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Good afternoon. So let's wait for everyone to come to join. Just for a few seconds. Hello. Hello, everyone. Good afternoon. So we are just, I think we did not, not everyone's joined yet. So we have like, maybe around five students did not join yet. Guys are late this time. Same like me, last lecture. So we should, we should start with a few seconds, but let's just wait for a few seconds. Okay, so I think we should start. So good afternoon, everyone. Again, one more time. So we, we will have the lecture, as you know, this is the last lecture in the course, and then we will have next week, just presentations, as we did in the midterm and as we agree earlier in this course. So today I would like, I hope the lecture will be, the last lecture will be light and simple. So we will have, we will continue talking about writing thesis. And maybe last time, if you remember, we were talking about the main components of the thesis. This is very important for you guys, for the, for, we talk about, I just want to recap what we have, we have covered last time. So we talk about writing thesis, and we had several, we show you even in the link here, several templates for SKQ that you have to follow. There is templates in English and same. There are also, if you search for thesis template in Korean, you will find also template and will be very similar. So even whatever I say in this lecture and last lecture are applied to Korean thesis or English thesis, they are same. So we are talking about components and what we should be, what we should be there in the chapters of the thesis. So this is our main focus when we are talking about thesis, structure and thesis contents. So these contents and structure is same regardless of its Korean language or its English language. So last time we talk about the structure and we said that you can write your thesis in, there is a template in Microsoft Word or in Hangul, or even you can write your thesis in template in Letix. And then we talk about the main components of the thesis. And by the way, those components, if you think about them, they are very similar to the components of a paper. Like if you want to publish a research paper, you know, thesis is just extension of research paper. So if you have, for example, for a master degree student, I showed you last time two of my students thesis, and both of them publish international journal or conference and then they extend this one in the thesis. That's it. Even I show you evidence when I show you the draft and they just even copy the structure of the paper and put it in the structure of the thesis, if you remember. So what I want to say that whatever I say here also is same for paper, if you would like to publish paper. OK, so again, for you as a master degree students, there is no difference. So that your research paper that you will publish in a conference or a workshop or a journal, Korean journal, international journal, these contents, you just extend a little bit. It just make it a little bit bigger, more details, and you will write your thesis. That's it. So that means this is why when we are asking master students to master degree students to publish at least one paper, we know that if the student published that paper, then he can write his thesis without any issue. OK, so this is why we ask the students all the time, try to publish paper before third semester of your master degree. Hopefully, when you publish your paper in a conference or in a journal, then later you just take that contents of the paper you publish and you extend a little bit and you put it in your thesis. And that's it. And usually the thesis could be around 40 pages or 50 pages maximum, even with sometimes with references. It would be this size. So it's not big, especially if you publish paper already in a conference or in a journal. So last time we talked about the title of the title page and how important is the title. We told you last time, if you remember, title will stay in your thesis and your resume or CV. So be careful when you are choosing the title because it will stick in your CV for the rest of your career. And also we talk about abstract and what are the main things that should be there in the abstract. And then we talk about the table of contents, list of figures, list of tables and show you even examples of that. Then I talk about the introduction and what is the main components that should be there in the introduction. Even I put some slides with a lot of text, which is unusual in my case. But just to let you remember later what will be there in the thesis. Even I went farther and I uploaded the video recording of last lecture and I will upload the video recording of this lecture. Why? Because maybe after one semester or after two semesters when you are preparing for your thesis, maybe you just want to come back here and just listen to the video and see what are the things that you should consider in the introduction, in the methods, in the result, in the discussion and so on. So even I uploaded the recorded video of last lecture and this lecture I will upload. And also I start uploading some of the videos just for you to, in case later if you would like to go back to this course and listen to some of the lectures by yourself. Okay. So even we talked last time about methods and we said that methods is where you are placing your algorithm. And then you are placing all details about how you are going to solve the problem and what are the techniques that you require to run the experiments. Okay. To run the experiments or run the simulation to evaluate your algorithm or evaluate your technique. Okay. So maybe your technique will be for hardware. Maybe your technique will be software. Your technique will be whatever algorithm. It will be structure, whatever framework. So whatever your technique or whatever your solution, maybe your solution or your technique needs some component or some knowledge. All of this one you will add in the method. And if I read the method section, I will totally understand how you conduct the experiments and how you generate the result, which will be the next section. Okay. So result section will be the next. So today I will talk in details about the result section, what should be there. And also I will talk about some other section we call it discussion section. Sometimes we merge result and discussion together. Sometimes we separate them. What I recommend is to separate them, each one alone. The result section alone and discussion section alone. But if you have few results, then in this case you can merge the result and discussion. But you have to merge them in a specific way that I will tell you later. Okay. So there is a way to merge that result and discussion. And this is what I'm going to talk about. Then I will talk about other parts. And as I told you last time, and I'm repeating this one again, those are the basic chapters or sections in your thesis. But you can add more. So those are the basic. But maybe the professor will ask you to add one section about, for example, mobile antennas or mobile communication, or he will ask you add one section about, for example, a specific technology. So you will add whatever the professor could suggest on you. Or maybe you will find in the literature or when you read another paper, you said, oh, I think I need one chapter more about something. Okay. So you can. But those are the basic things. Okay. All of the pieces and even papers require those sections. Okay. So let's move on. So I will jump to where did we stop? We finished the methods, as you remember last time. And now we are going to talk about results. So results are all the observations that you get are after you run experiment or after you run a simulation. So you will be around simulation or you run experiments on software or experiment on hardware and you collect some observations. Okay. You have some observation. You collect them and save them in tables or graphs. Okay. So in this case, those results are the core, the core of or the evidence of how you did well or how is your algorithm is efficient or your framework is efficient or how good is your proposal. Okay. So those are the evidence of your proposal. If you do them well, then in this case, if you get some good observation or good results, then your paper or your thesis will be appreciated by professor and also by the research or by the thesis committee. And also maybe appreciated if you publish this one in a conference or in a journal. Okay. So now the most important thing is results depends a lot on the, on the method. So when I first check, for example, I told you last time when, when some research committee or I would like to check our review research paper or research thesis. First thing I check as a, as a professor, I check the abstract. Then I will check the introduction. Why I will check the introduction? Because it include what exactly the problem and what is your contribution? You will add your contribution list in the introduction. Okay. So this is the first thing I check. Then I will go to the conclusion to check what is the conclusion of your, or what are