检索结果-内蒙古大学图书馆

Simulation Winter Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz Modeling and Analysis Innovation Center The MITRE Corporation Charlottesville VA USA Mission Analysis Group L3Harris Corporation Herndon VA USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA University of Central Florida School of Modeling Simulation and Training Orlando FL USA Department of Systems and Information Engineering University of Virgina Charlottesville VA USA Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn University Auburn AL USA

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within Modeling and Simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Empirical Study on Secure Usage of Mobile Health Apps: The Attack Simulation Approach

arXiv

引用

arXiv 2022年

作者： Aljedaani, Bakheet Ahmad, Aakash Zahedi, Mansooreh Babar, M. Ali Computer Science Department Aljumum University College Umm Alqura University Makkah Saudi Arabia School of Computing and Communications Lancaster University Leipzig Germany School of Computing and Information Systems University of Melbourne Australia CREST – the Centre for Research on Engineering Software Technologies University of Adelaide Australia Australia

Context: Mobile applications (mobile apps for short) have proven their usefulness in enhancing service provisioning across a multitude of domains that range from smart healthcare, to mobile commerce, and areas of context-sensitive computing. In smart healthcare context, mobile health (mHealth) apps - representing a specific genre of mobile apps that manage health-critical information - face some critical challenges relating to security and privacy of device and user data. In recent years, a number of empirically grounded, survey-based studies have been conducted to investigate secure development and usage of mHealth apps. However, such studies rely on self-reported behaviors documented via interviews or survey questions that lack a practical, i.e. action-based approach to monitor and synthesise users’ actions and behaviors in security critical scenarios. Objective and Method: Our objective was to conduct an empirical study - engaging participants with attack simulation scenarios and analyse their actions - for investigating the security awareness of mHealth app users via action-based research. To conduct the study, we simulated some common security attack scenarios in mHealth context and engaged a total of 105 app users (14 countries across 5 continents) to monitor their actions and analyse their behavior. We analysed users' data with statistical analysis including reliability and correlations tests, descriptive analysis, and qualitative data analysis (i.e., thematic analysis method) for the open-ended questions. Results: Our results indicate that whilst the minority of our participants perceived access permissions positively, the majority had negative views by indicating that such an app could violate or cost them to lose privacy. Users provide their consent, granting permissions, without a careful review of privacy policies that leads to undesired or malicious access to health critical data. The results also indicated that 73.3% of our participants had denied at l

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

Text-based automatic personality prediction: A bibliographic review

arXiv

引用

arXiv 2021年

作者： Feizi-Derakhshi, Ali-Reza Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Akan, Taymaz Ranjbar-Khadivi, Mehrdad Zafarni-Moattar, Elnaz Jahanbakhsh-Naghadeh, Zoleikha Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Software Engineering Ayvansaray University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran

Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three;pre-trained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets. © 2021, CC BY-NC-SA.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Deep learning approach for forecasting athletes' performance in sports tournaments 20

Deep learning approach for forecasting athletes' performance...

引用

13th International Conference on Intelligent systems: Theories and Applications, SITA 2020

作者： El-Kassabi, Hadeel T. Khalil, Khaled Serhani, M. Adel Computer Science and Software Engineering Cit Uae University Al-Ain United Arab Emirates Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Information Systems and Security Cit Uae University Al-Ain United Arab Emirates

ISBN: (纸本)9781450377331

Sports and international tournaments have gained world attention in the past decade. Enhancing sports activities and promoting sports to participate in international events competitions and tournaments play a substantial role in the development and advancement of nations around the globe. In this paper we applied different deep learning models for predicting athletes' performance in tournaments to help them improve their results. We propose a deep learning selection algorithm to evaluate the effectiveness of the athletes' current training by predicting their race results upon completing each additional training which potentially improves their performance. We gathered public training data for athletes who participated in the 2017 Boston Marathon within a five-month window prior to the race. Deep learning models were applied and evaluated to predict marathon finishing times. These include Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). Results show that Deep Learning models give improved race time prediction accuracy over the baseline machine learning model such as standard Linear Regression (LR). © 2020 ACM.

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

Independent and Identically Distributed (IID) Data Assessment in Federated Learning

Independent and Identically Distributed (IID) Data Assessmen...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Mohamad Arafeh Ahmad Hammoud Hadi Otrok Azzam Mourad Chamseddine Talhi Zbigniew Dziong Department of Software Engineering Ecole de Technologie Superieure (ETS) Montreal QC Canada Department of Electrical Engineering Ecole de Technologie Superieure (ETS) Montreal Canada Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Department of Computer science and Mathematics Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE

ISBN: (纸本)9781665435413

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.

关键词： Data privacy Federated learning Distributed databases Coherence Global communication Convergence

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for modelling infectious disease epidemics

引用

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Neuron signal propagation analysis of cytokine-storm induced demyelination

arXiv

引用

arXiv 2021年

作者： Adonias, Geoflly L. Siljak, Harun Barros, Michael Taynnan Balasubramaniam, Sasitharan Telecommunications Software and Systems Group Waterford Institute of Technology Waterford Ireland Department of Electronic and Electrical Engineering Trinity College Dublin Dublin Ireland School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

The COVID-19 pandemic has shaken the world unprecedentedly, where it has affected the vast global population both socially and economically. The pandemic has also opened our eyes to the many threats that novel virus infections can pose for humanity. While numerous unknowns are being investigated in terms of the distributed damage that the virus can do to the human body, recent studies have also shown that the infection can lead to lifelong sequelae that could affect other parts of the body, and one example is the brain. As part of this work, we investigate how viral infection can affect the brain by modelling and simulating a neuron’s behaviour under demyelination that is affected by the cytokine storm. We quantify the effects of cytokine-induced demyelination on the propagation of action potential signals within a neuron. We used information and communication theory analysis on the signal propagated through the axonal pathway under different intensity levels of demyelination to analyse these effects. Our simulations demonstrate that virus-induced degeneration can play a role in the signal power and spiking rate and the probability of releasing neurotransmitters and compromising the propagation and processing of information between the neurons. We also propose a transfer function that models these attenuation effects that degenerates the action potential, where this model has the potential to be used as a framework for the analysis of virus-induced neurodegeneration that can pave the way to improved understanding of virus-induced demyelination. Copyright © 2021, The Authors. All rights reserved.

关键词： Viruses

来源：评论

学校读者我要写书评

暂无评论

Improved Method of Segmentation of Images From Space-Based Optoelectronic Observation systems Based on Otsu's Algorithm with Global and Adaptive Thresholds

Improved Method of Segmentation of Images From Space-Based O...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Hennadii Khudov Temir Kalimulin Rostyslav Khudov Ihor Butko Viktoriia Burtseva Valeriy Burtsev Department of Radar Troops Tactic Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Theoretical and Applied Informatics V.N.Karazin Kharkiv National University Kharkiv Ukraine Department of Computer Sciences and Software Engineering Academician Y. Bugay International Scientific and Technical University Kyiv Ukraine Scientific Research Department of Military Standards Metrology Center of Military Standards Kharkiv Ukraine Department of Control Systems of Antiaircraft Missile Troops Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

An analysis of optoelectronic image, received from space-based observation system has been carried out. It has been found out, that this image has a complex structure and a unimodal intensity histogram. Otsu method is chosen as to segment the image. This method is easy and relatively quick, when talking of its realization. Methods of global and adaptive thresholding have been researched to perform image segmentation. Results of experimental research of segmentation of optoelectronic image from space-based observation system with use of Otsu method are developed. Ways for future research in this area are defined.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Multiclass Depression Detection in Social Media Based on Sentiment Analysis 17th

A Multiclass Depression Detection in Social Media Based on S...

引用

17th International Conference on Information Technology: New Generations, ITNG 2020

作者： Mustafa, Raza Ul Ashraf, Noman Ahmed, Fahad Shabbir Ferzund, Javed Shahzad, Basit Gelbukh, Alexander Department of Computer Science COMSATS University Islamabad Islamabad Pakistan IPN - Computing Research Center Mexico City Mexico Yale School of Medicine New HavenCT United States Deptartment of Software Engineering National University of Modern Languages NUML Islamabad Pakistan

ISBN: (纸本)9783030430191

Depression is a common mental health disorder. Despite its high prevalence, the only way of diagnosing depression is through self-reporting. However, 70% of the patients would not consult doctors at an early stage of depression. Meanwhile people increasingly relying on social media for sharing emotions, and daily life activities thus helpful for detecting their mental health. Inspired by these a total of 179 depressive individuals selected from Twitter, who have reported depression and they are on medical treatment. A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers. © Springer Nature Switzerland AG 2020.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Enabled Economic Transactions: Recurring Financial Accruals and Payments

Blockchain-Enabled Economic Transactions: Recurring Financia...

引用

Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Razvan Mihai Omer Faruk Ozkul Gora Datta Nicolae Goga Sergii Grybniak Constantin Viorel Marian Faculty of Electronics Telecommunications and Technology of Information (ETTI) Politehnica University of Bucharest Bucharest Romania Faculty of Engineering in Foreign Languages (FILS) Politehnica University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering University of California Berkeley Berkeley United States Molecular Dynamics Group University of Groningen Groningen Netherlands Institute of Computer Systems Odesa Polytechnic State University Odesa Ukraine

Economic transactions are based upon implicit or explicit contracts that set out the rights and obligations of the parties to the transactions. Blockchain technology can address fundamental financial, economic, and accounting challenges. We propose a blockchain-based prototype capable of capturing the essence of recurring economic transactions. For this purpose, we have devised an asset rental contract to show how economic transactions are recorded and tracked more effectively, efficiently, and in quasi-real-time, thus changing the traditional way of account keeping and auditing. We use the Ethereum blockchain as the most evolved and widely used smart contract platform. We showcase a significant discovery related to current blockchain technology limitations to making automatic non-custodial recurring payments. We show how the prototype can be extended to a range of recurring transactions.

关键词： Economics Smart contracts Prototypes Finance Blockchains Standards

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：