Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where unpredictability of new terms requires adaptation in the vector space model. We present a novel event detection system based on the Incremental Term Frequency-Inverse Document Frequency (TF-IDF) weighting incorporated with Locality Sensitive Hashing (LSH). Our system could efficiently and effectively adapt to the changes within the data streams of any new terms with continual updates to the vector space model. Regarding miss probability, our proposed novelty detection framework outperforms a recognised baseline system by approximately 16% when evaluating a benchmark dataset from Google News.
For the first time, an eco-friendly
and sustainable tandem [5C
+ 1C] cycloaromatization of α-alkenoyl ketene dithioacetals
and nitroethane in water for the efficient synthesis of ortho-acylphenols was reported. In refluxing water, a range of α-alkenoyl
ketene dithioacetals and nitroethane smoothly underwent tandem Michael
addition/cyclization/aromatization reactions in the presence of 2.0
equivalents of DBU to provide various ortho-acylphenols
in excellent yields. The green approach to ortho-acylphenols
not only avoided the use of harmful organic solvents, which could
result in serious environmental and safety issues, but also exhibited
fascinating features such as good substrate scope, excellent yields,
simple purification for desired products, ease of scale-up, and reusable
aqueous medium.
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