the main things you get out of your results? You will put it in your conclusion and then I will go and check the results. Okay. Now, if I found the result, something does not go intuitive with my feeling about how you get the result, then I will check the methods carefully. I will check your method. Did you follow the official scientific procedure? Well or not? Okay. If I find something problem in the method, then your results are not okay. So that means your results are not correct. Okay. So this is very important. Okay. Now results, as you know, everyone knows that results are just statistics, table, graph, these kinds of things. Okay. And usually, usually the results should be including positive results and negative results. Negative results, you say, wait, what I mean by negative results? Negative results mean some situation or some scenario where your results are not, or your algorithm will not do well, or your framework will not do well. You might ask, do we have to announce or mention that our algorithm is not work well in a specific situation or in a specific scenario or in a specific, for example, environment? Should we do, like say that in a specific situation, the results will be bad. Should we say that? Yes, we do that. We do that. And I will show you even from some of my paper, we call this one limitation. We call them limitations. Okay. So why do we mention the negative results? We mentioned the negative result to still other people who read your research that, okay, my algorithm work well in the case one, case two, case three, but my algorithm does not work well in case four. So in this case, other researchers will try to find a solution for case four, how to make some good algorithm or good technique or good approach for case four where you have limitation. Okay. So yes, we put limitations for our work. Okay. So I can come up, there are several papers where we, I will show you later from my research work with my students that we put many papers with some limitations. Okay. So this is fine. This is no problem. No one will tell you this is bad. Okay. It's good to even researchers and professor when they see that you are honest and you just say the truth, regardless, you say, where is your algorithm work well and where is your algorithm not work well, they will appreciate that. They will know that you are honest because there are many researchers do this way. Okay. So mention negative results as well as positive results. But there is something here important in the sentence here. Do not interpret results. Save this one for discussion. Why? That means when you are put the results in the results section, do not interpret. Interprets means explain, do not explain. Why? Because if you are for the someone who is reading your results section, you want to see the result and try himself to comprehend or to understand your results. But when you interpret, you are changing the reader mode. So every time you put some result, you explain why this is like this. You are making the reader flip from understanding to analysis. So you want me to, so when you I'm seeing your results, I try to understand, but when you say for me, why there is like this, I will change my brain mode from understand to what to analysis and try to analyze what's going on. Okay. So this is good or bad. This is bad. Why it's bad because the reader will feel tired, will feel tired when he's reading your results section. He will feel tired. Why? Because he flipped from understanding to analysis, analysis mode. To understand mode, understand mode, then analysis mode, which will make him read your results section very slow and it will be very tiring for him. So this is why in research, we are in research. We put the results first. And then after we put the results, we try to analyze what we observe or we try to say why there is like this in the discussion section. Okay. Or we can put why there is like this after we explain, after we put all the results. So this is very important, by the way, guys, this is really important. You should put results first and then just put tables. Just tell what is these numbers in the tables and is the units correct? For example, kilogram, milliseconds, watt, joule, whatever. So you explain, not explain the result, we just tell what is in the table. You just tell what is in the table. That means you just mention the results. So when you mention the results, this is the first stage. Then after that, why there is like this, this is we call it discussion mode. Okay. When you are interpret why there is like this. Okay. This is very important to know. This is why I'm emphasizing this point. Okay. Now, after that, so usually presents sufficient detail so the other can draw their own inference. So you will, when you are showing the results, you are just put, show some details about how these results are collected. And that's it. Just that's it. This is what I want you to do. And let the people who are reading your result get their own conclusion. They will get their own conclusion and later, later you will, they will match their conclusion with your explanations when you talk about discussion. So they will match what they try to infer from your results and they will match this one with your explanation. Is it okay? Is it clear this point? Okay. Okay. So this is an important thing. Also, another thing you have to take care of in the results section is the units. For example, millisecond, second. So every time when you are, for example, drawing a graph, you are drawing a graph, be sure that you put the units here. What is that unit here? For example, millisecond, microsecond, whatever. For example, this one is accuracy percentage or whatever. So you would put the units very correct, okay, for the drawing. Okay. And also break up your result into logical segments by using subheading. So, for example, you will show result for a specific situation and then another, so you will divide the results section. In the results section, you will divide the results section into subsections, okay. One section handle, for example, one section handle one issue, okay, or collect result for a specific scenario. Another subsection handle for another scenario. Another section show results for another scenario and so on, okay, and so on. So you will do this way, okay. So you should show the results for each scenario. So let me just show you some example because I don't want to speak all the time. Just show you, I can pick one of the papers that I have already published. So here is one paper, okay. So here is, sorry. So this paper is security paper. It's about, just to give you context, what is this paper? This paper is about user authentication. So in this one, we design using deep learning and we collect from your mobile the sensors of your mobile. And based on the sensor, if you hold your mobile, I can know that you are Tamir, okay. But if you give your mobile to someone else, I know that this is not Tamir, so I will lock the mobile. I will not allow the user to use the mobile. So this is what something we call it continuous authentication. Nowadays, it's a very active research area where you continuously authenticate the user from his sensor. You know, in mobile, in your mobile, there is a gyroscope, accelerometer, there is pressure sensors, there is a touch screen sensor, there are many sensors. So from these sensors, we collect information about you and we will create some profile for you. And then later, if when the deep learning model will run in your mobile in the background in software, and if you are using your mobile, the mobile will run fine. But if you leave your mobile and someone else touch the mobile, the mobile will lock. He knows that you are not, it's not the owner of the mobile, okay. So in this one, let me just, as you see guys here, again, one more time, in the introduction section, introduction section here, the most important thing is the list of contribution. If you see here, I put the list of contribution, this is in the introduction. That must be there, the list of contribution. Even in your thesis, you will have the list of contribution in the introduction. This is very important. And there is the related work, you see here, this is the related work, second section. And then after that, now the methods, okay, this is the methods. So I call the methods the name of the system that I design, okay, with my co-authors. So this is the system, but this is the methods. And now, let's go to the results section. I just want to go to the results section. As you see, in the results section, we put, where is the results section? I just, I'm looking for the title of the section. Yeah, here is it. Experiments and evaluation results, okay. So here, if you see, I will put the graphs, I will put tables, I put these results. And it's really important for the results section, I will give good caption. You see here, I'm giving long caption for the figure to make it self-explanatory. Self-explanatory means if I read this one, I will understand what is this figure about without going and reading from the table, go read from the text, okay. So this is the results section, okay, we put evaluation