Scientific Me Tho Do Log Ye

8/4/2019 Scientific Me Tho Do Log Ye

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1

Mt s vn v phng phphc tp v nghin cu khoa hc

H T Bo

Japan Advanced Institute Vietnamese Academyof Science and Technology of Science and Technology

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Phn 1

V vic hc cao hc v nghin cu khoa hc

(from talks at HCMUT 2007, ICT-Hanoi 2007, and some writings)

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Bn cht ca o to sau i hc

Bn cht ca o to thc sl hc

Hc l vic chuyn tri thc con

ngi bit thnh tri thc cacc c nhn hoc t chc.

i hc: hc cc tri thc chungca ngh; Thc s: hc cc trithc chuyn su ca ngh.

Thc sl ngi tinh thngngh nghip (master, tudeapprofondie).

Bn cht ca o totin sl nghin cu

Nghin cu l vic tm

v to ra cc tri thcmi v c ngha bicc c nhn hoc tchc.

Tin sl ngi bitlm nghin cu, vch yu lm vicnghin cu.

4

Chng trnh thc sphbin trn th giiHc hai nm vi tn ch

Nm u ch yu hc ccmn cn thit (khong 10mn, phn ln t chn)

Nm th hai ch yu cho vicrn luyn seminar, reading, hot ng

ca lab

lm ti nghin cu, vit v

bo v lun vn.

Chng trnh thc sph bin ca ta Phn ln thi gian cho cc

mn hc trn lp (khong20 mn)

Cha dng h tn ch t thi gian cho rn luyn

v lm lun vn t rn kh nng t hc Tiu ch v cch nh gi

cha thch hp (lunyu cu ci mi)?

Bn cht ca o to thc s l hc


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3/16

9

Khoa hc v Cng ngh

Khoa hc l vic kho st cc hin tng t nhin v x

hi tm tri thc mi. Cng ngh l cch dng cc tri thc khoa hc v vt liut mc tiu lm sn phm (technology is not abouttools, it deals with how humans work, Peter Drucker).

Thay i khp ni Vit Nam trong cc nm 1990s: khoahc khoa hc & cng ngh (Vin KHVN VinKH&CNVN, B Khoa hc v Cng ngh, etc.)

Khoa hc v cng ngh rt lin quan n nhau nhng l hai

th khc nhau. KH-CN ang c dng ln vo nhau nhmt n v ca nhn thc (khng lun lun tt).

Vit Nam cn t l khoa hc IC v cng ngh IC bao nhiu?

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ng dng: Dng tri thc bit gii quyt cc vn thc t.

Trong ICT Nghin cu c bn c th

nhanh chng chuyn vonghin cu ng dng

Nghin cu ng dng c th

nhanh chng chuyn thnhsn phm

ng dng c khp ni

Nghin cu c bn: Tm trithc mi cho cc nghincu c bn khc haynghin cu ng dng Gene finding

M hnh ngn ng ting Vit

Kernel methods

Nghin cu ng dng: Tmtri thc khoa hc giiquyt cc vn thc t Dch my Anh-Vit

(http://vietnamnet.vn/khoahoc/vande/2006/01/532815)

Nghin cu c bn, nghin cu ngdng v ng dng?

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Nghin cu c bn, nghin cu ngdng v ng dng?

Trong khi khng phi mi t nc u cn tin hnh nghin cuc bn nhiu lnh vc khc nhau, mi t nc cn phi xem xtcc loi nghin cu khoa hc v cng ngh c th trc tip nggp vo s pht trin ca mnh.

... C l cu hi cn hi nht l: u l mc ti thiu cc hot ngkhoa hc v cng ngh cn phi c t c cc mc tiu caquc gia?

Nghin cu c bn bao nhiu phn trm? Vo vn gi? Lnh vcno? nn tp trung cho cc nghin cu lm nn tng cho nghin cung dng.

Cn khuyn khch v t chc nghin cu cng ngh cao v u tin cho sng lm nghin cu ng dng.

