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arXiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani SulaymaniyahKRG46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler ErbilKRG44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Sydney BathurstNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil ErbilKRG44001 Iraq Department of Information Technology Lebanese French University ErbilKRG44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan BurdwanWest Bengal713101 India

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location;then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs. © 2020, CC BY.

关键词： Location

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GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series

arXiv

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arXiv 2024年

作者： Piao, Chengzhe Zhu, Taiyu Baldeweg, Stephanie E. Taylor, Paul Georgiou, Pantelis Sun, Jiahao Wang, Jun Li, Kezhi Institute of Health Informatics University College London LondonNW1 2DA United Kingdom Department of Psychiatry University of Oxford OxfordOX3 7JX United Kingdom Department of Diabetes & Endocrinology University College London Hospitals LondonNW1 2PG United Kingdom Centre for Obesity & Metabolism Dept of Experimental & Translational Medicine University College London LondonWC1E 6JF United Kingdom Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom Department of Computer Science University College London LondonWC1E 6EA United Kingdom

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multi-modal data, i.e., sensor data and self-reported event data, organised as multi-variate time series (MTS). However, these methods are mostly regarded as "black boxes" and not entirely trusted by clinicians and patients. In this paper, we propose interpretable graph attentive recurrent neural networks (GARNNs) to model MTS, explaining variable contributions via summarizing variable importance and generating feature maps by graph attention mechanisms instead of post-hoc analysis. We evaluate GARNNs on four dataset, representing diverse clinical scenarios. Upon comparison with twelve well-established baseline methods, GARNNs not only achieve the best prediction accuracy but also provide high-quality temporal interpretability, in particular for postprandial glucose levels as a result of corresponding meal intake and insulin injection. These findings underline the potential of GARNN as a robust tool for improving diabetes care, bridging the gap between deep learning technology and real-world healthcare solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Reconstruction Model of Ocean Observing Complex Virtual Instrument

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Journal of Ocean University of China 2018年第5期17卷 1159-1170页

作者： QIU Zhijin GUO Zhongwen WANG Yu HU Tong LIU Chao LI Ting Institute of Oceanographic Instrumentation Qilu University of Technology(Shandong Academy of Sciences) Department of Computer Science and Technology College of Information Science and EngineeringOcean University of China Department of Computer Science College of Computing and InformaticsUniversity of North Carolina at Charlotte

With the newly proposed Global Ocean Observing Integration, ocean observing scope has been expanded from the region to the global, therefore the need of large-scale ocean observing system integration has become more and more urgent. Currently, ocean observing systems enabled ocean sensor networks are commonly developed by different organizations using specific technologies and platforms, which brings several challenges in ocean observing instrument （OOI） access and ocean observing system seamless integration. Furthermore, the development of ocean observing systems often suffers from low efficiency due to the complex prograrmning and debugging process. To solve these problems, a novel model, Complex Virtual Instrument （CVI） Model, is proposed. The model provides formal definitions on observing instrument description file, CVI description file, model calculation method, development model and interaction standard. In addition, this model establishes mathematical expressions of two model calculation operations, meanwhile builds the mapping relationship between observing instrument description file and CVI description file. The CVI based on the new model can achieve automatic access to different OOIs, seamless integration and communication for heterogeneous environments, and further implement standardized data access and management for the global unified ocean observing network. Throughout the development, integration and application of such CVI, the rationality and feasibility of the model have been evaluated. The results confirm that the proposed model can effectively implement heterogeneous system integration, improve development efficiency, make full usage of reusable components, reduce development cost, and enhance overall software system quality. We believe that our new model has great significance to promote the large-scale ocean observing system integration.

关键词： ocean observing complex virtual instrument seamless integration model calculation development platform

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Rank the spreading influence of nodes using dynamic Markov process

arXiv

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arXiv 2022年

作者： Lin, Jian-Hong Yang, Zhao Liu, Jian-Guo Chen, Bo-Lun Tessone, Claudio J. Blockchain and Distributed Ledger Technologies Institute of Informatics University of Zürich Andreasstrasse 15 ZürichCH-8050 Switzerland UZH Blockchain Center University of Zürich Andreasstrasse 15 ZürichCH-8050 Switzerland ETH Zürich Department of Management Technology and Economics Scheuchzerstrasse 7 ZürichCH-8092 Switzerland Global People Analytics Talent Rewards & Insights Roche BaselCH-4070 Switzerland Institute of Accounting and Finance Shanghai University of Finance and Economics Shanghai200433 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian233003 China Swiss Center for Data and Network Sciences University of Zurich ZürichCH-8050 Switzerland

Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node's spreading ability is to evaluate the fraction of susceptible nodes been infected by the target node during the outbreak, i.e., the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbours. We have employed the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes' outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Explaining predictions of deep neural classifier via activation analysis

arXiv

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arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Opinions regarding virtual reality among older people in Taiwan 6

Opinions regarding virtual reality among older people in Tai...