or results section, it's okay. And now, if you see here, I have one subsection. So this subsection, I only show the result of using sensor data, and then I will go and show you another section, subsection, sorry, those are results. Another section that I have result that show that the impact of doing sampling, sampling over the sensor data. I don't include all sensor data, I sample some of it. And I will show how is the accuracy of the authentication of the user based on some of the data, not the entire data. And let's go on, let's move on to another result. Performance of, no, no, no, this is discussion, okay. Yeah, this is the discussion. Yeah, so I have two parts, two parts for the results, okay. So you see here, I put the results, and for the results here, most of the time I add figures, okay. So yeah, this is the figures. So if you see here, the figures are long caption, this is long caption here, there is a long caption here, and this one to explain ABC figures, okay. And here also long caption. So this one explain the first experiment results, those are just, I'm showing results, okay. And now here I'm showing another results, and you see here I'm talking about figures. I'm saying in figure this one we put this result. But if you see here, I am putting some numbers from the figure here, from the figures I put some accuracy numbers, but I do not explain everything. This is a common mistake, by the way, common mistake. When you have a lot of results, you do not explain everything. Keep the user or keep the reader read all result by himself. But what you will put here, the most important results. So from the figure, I just put the most important numbers, the most important numbers. Others I don't put, okay. Yes, so I put the result here, I do not explain anything. You see here, I just put some result. So this result is like this. In figures I show the results that we obtain with this situation, and the best results are showing in this accuracy, okay. So this is an LSTM situation, we are achieving this kind of, that's it, okay. So I am showing from A only one thing, okay. Look here, A how many accuracy I have, one, two, three, four, five. But I'm just showing one of them, okay. So just I show the most important one, okay. So this is after I show results, and here is another one, this one comparison with many situation, and you see the caption is also long. And here also the caption is all, by the way, this paper has no tables, but maybe later I will show another paper, maybe, oh, it has table, it has table. Okay, here is the table, okay. So you see here, those are results. So those all are results, okay. And you see here, even I highlighted the most important result, and I explained this one in the text. And if you see here, the caption of the table is very long, because it need to explain what's going on here, okay. So why the caption should be long, to explain everything without going and reading the text, okay. And if you see here, the caption of the table is at the top of the table, but the caption of the figure is below the figure. So this is the rule, okay, and scientific writing, okay. Now after that, after that, I show the discussion. So let me go to the discussion. Here is it. You see, I put it as a subsection. I put it as a subsection, not section alone. And you see, I also have analysis, more analysis. So this is analysis and discussion, okay. So just want to tell you that, by the way, if you merge these two things together, that's okay. But it depends on you, what you like. So sometimes you like to separate the discussion into two parts or three parts. That's okay. So I just want to tell you, discussion come after result. This is what I want to tell you, okay. Do not put discussion and result together, merge. Make them separated, okay. You can put them in one chapter or in one section, or you can put them in two separate sections. That's okay. But separate them, okay. You should physically separate them, okay. So this is, and then here I just give some discussion why the results are like this, why the results are showing like this, and so on, okay. So this is the important thing. Even I put, I think I put some, I'm trying to find if, yeah, maybe this paper, I did not put limitations, but let me just bring another paper. The problem that I always pick some papers which are available outside of the compass. Because I'm outside, so this is another paper. Okay, let me just see if there is, this is, I don't want, here is table and data set and results, and the results are divided to some parts. But I just want to show something. Oh, here we go. You see, you see guys, I show negative results, you see. I show the negative result. I'm not afraid to say that. While our work provide high accuracy in a specific situation, it has several shortcomings, mean bad things. Should I mention that? Yes, you mentioned that, no problem. No one will say for you anything bad. It does not work with this situation. It does not work with this situation. And by the way, this is another work, by the way. This is another work. I just want to prove for you, I want to prove for you that we, and you know, this paper, by the way, this paper is published in number one conference in security worldwide. So this one, I put limitation and say that this is not working. And this one published in CCS. Okay. CCS is a computer and communication security conference. And if you go here and say top conferences in, I just want to tell you that don't feel afraid of top conferences in, and what you see, tier number one CCS. It's the number one worldwide. Okay. So it's a top conference and I put there, I put there limitations. I am not afraid to say limitations in the results. Okay. And here is it. You see, I have discussions alone. I put here, I, by the way, I put evaluation, mean results and discussion. So I put them in one section, but I separate them. Look here. I put the result. First, I put the result and here is the results, results, results, results. And then wait, you will see the discussion. So I will put the results and then we will go to the discussion. Where is the discussion? So here is the limitations. So this is the limitations. Yeah. Oh, okay. Okay. Yeah. I, I, I remember now I put the results, I put the results and then I separate them with the key insight, which is the discussion. So I separate them. You see, I did not merge. So I show the result of the experiment number one, and then I will put them my discussion, my interpretations. And then again, same thing. I will go to experiment number two. Okay. Here is experiment number two. I just show the results. Okay. Oh, here's experiment number. Okay. Let me just go to experiment. Okay. Here is another experiment. This is another experiment and show the results, show the result and then discussion. Okay. So I want to tell you that there is also another experiment and discussion. Okay. So I just want to tell you, you have to separate, you have to separate discussions and results. Okay. And don't feel afraid in the result to put some limitations. Where is your system is not working. Don't feel afraid about that. And by the way, limitations are different than future work. Different. Limitation mean your system does not work in a specific scenario, but the future work mean your system will be extended. I want to extend my system. I'm not talking about my system is not working in a specific scenario. I want my system to be extended to cover more things, more features, more stuff. Okay. So the future work, yeah, could be the limitation could be changed to future work. Can be. Okay. Can be changed to future work. That's possible. But mainly in the future work, when you put limitation in the paper, future work, we are talking about how you can extend your system to have more capabilities, more power things. Okay. This is the future work. But limitation mean something where your system does not work. Do I have to in the future work say that some of the limitation I will make them work? It depends. It depends. Maybe your system is impossible to make work in this scenario. Impossible. Okay. So in this case, your system is not designed for this scenario. So it's designed for another scenario, but that scenario you can extend it to make more, more features. Okay. Or more capabilities. But if you go to another scenario, impossible to work. So in this case, no, that's okay. That's okay. That's for to define. So some of the limitation can be future work and some of them are impossible to make them a future work. You have to solve the problem in different way, not in the way that you solve in this paper. Okay. You got my points. So it's really important to get these points because this one later you need it in your, when you are writing your paper or your, your thesis. Okay. Okay. So let's move on. Any question, by the way, guys, anything in mind you would like to ask me? I'm trying to show you some real example and then back again to the slides. But is there any question, anything, something in mind? Okay. So I think some of you already start doing some experiment and they are trying to collect results. So maybe these things now is important, but