Data Science and Management最新文献

{"title":"Corrigendum regarding previously published articles","authors":"","doi":"10.1016/j.dsm.2024.12.006","DOIUrl":"10.1016/j.dsm.2024.12.006","url":null,"abstract":"","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Page 116"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738515","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":"Does the application of industrial robots reduce the intensity of CO2 emissions embodied in manufacturing exports?","authors":"Yaya Li ,&nbsp;Yun Zhang ,&nbsp;Xiaoli Wu","doi":"10.1016/j.dsm.2024.09.003","DOIUrl":"10.1016/j.dsm.2024.09.003","url":null,"abstract":"<div><div>Industrial robot application (IRA) provides an opportunity for the low-carbon development of trade. This study focuses on the green revolution of manufacturing export trade, analyzes the mechanism by which IRA affects CO₂ emissions embodied in manufacturing exports (CIE), and conducts an empirical test based on panel data from 37 countries from 2000 to 2019. This study found that first, IRA can significantly reduce CIE, but there is a U-shaped nexus between the two, which shows a rebound effect. Second, the heterogeneity test demonstrates that in comparison to both the low-tech and high-tech sectors, IRA in the medium-tech industry can significantly reduce CIE; compared with the low-IRA sectors, the high-IRA sectors exhibit a more obvious reduction. In addition, IRA has a stronger effect on high-carbon-intensity areas. Third, the mechanism test shows that IRA mainly affects CIE through low-carbon technology and productivity effects. Moreover, environmental regulations and the manufacturing intelligence process positively moderate the nexus between IRA and CIE. Finally, these conclusions provide possible empirical evidence for the smart evolution of the manufacturing industry and the green development of trade.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 117-126"},"PeriodicalIF":0.0,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139014","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":"Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks","authors":"Wasim Khan ,&nbsp;Afsaruddin Mohd ,&nbsp;Mohammad Suaib ,&nbsp;Mohammad Ishrat ,&nbsp;Anwar Ahamed Shaikh ,&nbsp;Syed Mohd Faisal","doi":"10.1016/j.dsm.2024.09.002","DOIUrl":"10.1016/j.dsm.2024.09.002","url":null,"abstract":"<div><div>In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data. This is further bolstered by deep residual learning, which is employed to model intricate nonlinear connections directly from input data. A pivotal innovation in our approach is the incorporation of a residual-based attention mechanism. This mechanism dynamically adjusts the importance of nodes based on their residual information, thereby significantly enhancing the sensitivity of the model to subtle anomalies. Furthermore, we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data. This mapping is the key to our model’s ability to pinpoint anomalies with greater precision. An extensive experimental setup was used to validate the efficacy of the proposed model. Using attributed social network datasets, we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection. The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 137-146"},"PeriodicalIF":0.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146833","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":"Understanding users’ effective use of generative conversational AI from a media naturalness perspective: a hybrid structural equation modeling-artificial neural network (SEM-ANN) approach","authors":"Kun Wang,&nbsp;Yaobin Lu,&nbsp;Zhao Pan","doi":"10.1016/j.dsm.2024.09.001","DOIUrl":"10.1016/j.dsm.2024.09.001","url":null,"abstract":"<div><div>Although generative conversational artificial intelligence (AI) can answer questions well and hold conversations as a person, the semantic ambiguity inherent in text-based communication poses challenges to effective use. Effective use reflects the users’ utilization of generative conversational AI to achieve their goals, which has not been previously studied. Drawing on the media naturalness theory, we examined how generative conversational AI’s content and style naturalness affect effective use. A two-wave survey was conducted to collect data from 565 users of generative conversational AI. Two techniques were used in this study. Initially, partial least squares structural equation modeling (PLS-SEM) was applied to determine the variables that significantly affected the mechanisms (i.e., cognitive effort and communication ambiguity) and effective use. Secondly, an artificial neural network model was used to evaluate the relative importance of the significant predictors of mechanisms and effective use identified from the PLS-SEM analysis. The results revealed that the naturalness of content and style differed in their effects on cognitive effort and communication ambiguity. Additionally, cognitive effort and communication ambiguity negatively affected effective use. This study advances the literature on effective use by uncovering the psychological mechanisms underlying effective use and their antecedents. In addition, this study offers insights into the design of generative conversational AI.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 147-159"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146834","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":"An explainable feature selection framework for web phishing detection with machine learning","authors":"Sakib Shahriar Shafin","doi":"10.1016/j.dsm.2024.08.004","DOIUrl":"10.1016/j.dsm.2024.08.004","url":null,"abstract":"<div><div>In the evolving landscape of cyber threats, phishing attacks pose significant challenges, particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy. Conventional and machine learning (ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics. Furthermore, newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable. Hence, effective detection depends on identifying the most critical features. Traditional feature selection (FS) methods often struggle to enhance ML model performance and instead decrease it. To combat these issues, we propose an innovative method using explainable AI (XAI) to enhance FS in ML models and improve the identification of phishing websites. Specifically, we employ SHapley Additive exPlanations (SHAP) for global perspective and aggregated local interpretable model-agnostic explanations (LIME) to determine specific localized patterns. The proposed SHAP and LIME-aggregated FS (SLA-FS) framework pinpoints the most informative features, enabling more precise, swift, and adaptable phishing detection. Applying this approach to an up-to-date web phishing dataset, we evaluate the performance of three ML models before and after FS to assess their effectiveness. Our findings reveal that random forest (RF), with an accuracy of 97.41% and XGBoost (XGB) at 97.21% significantly benefit from the SLA-FS framework, while k-nearest neighbors lags. Our framework increases the accuracy of RF and XGB by 0.65% and 0.41%, respectively, outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset, showcasing its potential.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 127-136"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139015","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":"Design of knowledge transaction protection mechanism in the open innovation community based on blockchain technology","authors":"Dongshan Yang ,&nbsp;Ling Zhao ,&nbsp;Feng Leng ,&nbsp;Zujun Shi","doi":"10.1016/j.dsm.2024.08.002","DOIUrl":"10.1016/j.dsm.2024.08.002","url":null,"abstract":"<div><div>Open innovation communities (OICs) are important for the development of modern enterprises. However, there are still many safety hazards and trust crises on community platforms. This paper proposes an approach that combines blockchain technology and an OIC, as blockchain technology provides a direction for solving trust and security problems in community platform transactions. Moreover, the design of smart contracts can ensure the implementation of transactions and improve their reliability. In this study, the participants in an OIC and three types of infringement problems were analyzed. The Byzantine Fault Tolerant (BFT) algorithm was applied to design the logic of smart contracts and the coupling between idea platforms and the blockchain. The protection mechanism of an OIC was established based on blockchain smart contract supervision to provide a better knowledge co-creation environment for OICs.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 86-93"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143296563","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":"Effects of feature selection and normalization on network intrusion detection","authors":"Mubarak Albarka Umar ,&nbsp;Zhanfang Chen ,&nbsp;Khaled Shuaib ,&nbsp;Yan Liu","doi":"10.1016/j.dsm.2024.08.001","DOIUrl":"10.1016/j.dsm.2024.08.001","url":null,"abstract":"<div><div>The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence (AI) techniques (such as machine learning (ML) and deep learning (DL)) to build more efficient and reliable intrusion detection systems (IDSs). However, the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs. Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues. While most of these researchers reported the success of these preprocessing techniques on a shallow level, very few studies have been performed on their effects on a wider scale. Furthermore, the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used, which most of the existing studies give little emphasis on. Thus, this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets: NSL-KDD, UNSW-NB15, and CSE–CIC–IDS2018, and various AI algorithms. A wrapper-based approach, which tends to give superior performance, and min-max normalization methods were used for feature selection and normalization, respectively. Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization. The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. The RF models also achieved an excellent performance compared to recent works. The results show that normalization and feature selection positively affect IDS modeling. Furthermore, while feature selection benefits simpler algorithms (such as RF), normalization is more useful for complex algorithms like ANNs and DNNs, and algorithms such as NB are unsuitable for IDS modeling. The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset. Our findings suggest that prioritizing robust algorithms like RF, alongside complex models such as ANN and DNN, can significantly enhance IDS performance. These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 23-39"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163848","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":"Digital volunteer services in emergency situations: Typological characteristics, advantages, and challenges","authors":"Yongzhong Sha ,&nbsp;Xingfei Wei ,&nbsp;Chunhua Niu ,&nbsp;Yongbao Zhang ,&nbsp;Lu He","doi":"10.1016/j.dsm.2024.08.003","DOIUrl":"10.1016/j.dsm.2024.08.003","url":null,"abstract":"<div><div>Volunteer engagement in emergency management, focusing on mitigating adverse consequences, has attracted scholarly and practitioner attention. Digital volunteering helps overcome the limitations of traditional on-site volunteering through extensive volunteering opportunities in emergency management. This study utilizes a case text analysis and interviews to investigate and categorize digital volunteer services in emergency scenarios. Based on two key dimensions—direct recipients of volunteer services and the nature of the services rendered—the study presents four types of digital volunteer services: bridging, supportive, complementary, and collaborative. Moreover, it delineates eight role archetypes digital volunteers assume in emergency response situations along with their primary service contributions. Compared to conventional on-site volunteer services, digital volunteer services offer unique advantages while facing specific challenges. Finally, this study offers recommendations in four dimensions for the robust development of digital volunteer services, contributing to more effective and sustainable emergency management practices.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165086","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":"Forecast uncertainties real-time data-driven compensation scheme for optimal storage control","authors":"Arbel Yaniv,&nbsp;Yuval Beck","doi":"10.1016/j.dsm.2024.07.002","DOIUrl":"10.1016/j.dsm.2024.07.002","url":null,"abstract":"<div><div>This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 59-71"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846117","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":"Dual-market quantitative trading: The dynamics of liquidity and turnover in financial markets","authors":"Qing Zhu ,&nbsp;Chenyu Han ,&nbsp;Yuze Li","doi":"10.1016/j.dsm.2024.07.003","DOIUrl":"10.1016/j.dsm.2024.07.003","url":null,"abstract":"<div><div>Financial market liquidity is a popular research topic. Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate, the lower the returns. However, the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models. To explore high machine-driven liquidity and the impact of high turnover rates on returns, this study establishes a dual-market quantitative trading system, introduces a variational modal decomposition (VMD)-bidirectional gated recurrent unit (BiGRU) model for data prediction, and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S. and Chinese stock markets. The experimental results show that given a principal amount of 210,000.00 CNY, the final predicted net return is 226,538.30 CNY, a net return of 107.86%, which is 40.6% higher than the net return of a single Chinese market. We conclude that, under machine-driven trading, increasing liquidity and turnover increase returns. This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851279","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}