Big Data最新文献_第10页

Cybersecurity Practices of Rural Underserved Communities in Africa: A Case Study from Northern Namibia 非洲农村服务不足社区的网络安全实践:来自纳米比亚北部的案例研究

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220449

G. Nhinda, Fungai Bhunu Shava

{"title":"Cybersecurity Practices of Rural Underserved Communities in Africa: A Case Study from Northern Namibia","authors":"G. Nhinda, Fungai Bhunu Shava","doi":"10.1109/icABCD59051.2023.10220449","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220449","url":null,"abstract":"Globally, Information Communication Technology (ICT) device usage has seen a steep rise over the last few years. This also holds in developing countries, which have embarked on connecting the unconnected or previously disadvantaged parts of their populations. This connectivity enables people to interact with cyberspace, which brings opportunities and challenges. Opportunities such as the ability to conduct business online, attend online education, and perform online banking activities. Challenges experienced are the cost of Internet access and more worrying cyber-risks and potential for exploitation. There remain pockets of communities that experience sporadic connectivity to cyberspace, these communities tend to be more susceptible to cyber-attacks due to issues of lack/limited awareness of cyber secure practices, an existent culture that might be exploited by cybercriminals, and overall, a lackluster approach to their cyber-hygiene. We present a qualitative study conducted in rural Northern Namibia. Our findings indicate that both secure and insecure cybersecurity practices exist. However, through the Ubuntu and Uushiindaism Afrocentric lenses, practices such as sharing mobile devices without passwords among the community mirror community unity. Practices such as this in mainstream research can be considered insecure. We also propose interrogating “common” secure cybersecurity practices in their universality of applicability.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"31 1","pages":"1-7"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72963314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VTCGAN: A Proposed Multimodal Approach to Financial Time Series and Chart Pattern Generation for Algorithmic Trading 基于算法交易的金融时间序列和图表模式生成的多模态方法

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220544

Joseph Tafataona Mtetwa, K. Ogudo, S. Pudaruth

引用次数: 0

The Next Evolution of Web Browser Execution Environment Performance Web浏览器执行环境性能的下一个演变

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220564

Zahir Toufie, Boniface Kabaso

{"title":"The Next Evolution of Web Browser Execution Environment Performance","authors":"Zahir Toufie, Boniface Kabaso","doi":"10.1109/icABCD59051.2023.10220564","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220564","url":null,"abstract":"Web browsers have for long been wanting to host and execute feature-rich, compute-intensive, and complex applications or simply Compute-Intensive Applications (CIAs), within their Execution Environment (EE), with native desktop performance. There was Adobe Shockwave, Macromedia Flash, Java Applets, JavaScript Programming Language (JS) and recently WebAssembly Programming Language (WASM), but also short-lived relationships, such as Microsoft ActiveX, Silverlight and Apple Quicktime. One hindrance to web browsers hosting and executing CIAs with native desktop performance is that currently there is no web browser technology with the software architecture and design that can support them. This paper aims to review the evolution of the Web as an application platform since the rise of WASM, over the last decade or so, within the context of application performance relative to that of native desktop application performance. As well as to propose where researchers should focus their efforts in order to advance the Web as an application platform that is capable of executing CIAs. In future work, we plan to extend our study to include theoretical contributions, such as providing insights into how to improve the performance of web applications based on various software architectures and designs for web browser EEs, methodological contributions, such as providing methods and approaches developed, adapted or enhanced which detail the software architecture and design for web browser EEs that have higher performance than currently available, and practical contributions that will lay the groundwork for a production-ready web browser EE based on the prototype web browser EE produced by our study.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"183 1","pages":"1-7"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76639144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Rest API to Classify Pneumonia Infection From Chest X-ray Images Using Multi-Layer Perceptron and LeNet 基于多层感知机和LeNet的胸片肺炎感染分类Rest API

