To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments,which results from anomaly detection mechanisms in location-based service(LBS)applications,thi...To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments,which results from anomaly detection mechanisms in location-based service(LBS)applications,this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis.The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns.First,we design an automated data extraction algorithm that recognizes graphical user interface(GUI)elements to collect spatio-temporal behavior data.Then,by analyzing the automatically collected user data,we identify normal users’spatio-temporal patterns and extract their features such as high-activity time windows and spatial clustering characteristics.Subsequently,an antidetection scheduling strategy is developed,integrating spatial clustering optimization,load-balanced allocation,and time window control to generate probe scheduling schemes.Additionally,a self-correction mechanism based on an exponential backoff strategy is implemented to rectify anomalous behaviors andmaintain system stability.Experiments in real-world environments demonstrate that the proposed method significantly outperforms baseline methods in terms of both probe ban rate and task completion rate,while maintaining high time efficiency.This study provides a more reliable and clandestine solution for geosocial data collection and lays the foundation for building more robust virtual probe systems.展开更多
The successful deployment of autonomous vehicles(AVs)relies heavily on their ability to interact safely and effectively with other road users.External human–machine interfaces(eHMIs)have emerged as critical component...The successful deployment of autonomous vehicles(AVs)relies heavily on their ability to interact safely and effectively with other road users.External human–machine interfaces(eHMIs)have emerged as critical components in facilitating these interactions.Rigorous evaluation and testing of eHMIs are essential for realizing their intended safety and communication benefits.This study provides a comprehensive review of current eHMI research,focusing on their impact on road users’behavior and perceptions,as well as the methods used for evaluation.Key behavioral factors—such as eHMI modality,information type,location,vehicle kinematics,traffic environment,and user characteristics—are systematically reviewed and summarized.The influence of eHMIs on user perceptions is also explored through indicators such as perceived safety,comprehensibility,trust,cognitive load,and user experience.Despite notable advancements,several critical research gaps remain underexplored.Most studies focus on one-toone interactions,neglecting the complexities of mixed-traffic environments involving AVs,conventional human-driven vehicles,pedestrians,and cyclists.Current evaluation methods largely rely on virtual reality and Wizard-of-Oz experiments,which may fail to fully capture real-world dynamics.Additionally,subjective questionnaires,which are often used in these studies,do not guarantee high reproducibility of findings.Moreover,insufficient attention has been given to the synchronization of eHMI signals with vehicle kinematics.Furthermore,the absence of standardized evaluation frameworks limits cross-study comparability and the development of universally applicable eHMI solutions.To address these challenges,future research should prioritize the integration of naturalistic traffic scenarios,the adoption of objective and reproducible evaluation methods,the exploration of multimodal eHMI designs,and the development of standardized assessment protocols.These efforts are crucial for improving AV communication with diverse road users and ensuring safety in increasingly complex traffic ecosystems.展开更多
The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related in...The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.展开更多
This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and...This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.展开更多
Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling...Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.展开更多
With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis o...With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.展开更多
Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a...Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.展开更多
A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by ana...A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.展开更多
Purpose： In order to further the understanding of Chinese Web users＇ image-seeking behavior, this study explores the kinds of images that Chinese Web users seek online and how they express their requests.Design/meth...Purpose： In order to further the understanding of Chinese Web users＇ image-seeking behavior, this study explores the kinds of images that Chinese Web users seek online and how they express their requests.Design/methodology/approach： We used five pairs of simulated keywords to collect 893 image-seeking questions from Baidu Zhidao. Then, we revised the subject category of questions to analyze popular image needs. In addition, we conducted content analysis and descriptive statistical analysis to identify image-seeking motivations and image features used in the requests in terms of the two theories of image feature classification and image use. Findings： Among the 893 questions, the