About us

The conference will held a series of Tutorials & Workshops that will be included in its program and will be placed in the same dates. The topics emphasizes emergent aspects, relevant issues, challenges etc. of AI.
Tutorials will be held 23th of October 2018.


Chairman of Tutorials and Workshops

Granada

Granada, más deslumbrante que la flor, más sabrosa que la fruta de la que toma su nombre, parece una virgen tumbada al sol - Alejandro Dumas

Tutorials

Tutorial #1

Natural Language Processing and Sentiment Analysis

The communication protocol between humans is natural language. Humans communicate with each other and express their private states using language in the form of text or speech. Furthermore, language may be considered as the projection of human knowledge and intelligence. Hence, natural language is the right source of data to understand humans, to represent their intelligence and to communicate with them. However, natural language is unstructured data and it cannot be directly used by a computer system.

In this tutorial, we will describe how to represent natural language in order to be processed by a computer, or in other words, how to build features that represent natural language. We will introduce Deep Learning methods for the extraction of rich linguistic features from written utterances, not limited to superficial views of the text as a bag or sequence of words, but including the syntactic (structural) analysis of the text. Moreover, we will show the usefulness of these linguistic features for the development of opinion classification systems, which is crucial for Sentiment Analysis tasks. Likewise, we will focus on the development of polarity classification methods grounded in Deep Learning.

Tutorial #2

Deep Learning: Fundamentals, Models and Applications

Summary to be here soon.

Tutorial #3

Applying computer learning to detect malware in Android: AndroPyTool and OmniDroid

The possibilities and advantages of applying Computational Learning to solve a multitude of problems are beyond doubt. It has been demonstrated how this broad set of techniques can help solve problems in areas as diverse as computer vision, natural language processing, fraud detection, robotics and bioinformatics, among others. Our aim in this tutorial is to present the possibilities offered by this field when dealing with a current, complex problem and críıtico: the detection of malware on Android devices. As we will show in this tutorial, Computational Learning techniques such as classification and clustering algorithms, evolutionary computing, and deep learning are currently being used to detect those malware samples whose behavior shows malicious patterns. In addition, we will explain the different tools developed to analyze Android malware and perform reverse engineering. Finally, we will first describe our software AndroPyTool, designed to extract a wide range of features from Android applications in order to deeply characterize their behavior. Secondly, we will talk about Omnidroid, a wide set of data on characteristics extracted from Android applications, both benign and malignant.

Dates

Tutorials & Workshops

Proposal reception 30 April

Waiting for your contributions!

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