later, if you are not started yet, that's totally fine. Maybe later you can back again to this, this is a recorded video and you listen to it again when you are writing your paper or writing your thesis. Okay. Okay. Okay. So we talk about break results into two logical segments, like mean a result section A will be about data, check all sensor data and then section check about sampling data. And section A3 is about experiment where you are applying specific kind of data or features. Okay. So you can divide this one. Okay. Also another thing that you can, it's also usually when you are writing your results, it's important to support your result. Because when you are saying, for example, my results are significantly better than other people or significantly important, more efficient than related word of X. So when you are saying that usually we are doing this well in computer science by applying statistic, statistical tests. Did you hear about statistical tests? Like for example, T-test, ANOVA test, those are things. So what is this test? Maybe later you can need them. So for example, imagine you have results, imagine you have results, these are our numbers, you have results. Okay. And you just want to say, your results are significantly better than related work of A. Okay. Your results are significantly better than results of A. How you can prove that? You can prove this one. If the difference between the results here and the results here, the results of A and your results are far away from each other, then in this case, you can say significantly different. But if they are near each other, how you can say significantly different? You will apply some statistical tests. So you will apply some statistical tests. And these statistical tests could be, for example, T-test or could be ANOVA test. You just said on the internet, I want to compare between two groups of results, what kind of test I can use. They will tell you what kind of test you can use. You can use ANOVA test, you can use T-test, you can use a student test, whatever. And these tests will give you P-value. And if these P-value, automatically the T-test, the statistical test will generate for you P-value. If this P-value is below 0.01, that means the difference between these results and these results are significant. And in this case, you can say, oh, my results are way significant than work of A and with a P-value is this then 0.01. So in this case, this is we could statistically prove, statistically prove that your results are way better than other people results. Okay? Anyone heard about statistical tests here? If you heard about statistical tests, say yes. Please say yes. I want to know how many know about what I'm talking about here. Statistical test. Did you hear about statistical tests to compare between two group of results or two group of values? So if I have a group of values and another group of values, say this one are result and this one are result, and I just want to say results of A are significant different than results of B. If significant mean they are different. So if they are different, that means your results maybe are better than his results. Yes, exactly. You have T-test or you have which we call student test. And also, if you are comparing three groups or four groups, we will use ANOVA test or another test. Okay? So this is kind of test. But anyway, later maybe your professor will ask you to do this kind of test. So he will ask you, okay, run your results, collect your results, and then go to another related work results and collect them and then compare between them. To compare between them and say your results are significantly different than this result, you need to apply some tests. Okay? Okay. Maybe you will need this one later. Okay. So this is also the test when you are applying some test. Those are also results. Those are also results. Okay? Yes. Okay. Now let's move on to the discussion. So we finished about the results section and we said that the results section is only to show your results without any interpretations. Now let's go move to the discussion part. Discussion part could be section or subsection, but should be alone. We can say in Korean, Tarot Tarot. Okay? Tarot Tarot. Results alone, discussion alone. Okay? So do it as you like, but alone. Tarot Tarot. Okay? So start with a few sentences that summarize your important results. This is the first thing in the discussion. You will say, I achieve, my system achieve, or my framework or my method, achieve these results. A, B, C, D. Okay? And then after that, after you are saying a brief overview about what is your best result, then you will start telling your interpretations. You start telling, oh, my results achieved this one due to blah, blah, blah. Due to blah, blah. This is better than these results because of one, two, three, four. And these results are have is outperformed existing results because I use this component in my distribution. I use this component and you start interpreting why your results are like this. Why your results are like this. Okay? Is this okay? Yes. So you need to highlight what are the major patterns in your observation. Okay. What are the major patterns in your observations? So in this case, for example, there is a change in the observations over time or there is a relation between, for example, and also you will show if there is any relationship between two components, for example, relationship between model complexity and accuracy, for example. So for example, let me just show you one example from myself. So let's assume in your results you have, for example, let's say I'm talking about, for example, let's talk about deep learning models. So I'm using, for example, C1 and C1 is one model and another more complex model. We call it C10. This is more complex. Okay? And then we have another model, C100. Okay. And in this case, you achieve results here, for example, in C1 you achieve this accuracy and this is the accuracy. Okay. Okay. And this accuracy in percentage, for example. Okay. So you are achieving, sorry, I'm writing by mouse. Okay. So here you are achieving this kind of accuracy. Then you're achieving these kinds of accuracy. And then you need to share the relationship between some trends. So you have here some trends. If you see guys, you have some trends here. Okay. So this is me going up as the model or as the system is more complex, the accuracy go up. Okay. So you need to share that. You say, for example, I notice that the complexity of the model is related to that high accuracy. So this is you need to notice these kind of thing. Like if there is any trend in your results, any pattern in your results, you need to explain this one. Also, you need to show if there is an exception or not. For example, everything is going well. Except for example, you are applying here some system with different setting. For example, this one, D50. Okay. With D50, this is exceptional situation. D50 you achieve even it's complex, but it's achieved accuracy less than D10. So this is less than C10. Even it's more complex, but it's achieved accuracy less than C10. Why? Maybe because the structure, I use different components here. So we call this one exceptional situation. So I notice that complexity was when complexity is going high, the accuracy is going high, except in the situation where I change some components, the accuracy is not going up. Okay. So in this case, we call this one exceptional situation. Okay. Yeah. And then you just also in the discussion, you will explain what is the cause of this, why this pattern is there. And you will know that because you know your system, you know your method, you know your algorithm. Maybe you know the mathematics behind it, or you know the physical phenomena behind it, or you know the chemical phenomena behind it. So you'll know why this one, this kind of pattern is there. Or you'll know this one from a related world. You know what is the pattern. So you will need to explain why this pattern is there. So first you will tell the reader, oh, there is a pattern. There is a relationship. And then after that, there is exceptional situation for this relationship. And then why you have this relationship, why this relationship is there. Okay. Those are very important questions you need to answer. If you answer them, you will have amazing discussion. Okay. So if you answer this one, you will have amazing discussion. Also, is your results agree with the previous work or related to work or not? You need to mention that. Okay. And the discussion. Or you agree, you don't agree. Maybe agree, partial of your system agree on a specific scenario, but if you change the setting, it will not agree. So you need to mention this kind of thing. Okay. So, okay, so interpret the result in terms of background lead to lead by the introduction. This is will make things important. So sometimes when you are interpret and you discuss this one, then part of your discussion, you will add to that introduction later. Because in the introduction, you will make people excited about your work and to make people excited about your work. You will bring some part of your discussion and put it in the