14575854
Query Translation Disambiguation as Graph Partitioning
Resolving ambiguity in the process of query translation is crucial to cross-language information retrieval when only a bilingual dictionary is available. In this paper we propose a novel approach for query translation disambiguation, named "spectral query translation model". The proposed approach views the problem of query translation disambiguation as a graph partitioning problem. For a given query, a weighted graph is first created for all possible translations of query words based on the co-occurrence statistics of the translation words. The best translation of the query is then determined by the most strongly connected component within the graph. The proposed approach distinguishes from previous approaches in that the translations of all query words are estimated simultaneously. Furthermore, translation probabilities are introduced in the proposed approach to capture the uncertainty in translating queries. Empirical studies with TREC datasets have shown that the spectral query translation model achieves a relative 20% -50% improvement in cross-language information retrieval, compared to other approaches that also exploit word co-occurrence statistics for query translation disambiguation.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
14752852
A toolkit for automatically constructing outdoor radio maps
Outdoor location systems based on wireless access point (WAP) signal strength must either know the exact location of the WAPs in order to use a triangulation algorithm, or must construct a radio-map of the signal strengths. While the radio-map technique increases accuracy and can accommodate a wireless network that is not owned by any one entity, conventional methods for constructing a radio-map are labor-intensive and impractical in such environments. We introduce a toolkit for automatically constructing outdoor radio-maps. Our toolkit can easily be carried by delivery personnel and security guards during their normal work duties to obtain signal readings. The scattered data readings are then fed into an interpolation algorithm to construct a more complete grid that can be used as the radio-map.
13048061
Caching intermediate results for program improvement
A systematic approach is given for symbolically caching intermediate results useful for deriving incremental programs from non-incremental programs. We exploit a number of program analysis and transformation techniques, centered around effective caching based on its utilization in deriving incremental programs, in order to increase the degree of incrementality not otherwise achievable by using only the return values of programs that are of direct interest. Our method can be applied straightforwardly to provide a systematic approach to program improvement via caching.
30505871
User Interest Detection on Web Pages for Building Personalized Information Agent
In this paper we propose a vision-based approach to detecting user’s interests on web pages. This approach first splits a web page into several blocks of topics according to visual cues and then analyzes the layout structure of the page based on these blocks. The user’s interests are detected according to his browsing behaviors on the blocks and the layout structure of the web page. A personalized information agent is also developed to automatically extract new content from those web pages according to the user’s interests. Our experiments show that our proposed approach to detecting user’s interests on web pages achieves satisfactory accuracy and the personalized information agent significantly saves user’s browsing time and improves user’s browsing experience.
62742629
Local facial asymmetry for expression classification
We explore a novel application of facial asymmetry: expression classification. Using 2D facial expression images, we show the effectiveness of automatically selected local facial asymmetry for expression recognition. Quantitative evaluations of expression classification using local asymmetry demonstrate statistically significant improvements over expression classification results on the same data set without explicit representation of facial asymmetry. A comparison of discriminative local facial asymmetry features for expression classification versus human identification is given.
10720584
Graph-Based Semi-Supervised Learning as a Generative Model
This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional probabilities are estimated by graph propagation and the class priors are estimated by linear regression. Experimental results on various datasets show that the proposed method is superior to existing graph-based semi-supervised learning methods, especially when the labeled subset alone proves insufficient to estimate meaningful class priors.