image searches for entertainment accounted for 47.59%, more than the searches for professional knowledge （37.40%） and personal daily activities （15.01%）. With regard to motivation, over 60% of the questions were identified as used for learning, which is well over the proportion of questions used for illustrating. Thus, these questions requested images as sources of data rather than sources of objects. Non-visual features （47.58%） were used most frequently in question descriptions, slightly higher than semantic features （45.96%）. Users who lacked domain knowledge tended to use general words rather than specific words to describe their requests. However, not many users used syntactic featm＇es when seeking images. Nevertheless, most of the users had a fairly clear idea about what the target image should look like. Research limitations： We studied only one question and answer （Q＆A） community using five pairs of simulated keywords. Practical implications： The findings should be helpful in strengthening the functionality of Q＆A systems, promoting the theories of image feature classification, and shedding light on information literacy training.Originality/value： This study is one of the first research efforts that discusses Chinese Web users＇ daily image searches and querying behavior in natural language in a Q＆A community, which should help to further the understanding of the principles of image-seeking behavior among Chinese Web users.展开更多
Purpose:As smartphones become ubiquitous,it is important to understand emerging information behavior as a result of wide spread use of smartphones.The purpose of this study is to investigate information behavior in th...Purpose:As smartphones become ubiquitous,it is important to understand emerging information behavior as a result of wide spread use of smartphones.The purpose of this study is to investigate information behavior in the mobile environment by studying undergraduate smartphone users in China.Design/methodology/approach:This study is based on a survey of 205 undergraduate students in China.Findings:Smartphones are used predominantly for accessing news and connecting to social media,rather than for academic purposes such as accessing library resources or researching.While students use smartphones for reading e-books,much of this reading is recreational during their spare time.Research limitations:The inherent limitations of self-reported measures and the small sample size of this study mean that the results cannot be generalized across different age groups and cultures.Practical implications:When targeting users on the move,information professionals should be aware that the needs and behaviors of smartphone readers are significantly different compared to users of fixed devices,and should provide services in a mobile-friendly way.Originality/value:The younger generation is accustomed to instant information access.For libraries to relevant,they must redesign their services.It is important for libraries to leverage the strengths of mobile technology and to balance traditional services with mobile delivery.Even though many mobile users will use desktop or laptop computers to access library resources,they will benefit from the availability of mobile-friendly library services.展开更多
Purpose:To have a better understanding of library users' social bookmarking behaviors,we conducted a survey of college students to examine how much attention they have paid to social bookmarking results,their soci...Purpose:To have a better understanding of library users' social bookmarking behaviors,we conducted a survey of college students to examine how much attention they have paid to social bookmarking results,their social bookmarking motivations and behaviors on book sharing sites and in their library's OPAC,and their expectations to their library's social bookmarking service.Design/methodology/approach:A questionnaire survey was conducted to investigate social bookmarking behaviors and motivations of users of Wuhan University Library(WUL) on book sharing sites and in WUL's OPAC.A total of 700 questionnaires were distributed and357 valid questionnaires were retrieved.SPSS was used for data analysis.Findings:The results revealed that there were differences between users' social bookmarking behaviors on book sharing sites and in the Library's OPAC.Users preferred to use tags than add tags on book sharing sites.The main tagging motivation on book sharing sites was for meeting users' specific needs such as collection management and book recommendations.As for the Library,the lack of publicity and promotion of the social bookmarking service led to insufficient use of the Library's service.Due to a lack of knowledge of the social bookmarking service,users did not care about the lectures or publicity campaigns about the Library's social bookmarking service.Research limitations:Because social bookmarking was not common among WUL users,and the questionnaires could be handed out to a limited number of people,it was hard to describe users' social bookmarking behaviors in the Library's OPAC by investigating a sample of 357Library users,and therefore we only investigated users' social bookmarking behaviors on the book sharing sites.Practical implications:The survey results provide insights into library users' social bookmarking behaviors and motivations.The study will help academic libraries improve their social bookmarking service.Originality/value:In spite of growing popularity of social bookmarking sites,little has been known about the social bookmarking behaviors of library users.This study investigated college students' social bookmarking behaviors and motivations,providing suggestions for academic libraries to improve their social bookmarking services.展开更多
Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often l...Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often lead to yield inaccurate features of influential users due to neighborhood aggregation,and require a large substantial amount of labeled data for training,making them difficult and challenging to apply in practice.To address this issue,we propose a semi-supervised contrastive learning method for identifying influential users.First,the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics related to influence;then,contrastive learning is employed to guide the encoder to generate various influence-related features for users;finally,with only a small amount of labeled data,an attention-based user classifier is trained to accurately identify influential users.Experiments conducted on three public social network datasets demonstrate that the proposed method,using only 20%of the labeled data as the training set,achieves F1 values that are 5.9%,5.8%,and 8.7%higher than those unsupervised EVC method,and it matches the performance of GNN-based methods such as DeepInf,InfGCN and OlapGN,which require 80%of labeled data as the training set.展开更多
Generically system includes two types of categories, computerized based system and socio-technical system;the difference between both types of systems is that the socio-technical systems deals with technical operation...Generically system includes two types of categories, computerized based system and socio-technical system;the difference between both types of systems is that the socio-technical systems deals with technical operation and involves the users. Information system that deals with the flow of information regarding business and social perspective falls in socio-technical based system. ERP (Enterprise Resource Planning) is the best example of Information system that organization adopt to make fast the business process. The ERP systems are suffering from multifaceted interface. Previous research shows that there is a need for an improvement regarding user interface because it is the way of communicate with system. As the emphasis of HCI (Human Computer Interaction) is to clearly understand the users’ philosophy, reminiscence and problems solving techniques in order to design users oriented applications. Therefore the usability engineering is the only way to study the deeds of users by getting analysis on prototype or system by offering different methods and techniques. This paper will focus on the users’ experiences in view of financial module in ERP system which is based on different sub-components. To carry this research work HCI research methods, automated explicitly survey questionnaire method and focus group were adopted to gather users understanding in order to evaluate the selected application in ERP system. This study involved group of users from various corporate level industries in textile sector. It is the extended work as in previous only two industries were selected.展开更多
With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce opera...With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.展开更多
Objective: It is in order to estimate the prevalence and incidence of HIV, the frequency of sexual risk behaviors, and perceptions of available resources to prevent and treat HIV among crack users in the San Salvador ...Objective: It is in order to estimate the prevalence and incidence of HIV, the frequency of sexual risk behaviors, and perceptions of available resources to prevent and treat HIV among crack users in the San Salvador Metropolitan Area. Methods: We conducted a survey of 420 crack users by using respondent-driven sampling to measure demographic characteristics, the quantity and frequency of drug use, history of STIs, including HIV, and experiences with organizations which provide prevention and treatment of HIV. Each participant offered a free and voluntary HIV test and was asked permission to share the results of the test with the study. Bernoullian modeling was used to estimate the prevalence and incidence of HIV among heterosexual males in this population. Results: The estimated prevalence was 7% (95% CI: 2.3% -9.8%) among participants who agreed to take the test and share the results, and 4.9% (95% CI: 2.8% -7.8%) assuming that those who did not take the test or share results were seronegative. Participants reported a high frequency of sexual risk behaviors. In addition, participants were reported to have little knowledge of organizations to prevent or treat HIV/AIDS;58% had never taken an HIV test prior to survey administration. Conclusions: Crack users in San Salvador are at high risk for HIV acquisition. HIV prevention interventions are urgently needed, especially interventions increasing access to HIV testing and prevention.展开更多
Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep perf...Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.展开更多
