Corpus stylistics and colour symbolism in The Great Gatsby and its Thai translations

The present study adopts a corpus stylistic approach to: (1) examine a relationship between textual patterns of colour words in The Great Gatsby and their symbolic interpretations and (2) investigate the ways those patterns are handled in Thai translations. Distribution and co-occurrence patterns were analysed for colour words that are key in the novel: white, grey, yellow and lavender. The density and frequent patterns of each word are argued to foreground an association between the colour word and particular concepts, pointing to symbolic meaning potentials related to the novel’s themes of socioeconomic inequality and destructive wealth. The textual patterns are compared with what occurs in three Thai translations of the novel. While most of the colour images are directly translated, non-equivalents tend to be applied to figurative uses of the colour terms. This results in some changes in textual patterns of the colour words in the translated texts, which can in turn affect readers’ interpretations of colour symbolism in the novel.

The Status of English in Morocco: Lessons from Spontaneous Debates in Social Media

Morocco, a multilingual country with historical and geo-political legacies, has opened a hot debate on languages recently. Within this debate, this article investigates spontaneous comments in social media on languages in Morocco, especially adopting English as a first foreign language. It aims to bring this topic to the surface and thus discuss it in the light of research on language attitudes and language awareness. To do so, it analyses the reactions to texts about the declarations by the Minister of Higher Education shared in social networks and sites. The data consisting of 2,018 comments is classified according to 12 frequent patterns whose frequencies are calculated. The findings show that most of Moroccans have positive attitudes towards English while some show opposing reactions towards French. These participants hold ambivalent opinions about the rest of languages used in Morocco; however, they tend to insist on Morocco having a clear language policy which, seemingly, prioritises the mother tongues, Arabic and Amazigh. The discussions show that some investigated reactions are mitigated as they may be illusionary.

A bit-object-based method for mining maximum frequent patterns in intensive cloud computing data

In order to overcome the problems of poor timeliness and low accuracy of mining existing in traditional methods, this paper designs a bit-object based maximum frequent pattern mining method for intensive cloud computing data. After judging the support number according to the bit object of the maximum frequent pattern, the intensive cloud computing data is accurately collected according to the difference between the load value of cloud data and the true value of load, so as to improve the accuracy of subsequent mining results, and then the maximum frequent pattern of data is accurately mined by combining the bit object. Experimental results show that the maximum time to generate mining results is only 4.6 s, the maximum bit error rate of output results is only 7%, and the maximum memory occupancy is only 3.90%. The above results show that this method is more suitable for practical excavation.

Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.

Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

An Enhanced Apriori with Interestingness of Patterns using cSupport and rSupport

Data mining is wide spreading its applications in several areas. There are different tasks in mining which provides solutions for wide variety of problems in order to discover knowledge. Among those tasks association mining plays a pivotal role for identifying frequent patterns. Among the available association mining algorithms Apriori algorithm is one of the most prevalent and dominant algorithm which is used to discover frequent patterns. An enhancement to Apriori algorithm is done i.e. Apriori2 which minimized the number of scans. In this research Apriori2 is modified by including rSupport or cSupport. Also includes the comparison of these variants of APRIORI along with the proposed.