1	Continuous Assessment Overview Module 7 KAN Min-Yen
2	Survey Paper Grading Metric: Average: Std. Dev: Disputes on grade should be brought up by the end of this week
3	Proposals Need a clear formulation of the problem: Scope of the problem Limit what you are going to examine Be precise about what you are going to tackle Motivation - reason why applicable to DL A reasonably detailed idea of how you will approach the problem How would one evaluate your project?
4	Timeline for Final Project 24 Sep: Today 2 week interval: have a clear idea what your project is going to be 15 – 22 Oct: lecture presentation (done in pairs, sign up during break) 2 week interval: have finished the bulk of the programming, and thought about how to evaluate your work 13 Nov (Thursday, from 1-5 pm): final presentation
5	Lecture presentation groups HENDRA SETIAWAN & EDWARD WIJAYA (Mixed-media Mining in DL) LIN LI & WONG SWEE SEONG (Document Clustering for DLs) HUANG WENDONG & WANG GANG (Music & Speech in Digital Libraries) LI YINGGUANG & VOROBIEV ARTEM (Peer to Peer in DLs) GUO SHUQIAO & YANG HUI (Web/Hypertext Information Seeking) MASLENNIKOV MSTISLAV & CHAN YEE SENG (Spatial and Temporal DLs) QIU LONG & TOK WEE HYONG (Intelligent Agents in DLs) CHEN XI & WANG XIAOHANG (Metadata Extraction and Indexing)
6	Project grading dimensions Grade will depend more on quality, ideas, crisp results than number of hours spent Creativity in defining the problem to be investigated Quality of the methods used investigating it Thoroughness in justifying your design decisions Quality of your write-up Reporting methods, results, discussion, etc. Quality of evaluation of your system You will not be penalized if your system performs poorly
7	Quality Try to make it something that shows something interesting, not just an exercise in programming But it can be a small something – be focused and not too ambitious
8	What should be in the report? A clear research question or application, and hypotheses about a good approach to it A clear and complete discussion of the theory / algorithms / method used A high-level description of the implementation Testing, and discussion of results Clear graphs, tables, experimental comparisons A discussion of alternatives and their performance Brief but adequate discussion of related work
9	Rough grading guidelines Idea’s motivation, originality, scoping: 25% A chance for you to refine your proposal Experiment methods, implementation: 25% Evaluation methods, results and post-analysis: 25% Quality of write-up Including adequate comparison to prior work, proper citing: 25% This part should be easy since you have done the survey paper
10	Outline for today… Bibliometrics Citation typing and visualization Pagerank HITS