The aim of the current study was to examine the prevalence of HIV, past six-month illicit drug use, and risk behaviors among a population of heavy drug users living in an urban setting. Although many studies investiga...The aim of the current study was to examine the prevalence of HIV, past six-month illicit drug use, and risk behaviors among a population of heavy drug users living in an urban setting. Although many studies investigate substance use, sex-risk behavior, and HIV by race and gender, no studies have examined these variables simultaneously. The current study seeks to fill this gap in the literature by exploring HIV prevalence among a predominantly heterosexual sample of recent substance users by injection drug use (IDU) status, race, and sex. Baseline data from the Baltimore site of the NEURO-HIV epidemiologic study was used in this study. This study examines neuropsychological and social-behavioral risk factors of HIV, hepatitis A, hepatitis B, and hepatitis C among both injection and non-injection drug users. Descriptive statistics and chi-square statistics were used in data analyses. Blood and urine samples were obtained to test for the presence of recent drug use, viral hepatitis, HIV, and other sexually transmitted diseases (STDs). Findings presented here have several important implications for HIV prevention and care among substance users. Intervention programs that incorporate substance use treatment in addition to HIV education, particularly with respect to substance use and sex risk behavior are imperative.展开更多
To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator a...To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator at temperatures of 380-440℃and strain rates of 0.05-1 s^(−1).The Johnson-Cook model,Hensel-Spittel model,strain-compensated Arrhenius model,and critical fracture strain model were established.Results show that through the evaluation of the models using the correlation coefficient(R)and the average absolute relative error,the strain-compensated Arrhenius model can represent the flow behavior of the alloy more accurately.Shear bands are more pronounced in the as-homogenized specimens,whereas dynamic recrystallization is predominantly observed in as-rolled specimens.Fracture morphology analysis reveals that a mixed fracture mechanism is prevalent in the as-homogenized specimen,whereas a ductile fracture mechanism is predominant in the as-rolled specimen.The processing maps indicate that the unstable region is reduced in the as-rolled specimens compared with that in the as-homogenized specimens.The optimal hot working windows for the as-homogenized and as-rolled specimens are determined as 410-440℃/0.14-1 s^(−1)and 380-400℃/0.05-0.29 s^(−1),respectively.展开更多
The paper focuses on the habits of China Web users' language utilization behaviors in accessing the Web. It also seeks to make a general study on the basic nature of language phenomenon with regard to digital acce...The paper focuses on the habits of China Web users' language utilization behaviors in accessing the Web. It also seeks to make a general study on the basic nature of language phenomenon with regard to digital accessing. A questionnaire survey was formulated and distributed online for these research purposes. There were 1,267 responses collected. The data were analyzed with descriptive statistics, Chi-square testing and contingency table analyses. Results revealed the following findings. Tagging has already played an important role in Web2.0 communication for China's Web users. China users rely greatly on all kinds of taxonomies in browsing and have also an awareness of them in effective searching. These imply that the classified languages in digital environment may aid Chinese Web users in a more satisfying manner. Highly subject-specific words, especially those from authorized tools, yielded better results in searching. Chinese users have high recognition for related terms. As to the demographic aspect, there is little difference between different genders in the utilization of information retrieval languages. Age may constitute a variable element to a certain degree. Educational background has a complex effect on language utilizations in searching. These research findings characterize China Web users' behaviors in digital information accessing. They also can be potentially valuable for the modeling and further refinement of digital accessing services.展开更多
A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this work.Firstly, the classifier is integrated based on...A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this work.Firstly, the classifier is integrated based on the dimension of predicted probability, and the pruning algorithm based on greedy forward search is obtained by combining the two indicators of accuracy and complementarity.Then the pruning algorithm is integrated into the Stacking ensemble method to establish a user online shopping behavior prediction model based on the probabilistic multi-dimensional selective ensemble method.Finally, the research method is compared with the prediction results of individual learners in ensemble learning and the Stacking ensemble method without pruning.The experimental results show that the proposed method can reduce the scale of integration, improve the prediction accuracy of the model, and predict the user's online purchase behavior.展开更多
基金supported by theNationalNatural Science Foundation of China(No.U23A20305)National Key Research and Development Program of China(No.2022YFB3102900)+1 种基金Innovation Scientists and Technicians Troop Construction Projects of Henan Province,China(No.254000510007)Key Research and Development Project of Henan Province(No.221111321200).
