Identifying idiolect in forensic authorship attribution: an n-gram textbite approach

Forensic authorship attribution is concerned with identifying authors of disputed or anonymous documents, which are potentially evidential in legal cases, through the analysis of linguistic clues left behind by writers. The forensic linguist “approaches this problem of questioned authorship from the theoretical position that every native speaker has their own distinct and individual version of the language [. . . ], their own idiolect” (Coulthard, 2004: 31). However, given the diXculty in empirically substantiating a theory of idiolect, there is growing concern in the Veld that it remains too abstract to be of practical use (Kredens, 2002; Grant, 2010; Turell, 2010). Stylistic, corpus, and computational approaches to text, however, are able to identify repeated collocational patterns, or n-grams, two to six word chunks of language, similar to the popular notion of soundbites: small segments of no more than a few seconds of speech that journalists are able to recognise as having news value and which characterise the important moments of talk. The soundbite oUers an intriguing parallel for authorship attribution studies, with the following question arising: looking at any set of texts by any author, is it possible to identify ‘n-gram textbites’, small textual segments that characterise that author’s writing, providing DNA-like chunks of identifying material

The Incidence and Clinical Relevance of Graft Hypertrophy After Matrix-Based Autologous Chondrocyte Implantation

Background: Graft hypertrophy is the most common complication of periosteal autologous chondrocyte implantation (p-ACI). Purpose: The aim of this prospective study was to analyze the development, the incidence rate, and the persistence of graft hypertrophy after matrix-based autologous chondrocyte implantation (mb-ACI) in the knee joint within a 2-year postoperative course. Study Design: Case series; Level of evidence, 4. Methods: Between 2004 and 2007, a total of 41 patients with 44 isolated cartilage defects of the knee were treated with the mb-ACI technique. The mean age of the patients was 35.8 years (standard deviation [SD], 11.3 years), and the mean body mass index was 25.9 (SD, 4.2; range, 19-35.3). The cartilage defects were arthroscopically classified as Outerbridge grades III and IV. The mean area of the cartilage defect measured 6.14 cm2 (SD, 2.3 cm2). Postoperative clinical and magnetic resonance imaging (MRI) examinations were conducted at 3, 6, 12, and 24 months to analyze the incidence and course of the graft. Results: Graft hypertrophy developed in 25% of the patients treated with mb-ACI within a postoperative course of 1 year; 16% of the patients developed hypertrophy grade 2, and 9% developed hypertrophy grade 1. Graft hypertrophy occurred primarily in the first 12 months and regressed in most cases within 2 years. The International Knee Documentation Committee (IKDC) and visual analog scale (VAS) scores improved during the postoperative follow-up time of 2 years. There was no difference between the clinical results regarding the IKDC and VAS pain scores and the presence of graft hypertrophy. Conclusion: The mb-ACI technique does not lead to graft hypertrophy requiring treatment as opposed to classic p-ACI. The frequency of occurrence of graft hypertrophy after p-ACI and mb-ACI is comparable. Graft hypertrophy can be considered as a temporary excessive growth of regenerative cartilage tissue rather than a true graft hypertrophy. It is therefore usually not a persistent or systematic complication in the treatment of circumscribed cartilage defects with mb-ACI

Knowledge Discovery in Online Repositories: A Text Mining Approach

Before the advent of the Internet, the newspapers were the prominent instrument of mobilization for independence and political struggles. Since independence in Nigeria, the political class has adopted newspapers as a medium of Political Competition and Communication. Consequently, most political information exists in unstructured form and hence the need to tap into it using text mining algorithm. This paper implements a text mining algorithm on some unstructured data format in some newspapers. The algorithm involves the following natural language processing techniques: tokenization, text filtering and refinement. As a follow-up to the natural language techniques, association rule mining technique of data mining is used to extract knowledge using the Modified Generating Association Rules based on Weighting scheme (GARW). The main contributions of the technique are that it integrates information retrieval scheme (Term Frequency Inverse Document Frequency) (for keyword/feature selection that automatically selects the most discriminative keywords for use in association rules generation) with Data Mining technique for association rules discovery. The program is applied to Pre-Election information gotten from the website of the Nigerian Guardian newspaper. The extracted association rules contained important features and described the informative news included in the documents collection when related to the concluded 2007 presidential election. The system presented useful information that could help sanitize the polity as well as protect the nascent democracy

A Planning Approach to Migrating Domain-specific Legacy Systems into Service Oriented Architecture

The planning work prior to implementing an SOA migration project is very important for its success. Up to now, most of this kind of work has been manual work. An SOA migration planning approach based on intelligent information processing methods is addressed to semi-automate the manual work. This thesis will investigate the principle research question: “How can we obtain SOA migration planning schemas (semi-) automatically instead of by traditional manual work in order to determine if legacy software systems should be migrated to SOA computation environment?”. The controlled experiment research method has been adopted for directing research throughout the whole thesis. Data mining methods are used to analyse SOA migration source and migration targets. The mined information will be the supplementation of traditional analysis results. Text similarity measurement methods are used to measure the matching relationship between migration sources and migration targets. It implements the quantitative analysis of matching relationships instead of common qualitative analysis. Concretely, an association rule and sequence pattern mining algorithms are proposed to analyse legacy assets and domain logics for establishing a Service model and a Component model. These two algorithms can mine all motifs with any min-support number without assuming any ordering. It is better than the existing algorithms for establishing Service models and Component models in SOA migration situations. Two matching strategies based on keyword level and superficial semantic levels are described, which can calculate the degree of similarity between legacy components and domain services effectively. Two decision-making methods based on similarity matrix and hybrid information are investigated, which are for creating SOA migration planning schemas. Finally a simple evaluation method is depicted. Two case studies on migrating e-learning legacy systems to SOA have been explored. The results show the proposed approach is encouraging and applicable. Therefore, the SOA migration planning schemas can be created semi-automatically instead of by traditional manual work by using data mining and text similarity measurement methods

Pattern Mining and Sense-Making Support for Enhancing the User Experience

While data mining techniques such as frequent itemset and sequence mining are well established as powerful pattern discovery tools in domains from science, medicine to business, a detriment is the lack of support for interactive exploration of high numbers of patterns generated with diverse parameter settings and the relationships among the mined patterns. To enhance the user experience, real-time query turnaround times and improved support for interactive mining are desired. There is also an increasing interest in applying data mining solutions for mobile data. Patterns mined over mobile data may enable context-aware applications ranging from automating frequently repeated tasks to providing personalized recommendations. Overall, this dissertation addresses three problems that limit the utility of data mining, namely, (a.) lack of interactive exploration tools for mined patterns, (b.) insufficient support for mining localized patterns, and (c.) high computational mining requirements prohibiting mining of patterns on smaller compute units such as a smartphone. This dissertation develops interactive frameworks for the guided exploration of mined patterns and their relationships. Contributions include the PARAS pre- processing and indexing framework; enabling analysts to gain key insights into rule relationships in a parameter space view due to the compact storage of rules that enables query-time reconstruction of complete rulesets. Contributions also include the visual rule exploration framework FIRE that presents an interactive dual view of the parameter space and the rule space, that together enable enhanced sense-making of rule relationships. This dissertation also supports the online mining of localized association rules computed on data subsets by selectively deploying alternative execution strategies that leverage multidimensional itemset-based data partitioning index. Finally, we designed OLAPH, an on-device context-aware service that learns phone usage patterns over mobile context data such as app usage, location, call and SMS logs to provide device intelligence. Concepts introduced for modeling mobile data as sequences include compressing context logs to intervaled context events, adding generalized time features, and identifying meaningful sequences via filter expressions