2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献_第3页

A Serverless Electroencephalogram Data Retrieval and Preprocessing Framework 无服务器脑电图数据检索与预处理框架

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00045

Bathsheba Farrow, S. Jayarathna

{"title":"A Serverless Electroencephalogram Data Retrieval and Preprocessing Framework","authors":"Bathsheba Farrow, S. Jayarathna","doi":"10.1109/IRI58017.2023.00045","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00045","url":null,"abstract":"Electroencephalogram (EEG) research continues to rely heavily on data silos used in isolated physical lab environments. However, as a part of the digital transformation, the EEG community has begun its exploration of the public cloud to determine how it can be best utilized to increase collaboration and accelerate research outcomes. The growing number of online repositories for data and tools has provided additional computational resources but the process of downloading data and software along with the installation and configuration requirements is cumbersome and prone to error. To break away from this research paradigm, we present a novel application of cloud technologies to provide reusable EEG data acquisition and preprocessing software as a service (SaaS) that eliminates data and software downloading prerequisites. We utilize the Amazon Web Services (AWS) cloud platform and serverless technologies to create a distributed, highly scalable and extensible solution for EEG signal data preprocessing that is more conducive to effective collaboration and data reproducibility with the potential to expedite neurotechnology breakthroughs.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133644522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Data Science Solution for Analyzing Long COVID Cases 分析COVID长病例的数据科学解决方案

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00046

Dandan Tan, C. Leung, Katrina Dotzlaw, Ryan Dotzlaw, Adam G. M. Pazdor, Sean Szturm

{"title":"A Data Science Solution for Analyzing Long COVID Cases","authors":"Dandan Tan, C. Leung, Katrina Dotzlaw, Ryan Dotzlaw, Adam G. M. Pazdor, Sean Szturm","doi":"10.1109/IRI58017.2023.00046","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00046","url":null,"abstract":"Many people around the world have witnessed various repercussions caused by the COVID-19 pandemic, such as a decline in industrial activities and business closures. A notable negative consequence of this situation is the potential impact of long COVID on workers across multiple industries, particularly in the industrial sector. As significant volumes of data have been collected during both the COVID-19 period and the subsequent post-COVID-19 period, researchers have initiated investigations into the condition commonly known as long COVID. In this paper, we present a data science solution that integrates data from diverse and comprehensive sources to uncover meaningful associations within demographic data related to long COVID. Leveraging this integrated information, our solution identifies features leading to long COVID in patients. Evaluation results on real-life datasets demonstrate practicality of our solution in identifying individuals who may be prone to long COVID, while also highlighting demographic factors that may indicate an elevated risk. Through evaluation, we show the practicality of our solution in analyzing and predicting long COVID cases.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127262004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating Security Vulnerability Related to Exposure and TLS Ecosystem in IoT Devices 研究物联网设备中与暴露和TLS生态系统相关的安全漏洞

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00009

Y. Siwakoti, D. Rawat

引用次数: 0

Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud 评估识别医疗保险欺诈的一类和二元分类方法

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00053

Joffrey L. Leevy, John T. Hancock, T. Khoshgoftaar

引用次数: 0

MRHN: Hypergraph Convolutional Network for Web API Recommendation 用于Web API推荐的超图卷积网络

