As we become increasingly dependent on our computers and spending a major part of our day interacting with these machines, it is becoming important for lifeloggers and human-computer interaction (HCI) researchers to capture this aspect of our life. In this paper, we present LoggerMan, a comprehensive logging tool to capture many aspects of our computer usage. It also comes with reporting capabilities to give insights to the data owner about his/her computer usage. By this work, we aim to fill the current lack of logging software in this domain, which would help us and other researchers as well to build data sets for HCI experiments and also to better understand computer usage patterns. Our tool is published online (loggerman.org) to be used freely by the community

Albatal, Rami

Gurrin, Cathal

Hinbarji, Zaher

O'Connor, Noel E.

Name not available

LoggerMan, A Comprehensive Logging andVisualization Tool to Capture Computer UsageZaher Hinbarji, Rami Albatal, Noel O’Connor, and Cathal GurrinInsight Centre for Data Analytics,Dublin City Universityzaher.hinbarji@insight-centre.orghttps://www.insight-centre.orgAbstract. As we become increasingly dependent on our computers andspending a major part of our day interacting with these machines, itis becoming important for lifeloggers and human-computer interaction(HCI) researchers to capture this aspect of our life. In this paper, wepresent LoggerMan, a comprehensive logging tool to capture many as-pects of our computer usage. It also comes with reporting capabilities togive insights to the data owner about his/her computer usage. By thiswork, we aim to fill the current lack of logging software in this domain,which would help us and other researchers as well to build data sets forHCI experiments and also to better understand computer usage patterns.Our tool is published online 1 to be used freely by the community.Keywords: logging tool, computer usage, lifelogging, human-computerinteraction, user modeling1 IntroductionLifelogging is seen as the process of tracking personal data generated by ourown behavioral activities. While most of the related work on lifelogging is fo-cused on gathering and analysing visual and physical activity data, in this workwe take this further by including HCI tracking and analysis. In our modernsociety computers have become a core part of our professional and personal en-vironments, which makes capturing HCI activities a rich source of informationfor better insights into our life. Capturing HCI data is the first step toward sev-eral applications, ranging from simple automatic memory/diary generation, tothe discovery of user’s interests, skills, preferences and mood. Personalisation,profiling and security applications are common examples of how such lifelog datacan be utilised. We believe that the passive continuous tracking of HCI can revealimportant information about the user, which can be used to perform advancedanalytics and to build various personalised services. In this work, we presenta comprehensive logging tool to capture a wide range of our human-computerinteractions.1 http://loggerman.org/2 LoggerMan, A Comprehensive Logging Tool to Capture Computer Usage2 BackgroundAlthough one can easily find a commercial spyware/surveillance software pro-viding the ability to monitor and record the computer activities, it is not easyto find dedicated and comprehensive tools for logging computer usage for analy-sis purposes. In [1], authors discussed using surveillance software for conductingHCI experiments. They described several issues related to time stamps, mouseclicks, web usage and some other missing functionality in such software. Thecurrent available software is designed to track interaction with one application.For example, WOSIT [2] can be used to observe user actions on one application’sUI in UNIX systems. OWL[3] is another application-specific logging tool thattracks Microsoft Word usage.Several works have utilised HCI logging for various applications. For exam-ple, the use of keystroke logging tools in the domain of cognitive writing researchhas created new possibilities by providing detailed information about the writingprocess that was not accessible previously. Particularly, key-loggers are utilisedin studies on writing processes, description of writing strategies, children writingdevelopment, writing and spelling difficulties analysis [4] [5] [6]. Inputlog is anexample of such logging tool [7]. Keyboard logging also can be integrated in edu-cational fields for programming and typing skills [8]. In addition, detailed timingof keystrokes can be employed for security purposes [9], [10] and for stress [11]and emotion detection [12]. Screenshots are another example of possible loggingtechnique that can be used to capture and analyse the content consumed or pro-duced by the user. Screenshots were applied for search and task automation[13],and for building user friendly help manuals and demonstrations [14].Furthermore, intrusion detection and authentication are common applica-tions based on HCI logging. In [15], mouse curves are logged and used as asignature to authenticate users. Several mouse usage features are used to builduser profile [16] [17]. Log