Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
First story detection in TDT is hard
Proceedings of the ninth international conference on Information and knowledge management
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
"Constant, constant, multi-tasking craziness": managing multiple working spheres
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TaskTracer: a desktop environment to support multi-tasking knowledge workers
Proceedings of the 10th international conference on Intelligent user interfaces
A hybrid learning system for recognizing user tasks from desktop activities and email messages
Proceedings of the 11th international conference on Intelligent user interfaces
A simple-transition model for relational sequences
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Detecting and correcting user activity switches: algorithms and interfaces
Proceedings of the 14th international conference on Intelligent user interfaces
Discovering frequent work procedures from resource connections
Proceedings of the 14th international conference on Intelligent user interfaces
Proceedings of the 1st Workshop on Context, Information and Ontologies
From documents to tasks: deriving user tasks from document usage patterns
Proceedings of the 15th international conference on Intelligent user interfaces
Activity recognition using eye-gaze movements and traditional interactions
Interacting with Computers
Switch detector: an activity spotting system for desktop
Proceedings of the 20th ACM international conference on Information and knowledge management
Understanding user behavior through summarization of window transition logs
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Interaction history visualization
Proceedings of the 30th ACM international conference on Design of communication
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Desktop users commonly work on multiple tasks. The TaskTracer system provides a convenient, low-cost way for such users to define a hierarchy of tasks and to associate resources with those tasks. With this information, TaskTracer then supports the multi-tasking user by configuring the computer for the current task. To do this, it must detect when the user switches the task and identify the user's current task at all times. This problem of "task switch detection" is a special case of the general problem of change-point detection. It involves monitoring the behavior of the user and predicting in real time when the user moves from one task to another. We present a framework that analyzes a sequence of observations to detect task switches. First, a classifier is trained discriminatively to predict the current task based only on features extracted from the window in focus. Second, multiple single-window predictions (specifically, the class probability estimates) are combined to obtain more reliable predictions. This paper studies three such combination methods: (a) simple voting, (b) a likelihood ratio test that assesses the variability of the task probabilities over the sequence of windows, and (c) application of the Viterbi algorithm under an assumed task transition cost model. Experimental results show that all three methods improve over the single-window predictions and that the Viterbi approach gives the best results.