introduction in another word. Why? Because you want the people who are reading the introduction to be excited to go and read your discussion to see what did you find. Because here you will tell them what did you find. Okay. You will find that there is a pattern and you say why this pattern is there. Okay. Then maybe you will not find one pattern. You will find different patterns. You will find there is a relationship between sensors and accuracy. You will find there is a relationship between sampling rate and accuracy. You will find that is if there is a relationship between time of data collection and stability of the system, you will find there is a complexity relationship between complexity and accuracy and so on. You will find maybe many relationships. Okay. So you explain all of them and you will put this one part of this discussion will put it in the end of the introduction section. And this one will motivate people to come and read more details in the discussion section. Okay. So, yeah, this is what is the relation presents result to the original result. This one, how is your results and how is your discussion help to answer the original question of your research? This is very important. So your discussion section must answer many questions related to that research questions of your research. Okay. So did you answer that? Did you find some solution that would be more efficient? Did you find some solution which is more effective? Did you find some solution which is more memory efficient or more power efficient than existing solution? So this one is the original research question. So your discussion will answer the discussion will answer this question. Okay. You might ask when there is a research question. I put the research question. Anyone can tell me where I put the research question or I will put the objective of my research. Where I put my objective of my research. Why I'm conducting this research. Where I put this one. In which section? The objective or research question. Where I put them. Please answer in the chat. This is the last thing. And then I will give you a break. I will give you a break. But where we put research question or problem statement or we call it objective of my research. Where I put this one. In which part of the thesis? In which section? Introduction. Yes. Excellent. Excellent. It's in the introduction. Okay. So in the introduction, I will put my research question or my problem statement. Okay. I will put my problem statement or what's the problem? What is the question I want? I'm trying to do in this research. I will put this one introduction. Why? Because I want the reader to know what is the problem I'm trying to solve before he continue reading. I want him to know what is the problem I'm trying to solve when I know what is the problem. Then he will know how I'm trying to solve. Okay. So you have to do this one in the introduction. So that means discussion will answer the question of research in the discussion. Okay. So in the of the introduction. So whatever you raise as a question in the introduction or as a problem in the introduction, you try to answer it in the discussion. Okay. After the results. Continue. Okay. Any question? Okay. So if there is no question, you can if you come up with any question during the break, then you can write it or you can speak. You open the mic and speak. So let's give you 10 minutes a break and then we will back after 10 minutes. Okay. So thank you. And let's have a break. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Okay. Okay. Okay. Hey, guys. So it's back. Okay. So I will try to finish early for today as it's the last class. So as I told you before, discussion section is really important for focusing on how you handle the problem or how your results prove that you did a good job. Okay. So by answering those questions here I listed, you will show the reader that you did a good job by implementing a good method or good algorithm, and you collected results that support your way of solving this problem. Also, one more question here is what is the implication of results, the current results you did, on other unanswered questions. So maybe you raise at the introduction section several research questions and you try to answer one of them or two of them in your results or in your simulation, and others are still not answered, fully unanswered. But you give or bathe the way for other people to answer those questions by your results. You have some impact on answering some questions. You did not answer them, but you help other people later to answer those questions. So you need to discuss these things if possible. This is in the case if you have multiple questions. Also, there is another thing sometimes when you are explaining the results, why the results are like this. For example, if you find that there is a relation between complexity and accuracy, and there is many reasons for saying that there is a relationship, for example, positive relationship between complexity and accuracy. Maybe there are many reasons. So you should not choose the reason that you like. When you, for example, if there is many reasons, you try to eliminate from these reasons as much as you can, and then you tell the reader, for example, you need to tell the reader, based on the understanding of the method, we have these reasons. So the first one is, could be the relation for complexity, the positive relation between complexity and accuracy. And we believe that this one and this one are the main reasons, but others are not. But in this case, you should eliminate with some evidence or with a strong evidence that those are not related to the complexity and accuracy. And others are, and there is one or two are related. But when you are come to the conclusion that this is the reason for these results, you should support your answer with some evidence. Okay. What kind of evidence I can support when I say the reason that complexity, the accuracy, the relationship between accuracy and complexity is that the more complex system has more capacity and more capacity mean high accuracy. So this is a conclusion. You need to get some references that, oh, sorry. Okay. You need to bring some references that support your conclusion or your interpretation. How you can do that from literature, from related world. So from related world, maybe some other people found there is a relationship between complexity and accuracy, then you will cite them when you are put your reason. Okay. So that means you should not jump to the reason that you like, and you should consider, eliminate as much reasons as you can, but keep the reasons that you cannot eliminate. For example, maybe capacity and also hyper parameter tuning are the main reasons for this relationship. So you put two reasons. That's okay. Okay. And you should also cite. So you might ask, oh, yeah, professor, do we have to make some citations in the discussion? Yes, because you are explaining and to explain something, you need proof. You need proof for your explanation. And this proof is what? Citation of other people. Okay. So you are explained based on your understanding. How you got your understanding from other related people or other related world. So you need to sign this related work when you are explaining. Okay. So related. So discussion section should include a lot of citations because you are give proof. You give a proof from related work for your interpretation, for your explanation. Okay. Okay. Okay. Then another thing that you should include in the discussion is you need to tell the reader what are the things we know now from these results that we didn't know before from other results from previous results. Maybe you come up with a new pattern. No one came with this pattern before. So you can explain. And this is will be good actually, because you can explain this one based on your understanding and also put some reference for your understanding. And this one would be a good contribution that you come up with something or with some patterns and the result, no one else mentioned about it before. Okay. Maybe, for example, you found there is a relationship between, for example, using a specific transistors and the overall performance of the system. Maybe with a specific. No one know this one before. Everyone use any type of transistor, but when you use a specific type of transistor, you got the specific performance. No one get before. So in this case, you have to tell people, oh, we now know that with this specific type of transistor, we got some results. No one get this one before. Okay. You need to highlight this one. Okay. So again, include evidence or reasons support each interpretation. You need to get some evidence for each interpretation or each explanation of your results interpretation. How when you explain the results, why there is a plug this or why this pattern is like this, you support by references. Okay. Or evidence. Sometimes the evidence could be a reference or could be statistical test. This could be also evidence. Okay. Or proof. Or maybe it could be math proof. You can include math proof. It depends on your major. Okay. You sometimes kind of prove results are