Automatic text summarization attempts to provide an effective solution to today's unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL and ESA methods are used to achieve sentence semantic parsing and to represent its words as a vector of weighted Wikipedia concepts, respectively. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. Results also tell that the system is scalable, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectoral semantic representation when combined with the crowd-sourced knowledge base in Wikipedia.

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An Electrocardiogram (ECG) signal describes the electrical activity of the heart recorded by electrodes placed on the surface of human body. It summarizes an important electrical activity used for the primary diagnosis of heart abnormalities such as Tachycardia, Bradycardia, Normalcy, Regularity and Heart Rate Variation. The most clinically useful information of the ECG signal is found in the time intervals between its consecutive waves and amplitudes defined by its features. In this paper, an ECG feature extraction algorithm based on Daubechies Wavelet Transform is presented. DB4 Wavelet is selected due to the similarity of its scaling function to the shape of the ECG signal. R peaks detection is the core of this algorithm's feature extraction. All other primary peaks are extracted with respect to the location of R peaks through creating windows proportional to their normal intervals. The proposed extraction algorithm is evaluated on MIT-BIH Arrhythmia Database. Experimental results indicate that the algorithm can successfully detect and extract all the primary features with a deviation error of less than 10%.

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Small and Medium Enterprises (SMEs) now generate digital data at an unprecedented rate from online transactions, social media marketing and associated customer interactions, online product/service reviews and feedback, clinical diagnosis, Internet of Things (IoT) sensors, and production processes. All these forms of data can be transformed into monetary value if put into a proper data value chain. This requires both skills and IT investments for the longterm benefit of businesses. However, such spending is beyond the capacity of most SMEs due to their limited resources and restricted access to finance. This paper presents lessons learned from a case study of 53 UK SMEs, mostly from the West Midlands region of England, supported as part of a 3-year ERDF 1 project -Big Data Corridor 2 -in the areas of big data management, analytics and related IT issues. Based on our study's sample companies, several perspectives including digital technology trends, challenges facing the UK SMEs, and the state of their adoption in data analytics and big data, are presented.