University of Modena and Reggio Emilia

About: University of Modena and Reggio Emilia is a education organization based out in Modena, Italy. It is known for research contribution in the topics: Population & Medicine. The organization has 8179 authors who have published 22418 publications receiving 671337 citations. The organization is also known as: Università degli Studi di Modena e Reggio Emilia & Universita degli Studi di Modena e Reggio Emilia.

A Tabu search heuristic for the vehicle routing problem with two-dimensional loading constraints

Abstract: This article addresses the well-known Capacitated Vehicle Routing Problem (CVRP), in the special case where the demand of a customer consists of a certain number of two-dimensional weighted items. The problem calls for the minimization of the cost of transportation needed for the delivery of the goods demanded by the customers, and carried out by a fleet of vehicles based at a central depot. In order to accommodate all items on the vehicles, a feasibility check of the two-dimensional packing (2L) must be executed on each vehicle. The overall problem, denoted as 2L-CVRP, is NP-hard and particularly difficult to solve in practice. We propose a Tabu Search algorithm, in which the loading component of the problem is solved through heuristics, lower bounds, and a truncated branch-and-bound procedure. The effectiveness of the algorithm is demonstrated through extensive computational experiments. © 2007 Wiley Periodicals, Inc. NETWORKS, 2008

On the effectiveness of machine and deep learning for cyber security

Abstract: Machine learning is adopted in a wide range of domains where it shows its superiority over traditional rule-based algorithms. These methods are being integrated in cyber detection systems with the goal of supporting or even replacing the first level of security analysts. Although the complete automation of detection and analysis is an enticing goal, the efficacy of machine learning in cyber security must be evaluated with the due diligence. We present an analysis, addressed to security specialists, of machine learning techniques applied to the detection of intrusion, malware, and spam. The goal is twofold: to assess the current maturity of these solutions and to identify their main limitations that prevent an immediate adoption of machine learning cyber detection schemes. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic.