Abstract
Bugs in multithreaded application can be elusive. They are often hard to trace and replicate, given the usual non-determinism and irreproducibility of scheduling decisions at runtime. We present Cooperari, a tool for deterministic testing of multithreaded Java code based on cooperative execution. In a cooperative execution, threads voluntarily suspend (yield) at interference points (e.g., lock acquisition), and code between two consecutive yield points of each thread always executes serially as a transaction. A cooperative scheduler takes over control at yield points and deterministically selects the next thread to run. An application test runs multiple times, until it either fails or the state-space of schedules is deemed as covered by a configurable policy that is responsible for the scheduling decisions. Beyond failed assertions in software tests, deadlocks and races are also detected as soon as they are exposed in the cooperative execution. Cooperari effectively finds, characterizes, and deterministically reproduces bugs that are not detected under unconstrained preemptive semantics, as illustrated by standard benchmark examples.

Abstract
Query specification for 3D object retrieval still relies on traditional interaction paradigms. The goal of our study was to identify the most natural methods to describe 3D objects, focusing on verbal and gestural expressions. Our case study uses LEGOR blocks. We started by collecting a corpus involving ten pairs of subjects, in which one participant requests blocks for building a model from another participant. This small corpus suggests that users prefer to describe 3D objects verbally, rarely resorting to gestures, and using them only as complement. The paper describes this corpus, addressing the challenges that such verbal descriptions create for a speech understanding system, namely the long complex verbal descriptions, involving dimensions, shapes, colors, metaphors, and diminutives. The latter connote small size, endearment or insignificance, and are only very common in informal language. In this corpus, they occurred in one out of seven requests. This experiment was the first step of the development of a prototype for searching LEGOR blocks combining speech and stereoscopic 3D. Although the verbal interaction in the first version is limited to relatively simple queries, its combination with immersive visualization allows the user to explore query results in a dataset with virtual blocks.

Abstract
The expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs' characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

Abstract
The mature bulk power system requires to meet the needs of 21th century in terms of efficient and effective utilization of electric energy, together with the capability of accommodating recently growing renewable energy resources penetration. As a new idea of modernizing the current grid structure, the smart grid issue is a widely growing area of interest with investments from developed/developing country governments. As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest as such strategies provide consumers the chance for the real-time control of their consumption to reduce their bills, while utilities can lower the peak power value to be supplied to consumers. As a new type of consumer load in the electric market, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-grid (V2G) option instead of peak power procurement from utility. This study aims to discuss the impacts of different DR strategies and EV owner consumer preferences on the reduction of total electricity prices. Different case studies are conducted to better analyze the price reduction potential of different operating strategies.

Abstract
Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. © 2014 Springer-Verlag.

Abstract
This paper presents an approach for testing mobile applications using reverse engineering and behavioural patterns. The goal of this research work is to ease the testing of mobile applications by automatically identifying and testing behaviour that is common in this type of applications, i.e., behaviour patterns. The approach includes a tool to automatically explore an Android application. This tool also identifies patterns in the behaviour of the application and apply tests previously associated with those patterns. The final results of this research work will be a catalogue of behavioural patterns and the tool which will output a report on the matched patterns and another one on the testing of those patterns.