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Journal articles on the topic 'User activity'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Zhang, Tongda, Xiao Sun, Yueting Chai, and Hamid Aghajan. "Human Computer Interaction Activity Based User Identification." International Journal of Machine Learning and Computing 4, no. 4 (2014): 354–58. http://dx.doi.org/10.7763/ijmlc.2014.v4.436.

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2

Santosa, Paulus Insap, Kwok Kee Wei, and Hock Chuan Chan. "User involvement and user satisfaction with information-seeking activity." European Journal of Information Systems 14, no. 4 (December 2005): 361–70. http://dx.doi.org/10.1057/palgrave.ejis.3000545.

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3

Sanklecha, Ms Sakshi, Mr Darshit Deotale, Ms Jyoti Yadav, Ms Dipti Mishra, and Prof V. P. Yadav. "User Activity Monitoring System / SPYWARE." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1382–89. http://dx.doi.org/10.22214/ijraset.2022.40854.

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Abstract: User activity monitoring (UAM) do the task of monitoring and recording of user actions, in the field of information security or cyber security. Including the use of applications, windows opened, system commands executed, checkboxes clicked, text entered/edited, URLs visited and nearly every everything on-screen event to protect data by ensuring that employees and contractors are performing their assigned tasks and not posing any risk to the organization are all captured and recorded in the system by the UAMS. Video-like playback of user activity and process the videos into user activity logs that keep step-by-step records of user actions that can be searched and analysed is delivered by the User Activity Monitoring System to investigate any out-of-scope activities. Creating a visual record of potentially hazardous user activity are all involved in Visual Forensics. Each user action is logged, and recorded. Once a user session is completed, UAM creates a written record as well as visual record. It can be screen- captures/screenshots or video of exactly what kind of activity a user has done. This written record of our UAMS differs from that of a SIEM or logging tool, because it captures data at a user-level not at a system level –providing plain English logs rather than System Logs (which is originally created for debugging purposes). These textual logs can be used to pair with the corresponding screen- captures/screenshots or video summaries. Using these corresponding logs and images, the visual forensics component of UAM allows for organizations to search for exact user activity in case of a security incident. In the case of a security threat, i.e. a data breach or data leak, visual Forensics are used to show exactly what kind of activity a user has done, and everything leading to the incident. Visual Forensics can also be used to provide evidence to any law enforcement that investigate the intrusion or leak.

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4

Herzberg, Rafael. "BRAZIL: Energy End–user Activity." Strategic Planning for Energy and the Environment 21, no. 4 (April 2002): 74–79. http://dx.doi.org/10.1080/10485230209509598.

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5

Herzberg, Rafael. "BRAZIL Energy End-user Activity." Strategic Planning for Energy and the Environment 21, no. 4 (April 1, 2002): 74–79. http://dx.doi.org/10.1092/8k2g-8k25-rb8u-pge9.

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6

Abitbol, Jacob Levy, and Alfredo J. Morales. "Socioeconomic Patterns of Twitter User Activity." Entropy 23, no. 6 (June 19, 2021): 780. http://dx.doi.org/10.3390/e23060780.

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Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual’s income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.

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7

Yin, Jie, Qiang Yang, Dou Shen, and Ze-Nian Li. "Activity recognition via user-trace segmentation." ACM Transactions on Sensor Networks 4, no. 4 (August 2008): 1–34. http://dx.doi.org/10.1145/1387663.1387665.

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8

Mortazavi, Bobak J., Mohammad Pourhomayoun, Sunghoon Ivan Lee, Suneil Nyamathi, Brandon Wu, and Majid Sarrafzadeh. "User-optimized activity recognition for exergaming." Pervasive and Mobile Computing 26 (February 2016): 3–16. http://dx.doi.org/10.1016/j.pmcj.2015.11.001.

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9

Shah, Syed W., and Salil S. Kanhere. "Smart user identification using cardiopulmonary activity." Pervasive and Mobile Computing 58 (August 2019): 101024. http://dx.doi.org/10.1016/j.pmcj.2019.05.005.

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10

Di Lascio, Luigi, Antonio Gisolfi, and Vincenzo Loia. "Uncertainty processing in user-modeling activity." Information Sciences 106, no. 1-2 (April 1998): 25–47. http://dx.doi.org/10.1016/s0020-0255(97)10009-3.

