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The increasing growth of electricity usage in buildings points out the significant role of residential users in the programs for the efficient control and management of electrical energy. The shaving of consumption peaks in household is becoming an integral part of the national energy strategies to reduce the risk of blackouts and ensure environmental sustainability of new urban context. This paper investigates the use of the paradigm of swarm intelligence to scheduling the operation of household appliances in order to reduce to smooth the variation and reduce the peak-to-average ratio of total electricity demand at home. Simulation results confirm the proposed approach

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""\\"The transformation of the traditional power grid into a network of intelligent energy distribution able to meet the growing needs of efficiency and environmental impact involves, not only a historical and technical development, but also a profound transformation of the entire existing infrastructure. Given the undeniable benefits introduced, the new smart energy network vulnerabilities are found both in the communication system and power distribution. This paper aims to explore requirements for the smart grid security issues.\\""

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""In this work the authors propose a classification method based on Support Vector Machine (SVM) and key frames features extraction to classify historical sport video contents. In the context of the Italian Project, IRMA (Information Retrieval in Multimedia Archives), with the goal to recover and preserve historical videos of proven cultural interest, a data set made up of several hours of videos from the 1960 Olympic games, provided by RAI and Teche RAI, is adopted as testbed. Each video is summarized by its key frames and represented by the features vectors computed in the Laguerre Gauss transformed domain. The high-level video classification starts from these vectors that are the input of the SVM classifier. The experimental results show the effectiveness of the proposed method."

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