Peril and Promise: Higher Education in Developing Countries, World Bank and UNESCO12

Cc lnh vc thit yu ca ICT: nh k thut mng, cng nghphn mm, an ton thng tin, tr tu nhn to, v.v. cng bquc t

Cc lnh vc mi, thch hp v trin vng

Tin sinh hc, cng ngh Web, cc lai d liu phc tp ...thay v cc ch qu quen thuc nh tp m, tp th, cs d liu quan h, ...

Cc lnh vc cn cho nhu cu Vit Nam v ngi Vit philm, nh:

Hnh chnh in t, h tng c s ICT ... vs. thc ti o

X l vn bn v ting ni ting Vit

Pht hin o vn v c s d liu lun vn

Nghin cu cho nhu cu ICT caVietnam


4/16

13

25,000 Genes25,000 Genes

100,000 Proteins100,000 Proteins

1400Chemicals

Metabolomics

Proteomics

Genomics

ProteinGene Complex disease

Protein

Gene

Protein

Gene

Protein

Gene

Protein

Gene

Protein

Gene

ProteinGene

Protein

Gene

ProteinGene Complex disease

Protein

Gene

Protein

Gene

Protein

Gene

Protein

Gene

Protein

Gene

ProteinGene

Protein

Gene

Don gene gy bnh v tin y-sinhhc

50 putativedisease genes

addition to3053 known

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H tng c s cho x l ting ni vvn bn ting Vit

SP7.3Vietnamese tree bank

SP7.3Vietnamese tree bank

SP7.4E-V corpora of

aligned sentences

SP7.4E-V corpora of

aligned sentences

SP3English-Vietnamesetranslation system

SP4IREST: Internet use

support system

SP5Vietnamese

spelling checker

SP8.2Vietnamese word

segmentation

SP8.2Vietnamese word

segmentation

SP8.3

Vietnamese POS tagging

SP8.3

Vietnamese POS tagging

SP8.4Vietnamese chunking

SP8.4Vietnamese chunking

SP8.5Vietnamese

syntax analyser

SP8.5Vietnamesesyntax analyser

SP7.1English-Vietnamese

dictionary

SP7.1English-Vietnamese

dictionary

SP7.2Viet dictionary

SP7.2Viet dictionary

SP1Apllicationorientedsystems based onVietnamese speech

recognition & synthesis

SP2Speech recognition

system withlarge vocabulary

SP8.1Speech analysis tools

SP8.1Speech analysis tools

SP6.1

Corpora forspeech recognition

SP6.1

Corpora forspeech recognition

SP6.2

Corpora forSpeech synthesis

SP6.2

Corpora forSpeech synthesis

SP6.3

Corpora forspecific words

SP6.3

Corpora forspecific words

National project KC01-01/06-10 on Vietnamese Language and Speech Processing

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Dch my Anh-Vit

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Nghin cu cn hng n cng b trn cc tpch v hi ngh quc t *

Cn khuyn khch v cao cc nghin cucht lng cao, v phn bit gi tr khc nhauca kt qu nghin cu (rt cnh tranh)

Cn dy v hc phng phpnghin cu khoa hc

Tng bc t mc tiu trn

* Vn ha ngnh trong tiu ch nh gi, http://www.tiasang.com.vn/news?id=1771

Hng n cc cng b quc t


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6/16

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So snh Thailand & Vietnam

0.7/110468# Articles in Math & Physics

17/1691208Made by universities

16/13235324# Citations (B) until 12.2006

8/11731364Made in the country (B)

3/1468113912# Citations (A) until 12.2006

3/15461739Made with foreigners (A)

4/17373103# Articles in inter. journals

Rate (TL/VN)VietnamThailand

In 2001-2002

0.53/172115Articles in Math & Physics

12.47/11363948# Citations

9.15/1825302# Articles

Rate (Chula/

VNUHN+VNUHCM)VNU-HCMVNU-HNChulalongkorn

In 2001-2002

Source: Phm Duy Hin, http://vietnamnet.vn/nhandinh/2007/01/649976/22

Rt t ngi t cc nc ang pht trin tham dc cc hi ngh khoa hc quc t hng u v ICT

(NIPS, ICML, KDD, IJCAI, ) L do v khng c bi lt vo cc ni ny v khng c

tin i (th d ca IJCAI 2007 ti n )

Cu hi l lm sao em c nhiu hi ngh quc t ttn Vit Nam (e.g., PAKDD05, RIVF07, RIVF08,PRICAI08, etc.)