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11

Cole, Michael J., Chathra Hendahewa, Nicholas J. Belkin, and Chirag Shah. "User Activity Patterns During Information Search." ACM Transactions on Information Systems 33, no. 1 (February 17, 2015): 1–39. http://dx.doi.org/10.1145/2699656.

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12

De Bock, Yannick, Andres Auquilla, Ann Nowé, and Joost R. Duflou. "Nonparametric user activity modelling and prediction." User Modeling and User-Adapted Interaction 30, no. 5 (March 14, 2020): 803–31. http://dx.doi.org/10.1007/s11257-020-09259-3.

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13

Vermol, Verly Veto, Shahriman Zainal Abidin, Rusmadiah Anwar, and Oskar Hasdinor Hassan. "Designer Activity Experience: Blind User-Designer Activity Model in Knowing Product Influence Through Blind User Perspective." Advanced Science Letters 23, no. 11 (November 1, 2017): 10815–21. http://dx.doi.org/10.1166/asl.2017.10160.

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14

Zeng, Jianping, Shiyong Zhang, and Chengrong Wu. "A framework for WWW user activity analysis based on user interest." Knowledge-Based Systems 21, no. 8 (December 2008): 905–10. http://dx.doi.org/10.1016/j.knosys.2008.03.049.

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15

Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. "Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 1 (January 2015): 129–42. http://dx.doi.org/10.1109/tsmc.2014.2327053.

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16

Belikova, S. A., Y. I. Rogozov, A. S. Sviridov, O. V. Shevchenko, A. V. Egorov, and L. V. Koltunova. "Approach to user interfaces development based on semantic model of user activity." Journal of Physics: Conference Series 1457 (January 2020): 012012. http://dx.doi.org/10.1088/1742-6596/1457/1/012012.

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17

Wardhanie, Ayouvi Poerna, and Endra Rahmawati. "Pengenalan dan Penerapan User Interface and User Experience Design for Beginners." Batara Wisnu : Indonesian Journal of Community Services 2, no. 3 (December 12, 2022): 536–44. http://dx.doi.org/10.53363/bw.v2i3.129.

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One indicator of the success of a platform so as it can attract customers to visit is a user-friendly and creative application design, in the area of information technology known as User Interface and User Experience. Therefore, to improve the ability, both intellectually and practically, a community service activity is carried out, especially for the younger generation in the form of introducing and implementing UI/UX designs for beginners so that they can compete with today's business and technology world.This activity, which is packaged in the form of a webinar, focuses on understanding the basics of UI/UX design and its methodology as well as how to apply the tools, namely Figma, to design a website. Participants in this workshop amounted to 166 people spread across various parts of Indonesia, with an online learning method using Zoom for two hours. As a result of this activity, participants were able to follow the material given by the two speakers well, but in terms of time efficiency, there were delays due to technical problems. Participants' input for the material in the next activity is more focused on graphic design, coding, big data and cyber security

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18

Bodker, Susanne. "A Human Activity Approach to User Interfaces." Human–Computer Interaction 4, no. 3 (September 1989): 171–95. http://dx.doi.org/10.1207/s15327051hci0403_1.

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19

Trevisiol, Michele. "Exploiting Implicit User Activity for Media Recommendation." ACM SIGIR Forum 49, no. 1 (June 23, 2015): 70. http://dx.doi.org/10.1145/2795403.2795421.

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20

Walters, Richard. "Managing privileged user activity in the datacentre." Network Security 2010, no. 11 (November 2010): 6–10. http://dx.doi.org/10.1016/s1353-4858(10)70134-3.

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21

Shye, Alex, Benjamin Scholbrock, Gokhan Memik, and Peter A. Dinda. "Characterizing and modeling user activity on smartphones." ACM SIGMETRICS Performance Evaluation Review 38, no. 1 (June 12, 2010): 375–76. http://dx.doi.org/10.1145/1811099.1811094.

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22

Zhu, Hao, and Georgios B. Giannakis. "Exploiting Sparse User Activity in Multiuser Detection." IEEE Transactions on Communications 59, no. 2 (February 2011): 454–65. http://dx.doi.org/10.1109/tcomm.2011.121410.090570.