文摘To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments,which results from anomaly detection mechanisms in location-based service(LBS)applications,this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis.The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns.First,we design an automated data extraction algorithm that recognizes graphical user interface(GUI)elements to collect spatio-temporal behavior data.Then,by analyzing the automatically collected user data,we identify normal users’spatio-temporal patterns and extract their features such as high-activity time windows and spatial clustering characteristics.Subsequently,an antidetection scheduling strategy is developed,integrating spatial clustering optimization,load-balanced allocation,and time window control to generate probe scheduling schemes.Additionally,a self-correction mechanism based on an exponential backoff strategy is implemented to rectify anomalous behaviors andmaintain system stability.Experiments in real-world environments demonstrate that the proposed method significantly outperforms baseline methods in terms of both probe ban rate and task completion rate,while maintaining high time efficiency.This study provides a more reliable and clandestine solution for geosocial data collection and lays the foundation for building more robust virtual probe systems.
基金supported by the National Natural Science Foundation of China(Nos.52472360,72101128,and 72471070)the China Postdoctoral Science Foundation(No.2023M730560).
文摘The successful deployment of autonomous vehicles(AVs)relies heavily on their ability to interact safely and effectively with other road users.External human–machine interfaces(eHMIs)have emerged as critical components in facilitating these interactions.Rigorous evaluation and testing of eHMIs are essential for realizing their intended safety and communication benefits.This study provides a comprehensive review of current eHMI research,focusing on their impact on road users’behavior and perceptions,as well as the methods used for evaluation.Key behavioral factors—such as eHMI modality,information type,location,vehicle kinematics,traffic environment,and user characteristics—are systematically reviewed and summarized.The influence of eHMIs on user perceptions is also explored through indicators such as perceived safety,comprehensibility,trust,cognitive load,and user experience.Despite notable advancements,several critical research gaps remain underexplored.Most studies focus on one-toone interactions,neglecting the complexities of mixed-traffic environments involving AVs,conventional human-driven vehicles,pedestrians,and cyclists.Current evaluation methods largely rely on virtual reality and Wizard-of-Oz experiments,which may fail to fully capture real-world dynamics.Additionally,subjective questionnaires,which are often used in these studies,do not guarantee high reproducibility of findings.Moreover,insufficient attention has been given to the synchronization of eHMI signals with vehicle kinematics.Furthermore,the absence of standardized evaluation frameworks limits cross-study comparability and the development of universally applicable eHMI solutions.To address these challenges,future research should prioritize the integration of naturalistic traffic scenarios,the adoption of objective and reproducible evaluation methods,the exploration of multimodal eHMI designs,and the development of standardized assessment protocols.These efforts are crucial for improving AV communication with diverse road users and ensuring safety in increasingly complex traffic ecosystems.
基金sponsored by the National Natural Science Foundation of China under grant number No.61100008,61201084the China Postdoctoral Science Foundation under Grant No.2013M541346+3 种基金Heilongiiang Postdoctoral Special Fund(Postdoctoral Youth Talent Program)under Grant No.LBH-TZ0504Heilongjiang Postdoctoral Fund under Grant No.LBH-Z13058the Natural Science Foundation of Heilongjiang Province of China under Grant No.QC2015076The Fundamental Research Funds for the Central Universities of China under grant number HEUCF100602
文摘The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.
文摘This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.
基金supported by the State Grid Science and Technology Project (No.5442AI90009)Natural Science Foundation of China (No. 6170337)
文摘Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.
文摘With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.
基金supported by the National Natural Science Foundation of China(Grant No.:71203163)the Foundation for Humanities and Social Sciences of the Chinese Ministry of Education(Grant No.:12YJC870011)
文摘Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.