like this because of this mathematical proof. You can do that. Okay. What is the significance of results? Why should we care about this result? So you need to highlight in the discussion why we should care about these results. Okay. So this is very important. Okay. So any questions so far about the discussion section, which is really, really important to highlight your work is, is deserve to be recognized by community, by the social community. Any question about this one? Any question about discussion section? Without discussion section, no one will care about your results. No one care. But when you are discussing your results, well, everyone will care to say, wow, you did good job. Okay. So as you explain your results well in the discussion and you answer many questions that I put here, then you will have wow paper at the end or wow thesis at the end. Okay. Now these section should be rich with references similar to that related work or we call it pecky ground or related work because you need to support all of your explanations by some citations by some papers. Okay. Another thing about discussion. Sometimes we divide the discussion section to sub sections. Same like what I did here. If you remember in the paper, I show you, I divided the paper to. Yeah, I think this is the paper that I divided the discussions. I think maybe another paper I okay here, this bill, for example, I just showed subsections, the discussion divided to subsections. So if you be here, let's go to the discussion. Here's the results. And then after the results, I divided the result to subsection and here I discuss the performance and then I discuss or did analysis for that for the authentication based on these results. Okay. So I divided the discussion to two parts. Okay. I can do that. Why? To make the reader easy to follow my discussion. Okay, so you can divide that discussion to many parts to make it more and usually the discussion has a lot of opinion, your opinion. So sometimes it will be long. So usually when we write the discussion, this is our, my experience with my student. When we write the discussion, we write the discussion long, long, and then later I can please summarize it more, summarize it more, summarize it more until we get good size of discussion. Okay. So we need at the beginning, when you write the discussion, you will explain a lot of things. You will do a lot of interpretations, a lot of discussion from different perspective. You will try to answer many questions and then later you need to say, oh, this is too much talk, too much discussion. So in this case, you will try to compress and make it smaller, smaller, smaller until you will get some good size for the discussion. Usually the discussion will be half page or three or around less than one page. So from half page to less than one page. Okay. Not more than that. So the discussion should not be more than that. So you need to compress your discussion and try to remove all of unnecessary things. Or if the discussion is too long, you can divide this discussion to small, small parts, subsection, subsection to make it easy for the reader to follow your discussion. Okay. Is it clear at this point? Okay. So what is the difference? I think after all of these things I mentioned before, it's easy for you to know what's the difference between results and discussion. And I already explained this one many times. So the writer must make it crystal clear to the reader which statement are observation, observation results and which statement is interpretation. So this is easy. So usual results are related to you write results or you write observations and discussion. You are writing a clear explanation for these results. Okay. Explanation. So this is the difference between them. Okay. So the good solution to make them different is to physically separate them separate this research section alone and discussion section alone or paragraph of the results alone and the paragraph of the discussion alone. Okay. Do not overlay interpretation on the top of the data and figures. Do not make explanations with the results. Okay. Careful use of the phrases such as we infer or we observe. So this kind of thing means discussion. Okay. So be careful about that. Do not worry if results seem short. Don't worry about that. That's okay. Sometimes the results are short but maybe the discussion will be longer than the results. That's pretty far. Okay. Okay. Now what is in the conclusion? Conclusion is usually focus on what is the best result you got. You will put it in the conclusion. So usually the conclusion is focusing on the observations or the results. So usually the conclusion focus on the observation. You repeat for the user or for the reader the most important result you got. Okay. Now the question is should I put some negative results in the conclusion? No. You should not. You should not put negative results in the conclusion. Why? Because conclusion is for the strong things from your paper. Okay. The strong results from your paper. The limitations and weaknesses and things which are not good you put them in a separate section in the discussion. But you don't put them in the conclusion because the conclusion you read the second sentence here say if you meet the reader at meeting six months from now after he read your paper what do you want the reader to remember from your paper? So this is what you write in the conclusion. So you write in the conclusion things that you will let the reader remember after a long time. So what you will put there the strong results. The strong results you will put them there. Okay. So this is why I told you at the beginning of the class today I will go and read abstract which will tell me something about the problem. What is the problem you are trying to solve? And then I will read the introduction because I need to check what is your list of contribution and what is the problem you are solving and then I will go to the conclusion. I will go to the conclusion to see what is your best result you did in this paper or in this thesis. So to do that and every time go and read some of the conclusions you will see that most of the like all the time people write in the conclusion their best result or the what we call in Korea a bunch of things. The most important thing. Okay you will put them in the conclusion because you want the reader to remember your paper in a good way in a positive way. Okay. So this is clear. That's it. This is what you need to put in the conclusion. The conclusion will be one paragraph or two paragraphs. It's short not long. Okay. So you will put something you will re-describe the problem you try to solve or the question you try to solve you put the best result you are trying to the results you get or from and their interpretations or the insightful results and then you will put a little bit about future work. Sometimes we put future work in the conclusion, but things that we are plan to do next. Okay. So now we have around one sentence about the problem one sentence about how did you solve this problem and two sentences or three sentences about the best result you did and then two or three sentences about your future work and that's done. This is the conclusion. Okay. So it's like around six sentences or seven sentences. The conclusion is finished. Okay. So that's about the conclusion. So do you understand now what is the difference between conclusion and abstract? So usually abstract introduce the problem and tell why this problem is important and then try to highlight for you the way that we solve the problem. In the conclusion, you are telling what is the problem or problem in direct sentence, how you solve it in a direct sentence and then you will put the best result you did with their interpretations. The best result you did with their interpretations in two or three sentences and then future work. Can you hear me? Is it clear? So you will understand this one. Just remember this one for now in a very simple way, but you will understand this one well as you read more abstract and conclusion from related work. So when you read more papers and you read the abstract and conclusion of these related papers, you will understand what should be there in the abstract and what should be in the conclusion. Okay, but I already wrote it here in a very long sentences because later maybe after six months or after one year you will back to this picture and you will remember what should be there in the conclusion and what should be in the abstract. Continue. Okay, so any question, anything in mind you would like to ask me? Okay. Okay. So, order of writing. I already mentioned this one before, how we can start writing. So when you are writing your thesis, first you will start with results. You will start with results and figures. You will write them first and then after that you will go and strike what? You will write the introduction. You will write the introduction and then you will write the related work, related work and then you will go back again to the discussion. You will write the discussion because you need to explain what are those results and then finally you will write the abstract and conclusion. So