AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

The Intersection of Privacy by Design and Behavioral Economics: Nudging Users towards Privacy-Friendly Choices

This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

The clutch size,incubation behavior of Reeves’s Pheasant(Syrmaticus reevesii) and their responses to ambient temperature and precipitation

Weather conditions play a pivotal role in embryo development and parental incubation costs,potentially impacting the clutch size and incubation behavior of birds.Understanding these effects is crucial for bird conservation.Reeves’ s Pheasant(Syrmaticus reevesii) is a threatened species endemic to China,which is characterized by female-only incubation.However,there is a lack of information regarding the impact of weather conditions on clutch size and incubation behavior in this species.Using satellite tracking,we tracked 27 wild female Reeves’ s Pheasants from 2020 to 2023 in Hubei Province,China.We explored their clutch size and incubation behavior,as well as their responses to ambient temperature and precipitation.Clutch size averaged 7.75 ±1.36,had an association with average ambient temperature and average daily precipitation during the egglaying period,and was potentially linked to female breeding attempts.Throughout the incubation period,females took an average of 0.73 ±0.46 recesses every 24 h,with an average recess duration of 100.80 ±73.37 min and an average nest attendance of 92.98 ±5.27%.They showed a unimodal recess pattern in which nest departures peaked primarily between 13:00 and 16:00.Furthermore,females rarely left nests when daily precipitation was high.Recess duration and nest attendance were influenced by the interaction between daily mean ambient temperature and daily precipitation,as well as day of incubation.Additionally,there was a positive correlation between clutch size and recess duration.These results contribute valuable insights into the lifehistory features of this endangered species.

Interconnected microstructure and flexural behavior of Ti_(2)C-Ti composites with superior Young’s modulus

To enhance the Young’s modulus(E)and strength of titanium alloys,we designed titanium matrix composites with intercon-nected microstructure based on the Hashin-Shtrikman theory.According to the results,the in-situ reaction yielded an interconnected microstructure composed of Ti_(2)C particles when the Ti_(2)C content reached 50vol%.With widths of 10 and 230 nm,the intraparticle Ti lamellae in the prepared composite exhibited a bimodal size distribution due to precipitation and the unreacted Ti phase within the grown Ti_(2)C particles.The composites with interconnected microstructure attained superior properties,including E of 174.3 GPa and ultimate flexural strength of 1014 GPa.Compared with that of pure Ti,the E of the composite was increased by 55% due to the high Ti_(2)C content and interconnected microstructure.The outstanding strength resulted from the strong interfacial bonding,load-bearing capacity of interconnected Ti_(2)C particles,and bimodal intraparticle Ti lamellae,which minimized the average crack driving force.Interrupted flexural tests revealed preferential crack initiation along the{001}cleavage plane and grain boundary of Ti_(2)C in the region with the highest tensile stress.In addition,the propagation can be efficiently inhibited by interparticle Ti grains,which prevented the brittle fracture of the composites.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

Research on the Influence of Anchor Attributes on Consumers’Online Behaviors in Social E-Commerce Platforms:The Moderating Effect of Platform Contextual Factors

As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.

Research on the Influence of Family Education on Children’s Behavioral Habits

Under the background of the all-round deepening of quality education,the cultivation of comprehensive quality has become the main theme of contemporary education reform.Good behavior and habits are of great significance to children’s future learning,growth,and development.Through literature review and other methods,this paper analyzes the current situation of children’s family education and the influence of family education on the cultivation of children’s behavioral habits and provides some strategies for cultivating children’s good behavioral habits in family education.

Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics.The framework models the user behavior as sequences of events representing the user activities at such a network.The represented sequences are thenfitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users.Thus,the model can recognize frequencies of regular behavior to profile the user manner in the network.The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular behavior.The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network.Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including users.Therefore,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow.In contrast,the irregular patterns can trigger an alert for a potential cyber-attack.The framework has been fully described where the evaluation metrics have also been introduced.The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1.The paper has been concluded with pro-viding the potential directions for future improvements.

基于SAPAD-QFD的施工升降机驾驶室设计研究

目的 为提高施工升降机驾驶员工作效率,改善其工作体验,设计出满足驾驶员实际需求的施工升降机驾驶室。方法 以SAPAD理论和QFD理论为理论框架,梳理出用户需求及与其对应的设计特征,并在此基础上开展设计实践。首先,通过观察法和访谈法来获取驾驶员日常行为信息,根据SAPAD理论完成“行为-产品-意义”的映射分析,并借助聚类分析法获取用户需求;其次,利用AHP法对用户需求进行权重分析并计算权重值;最后,根据QFD理论构建施工升降机驾驶室的质量屋,将各项用户需求转化为设计特征,指导设计实践。结果 根据研究结果,提出一种符合驾驶员实际需求的施工升降机驾驶室设计方案。结论 SAPAD理论与QFD理论的综合应用,一方面可以挖掘出用户的核心需求,另一方面增加了设计方案的科学性,在优化施工升降机驾驶室用户体验的同时,也为其他工业产品设计提供了有价值的参考。

一种基于XGBoost的用户行为异常检测方法

伴随企业业务的不断扩增和电子化发展,企业自身数据和负载数据都开始暴增。然而,作为企业核心资产之一的内部数据,却面临着日益严峻的安全威胁。越来越多以周期长、频率低、隐蔽强为典型特征的非明显攻击绕过传统安全检测方法,对大量数据造成损毁。对此,根据用户、实体、行为关联,提出整合可以反映用户行为基线的各类数据,提取几种最能反映用户异常的基础特征,利用XGBoost的特征选择策略与FCM聚类算法结合,通过异常打分,定位异常风险最大的一批用户。结果显示,新提出的异常检测算法有着82%以上的准确率,以及86%以上的召回率。

基于特征分箱和K-Means算法的用户行为分析方法

针对网购用户所产生的购物行为进行分析,首先通过数据处理构建客户关系管理模型(RFM模型),在此模型的基础上采用特征分箱法和K-Means聚类两种方法对用户进行细分,并对2种模型结果进行比较分析,讨论二者的差异性和具体的应用范围和意义.其中,基于特征分箱法的RFM模型将变量转化到相似的尺度上并将变量离散化,使得用户分类标签更加清晰,也可依据各类标签分类出不同类型的用户.K-Means算法通过轮廓系数评估聚类算法质量以至于选取最优K值.本文实验分析结果可为运营商提供更加可靠直观的数据,使得运营商可以根据不同用户的不同行为进行市场细分,进而进行精准营销和服务设置.

