3781215
Web mining from competitors' websites
This paper presents a framework for user-oriented text mining. It is then illustrated with an example of discovering knowledge from competitors' websites. The knowledge to be discovered is in the form of association rules. A user's background knowledge is represented as a concept hierarchy developed from documents on his/her own website. The concept hierarchy captures the semantic usage of words and relationships among words in background documents. Association rules are identified among the noun phrases extracted from documents on competitors' websites. The interestingness measure, i.e. novelty, which measures the semantic distance between the antecedent and the consequent of a rule in the background knowledge, is computed from the co-occurrence frequency of words and the connection lengths among words in the concept hierarchy. A user evaluation of the novelty of discovered rules demonstrates that the correlation between the algorithm and the human judges is comparable to that between human judges.
16016667
Finding more useful information faster from web search results
In this paper, we propose a prototype system for automatic generation of concept hierarchies to be used as an overview of search results. The system sends a user's query to five search engines and receives a returned list of relevant web pages. The system then extracts query-oriented concept terms from snippets that come with the returned hits. Concept terms are organized into a concept hierarchy using a co-occurrence-based classification technique. Finally, concepts in returned documents are dynamically highlighted according to terms in the selected concept branch that lead to the chosen document. The user study shows that concept hierarchies do provide easy navigation and browsing of web returned documents. The results also show that users can find a document of interest no matter how low it is ranked in the retrieved list.
3764367
eLearning assessment through textual analysis of class discussions
Most of the elearning classes use discussion forums to support class interaction. This paper presents a novel approach that automatically assesses student class performance by analyzing knowledge density in online class messages. Class performance of students is evaluated from three perspectives: the quality of their course work, the quantity of their efforts, and the activeness of their participation; three measures - knowledge density, message length, and message count, are derived from class messages respectively. Together, they are used to calculate a performance indicator score. The experiment shows that there is a high correlation between the performance indicator scores and the actual grades assigned by instructors. The rank orders of students by the performance indicator score are highly correlated with those by the actual grades as well.
2743837
A hybrid classifier approach for Web retrieved documents classification
The paper presents a hybrid technique for the classification of Web returned hits into concept hierarchies. The technique involves a combination of manual and automatic classifiers. At first, all Web returned documents are assigned to human defined categories using manual classifiers, and then automatic classifiers are used to generate a concept hierarchy for each of these categories. The results of the evaluation reveal the following: (a) for polysemous queries, our system is able to generate meaningful categories corresponding to (but not limited to), the different semantic facets of the queries; (b) as expected, for non-polysemous queries the system generates fewer categories; (c) the hierarchy precision of the concept hierarchies generated for polysemous queries is found to be significantly better when compared to the one obtained using a baseline system.
3763580
Extracting Features from Web Search Returned Hits for Hierarchical Classification
3822993
Learning by Examples: Identifying Key Concepts from Text Using Pre-Defined Inputs
1385145
Simulation based deadlock analysis for system level designs
In the design of highly complex, heterogeneous, and concurrent systems, deadlock detection and resolution remains an important issue. In this paper, we systematically analyze the synchronization dependencies in concurrent systems modeled in the Metropolis design environment, where system functions, high level architectures and function-architecture mappings can be modeled and simulated. We propose a data structure called the dynamic synchronization dependency graph, which captures the runtime (blocking) dependencies. A loop-detection algorithm is then used to detect deadlocks and help designers quickly isolate and identify modeling errors that cause the deadlock problems. We demonstrate our approach through a real world design example, which is a complex functional model for video processing and a high level model of function-architecture mapping.
1529480
A compression algorithm for DNA sequences and its applications in genome comparison
We present a lossless compression algorithm, <italic>Gen-Compress</italic>, for DNA sequences, based on searching for approximate repeats. Our algorithm achieves the best compression ratios for benchmark DNA sequences, comparing to other DNA compression programs [3, 7]. Significantly better compression results show that the approximate repeats are one of the main hidden regularities in DNA sequences.
We then describe a theory of measuring the relatedness between two DNA sequences. We propose to use <italic>d</italic>(<italic>x</italic>, <italic>y</italic>) = 1 — <italic>K</italic>(<italic>x</italic>) - <italic>K</italic>(<italic>x</italic>|<italic>y</italic>)/<italic>K</italic>(<italic>xy</italic> to measure the distance of any two sequences, where <italic>K</italic>  stands for Kolmogorov complexity [5]. Here, <italic>K</italic>(<italic>x</italic>) - <italic>K</italic>(<italic>x</italic>|<italic>y</italic>) is the mutual information shared by <italic>x</italic> and <italic>y</italic>. But mutual information is not a distance, there is no triangle inequality. The distance <italic>d</italic>(<italic>x</italic>, <italic>y</italic>) is symmetric. It also satisfies the triangle inequality, and furthermore, it is universal [4].
It has not escaped our notice that the distance measure we have postulated can be immediately used to construct evolutionary trees from DNA sequences, especially those that cannot be aligned, such as complete genomes. With more and more genomes sequenced, constructing trees from genomes becomes possible [1, 2, 6, 8].  Kolmogorov complexity is not computable. We use <italic>GenCompress</italic> to approximate it. We present strong experimental support for this theory, and demonstrate its applicability by correctly constructing a 16S (18S) rRNA tree, and a whole genome tree for several species of bacteria. Larger scale experiments are underway at the University of Waterloo, with very promising results.
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
3226359
Segmentation, Registration, and Deformation Analysis of 3D MR Images of Mice
We demonstrate our mouse MR image and shape analysis pipeline. The long term goal of our work is the description of structural shape variations in normal, genetically identical mice and the subsequent detection of pathological phenotypes in genetically modified mice. The pipeline begins with the acquisition and reconstruction of high resolution MR images of mice. Regions of the images containing organs under study are enhanced via edge-preserving smoothing and mouse organs are segmented using a deformable mesh model. Images are registered using a multi-resolution approach and displacement fields are used to calculate local volume changes. Detailed descriptions and results for each step of the pipeline are presented.
11357832
A latent semantic indexing based method for solving multiple instance learning problem in region-based image retrieval
Relevance feedback (RF) is a widely used technique in incorporating user's knowledge with the learning process for content-based image retrieval (CBIR). As a supervised learning technique, it has been shown to significantly increase the retrieval accuracy. However, as a CBIR system continues to receive user queries and user feedbacks, the information of user preferences across query sessions are often lost at the end of search, thus requiring the feedback process to be restarted for each new query. A few works targeting long-term learning have been done in general CBIR domain to alleviate this problem. However, none of them address the needs and long-term similarity learning techniques for region-based image retrieval. This paper proposes a latent semantic indexing (LSI) based method to utilize users' relevance feedback information. The proposed region-based image retrieval system is constructed on a multiple instance learning (MIL) framework with one-class support vector machine (SVM) as its core. Experiments show that the proposed method can better utilize users' feedbacks of previous sessions, thus improving the performance of the learning algorithm (one-class SVM).