again, let me repeat it one more time. You will start with the results. You will put the tables of the results. You will put the figures of the results in the results and you will write the results section. Okay. And which will be, and then after that you will go to the introduction section and you will write what is the problem and what is the motivation behind this problem, why this problem is important. And then after that you will go and write the related work. You will write the related work, what's going on in the related work. Then after that, maybe also you will go and write the methods or the way you solve the problem, how you solve the problem. Okay. And then back again to the results and write the discussion. And then finally, finally you will write the conclusion and abstract. Okay. So this is how we are writing in the paper. Maybe you will change it a bit, but usually this is the way we are writing. So we start by the results. We start by the result and we end by the abstract and conclusion. Continue. Yeah. So this is in the thesis and also the same thing in the paper. And I don't care about if it's Korean language paper or thesis or it's English language thesis or paper. So all are same. Okay. So we are, I'm teaching you now, regardless of the language, I'm teaching you the way of writing. Okay. So it's not about which language you are using. Okay. So here is some tips or things that you need to consider before we finish. When you are, so you have to check your tables and figures with answering this question. Is the figure self-explanatory? How we can know if the figure is self-explanatory? The caption should include all details. As I showed you before, the caption could be two lines or three lines. That's totally fine. So the caption of the figure and the caption of the table should be self-explanatory. That means no need to go to understand the figure to go and read the manuscript or the paper or go and read the text. So you just read and then fully understand you go to read the text. But initially you can understand the text, the figure from the caption. Okay. Are your axes that for the figure, are your X axis and Y axis or the Z axis have good units? The units are here. What, for example, time in second, milliseconds or a kilometer or a micro watt, micro joule, whatever. And this one have the units. You have to be careful about that. Okay. So the unit must be there. Okay. Did you show in your drawing of the graph or the performance, the error bar? Error bar means usually, for example, you draw, for example, results like this achieve this kind of value and this value go up and down with this value. Usually this one, we call this one error bar and we call this one the standard deviation usually. Most of my work, we assume the standard deviation. So we can put the average, for example, average accuracy and the results go up and down with this standard deviation. Okay. So we call this one error bar. Okay. Did you include error bar in your results or not? So, for example, let me see if in my paper here, usually we add this one in most of the papers. Okay. So it depends on how complex is the system. Oh, okay. Okay. Yeah. You see, you see, we add the bar, you see the bar, it's going to this direction more than this direction. So this is fluctuation, how much the error bar. Okay. But by the way, this error bar fluctuate, like around, they fluctuate around four percent, four percent. So this one is a small scale from 84 percent to 100 percent. So the fluctuation is not high. Okay. It's around four percent fluctuation, go up and down. So this is record error bar. Did you add the error bars and did you add the units here? What are the units? Okay. So this is important to check in your paper. Also, if you have a data and this data is drawn. Okay. So let me just see, show you. If you have a data and this data are like this, for example. Sorry, I'm trying to use. Sometimes you have fit, we use sometimes we call it fit line. So when you are using fit lines, so sometimes we draw fit line this way. This is a fit line. This is one fit line. And there is another fit line I can draw here is this one. Which one is more accurate? The orange one, right? The orange one is more accurate to fit the data than the blue one, right? Yes. So you need to be careful about the fit line when you are using drawing some data. If you're adding some, we call this one a trend fit line or something. So you sometimes need to draw this one in a good way. Okay. Good curve. Okay. So this is also something you need to be careful about. Don't make redundant data. What I mean by redundant data? So for example, you have, you draw a figure for accuracy. So for example, you draw accuracy of some system. Okay. And then after that, the same results of this accuracy, you put it in the table. This is not good. And this is common mistake, by the way. All the things I put here are common mistake. The students put the results in a table and figure. This is wrong. Okay. So usually your professor or your committee will easily detect this one. Oh, you say, oh, you repeat results in figure and table. This is not acceptable. You put them in a table or in a figure, but you do not repeat the results. Okay. So don't put the results in a table and then in a figure. Okay. So remove redundancy of the results. Okay. Okay. So could the data density be increased by eliminating non data bearing space? So sometimes when you are drawing figures, you will. Okay. So for example, in a figure of you have a drawing figure like this, for example, let's say you draw figure like this. And this figure is this one, this system. And then after that, this one go to this system. Let's say this is a results results. Okay. This is have a lot of space. Usually when you draw figures, we don't like a space in your drawing or in your figure. So you should you can draw this one this way so you can have like this and you can have this one like this and this one like this. And this one will be like this. Okay. So this one is similar to this one, but here you have a lot of space here. Okay. We usually we don't like a space. Not only me, all scientific community. They don't like space to have a lot of space in the normal figure or even in the results figure. So they don't like. So, for example, let me show you here. You see, there is no space. So I can't. Why I did not put from zero. Why did not put from zero to one hundred. If I put from zero to one hundred, what will happen? There will be many white space here. Right. So I put the results from forty eight to one hundred because I don't want space. And only this is not my choice because all research community don't like this one. Okay. They don't like adding a lot of space in the paper. So here, even here, you see forty eight, forty eight, because most of the accuracy is above forty eight. Look here from 90 you see from 90 to 100. So why did not start from zero to one hundred? Because usually accuracy starts from zero. But it would be a lot of space. So we try to compress the figure and try to avoid the space as much as we can. Okay. So I'm just showing you example here. Is this a sparse data set that could be better be expressed as a table? Usually if you have some sparse data, it's better to put it in table, not in figure, because figures are for dense data. But if the data you don't have dense results, then you can put them in table. Okay. So, for example, you have results for one situation, you have the results for another situation. Then in this case, you put them in table. So, okay, you need to answer this question. Does the figure distort the data in any way, like make distortion for the data? Are the data presented in a context or without context? Okay. Do you I mean, this data that you put there, does it have a meaningful things in your results or does not have any meaningful things? Okay. So you should pick what are the results that you need to put in the thesis. Some results you should not put in the thesis. Does not make any sense. Okay. Or does not have any context. Okay. Are the data presented in a context or not? Does the figure caption guide the reader to some eye to take home lesson on the figure? Like it will tell him what is the important thing in the figure. So the caption should explain what is the figure and tell him what is the important thing in the figure. Okay. Okay. So another thing, if there is no data provided to support given statement on the result observation, consider add more data. Okay. So or delete the unsupported observation. So sometimes you explain. But when you are explaining, you need for the explanation, you need some supported results to tell you that, oh, what you are saying is correct. For example, you say, oh, there is a positive relationship between complexity and accuracy, but you don't have any results that show a complexity relationship between complexity and results. You should not put this statement. So, for example, you say there is a positive relationship between complexity and accuracy, but there's no result for that. You should not put this sentence. Okay. You should put sentence that supported by your results. Okay. And also when you