电商直播中主播互动性、感知价值与用户参与行为——基于SOR理论的视角

随着社交媒体平台的快速发展,直播销售已成为电子商务的重要部分。然而,电商直播的特性在于实时互动,如何通过有效的互动获得更好的直播效果,特别是主播互动类型如何影响用户参与行为的机制尚不清晰。针对这一问题,本研究基于SOR模型,运用Python爬虫技术获取真实的直播动态数据,探究了任务型和关系型互动对不同用户参与行为(在线观看、点赞与评论)的影响,并研究了感知价值(包括感知信息价值与感知娱乐价值)的中介作用。研究结果显示,任务型互动对于用户评论有显著影响,但对用户在线观看和点赞影响不显著;相比之下,关系型互动对所有因变量均有显著影响。此外,作为中介变量的感知信息价值和感知娱乐价值,在主播互动和用户参与行为之间发挥了部分的中介作用。本研究扩大了SOR理论使用范围,丰富了电商直播中用户参与行为的研究,深化了对电商直播中感知价值的理解,研究结论对提升主播互动策略及优化直播效果具有一定的指导意义。

基于SEM模型的新能源网约车用户充电行为关联因素分析

针对新能源网约车用户的充电行为与个人属性、车辆属性、用户满意度等多重因素之间的复杂互动问题,运用结构方程模型(Structural Equation Modeling,SEM)详细探究各定量因素之间的影响作用,并采用关联规则挖掘技术结合定类数据和定量数据分析405份样本,得到用户与充电行为相关决策的具体表现形式.研究结果表明:每增加40 km的行驶里程,充电次数将平均增加0.407次;用户个人和车辆的属性反映出不同群体的充电决策差异,路径系数分别为0.336、0.159;排队时间阈值每增加1个单位,到站时间阈值平均增加0.231个单位,说明不同场景下用户可接受的时间阈值具有一致性,通过提供针对性服务可以满足不同用户的需求;新能源网约车的预估里程准确度与用户对充电设施的满意度正相关,正相关结果的统计显著性水平P值为0.273,反映了准确预估里程能力在促进新能源网约车普及中的重要性;用户对价格和服务的多样性说明市场的灵活定价机制具有潜在价值;时间和金钱在决策中的复杂权衡,反映了行为背后的复杂性和多元性.这些结果揭示了不同因素之间的复杂关联,为未来相关领域的研究提供了理论基础和实证依据.

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

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.

基于CPSS视角的需求响应能力评估综述

对信息—物理—社会系统与需求响应评估的结合进行综述。首先对信息物理系统的历史和框架进行分析。接着总结了现有的需求响应研究现状,包括需求响应潜力评估的意义、分类和评估方法。对于需求响应能力评估的数据来源进行总结,包括问卷调查和运行数据采集。在信息—物理—社会系统与需求响应结合方面,分别分析需求响应的物理域、信息域和社会域基础,介绍相应的建模方法和研究内容。最后,对未来的研究展望进行讨论,包括市场考核机制、快速仿真与建模技术以及综合能源下的需求响应管理。

基于拓展计划行为理论的电子竞技用户行为SEM模型构建及分析

为进一步厘清电竞用户行为的形成过程,给电竞产业持续健康发展提供一定的决策参考,基于拓展计划行为理论和使用与满足理论对我国电竞用户行为进行SEM模型的构建及分析。结论表明:替代成就、审美享受、戏剧体验、社会互动四类需求动机因素均能正向影响电竞用户的行为态度。其中,社会互动的影响最为显著;电竞用户的行为态度、主观规范和知觉行为控制既能通过电竞参与意向的中介效应正向影响电竞参与行为,也能直接产生正向影响,且主观规范对电竞参与意向的影响最为显著,知觉行为控制对电竞参与行为的直接影响最为显著;电竞参与意向在以上三个自变量与电竞参与行为之间均起到了不完全中介作用。

Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis

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.

Free Riding Inhibition Mechanism Based on User Behavior in P2P File-Sharing System

In order to inhibit Free Riding in Peer-toPeer(P2P) file-sharing systems,the Free Riding Inhibition Mechanism Based on User Behavior(IMBUB) is proposed.IMBUB considers the regularity of user behavior and models user behavior by analyzing many definitions and formulas.In IMBUB,Bandwidth Allocated Ratio,Incentive Mechanism Based on User Online Time,Double Reward Mechanism,Incentive Mechanism of Sharing for Permission and Inhibition Mechanism of White-washing Behavior are put forward to inhibit Free Riding and encourage user sharing.A P2P file system BITShare is designed and realized under the conditions of a campus network environment.The test results show that BITShare's Query Hit Ratio has a significant increase from 22% to 99%,and the sharing process in BITShare is very optimistic.Most users opt to use online time to exchange service quality instead of white-washing behavior,and the real white-ishing ratio in BITShare is lower than 1%.We confirm that IMBUB can effectively inhibit Free Riding behavior in P2P file-sharing systems.

Behavioral Intention to Continue Using a Library Mobile App

To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.