Abstract

Abstract
The ANTAREX 1 project aims at expressing the application selfadaptivity through a Domain Specific Language (DSL) and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to Exascale. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. We show through a mini-App extracted from one of the project application use cases some initial exploration of application precision tuning by means enabled by the DSL. © 2016 Copyright held by the owner/author(s).

Abstract

Abstract
This work presents the results of the characterization of lung nodules in chest Computerized Tomography for benign/malignant classification. A set of image features was used in the Computer-aided Diagnosis system to distinguish benign from malignant nodules and, therefore, diagnose lung cancer. A filter-based feature selection approach was used in order to define an optimal subset with higher accuracy. A large and heterogeneous set of 293 features was defined, including shape, intensity and texture features. We used different KNN and SVM classifiers to evaluate the features subsets. The estimated results were tested in a dataset annotated by radiologists. Promising results were obtained with an area under the Receiver Operating Characteristic curve (AUC value) of 96:2 ± 0:5% using SVM.

Abstract
Poker has been gradually gaining the attention of the scientific community, mostly in researchers on Artificial Intelligence. The main reason is concerned with the fact that Poker provides great challenges to the research in the area. Unlike many other games, poker is a stochastic game of imperfect information, which creates a high amount of possibilities to every state of the game. In this work a different line of thought is followed by trying to create an agent capable of reproducing the way a professional Poker human player plays for all stages in a Texas Hold'em Poker game. For this purpose, a high level data model able to comprehend the maximum of information relevant to every state of the game was built, loaded with data from a database containing millions of plays made by a professional poker players, by using Talend Data Integration. To execute Data mining techniques Weka software package was used. The final results show that it is possible to create a virtual poker player that make very similar decisions of a professional poker player.

Abstract
Social networks have an evolving characteristic because of continuous interaction between users. Existing event detection tasks do not consider the analysis under a user-centric perspective. In this paper we propose to detect node centrality events, that is the task of finding events based on the position and roles of the nodes. We present a naive algorithm for detecting such events in network streams. Moreover, we apply our proposal in a case study, showing how node centrality events can be used for tracking user preferences changes.