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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keyuan Jiang Valli Devendra Soniya Chavan Gordon R. Bernard Computer Information Technology & Graphics Purdue University Northwest Hammond IN U.S.A. Department of Medicine Vanderbilt University Nashville TN U.S.A.

Gathering the information pertaining to health evidence occurring on particular days can help understand the progression of a disease over time, and such evidence as symptoms was widely shared by the social media users during the COVID-19 pandemic. Identifying this type of evidence is challenging. In this work, we investigated pretrained large language models on their ability to identify the day-based COVID-19 symptom mentions in Twitter posts. Our results on a corpus of 635 tweets show that without any supervision and optimization both GPT-3.5 and GPT 4 models achieved impressive performance, much better than Web-based ChatGPT and Google Bard. In addition, we explored and utilized GPT-4’s ability to determine the number of matches between a list of predicted symptom expressions and a list of annotated symptom expressions, reducing the effort of designing a sophisticated algorithm for finding the matches.

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ROStopbox: A Human-Centered Vehicle Environment Data Acquisition System

ROStopbox: A Human-Centered Vehicle Environment Data Acquisi...

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Automated Vehicle Validation Conference (IAVVC), IEEE International

作者： Daniel Schneider Jonas Nesensohn Bernhard Schick Daniel Watzenig Institute for Advanced Driver Assistance Systems and Connected Mobility Kempten University of Applied Sciences Institute of Computer Graphics and Vision Graz University of Technology AVL Deutschland GmbH Advanced Solution Lab Karlsruhe

ISBN: (数字)9798350354072

ISBN: (纸本)9798350354089

As the development towards fully automated driving continues, passenger trust in automation and human well-being when using advanced driver assistance systems will become increasingly important. To support the existing data-driven development process, systems for acquiring perceptual data such as from camera and LiDAR along with physiological and subjective information will be required in the future. The simultaneous recording of physiological signals alongside data from traditional perception sensors can therefore help to identify scenarios in which passengers are stressed, have an increased workload, or feel unsafe. By recording questionnaires in parallel, it is possible to also correlate the physiological signals with passengers’ subjective perception. The combination of these signal sources makes it possible to identify causes of drivers’ increased workload in the environmental data, describe development targets, and safeguard rules for future assistance systems and automated driving functions, keeping humans in the loop. Here, we present an approach for the simultaneous acquisition of sensor information from multiple domains such as physiological measurement data, environmental sensors, vehicle-internal communication, and subjective questionnaires, paying attention to an unobtrusive measurement setup. We demonstrate the benefits of our approach by identifying a relevant scenario in real traffic conditions in which we observe the impact on the driver’s physiological parameters.

关键词： Laser radar Operating systems Data acquisition Human factors Cameras Physiology Human in the loop Sensors Recording Testing

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Eye fixations and electrodermal activity during low-budget virtual reality embodiment

Eye fixations and electrodermal activity during low-budget v...

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作者： Krogmeier, Claudia Mousas, Christos Department of Computer Graphics Technology Purdue University IN United States

As motion-sensing devices become more accessible to consumers, it is important to understand how users experience embodiment when using these devices. In our 3 × 2 between-groups study, we explored eye fixations and electrodermal activity (EDA) in order to more objectively understand potential interaction effects between the self-avatar body, and the presence of a mirror within the context of low-budget embodiment. We developed six experimental conditions concerning Body (human, mannequin, and zombie self-avatars) and Mirror (mirror and no mirror) factors, and presented participants with a virtual environment in which they could control their self-avatars by using HTC Vive controllers and trackers. In addition to eye fixations and EDA, we assessed self-reported data concerning body ownership, agency, self-location, as well as enjoyment of the experience. Our results suggest that the Body may have been more influential in eliciting body ownership than the Mirror, and that an interaction effect between Gender and Body may influence eye gaze behavior. Additionally, female participants reported significantly higher agency than males. We consider logical next steps for similar research which might elaborate upon our findings. © 2020 John Wiley & Sons, Ltd.

关键词： Mirrors

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Time Series Forecasting of Stock Prices Using Neural Networks LSTM and GAN

Time Series Forecasting of Stock Prices Using Neural Network...

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AIML-Applications for Engineering & technology (ICAET), International Conference on

作者： Vijay Mane Ruturaj Patil Shantanu Pawar Nagesh Pujari Arjun Jaggi Preetish Kakkar Electronics and Telecommunication Engineering. Vishwakarma Institute of Technology Pune Pune India Electronics and Telecommunication Engineering. Vishwakarma Institute of Technology Pune India HCL Tech California US Senior Computer Graphics Engineer at Adobe Seattle Washington United State

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The project seeks to improve the precision of Amazon stock price prediction using advanced neural network techniques, specifically Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs). The analysis is based on OHLCV (Open, High, Low, Close, Volume) data, and various models were employed, including both conventional statistical methods like ARIMA and Fourier Transform models, as well as cutting-edge deep learning approaches. Feature extraction plays a critical role in improving model performance, with 19 features being created, such as momentum, Bollinger Bands, moving averages, and exponential moving averages (EMA), which significantly improved the accuracy of predictions. While ARIMA and Fourier Transform models yielded precise results with low AIC, BIC values, and close p-values, LSTM models faced challenges in short-term price prediction. Despite utilizing both single and multiple features, LSTMs were more effective in forecasting long-term trends rather than capturing short-term price fluctuations. Sentiment analysis from news and Reddit data is also incorporated to further refine stock price predictions. In addition to LSTMs, GANs were utilized to generate synthetic stock data for further training the models, where the historical trends are created using a Generator, and CNN acts as the Discriminator. By clubbing the advantages of neural networks for processing sequential data and also the use of GANs for generating realistic stock data, this unique method aims to increase the overall model accuracy.

关键词： Analytical models Adaptation models Sentiment analysis Social networking (online) Neural networks Predictive models Generative adversarial networks Data models Forecasting Long short term memory

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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CLEO: Applications and technology, CLEO:A and T 2023 - Part of Conference on Lasers and Electro-Optics 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. Boltasseva, Alexandra School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

ISBN: (纸本)9781957171258

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 µm for macroscopic color printing by utilizing femtosecond laser photomodification of multi-functional optical elements. CLEO 2023 © Optica Publishing Group 2023, © 2023 The Author(s)

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 urn for macroscopic color printing by utilizing femtosecond lase... 详细信息

ISBN: (纸本)9781957171258

关键词： Color

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Insulator Defect Detection Algorithm Based on Improved YOLOv5s 5

Insulator Defect Detection Algorithm Based on Improved YOLOv...

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5th International Conference on computer Information Science and Application technology, CISAT 2022

作者： Huang, Wenming Li, Tingting Xiao, Yannan Wen, Yayuan Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphics Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9781510660076

As one of the extremely important components on the transmission tower, the insulator has two functions of electrical insulation and wire fixing, which directly affects the operation of the power system. Defects in insulators can impair the service life of transmission lines. UAV aerial photography of electric power towers has problems such as small number of defective insulator samples, small area, large aspect ratio of insulator strings, and variable inclination angle, coupled with the influence of environmental factors such as light, interference, distance, etc., which lead to low detection accuracy of insulator defects. Aiming at the above problems, an improved YOLOv5 insulator defect detection algorithm is proposed. First, screen the aerial images and use data augmentation to obtain a sufficient number of defective insulator images to enrich the dataset and avoid model overfitting. Secondly, the convolutional attention module CBAM is introduced to improve the expression ability of defect insulator features and strengthen the network's ability to identify targets. Finally, the Leaky ReLU activation function of the hidden layer of the original YOLOv5 algorithm is replaced by the Mish function to improve the generalization ability of the network. The experimental results show that compared with the original YOLOv5 algorithm, the average precision mAP (IOU=0.5) of the improved algorithm is increased by 7.8%, which effectively improves the problems of false detection and missed detection in the original algorithm. Compared with other mainstream object detection algorithms, the algorithm proposed in this paper has better detection effect on insulator defects. © 2022 SPIE.

