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.

Many of the current approaches used to teaching and learning in engineering education are not the most appropriate to prepare students for the challenges they will face in their professional careers. The active involvement of students in their learning process facilitates the development of the technical and professional competencies they need as professionals. This article describes the organisation and impact of a mini-conference and project work - the creation of a software product and its introduction in the market - aimed at the development of professional competencies in general and writing skills in particular. The course was evaluated by assessing the students’ perception of the development of a number of professional competencies through a questionnaire completed by 125 students of two consecutive editions. The results indicate that the project work and the mini-conference had a positive impact on students’ perceptions of the development of professional competencies.

Software QoS properties, such as response time, availability, bandwidth requirement, memory usage, among many others, play a major role in the processes of selecting and composing software components. This paper extends a component calculus to deal, in an effective way, with them. The calculus models components as generalised Mealy machines, i.e., state-based entities interacting along their life time through well defined interfaces of observers and actions. QoS is introduced through an algebraic structure specifying the relevant QoS domain and how its values are composed under different disciplines. A major effect of introducing QoS-awareness is that a number of equivalences holding in the plain calculus become refinement laws. The paper also introduces a prototyper for the calculus developed as a ‘proof-of-concept’ implementation.

Weighted automata are a generalisation of non-deterministic automata where each transition, in addition to an input letter, has also a quantity expressing the weight (e.g. cost or probability) of its execution. As for non-deterministic automata, their behaviours can be expressed in terms of either (weighted) bisimilarity or (weighted) language equivalence.

Coalgebras provide a categorical framework for the uniform study of state-based systems and their behaviours. In this work, we show that coalgebras can suitably model weighted automata in two different ways: coalgebras on Set (the category of sets and functions) characterise weighted bisimilarity, while coalgebras on Vect (the category of vector spaces and linear maps) characterise weighted language equivalence.

Relying on the second characterisation, we show three different procedures for computing weighted language equivalence. The first one consists in a generalisation of the usual partition refinement algorithm for ordinary automata. The second one is the backward version of the first one. The third procedure relies on a syntactic representation of rational weighted languages.

A considerable amount of time in software engineering is spent in debugging. In practice, mainly debugging tools which allow for executing a program step-by-step and setting break points are used. This debugging method is however very time consuming and cumbersome. There is a need for tools which undertake the task of narrowing down the most likely fault locations. These tools must complete this task with as little user interaction as possible and the results computed must be beneficial so that such tools appeal to programmers. In order to come up with such tools, we present three variants of the well-known spectrum-based fault localization technique that are enhanced by using methods from Artificial Intelligence. Each of the three combined approaches outperforms the underlying basic method concerning diagnostic accuracy. Hence, the presented approaches support the hypothesis that combining techniques from different areas is beneficial. In addition to the introduction of these techniques, we perform an empirical evaluation, discuss open challenges of debugging and outline possible solutions.

Modern software systems must support a high degree of variability to accommodate a wide range of requirements and operating conditions. This paper introduces the Abstract Behavioural Specication (ABS) language and tool suite, a comprehensive platform for developing and analysing highly adaptable distributed concurrent software systems. The ABS language has a hybrid functional and object-oriented core, and comes with extensions that support the development of systems that are adaptable to diversied requirements, yet capable to maintain a high level of trustworthiness. Using ABS, system variability is consistently traceable from the level of requirements engineering down to object behaviour. This facilitates temporal evolution, as changes to the required set of features of a system are automatically re ected by functional adaptation of the system's behaviour. The analysis capabilities of ABS stretch from debugging, observing and simulating to resource analysis of ABS models and help ensure that a system will remain dependable throughout its evolutionary lifetime. We report on the experience of using the ABS language and the ABS tool suite in an industrial case study.

Detection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, called UEOC, which means, 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge on the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and was compared with a set of competing algorithms. Experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities.