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arXiv 2024年

作者： Sen, A. Sumnicht, C. Adeniye, S. Patel, D. Choudhuri, S. School of Computing Informatics and Decision Systems Engg. Arizona State University TempeAZ85287 United States

Communication network design for monitoring the state of an electric power grid has received significant attention in recent years. In order to measure stability of a power grid, it is imperative that measurement data collected by the Phasor Measurement Units (PMUs) located at the Sub-Stations (SS) must arrive at the Control Center (CC) within a specified delay threshold δ. In earlier papers we formalized the design problem as the Rooted Delay Constrained Minimum Spanning Tree (RDCMST) problem. However, RDCMST does not provide any fault-tolerance capability, as failure of just one communication link would prevent PMU data from reaching the CC. In this paper, we study the optimal cost network design problem with fault tolerance capability. In our model the PMU data from the SSs will have a path to reach the CC in spite of the failure of at most R links within the delay threshold δ. If R = 1, each SS will have two link disjoint paths of length at most δ to the CC. In other words, each SS will be on a cycle with the CC. We refer to this problem as the Rooted Delay Constrained Minimum Cost Cycle Cover (RDCMCCC) problem. We provide computational complexity analysis, an Integer Linear Programming formulation to find the optimal solution and a heuristic based on the sweeping technique. We evaluate the performance of our heuristic with real substation location data of Arizona. Copyright © 2024, The Authors. All rights reserved.

关键词： Phasor measurement units

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Fostering interdisciplinary data cultures through early career development: The RDA/US data share fellowship

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Data Science Journal 2021年第1期20卷 1-19页

作者： Kouper, Inna Scheidt, Lois A. Plale, Beth A. Department of Informatics School of Informatics Computing and Engineering Indiana University Bloomington United States School of Information Studies University of Wisconsin Milwaukee United States Department of Intelligent Systems and Engineering School of Informatics Computing and Engineering Indiana University Bloomington United States

Openness and interdisciplinarity in research and data are among the challenges that are frequently discussed in the context of changing scientific and scholarly practices. Gradually, the visions of open and widely shared data are being reconciled with complex realities that stem from the disciplinary differences in data cultures. In this paper we discuss interdisciplinarity through data as a way to create research environments that are more flexible and, as a result, more amenable to change. We report our findings from facilitating and evaluating a data-oriented early-career fellowship program that was administered as part of the Research Data Alliance (RDA), a global organization that aims to enable open sharing and re-use of data. We identify ways to foster interdisciplinary data cultures among the future researchers and professionals and propose recommendations for future programs. While the short-term early career programs cannot address the systemic factors that impact openness and interdisciplinarity, such as the systems of reward and recognition or the funding structures, they can introduce mechanisms that support diversity, learning, and leadership and, ultimately, contribute to a culture change. © 2021 The Author(s).

关键词： Data culture Data stewardship Early career training Open science

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Reconstruction of multiplex networks via graph embeddings

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Physical Review E 2024年第2期109卷 024313-024313页

作者： Daniel Kaiser Siddharth Patwardhan Minsuk Kim Filippo Radicchi Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations of a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper for network science. Here, we develop a machine-learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques.

关键词： Interconnected & interdependent networks

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GeoLinter: A Linting Framework for Choropleth Maps

arXiv

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arXiv 2023年

作者： Lei, Fan Fan, Arlen MacEachren, Alan M. Maciejewski, Ross The School of Computing Informatics & Decision Systems Engineering Arizona State University United States Pennsylvania State University United States

Visualization linting is a proven effective tool in assisting users to follow established visualization guidelines. Despite its success, visualization linting for choropleth maps, one of the most popular visualizations on the internet, has yet to be investigated. In this paper, we present GeoLinter, a linting framework for choropleth maps that assists in creating accurate and robust maps. Based on a set of design guidelines and metrics drawing upon a collection of best practices from the cartographic literature, GeoLinter detects potentially suboptimal design decisions and provides further recommendations on design improvement with explanations at each step of the design process. We perform a validation study to evaluate the proposed framework's functionality with respect to identifying and fixing errors and apply its results to improve the robustness of GeoLinter. Finally, we demonstrate the effectiveness of the GeoLinter - validated through empirical studies - by applying it to a series of case studies using real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach

Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecur...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Phan, Quoc Bao Nguyen, Linh Nguyen, Tuy Tan Nguyen, Dinh C. Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350324136

This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-Theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-Term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-Time and secure access to critical healthcare information. © 2024 IEEE.

关键词： GANs Images denoising Kyber number theoretic transform

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

A Comparison Study on Generative Adversarial Networks (GANs)...

