Abstract
Large-scale distributed systems appear as the major infrastructures for supporting planet-scale services. These systems call for appropriate management mechanisms and protocols. Slicing is an example of an autonomous, fully decentralized protocol suitable for large-scale environments. It aims at organizing the system into groups of nodes, called slices, according to an application-specific criteria where the size of each slice is relative to the size of the full system. This allows assigning a certain fraction of nodes to different task, according to their capabilities. Although useful, current slicing techniques lack some features of considerable practical importance. This paper proposes a slicing protocol, that builds on existing solutions, and addresses some of their frailties. We present novel solutions to deal with non-uniform slices and to perform online and dynamic slices schema reconfiguration. Moreover, we describe how to provision a slice-local Peer Sampling Service for upper protocol layers and how to enhance slicing protocols with the capability of slicing over more than one attribute. Slicing is presented as a complete, dependable and integrated distributed systems primitive for large-scale systems.

Abstract
Traffic Sampling is a crucial step towards scalable network measurements, enclosing manifold challenges. The wide variety of foreseeable sampling scenarios demands for a modular view of sampling components and features, grounded on a consistent architecture. Articulating the measurement scope, the required information model and the adequate sampling strategy is a major design issue for achieving an encompassing and efficient sampling solution. This is the main focus of the present work, where a layered architecture, a taxonomy of existing sampling techniques distinguishing their inner characteristics and a flexible framework able to combine these characteristics are introduced. In addition, a new multiadaptive technique proposal, based on linear prediction, allows to reduce the measurement overhead significantly, while assuring that traffic samples reflect the statistical behavior of the global traffic under analysis.

Abstract
Rationale, aims and objectivesTraditional complexity measures are used to capture the amount of structured information present in a certain phenomenon. Several approaches developed to facilitate the characterization of complexity have been described in the related literature. Fetal heart rate (FHR) monitoring has been used and improved during the last decades. The importance of these studies lies on an attempt to predict the fetus outcome, but complexity measures are not yet established in clinical practice. In this study, we have focused on two conceptually different measures: Shannon entropy, a probabilistic approach, and Kolmogorov complexity, an algorithmic approach. The main aim of the current investigation was to show that approximation to Kolmogorov complexity through different compressors, although applied to a lesser extent, may be as useful as Shannon entropy calculated by approximation through different entropies, which has been successfully applied to different scientific areas. MethodsTo illustrate the applicability of both approaches, two entropy measures, approximate and sample entropy, and two compressors, paq8l and bzip2, were considered. These indices were applied to FHR tracings pertaining to a dataset composed of 48 delivered fetuses with umbilical artery blood (UAB) pH in the normal range (pH7.20), 10 delivered mildly acidemic fetuses and 10 moderate-to-severe acidemic fetuses. The complexity indices were computed on the initial and final segments of the last hour of labour, considering 5- and 10-minute segments. ResultsIn our sample set, both entropies and compressors were successfully utilized to distinguish fetuses at risk of hypoxia from healthy ones. Fetuses with lower UAB pH presented significantly lower entropy and compression indices, more markedly in the final segments. ConclusionsThe combination of these conceptually different measures appeared to present an improved approach in the characterization of different pathophysiological states, reinforcing the theory that entropies and compressors measure different complexity features. In view of these findings, we recommend a combination of the two approaches.

Abstract
User Interface (UI) patterns are used extensively in the design of today's software. UI patterns embody commonly recurring solutions that solve common GUI design problems, such as "login," "file-open," and "search." Yet, testing of GUIs for functional correctness has largely ignored UI patterns. This paper formalizes the notion of a Pattern-Based Graphical User Interface (GUI) Testing method (PBGT) for systematizing and automating the GUI testing process. The space of all possible interactions with a GUI is typically very large. PBGT presents a new methodology to sample the input space using " UI Test Patterns," that embody commonly recurring solutions to test GUIs. Our empirical studies show that the PBGT methodology is effective in revealing faults in fielded GUIs.

Abstract
The presence of code replication can be a consequence of a lack in the composition mechanisms where classes are insufficient to reuse the code that is replicated. To extend the reuse of pieces of code some proposals have been made that try to compose classes using those pieces of code. In this paper we compare two of those approaches: Traits and Roles. We compare their compositions mechanisms and how we can use them to reduce code replication. To study the extent to which they reduce code replication we conducted a case study using the JHotDraw framework where we detect and remove code replication using each technique. Results from the case study show that roles have an advantage over traits, as they are capable of removing more code replication.

Abstract