Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations

Identifying academic plagiarism is a pressing task for educational and research institutions, publishers, and funding agencies. Current plagiarism detection systems reliably find instances of copied and moderately reworded text. However, reliably detecting concealed plagiarism, such as strong paraphrases, translations, and the reuse of nontextual content and ideas is an open research problem. In this paper, we extend our prior research on analyzing mathematical content and academic citations. Both are promising approaches for improving the detection of concealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detection process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similarity measures that consider the order of mathematical features and outperform the measures in our prior research. iii) We compare the effectiveness of the math-based, citation-based, and text-based detection approaches using confirmed cases of academic plagiarism. iv) We demonstrate that the combined analysis of math-based and citation-based content features allows identifying potentially suspicious cases in a collection of 102K STEM documents. Overall, we show that analyzing the similarity of mathematical content and academic citations is a striking supplement for conventional text-based detection approaches for academic literature in the STEM disciplines.Comment: Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) 2019. The data and code of our study are openly available at https://purl.org/hybridP

Epidemiology of invasive aspergillosis and azole resistance in patients with acute leukaemia: the SEPIA Study

Invasive aspergillosis (IA) is a serious hazard to high-risk haematological patients. There are increasing reports of azole-resistant Aspergillus spp. This study assessed the epidemiology of IA and azoleresistant Aspergillus spp. in patients with acute leukaemia in Germany. A prospective multicentre cohort study was performed in German haematology/oncology centres. The incidence of probable and proven aspergillosis according to the revised EORTC/MSG criteria was assessed for all patients with acute leukaemia [acutemyeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL)]. Caseswere documented into a web-based case report form, and centres provided data on standards regarding prophylactic and diagnostic measures. Clinical isolates were screened centrally for azole resistance and, if applicable, underlying resistance mechanisms were analysed. Between September 2011 and December 2013, 179 cases of IA [6 proven (3.4%) and 173 probable (96.6%)] were diagnosed in 3067 patients with acuteleukaemia. The incidence of IA was 6.4% among 2440 AML patients and 3.8% among 627 ALL patients. Mortality at Day 84 was 33.8% (49/145) and attributable mortality was 26.9% (39/145). At Day 84, 53 patients (29.6%) showed a complete response, 25 (14.0%) a partial response and 17 (9.5%) a deterioration or failure. A total of 77 clinical Aspergillus fumigatus isolates were collected during the study period. Two episodes of azole-resistant IA (1.1%) were caused by a TR/L98H mutation in the cyp51A gene. With only two cases of IA due to azole-resistant A. fumigatus, a change of antifungal treatment practices in Germany does not appear warranted currently. (C) 2016 Elsevier B.V. and International Society of Chemotherapy. All rights reserved