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220479

Tinashe Crispen Gadzirai, W. T. Vambe

{"title":"A Rest API to Classify Pneumonia Infection From Chest X-ray Images Using Multi-Layer Perceptron and LeNet","authors":"Tinashe Crispen Gadzirai, W. T. Vambe","doi":"10.1109/icABCD59051.2023.10220479","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220479","url":null,"abstract":"Pneumonia remains the most common reason for inpatient stays and fatalities among adults and children in the world. It became worse during Covid 19 pandemic. Most African countries like South Africa were and are still seriously affected. The situation is worse in rural areas because of several reasons, among them; not having enough X-rays machines, having no or few radiologists to analyze and interpret the X-ray pictures to determine if the pictures are normal pictures or pneumonia. The ability to accurately classify these two types of pneumonia can guarantee effective treatment which will boost survival chances. Artificial Intelligence (AI) is a cost-effective approach and can play a pivotal role in easily analyzing and interpreting X-ray images. This research used CRoss Industry Standard Process for Data Mining methodology in developing a simple Rest API model that would classify the chest X-ray image if it were normal, the person has pneumonia caused by bacteria or virus. Multi-Layer Perceptron (MLP) model had a training accuracy of 73.89%, validation accuracy of 75.46%, and test accuracy of 75.46% whereas LeNet had 78.49%, 76.51%, and 76,51%, respectively. This study demonstrated to the public that AI models may be developed to aid health professionals in the early diagnosis, classification, analysis, and interpretation of X-ray images for pneumonia. In the future, the model created should convert the English interpretations into South African local languages like isiXhosa, Zulu, Venda, and many others. Thus, making it easier for the local communities to understand giving them a sense of belonging.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"1 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84198355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deploying a Stable 5G SA Testbed Using srsRAN and Open5GS: UE Integration and Troubleshooting Towards Network Slicing 使用srsRAN和Open5GS部署稳定的5G SA测试平台:面向网络切片的UE集成和故障排除

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220512

Lusani Mamushiane, A. Lysko, H. Kobo, Joyce B. Mwangama

{"title":"Deploying a Stable 5G SA Testbed Using srsRAN and Open5GS: UE Integration and Troubleshooting Towards Network Slicing","authors":"Lusani Mamushiane, A. Lysko, H. Kobo, Joyce B. Mwangama","doi":"10.1109/icABCD59051.2023.10220512","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220512","url":null,"abstract":"Field trials and experimentation are crucial for accelerating the adoption of standalone (SA) 5G in Africa. Traditionally, only network operators and vendors had the opportunity for practical experimentation due to proprietary systems and licensing restrictions. However, the emergence of open source cellular stacks and affordable software-defined radio (SDR) systems is changing this landscape. Although these technologies are not yet fully developed for complete 5G systems, their progress is rapid, and the research community is using them to test different use cases like network slicing. Building a 5G network is complex, especially in uncontrolled RF environments with fluctuating physical conditions such as noise and interference. This necessitates proper RF planning and performance optimization. The complexity is further compounded by the variety of 5G end-user devices, each with unique configurations and integration requirements. Some devices are network locked and require rooting to connect to a 5G testbed, while others need expert APN configurations or have specific compatibility specifications like sub-carrier spacing (SCS) and duplex mode. Unfortunately, vendors often provide limited information about RF compatibility, making trial-and-error techniques necessary to uncover compatibility details. This paper presents best practices for deploying and configuring a 5G SA testbed, focusing on the integration challenges of consumer-grade devices, specifically 5G mobile phones connected to a 5G testbed. Additionally, the paper offers solutions for troubleshooting integration errors and performance issues, as well as a brief discussion on the realization of basic network slicing in a 5G SA network.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"390 1","pages":"1-10"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80438244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Scalable Semantic Framework for an Integrated Multi-Hazard Early Warning System 多灾种综合预警系统的可扩展语义框架

