Ricerc@Sapienza

New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload.

The vast majority of today's mobile malware targets Android devices. An important task of malware analysis is the classification of malicious samples into known families. In this paper we propose AndroDFA: an approach to Android malware family classification based on dynamic analysis of resource consumption metrics available from the proc file system. These metrics can be easily measured during sample execution.

Coping with malware is getting more and more challenging, given their
relentless growth in complexity and volume. One of the most common approaches
in literature is using machine learning techniques, to automatically learn
models and patterns behind such complexity, and to develop technologies for
keeping pace with the speed of development of novel malware. This survey aims
at providing an overview on the way machine learning has been used so far in
the context of malware analysis. We systematize surveyed papers according to

Timely weed mapping in crop post-emergence situations is a challenging task required for developing precision weed management solutions. It is necessary to discriminate the crop from the weeds and, if possible, to distinguish different weed species. The ability to map weeds using hyperspectral images acquired from an unmanned airborne vehicle (UAV) over a maize field was evaluated by comparing different classification strategies. The results were mainly affected by the variability in crop and weed spectral signatures.

Purpose: To analyse the classification performances of a decision tree method applied to predictor variables in survival outcome in patients with locally advanced rectal cancer (LARC). The aim was to offer a critical analysis to better apply tree-based approach in clinical practice and improve its interpretation. Materials and methods: Data concerning patients with histological proven LARC between 2007 and 2014 were reviewed. All patients were treated with trimodality approach with a curative intent. The Kaplan–Meier method was used to estimate overall survival (OS).