Abstract
Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in the data mining communities to evaluate extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioinformatics community by using a symbolic definition of time series motifs to derive each motif's p-value. We estimate the expected frequency of a motif by using Markov Chain models. The p-value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique-statistical tests-to a time series setting. This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif. © 2012 Wiley Periodicals, Inc.

Abstract
In this paper we present a quality model for spreadsheets based on the ISO/IEC 9126 standard that defines a generic quality model for software. To each of the software characteristics defined in the ISO/IEC 9126, we associate an equivalent spreadsheet characteristic. Then, we propose a set of spreadsheet specific metrics to assess the quality of a spreadsheet in each of the defined characteristics. To obtain the normal distribution of expected values for a spreadsheet in each of the proposed metrics, we have executed them in the widely used EUSES spreadsheet corpus. Then, we quantify each characteristic of our quality model after computing the values of our metrics, and we define quality scores for the different ranges of values. Finally, to automate the quality assessment of a given spreadsheet, according to our quality model, we have integrated the computation of the metrics it includes in both a batch and a web-based tool.

Abstract
This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economical situation.

Abstract
The spectral behavior in the C-band of fiber Bragg gratings (FBGs) was analyzed as a function of temperature and strain. The FBGs were fabricated in pure silica four-leaf-clover- shaped suspended-core fibers by (DUV) femtosecond laser exposure (3.6 W at 800 nm, 130 fs, 1 kHz frequency tripled to 350 fs, 650 mW at 267 nm). A defect fiber (with a hollow hole in the core) and nondefect fiber were compared both yielding approximate to 1 pm/mu epsilon sensitivity to strain but different sensitivity to temperature (from 3.0 pm/degrees C to 8.4 pm/degrees C for the defect fiber and 10 pm/degrees C for the nondefect fiber). The 16% to 70% relative difference between the thermal coefficients of the two fibers, together with their similar strain sensitivity enables the simultaneous measurement of strain and temperature.

Abstract
News can contain information which may provide an indication of the future direction of a share or stock market index. The possibility of predicting future stock market prices has attracted an increasing numbers of industry practitioners and academic researchers to this area of investigation. Popular approaches have relied upon either: models constructed from manually selected training or manually constructed dictionaries. A potential flaw of manually selecting data is that the effectiveness of the trained model is dependent upon the ability of the human annotator. An alternative approach is to manually align news stories with trends in a specific market. A negative story is inferred if it co-occurs with a market losing value where as positive story is associated with a rise. This approach may have its flaws because news stories may co-occur with market movements by chance and consequently may inhibit the construction of a robust classifier with data gathered by this method. This paper presents a strategy which combines a: rule classifier, alignment strategy and self-training to induce a robust model for classifying news stories. The proposed method is compared with several competing methodologies and is evaluated with: estimated F-Measure and estimated trading returns. In addition the paper provides an evaluation of classifying a news story with its: headline, description or story text with: Language Models and Naive Bayes. The results demonstrate a clear advantage for the proposed methodology when evaluated by estimated F-Measure. The proposed strategy also produces the highest trading returns. In addition the paper clearly demonstrates that a news story's headline provides the greatest assistance for classification. The models induced from headlines gained the highest estimated F-Measure and trading returns for each strategy with the exception of the alignment method which performed uniformly poorly. © Technomathematics Research Foundation.

Abstract
The vision towards the Network of the Future cannot be separated from the fact that today's networks, and networking services are subject to sophisticated and very effective attacks. When these attacks first appeared, spoofing and distributed denial-of-service attacks were treated as apocalypse for networking. Now, they are considered moderate damage, whereas more sophisticated and inconspicuous attacks, such as botnets activities, might have greater and far reaching impact. As the Internet is expanding to mobile phones and smart dust and as its social coverage is liberalized towards the realization of ubiquitous computing (with communication), the concerns on security and privacy have become deeper and the problems more challenging than ever. Re-designing the Internet as the Network of the Future is self-motivating for researchers, and security and privacy cannot be provided again as separate, external, add-on, solutions. In this paper, we discuss the security and privacy challenges of the Network of the Future and try to delimit the solutions space on the basis of emerging techniques. We also review methods that help the quantification of security and privacy in an effort to provide a more systematic and quantitative treatment of the area in the future. Copyright (c) 2011 John Wiley & Sons, Ltd.