Frontiers in Big Data最新文献

{"title":"Hybridization of long short-term memory neural network in fractional time series modeling of inflation","authors":"Erman Arif, Elin Herlinawati, D. Devianto, Mutia Yollanda, Dony Permana","doi":"10.3389/fdata.2023.1282541","DOIUrl":"https://doi.org/10.3389/fdata.2023.1282541","url":null,"abstract":"Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates. Given the significant relationship between inflation and monetary, it becomes feasible to detect long-memory patterns within the data. To capture these long-memory patterns, Autoregressive Fractionally Moving Average (ARFIMA) was developed as a valuable tool in data mining. Due to the challenges posed in residual assumptions, time series model has to be developed to address heteroscedasticity. Consequently, the implementation of a suitable model was imperative to rectify this effect within the residual ARFIMA. In this context, a novel hybrid model was proposed, with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) being replaced by Long Short-Term Memory (LSTM) neural network. The network was used as iterative model to address this issue and achieve optimal parameters. Through a sensitivity analysis using mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), the performance of ARFIMA, ARFIMA-GARCH, and ARFIMA-LSTM models was assessed. The results showed that ARFIMA-LSTM excelled in simulating the inflation rate. This provided further evidence that inflation data showed characteristics of long memory, and the accuracy of the model was improved by integrating LSTM neural network.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"3 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384694","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}

{"title":"Criminal clickbait: a panel data analysis on the attractiveness of online advertisements offering stolen data","authors":"Renushka Madarie, Christianne J. de Poot, Marleen Weulen Kranenbarg","doi":"10.3389/fdata.2023.1320569","DOIUrl":"https://doi.org/10.3389/fdata.2023.1320569","url":null,"abstract":"Few studies have examined the sales of stolen account credentials on darkweb markets. In this study, we tested how advertisement characteristics affect the popularity of illicit online advertisements offering account credentials. Unlike previous criminological research, we take a novel approach by assessing the applicability of knowledge on regular consumer behaviours instead of theories explaining offender behaviour.We scraped 1,565 unique advertisements offering credentials on a darkweb market. We used this panel data set to predict the simultaneous effects of the asking price, endorsement cues and title elements on advertisement popularity by estimating several hybrid panel data models.Most of our findings disconfirm our hypotheses. Asking price did not affect advertisement popularity. Endorsement cues, including vendor reputation and cumulative sales and views, had mixed and negative relationships, respectively, with advertisement popularity.Our results might suggest that account credentials are not simply regular products, but high-risk commodities that, paradoxically, become less attractive as they gain popularity. This study highlights the necessity of a deeper understanding of illicit online market dynamics to improve theories on illicit consumer behaviours and assist cybersecurity experts in disrupting criminal business models more effectively. We propose several avenues for future experimental research to gain further insights into these illicit processes.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"1 11","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944240","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}

{"title":"Corrigendum: Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks.","authors":"Shekhar Mahmud, Mohammed Mansour, Turker Berk Donmez, Mustafa Kutlu, Chris Freeman","doi":"10.3389/fdata.2023.1338363","DOIUrl":"https://doi.org/10.3389/fdata.2023.1338363","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fdata.2023.1291329.].</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1338363"},"PeriodicalIF":3.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10762862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139089307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions","authors":"Brendan Hoover, Dakota Zaengle, M. Mark-Moser, Patrick C. Wingo, Anuj Suhag, Kelly Rose","doi":"10.3389/fdata.2023.1227189","DOIUrl":"https://doi.org/10.3389/fdata.2023.1227189","url":null,"abstract":"Subsurface interpretations and models rely on knowledge from subject matter experts who utilize unstructured information from images, maps, cross sections, and other products to provide context to measured data (e. g., cores, well logs, seismic surveys). To enhance such knowledge discovery, we advanced the National Energy Technology Laboratory's (NETL) Subsurface Trend Analysis (STA) workflow with an artificial intelligence (AI) deep learning approach for image embedding. NETL's STA method offers a validated science-based approach of combining geologic systems knowledge, statistical modeling, and datasets to improve predictions of subsurface properties. The STA image embedding tool quickly extracts images from unstructured knowledge products like publications, maps, websites, and presentations; categorically labels the images; and creates a repository for geologic domain postulation. Via a case study on geographic and subsurface literature of the Gulf of Mexico (GOM), results show the STA image embedding tool extracts images and correctly labels them with ~90 to ~95% accuracy.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":" 31","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138962433","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}

