Research in Computing
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Transcript of Research in Computing
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Research in Computing
สมชาย ประสทธิ์ จู�ตระกู�ล
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Success Factors in Computing Research
Research
Computing Knowledge
Scientific Method Analytical Skill
FundingDetermination
Motivation Maturity
Independence
Luck
English
Reading & Writing Skills
Perseverance
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Discipline in Computing
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Scientific Method
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Engineering
• Construction of (useful) products• Solving problems
– understand the problem– analyse the problem
• Find solutions– Constructing the solution from parts that address the
- problem's various aspects do a synthesis
• Engineers– apply theories, methods and tools from different disci
plines– Search for solutions even when there is not theory or
methods
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Distinctions between S & T
• Unchangeable vs. Changeable• Inherent vs. Imposed• General vs. Specific• End in Itself vs. End in Something Else• Abstracting vs. Modeling Complex Systems• Conceptualizing vs. Optimizing• Discovery vs. Invention• - -Long term vs. Short term
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Research
• Careful or diligent search
• Studious inquiry or examination; especially : i nvestigation or experimentation aimed at the
discovery and interpretation of facts, revision of accepted theories or laws in the light of ne w facts, or practical application of such new o
r revised theories or laws
• The collecting of information about a particul ar subject
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Building Blocks for Research
Feasibility
Characterization
Method / Means
Generalization
Discriminization
Qualitative model
Technique
System
Empirical model
Analytic model
Persuasion
Implementation
Evaluation
Analysis
Experience
Questions Result Validation
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Building Blocks for Research
Feasibility
Characterization
Method / Means
Generalization
Discriminization
Qualitative model
Technique
System
Empirical model
Analytic model
Persuasion
Implementation
Evaluation
Analysis
Experience
Questions Result Validation
A "Good" Plan
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Building Blocks for Research
Feasibility
Characterization
Method / Means
Generalization
Discriminization
Qualitative model
Technique
System
Empirical model
Analytic model
Persuasion
Implementation
Evaluation
Analysis
Experience
Questions Result Validation
Common "Bad" Plan
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Building Blocks for Research
Feasibility
Characterization
Method / Means
Generalization
Discriminization
Qualitative model
Technique
System
Empirical model
Analytic model
Persuasion
Implementation
Evaluation
Analysis
Experience
Questions Result Validation
Common Plan
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Validation of CS Papers
• CS published relatively few papers with experi mentally validated results.
• Sampling CS articles from ACM– 40% have no experimental validation– 30 15only % devote / space to experimental valid
aaaaa• Sampling articles from IEEE Trans. on SE
– 5 0 % have no experimental validation– aaaaa aa aaaaaaa aaaaa aaaaaaaaaa2 0% 1 /5
• Paul Lukowicz and et.al., "Experimental Evaluation in Computer Science:AQuantitativeStudy",J our nal of Syst ems and Sof t war e, J anuar y 1 9 9 5
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Validation in NN Papers
• Only 22% of the top NN journal articles use mor e than one real world problem data and compar e the results to at least one alternative algorith
m.
• Lutz Prechelt, "A Quantitative Study of Experimental Evaluation s of Neural Network Learning Algorithms: Current Research Pra
ctice", Neural Networks Vol. 9 , 1 9 9 6
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Reading is Fundamental
• Finding and reading related work is the foundat ion of good research
– ACM Guide to Computing Literature– Computing Reviews
• Developing a bibliography of related works• Background reading + Important reading
– Journal + Proceeding
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Reading with care
• Abstract, introduction, conclusion• Get important points• If relevant, read the whole thing• Take note during reading
(make your thought organized)
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Reading with Care
• Ask questions when reading– what is the motivation ?– what is the contribution ?– a aa aaaa aaaa aaaaaaaaaaaa aaaaaa aa a aaa aaaaaaaaaa a
ncountered ?– a aaa aaa aaa aa aaaaaaa aaaaaaaaaa aaaaa a– What questions are left unanswered ?– Can the results be generalized ?– Can the specific result be improved ?
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Writing is Fundamental
• Good writing is the only lasting medium of the s cientific process.
• Mathematics or code are not substitutes for English
• Document your work regularly
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Working with Others
• Success comes from work with others• Share ideas and let them develop in group atm
osphere• Carefully consider criticism, use it as a guidelin
e
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Programming
• A programming project is not research• It is a mechanism for performing experiment• Experiment
– Establish goals– Thinksimple(devel ope managebl e pr oj ect )– build prototype (not a complete product)– use tools (perl, MathLab, Mathematica, Excel, SPSS, ..
.)– Collaborate– Document results
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David Patterson's Six Steps
• Selecting a problem• Picking a solution• Running a project• Finishing a project• Quantitative evaluation• Transferrring technology
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Recommendation
• Grad school is unstructured environment– reading papers– discussing ideas with colleagues– writing and revising papers– staring blankly in space– having brillant idea and implementing them
• Spend your time wisely
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