Objective vs. Motivation



Objective= Your want. What do you want to do in this particular scene?

Motivation=WHY you have the want? What's going to happen if you do or don't get it? What are the huge consequences?, etc.

https://answers.yahoo.com/question/index?qid=20100111184253AAN31Uc

Thesis Presentation Tips & Mistakes :)

 



I was searching about how to present your thesis, since it is a critical task to present 3 years work in 15 minutes :) .. wooow isn't it huge?!!

Well, I am going to summarize my findings so it would benefit people who are going through the same struggle.

Presentation Tips:

1. Contribution List: Emphasize on what you have been doing and what your contribution is. For example, if you have worked 20 weeks, have you identified 20 bullets of things that you have done that you can be proud of?

2.  Practice: Have a friend practice and scrutinize your presentation/report. This makes the presentation/opposition much more interesting. Hopefully that can bring up a nice discussion during the presentation.

3. Thesis Motivation: Motivate why your work is important in the context.

4. Chairman Interruption: Don’t interrupt the chairman! It is not up to you as respondent to outline the defence. The examiner/chairman will introduce you to the audience and tell them and you what is expected.
5. Thank you/Questions Slide: You should not present a final slide with “Questions” on. It is not up to you to decide that. Even the “Thank you” slide should be omitted in my opinion … wait for the verdict from the examiner first ;)

6. Enjoy Silence: You do not have to talk all the time. Use silence as a way to show and emphasize your results. A 5-second delay is OK. (A 40-second delay is awkward, but try and see…)

7. Results Table: At some point you get to the end when you eagerly want to present the final results of your work. Then all the tables of more or less random data (for the audience) are presented. These probably do not make sense unless you are very careful with the way you present them. Remember that approximately 5 seconds after you presented the table-or-figure slide people will have forgotten it.

Presentation Mistakes:

1. Too much information: You know that you are heading for TMI when you start to feel like you are drowning in facts and figures which don’t seem to relate to each other. A presentation like this is unlikely to make you look like a lightweight, but it can make you look more confused than you are.

2. All theory, no action: It’s a difficult line to walk with theory sometimes. Not enough can make your project look lightweight. The student spent the majority of her presentation explaining the theory behind practice based research in exquisite detail. It must have seemed like a good strategy because her examiners were not from the design research field, unfortunately these people had already read her text, which went through much of the same explanation, and the rest of the audience were designers – who already knew the arguments. lengthy exposition gave the perverse impression that the student was defensive and unsure of herself.

3. Why are we here: Sometimes students race through an explanation of data without enough lead in for me to understand what the problem was in the first place, why the research matters.how it changes anything in that bigger world beyond the thesis.

4. Undigested text: Someone estimated that a good one hour presentation takes about 30 hours to prepare – they are probably right. (less text in presentation)

5. Question time = fail: Being able to give a good performance during question time is a vital skill because it shows people what kind of academic you are when you are when you are off script. Unfortunately a lot of academics are old hands at asking tricky questions of research students – and they know all the brutal ones. The most common one in a confirmation presentations is “What is your research question?”. I think the key is to stay calm and take your time to answer. It can help to write the question on a piece of paper.

Presentation Preperation:

1. Go to defences: The best way to find out what happens in a defence is to make a point of going to several defences before your own.

2. Timing: For our department, plan your presentation to run about 20-25 minutes, 30 min. max! Remember, the presentation is primarily for the benefit of the Examination Committee, not for additional people who may wander in to listen--it's not a public lecture!--and the Exam Committee will all have read your thesis. They don't need or want an exhaustive description.

3. Contributions: Don't be modest; be clear on your contributions! If someone asks, "Why should you get a degree?," how will you justify yourself? "I've been here 2 years" won't cut it.
4. Slides:

Turn on slide numbering. This makes it easy for viewers to jot them down and then say, "Please go back to slide 23..." With PowerPoint you can press 2-3-Enter and zap right to the given number.

Avoid using acronyms on the slides without defining them.

Identify slides that you can afford to skip over if you see time is getting tight. Some Exam Chairs will cut you off, so don't assume you can talk forever.
Think of some expected questions and prepare some extra slides to answer them (the dry run really helps for this, see next point).
5. Rehearsal: Do a "dry run" with faculty and grad students from your research group. Pay attention to their suggestions for improvement, but realize that "you can't please everyone" and tastes will differ.

6. Paper copy: Make sure you bring along a copy of your own thesis, since numerous questions will take the form "On page 25, what did you mean by...?" or "Table 3-1 is not clearly labelled," etc. You will need to be able to turn rapidly to those references and give a suitable explanation. Do not make the mistake of bringing a later revision of your thesis than the one handed out to the committee! Such things make the defence ridiculous and annoying for the examiners.

Defence Expectation:

If your Advisory Committee has approved the thesis as "defensible," you will almost certainly pass and get your degree. Don't worry about that! So what is really at stake?

1. Everyone wants to look good: You want to look smart, your supervisor wants to be proud of you, the examiners want to look insightful and thorough, and the department wants to maintain high standards. You should contribute to all of that, and not undermine it by being poorly prepared, disrespectful, sloppily dressed, late arriving, showing irritation or anger, and so on. Respect the integrity of the process, and take what the examiners dish out without complaining.

2. You want to minimize your "damage": this refers to how much additional work you have to put into corrections and revisions. At worst, examiners will demand more research and/or experiments, and they will insist on rereading the thesis before they sign off. That could take you weeks, even months! At best, there will be some minor wording improvements, checked only by your supervisor. Ordinarily, you'll be asked to insert or clarify some explanations. If you explain your work well and answer questions well, it is less likely that many or major revisions will be demanded.

3. Taking a philosophical view: whether the revisions are little or much trouble, they will make your thesis a better document . Admittedly, it's possible that no one may ever read your thesis again; but it's also likely that you or your supervisor will write one or more articles based on your thesis, in order to disseminate your work. If the thesis is better because of the revisions, those articles may be more publishable and/or easier to write. If you are continuing in an academic or research career, high quality publications in reputable conferences and journals are extremely valuable to you.

4. Occasionally, a defence will transcend a mundane rite of passage. For this to happen, the examiners must really be engaged with the research and the student, and the "chemistry" will be right. At such times, the questions, the speculation, the theorizing, the discussion, the proposals, the unexpected connections that spontaneously flow can open up fruitful avenues of research and even answer open problems. If you attend even one such defence--let alone your own--you will feel that it was an honour and a pleasure, and that this is what academe is supposed to be about.

"Defence" implies "attack."
Expect to be attacked, and take a confident attitude anyway. Probably you know more than anyone in the room on your particular topic, so don't feel frightened!

Answering questions from the examiners:
Examiners get annoyed when (a) students don't understand their questions, (b) students don't answer them directly, forcing them to repeat/reword, (c) students blab too much, using up the limited defence time. Annoyed examiners tend to demand MORE REVISIONS, then you will be sorry.

Pay careful attention to questions and try to answer what is really asked! If you must, frankly ask, "Can you please repeat that?" Don't let your mouth run on beyond the basic answer, or you may say something flaky that invites more probing. Be aware that an outside examiner with insufficient background in your area may really ask a "dumb question," but you should give a polite answer that doesn't appear to put them down.
Don't look pleadingly at your supervisor(s) for help! It's your thesis, not theirs. If they sense you need help, they can ask some leading "softball" questions during their turns, and they always get the last questions by convention.

Don't worry about writing down everything people say needs to be changed in your thesis. Your supervisor(s) will keep careful notes of this for you. Will the general audience ask questions? If there is time and the Exam Chair invites them to, they may. But you should not invite them; it is the Exam Chair who is running the defence.

When the questions are over--typically there will be two rounds, and the Exam Chair may or may not ask some--you will be thanked for your presentation and put out of the room, along with any other audience members. Hopefully, some supporters will keep you company in the hallway, so you don't get too anxious.

The examiners will then decide, first, whether you have passed, and second, what revisions are required. If things go smoothly this phase may take as little as ten minutes. Eventually you will be called back into the room, most likely congratulated on passing, and then the demanded revisions will be outlined. Most likely, it will be left to your supervisor(s) to detail the revisions. If the examiners ask for more revisions than you wanted, this is not the time to argue and pout. Instead, accept their criticisms graciously.

Source:
http://mixedsignal.wordpress.com/2013/06/13/top-ten-tips-for-master-thesis-presentations/
http://thesiswhisperer.com/2010/11/25/5-classic-research-presentation-mistakes/
http://www.uoguelph.ca/~gardnerw/research/defence.htm 

How to write my Thesis Conclusion?

 

 

I found a presentation about "Writing the Conclusion Chapter for your Thesis" by Louise Edwards, I think it is an amazing small material for those who are searching how to write this chapter just read a 2 page pdf including the presentation's slides.

 

You can download the material from this link.

 

The best part of the presentation from my opinion is this one..

