Plagiarism detection using feature-based neural networks

SIGCSE 2007: 38th SIGCSE Technical Symposium on Computer Science Education

Volumn , Issue , 2007, Pages 34-38

  Engels, Steve ^a   Lakshmanan, Vivek ^a   Craig, Michelle ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

Cheating; Features; Introductory computer science; Neural networks; Plagiarism detection

Indexed keywords

CODES (SYMBOLS); COMPUTER SCIENCE; FEATURE EXTRACTION; TEXT PROCESSING;

COURSE INSTRUCTORS; FEATURE-BASED PLAGIARISM DETECTOR; INTRODUCTORY COMPUTER SCIENCE; PLAGIARISM DETECTION;

NEURAL NETWORKS;

EID: 34748904624     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1227310.1227324     Document Type: Conference Paper

References (6)

1. Hinton, G.E. Supervised learning in multilayer neural networks. In The MIT Encyclopedia of the Cognitive Sciences. The MIT Press.
2. D. McCabe. Levels of Cheating and Plagiarism Remain High. Center for Academic Integrity, Duke University, 2005. Website: http:// academicintegrity.org
3. Lutz Prechelt, Guido Malpohl, Michael Philippsen. Finding plagiarisms among a set of programs with JPlag. Journal of Universal Computer Science, 8(11):1016-1038, November 2002.
4. S. Schleimer, D. Wilkerson, and A. Aiken. Winnowing: Local Algorithms for Document Fingerprinting. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 76-85, June 2003.
5. Chen, O.T.-C. and Sheu, B.J.; Optimization schemes for neural network training. 1994 IEEE International Conference on Computational Intelligence, pages 817-822 vol.2, June 1994.
6. I. Nabney, Netlab Software, Neural Computing Research Group, Information Engineering, Aston University, Birmingham, U.K. http://www.ncrg. aston.ac.uk/netlab/index.php