXiv 2022年

作者： Nguyen, Thanh Tin Nguyen, Long H. Pham, Nhat Truong Nguyen, Liu Tai Do, Van Huong Nguyen, Hai Nguyen, Ngoc Duy Human Computer Interaction Lab Sejong University Seoul Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam ASICLAND Suwon Korea Republic of Khoury College of Computer Sciences Northeastern University Boston United States Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia

This study presents our approach on the automatic Vietnamese image captioning for healthcare domain in text processing tasks of Vietnamese Language and Speech Processing (VLSP) Challenge 2021, as shown in Figure 1. In recent years, image captioning often employs a convolutional neural network-based architecture as an encoder and a long short-term memory (LSTM) as a decoder to generate sentences. These models perform remarkably well in different datasets. Our proposed model also has an encoder and a decoder, but we instead use a Swin Transformer in the encoder, and a LSTM combined with an attention module in the decoder. The study presents our training experiments and techniques used during the competition. Our model achieves a BLEU4 score of 0.293 on the vietCap4H dataset, and the score is ranked the 3rd place on the private leaderboard. Our code can be found at https://***/JDdJm. © 2022, CC BY.

关键词： Long short-term memory

DVP: Data visualization platform

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arXiv 2019年

作者： Yousefa, Waleed A. Abouelkahire, Ahmed A. Marzouk, Omar S. Mohame, Sameh K Alaggan, Mohammad Human Computer Interaction Laboratory HCI Lab Computer Science Department Faculty of Computers and Information Helwan University

We identify two major steps in data analysis, data exploration for understanding and observing patterns/relationships in data;and construction, design and assessment of various models to formalize these relationships. For each step, there exists a large set of tools and software. For the first step, many visualization tools exist, such as, GGobi, Parallax, and Crystal Vision, and most recently tableau and plottly. For the second step, many Scientific Computing Environments (SCEs) exist, such as, Matlab, Mathematica, R and Python. However, there does not exist a tool which allows for seamless two-way interaction between visualization tools and SCEs. We have designed and implemented a data visualization platform (DVP) with an architecture and design that attempts to bridge this gap. DVP connects seamlessly to SCEs to bring the computational capabilities to the visualization methods in a single coherent platform. DVP is designed with two interfaces, the desktop stand alone version and the online interface. To illustrate the power of DVP design, a free demo for the online interface of DVP is available [1] and very low-level design details are explained in this article. Since DVP was launched, circa 2012, the present manuscript was not published since today for commercialization and patent considerations. Copyright © 2019, The Authors. All rights reserved.

关键词： Data visualization

Seeking common but distinguishing difference, a joint aspect-based sentiment analysis model

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arXiv 2021年

作者： Jing, Hongjiang Li, Zuchao Zhao, Hai Jiang, Shu Department of Computer Science and Engineering Shanghai Jiao Tong University 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

Aspect-based sentiment analysis (ABSA) task consists of three typical subtasks: aspect term extraction, opinion term extraction, and sentiment polarity classification. These three subtasks are usually performed jointly to save resources and reduce the error propagation in the pipeline. However, most of the existing joint models only focus on the benefits of encoder sharing between subtasks but ignore the difference. Therefore, we propose a joint ABSA model, which not only enjoys the benefits of encoder sharing but also focuses on the difference to improve the effectiveness of the model. In detail, we introduce a dual-encoder design, in which a pair encoder especially focuses on candidate aspect-opinion pair classification, and the original encoder keeps attention on sequence labeling. Empirical results show that our proposed model shows robustness and significantly outperforms the previous state-of-the-art on four benchmark datasets. © 2021, CC BY.

关键词： Signal encoding

NEURAL MACHINE TRANSLATION WITH UNIVERSAL VISUAL REPRESENTATION 8

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NEURAL MACHINE TRANSLATION WITH UNIVERSAL VISUAL REPRESENTAT...

8th International Conference on Learning Representations, ICLR 2020

作者： Zhang, Zhuosheng Chen, Kehai Wang, Rui Utiyama, Masao Sumita, Eiichiro Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University 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 China Kyoto Japan

Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we present a universal visual representation learned over the monolingual corpora with image annotations, which overcomes the lack of large-scale bilingual sentence-image pairs, thereby extending image applicability in NMT. In detail, a group of images with similar topics to the source sentence will be retrieved from a light topic-image lookup table learned over the existing sentence-image pairs, and then is encoded as image representations by a pre-trained ResNet. An attention layer with a gated weighting is to fuse the visual information and text information as input to the decoder for predicting target translations. In particular, the proposed method enables the visual information to be integrated into large-scale text-only NMT in addition to the multimodal NMT. Experiments on four widely used translation datasets, including the WMT'16 English-to-Romanian, WMT'14 English-to-German, WMT'14 English-to-French, and Multi30K, show that the proposed approach achieves significant improvements over strong baselines. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Neural machine translation