 

Basic Functions of a conclusion

 

 1. To summarize:

– What you researched
– Nature of your main arguments
– How you researched it
– What you discovered
– What pre-existing views were challenged

2. To provide an overview of:

• The new knowledge or information discovered
• The significance of your research (where is it new?)
• The limitations of your thesis (concepts, data)
• Speculation on the implications of these limitations
• Areas for further development and research
(alternative data sets; links with other fields; different
method applied to same data)

 

 Hope you'll find this useful.. I really liked that the presenter asks us to be more positive while writing this chapter since it plays an important role in wrapping up your work and your long-term effort :)

 





How to write your Thesis Abstract?

 

 

 

 

I am in the middle of the "Thesis Abstract" writing process and I wanted to know how this should be done appropriately.. so I googled and I wanted to share this useful website summarized important tips.

 

1. Abstract REAL Goal:

 

An abstract is not merely an introduction in the sense of a preface, preamble, or advance organizer that prepares the reader for the thesis. In addition to that function, it must be capable of substituting for the whole thesis when there is insufficient time and space for the full text.

 

2. Abstract Content:

 

The structure of the abstract should mirror the structure of the whole thesis, and should represent all its major elements. For example, if your thesis has five chapters (introduction, literature review, methodology, results, conclusion), there should be one or more sentences assigned to summarize each chapter.

3. Research Question included:

As in the thesis itself, your research questions are critical in ensuring that the abstract is coherent and logically structured. They form the skeleton to which other elements adhere. They should be presented near the beginning of the abstract.

4. Results are included:

The primary function of your thesis (and by extension your abstract) is not to tell readers what you did, it is to tell them what you discovered. Other information, such as the account of your research methods, is needed mainly to back the claims you make about your results. Approximately the last half of the abstract should be dedicated to summarizing and interpreting your results.

Reference:
http://www.sfu.ca/~jcnesbit/HowToWriteAbstract.htm

I think it is also useful to check abstract of other thesis, you can search for thesis in your same field, also ask your colleagues from the same faculty to send you their thesis and check your faculty if it has a detailed specification for the abstract, so you can figure out what is your limitations and what others do as a sample.

Hope this was helpful enough for you and me to write a good abstract :)

 

Writing - Transition words

If you have a problem in connecting between two sentences then you need this site, it is very easy to use and to pick the transition words you need since it's categorized differently and you also can used various transition words from a certain category for sake of diversity.

http://www.studygs.net/wrtstr6.htm



Writing - Phrase Bank

  







http://www.phrasebank.manchester.ac.uk/

This is a website which assists in research writing process through offering written examples per the following sections and functions.

Major Sections
Introducing work
Reviewing literature
Describing methods
Reporting results
Discussing findings
Writing conclusions   

General Functions
Being critical
Classifying and listing
Compare and contrast
Defining terms
Explaining causality
Expressing caution
Describing trends
Describing quantities
Giving examples
Writing about the past
Preview and transition
 

Matching Recommendation Technologies and Domains - Part 2

 

 



 

 

Matching Domain Characteristics with Knowledge Sources

 

The choice of domain and the characteristics of the application place certain constraints on the kinds of knowledge sources that a recommender system may deploy. In turn, the availability and quality of knowledge sources influences what recommendation technologies a recommender can profitably use.

 

 

Effect @Social Knowledge

 

In heterogenous domains, social knowledge should be considered as a knowledge source since it is gathered by user’s input and does not need extensive knowledge engineering.

 

However, social knowledge is not sufficiently accurate and reliable for high risk domains or for domains which need explanation.

 

Social knowledge will tend to be sparse for high churn domains.

 

Using social knowledge is appropriate for domains with implicit type of interaction since it is possible to mine the users’ behavior using machine learning and statistical techniques which are the typical algorithms in collaborative filtering.
 

In domains with unstable user preference, the social knowledge can be misleading since the historical data is unreliable.

 

Effect @Individual Knowledge

In heterogenous domains, it might be difficult to transfer user’s input on certain items for recommending other items. For example, it is not certain that two users who have similar taste about movies, would also like similar music.

 

* A domain that requires knowledge of the user’s short-term requirements is most likely suited to some kind of knowledge-based recommendation.Constraints and preferences allow the user to limit and to rank options. For example, a dog owner might have a strict constraint that any apartment he rents accept his pet. A parent with young children might have a preference to be close to parks and playgrounds.

 

In high risk domains and domains which need explanation, it is usually necessary to have explicit requirements and constraints from the user. Similarly, user requirements are more likely to be needed in domains with unstable preferences since the historical data are unreliable.