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00037

Gang Xiao, Jiahuan Fei, Dongliu Li, Cece Wang, Zhenbo Cheng, Jiawei Lu

{"title":"MRHN: Hypergraph Convolutional Network for Web API Recommendation","authors":"Gang Xiao, Jiahuan Fei, Dongliu Li, Cece Wang, Zhenbo Cheng, Jiawei Lu","doi":"10.1109/IRI58017.2023.00037","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00037","url":null,"abstract":"With the development of service-oriented computing, Mashup technology has emerged that uses web API as reusable components to create new products. How to achieve efficient and accurate service recommendation has attracted the attention of researchers in the field of service computing. The call relationship between mashups and APIs in real service data is intricate, and the information carried by the service further increases the complexity of the relationship between them. Most existing mashup recommendation models hardly mine such complex relationships effectively. To this end, this paper proposes the MRHN method. This method uses motifs to extract the hypergraph structure from services. While studying the complex relationship between service data, it also solves the problem of data sparsity, and uses the hypergraph convolutional network to extract the features of Mashup. Further, the weights of channels are adjusted using channel error attention mechanism. Finally, the performance of the proposed method is comprehensively evaluated. The experimental results show that compared with the existing service recommendation methods, the proposed method has significantly improved in terms of evaluation indicators such as NDCG and HR.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Convolutional Autoencoder-Hierarchical Clustering Algorithm To Reveal Image Spam Sources 混合卷积自编码器-分层聚类算法揭示图像垃圾源

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00013

Yongiin Lu, Wei-bang Chen, Zanyah Ailsworth, Xiaoliang Wang, Chengcui Zhang, Kaixuan Li

{"title":"Hybrid Convolutional Autoencoder-Hierarchical Clustering Algorithm To Reveal Image Spam Sources","authors":"Yongiin Lu, Wei-bang Chen, Zanyah Ailsworth, Xiaoliang Wang, Chengcui Zhang, Kaixuan Li","doi":"10.1109/IRI58017.2023.00013","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00013","url":null,"abstract":"We propose a novel hybrid algorithm framework to address the problem of clustering images received in spam emails based on authorship. The multimodal nature of these images, containing foreground objects, text, or a combination of both, poses a significant challenge for grouping them effectively. To address this challenge, we train convolutional autoencoders (CAE) to extract visual features from the images, which are produced by the encoder of the trained CAEs. Furthermore, we utilize an optical character recognition (OCR) algorithm to extract text information from the images. The extracted text and visual features, in conjunction with layout features, are employed to construct matrices that measure the similarities between each pair of images in our experiment dataset. We subsequently apply a two-stage hierarchical clustering algorithm to cluster the images into groups. We compare the results produced by our proposed algorithm with the ground truth collected by a domain expert. Our experimental findings reveal that our relatively simple CAEs, with as few as thirty-seven visual features, can achieve homogeneity, completeness, and V-measures that are as high as those obtained from more complex convolutional neural networks (CNNs).","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130191996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Explainable AI based Clinical Assistance Model for Identifying Patients with the Onset of Sepsis 一种可解释的基于AI的脓毒症患者识别临床辅助模型

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00059

Snehashis Chakraborty, Komal Kumar, Balakrishna Pailla Reddy, Tanushree Meena, S. Roy

{"title":"An Explainable AI based Clinical Assistance Model for Identifying Patients with the Onset of Sepsis","authors":"Snehashis Chakraborty, Komal Kumar, Balakrishna Pailla Reddy, Tanushree Meena, S. Roy","doi":"10.1109/IRI58017.2023.00059","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00059","url":null,"abstract":"The high mortality rate of sepsis, especially in Intensive Care Unit (ICU) makes it third-highest mortality disease globally. The treatment of sepsis is also time consuming and depends on multi-parametric tests, hence early identification of patients with sepsis becomes crucial. The recent rise in the development of Artificial Intelligence (AI) based models, especially in early prediction of sepsis, have improved the patient outcome. However, drawbacks like low sensitivity, use of excess features that leads to overfitting, and lack of interpretability limit their ability to be used in a clinical setting. So, in this research we have developed a smart, explainable and a highly accurate AI based model (called XAutoNet) that provides quick and early prediction of sepsis with a minimal number of features as input. An application based novel convolutional neural network (CNN) based autoencoder is also implemented that improves the performance of XAutoNet by dimensional reduction. Finally, to unbox the “Black Box” nature of these models, Gradient based Class Activation Map (GradCAM) and SHapley Additive exPlanations (SHAP) are implemented to provide interpretability of autoencoder and XAutoNet in the form of visualization graphs to assist clinicians in diagnosis and treatment.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127550548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microsaccade Segmentation using Directional Variance Analysis and Artificial Neural Networks 基于方向方差分析和人工神经网络的微眼动分割