Cn s tham gia vi nhiu c gng,chun b v ng gp t Vit Nam.

em hi ngh quc tn Vietnam

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Phn 2

Xc nh ti nghin cu(Adapted from the lecture of/with Prof. Duong Nguyen Vu, HCMC, November 2007)

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Finding a research topic: first step

The difference between a trivial project and a

significant project is not the amount of work

required to carry it out, but the amount of thoughtthat you apply in the selection and definition of

your problem.

David P. Beach & Torsten K.E. AlvagerHandbook for Scientific and Technical Research,

Prentice-Hall, 1992, p. 29


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Formulating a problem

The process of problem formulation implies a seriesof reiterated actions:

Original problem

Discussions:

modified problem

Finalize problem

Research Planning

literatureor public ?

Decision on

the problem

Bibliographic

Search

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Problem Formulation

First step in all research projects.

Generally, most difficult element of this process is the

starting point: original idea/topic. Usually, a research topic is suggested by the research

director/supervisor: the topic is often chosen among theset of problems that has been actively investigated bythe group for a while.

Even though, initial idea is usually vague or brut, andnecessitates improvements and/or developments.

For these reasons, this is the stage where experience,creativity, originality, etc. are more than necessary.

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Research a Research Problem

A Doctoral topic consists normally to: Develop a new theory, a new formalism, or

Contribute to an existing theory or formalism.

A Master topic aims at To master the knowledge and skill of an area in

the profession (to go to industry)

To train for experience of doing research (if going

to doctor course)

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Refining a Research Topic

Topics that are too vague or imprecise require alarge quantity of work.

Mathematically, a problem leading to more than

one solution is called ill-posed problem.

Problem Space Solution space

Constraints


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Hypothesis

Hypothesis expresses the elements of a researchproblem.

Therefore, the hypotheses define the set of experimentsto be conducted during the research.

In practice, a research topic contains more than oneunknown.

During the scientific or technologic research process, theresearcher drives for the clarification of this unknown withirrefutable evidences or proofs.

It is important that these hypotheses be well-posed.

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Results of a Hypothesis

Hypothesis providing experimentations for a theory,through an affirmation or an

invalidation/disqualification of the specific predictionsestablished from this theory, must be tested in one of4 ways:

For an extended scope of the theory,

For the limits of the applicability of the theory,

For an improved exactitude of the theory,

For the validation of the basic assumptions of the theory.

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Hypothesis Validating a Theory

Test for an extended scope:

A theory is often applicable to sole certain restrictedsituations or conditions. The theory can become more

powerful if it is demonstrated to be applicable toother instances.

In the contrary, it reinforces the limits of theapplicability of the theory.

Test for the limits of the applicability:

E.g., Einsteins theory of relativity doesnt falsify

Newtons mechanic. It only describes the limits towhich the theory is applicable.

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Hypothesis Validating a Theory

Test for improving the exactitude of the theory, Theories are often a generalization of observed facts, via

objective measurements issued from heuristics analysis.

Generalization and Application are not always bijective.

Exactitude of a theory is always desired.

Test for validating or invalidating a basic assumption, Is a baseline assumption correct? Why?

A theory could become ridiculous if its basic assumptionsdoesnt have any scientific value or are not convincing vis--vis scientific community.

Scientific Warfare!!


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Estimating Feasibility (1)

If the chance of success or failure of an investigationcould be anticipated as precise as possible at theresearch definition phase, then useless efforts couldbe saved.

A research problem shall be evaluated, for itsfeasibility, under the following angles: Investigation domains: is the research topic the

development of a thought, an idea, or a mixture of both?What are the associated scientific domains? Do we haveenough knowledge to evaluate it?