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6th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2020

作者： Barsasella, Diana Malwade, Shwetambara Chang, Chia-Chi Liu, Megan F. Srikanth, Sruthi Panja, Ajith Kumar Jack Li, Yu-Chuan Syed-Abdul, Shabbir International Center for Health Information Technology Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Health Polytechnic of Health Ministry Tasikmalaya Tasikmalaya West Java Indonesia School of Gerontology Health Management College of Nursing Taipei Medical University Taipei Taiwan College of Interdisciplinary Studies Taipei Medical University Taipei Taiwan Department of Biomedical Engineering College of Engineering Guindy Anna University Chennai India Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai India

ISBN: (纸本)9789897584206

In recent years, older population aged 60 years and above has been increasing from 900 million in 2015 into 2 billion by 2050. With advancing age, older people experience decreasing social activities, decreased physical activities, issues related to mental health, disturbed sleep and overall poor quality of life. Virtual reality has shown applications in healthcare domain to help mitigate these problems. The aim of our study aim is to investigate the opinions of older population about virtual reality through dimensions of technology acceptance model. We used the data generated in our previously published research to measure the opinions of older population toward VR use. Thirty participants of older age group were involved in this study (twenty-four females and 6 males) from March to May 2018. They were exposed to 12 sessions of VR experiences for 15 minutes each, twice a week for 6 weeks. Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's Test were conducted to test the reliability of each questions. From Likert scale analysis, positive opinions (more than 70%) were seen towards the use of VR for entertainment, raising mood, attractiveness and fun to use VR. While the highest choice showing negative opinions in the difficulty of learning to use VR (33.3%). Our study indicates the opinion of older population that they showed positive opinions for all of the TAM variables, the index being 72% to 78.44%. The perceived VR as enjoyable to use, but they still needed time to be skillful in using it. Our study showed that providing training and ease of use is an essential element while introducing VR among older adults. Copyright © 2020 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved

关键词： Population statistics

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And Forgive & Forget: Self-stabilizing Swarms in Spite of Byzantine Robots 21st

And Forgive & Forget: Self-stabilizing Swarms in Spite of B...

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21st International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2019

作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Be’er Sheva Israel Department of Information Engineering Graduate School of Engineering Hiroshima University Higashihiroshima Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Tokyo Japan

ISBN: (纸本)9783030349912

In this paper, we consider the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the program/policy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to Forgive & Forget (F&F), otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. We demonstrate the need to periodically F&F to realize swarm several tasks including patrolling/cleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrol/clean the plane. © Springer Nature Switzerland AG 2019.

关键词： Robots

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MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

arXiv

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arXiv 2023年

作者： Fleming, Scott L. Lozano, Alejandro Haberkorn, William J. Jindal, Jenelle A. Reis, Eduardo Thapa, Rahul Blankemeier, Louis Genkins, Julian Z. Steinberg, Ethan Nayak, Ashwin Patel, Birju Chiang, Chia-Chun Callahan, Alison Huo, Zepeng Gatidis, Sergios Adams, Scott Fayanju, Oluseyi Shah, Shreya J. Savage, Thomas Goh, Ethan Chaudhari, Akshay S. Aghaeepour, Nima Sharp, Christopher Pfeffer, Michael A. Liang, Percy Chen, Jonathan H. Morse, Keith E. Brunskill, Emma P. Fries, Jason A. Shah, Nigam H. Department of Biomedical Data Science Stanford School of Medicine StanfordCA United States Department of Computer Science Stanford School of Engineering StanfordCA United States Department of Anesthesiology Peri-operative and Pain Medicine Stanford School of Medicine StanfordCA United States Department of Pediatrics Stanford School of Medicine StanfordCA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Department of Radiology Stanford School of Medicine StanfordCA United States Stanford University StanfordCA United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Technology and Digital Solutions Stanford Health Care Palo AltoCA United States Department of Electrical Enginering Stanford School of Engineering StanfordCA United States Department of Medicine Vanderbilt University School of Medicine NashvilleTN United States Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Medicine Stanford School of Medicine StanfordCA United States Department of Neurology Mayo Clinic RochesterMN United States Human-Centered Artificial Intelligence Institute Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Clinical Excellence Research Center Stanford School of Medicine StanfordCA United States

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. MedAlign is provided under a research data use agreement1 to enable LLM evaluations on tasks aligned with clinician needs and preferences. © 2023, CC BY.

