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Computing & Information Sciences Kansas State University Paper Review Guidelines KDD Lab Course Supplement William H. Hsu Kansas State University Department.

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Presentation on theme: "Computing & Information Sciences Kansas State University Paper Review Guidelines KDD Lab Course Supplement William H. Hsu Kansas State University Department."— Presentation transcript:

1 Computing & Information Sciences Kansas State University Paper Review Guidelines KDD Lab Course Supplement William H. Hsu Kansas State University Department of Computing and Information Sciences Laboratory for Knowledge Discovery in Databases (KDD Lab) Course web site: http://www.kddresearch.org/Courses/http://www.kddresearch.org/Courses/ Instructor home page: http://www.cis.ksu.edu/~bhsuhttp://www.cis.ksu.edu/~bhsu Paper Review Guidelines for AI, Database, and Computer Graphics Courses

2 Computing & Information Sciences Kansas State University Paper Review Guidelines KDD Lab Reviews and Literature Reviews Courses with Paper Reviews  CIS 598: Undergraduate Independent Study  CIS 690: Data Mining Systems (CIS 535)  CIS 636: Introduction to Computer Graphics  CIS 732: Machine Learning and Pattern Recognition  CIS 736: Computer Graphics  CIS 798, CIS 890: Topics in Computer Science  CIS 830: Topics in Artificial Intelligence  CIS 990: Graduate Seminar in Computer Science Courses with Literature Review Requirements for Reports  CIS 530, CIS 730: Artificial Intelligence  CIS 560: Database System Concepts

3 Computing & Information Sciences Kansas State University Paper Review Guidelines KDD Lab Guidelines “It’s More of a Guideline” – Barbarossa, Pirates of the Caribbean Approximately one page 10-12 point font 3-6 paragraphs 3 sections  Synopsis / Summary: < 1/3 of review  Critique of Content: > ½ of review  Critique of Presentation: the rest

4 Computing & Information Sciences Kansas State University Paper Review Guidelines KDD Lab Writing Good Reviews First: What Kind of Paper Is It? Content Critique  Novel contribution  Key strengths  Key weaknesses  Open questions Presentation Critique  Accessibility  Target audience?  Reproducibility of experimental results  Soundness  Overall: was the paper convincing?


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