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23

Mitra, U., and H. V. Poor. "Activity detection in a multi-user environment." Wireless Personal Communications 3, no. 1-2 (1996): 149–74. http://dx.doi.org/10.1007/bf00333928.

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24

Angelova, Maia, Jeremy Ellman, Helen Gibson, Paul Oman, Sutharshan Rajasegarar, and Ye Zhu. "User Activity Pattern Analysis in Telecare Data." IEEE Access 6 (2018): 33306–17. http://dx.doi.org/10.1109/access.2018.2847294.

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25

Zaytseva, Anna, and Olga Shuvalova. "Changing Emphases in Innovation Activity: User Innovation." Foresight-Russia 5, no. 2 (June 30, 2011): 16–32. http://dx.doi.org/10.17323/1995-459x.2011.2.16.32.

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26

Hwang, Myunggwon, Do-Heon Jeong, Jinhyung Kim, Sa-Kwang Song, and Hanmin Jung. "Activity inference for constructing user intention model." Computer Science and Information Systems 10, no. 2 (2013): 767–78. http://dx.doi.org/10.2298/csis121101033h.

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User intention modeling is a key component for providing appropriate services within ubiquitous and pervasive computing environments. Intention modeling should be concentrated on inferring user activities based on the objects a user approaches or touches. In order to support this kind of modeling, we propose the creation of object-activity pairs based on relatedness in a general domain. In this paper, we show our method for achieving this and evaluate its effectiveness.

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27

Vazquez, Manuel A., and Joaquin Miguez. "User Activity Tracking in DS-CDMA Systems." IEEE Transactions on Vehicular Technology 62, no. 7 (September 2013): 3188–203. http://dx.doi.org/10.1109/tvt.2013.2251024.

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28

Li, Qimeng, Raffaele Gravina, Ye Li, Saeed H. Alsamhi, Fangmin Sun, and Giancarlo Fortino. "Multi-user activity recognition: Challenges and opportunities." Information Fusion 63 (November 2020): 121–35. http://dx.doi.org/10.1016/j.inffus.2020.06.004.

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29

König-Ries, Birgitta, Michael Klein, and Tobias Breyer. "Activity-Based User Modeling in Wireless Networks." Mobile Networks and Applications 11, no. 2 (March 31, 2006): 267–77. http://dx.doi.org/10.1007/s11036-006-4478-4.

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30

Cuomo, Donna L., and Charles D. Bowen. "Stages of User Activity Model as a Basis for User-System Interface Evaluations." Proceedings of the Human Factors Society Annual Meeting 36, no. 16 (October 1992): 1254–58. http://dx.doi.org/10.1177/154193129203601616.

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This paper discusses the results of the first phase of a research project concerned with developing methods and measures of user-system interface effectiveness for command and control systems with graphical, direct manipulation style interfaces. Due to the increased use of prototyping user interfaces during concept definition and demonstration/validation phases, the opportunity exists for human factors engineers to apply evaluation methodologies early enough in the life cycle to make an impact on system design. Understanding and improving user-system interface (USI) evaluation techniques is critical to this process. In 1986, Norman proposed a descriptive “stages of user activity” model of human-computer interaction. Hutchins, Hollin, and Norman (1986) proposed concepts of measures based on the model which would assess the directness of the engagements between the user and the interface at each stage of the model. This first phase of our research program involved applying three USI evaluation techniques to a single interface, and assessing which, if any, provided information on the directness of engagement at each stage of Norman's model. We also classified the problem types identified according to the Smith and Mosier (1986) functional areas. The three techniques used were cognitive walkthrough, heuristic evaluation, and guidelines. It was found that the cognitive walkthrough method applied almost exclusively to the action specification stage. The guidelines were applicable to more of the stages evaluated but all the techniques were weak in measuring semantic distance and all of the stages on the evaluation side of the HCI activity cycle. Improvements to existing or new techniques are required for evaluating the directness of engagement for graphical, direct manipulation style interfaces.

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31

Liu, Xiufeng, Yanyan Yang, Rongling Li, and Per Sieverts Nielsen. "A Stochastic Model for Residential User Activity Simulation." Energies 12, no. 17 (August 28, 2019): 3326. http://dx.doi.org/10.3390/en12173326.

