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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Alabandi, Ghadeer Sands, William Biros, George Burtscher, Martin Department of Computer Science Texas State University San MarcosTX United States Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX United States

ISBN: (纸本)9798400701092

Detecting strongly connected components (SCCs) is an important step in various graph computations. The fastest GPU and CPU implementations from the literature work well on graphs where most of the vertices belong to a single SCC and the vertex degrees follow a power-law distribution. However, these algorithms can be slow on the mesh graphs used in certain radiative transfer simulations, which have a nearly constant vertex degree and can have significant variability in the number and size of SCCs. We introduce ECL-SCC, an SCC detection algorithm that addresses these shortcomings. Our approach is GPU friendly and employs innovative techniques such as maximum ID propagation and edge removal. On an A100 GPU, ECL-SCC performs on par with the fastest prior GPU code on power-law graphs and outperforms it by 7.8× on mesh graphs. Moreover, ECL-SCC running on the GPU outperforms fast parallel CPU code by three orders of magnitude on meshes. © 2023 ACM.

关键词： Graphics processing unit

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Robust Multi-Modal Speech In-Painting: A Sequence-to-Sequence Approach

arXiv

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arXiv 2024年

作者： Elyaderani, Mahsa Kadkhodaei Shirani, Shahram The Department of Computational Science and Engineering McMaster University Hamilton Canada The Department of Electrical and Computer Engineering McMaster University Hamilton Canada

The process of reconstructing missing parts of speech audio from context is called speech in-painting. Human perception of speech is inherently multi-modal, involving both audio and visual (AV) cues. In this paper, we introduce and study a sequence-to-sequence (seq2seq) speech in-painting model that incorporates AV features. Our approach extends AV speech in-painting techniques to scenarios where both audio and visual data may be jointly corrupted. To achieve this, we employ a multi-modal training paradigm that boosts the robustness of our model across various conditions involving acoustic and visual distortions. This makes our distortion-aware model a plausible solution for real-world challenging environments. We compare our method with existing transformer-based and recurrent neural network-based models, which attempt to reconstruct missing speech gaps ranging from a few milliseconds to over a second. Our experimental results demonstrate that our novel seq2seq architecture outperforms the state-of-the-art transformer solution by 38.8% in terms of enhancing speech quality and 7.14% in terms of improving speech intelligibility. We exploit a multi-task learning framework that simultaneously performs lip-reading (transcribing video components to text) while reconstructing missing parts of the associated speech. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech enhancement

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Enhancing Chronic Kidney Disease Prediction Through Data Preprocessing Optimization and Machine Learning Techniques

Enhancing Chronic Kidney Disease Prediction Through Data Pre...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Ekwe, Stephen Obono Oladejo, Sunday Oladayo University of South Africa Cset Department of Compt. Science & Electrical Engineering Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

ISBN: (纸本)9798350327816

Health care is the improvement of health through the avoidance and cure of diseases. Chronic kidney disease (CKD) poses a substantial health issue, ranking as the eighth leading cause of death and the tenth leading cause of disability-adjusted life years for both genders. This research aims to utilize machine learning (ML) algorithms to accurately predict the presence of CKD, which affects people worldwide. Timely identification of CKD is crucial to prevent it from progressing to its final and most critical phase. Machine learning algorithms, such as Decision Tree (DT), Naïve Bayes (NB), and others, are used in this study for the prediction of CKD. Various health parameters such as age, albumin level, and more are used as input into each ML algorithm to predict the presence of CKD. Data preprocessing is carried out to improve the quality of the data for more accurate prediction. The data is analyzed to uncover the connections between CKD health parameters and their impact on the occurrence of CKD. This research further examines the influence of different train and test data ratios on the accuracy of the employed algorithms. Evaluation metrics such as accuracy, sensitivity, and more are employed to gauge the effectiveness of each algorithm in making predictions. Random Forest stood out as the most accurate algorithm, with a score of 96.25%. Naive Bayes and XGBoost demonstrated the highest sensitivity, achieving a perfect score of 100%. © 2023 IEEE.

关键词： Chronic Kidney Disease (CKD) Data Analysis Data Preprocessing Health Care Machine Learning

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一种比较学者学术影响力的新方法（英文）

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Journal of Data and Information science 2025年第2期 40-60+187页

作者： Anand Bihari Sudhakar Tripathi Akshay Deepak P.Mohan Kumar Department of Computational Intelligence School of computer Science and Engineering Vellore Institute of Technology Department of Information Technology Rajkiya Engineering College Department of Computer Science and Engineering National Institute of Technology Patna Department of Database Systems School of computer Science and Engineering Vellore Institute of Technology