文摘A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.
基金supported by Humanities and Social Science Fund from the Chinese Ministry of Education (Grant No.: 11YJC870010)
文摘Purpose： In order to further the understanding of Chinese Web users＇ image-seeking behavior, this study explores the kinds of images that Chinese Web users seek online and how they express their requests.Design/methodology/approach： We used five pairs of simulated keywords to collect 893 image-seeking questions from Baidu Zhidao. Then, we revised the subject category of questions to analyze popular image needs. In addition, we conducted content analysis and descriptive statistical analysis to identify image-seeking motivations and image features used in the requests in terms of the two theories of image feature classification and image use. Findings： Among the 893 questions, the image searches for entertainment accounted for 47.59%, more than the searches for professional knowledge （37.40%） and personal daily activities （15.01%）. With regard to motivation, over 60% of the questions were identified as used for learning, which is well over the proportion of questions used for illustrating. Thus, these questions requested images as sources of data rather than sources of objects. Non-visual features （47.58%） were used most frequently in question descriptions, slightly higher than semantic features （45.96%）. Users who lacked domain knowledge tended to use general words rather than specific words to describe their requests. However, not many users used syntactic featm＇es when seeking images. Nevertheless, most of the users had a fairly clear idea about what the target image should look like. Research limitations： We studied only one question and answer （Q＆A） community using five pairs of simulated keywords. Practical implications： The findings should be helpful in strengthening the functionality of Q＆A systems, promoting the theories of image feature classification, and shedding light on information literacy training.Originality/value： This study is one of the first research efforts that discusses Chinese Web users＇ daily image searches and querying behavior in natural language in a Q＆A community, which should help to further the understanding of the principles of image-seeking behavior among Chinese Web users.
文摘Purpose:As smartphones become ubiquitous,it is important to understand emerging information behavior as a result of wide spread use of smartphones.The purpose of this study is to investigate information behavior in the mobile environment by studying undergraduate smartphone users in China.Design/methodology/approach:This study is based on a survey of 205 undergraduate students in China.Findings:Smartphones are used predominantly for accessing news and connecting to social media,rather than for academic purposes such as accessing library resources or researching.While students use smartphones for reading e-books,much of this reading is recreational during their spare time.Research limitations:The inherent limitations of self-reported measures and the small sample size of this study mean that the results cannot be generalized across different age groups and cultures.Practical implications:When targeting users on the move,information professionals should be aware that the needs and behaviors of smartphone readers are significantly different compared to users of fixed devices,and should provide services in a mobile-friendly way.Originality/value:The younger generation is accustomed to instant information access.For libraries to relevant,they must redesign their services.It is important for libraries to leverage the strengths of mobile technology and to balance traditional services with mobile delivery.Even though many mobile users will use desktop or laptop computers to access library resources,they will benefit from the availability of mobile-friendly library services.
基金supported by the National Youth Top-notch Talent Support Program of China
文摘Purpose:To have a better understanding of library users' social bookmarking behaviors,we conducted a survey of college students to examine how much attention they have paid to social bookmarking results,their social bookmarking motivations and behaviors on book sharing sites and in their library's OPAC,and their expectations to their library's social bookmarking service.Design/methodology/approach:A questionnaire survey was conducted to investigate social bookmarking behaviors and motivations of users of Wuhan University Library(WUL) on book sharing sites and in WUL's OPAC.A total of 700 questionnaires were distributed and357 valid questionnaires were retrieved.SPSS was used for data analysis.Findings:The results revealed that there were differences between users' social bookmarking behaviors on book sharing sites and in the Library's OPAC.Users preferred to use tags than add tags on book sharing sites.The main tagging motivation on book sharing sites was for meeting users' specific needs such as collection management and book recommendations.As for the Library,the lack of publicity and promotion of the social bookmarking service led to insufficient use of the Library's service.Due to a lack of knowledge of the social bookmarking service,users did not care about the lectures or publicity campaigns about the Library's social bookmarking service.Research limitations:Because social bookmarking was not common among WUL users,and the questionnaires could be handed out to a limited number of people,it was hard to describe users' social bookmarking behaviors in the Library's OPAC by investigating a sample of 357Library users,and therefore we only investigated users' social bookmarking behaviors on the book sharing sites.Practical implications:The survey results provide insights into library users' social bookmarking behaviors and motivations.The study will help academic libraries improve their social bookmarking service.Originality/value:In spite of growing popularity of social bookmarking sites,little has been known about the social bookmarking behaviors of library users.This study investigated college students' social bookmarking behaviors and motivations,providing suggestions for academic libraries to improve their social bookmarking services.
