Abstract
This paper assesses if text possesses fractal properties, namely if several attributes that characterize sentences are self-similar. In order to do that, seven corpora were analyzed using several statistical tools, so as to determine if the empirical sequences for the attributes were Gaussian and self-similar. The Kolmogorov-Smirnov goodness-of-fit test and two Hurst parameter estimators were employed. The results show that there is a fractal beauty in the text produced by humans and suggest that its quality is directly proportional to the self-similarity degree.

Abstract

Abstract
The list of significant papers from the first 25 years of the Field-Programmable Logic and Applications conference (FPL) is presented in this paper. These 27 papers represent those which have most strongly influenced theory and practice in the field.

Abstract
In a modern industrial environment control programs are distributed among several devices. This raises new issues and challenges especially in failure modes. Building fault tolerant applications can be the solution in order a failure of one sub-component not to jeopardize the execution of the whole application. The authors have proposed a framework to support replicated IEC 61499 applications. In this paper we augment this framework with the support for different voting strategies, propose an extension of the replication communication protocol, and analyse the resulting fault-tolerance semantics. A limited implementation of the framework is also described.

Abstract
Data stream mining is an emergent research area that investigates knowledge extraction from large amounts of continuously generated data, produced by non-stationary distribution. Novelty detection, the ability to identify new or previously unknown situations, is a useful ability for learning systems, especially when dealing with data streams, where concepts may appear, disappear, or evolve over time. There are several studies currently investigating the application of novelty detection techniques in data streams. However, there is no consensus regarding how to evaluate the performance of these techniques. In this study, we propose a new evaluation methodology for multiclass novelty detection in data streams able to deal with: i) unsupervised learning, which generates novelty patterns without an association with the true classes, where one class may be composed of a novelty set, ii) confusion matrix that increases over time, iii) confusion matrix with a column representing unknown examples, i.e., those not explained by the model, and iv) representation of the evaluation measures over time. We propose a new methodology to associate the novelty patterns detected by the algorithm, in an unsupervised fashion, with the true classes. Finally, we evaluate the performance of the proposed methodology through the use of known novelty detection algorithms with artificial and real data sets.

Abstract
Internet sales channels, especially e-brokers that compare prices in the market, have a major impact on car rentals. As costs are heavily correlated with unoccupied fleet, occupation considerations should be integrated with swift responses to the market prices. This work was developed alongside Guerin, a Portuguese car rental, to build a tool that quickly updates prices on e-brokers websites to increase total value. This paper describes the specificities of the problem and their implication on the solution, and presents an adaptative heuristic to update prices and the system’s architecture. © 2015, Springer International Publishing Switzerland.