files, resource and command usage are used to profileusers behaviour [18] [19]. In [20], user identification was explored via logging var-ious GUI interactions such as windows switching frequency, time between newwindows and number of opened windows.Motivated by the beneficial potential and applications of HCI logging, andby the lack of generic and comprehensive HCI tracking and visualisation tools,we present LoggerMan a generic, passive and application-independent loggingsolution for researchers and lifeloggers. LoggerMan provides a robust tool toassist data gathering, visualisation and analytics for research community.3 LoggerMan OverviewLoggerMan helps researchers and lifeloggers to collect interaction data producedduring normal computer usage. The main goal of LoggerMan is to work passivelyin the background, intercept usage events and store them for later analysis. Itgathers wide range of keyboard, mouse and UI actions. LoggerMan is publishedonline (LoggerMan.org) for interested researchers/lifeloggers. In the next sec-tions, we will describe the system main components and functionality.LoggerMan, A Comprehensive Logging Tool to Capture Computer Usage 33.1 Privacy and Technical SpecificationLoggerMan works under Mac OSX 10.7 or later. To avoid any privacy concernsamong users, all captured data is stored locally on the computer. This providesthe user with a full control of the data. Buffering techniques are used to storethe stream of events efficiently to the hard disk. All modules are designed to beUnicode compatible. Thus, the tool can log texts of different languages properly.It adds itself to the computer’s startup to run automatically after a systemrestart. To maintain user privacy, LoggerMan does not log data typed in securedfields (password fields).3.2 ModulesFig. 1: LoggerMan MenuLoggerMan has been designed to be flexi-ble and modular. Our tool consists of multi-ple logging modules which can be switchedon/off independently (Fig. 1). Each loggingmodule captures a different type of data andevery log-entry is timestamped to the cur-rent time.Keyboard related Modules. These mod-ules are responsible for capturing keystrokesevents and storing them in local files. Theyprovide two levels of detail: word basedand key based. Word-based mode (visible as”Keyboard” menu item on Fig. 1) segments and concatenates the events streaminto words. Whereas, key-based mode (visible as ”Keystrokes” menu item onFig. 1) tracks each key separately and stores it associated with its time stamp.This particularly important for the domain of keystrokes dynamics [9].Mouse Module. All mouse actions are traced by this module: move, right/leftup/down mouse buttons events, scroll and drag-and-drop actions. We previouslyutilised the data stored by this module to build an authentication system basedon mouse curves [15].Screenshots Module. This will capture a screenshot of the current active win-dow. The user can select one of the shooting intervals (every 5, 10, 30 sec) ora smart-shooting option, which takes a screenshot: after every window transi-tion (from app to app or even different windows in the same app) and at fixedone minute intervals also. This helps to ensure a reasonable trade-off betweencapture frequency and storage usage. The screenshot can then provide valuableinformation about the content that the user is consuming or non-texual contentthat the user is creating.Apps Module. This module is designed to track apps transitions. It only logsthe currently used app regardless of how it is being used, and as such it offersa simple overview of app usage, without any potential privacy issues of actuallycapturing the screens.Clipboard Module. Clipboard module is responsible for tracking copy-pasteoperations. Any text the user copy to the clipboard is captured and logged.4 LoggerMan, A Comprehensive Logging Tool to Capture Computer Usage3.3 Reporting and InsightThe current version of LoggerMan comes with a local reporting tool to visualiseand give insights to the data owner about his/her computer usage. Specifically,the reporting tool presents three tag clouds: used apps, typed words and windowstitles. These clouds are constructed by computing the normalised frequencies ofitems occurrence during the selected date range by the user (Fig. 2 and Fig. 4).Information about mouse clicks and typed keystrokes during a time frame is alsopresented as a graph (Fig. 3). In addition, LoggerMan organises the screenshotsand the app usage data in a zoom-able timeline to provide a dyanmic and up-to-date view of computer usage (Fig. 5).Fig. 2: Apps CloudFig. 3: Mouse/Keyboard UsageFig. 