Fractal-based Deep Convolutional Representations: Algorithms and Comparisons – The recent emergence of deep learning has brought many new challenges to human-computer interaction including the problem of designing and learning artificial agents. In fact, the task of real-time interaction with agents is important for a wide range of applications including interactive and game-like games. We tackle the problem in two steps. First, we model the world as a set of interconnected objects (i.e., agents) that interact with each other as a natural learning mechanism. Second, we learn agents that exploit the system resources. As with any learning system, a new agent could or could not be learned yet. To explore the potential for agents we apply deep learning techniques to the world of action and action recognition. We show that deep learning can help agents to better understand, perform and explore the system resources efficiently.

We present a new dataset for a novel kind of semantic discrimination (tense) task aiming at comparing two types of text: semantic and unsemantically. It includes large-scale annotated hand annotated datasets that are large in size and are capable of covering an entire language. We propose a two-stage multi-label task: a simple, yet effective and accurate algorithm to efficiently label text. Our approach takes the idea of big-data and tries to model the linguistic diversity for content categorization using a new class of features that are modeled both as data and concepts. From semantic and unsemantically rich text we then use information about the semantics of text for information processing, allowing each label to be inferred from context. Our results show that the semantic diversity of a given text significantly outperforms the unsemantically rich text.

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Ontology Management System Using Part-of-Speech Tagging Algorithm

Fractal-based Deep Convolutional Representations: Algorithms and Comparisons

A Unified Collaborative Strategy for Data Analysis and Feature Extraction

Using the Multi-dimensional Bilateral Distribution for Textual DiscriminationWe present a new dataset for a novel kind of semantic discrimination (tense) task aiming at comparing two types of text: semantic and unsemantically. It includes large-scale annotated hand annotated datasets that are large in size and are capable of covering an entire language. We propose a two-stage multi-label task: a simple, yet effective and accurate algorithm to efficiently label text. Our approach takes the idea of big-data and tries to model the linguistic diversity for content categorization using a new class of features that are modeled both as data and concepts. From semantic and unsemantically rich text we then use information about the semantics of text for information processing, allowing each label to be inferred from context. Our results show that the semantic diversity of a given text significantly outperforms the unsemantically rich text.