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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Gondhi, Spoorthy Reddy Janak, Urmisha Reddy Raja, Ashok Asrari, Arash Purdue University Northwest Electrical and Computer Engineering Department HammondIN United States Purdue University Northwest Computer Information Technology and Graphics Department HammondIN United States

ISBN: (纸本)9798331540104

The integration of diverse infrastructures in modern-day power systems facilitates unauthorized access and data manipulation by adversaries, as these systems heavily rely on information and Communication technology (ICT) for monitoring and control. A significant challenge within these power networks is the risk of operational disruptions, such as congestion and voltage instability, resulting from stealthy false data injection (FDI) cyberattacks. Different from the existing work, this paper proposes a solution by introducing a defense framework that utilizes a Wasserstein Generative Adversarial Network (WGAN) to generate synthetic data. This synthetic data closely resembles the output from actual Phasor Measurement Units (PMU) and is developed by training the WGAN with extensive real PMU datasets. Additionally, mixing synthetic and real data when sending it to the Supervisory Control and Data Acquisition (SCADA) system adds layers of complexity and obscures the data landscape for attackers, thereby hindering their ability to detect vulnerabilities and anomalies. © 2024 IEEE.

关键词： Phasor measurement units

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The Ability of Pretrained Large Language Models in Understanding Health Concepts in Social Media Posts

The Ability of Pretrained Large Language Models in Understan...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Keyuan Bernard, Gordon R. Purdue University Northwest Department of Computer Information Technology & Graphics HammondIN United States Vanderbilt University Department of Medicine NashvilleTN United States

ISBN: (纸本)9798350386226

Lay people's terms of health concepts are widely found in social medial data. Understanding their terms can help learn what they have experienced. The existing consumer health vocabularies do not include many health concept phrases found in social media. Recent advancement of pretrained large language models has demonstrated their state-of-the-art performance in a number of natural language processing tasks. In this work, we investigated 3 commonly used pretrained large language models on their ability of understanding consumer health concepts. The results on a corpus of 156 phrases related to 3 common COVID-19 symptoms showed that these pretrained general domain language models generated encouraging outputs, indicating that they may be a promising alternative to the current consumer health vocabularies. © 2024 IEEE.

关键词： Electronic health record

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Challenges of Distance Learning During the Coronavirus (COVID-19) Pandemic A Case Study of the University of Petra 2

Challenges of Distance Learning During the Coronavirus (COVI...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Omar, Khalil Saleh, Yaser Issa, Ghassan University Of Petra Computer Science Department Amman Jordan Princes Sumaya University For Technology Department Of Computer Graphics And Animation Amman Jordan School Of Information Technology Skyline University College Sharjah United Arab Emirates

ISBN: (纸本)9798350394962

The Coronavirus (COVID-19) pandemic has affected all aspects of our daily life and imposed social distancing among people. In higher education, the shift from traditional learning (TL) to the distance learning (DL) model has become inevitable to continue academic life. However, this unexpected shift is complex and poses new challenges and obstacles for the stakeholders of the DL system. In this research, the DL system's stakeholders have been classified into students, instructors, technical administrators, and educational administrators. This research study aims to gather the perceptions of and satisfaction level of these stakeholders with DL and explore the challenges encountered by these stakeholders during the shift to DL during the COVID-19 pandemic. Therefore, four questionnaires were developed and distributed to the identified stakeholders at the University of Petra, which has been adopted as a case study for this research. © 2024 IEEE.

关键词： Coronavirus

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Systematic analysis of artificial intelligence in the era of industry 4.0

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Journal of Management Analytics 2023年第1期10卷 89-108页

作者： Weiru Chen Wu He Jiayue Shen Xin Tian Xianping Wang Department of Information Technology&Decision Science Old Dominion UniversityNorfolkVAUSA Department of Computer&Information Science Arkansas Tech UniversityRussellvilleARUSA Department of Engineering Technology SUNY Polytechnic InstituteUticaNYUSA College of Computing and Software Engineering Kennesaw State UniversityMariettaGAUSA Computer Information Technology&Graphics Department Purdue University NorthwestHammondINUSA

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.

关键词： Industry 4.0 AI deep learning neural network supervised learning unsupervised learning reinforced learning

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Detection of Day-Based Health Evidence with Pretrained Large Language Models: A Case of COVID-19 Symptoms in Social Media Posts

Detection of Day-Based Health Evidence with Pretrained Large...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Keyuan Devendra, Valli Chavan, Soniya Bernard, Gordon R. Purdue University Northwest Computer Information Technology & Graphics HammondIN United States Vanderbilt University Department of Medicine NashvilleTN United States

ISBN: (纸本)9798350337488

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. © 2023 IEEE.