4: Windows Titles Cloud4 Demonstration and EvaluationThis tool is designed to be easy to install and run by any user. The user is allset by just double clicking the app icon for the first time. After that, the appwill keep working automatically and passively in the background. Three subjectshave been using LoggerMan to capture their computer usage for more than 2months. Although the data size is very dependent of the running modules andthe computer usage intensity, LoggerMan produced around 1 GB of data in aweek for average computer usage by each subject. Most of this space (98%) istaken by screenshots. During MMM 2016, our demo will be conducted in a demomode where conference participants will be able to log their interactions for ashort period of time, then they can visualise their data by the reporting moduleof LoggerMan. Participants can get a copy of their interaction log files beforeLoggerMan, A Comprehensive Logging Tool to Capture Computer Usage 5Fig. 5: Apps & Screenshots Timelinethe data being deleted at the end of the session. A personalized report will begenerated for each user to show their uniqueness compared to other users.5 ConclusionsThere is a lack of a useful logging tools in the domain of human-computer in-teraction. In this paper, we presented LoggerMan, a logging and visualisationtool to capture computer activities and to give some insight based on this data.LoggerMan can easily support a wide range HCI related research such as useridentification, personalisation, recommendation and cognition.Acknowledgments This publication has emanated from research conductedwith the financial support of Science Foundation Ireland (SFI) under grant num-ber SFI/12/RC/2289.References1. Blaise W. Liffick and Laura K. Yohe. Using surveillance software as an hci tool.In Information Systems Education Conference, 2001.2. The MITRE corporation. Widget observation simulation inspection tool. http://www.openchannelfoundation.org/projects/WOSIT. Accessed: 2015-08-07.3. Frank Linton. Owl: A recommender system for it skills.4. K.P.H. Sullivan and E. Lindgren. Studies in Writing: Vol. 18. Computer Key-Stroke Logging and Writing: Methods and Applications. 2006.5. Marille Leijten and Luuk Van Waes. Keystroke logging in writing research: Us-ing inputlog to analyze and visualize writing processes. Written Communication,30(3):358–392, 2013.6 LoggerMan, A Comprehensive Logging Tool to Capture Computer Usage6. Sven Stromqvist, Kenneth Holmqvist, Victoria Johansson, Henrik Karlsson, andAsa Wengelin. What keystroke-logging can reveal about writing, volume 18 of Com-puter key-stroke logging and writing: methods and applications (Studies in Writing),pages 45–72. Elsevier, 2006.7. Marille LEIJTEN and Luuk VAN WAES. Inputlog: A logging tool for the researchof writing processes. Technical report.8. Manuel Rodrigues, Srgio Gonalves, Davide Carneiro, Paulo Novais, and FlorentinoFdez-Riverola. Keystrokes and clicks: Measuring stress on e-learning students. InJorge Casillas, Francisco J. Martnez-Lpez, Rosa Vicari, and Fernando De la Prieta,editors, Management Intelligent Systems, volume 220 of Advances in IntelligentSystems and Computing, pages 119–126. Springer International Publishing, 2013.9. Daniele Gunetti and Claudia Picardi. Keystroke analysis of free text. ACM Trans.Inf. Syst. Secur., 8(3):312–347, August 2005.10. T. Shimshon, R. Moskovitch, L. Rokach, and Y. Elovici. Clustering di-graphs forcontinuously verifying users according to their typing patterns. In Electrical andElectronics Engineers in Israel (IEEEI), 2010 IEEE 26th Convention of, pages000445–000449, 2010.11. Lisa M. Vizer. Detecting cognitive and physical stress through typing behavior. InCHI ’09 Extended Abstracts on Human Factors in Computing Systems, CHI EA’09, pages 3113–3116. ACM, 2009.12. A. Kolakowska. A review of emotion recognition methods based on keystrokedynamics and mouse movements. In Human System Interaction (HSI), 2013 The6th International Conference on, pages 548–555, 2013.13. Tom Yeh, Tsung-Hsiang Chang, and Robert C. Miller. Sikuli: Using gui screenshotsfor search and automation. In Proceedings of the 22Nd Annual ACM Symposiumon User Interface Software and Technology, UIST ’09, pages 183–192. ACM, 2009.14. Tom Yeh, Tsung-Hsiang Chang, Bo Xie, Greg Walsh, Ivan Watkins, Krist Wong-suphasawat, Man Huang, Larry S. Davis, and Benjamin B. Bederson. Creatingcontextual help for guis using screenshots. In Proceedings of the 24th Annual ACMSymposium on User Interface Software and Technology, UIST ’11, pages 145–154,2011.15. Zaher Hinbarji, Rami Albatal, and Cathal Gurrin. Dynamic user authenticationbased on mouse movements curves. In 21st International Conference on MultiMediaModelling (MMM 2015), Sydney, Australia, 2015. Springer.16. Ahmed Awad E. Ahmed and Issa Traore. A new biometric technology based onmouse dynamics. IEEE Trans. Dependable Sec. Comput., 4(3):165–179, 2007.17. Maja Pusara and Carla E. Brodley. User re-authentication via mouse movements.In Proceedings of the 2004 ACM Workshop on Visualization and Data Mining forComputer Security, pages 1–8. ACM, 2004.18. James P. Anderson Co. Computer Security Threat Monitoring and Surveillance.2002.19. Dit yan Yeung and Yuxin Ding. Host-based intrusion detection using dynamic andstatic behavioral models. Pattern Recognition, 36:229–243, 2003.20. T. Goldring. User profiling for intrusion detection in windows nt. ComputingScience and Statistics, 35, 2003.

LoggerMan, a comprehensive logging and visualisation tool to capture computer usage

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O\u27Connor, Noel E.

Irish Universities

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LoggerMan, a comprehensive logging and visualisation tool to capture computer usage

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