Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2012

Devaurs Didier, Rath Andreas S., Lindstaedt Stefanie

Exploiting the User Interaction Context for Automatic Task Detection

Applied Artificial Intelligence, Taylor & Francis Group, 2012

Journal
Detecting the task a user is performing on his/her computer desktop is important in order to provide him/her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (1) capturing simple user interaction events on the computer desktop and (2) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task-detection performance. We also argue that good results can be achieved by training task classifiers “offline” on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones.
2011

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

An Ontology-Based Approach for Detecting Knowledge Intensive Tasks

Journal of Digital Information Management, Pichappan, P., Jacobs, D. , Digital Information Research Foundation, 2011

Journal
In the context detection field, an important challenge is automatically detecting the user's task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task detection approach, and evaluate it against previous work. We show that knowledge intensive tasks cannot be accurately classified using only the window title. We argue that our approach allows classifying such tasks better, by providing feature combinations that can adapt to the domain and the degree of freedom in task execution.
2010

Devaurs Didier, Gras R.

Species Abundance Patterns in an Ecosystem Simulation Studied through Fisher's Logseries

Simulation Modelling Practice and Theory, Elsevier, 2010

Journal
We have developed an individual-based evolving predator–prey ecosystem simulation thatintegrates, for the first time, a complex individual behaviour model, an evolutionary mechanismand a speciation process, at an acceptable computational cost. In this article, we analysethe species abundance patterns observed in the communities generated by oursimulation, based on Fisher’s logseries. We propose a rigorous methodology for testingabundance data against the logseries. We show that our simulation produces coherentresults, in terms of relative species abundance, when compared to classical ecological patterns.Some preliminary results are also provided about how our simulation is supportingecological field results.
2010

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier

Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Sustaining TEL: From Innovation to Learning and Practice, Lecture Notes in Computer Science, Springer, 2010

. Supporting learning activities during work has gained momentum fororganizations since work-integrated learning (WIL) has been shown to increaseproductivity of knowledge workers. WIL aims at fostering learning at the workplace,during work, for enhancing task performance. A key challenge for enablingtask-specific, contextualized, personalized learning and work support is to automaticallydetect the user’s task. In this paper we utilize our ontology-based usertask detection approach for studying the factors influencing task detection performance.We describe three laboratory experiments we have performed in twodomains including over 40 users and more than 500 recorded task executions.The insights gained from our evaluation are: (i) the J48 decision tree and Na¨ıveBayes classifiers perform best, (ii) six features can be isolated, which providegood classification accuracy, (iii) knowledge-intensive tasks can be classified aswell as routine tasks and (iv) a classifier trained by experts on standardized taskscan be used to classify users’ personal tasks.
2010

Pozzi, Francesca, Persico, Donatella, Fischer, Frank, Hofmann, Lena, Lindstaedt Stefanie , Cress, Ulrike, Rath Andreas S., Moskaliuk, Johannes, Weber, Nicolas, Kimmerle, Joachim, Devaurs Didier, Ney, Muriel, Gonçalves, Celso, Balacheff, Nicolas, Schwartz, Claudine, Bosson, Jean-Luc, Dillenbourg, Pierre, Jermann, Patrick, Zufferey, Guillaume, Brown, Elisabeth, Sharples, Mike, Windrum, Caroline, Specht, Marcus, Börner, Dirk, Glahn, Christian, Fiedler, Sebastian, Fisichella, Marco, Herder, Eelco, Marenzi, Ivana, Nejdl, Wolfgang, Kawese, Ricardo, Papadakis, George

D1.2 Trends in Connecting Learners. First Research & Technology Scouting Report

2010

In this first STELLAR trend report we survey the more distant future of TEL, as reflected in the roadmaps; we compare the visions with trends in TEL research and TEL practice. This generic overview is complemented by a number of small-scale studies, which focus on a specific technology, approach or pedagogical model.
2009

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier

UICO: An Ontology-Based User Interaction Context Model for Automatic Task Detection on the Computer Desktop

Proceedings of the Context Information and Ontology (CIAO2009) workshop as part of the ESWC 2009, Gomez-Perez, J. M., Haase, P., Tilly, M., Warren, P., ACM, 2009

Konferenz
‘Understanding context is vital’ [1] and ‘context is key’ [2]signal the key interest in the context detection field. Oneimportant challenge in this area is automatically detectingthe user’s task because once it is known it is possible tosupport her better. In this paper we propose an ontologybaseduser interaction context model (UICO) that enhancesthe performance of task detection on the user’s computerdesktop. Starting from low-level contextual attention metadatacaptured from the user’s desktop, we utilize rule-based,information extraction and machine learning approaches toautomatically populate this user interaction context model.Furthermore we automatically derive relations between themodel’s entities and automatically detect the user’s task.We present evaluation results of a large-scale user study wecarried out in a knowledge-intensive business environment,which support our approach.
2009

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

Detecting Real User Tasks by Training on Laboratory Contextual Attention Metadata

Proceedings of Exploitation User Attention Metadata (EUAM '09) held at Informatik '09, 2009

Konferenz
Detecting the current task of a user is essential for providing her with contextualizedand personalized support, and using Contextual Attention Metadata (CAM)can help doing so. Some recent approaches propose to perform automatic user task detectionby means of task classifiers using such metadata. In this paper, we show thatgood results can be achieved by training such classifiers offline on CAM gathered inlaboratory settings. We also isolate a combination of metadata features that present asignificantly better discriminative power than classical ones.
2009

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

KnowSe: Fostering User Interaction Context Awareness

Supplementary Proceedings of the 11th European Conference on Computer Supported Cooperative Work (ECSCW '09). Demo Paper, 2009

Konferenz
2009

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

Contextualized Knowledge Services for Personalized Learner Support

Fourth European Conference on Technology Enhanced Learning. Demo Paper. Lecture Notes in Computer Science. Springer, 2009

Konferenz
In this demonstration we present our KnowSe framework,developed for observing, storing, analyzing and leveraging ContextualAttention Metadata, utilizing our ontology-based user interactions contextmodel (UICO). It includes highly contextualized knowledge servicesfor supporting learners in a personalized and adaptive way, by exploitingthe learner’s user context.
2009

Gras R., Devaurs Didier, Wozniak A., Aspinall A.

An Individual-Based Evolving Predator-Prey Ecosystem Simulation Using a Fuzzy Cognitive Map as the Behavior Model

Artificial Life, Massachusetts Institute of Technology, 2009

Journal
We present an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a fuzzy cognitive map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g., distance to predator or prey, distance to potential breeding partner, distance to food, energy level) and its internal states (e.g., fear, hunger, curiosity), and to choose several possible actions such as evasion, eating, or breeding. The FCM of each individual is unique and is the result of the evolutionary process. The notion of species is also implemented in such a way that species emerge from the evolving population of agents. To our knowledge, our system is the only one that allows the modeling of links between behavior patterns and speciation. The simulation produces a lot of data, including number of individuals, level of energy by individual, choice of action, age of the individuals, and average FCM associated with each species. This study investigates patterns of macroevolutionary processes, such as the emergence of species in a simulated ecosystem, and proposes a general framework for the study of specific ecological problems such as invasive species and species diversity patterns. We present promising results showing coherent behaviors of the whole simulation with the emergence of strong correlation patterns also observed in existing ecosystems.
2009

Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.

Machine Learning based Work Task Classification

Journal of Digital Information Management, 2009

Journal
Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.
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