基金supported by the National Key Project of the National Natural Science Foundation of China under Grant No.U23A20305.
文摘Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often lead to yield inaccurate features of influential users due to neighborhood aggregation,and require a large substantial amount of labeled data for training,making them difficult and challenging to apply in practice.To address this issue,we propose a semi-supervised contrastive learning method for identifying influential users.First,the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics related to influence;then,contrastive learning is employed to guide the encoder to generate various influence-related features for users;finally,with only a small amount of labeled data,an attention-based user classifier is trained to accurately identify influential users.Experiments conducted on three public social network datasets demonstrate that the proposed method,using only 20%of the labeled data as the training set,achieves F1 values that are 5.9%,5.8%,and 8.7%higher than those unsupervised EVC method,and it matches the performance of GNN-based methods such as DeepInf,InfGCN and OlapGN,which require 80%of labeled data as the training set.
文摘Generically system includes two types of categories, computerized based system and socio-technical system;the difference between both types of systems is that the socio-technical systems deals with technical operation and involves the users. Information system that deals with the flow of information regarding business and social perspective falls in socio-technical based system. ERP (Enterprise Resource Planning) is the best example of Information system that organization adopt to make fast the business process. The ERP systems are suffering from multifaceted interface. Previous research shows that there is a need for an improvement regarding user interface because it is the way of communicate with system. As the emphasis of HCI (Human Computer Interaction) is to clearly understand the users’ philosophy, reminiscence and problems solving techniques in order to design users oriented applications. Therefore the usability engineering is the only way to study the deeds of users by getting analysis on prototype or system by offering different methods and techniques. This paper will focus on the users’ experiences in view of financial module in ERP system which is based on different sub-components. To carry this research work HCI research methods, automated explicitly survey questionnaire method and focus group were adopted to gather users understanding in order to evaluate the selected application in ERP system. This study involved group of users from various corporate level industries in textile sector. It is the extended work as in previous only two industries were selected.
基金grateful for the financial support from the National Key R&D Program of China(2023YFB2407300).
文摘With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.
文摘Objective: It is in order to estimate the prevalence and incidence of HIV, the frequency of sexual risk behaviors, and perceptions of available resources to prevent and treat HIV among crack users in the San Salvador Metropolitan Area. Methods: We conducted a survey of 420 crack users by using respondent-driven sampling to measure demographic characteristics, the quantity and frequency of drug use, history of STIs, including HIV, and experiences with organizations which provide prevention and treatment of HIV. Each participant offered a free and voluntary HIV test and was asked permission to share the results of the test with the study. Bernoullian modeling was used to estimate the prevalence and incidence of HIV among heterosexual males in this population. Results: The estimated prevalence was 7% (95% CI: 2.3% -9.8%) among participants who agreed to take the test and share the results, and 4.9% (95% CI: 2.8% -7.8%) assuming that those who did not take the test or share results were seronegative. Participants reported a high frequency of sexual risk behaviors. In addition, participants were reported to have little knowledge of organizations to prevent or treat HIV/AIDS;58% had never taken an HIV test prior to survey administration. Conclusions: Crack users in San Salvador are at high risk for HIV acquisition. HIV prevention interventions are urgently needed, especially interventions increasing access to HIV testing and prevention.