{"title":"Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks.","authors":"Tomislav Duricic, Dominik Kowald, Emanuel Lacic, Elisabeth Lex","doi":"10.3389/fdata.2023.1251072","DOIUrl":"10.3389/fdata.2023.1251072","url":null,"abstract":"<p><p>By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective. We begin by reviewing recent developments in approaches that improve not only the accuracy-diversity trade-off but also promote serendipity, and fairness in GNN-based recommender systems. We discuss different stages of model development including data preprocessing, graph construction, embedding initialization, propagation layers, embedding fusion, score computation, and training methodologies. Furthermore, we present a look into the practical difficulties encountered in assuring diversity, serendipity, and fairness, while retaining high accuracy. Finally, we discuss potential future research directions for developing more robust GNN-based recommender systems that go beyond the unidimensional perspective of focusing solely on accuracy. This review aims to provide researchers and practitioners with an in-depth understanding of the multifaceted issues that arise when designing GNN-based recommender systems, setting our work apart by offering a comprehensive exploration of beyond-accuracy dimensions.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1251072"},"PeriodicalIF":3.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10762851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139089306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum: Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse.","authors":"Paschal Ochang, Damian Eke, Bernd Carsten Stahl","doi":"10.3389/fdata.2023.1344345","DOIUrl":"10.3389/fdata.2023.1344345","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fdata.2023.1240660.].</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1344345"},"PeriodicalIF":3.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10758607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139089308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum: Do you hear the people sing? Comparison of synchronized URL and narrative themes in 2020 and 2023 French protests.","authors":"Lynnette Hui Xian Ng, Kathleen M Carley","doi":"10.3389/fdata.2023.1343108","DOIUrl":"https://doi.org/10.3389/fdata.2023.1343108","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fdata.2023.1221744.].</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1343108"},"PeriodicalIF":3.1,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10750104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139040893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum: Anemia detection through non-invasive analysis of lip mucosa images.","authors":"Shekhar Mahmud, Turker Berk Donmez, Mohammed Mansour, Mustafa Kutlu, Chris Freeman","doi":"10.3389/fdata.2023.1335213","DOIUrl":"https://doi.org/10.3389/fdata.2023.1335213","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fdata.2023.1241899.].</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1335213"},"PeriodicalIF":3.1,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10749427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis.","authors":"A K M Mubashwir Alam, Keke Chen","doi":"10.3389/fdata.2023.1296469","DOIUrl":"https://doi.org/10.3389/fdata.2023.1296469","url":null,"abstract":"<p><strong>Introduction: </strong>Big graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs of the key involved parties: data contributors and the data owner who collects data from contributors.</p><p><strong>Methods: </strong>We propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Our approach has several unique contributions compared to existing confidential graph analysis approaches. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers.</p><p><strong>Results: </strong>The TEE-Graph approach is much more efficient than software crypto approaches and also immune to access-pattern-based attacks. Compared with the best-known software crypto approach for graph spectral analysis, PrivateGraph, we have seen that TEE-Graph has 10³-10⁵ times lower computation, storage, and communication costs. Furthermore, the proposed access-pattern protection method incurs only about 10%-25% of the overall computation cost.</p><p><strong>Discussion: </strong>Our experimentation showed that TEE-Graph performs significantly better and has lower costs than typical software approaches. It also addresses the unique ownership and access-pattern issues that other TEE-related graph analytics approaches have not sufficiently studied. The proposed approach can be extended to other graph analytics problems with strong ownership and access-pattern protection.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1296469"},"PeriodicalIF":3.1,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10724017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138813061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The metaverse digital environments: a scoping review of the challenges, privacy and security issues.","authors":"Muhammad Tukur, Jens Schneider, Mowafa Househ, Ahmed Haruna Dokoro, Usman Idris Ismail, Muhammad Dawaki, Marco Agus","doi":"10.3389/fdata.2023.1301812","DOIUrl":"10.3389/fdata.2023.1301812","url":null,"abstract":"<p><p>The concept of the \"metaverse\" has garnered significant attention recently, positioned as the \"next frontier\" of the internet. This emerging digital realm carries substantial economic and financial implications for both IT and non-IT industries. However, the integration and evolution of these virtual universes bring forth a multitude of intricate issues and quandaries that demand resolution. Within this research endeavor, our objective was to delve into and appraise the array of challenges, privacy concerns, and security issues that have come to light during the development of metaverse virtual environments in the wake of the COVID-19 pandemic. Through a meticulous review and analysis of literature spanning from January 2020 to December 2022, we have meticulously identified and scrutinized 29 distinct challenges, along with 12 policy, privacy, and security matters intertwined with the metaverse. Among the challenges we unearthed, the foremost were concerns pertaining to the costs associated with hardware and software, implementation complexities, digital disparities, and the ethical and moral quandaries surrounding socio-control, collectively cited by 43%, 40%, and 33% of the surveyed articles, respectively. Turning our focus to policy, privacy, and security issues, the top three concerns that emerged from our investigation encompassed the formulation of metaverse rules and principles, the encroachment of privacy threats within the metaverse, and the looming challenges concerning data management, all mentioned in 43%, 40%, and 33% of the examined literature. In summation, the development of virtual environments within the metaverse is a multifaceted and dynamically evolving domain, offering both opportunities and hurdles for researchers and practitioners alike. It is our aspiration that the insights, challenges, and recommendations articulated in this report will catalyze extensive dialogues among industry stakeholders, governmental bodies, and other interested parties concerning the metaverse's destiny and the world they aim to construct or bequeath to future generations.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1301812"},"PeriodicalIF":2.4,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10702132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138813063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}