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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

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Real-Time Tracking of Misdiagnosis in Electronic Health Records for Improved Predictive Modeling

Real-Time Tracking of Misdiagnosis in Electronic Health Reco...

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IEEE Southeastcon

作者： Elizabeth Trader Varadraj Gurupur Department of Electrical and Computer Engineering Center for Decision Support Systems and Informatics University of Central Florida Orlando FL Center for Decision Support Systems and Informatics School of Global Health Management and Informatics University of Central Florida Orlando FL

Misdiagnosis, a persistent threat in healthcare, compromises patient outcomes and hinders effective treatment. Despite its prevalence, understanding and addressing misdiagnoses remains a complex challenge due to the limitations of traditional methods and fragmented data. The emergence of machine learning (ML) offers a potential game-changer, leveraging vast medical datasets to identify subtle disease signatures and personalize treatment plans with unprecedented accuracy. However, data quality remains a formidable barrier, as incompleteness, inconsistencies, and biases can distort ML models, leading to potentially detrimental misinterpretations. This paper highlights the critical role of misdiagnosis statistics in navigating this intricate landscape and creates a schema to address the problems caused by the lack of accessible misdiagnosis data.

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A Proposal to Study "Is High Quality Data All We Need?"

arXiv

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arXiv 2022年

作者： Mishra, Swaroop Arunkumar, Anjana School of Computing Informatics and Decision Systems Engineering Arizona State University United States

Even though deep neural models have achieved superhuman performance on many popular benchmarks, they have failed to generalize to OOD or adversarial datasets. Conventional approaches aimed at increasing robustness include developing increasingly large models and augmentation with large scale datasets. However, orthogonal to these trends, we hypothesize that a smaller, high quality dataset is what we need. Our hypothesis is based on the fact that deep neural networks are data driven models, and data is what leads/misleads models. In this work, we propose an empirical study that examines how to select a subset of and/or create high quality benchmark data, for a model to learn effectively. We seek to answer if big datasets are truly needed to learn a task, and whether a smaller subset of high quality data can replace big datasets. We plan to investigate both data pruning and data creation paradigms to generate high quality datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks

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Computer Modeling in engineering & Sciences 2025年第3期142卷 2487-2511页

作者： Asma Aldrees Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Mohd Anjum Department of Informatics and Computer Systems College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India

Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic *** disorders are a major global health concern,affecting millions of people worldwide due to their widespread *** photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic *** a result,accurate fundus detection is essential for early diagnosis and effective treatment,helping to prevent severe complications and improve patient *** address this need,this article introduces a Derivative Model for Fundus Detection using Deep NeuralNetworks(DMFD-DNN)to enhance diagnostic *** selects key features for fundus detection using the least derivative,which identifies features correlating with stored fundus *** filtering relies on the minimum derivative,determined by extracting both similar and varying *** this research,the DNN model was integrated with the derivative *** images were segmented,features were extracted,and the DNN was iteratively trained to identify fundus regions *** goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally,taking into account the least possible derivative across iterations,and using outputs from previous *** hidden layer of the neural network operates on the most significant derivative,which may reduce precision across *** derivatives are treated as inaccurate,and the model is subsequently trained using selective features and their corresponding *** proposed model outperforms previous techniques in detecting fundus regions,achieving 94.98%accuracy and 91.57%sensitivity,with a minimal error rate of 5.43%.It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead,thereby improving operational efficiency and ***,the propo

关键词： Deep neural network feature extraction fundus detection medical image processing

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Can we trust the evaluation on ChatGPT? 3

Can we trust the evaluation on ChatGPT?

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3rd Workshop on Trustworthy Natural Language Processing, TrustNLP 2023, co-located with ACL 2023

作者： Aiyappa, Rachith An, Jisun Kwak, Haewoon Ahn, Yong-Yeol Center for Complex Networks & Systems Luddy School of Informatics Computing & Engineering United States Indiana University Network Science Institute Indiana University BloomingtonIN47408 United States

ISBN: (纸本)9781959429869

ChatGPT, the first large language model with mass adoption, has demonstrated remarkable performance in numerous natural language tasks. Despite its evident usefulness, evaluating ChatGPT's performance in diverse problem domains remains challenging due to the closed nature of the model and its continuous updates via Reinforcement Learning from Human Feedback (RLHF). We highlight the issue of data contamination in ChatGPT evaluations, with a case study in stance detection. We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models. © 2023 Proceedings of the Annual Meeting of the Association for Computational Linguistics. All rights reserved.

关键词： Reinforcement learning

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