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User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

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32

Nasution, Muhammad Romi, Yudi Prayudi, and Ahmad Luthfi. "Investigating Social Media User Activity on Android Smartphone." International Journal of Computer Applications 183, no. 48 (January 18, 2022): 46–52. http://dx.doi.org/10.5120/ijca2022921890.

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33

Jeon, Myung Joong, and Young Tack Park. "Robust User Activity Recognition using Smartphone Accelerometer Sensors." KIPS Transactions on Software and Data Engineering 2, no. 9 (September 30, 2013): 629–42. http://dx.doi.org/10.3745/ktsde.2013.2.9.629.

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34

Lee, Junho, and Seung-Hwan Lee. "Low dimensional multiuser detection exploiting low user activity." Journal of Communications and Networks 15, no. 3 (June 2013): 283–91. http://dx.doi.org/10.1109/jcn.2013.000051.

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35

Yu, Chen, Yang Liu, Dezhong Yao, Laurence T. Yang, Hai Jin, Hanhua Chen, and Qiang Ding. "Modeling User Activity Patterns for Next-Place Prediction." IEEE Systems Journal 11, no. 2 (June 2017): 1060–71. http://dx.doi.org/10.1109/jsyst.2015.2445919.

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36

Chmiel, Anna, Pawel Sobkowicz, Julian Sienkiewicz, Georgios Paltoglou, Kevan Buckley, Mike Thelwall, and Janusz A. Hołyst. "Negative emotions boost user activity at BBC forum." Physica A: Statistical Mechanics and its Applications 390, no. 16 (August 2011): 2936–44. http://dx.doi.org/10.1016/j.physa.2011.03.040.

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37

Hayward, Mark, Rosalie Hughes, Debbie Southwood, Kathryn Pearce, and Nan Holmes. "User involvement in placement activity: The full monty." Clinical Psychology Forum 1, no. 167 (November 2006): 10–13. http://dx.doi.org/10.53841/bpscpf.2006.1.167.10.

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This article utilises multiple perspectives to explore a trainee’s experience of working within a service user organisation. In order to emphasise the relational nature of this experience, where possible, contributors are referred to by their first names rather than their role.

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38

Weber, Ingmar, and Venkata Rama Kiran Garimella. "Visualizing User-Defined, Discriminative Geo-Temporal Twitter Activity." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (May 16, 2014): 656–57. http://dx.doi.org/10.1609/icwsm.v8i1.14496.

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We present a system that visualizes geo-temporal Twitter activity. The distinguishing features our system offers include, (i) a large degree of user freedom in specifying the subset of data to visualize and (ii) a focus on *discriminative* patterns rather than high volume patterns. Tweets with precise GPS co-ordinates are assigned to geographical cells and grouped by (i) tweet language, (ii) tweet topic, (iii) day of week, and (iv) time of day. The spatial resolutions of the cells is determined in a data-driven manner using quad-trees and recursive splitting. The user can then choose to see data for, say, English tweets on weekend evenings for the topic "party". This system has been implemented for 1.8 million geo-tagged tweets from Qatar (http://qtr.qcri.org/) and for 4.8 million geo-tagged tweets from New York City (http://nyc.qcri.org/) and can be easily extended to other cities/countries.

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39

Muter, Paul, John J. Furedy, Alex Vincent, and Ted Pelcowitz. "User-hostile systems and patterns of psychophysiological activity." Computers in Human Behavior 9, no. 1 (March 1993): 105–11. http://dx.doi.org/10.1016/0747-5632(93)90025-n.

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40

Sciamanna, C. "User Attitudes toward a Physical Activity Promotion Website." Preventive Medicine 35, no. 6 (December 2002): 612–15. http://dx.doi.org/10.1006/pmed.2002.1103.

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41

Luo, Wen Hua. "Analysis of User Activity Based on Registry in RAM." Applied Mechanics and Materials 278-280 (January 2013): 1787–90. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1787.

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As important evidences and clue sources in computer crime investigation, the information of user activity plays an important role in the aspect of revealing detail of offender’s operation. The specific keys of registry in RAM are related to specific user activity. The structures of registry in RAM are different from in disk, especially in the aspect of cell index translation. Based on analysis of data structure for registry in RAM, this paper introduces the technology of cell index translation in detail. Also summarizes the keys closely related to user activity, and illustrates the method of analysis of user activity based on registry in RAM with real case. The method is proved to be accurate and efficient in real work of digital investigation.

