In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: The Complete Tang Poems (CTP) is the most important source to study Tang poems. We look into CTP with computational tools from specific linguistic perspectives, including distributional semantics and collocational analysis. From such quantitative viewpoints, we compare the usage of "wind" and "moon" in the poems of Li Bai () and Du Fu (). Colors in poems function like sounds in movies, and play a crucial role in the imageries of poems. Thus, words for colors are studied, and "" (bai2, white) is the main focus because it is the most frequent color in CTP. We also explore some cases of using colored words in antithesis() pairs that were central for fostering the imageries of the poems. CTP also contains useful historical information, and we extract person names in CTP to study the social networks of the Tang poets. Such information can then be integrated with the China Biographical Database of Harvard University.

A: Color Aesthetics and Social Networks in Complete Tang Poems: Explorations and Discoveries
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Q: The ability to monitor the progress of students' academic performance is a critical issue to the academic community of higher learning. A system for analyzing students' results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k-mean clustering algorithm for analyzing students' result data. The model was combined with the deterministic model to analyze the students' results of a private Institution in %igeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners. Keywordsk mean, clustering, academic performance, algorithm.

A: Application of k-Means Clustering algorithm for prediction of Students' Academic Performance
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Q: It was recently shown that the problem of de<lb>coding messages transmitted through a noisy<lb>channel can be formulated as a belief up<lb>dating task over a probabilistic network (14).<lb>Moreover, it was observed that iterative ap<lb>plication of the (linear time) belief propa<lb>gation algorithm designed for polytrees (15)<lb>outperformed state of the art decoding algo<lb>rithms, even though the corresponding net<lb>works may have many cycles.<lb>This paper demonstrates empirically that<lb>an approximation algorithm approx-mpe<lb>for solving the most probable explana<lb>tion<lb>(MPE) problem, developed within the<lb>recently proposed mini-bucket elimination<lb>framework (4), outperforms iterative belief<lb>propagation on classes of coding networks<lb>that have bounded induced width.<lb>Our ex<lb>periments suggest that approximate MPE de<lb>coders can be good competitors to the ap<lb>proximate belief updating decoders.

A:
Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding
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