0000000001205448
AUTHOR
A. Paola
Introducing automated reasoning in network management
This paper proposes the adoption of Artificial Intelligence techniques in the field of network management and moni toring. In order to allow automated reasoning on network ing topics, we constructed an accurate ontological model capable of fitting as more as possible networking concepts. The thoroughly representation of the domain knowledge is used by a Logical Reasoner, which is an expert system ca pable of performing management tasks typically executed by human experts. The Logical Reasoner is integrated in a distributed multi-agent architecture for network manage ment, which exploits the dynamic reasoning capabilities of the Situation Calculus formalism to provide a powerful sys tem capa…
User detection through multi-sensor fusion in an AmI scenario
Recent advances in technology, with regard to sensing and transmission devices, have made it possible to obtain continuous and precise monitoring of a wide range of qualitatively diverse environments. This has boosted the research on the novel field of Ambient Intelligence, which aims at exploiting the information about the environment state in order to adapt it to the user’s preference. In this paper, we analyze the issue of detecting the user’s presence in a given region of the monitored area, which is crucial in order to trigger subsequent actions. In particular, we present a comprehensive framework that turns data perceived by sensors of different nature, and with possible imprecision, …
An Intelligent System for Energy Efficiency in a Complex of Buildings
Energy efficiency has nowadays become one of the most challenging task for both academic and commercial organizations, and this has boosted research on novel fields, such as Ambient Intelligence. In this paper we address the issue of timely and ubiquitous monitoring of building complexes in order to optimize their energy consumption, and present an intelligent system addressed to the typical end user, i.e. the administrator, or responsible operator, of the complex. A three-level architecture has been designed for detecting the presence of the building inhabitants user and understanding their preferences with respect to the environmental conditions in order to optimize the overall energy eff…
Malware detection through low-level features and stacked denoising autoencoders
In recent years, the diffusion of malicious software through various channels has gained the request for intelligent techniques capable of timely detecting new malware spread. In this work, we focus on the application of Deep Learning methods for malware detection, by evaluating their effectiveness when malware are represented by high-level, and lowlevel features respectively. Experimental results show that, when using high-level features, deep neural networks do not significantly improve the overall detection accuracy. On the other hand, when low-level features, i.e., small pieces of information extracted through a light processing, are chosen, they allow to increase the capability of corr…