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我... 详细信息

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我们不能直接比较位于不同职业生涯阶段的学者的影响力。已有多种文献计量指标尝试考虑时间跨度因素，其中h指数序列和EM/EM'指数序列已经被用于评估和比较学者的科学影响力。h指数序列、EM指数序列和EM'指数序列考虑了学者的年度影响力，并通过指标值及各个年度的分量值对学者进行比较。然而当两位学者在科研年限上存在显著差距时，基于时间序列的文献计量指标难以实现对其学术影响力的有效比较。研究方法：研究提出了一种累积指数计算方法，用于评估学者在其科研生涯某一年龄节点前的科学影响力，并使用89位学者的数据进行了实证测试。研究发现：本研究提出的方法已在89位学者的发表数据上进行了测试，能够清晰地区分两类学者之间的科学影响力差异。此外，该方法还有助于根据研究影响预测未来有潜力的杰出学者。实践意义：本研究提出的方法可被各类权威机构用于比较其学者的科研影响力，资助机构亦可将其用于类似的评估目的。原创性/价值：本研究通过引入一种新的学者科研影响力比较方法，丰富了现有文献体系。研究成果对于构建更加精确且公正的科研评估框架具有重要意义，有助于实现对学术贡献更为公平的评价。

关键词： h指数 EM指数 EM’指数 h指数序列 EM指数序列 EM’指数序列科学影响力比较科研影响力

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Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks 2

Optimizing Wireless Sensor Networks in E-Commerce with Messa...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jeyaselvi, M. Saravanan, B. Boobalan, M. Ganesha, M. Lamba, Anil Kumar Suganthi, D. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Sri Eshwar College of Engineering Department of Cse Tamilnadu Coimbatore India Vivekanandha College of Engineering for Women Tiruchengode Namakkal India Ajiet Department of Cse Karnataka Mangaluru India Geeta University School of Computer Science and Engineering Haryana India Saveetha College of Liberal Arts and Sciences Simats Department of Computational Intelligence Thandalam Chennai India

ISBN: (纸本)9798350388916

In today's e-commerce landscape, successful logistics management is essential for achieving success. In this post, we will go over how wireless sensor networks (WSNs) can help online stores streamline their logistics. Data collection and real-time monitoring made possible by wireless sensor networks (WSNs) increase supply chain visibility and control. The proposed approach includes both preprocessing and training the model. Part of the preprocessing phase is principal component analysis (PCA), which entails cleaning and transforming raw data into an appropriate format for analysis or model training. Throughout the training, CNN-BiGRU-BiLSTM was continuously utilized. With a 93.46% average accuracy, this innovative method surpasses the BiGRU and BiLSTM. © 2024 IEEE.

关键词： Long short-term memory

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Oral Cancer Detection and Classification Using Deep Learning with DenseNet121-CatBoost Classifier 2

Oral Cancer Detection and Classification Using Deep Learning...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Anitha, D. Soujanya, Thota Chakraborty, Subhra Alkhayyat, Ahmad Revathi, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Sr University School of Agricultural Sciences Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350372892

Oral cancer is a growing universal health challenge, which is rising in the current situation. Early detection is vital for effective treatment and better patient results. However, current diagnostic methods, notably manual histopathological analysis, suffer from subjectivity and time limitations due to classifying the images according to their type. The dependence on manual analysis can lead to delays in accurately diagnosing oral cancer. The proposed method DenseNet121-CatBoost classifier addresses this challenge by using the Histopathological Images dataset. The DenseNet121 for the extracting process and the CatBoost classifier for classifying the images according to their classes. DenseNet121 is utilized to extract features and patterns from images and the CatBoost classifier in the classification process helps to classify the dataset according to its classes using the extracted information. The performance of the DenseNet121-CatBoost method is analyzed using accuracy, specificity, sensitivity, f1score, precision, and Area Under Curve (AUC). This paper shows that the proposed DenseNet121-CatBoost outperforms the existing methods such as ResNet50+CatBoost, DenseNet201+SVM (Support Vector Machine), and GoogLeNet+XGBoost in terms of accuracy of 97.62%, specificity of 98.96%, sensitivity of 94.55%, f1-score of 93.87%, precision of 98.65%, and AUC of 95.26%. © 2024 IEEE.

关键词： Gabor filters

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Trust-Region Based Stochastic Variational Inference for Distributed and Asynchronous Networks

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Journal of Systems science & Complexity 2022年第6期35卷 2062-2076页

作者： FU Weiming QIN Jiahu LING Qing KANG Yu YE Baijia Department of Automation University of Science and Technology of ChinaHefei 230027China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational ScienceSun Yat-Sen UniversityGuangzhou 510006China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230027China

Stochastic variational inference is an efficient Bayesian inference technology for massive datasets,which approximates posteriors by using noisy gradient *** stochastic variational inference can only be performed in a centralized manner,which limits its applications in a wide range of situations where data is possessed by multiple ***,this paper develops a novel trust-region based stochastic variational inference algorithm for a general class of conjugate-exponential models over distributed and asynchronous networks,where the global parameters are diffused over the network by using the Metropolis rule and the local parameters are updated by using the trust-region ***,a simple rule is introduced to balance the transmission frequencies between neighboring nodes such that the proposed distributed algorithm can be performed in an asynchronous *** utility of the proposed algorithm is tested by fitting the Bernoulli model and the Gaussian model to different datasets on a synthetic network,and experimental results demonstrate its effectiveness and advantages over existing works.