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42

Parwez, Md Salik, Danda B. Rawat, and Moses Garuba. "Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network." IEEE Transactions on Industrial Informatics 13, no. 4 (August 2017): 2058–65. http://dx.doi.org/10.1109/tii.2017.2650206.

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43

Shen, Weilin, Qiping Shen, and Quanbin Sun. "Building Information Modeling-based user activity simulation and evaluation method for improving designer–user communications." Automation in Construction 21 (January 2012): 148–60. http://dx.doi.org/10.1016/j.autcon.2011.05.022.

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44

Gardner, David W., Christian B. Redd, John C. Cagle, Brian J. Hafner, and Joan E. Sanders. "Monitoring Prosthesis User Activity and Doffing Using an Activity Monitor and Proximity Sensors." Journal of Prosthetics and Orthotics 28, no. 2 (April 2016): 68–77. http://dx.doi.org/10.1097/jpo.0000000000000093.

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45

Abdul Rehman Baloch, Kamran Taj Pathan, Azhar Ali Shah, Mujeeb-ur-Rehman Jamali, and Muhammad Ali Baloch. "User-Centric Context-Aware Location-Based Service for ATM’s Users." VAWKUM Transactions on Computer Sciences 11, no. 2 (December 6, 2023): 60–69. http://dx.doi.org/10.21015/vtcs.v11i2.1627.

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The article discusses a context-aware system designed to help Automated Teller Machine (ATM) users quickly locate a working ATM with cash. Many people rely on ATMs for quick cash withdrawals, but often waste time searching for a working machine. The proposed system takes into account the user’s environmental context, such as their activity, the availability of cash in the ATM, the on/off status of the machine, and the presence of a line or crowd at the ATM booth. The objective of the system is to enhance the ATM locator according to the user’s specific needs, utilizing advanced features to recommend the best option for ATM customers based on their current situation. This user-centric approach aims to provide a more efficient and effective system for ATM users.

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46

Liao, Xiangwen, Lingying Zhang, Jingjing Wei, Dingda Yang, and Guolong Chen. "Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach." Wireless Communications and Mobile Computing 2018 (October 3, 2018): 1–11. http://dx.doi.org/10.1155/2018/9434239.

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User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

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47

Lazarov, Andon D., and Petia Petrova. "Modelling Activity of a Malicious User in Computer Networks." Cybernetics and Information Technologies 22, no. 2 (June 1, 2022): 86–95. http://dx.doi.org/10.2478/cait-2022-0018.

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Abstract In the present study, an extended classification of Internet users penetrating in computer networks and a definition of the motivation as a psychological and emotional state and main prerequisites for modelling of network intruder’s activity are suggested. A mathematical model as a quadratic function of malicious individual’s behavior and impact on the computer network based on three quantified factors, motivation, satisfaction and system protection is developed. Numerical simulation experiments of the unauthorized access and its effect onto the computer network are carried out. The obtained results are graphically illustrated and discussed.

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48

Urbaniak, Rafal, Michał Ptaszyński, Patrycja Tempska, Gniewosz Leliwa, Maciej Brochocki, and Michał Wroczyński. "Personal attacks decrease user activity in social networking platforms." Computers in Human Behavior 126 (January 2022): 106972. http://dx.doi.org/10.1016/j.chb.2021.106972.

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49

Dhinesh Kumar, P., and S. Anusuya. "Identifying User Personality in Facebook Using their Activity Data." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 89–93. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1943.

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The facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modelling user’s behaviour. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modelling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be presented. Gaining insight in a web user’s personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user’s personality traits from their Facebook status updates. Even with a small set of training examples we can outperform the majority class baseline algorithm. Furthermore, the results are improved by adding training examples from another source. This is an interesting result because it indicates that personality trait recognition generalises across social media platforms.

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50

Rajabi, Maryam, and Elham Shrifian. "User activity impact assessments in a sustainable public space:." International Review for Spatial Planning and Sustainable Development 10, no. 2 (April 15, 2022): 111–30. http://dx.doi.org/10.14246/irspsd.10.2_111.

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