Research Problem: Can we illustrate the topic by a simpledescription (with short and concise wordings) under theform of a question?

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Estimating Feasibility (2)

Availability of Basic Data: do we have access tobasic data that would be needed? Familiarization with raw data, Data Sources, Data Collection Method, Requirement for Specific Equipments, Operating Specific Equipments.

Qualification: what are the primary and secondary

scientific or technological fields that would beneeded in the search for the solutions? Are wecapable to perform research in these fields?

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Decision Criteria

Primary Criterion: Personal Interests: research topic must stimulate

imagination, creativity. The researcher must be

in. Size of the study: research topic must be

manageable.

Competence / qualification of the researchteam

Potentiality for original contribution.

Mastering of the domains related to the researchtopic.

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Decision Criteria

Advanced Decisional Criterion: Timing : Is the subject hot?

Originality: Has it been treated before?

Solidity: Is the research proposal complete? Is thereany contradiction?

Utility: What is the scope of the study? Is it useful?For which audience? Probable application domains?

Morality: is there any moral or ethical issues?

Feasibility: What are the associated constraints?

Human Resources? Time? Financial Resources? Cost?

Availability of initial data?


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Research Proposal

Contents of a research proposal: Characteristics of the research; Objectives and

Context. (use decision criteria to establish skeleton)

Contents and Organization: Information to be highlighted, keywords

Executive Summary,

Introductory Notes,

Major elements of the Proposal, Deliveries and Annexes.

Financial Proposal

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Trnh by v gii thiu kt qu

nghin cu:Papers, Talks, and Chats

(from tutorial of Prof. Marie desJardins (University of Maryland)ICML/KDD 2003

Phn 3

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Research isnt just research

Who cares what you do, if you never tell them?

Youll need to present your ideas in various forms andvenues: Networking with colleagues at UMBC and elsewhere Writing and submitting papers to workshops, conferences,

and journals

Presenting papers at workshops and conferences

Putting together a website that highlights your interests andresearch activities

oh, and these things also provide useful experiencefor job interviews, not to mention valuable job skills

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Networking

Meet people! It helps to have an objective: Find out what research theyre currently working on

Tell them what youre currently working on

Find an area of common interest Learn what their visions/future directions are

Suggest a new direction for research or topic for a class

Whats in this interaction for you?

Whats in it for them?

If you know two friends, and they know two friends,

and they know two friends Pretty soon you knoweverybody!


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Networking II

You need to be prepared to summarize yourresearch For a thesis topic, you should have a 1-minute,

5-minute, and 15-minute presentation alreadythought through

The same goes for other projects youve beenworking on

Be able to distinguish between your original

contributions, your advisors contributions, andideas drawn from previous research

Practice with other students!

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Writing and submitting papers

For a masters thesis, you should aim to have at leastone good conference paper by the time yougraduate

For a doctoral dissertation, you should aim for acouple of good conference papers and a journalpaper

Writing these papers is great practice for the thesisitself (and you can reuse the material!)

Where to submit?

Look at publication lists of people doing research related toyours, and see where they publish Publish at the conferences that have the most interesting

papers

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Writing papers: Strategy

First, decide where you plan to submit the paper

You may not finish in time, but having a deadline is alwayshelpful

Two to four months away is a good planning horizon

Next, decide what you will say

What are the key ideas? Have you developed them yet?

What are the key results? Have you designed and run theexperiments yet? Have you analyzed the data?

What is the key related work? Have you read the relevantbackground material? Can you give a good summary of it?

Now get started on the work you need to do to fill in themissing holes! (You can write in parallel)

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Writing papers: Design

Abstractsummarizes the research contributions, not the paper(i.e., it shouldnt be an outline of the paper)

Introduction/motivation what youve donewhat youve doneand why the readerwhy the readershould careshould care, plus an outline of the paper

Technical sections one or more sections summarizing theresearch ideas youve developed

Experiments/results/analysis one or more sections presentingexperimental results and/or supporting proofs

Future work summary of where youre headed next and openquestions still to be answered

Conclusions reminder of what youve said and why its important

Related work sometimes comes after introduction, sometimes

before conclusions (depends to some extent on whether yourebuilding on previous research, or dismissing it as irrelevant)


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Writing papers: Tactics

Top-down design (outline) is very helpful

Bulleted lists can help you get past writers block

Unless youre a really talented/experienced writer, you should usethese tools beforeyou start writing prose

Neatness counts! Check spelling, grammar, consistency offonts and notation beforeshowing it to anyone for review

If theyre concentrating on your typos, they might miss whatsinteresting about the content

Leave time for reviews!

Fellow students, collaborators, advisors,

A paper is only done when its submitted... and usually not eventhen.

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Authorship Who should be an author?

Anyone who contributed significantly to the conceptualdevelopment or writing of the paper

Not necessarilypeople who provided feedback, implementedcode, or ran experiments

What order should the authors be listed in?

If some authors contributed more of the conceptual developmentand/or did most/all of the writing, they should be listed first

If the contribution was equal or the authors worked as a team, theauthors should be listed in alphabetical order

Sometimes the note The authors are listed in alphabetical orderis explicitly included

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Giving talks Know how long you have

How long is the talk? Are questions included?

A good heuristic is 2-3 minutes per slide

If you have too many slides, youll skip some orworserushdesperately to finish. Avoid this temptation!!

Almost by definition, you neverhave time to say everythingabout your topic, so dont worry about skipping some things!

Unless youre very experienced giving talks, you should practiceyour timing:

A couple of times on your own to get the general flow

At least one dry run to work out the kinks

A run-through on your own the night before the talk

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Giving talks II

Know who your audience is Dont waste time on basics if youre talking to an

audience in your field

Even for these people, you need to be sureyoure explaining each new concept clearly

On the other hand, youll lose people in ageneral audience if you dont give the necessarybackground

In any case, the most important thing is to

emphasize what youve doneand why theyshould care!


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Giving talks III

Know what you want to say

Just giving a project summary is not interesting to most people

You should give enough detail to get your interesting ideasacross (and to show that youve actually solved, but not enoughto lose your audience

They want to hearwhat you did that was cooland why theyshould care

Preferably, theyll hear the above two points at the beginning ofthe talk, over the course of the talk, and at the end of the talk

If theyre intrigued, theyll ask questions or read your paper Whatever you do, dont just read your slides!

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Preparing slides

Dont just read your slides! Use the minimum amount of text necessary

Use examples Use a readable, simple, yet elegant format Use color to emphasize important points, but avoid

the excessive use ofcolor Hiding bullets like this is annoying (but sometimes

effective), but

Dont fidget, and Dont just read your slides!

Abuse of animation is a cardinal sin!

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How to give a bad talkAdvice from Dave Patterson, summarized by Mark Hill

1. Thou shalt not be neat

2. Thou shalt not waste space

3.

Thou shalt not covet brevity4. Thou shalt cover thy naked slides

5. Thou shalt not write large

6. Thou shalt not use color7. Thou shalt not illustrate8. Thou shalt not make eye contact

9. Thou shalt not skip slides in a long talk10. Thou shalt not practice

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Some useful resources

Writing: Lynn DuPre, Bugs in Writing

Strunk & White, Elements of Style

Giving talks: Mark Hill, Oral presentation advice

Patrick Winston, Some lecturing heuristics

Simon L. Peyton Jones et al., How to give agood research talk

Dave Patterson, How to have a bad career inresearch/academia


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Phn 4

Mt vi kinh nghim c nhn(from what I have discussed with students from Vietnam)

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English pronunciation

Usually do not pronounce vowel

Examples book vs. books

Text mining vs tech mining

Adapter [a-da-pu-ta]

She asks me if I can fly to the moon

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Research proposal (typically in Japan)

Objectives What to do clearly

Its significance Background and research context

Who are doing similar research, related research?

What are approaches to solve the problems?

Your critical view on the related work

Methodology and plan What could be the key idea of the solution?

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If I need to give an advice

To be self-confident

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Scientific Me Tho Do Log Ye