In this article, we propose a new automatic topic and subtopic detection method from a document called paragraph extension. In paragraph extension, a document is considered as a set of paragraphs and a paragraph merging technique is used to merge similar consecutive paragraphs until no similar consecutive paragraphs left. Following this, similar word counts in merged paragraphs are summed up to construct subtopic scores by using a corpus which is designed so that we can find words related to a subtopic. The paragraph vectors are represented by subtopics instead of the words. The subtopic of a paragraph is the most frequent one in the paragraph vector. On the other hand, topic of the document is the most dispersive subtopic in the document. An experimental topic/subtopic corpus is constructed for sport and education topics. We also supported corpus by WordNet to obtain synonyms words. We evaluate the proposed method on a data set contains randomly selected 40 documents from the education and sport topics. The experiment results show that average of topic detection success ratio is about %83 and the subtopic detection is about %68.

Recent advances in the automotive industry enabled us to build fast, reliable, and comfortable vehicles with lots of safety features. Also, roads are designed and made safer than ever before. However, traffic accidents remain one of the major causes of death. Intelligent transport systems are expected to reduce if not prevent accidents with interconnected vehicles and infrastructures. These vehicular ad hoc networks are highly dynamic and fragile. Although the standardization efforts are mature enough, the non-emergency/service channel selection mechanisms are not explicitly defined. In this paper, a novel cross-layer prediction-based algorithm is proposed to select the best possible service channel to decrease collisions beforehand. Theoretical analysis regarding the mean squared error prediction performance is established. It is shown that the proposed method outperforms the general Markovian-based prediction schemes under various traffic load scenarios.

We studied outlier document filtering (ODF) for extractive sentence summarization. Our results are superior compared to the average of the participant systems' using DUC 2006. Furthermore, we add extractive paragraph summarization to the same system. It is surprising that the results are nearly the same for ROUGE metrics. Although extractive paragraph summarization has a better performance for precision, extractive sentence summarization has a slightly better performance on the recall and F-Score which is the harmonic mean of recall and precision. The ODF is successful for both extractive sentence and paragraph summarization. The similarity metric (match percent) suggested in the article prevents the domination of longer sentences/paragraphs on shorter sentences/paragraphs in selection. As a result, the ODF provides the flexibility of paragraph extraction instead of sentence extraction for simplicity and readability and less work load.

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In this study, color rendering index and gamut area index related to the light quality of a light source are compared. Color rendering index is a measure that demonstrates how much a light source reveals colors in nature. In color rendering index measurements, average of reference colors is taken to obtain an overall measurement value. As to gamut area index measurement, the result is obtained through the field measurement formed by the results of reference colors in the color space.