关键词： Asynchronous networks Bayesian inference distributed algorithm stochastic variational inference trust-region method

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Improving the Efficiency of Multimodal Approach for Chest X-Ray 2nd

Improving the Efficiency of Multimodal Approach for Chest...

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Upadhya, Jiblal Vargas, Jorge Poudel, Khem Ranganathan, Jaishree Computational and Data Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Engineering Technology Middle Tennessee State University MurfreesboroTN37132 United States Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9783031569494

Medical data analysis with limited data has been a challenging problem in machine learning. Privacy regulations limit the sharing and use of patient data, making it challenging to access and aggregate large datasets for research purposes. To address this challenge, we propose a deep learning-based Multimodal network that incorporates data from multiple data sources: images, and text in the learning process. When faced with limited data of one type, incorporating data from multiple sources can enhance the model’s performance and generalization. Our approach integrates the text sub-model and image sub-model with deep network of Dense 512 using the transfer learning process. Specifically, the goal is to explore the impact of hyperparameter adjustments on the performance of the model with different learning rates. Evaluation of our proposed approach on the Indiana University Chest X-ray dataset that consists of textual (radiological reports) and visual (X-ray images) show that our approach significantly outperforms several baseline methods in classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Large datasets

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Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation

arXiv

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arXiv 2025年

作者： Qiu, Zhongyi Lyu, Hanjia Xiong, Wei Luo, Jiebo School of Computational Science and Engineering Georgia Institute of Technology United States Department of Computer Science University of Rochester United States

Social media enables dynamic user engagement with trending topics, and recent research has explored the potential of large language models (LLMs) for response generation. While some studies investigate LLMs as agents for simulating user behavior on social media, their focus remains on practical viability and scalability rather than a deeper understanding of how well LLM aligns with human behavior. This paper analyzes LLMs’ ability to simulate social media engagement through action-guided response generation, where a model first predicts a user’s most likely engagement action—retweet, quote, or rewrite—towards a trending post before generating a personalized response conditioned on the predicted action. We benchmark GPT-4o-mini, O1-mini, and DeepSeek-R1 in social media engagement simulation regarding a major societal event discussed on X. Our findings reveal that zero-shot LLMs underperform BERT in action prediction, while few-shot prompting initially degrades the prediction accuracy of LLMs with limited examples. However, in response generation, few-shot LLMs achieve stronger semantic alignment with ground truth posts. Copyright © 2025, The Authors. All rights reserved.

关键词： Social behavior

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A Fast and Accessible Neural Network Based Eye-Tracking System for Real-Time Psychometric and HCI Applications 10

A Fast and Accessible Neural Network Based Eye-Tracking Syst...

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10th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2024

作者： Iacobelli, Emanuele Pelella, Davide Ponzi, Valerio Russo, Samuele Napoli, Christian Department of Computer Automatic and Management Engineering Sapienza University of Rome Rome00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Psychology Sapienza University of Rome Rome00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Eye-tracking technology has long been a valuable tool across various domains, and recent advancements in neural networks have significantly expanded its versatility and potential. However, real-world applications continue to face challenges such as accommodating users’ natural movements, variations in lighting, occlusions of the eyes, and the limited availability of large, open-source datasets for training models. To address these issues, we developed a comprehensive pipeline that produces a lightweight and efficient model, requiring only an RGB camera as external hardware, making it easily deployable on standard PCs. Key input features include facial images, eye regions, head pose angles, the Eye Aspect Ratio (EAR), and a face grid that determines the face’s location within the camera’s frame. The model was trained using a custom dataset, in which participants were instructed to fixate on both randomly positioned points and the standard 9-point grid commonly employed in eye-tracking calibration. The resulting system was integrated into a real-time application, offering fast and accessible gaze tracking, making it well-suited for studies requiring rapid gaze assessments across broad regions of the screen, such as psychometric research and Human-computer Interaction (HCI) tasks. Its design is particularly advantageous for gaze laterality studies, which explore hemispheric dominance and attentional bias in cognitive and emotional processing, key concepts relevant to ADHD and dyslexia. Moreover, the system’s capabilities naturally extend to emotional and decision-making tasks, where broad-area gaze tracking can support the analysis of preference formation and attentional patterns without the need for specialized hardware. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Open systems

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