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00008

S. Suthaharan, Lee Daniel M.W., Min Zhang, E. Rossi

{"title":"Microsaccade Segmentation using Directional Variance Analysis and Artificial Neural Networks","authors":"S. Suthaharan, Lee Daniel M.W., Min Zhang, E. Rossi","doi":"10.1109/IRI58017.2023.00008","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00008","url":null,"abstract":"Fixational eye movements (FEMs) are an essential component of vision and there is considerable research interest in using them as biomarkers of brain injury and neurodegeneration. Study of FEMs often involves segmenting them into their individual components, primarily microsaccades and drifts. In practice, velocity (or acceleration) thresholds are commonly adapted-while they are generally imperfect-requiring tuning of thresholds and manual correction and verification by human graders. Manual segmentation and correction is a tedious and time-consuming process for human graders. Fortunately, it can be observed from Tracking scanning laser ophthalmoscopy (TSLO) video recordings that the directional variances of FEMs can be extracted to mathematically characterize microsaccades for segmentation and distinguish from drift. Therefore, we perform a directional variance analysis, extract relevant features, and automate the model using artificial neural networks (ANN). We propose and compare two directional variance approaches along with an ANN model for the segmentation of microsaccades. The first approach utilizes a single-point based feature variance, whereas the second approach utilizes a sliding-window based feature variance with the information from several time points. We calculate several statistical metrics to characterize the features of the microsaccades such as the number of microsaccades, microsaccade peak velocity and acceleration, and microsaccade duration. We have also calculated the accuracy, precision, sensitivity, and specificity scores for each approach to compare their performance. The single-point models labeled the FEM data with an accuracy of 70% whereas the sliding-window approach had an accuracy of 85%. When comparing the percent errors of the approaches to the ground truth, the sliding-window approach performs significantly better than the single-point approach as it captures more relevant directional variance features of FEMs.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130799577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Agent-Based Parallelization of a Multi-Dimensional Semantic Database Model 多维语义数据库模型的基于agent的并行化

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00019

Alex Li, M. Fukuda

{"title":"Agent-Based Parallelization of a Multi-Dimensional Semantic Database Model","authors":"Alex Li, M. Fukuda","doi":"10.1109/IRI58017.2023.00019","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00019","url":null,"abstract":"Responses to database queries that may be even identical should vary if they are given under a different user context. For instance, queries for wild animals in the context of the ocean versus mountains should be different. Announced in 1993 [1], Mathematical Model of Meaning (MMM) provides users with capabilities to extract data items tightly coupled under different semantic spaces. Such a space is created dynamically with user-defined impression words to compute semantic equivalence and similarity between data items. MMM computes semantic correlations between the key and other data items to achieve dynamic data querying. However, a semantic space creation and a data correlative calculation are computationally demanding. We consider MMM as a practical database application of multi-agent technologies, construct a space over a cluster system, and have multi-agents explore for a given target and its surrounding data items. We use the Multi-Agent Spatial Simulation (MASS) library to implement an agent-based semantic database system and to measure its parallel execution. Compared to a sequential MMM implementation, MASS-based parallelization yielded a 22-time speedup when creating a space, mainly achieved with matrix multiplication. MASS also reduced the time required for distance sorting of multi-dimensional vectors by 23.7%.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124422093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cost Efficient Mammogram Segmentation and Classification with NeuroMem® Chip for Breast Cancer Detection NeuroMem®芯片用于乳腺癌检测的低成本乳房x光片分割和分类

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00054

Soumeya Demil, Lydia Bouzar-Benlabiod, G. Paillet

引用次数: 0