UROP Proceedings 2010-11

“Where To”: A Collaborative Andriod Application Providing an OnlineGoogle Map with Audible Cantonese Location Pronounciation forExpats and Tourists in Hong KongAdvisor : MUPPALA K R Jogesh / CSEStudent : CHEUNG Ka Chun / COMP(UROP1100, Summer 2011)Hong Kong has hundreds of different locations which have names that are difficult forforeigners to pronounce and remember. Foreigners often have trouble communicating tolocals, especially taxi drivers, about where they want to go, such as tourist attractions orpopular sites. In this Android application, users can select the text language and when userstap on a location on the Google map of Hong Kong, the system displays the address in threedifferent languages/dialects (English, Cantonese and Putonghua). The users can also listen tothe pronunciation in the language/dialect of their choice.An Evaluation Framework for Search Engine PersonalizationAdvisor : NG Wilfred Siu Hung / CSEStudent : JIANG Yuxiang / COMP(UROP1000, Summer 2011)Many factors need to be considered when evaluating web search engines. We propose that theuser’s search intent should also be considered as an important factor in the evaluation. Afterdetermining the user’s search intent by analyzing the search queries, the search engine canreturn more personalized search results. In this project, search sessions are manually analyzedto figure out their search intents, and those search intents are then used in our personalizedsearch engine. We compare three types of search engines (Google, Apache Lucene and ourpersonalize search engine) against our reference rankings created manually.Mining Generalized Order-Preserving Submatrices in Gene ExpressionDataAdvisor : NG Wilfred Siu Hung / CSEStudent : LI Yuliang / COMP(UROP1100 & 1200, Fall 2010 & Summer 2011)To address the noisy nature of gene expression data, microarray experiments are usuallyconducted for multiple times. We propose to model the expression level as continuousdistributions in order to facilitate effective pattern mining from the data. In this project, thetwo gene expression matrix are defined according to uniform and normal distributions andthey are called the UniDist matrix and the NormDist matrix. Based on these two new matrices,we formalize a novel probabilistic OPSM (POPSM) model, which adopts a new probabilisticsupport measure to evaluate the extend to which a row belongs to a POPSM pattern. Ourempirical studies show that our proposed POPSM model better captures the characteristics ofthe expression levels of strongly associated genes and greatly promotes the discovery ofpatterns with high biological significance.41