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220560

Yolo Madani, Adeyinka K. Akanbi, Mpho Mbele, M. Masinde

{"title":"A Scalable Semantic Framework for an Integrated Multi-Hazard Early Warning System","authors":"Yolo Madani, Adeyinka K. Akanbi, Mpho Mbele, M. Masinde","doi":"10.1109/icABCD59051.2023.10220560","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220560","url":null,"abstract":"The application of modern technologies in the environmental monitoring domain through the deployment of interconnected Internet of Things (loT) sensors, legacy systems, and enterprise networks has become an invaluable component of realising an efficient environmental monitoring system. Monitoring systems' requirements are extremely different depending on the environment, leading to ad-hoc implementations and integration of heterogeneous systems and applications. The resulting distributed systems lack flexibility with inherent issues such as data incompatibility, lack of data integration, and systems interoperability. Semantic representation of data is necessary to combine data from heterogeneous sources for consolidation into meaningful and valuable information and unlock the reusability of data between the monitoring systems. This research explores how a scalable semantic framework can ensure data representation using machine-readable languages for seamless data integration and interoperability of other heterogeneous sub-systems in a Multi-Hazard Early Warning System (MHEWS) as a case study. The study hypothesises that the challenge of ensuring data representation, data integration, and system interoperability within an MHEWS can be overcome through the application of semantic middleware.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"54 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90753084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a Sign Language Recognition System Using Machine Learning 基于机器学习的手语识别系统的开发

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220456

H. Orovwode, Ibukun Deborah Oduntan, J. Abubakar

{"title":"Development of a Sign Language Recognition System Using Machine Learning","authors":"H. Orovwode, Ibukun Deborah Oduntan, J. Abubakar","doi":"10.1109/icABCD59051.2023.10220456","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220456","url":null,"abstract":"Deafness and voice impairment have been persistent disabilities throughout history, hindering individuals from engaging in verbal communication and leading to their isolation from the predominantly vocally communicating society. Sign language has emerged as the primary mode of communication for people with these disabilities. However, it presents a language barrier as it is not commonly understood by those who can hear. To address this issue, various methods for recognizing sign language have been proposed. This paperaims to develop a machine learning-based system that can recognize sign language in real-time. The paper involved the acquisition of a dataset consisting of 44,654 images representing the static American Sign Language (ASL) alphabet signs. The HandDetector module was utilized to detect and capture images of the signer's hand forming each sign through a PC webcam. The dataset was split into three sets: training data (20,772 cases), validation data (8,903 cases), and test data (14,979 cases). Image pre-processing techniques were implemented on the images and a convolutional neural network (CNN) model was trained and compiled. The CNN utilized in the paper comprised of three convolutional layers and a SoftMax output layer and it was compiled using the Adam optimizer and categorical cross-entropy loss function. The performance of the system was evaluated using the test dataset. Notably, the system achieved remarkable accuracy rates, having a training accuracy of 99.86%, a validation accuracy of 99.94%, and a test accuracy of 94.68%. The results obtained from this study demonstrated significant advancements in sign language recognition, surpassing previous findings in the literature.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"15 1","pages":"1-8"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82770967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ocular Cataract Identification Using Deep Convolutional Neural Networks 基于深度卷积神经网络的白内障识别

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220532

Feliciana M. E. Manuel, S. Saide, Felermino M. D. A. Ali, Sanae Lotfi

引用次数: 0

Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition 基于时空卷积和视频视觉变换的独立手语识别

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.1109/icABCD59051.2023.10220534

Mark Marais, Dane Brown, James Connan, Alden Boby

{"title":"Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition","authors":"Mark Marais, Dane Brown, James Connan, Alden Boby","doi":"10.1109/icABCD59051.2023.10220534","DOIUrl":"https://doi.org/10.1109/icABCD59051.2023.10220534","url":null,"abstract":"Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"7 1","pages":"1-6"},"PeriodicalIF":4.6,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80853503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Swimming Goggles Optical Design by Fresnel Lenses 菲涅耳透镜的泳镜光学设计

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-08-03 DOI: 10.3390/engproc2023038090

Feng-Ming Yeh, Liang-Ying Huang, Chao-Kai Chang, Ya-Hui Hsieh, Hsuan-Fu Wang, Rong-Seng Chang, Der-Chin Chen

引用次数: 0