Dialogue-oriented pre-training

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arXiv 2021年

作者： Xu, Yi 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 Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Pre-trained language models (PrLM) has been shown powerful in enhancing a broad range of downstream tasks including various dialogue related ones. However, PrLMs are usually trained on general plain text with common language model (LM) training objectives, which cannot sufficiently capture dialogue exclusive features due to the limitation of such training setting, so that there is an immediate need to fill the gap between a specific dialogue task and the LM task. As it is unlikely to collect huge dialogue data for dialogue-oriented pre-training, in this paper, we propose three strategies to simulate the conversation features on general plain text. Our proposed method differs from existing post-training methods that it may yield a general-purpose PrLM and does not individualize to any detailed task while keeping the capability of learning dialogue related features including speaker awareness, continuity and consistency. The resulted Dialog-PrLM is fine-tuned on three public multi-turn dialogue datasets and helps achieve significant and consistent improvement over the plain PrLMs. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational linguistics

Sparse fuzzy attention for structured sentiment analysis

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arXiv 2021年

作者： Peng, Letian Li, Zuchao 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 Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Attention scorers have achieved success in parsing tasks like semantic and syntactic dependency parsing. However, in tasks modeled into parsing, like structured sentiment analysis, "dependency edges" are very sparse which hinders parser performance. Thus we propose a sparse and fuzzy attention scorer with pooling layers which improves parser performance and sets the new state-of-the-art on structured sentiment analysis. We further explore the parsing modeling on structured sentiment analysis with second-order parsing and introduce a novel sparse second-order edge building procedure that leads to significant improvement in parsing performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Sentiment analysis

Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI

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Informatics in Medicine Unlocked 2023年 42卷

作者： Hossain, Md Maruf Ali, Md Shahin Ahmed, Md Mahfuz Rakib, Md Rakibul Hasan Kona, Moutushi Akter Afrin, Sadia Islam, Md Khairul Ahsan, Md Manjurul Raj, Sheikh Md Razibul Hasan Rahman, Md Habibur Department of Biomedical Engineering Islamic University Kushtia 7003 Bangladesh Department of Food and Nutrition Government College of Applied Human Science Azimpur Dhaka 1205 Bangladesh Department of Radiology Northwestern University Machine and Hybrid Intelligence Lab Chicago 60611 IL United States Industrial & Production Engineering Shahjalal University of Science and Technology Sylhet 3114 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Center for Advanced Bioinformatics and Artificial Intelligent Research Islamic University Kushtia 7003 Bangladesh

Cardiovascular disease (CVD) is a leading cause of death worldwide, with millions dying each year. The identification and early diagnosis of CVD are critical in preventing adverse health outcomes. Hence, this study proposes a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) and long short-term memory (LSTM) to identify CVD from the clinical data. This study utilizes CNN to extract the relevant features from the input data and the LSTM network to process sequential data and capture dependencies and patterns over time. This study provides insights into the potential of a hybrid DL model combined with feature engineering and explainable AI to improve the accuracy and interpretability of CVD prediction. We evaluated our model on a publicly available dataset where the proposed CNN-LSTM achieved a high accuracy of 73.52% and 74.15% with and without feature engineering, respectively, in identifying individuals with CVD, which is the best result compared to the current state-of-the-art model. The results of this study demonstrate the potential of DL models for the early diagnosis of CVD. Our proposed CNN-LSTM model also incorporates explainable AI to identify the top features responsible for CVD. They could be used to develop more eﬀective screening tools in clinical practice. © 2023 The Authors

关键词： Cardiovascular disease CNN-LSTM Deep learning Explainable AI Feature engineering

Multilingual pre-training with universal dependency learning 21

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Multilingual pre-training with universal dependency learning

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Kailai Sun Zuchao Li Hai Zhao Department of Computer Science and Engineering Shanghai Jiao Tong University 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

ISBN: (纸本)9781713845393

The pre-trained language model (PrLM) demonstrates domination in downstream natural language processing tasks, in which multilingual PrLM takes advantage of language universality to alleviate the issue of limited resources for low-resource languages. Despite its successes, the performance of multilingual PrLM is still unsatisfactory, when multilingual PrLMs only focus on plain text and ignore obvious universal linguistic structure clues. Existing PrLMs have shown that monolingual linguistic structure knowledge may bring about better performance. Thus we propose a novel multilingual PrLM that supports both explicit universal dependency parsing and implicit language modeling. Syntax in terms of universal dependency parse serves as not only pre-training objective but also learned representation in our model, which brings unprecedented PrLM interpretability and convenience in downstream task use. Our model outperforms two popular multilingual PrLM, multilingual-BERT and XLM-R, on cross-lingual natural language understanding (NLU) benchmarks and linguistic structure parsing datasets, demonstrating the effectiveness and stronger cross-lingual modeling capabilities of our approach.

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