关键词： COVID-19 symptoms day-based evidence health evidence detection large language models Twitter

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Oh No, Not Another Trolley! on the Need for a Co-Liberative Consciousness in CS Pedagogy

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第1期3卷 67-74页

作者： Williams, Rua M. Smarr, Simone Prioleau, Diandra Gilbert, Juan E. Computer Graphics and Technology Department Purdue University West LafayetteIN47907 United States Computer and Information Sciences and Engineering Department University of Florida GainesvilleFL32611 United States

Due to growing concerns for the disproportionate dangers, artificial intelligence (AI) advances pose to marginalized groups, proposals for procedural solutions to ethics in AI abound. It is time to consider that some systems may be inherently unethical, even violent, whether or not they are fair. In this article, we deploy a feminist critical discourse analysis of long-format responses to ethical scenarios from computing science undergraduate students. We find that even among students that had a strong understanding of social justice and the power of AI to exacerbate existing inequities, most students contextualize these problems as the product of biased datasets and human mis/trust factors, rather than as problems of design and purpose. Further, while many students recognized racism and classism at play in the potential negative impacts of AI systems, most students failed to recognize ableism as a driving social force for inequity. As computing science faculty, we must recognize that our students graduate to become the researchers and developers of future technosocial systems. Pedagogically, we need more than procedural fixes to systemic inequities. We are not going to program our way into justice. We must learn to say no to building violent things. © 2022 IEEE.

关键词： Students

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WGAN-Based Synthetic Data Generation for Modeling Cyberattacks in Power Transmission Systems

WGAN-Based Synthetic Data Generation for Modeling Cyberattac...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Spoorthy Reddy Gondhi Urmisha Reddy Janak Ashok Raja Arash Asrari Electrical and Computer Engineering Department Purdue University Northwest Hammond IN USA Computer Information Technology and Graphics Department Purdue University Northwest Hammond IN USA

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

关键词： SCADA systems Power system stability Generative adversarial networks Phasor measurement units Data models Power system reliability information and communication technology Reliability computer crime Synthetic data

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Anomaly Detection of Motion Capture Data Based on the Autoencoder Approach 23rd

Anomaly Detection of Motion Capture Data Based on the Aut...

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23rd International Conference on Computational Science, ICCS 2023

作者： Hasiec, Piotr Świtoński, Adam Josiński, Henryk Wojciechowski, Konrad Department of Computer Graphics Vision and Digital Systems Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland Polish-Japanese Academy of Information Technology Aleja Legionów 2 Bytom41-902 Poland

ISBN: (纸本)9783031360206

Anomalies of gait sequences are detected on the basis of an autoencoder strategy in which input data are reconstructed from their embeddings. The denoising dense low-dimensional and sparse high-dimensional autoencoders are applied for segments of time series representing 3D rotations of the skeletal body parts. The outliers - misreconstructed time segments - are determined and classified as abnormal gait fragments. In the validation stage, motion capture data registered in the virtual reality of the Human Dynamics and Multimodal Interaction Laboratory of the Polish-Japanese Academy of information technology equipped with Motek CAREN Extended hardware and software are used. The scenarios with audio and visual stimuli are prepared to enforce anomalies during a walk. The acquired data are labeled by a human, which results in the visible and invisible anomalies extracted. The neural network representing the autoencoder is trained using anomaly-free data and validated by the complete ones. AP (Average Precision) and ROC-AUC (Receiver Operating Characteristic – Area Under Curve) measures are calculated to assess detection performance. The influences of the number of neurons of the hidden layer, the length of the analyzed time segments and the variance of injected Gaussian noise are investigated. The obtained results, with AP = 0.46 and ROC-AUC = 0.71, are promising. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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A Review of Satellite Image Analysis Tools 4th

A Review of Satellite Image Analysis Tools

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Department of Computer Science and Engineering Graphics Era Hill University Dehradun India Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta

ISBN: (纸本)9789819994410

The rapid growth of urban areas, population, and pollution presents a significant environmental challenge. Addressing these challenges requires innovative analytical approaches and data sources. This study focuses on the three-scale phenomena of the environmental system, physical environment, and regional surroundings, considering their implications on a global network scale. Remote sensing data, with its easy accessibility, offers new toolkits and methods to improve the global environment. It provides self-consistent measurements of vital physical attributes and surpasses traditional data sources regarding accuracy, efficiency, and coverage of larger geographical areas. This study aims to identify and analyze appropriate satellite image analysis tools for healthcare. The study objectives include studying available tools, analyzing their features, categorizing tools into open-source and commercial categories, and providing recommendations. This study emphasizes feature extraction and image enhancement parameters, which are crucial for image analysis. Further, this study provides a functional overview of various commercial and open-source satellite image analysis tools and suggests suitable tools for healthcare applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geographic information systems

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Satellite Image Analysis in Health Care—A Systematic Review 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta Department of Computer Science Graphics Era Hill University Dehradun India

ISBN: (数字)9789819995219

ISBN: (纸本)9789819995202

Rapid urbanization, population expansion, and escalating pollution levels have given rise to novel environmental concerns that demand the application of creative, analytical methodologies and diverse data sources. To ensure efficient urban ecological management, it is crucial to consider three levels of phenomena: the environmental system, the physical environment, and the regional surroundings. Utilizing remote sensing data is a viable avenue for enhancing the global environment due to its convenient accessibility and ability to measure crucial physical features regularly. The primary objective of this research study is to ascertain and evaluate suitable methods for analyzing satellite imagery in the healthcare context. The study’s aims encompass examining the diverse array of tools accessible in the market, scrutinizing the characteristics of each device, exploring research papers that have employed these tools, and classifying the tools into open-source and commercial classifications. This study emphasizes the parameters associated with feature extraction and picture enhancement in satellite imagery, as these factors hold significant importance in the analysis of images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Remote sensing

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