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Presenting Research. Dr. Anjum Naveed & Dr. Peter Bloodsworth. Discussion: What is research?. Discussion: How can research outcomes be communicated to others?. How to Make a Persuasive Argument (researcher’s perspective). Getting Started. Before you start: Have your literature review handy

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Presenting research

Presenting Research

Dr. Anjum Naveed &

Dr. Peter Bloodsworth

Getting started
Getting Started perspective)

  • Before you start:

    • Have your literature review handy

    • Be very clear regarding what you are trying to prove

    • Re-read your problem statement / research hypothesis

    • List the contributions that you want to claim in your thesis

    • For each point in your list you will need evidence to convince others that you have achieved it

    • People won’t just take your word for it

    • The most common reason for a rejected paper is getting this wrong

Learn from others
Learn From Others perspective)

  • Use your literature review

  • How have others doing similar work to you evaluated their research?

  • What metrics did they use and why?

  • Are there any common data sets that are used widely?

  • How much proof is normally expected / presented?

  • Some fields require more proof than others

Compared to what
Compared to What? perspective)

  • Are there any systems that you can compare your work with?

  • Caution 1 : It might sound clever to pick a legacy system to compare against

  • Your system will look better – right?

  • WRONG!! : Researchers will spot this straight away and it will harm your credibility in the future

  • Caution 2 : Be sure that you make fair comparisons

  • Don’t deliberately pick an inappropriate system to compare against

  • Don’t choose a data set that favours your system

  • Don’t make sweeping statements with limited proof!

Explicitly state assumptions and initial conditions
Explicitly State Assumptions and Initial Conditions perspective)

  • Carefully write down the assumptions that you have made

  • Are they reasonable?

  • Could others question them?

  • How would you answer tough questions?

  • Results need to be bullet-proof

  • What initial conditions were set?

  • Could they bias the results?

  • What have you done to avoid this?

  • Could other researchers repeat your tests to verify the results?

Types of evaluation
Types of Evaluation perspective)

  • Two main types of evaluation:

    • Quantitative Evaluation

    • Qualitative Evaluation

  • The choice / mix of evaluation techniques depends on your thesis topic

  • Generally quantitative results allow us to make stronger claims

  • We have to be more careful when taking a qualitative approach can’t claim too much

  • Ask for advice from your supervisor before starting on this

  • Try to write an early paper to get some feedback on your evaluation technique

Quantitative evaluation
Quantitative Evaluation perspective)

  • Numerical comparisons

  • System X performs 10% more accurately than System Y

  • The algorithm is 90% effective in classifying brain tumors

  • Makes use of statistical and other mathematical techniques

  • Includes formal mathematical proof

  • Logical proof and Model checking

  • Regression to identify trends

  • Is very powerful but care needs to be taken because errors can be very costly

  • Examiners tend to be very numerate and will spot mistakes

Simulating results
Simulating Results perspective)

  • In some research areas you won’t have access to the required resources needed for testing

  • In such cases simulating or modeling can help us to generate quantitative results

  • Caution : The results will only be as good as our simulation / model!!

  • Use established simulation / modeling techniques and packages wherever possible

  • Carefully show that your simulation / model is accurate and that its configuration doesn’t introduce bias

  • Run several experiments and gradually increase complexity

What can go wrong
What Can Go Wrong? perspective)

  • Claiming too much, justifying too little

  • Using an inappropriate mathematical technique which introduces bias to results

  • Making a basic mathematical error

  • Selecting data that isn’t representative of your problem domain

  • Choosing data which is biased in some way

  • Not doing enough testing

  • Not having a large enough data sample

  • Misinterpreting results – missing the point or drawing wrong conclusions

What can go wrong1
What Can Go Wrong? perspective)

  • Not using metrics that are expected in your domain

  • Misunderstanding metrics and applying incorrectly

  • Choosing the wrong simulation tool and trying to force it to fit your problem

  • Badly configuring your simulation / model so that it doesn’t really describe your problem

  • Doing things in a hurry at the last minute increases all of the above risks – take your time!!

Qualitative evaluation
Qualitative Evaluation perspective)

  • This is not numerical

  • Is more descriptive

  • May involve a criteria for success

  • Create a list of necessary features that your system needs to show to be deemed a success

  • Use a range of tests to show how the system behaves in response to stimuli

  • Try to anticipate the possible inputs to the system

  • Create a real use-case and make it the focus of your evaluation

What can go wrong2
What Can Go Wrong perspective)

  • You claim too much – remember that qualitative results give you less real evidence

  • You make sweeping statements that you don’t properly justify (avoid words like: generic, optimal , etc)

  • You cover only a small range of possible inputs

  • You create a basic prototype and try to claim that it shows much more than it does

  • You don’t do enough testing

  • You set biased tests in some way without noticing

  • Your criteria is too limited to really test your work