This study deals with the recently proposed concept of so-called Context Specificity of Lemma (CSL). CSL is based on the word embedding technique called Word2vec which enables measuring lexical context similarity between lemmas. Specifically, a recently proposed method Closest Context Specificity (CCS) is applied to a diachronic analysis of Czech texts. This method expresses how unique is a context within which a given lemma appears. The aim of the paper is to study what kind of semantic features can CCS detect and how useful could CCS be in a diachronic semantic analysis. The second goal is to observe the relation of CCS to frequencies in the corpora.

We introduce a problem of tracking small animals, especially insects. To solve this problem, we focus on visual tracking in recorded movies, propose our pattern tracking mechanism based on F -transform, and implement a user-friendly software to handle the movies. The tracking core is compared with five state-of-the-art tracking algorithms: KCF, MIL, TLD, Boosting and MedianFlow from processing time and algorithm failure rate point of views. Based on the results computed from 1000 movie frames, we observed that the proposed F-transform tracking core is the fastest and the most reliable method.