Abstract
Nowadays, video surveillance systems are taking the first steps toward automation, in order to ease the burden on human resources as well as to avoid human error. As the underlying data distribution and the number of concepts change over time, the conventional learning algorithms fail to provide reliable solutions for this setting. In this paper, we formalize a learning concept suitable for multi-camera video surveillance and propose a learning methodology adapted to that new paradigm. The proposed framework resorts to the universal background model to robustly learn individual object models from small samples and to more effectively detect novel classes. The individual models are incrementally updated in an ensemble-based approach, with older models being progressively forgotten. The framework is designed to detect and label new concepts automatically. The system is also designed to exploit active learning strategies, in order to interact wisely with operator, requesting assistance in the most ambiguous to classify observations. The experimental results obtained both on real and synthetic data sets verify the usefulness of the proposed approach.

Abstract
This paper presents the iMPAcT tool that tests recurring behaviour, i.e., UI Patterns, on mobile applications. This tool is implemented in Java and makes use of Android's APIs UI Automator and UiAutomation. The tool automatically explores a mobile application in order to automatically identify and test UI Patterns. Each UI Pattern has a test strategy, Test Patterns, associated, which is applied when an UI Pattern is found. The approach works on top of a catalogue of UI Patterns, which determines which UI Patterns are to be tested, and what should their correct behaviour be, and may be used for any application.

Abstract
The evolution of the electrical grid into a smart grid, allowing user production, storage, and exchange of energy; remote control of appliances; and, in general, optimizations over how the energy is managed and consumed, is an evolution into a complex information and communication technology (ICT) system. With the goal of promoting an integrated and interoperable smart grid, a number of organizations all over the world started uncoordinated standardization activities, which caused the emergence of a large number of incompatible architectures and standards. There are now new standardization activities that have the goal of organizing existing standards and produce best practices to choose the right approach(es) to be employed in specific smart grid designs. This paper follows the lead of the National Institute of Standards and Technology (NIST) and the European Telecommunications Standards Institute/European Committee for Standardization/European Committee for Electrotechnical Standardization (ETSI/CEN/CENELEC) approaches in trying to provide taxonomy of existing solutions; our contribution reviews and relates current ICT state of the art with the objective of forecasting future trends based on the orientation of current efforts and on relationships between them. The resulting taxonomy provides guidelines for further studies of the architectures, and highlights how the standards in the last mile of the smart grid are converging to common solutions to improve ICT infrastructure interoperability.

Abstract
The intermittent nature of wind generation will lead to greater demands for operational flexibility. Traditionally, reserves came from conventional power plants provide the majority of additional required flexibility leading to higher efficiency losses due to technical restrictions of such units. Recently, demand response programs and emerging utility-scale energy storages gained much attention as other flexible options. Under this perspective, this paper proposes a robust optimization scheduling framework to derive an optimal unit commitment decision in systems with high penetration of wind power incorporating demand response programs as well as bulk energy storages in co-optimized energy and reserve markets. In this regard, an improved demand response model is presented using the economic model of responsive loads based on customer's behavior concept that gives choice right opportunity to customers in order to participate in their desired demand response strategy. Moreover, bulk energy storages are considered to be as active market participants. Computational results demonstrate how coordinated operation of different type of demand response programs and bulk energy storages can help accommodate wind power uncertainty from the economic and technical points of view.

Abstract
The organizations' demand to integrate several heterogeneous data sources and an ever-increasing volume of data is revealing the presence of quality problems in data. Currently, most of the data cleaning approaches (for detection and correction of data quality problems) are tailored for data sources with the same schema and sharing the same data model (e.g., relational model). On the other hand, these approaches are highly dependent on a domain expert to specify the data cleaning operations. This paper extends a previously proposed data cleaning methodology that reuses cleaning knowledge specified for other data sources. The methodology is further detailed/refined by specifying the requirements that a data cleaning operations vocabulary must satisfy. Ontologies in RDF/OWL are proposed as the data model for an abstract representation of the data schemas, no matter which data model is used (e.g., relational; graph). Existing approaches, methods and techniques that support the implementation of the proposed methodology, in general, and specifically of the data cleaning operations vocabulary are also presented and discussed in this paper.

Abstract
Since high dynamic range (HDR) displays are not yet widely available, there is still a need to perform a dynamic range reduction of HDR content to reproduce it properly on standard dynamic range (SDR) displays. The most common techniques for performing this reduction are termed tone-mapping operators (TMOs). Although mobile devices are becoming widespread, methods for displaying HDR content on these SDR screens are still very much in their infancy. While several studies have been conducted to evaluate TMOs, few have been done with a goal of testing small screen displays (SSDs), common on mobile devices. This paper presents an evaluation of six state-of-the-art HDR video TMOs. The experiments considered three different levels of ambient luminance under which 180 participants were asked to rank the TMOs for seven tone-mapped HDR video sequences. A comparison was conducted between tone-mapped HDR video footage shown on an SSD and on a large screen SDR display using an HDR display as reference. The results show that there are differences between the performance of the TMOs under different ambient lighting levels and the TMOs that perform well on traditional large screen displays also perform well on SSDs at the same given luminance level.