文摘Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.
文摘The aim of the current study was to examine the prevalence of HIV, past six-month illicit drug use, and risk behaviors among a population of heavy drug users living in an urban setting. Although many studies investigate substance use, sex-risk behavior, and HIV by race and gender, no studies have examined these variables simultaneously. The current study seeks to fill this gap in the literature by exploring HIV prevalence among a predominantly heterosexual sample of recent substance users by injection drug use (IDU) status, race, and sex. Baseline data from the Baltimore site of the NEURO-HIV epidemiologic study was used in this study. This study examines neuropsychological and social-behavioral risk factors of HIV, hepatitis A, hepatitis B, and hepatitis C among both injection and non-injection drug users. Descriptive statistics and chi-square statistics were used in data analyses. Blood and urine samples were obtained to test for the presence of recent drug use, viral hepatitis, HIV, and other sexually transmitted diseases (STDs). Findings presented here have several important implications for HIV prevention and care among substance users. Intervention programs that incorporate substance use treatment in addition to HIV education, particularly with respect to substance use and sex risk behavior are imperative.
文摘To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator at temperatures of 380-440℃and strain rates of 0.05-1 s^(−1).The Johnson-Cook model,Hensel-Spittel model,strain-compensated Arrhenius model,and critical fracture strain model were established.Results show that through the evaluation of the models using the correlation coefficient(R)and the average absolute relative error,the strain-compensated Arrhenius model can represent the flow behavior of the alloy more accurately.Shear bands are more pronounced in the as-homogenized specimens,whereas dynamic recrystallization is predominantly observed in as-rolled specimens.Fracture morphology analysis reveals that a mixed fracture mechanism is prevalent in the as-homogenized specimen,whereas a ductile fracture mechanism is predominant in the as-rolled specimen.The processing maps indicate that the unstable region is reduced in the as-rolled specimens compared with that in the as-homogenized specimens.The optimal hot working windows for the as-homogenized and as-rolled specimens are determined as 410-440℃/0.14-1 s^(−1)and 380-400℃/0.05-0.29 s^(−1),respectively.
文摘The paper focuses on the habits of China Web users' language utilization behaviors in accessing the Web. It also seeks to make a general study on the basic nature of language phenomenon with regard to digital accessing. A questionnaire survey was formulated and distributed online for these research purposes. There were 1,267 responses collected. The data were analyzed with descriptive statistics, Chi-square testing and contingency table analyses. Results revealed the following findings. Tagging has already played an important role in Web2.0 communication for China's Web users. China users rely greatly on all kinds of taxonomies in browsing and have also an awareness of them in effective searching. These imply that the classified languages in digital environment may aid Chinese Web users in a more satisfying manner. Highly subject-specific words, especially those from authorized tools, yielded better results in searching. Chinese users have high recognition for related terms. As to the demographic aspect, there is little difference between different genders in the utilization of information retrieval languages. Age may constitute a variable element to a certain degree. Educational background has a complex effect on language utilizations in searching. These research findings characterize China Web users' behaviors in digital information accessing. They also can be potentially valuable for the modeling and further refinement of digital accessing services.
基金Supported by the Scientific Research Foundation of Liaoning Provincial Department of Education (No.LJKZ0139)。
文摘A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this work.Firstly, the classifier is integrated based on the dimension of predicted probability, and the pruning algorithm based on greedy forward search is obtained by combining the two indicators of accuracy and complementarity.Then the pruning algorithm is integrated into the Stacking ensemble method to establish a user online shopping behavior prediction model based on the probabilistic multi-dimensional selective ensemble method.Finally, the research method is compared with the prediction results of individual learners in ensemble learning and the Stacking ensemble method without pruning.The experimental results show that the proposed method can reduce the scale of integration, improve the prediction accuracy of the model, and predict the user's online purchase behavior.
