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Research in automatic text plagiarism detection focuses on algorithms that compare suspicious documents against a collection of reference documents. Recent approaches perform well in identifying copied or modified foreign sections, but they assume a closed world where a reference collection is given. This article investigates the question whether plagiarism can be detected by a computer program if no reference can be provided, e.g., if the foreign sections stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism analysis; it is closely related to the problem of authorship verification. Our contributions are threefold. (1) We organize the algorithmic building blocks for intrinsic plagiarism analysis and authorship verification and survey the state of the art. (2) We show how the meta learning approach of Koppel and Schler, termed 'unmasking', can be employed to post-process unreliable stylometric analysis results. (3) We operationalize and evaluate an analysis chain that combines document chunking, style model computation, one-class classification, and meta learning. [ABSTRACT FROM AUTHOR]

This paper is about tool support for knowledge-intensive engineering tasks. In particular, it introduces software technology to assist the design of complex technical systems. There is a long tradition in automated design problem solving in the field of artificial intelligence, where, especially in the early stages, the search paradigm dictated many approaches. Later, in the so-called modern period, a better problem understanding led to the development of more adequate problem solving techniques. However, search still constitutes an indispensable part in computer-based design problem solving—albeit many human problem solvers get by without (almost). We tried to learn lessons from this observation, and one is presented in this paper. We introduce design problem solving by functional abstraction which follows the motto: construct a poor solution with little search, which then must be repaired. For the domain of fluidic engineering we have operationalized the paradigm by the combination of several high-level techniques. The red thread of this paper is design automation, but the presented technology does also contribute in the following respects: (a) productivity enhancement by relieving experts from auxiliary and routine tasks; (b) formulation, exchange, and documentation of knowledge about design; (c) requirements engineering, feasibility analysis, and validation. [ABSTRACT FROM AUTHOR]

Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on 'exact' translations but does not generalize well. [ABSTRACT FROM AUTHOR]