Abstract
This paper seeks to identify and discuss mobile network operators' (MNOs) business opportunities in spectrum sharing where the several MNOs would share the same spectrum band. This sharing model, here called co-primary spectrum sharing (CoPSS), aims to increase the efficiency of spectrum use by introducing licensed sharing between MNOs in new bands where two or more MNOs would get the license to operate on a shared basis, instead of separate exclusive licenses for different MNOs or unlicensed sharing. The paper starts with a discussion concerning the domains needed for defining CoPSS: a) type of spectrum authorized for sharing, b) temporal, spatial, and spectral dimensions of shared resources, c) degree of information shared, and presents a definition of CoPSS. Regarding CoPSS business opportunities for MNOs we present CoPSS enabling and limiting factors and CoPSS related challenges within a framework of value co-creation, co-capture, and co-opetition. The paper concludes with a variety of alternative ways of doing business with CoPSS. © 2014 ICST.

Abstract
In order to develop an hybrid approach to the Reverse Engineer of Web applications, we need first to understand how much of the control logic of the user interface can be obtained from the analysis of event listeners. To that end, we have developed a tool that enables us to perform such analysis, and applied it to the implementation of the one thousand most widely used Websites (according to Alexa Top Sites). This paper describes our approach for analyzing the user interface layer of those Websites, and the results we got from the analysis. The conclusions drawn from the exercise will be used to guide the development of the proposed hybrid reverse engineering tool.

Abstract
Adapting technological environments to users is a concern since Mark Weiser launched the concept of ubiquitous computing and, in order to do that, is necessary to understand users' characteristics. In this context, the purpose of this paper is to present a study about students' mobility habits within a university campus, having the intention of getting insights towards the best place to set an interactive public display and of predicting the main characteristics of the audience that will be present on that spot in forthcoming periods. Thus, the envisioned results of this work will allow the adaptation of the contents exhibited on the device to the audience. To perform the study, a set of logs of accesses to the university's Wi-Fi was used, data mining techniques were implemented and forecasting models were built, using the line of work suggested by the CRISP-DM methodology. As result, students profile were built based on past wireless accesses and on their scholar schedules, and three time series models were used (Holt-Winters, Seasonal Naive and Simple Exponential Smoothing) to predict the presence of students on the envisioned spot in future periods.

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.