 

Effect @Content Knowledge

 

The most basic kind of content knowledge is item features. These features can typically be used as is in a recommender system, although implementers will often want to restrict the feature space. If items are represented by unstructured documents such as news stories, the implementer will need to draw from information extraction (IE) techniques to extract and select features for use in recommendation. Features can be reduced further by applying more sophisticated feature selection techniques.Content knowledge in multimedia format presents an additional challenge.

 

The quality of recommendations produced by a content-based or knowledgebased recommender will be entirely dependent on the quality of the content data on which its decisions are based. Indeed, the lack of reliable item features is often cited as a motivating factor for avoiding content-based recommendation. The cost involved in creating and maintaining a database of useful item features should not be underestimated, particularly for heterogeneous domains.

 

New technical innovations arrive regularly, requiring that the schema and the individual entries for each item be updated. If there are a large number of not-entirely-independent features extracted in a variety of ways, the system may be tolerant of noisy feature data. On the other hand, applications with high risk will need to pay special attention to having clean item features.

 

Effect @Domain Knowledge (sub-content)

 

A knowledge-based recommender will typically need to know more than just what features are associated with what items. The most basic form of domain knowledge that a recommender can employ is an ontology over the item features. Such an ontology allows the system to reason about the relationship between features at a level deeper than just raw equality or difference.

Many high risk choices have constraints imposed by the domain that a recommender needs to obey.

The recommendation problem can be in some cases formulated entirely as constraint satisfaction with constraints being contributed both by the user and by the system.

A final category of domain knowledge is means-ends knowledge, which is the knowledge that enables a system to map between the user’s goals (ends) and the products that might satisfy them (means).

Part of the reason that users benefit from recommender systems is that they can make good choices without necessarily being conversant with all of the complexities of the product space.

Mapping Domains to Technologies

@Collaborative

some domain types for which social knowledge seems not very useful, in particular, high risk domains and ones with high churn.

In high churn domains, there may not be enough time for an item to build up a reputation among a large number of peer users before it is replaced with other items.

When there is large risk associated with a domain, most users are going to need a more convincing explanation of the appropriateness of a recommendation beyond simply that others liked it. This is particularly important if we consider the problem of robustness in collaborative systems.

@Knowledge-based

Similarly, if we look at the interaction, we can see that it is not always possible to gather every kind of knowledge type from every type of interaction.In systems with implicit inputs, we do not gather any kind of direct requirements from the user.

Preference instability favors knowledge-based techniques. Learning over a user’s prior interactions may turn out to be a hinderance rather than a help. However, in certain cases, such as web personalization, users may provide enough implicit data in a single session to form a useful profile that can be compared to others.
 @General Tips

 In cases where the criteria do not help to reach a definitive conclusion, it is worth noting that the different technologies do have different implementation and maintenance costs.

Collaborative recommendation is likely to be the least expensive to implement. It requires a database of user ratings, but it does not require clean, wellengineered item features, which is the minimum requirement for the other recommendation technologies.

Knowledge-based technologies are going to be the most expensive approach requiring knowledge engineering and continuing maintenance. So, a developer might wish to start by implementing the least expensive solution compatible with the domain.

Another factor to consider is that with hybrid recommendation it is possible to combine techniques. For example, to deal with a heterogeneous environment with unstable preferences, a hybrid between content-based and collaborative recommendation may be desirable.

 Sample Recommendation Domains

Table 3 illustrates the application of these criteria in 10 different domains where recommendation applications exist. Not all combinations of the six criteria are represented, but we can see that the considerations given above are fairly predictive.

High-risk domains generally lead to knowledge-based recommendation; scrutability is also a good predictor of this. Heterogeneous domains are handled largely with collaborative recommendation.

Web page recommendation looks a bit contradictory when we consider high churn and preference instability, which would seem to militate against collaborative methods. However, as discussed above, database size can compensate for preference instability and these recommenders do collect large amounts of implicit preference data in each session. Also, heterogeneity is high, which argues in favor of using social knowledge.

Conclusion

This chapter considers recommender systems as intelligent systems, and as such, dependent on knowledge. The differences between recommendation approaches can be best understood through reference to the different knowledge sources that they employ. By considering how domain characteristics impact the availability and quality of knowledge sources, we can connect recommendation technologies and domain characteristics.

We have examined 6 different factors: heterogeneity, risk, churn, preference stability, interaction style, and scrutability, and considered their impact on the knowledge sources available for recommendation. From this analysis, we derive constraints on what recommendation technologies will be most appropriate for domains according to their characteristics. Application of these criteria to some existing systems shows that they do a reasonably good job of predicting what technologies have been successfully employed both in research and applications.