<abstract><p>Ant cuticle texture presumably provides some type of function, and therefore is useful to research for ecological applications and bioinspired designs. In this study, we employ statistical image texture analysis and deep machine learning methods to classify similar ant species based on morphological features. We establish a public database of ant cuticle images for research. We provide a comparative study of the performance of image texture classification and deep machine learning methods on this ant cuticle dataset. Our results show that the deep learning methods give higher accuracy than statistical methods in recognizing ant cuticle textures. Our experiments also reveal that the deep learning networks designed for image texture performs better than the general deep learning networks.</p></abstract>

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<abstract><p>Definition modeling, the task of generating a definition for a given term, is a relatively new area of research applied in evaluating word embeddings. Automatic generation of dictionary quality definitions has many applications in natural language processing, such as sentiment analysis, machine translation, and word sense disambiguation. Additionally, definition modeling is also helpful for evaluating the quality of word embeddings. As more research is done in this field, the need for a summary of different applications, approaches, and obstacles grows apparent. This review provides an overview of the current research in definition modeling and a list of future directions and trends.</p></abstract>

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