关键词： Defects

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Enhanced Data Augmentation Using Synthetic Data for Brain Tumour Segmentation 1

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Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for computer Assisted Intervention Conference, MICCAI 2023

作者： Ferreira, André Solak, Naida Li, Jianning Dammann, Philipp Kleesiek, Jens Alves, Victor Egger, Jan Center Algoritmi/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Department of Neurosurgery and Spine Surgery University Hospital Essen Essen Germany

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for adult glioma segmentation, the Task 1 of the BraTS 2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other’s knowledge gaps. Our solution achieves the lesion-wise Dice metric of 0.8851, 0.8719, 0.8685 and lesion-wise HD95 of 22.838, 22.974, 16.713 (whole tumour, tumour core and enhancing tumour) on the testing cohort. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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Multilingual Natural Language Processing Model for Radiology Reports The Summary is all you need!

arXiv

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arXiv 2023年

作者： Lindo, Mariana Santos, Ana Sofia Ferreira, André Li, Jianning Luijten, Gijs Correia, Gustavo Kim, Moon Schaarschmidt, Benedikt Michael Deuschl, Cornelius Haubold, Johannes Kleesiek, Jens Egger, Jan Alves, Victor ALGORITMI Research Centre/LASI University of Minho Braga Portugal Essen Germany Essen Germany Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

The impression section of a radiology report summarizes important radiology findings and plays a critical role in communicating these findings to physicians. However, the preparation of these summaries is time-consuming and error-prone for radiologists. Recently, numerous models for radiology report summarization have been developed. Nevertheless, there is currently no model that can summarize these reports in multiple languages. Such a model could greatly improve future research and the development of Deep Learning models that incorporate data from patients with different ethnic backgrounds. In this study, the generation of radiology impressions in different languages was automated by fine-tuning a model, publicly available, based on a multilingual text-to-text Transformer to summarize findings available in English, Portuguese, and German radiology reports. In a blind test, two board-certified radiologists indicated that for at least 70% of the system-generated summaries, the quality matched or exceeded the corresponding human-written summaries, suggesting substantial clinical reliability. Furthermore, this study showed that the multilingual model outperformed other models that specialized in summarizing radiology reports in only one language, as well as models that were not specifically designed for summarizing radiology reports, such as ChatGPT. Copyright © 2023, The Authors. All rights reserved.

关键词： Radiology

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Change Bit-Depth Procedure for Data Measured Preprocessing before Compression

Change Bit-Depth Procedure for Data Measured Preprocessing b...

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2022 IEEE International Multi-Conference on Engineering, computer and Information Sciences, SIBIRCON 2022

作者： Levenets, Alexey V. Bondarenko, Vladislav V. Bahenov, Ruslan I. Belozerov, Oleg I. Belozerova, Svetlana I. Bogatenkov, Sergey A. Pacific National University Dept. Automation and System Engineering Khabarovsk Russia Sholom-Aleichem Priamursky State University Dept. Information Systems and Mathematics Birobidzhan Russia Far Eastern State Transport University Dept. Computer Technology and Computer Graphics Khabarovsk Russia South Ural State University Dept. Information Technology in Economics Chelyabinsk Russia

ISBN: (纸本)9781665464802

The paper proposes to change the bit-depth of the original data sequence as a preprocessing of the measuring data, which is carried out before the compression procedure. In this case the digit capacity conversion is performed without accounting for the primary data sample boundaries. The simulation was carried out in the Matlab software environment. The findings of the authors' research are presented. They prove to be a promising approach for some types of the studied data at least and several digit capacity values. The analysis of autocorrelation function values showing the data correlation revealed by the conversion is used as an assessment of perspective of the active conversion. In this article the authors review the values of secondary maximums of autocorrelation function. Their occurrence shows the demonstration of the implied correlative correspondence. Such correlations can potentially allow the converted data to be compressed more effectively than the original data using the known compression methods. © 2022 IEEE.

关键词： Data compression

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