关键词： Natural language processing systems

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Weighted hypersoft configuration model

arXiv

引用

arXiv 2020年

作者： Voitalov, Ivan van der Hoorn, Pim Kitsak, Maksim Papadopoulos, Fragkiskos Krioukov, Dmitri Department of Physics Northeastern University BostonMA02115 United States Network Science Institute Northeastern University BostonMA02115 United States Department of Mathematics and Computer Science Eindhoven University of Technology Postbus 513 Eindhoven5600 MB Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology 2628 CD Delft Netherlands Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 33 Saripolou Street Limassol3036 Cyprus Department of Mathematics Northeastern University BostonMA02115 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Maximum entropy null models of networks come in different flavors that depend on the type of the constraints under which entropy is maximized. If the constraints are on degree sequences or distributions, we are dealing with configuration models. If the degree sequence is constrained exactly, the corresponding microcanonical ensemble of random graphs with a given degree sequence is the configuration model per se. If the degree sequence is constrained only on average, the corresponding grandcanonical ensemble of random graphs with a given expected degree sequence is the soft configuration model. If the degree sequence is not fixed at all but randomly drawn from a fixed distribution, the corresponding hypercanonical ensemble of random graphs with a given degree distribution is the hypersoft configuration model, a more adequate description of dynamic real-world networks in which degree sequences are never fixed but degree distributions often stay stable. Here, we introduce the hypersoft configuration model of weighted networks. The main contribution is a particular version of the model with power-law degree and strength distributions, and superlinear scaling of strengths with degrees, mimicking the properties of some real-world networks. As a by-product, we generalize the notions of sparse graphons and their entropy to weighted networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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AIROGS: ARTIFICIAL INTELLIGENCE FOR ROBUST GLAUCOMA SCREENING CHALLENGE

arXiv

引用

arXiv 2023年

作者： De Vente, Coen Vermeer, Koenraad A. Jaccard, Nicolas Wang, He Sun, Hongyi Khader, Firas Truhn, Daniel Aimyshev, Temirgali Zhanibekuly, Yerkebulan Le, Tien-Dung Galdran, Adrian Ballester, Miguel Ángel González Carneiro, Gustavo Devika, R.G. Hrishikesh, P.S. Puthussery, Densen Liu, Hong Yang, Zekang Kondo, Satoshi Kasai, Satoshi Wang, Edward Durvasula, Ashritha Heras, Jónathan Zapata, Miguel Ángel Araújo, Teresa Aresta, Guilherme Bogunović, Hrvoje Arikan, Mustafa Lee, Yeong Chan Cho, Hyun Bin Choi, Yoon Ho Qayyum, Abdul Razzak, Imran Van Ginneken, Bram Lemij, Hans G. Sánchez, Clara I. Group Informatics Institute Universiteit Van Amsterdam Noord-Holland Amsterdam Netherlands The Department of Biomedical Engineering and Physics Amsterdam Umc Locatie Amc Noord-Holland Amsterdam Netherlands Department of Radiology and Nuclear Medicine Radboudumc Gelderland Nijmegen Netherlands The Rotterdam Ophthalmic Institute Rotterdam Eye Hospital Rotterdam Netherlands Project Orbis International Inc. New York United States The Peking Union Medical College Hospital Beijing China The Xuanwu Hospital Capital Medical University Beijing100053 China The Tsinghua University Beijing China The Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Cmc Technologies Llp Nur-Sultan Kazakhstan Kbc Belgium Universitat Pompeu Fabra Barcelona Spain The Australian Institute for Machine Learning Aiml University of Adelaide Australia Icrea Barcelona Spain The Centre for Vision Speech and Signal Processing University of Surrey United Kingdom The College of Engineering Trivandrum India Founding Minds Software India The Institute of Computing Technology Chinese Academy of Sciences China The Muroran Institute of Technology Japan The Niigata University of Health and Welfare Japan The Schulich School of Medicine and Dentistry University of Western Ontario London Canada The Department of Mathematics and Computer Science University of la Rioja Spain Hospital Vall Hebron Passeig Roser 126 Sant Cugat del Vallès Barcelona08195 Spain UPRetina Barcelona Spain The Christian Doppler Laboratory for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria The Institute of Ophthalmology University College London London United Kingdom The Research Institute for Future Medicine Samsung Medical Center Seoul Korea Republic of Sungkyunkwan University Samsung Medical Center Seoul Korea Republic of The Department of Artificial Intelligence and Informatics Mayo Clinic Jacks

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in realworld scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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