HPBR:host packet behavior ranking model used in worm detection by XIAO Feng-tao1, HU Hua-ping1, LIU Bo1, CHEN Xin1

“…Based on defining the access behavior model of a user,a model named HPBR（host packet behavior ranking） was presented,which ranked the network packets from and to the host.Hierarchical accumulative ranking procedure,performance analysis and optimization procedure in HPBR were described.At last,an example on using HPBR is intro-duced.…”
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Feasibility assessment of a pharmaco-kinetic behavior based yield model of salivary cotinine among tobacco users by E.C. Hensel, R.J. Robinson

“…The study expands upon a previously validated behavior-based yield (BBY) which quantified aerosol emissions from a Juul ecig as a function of user behavior. …”
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A prediction model of massive 5G network users’ revisit behavior based on telecom big data by Yudi SUN

“…Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.…”
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Analyzing information sharing behaviors during stance formation on COVID-19 vaccination among Japanese Twitter users. by Sho Cho, Shohei Hisamitsu, Hongshan Jin, Masashi Toyoda, Naoki Yoshinaga

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User avoidance behavior in pharmaceutical e-commerce intelligent customer service: a stressor-strain-outcome perspective by Jing Jia, Lu Chen, Chengzhen Wu, Manling Xiao

“…IntroductionThis study explores the implementation of Intelligent Customer Service (ICS) in pharmaceutical e-commerce, aiming to enhance user acceptance and service efficiency while addressing the psychological factors influencing user behavior. …”
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Design and Implementation of DPI-Based User's Bebavior Perception System in LTE Network by Jian Wang, Zhizhong Zhang, Yunlong Luo

“…Aiming at the traditional monitoring system with poor user perception and insufficient statistical capacity, the proposed system achieved user's behavior refined analysis which combined protocol decoding and services call/transaction detail records（xDR）synthesis method with the advantages of current prevailing internet behavior analysis technologies—deep packet inspection（DPI）technique and focused crawler technology. …”
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An approach to boost adherence to self-data reporting in mHealth applications for users without specific health conditions by Maria Aguiar, Ander Cejudo, Gorka Epelde, Deisy Chaves, Maria Trujillo, Garazi Artola, Unai Ayala, Roberto Bilbao, Itziar Tueros

“…As a secondary objective, this work aims to explore the differences in the adherence when considering a longer study duration and make a comparative analysis of the gamification effect. Methods In this study, the effectiveness of various behavior change techniques strategies is evaluated through the analysis of two user groups. …”
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Performance analysis of multi-user detection of 5G overloaded sporadic system by Guoyu MA, Bo AI, Xian’an HU, Xiaobing JIANG

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User Behavior Traffi c Replay Technology for Space-IntegratedGround Information Network Based on Profi le Analyzing by Qingsong XUE, Xiaofeng WANG

“…Faced the needs of user behavior emulation of the space-integrated-ground information network with complex behaviors and diverse protocols, an architecture based on cloud computing and virtualization technologies for replaying user behavior traffi c in the spaceintegrated-ground information network was designed.Furthermore, a user behavior traffi c automatic analysis and traffi c profi le generation technology based on deep packet inspection, and a user behavior traffi c replay technology of the space-integrated-ground information network based on traffi c profi le were researched, to achieve unifi ed, automated, and realistic simulation of miscellaneous kinds of user behavior traffi c.An emulation scenario of a space-integrated-ground information network with 6 space-based backbone network satellites and 66 space-based access network satellites was constructed.The experimental verifi cation of protocol traffi c profi le generation and traffi c replay of the broadband user behavior and narrow-band user behavior showed that the emulation of diversifi ed user interaction behaviors could provide eff ective support for the emulation of the space-integrated-ground information network.…”
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Evaluation System of Mobile Internet User Experience Based on Big Data Analysis by Sen Chen, Chao Chen, Xiaoyong Zhang, Lei Wang, Peng Sun

“…Following the evolution of mobile network and the development of smart devices,the mobile internet services emerge rapidly with fierce competitions,in which,improving the users' perception is quite important to win the victory in the competition.A users' experience evaluation mechanism for the main stream mobile internet services was developed and a set of KQI（key quality indicator）with authentication,inspection,and calculation methods were designed.The evaluation system of mobile internet user experience employs the big data analysis to restore the online behaviors of users,and calculates the KQI values from different dimensions of the indexes.The system realizes the comprehensive,in time,and accurate evaluation for the user experience,and thus provides strong value-added supports to the network operators and mobile internet companies.…”
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Topic-oriented measurement of microblogging network by Wei LIU, Li-hong WANG, Rui-guang LI

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User filtering based campus WLAN user clustering method by Yi-hong QIU, Ting-juan YAO, Feng-lin QIN, Lian-sheng GE

“…With the widespread of smart terminals such as smart phones and smart pads,using MAC address as user identification in campus wireless local area network (WLAN) user clustering research cannot exactly represent user behavior.An user filtering based user clustering is proposed.This method filters users’ behavior data by their degree of activeness,and then further conducts clustering analysis of campus WLAN user behavior.The experimental result verifies the effectiveness of the proposed method.…”
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Analysis on the Behavior Characteristics of NOx in Blast Furnace Gas by Peng HOU, Changliang ZHEN, Zibing WANG, Jie XU, Long HE, Xing ZHANG, Huan LIU

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Evaluating mobile apps for sun protection: content analysis and user preferences in a two-part study by Angela M. Rodrigues, Faye L. Doughty, Caroline Charlton, Sarah Woodhouse, Elizabeth Sillence

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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. …”
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Analysis of Factors Affecting the Formation of Current Patterns of Behavior in Behavioral Setting (Case Study: Azadi Square in Kermanshah) by Mostafa Shahinifar

“…Each interview lasted an average of approximately 12 minutes. Analysis of behavioral pattern maps in the area revealed a mismatch between the form and function of Azadi Square. …”
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Flow-based electronic resource network behavior analysis practice by HE Haitao, LI Enlei, ZHAO Qiong, YAO Renlong, WEI Yujun

“…By finely classifying and statistically analyzing traffic, it accurately assessed the usage behaviors of different types of users. First, a statistical analysis of traffic related to electronic resource access was performed on the campus network.Then, traffic data related to accessing Sci-Hub was constructed and analyzed. …”
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To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention by Aniek M. Siezenga, Esther C.A. Mertens, Jean-Louis van Gelder

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Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework by Liang Yu, Yuanshen Hong, Hua Lin, Xu Jiang, Ziming Song

“…Abstract This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. …”
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Barriers and Facilitators to User Engagement and Moderation for Web-Based Peer Support Among Young People: Qualitative Study Using the Behavior Change Wheel Framework by Ananya Ananya, Janina Tuuli, Rachel Perowne, Leslie Morrison Gutman

“…Themes were coded using the Capability, Opportunity, Motivation, and Behavior (COM-B) model and the TDF. The BCW tools were then used to identify relevant behavior change techniques to improve user engagement in, and moderation of, the service. …”
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