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There are wide range of cloud services commercially available.However there is limited research that investigates the strengths and weaknesses of their cost models in relation to different types of usage requirements. We propose a new costing model that systematically evaluates cloud services,and which combines compute, disk storage, and memory requirements. This paper demonstrates the proposed costing model on a data set that was derived from a real-world industrial data centre workload by calculating the precise cost of service provision from two leading cloud providers.

This article reports on the use of ensemble learning to classify the sentiment of tweets as being either positive or negative. Tweets were chosen because Twitter is both a popular tool and a public, human annotated dataset was made available as part of the SEMVAL 2013 competition. Researchers report on an approach to classification that contrasts single machine learning algorithms with a combination of algorithms in an ensemble learning approach. The single machines learning algorithms used were Support Vector Machine (SVM) and Naive Bayes (NB), while the method of ensemble learning was the arbiter tree. Their system achieved an F score using the arbiter tree at 83.55%, which was the same as SVM but quite slightly than NB algorithm.

This thesis addresses the problem of extracting a representation of text's meaning from its content. The solution investigation is based on the use of Roget's thesaurus as an external knowledge source and can be used to analyse texts of any length or complexity. The resulting document representation can then be compared to others, producing a new method for text similarity assessment.All coherent texts contain embedded sequences of words that are related to meaning. These sequences can be detected by identifying simple relationships between the relevant thesaural entries in which the words are found. The identification of initial sequences drives the addition of further related words into conceptually related \"lexical chains\".Although they differ in content, it is shown that the distribution of the links in these \"lexical chains\" is independent of the type of text in which they are embedded, and therefore this technique is of general applicability.Every coherent text contains many lexical chains of different lengths and strengths. These may be used to represent the broad subject matter of a text. By identifying the key concept of each chain, and relating to this to its presence we may produce an attribute value vector of concepts and their strengths. This may then be used to identify other texts as closer or further away in meaning.This thesis describes the creation of a tool suitable for the detection of lexical chains in large texts, and the design and implementation of algorithms to measure text similarity. The performance of the algorithms has been compared with human judgements and experimentally verified. The results show that lexical chain based similarity matching is capable of producing a ranking between a source text and several examples equivalent to that produced by human subjects. This illustrates the utility of Roget's thesaurus as a resource of the determination of lexical chains.

We describe a simple approach to word sense disambiguation using information filtering and extraction. The method fully exploits and extends the information available in the Hector dictionary. The algorithm proceeds by the application of several filters to prune the candidate set of word senses returning the most frequent if more than one remains. The experimental methodology and its implication are also discussed.

The third sector provides valuable services for local communities but is struggling due to reduced funding. More effective community engagement is called for by UK Government to devolve power and enable local improvements. Devolved power is often gained through local community associations who are tasked to manage community assets, calling for highly skilled community participants. They are under tremendous stress, which may have a detrimental impact on individuals' wellbeing. Community associations often struggle for capable community participants as they compete with the well-known giants of the voluntary sector. When the public think about participating in community activities there are a series of local social interactions that take place, culminating in a tipping point, when they decide to participate, or not. This process is complex with varying sources of information linking into decision making, which, when coupled with the needs of community associations, necessitates careful management to ensure the wellbeing of both. Prior evidence of complex networks with active communicating social agents show emergent properties.

Ellman reviews The Fall of Soviet Communism, 1985-91 by Jeremy Smith.