• Publications
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Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
TLDR
We propose a new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization, which is equivalent to normalized spectral clustering. Expand
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A survey of transfer learning
TLDR
This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Expand
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SCENE: a scalable two-stage personalized news recommendation system
TLDR
We propose a scalable two-stage personalized news recommendation approach with a two-level representation, which considers the exclusive characteristics (e.g., news content, access patterns, named entities, popularity and recency) of news items when performing recommendation. Expand
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IMDS: intelligent malware detection system
TLDR
The proliferation of malware has presented a serious threat to the security of computer systems. Expand
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An intelligent PE-malware detection system based on association mining
TLDR
The proliferation of malware has presented a serious threat to the security of computer systems. Expand
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Integrating Document Clustering and Multidocument Summarization
TLDR
We propose a new language model to simultaneously cluster and summarize documents by making use of both the document-term and sentence-term matrices. Expand
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Data clustering with size constraints
TLDR
We propose a heuristic algorithm to transform size constrained clustering problems into integer linear programming problems where all constraints can be the same size. Expand
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Multi-Document Summarization using Sentence-based Topic Models
TLDR
We propose a new Bayesian sentence-based topic model for summarization by making use of both the term-document and term-sentence associations. Expand
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SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging
TLDR
We develop interpretable string based malware detection system (SBMDS), which is based on interpretable strings analysis and uses support vector machine (SVM) ensemble with Bagging to classify the file samples and predict the exact types of the malware. Expand
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Generating event storylines from microblogs
TLDR
We explore the problem of generating storylines from microblogs for user input queries. Expand
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