this paper proposes a novel method for analyzing PC usage logs aiming to find working patterns and behaviors of employees at work. the logs we analyze are recorded at individual PCs for employees in a company, and inc...
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ISBN:
(纸本)9783642257308
this paper proposes a novel method for analyzing PC usage logs aiming to find working patterns and behaviors of employees at work. the logs we analyze are recorded at individual PCs for employees in a company, and include active window transitions. Our method consists of two levels of abstraction: (1) task summarization by HMM: (2) user behavior comparison by kernel principle component analysis based on a graph kernel. the experimental results show that our method reveals implicit user behavior at a high level of abstraction, and allows us to understand individual user behavior among groups, and over time.
In the field of structural patternrecognitiongraphs constitute a very common and powerful way of representing objects. the main drawback of graphrepresentations is that the computation of various graph similarity m...
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In this paper we describe modifications of irregular image segmentation pyramids based on user-interaction. We first build a hierarchy of segmentations by the minimum spanning tree based method, then regions from diff...
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this paper introduces the concept of discrete multidimensional size function, a mathematical tool studying the so-called size graphs. these graphs constitutes an ingredient of Size theory, a geometrical/topological ap...
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the proceedings contain 74 papers. the topics discussed include: from region based image representation to object discovery and recognition;structural patterns in complex networks through spectral analysis;graph embed...
ISBN:
(纸本)3642149790
the proceedings contain 74 papers. the topics discussed include: from region based image representation to object discovery and recognition;structural patterns in complex networks through spectral analysis;graph embedding using an edge-based wave kernel;combining elimination rules in tree-based nearest neighbor search algorithms;entropy-based variational scheme for fast Bayes learning of Gaussian mixtures;learning graph quantization;high-dimensional spectral feature selection for 3D object recognitionbased on Reeb graphs;dissimilarity-based multiple instance learning;a game theoretic approach to learning shape categories and contextual similarities;a comparison between two representatives of a set of graphs: median vs. barycenter graph;automatic traffic monitoring from satellite images using artificial immune system;and graph embedding based on nodes attributes representatives and a graph of words representation.
In this paper, we investigate different methodologies of Arabic segmentation for statistical machine translation by comparing a rule-based segmenter to different statistically-based segmenters. We also present a new m...
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Sign languages represent an interesting niche for statistical machine translation that is typically hampered by the scarceness of suitable data, and most papers in this area apply only a few, well-known techniques and...
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Currently most state-of-the-art statistical machine translation systems present a mismatch between training and generation conditions. Word alignments are computed using the well known IBM models for single-word based...
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In this paper we describe the statistical machine translation system of the RWth Aachen University developed for the translation task of the IWSLT 2010. this year, we participated in the BTEC translation task for the ...
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the collection of behavior protocols is a common practice in human factors research, but the analysis of these large data sets has always been a tedious and time-consuming process. We are interested in automatically f...
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ISBN:
(纸本)9783642021237
the collection of behavior protocols is a common practice in human factors research, but the analysis of these large data sets has always been a tedious and time-consuming process. We are interested in automatically finding canonical behaviors: a small subset of behavioral protocols that is most representative of the full data set, providing a view of the data with as few protocols as possible. Behavior protocols often have a natural graph-based representation, yet there has been little work applying graphtheory to their study. In this paper we extend our recent algorithm by taking into account the graph topology induced by the paths taken through the space of possible behaviors. We applied this technique to find canonical web-browsing behaviors for computer users. By comparing identified canonical sets to a ground truth determined by expert human coders. we found that this graph-based metric outperforms our previous metric based on edit distance.
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