explain this sentence, you need to be supported by some evidence. Okay. Strong evidence. Okay. So evidence could be citation or could be many results that support your explanation. Okay. So the data support your text, the data or the results support your text or your explanation. The data contradict with the text. So be sure that there is no data contradict with your text. So, for example, you say there is a negative relationship between complexity and accuracy. Okay. But in real, in real, there is a positive relationship between accuracy, between accuracy and complexity. So, for example, this is a complexity and this is the accuracy. Okay. And the figures say positive relationship, but you are saying in the text negative, negative relationship. So this is contradict. So be careful. Be careful. When you are writing sentences, be sure that these sentences are agree with the results. Okay. So this is what I want to say here. And also you should have data or results sufficient to prove your text. So if you have the text, I mean, you write some sentence, except then some sentence or you discuss some results, you need to have enough results to support your discussion. Okay. Or you have some evidence from related work. Okay. Okay. Maybe there are some data or data, when I say data here, guys, when I say data, I mean results, results. When results may support the text, but they are not presented in a good way. So you should take care that results are presented in a good or a good visual way to support your text. Okay. Another thing, do not explain elements of table or figure one by one. I already showed you this one before. So, for example, look here, I have a lot of results here. Look here. I have this one, one, two, three, four results in here. One, two, three, four results here. One, two, three, four. But I don't explain all of them. You explain the most important one. Okay. For example, the most important one is the blue one. So I only explain the blue one. I do not explain others. I don't care. So the reader can see the figure. The reader can see the figure. Only I would explain for the reader the most important results. Okay. If you are explaining everything in the results, people will feel boring about your results or about your paper. They will feel bored. They will not enjoy reading your pieces or read your paper. Okay. So be careful. Do not explain all results. Only explain the most important results. Is it clear? Important result in figure and important result in table. So, for example, here, let me just show you. In here, I put here big table. Look here. I put big table. But only I explain this one. Only I explain this one. Others I did not explain. I did not explain this one. You see the other one? I did not. Only I explain the most important thing. For example, when I'm using three sensors, I achieve false. I achieved this kind of false. I think this is false. If I are, I forgot about what is that? Yeah, false positive or false? This is a metric. I'm repeating this one a lot of time, but I forgot about it. This is false acceptance rate. Sorry. False acceptance rate. So I achieved false acceptance rate and false rejection rate with this one. So it's just a metric. I just want to tell you this is just a metric. You don't need to care about. But I just want to say this is the most interesting part that I explain. I don't explain others. Okay. This is what I want to say here. Okay. So do not explain everything in the table. Let people read the tables by themselves. Read them. Read the figures by themselves. Okay. Do not explain everything. Before you submit your thesis, this is very important thing. And all before you submit your paper, please check the spelling, check the grammar. And you are lucky, guys, nowadays, because you have a lot of tools to check the spelling and check the grammar. We did not have these things at the time when I was a master degree student or B.H.D. student. We did not have these things. Only we have the spelling check in Microsoft Word and that's it. Okay. But even in grammar, we can't check the grammar. So we have to give our paper or our thesis to other people to read it and check for us. Okay. Even sometimes we give it to some English service to double check. Some other people pay money for them to check for if our thesis is well written or our paper is well written. So what I want to tell you before you submit your thesis or you submit your paper, please check the spelling and check the grammar well. And you can double check by give your paper or your thesis to someone else to read it for you. Okay. And maybe they will notice something. And so this is really important to to avoid any embarrassment at the time of the of the review or at the time when the professor checks your thesis. Because if if I am in the committee as a professor and I see the student did many spelling mistakes or many grammar mistakes, what I can get as a message is that this student is careless. This student is careless. And this means I will be very tough on judging this student because this student is careless. And he just submit for me a thesis without taking care of the spelling and the grammar. So this is in Korean or in English. So the professor or the reviewer who check your paper, when he see that there is a lot of spelling mistakes and a lot of grammar mistakes, he will feel that you are careless. You just put this paper, write this paper or write this thesis and you don't care about who is reading this paper or who read this thesis. So this will send bad message for the one who is reading your thesis or who is reading your paper. So be careful. OK, please double check before you submit. OK. Any question, anything? I'm talking, talking all the time. OK, so, yeah, this is some of the writing advices. So you should check time is as fast. So you need to anyway, those are some clear things about advices about how you should write, avoid some missed phrases or complex sentences like complex sentences. For example, you say this is something where something which something that something. So in this case, when you are using which that where those would mean complex sentence, those complex sentences are hard to understand. So try to split your sentences to short, simple sentences. OK, this will make your sentences more easy to read. OK, this is what I want to say here. Try to do not use double negative. I think most of these writing advices I already explained for you before you remember when I told you about some advice of writing at the beginning of the semester. So I saw those advices and more advices you can check from our briefest lectures about how you can write sentences of void, for example, male, female, avoid some negative sentences. Avoid there is there are you know, there are many things. Avoid I and G, for example, walking, moving. So avoid this I and G phrases because they are not good. OK, you try to avoid them. Avoid he or she or it. So avoid these things. OK, so I put a big list of how to avoid something. And if you back to that, I think it was lecture number two or lecture number three, go back again to that lecture and you will see some more advices about writing. OK, so I will try to skip this one because it's yeah, those are repeated things that we mentioned before. OK, so this is the main things about the main components of the thesis or even for your paper. We cover them all. So references and appendices, usually appendices, we add them, we add in the appendices, the unrelated, I think which are less important to add in the paper. And usually the appendices could be appendix A, appendix B, appendix C. You can make them, divide them to many appendices. OK, many appendix. So that's it for and for the main structure of your thesis. Any question, anything in mind you would like to ask me? OK, so, yeah, this is by this lecture, guys, we came to the end of this course. I would like to thank you for having me. Even we did not meet face to face in the classroom. But I really enjoy having this course with you guys. And I hope you enjoy this course. Get some good knowledge from this course. Again, I would like to thank you again for this is just a closure of this course. Still, we will have next week your presentations. But for the course contents and for the course material, we came to the end by today. Thank you again. And I hope you enjoy this course. If you have any question or anything, even now or even later, you just feel free to contact me. Even you can visit me. I will be by the beginning of this semester on the campus, on Eskeke campus and so on. So if you have any free time, just come cross by. You can find my you just search my name. You'll find my office. You will find all the things you are looking for. You can even find my phone number. You can call me anytime you can. You just search here. You can find my email. This is my yeah. This is my. If you search my name, you can find me here. Here is my phone number. It is my email. Here's my location. I am in this building. Even you can search in Korean. If you search Tamir in Korean, you will find all of this information in Korean. You can visit me anytime. This is when I was too young. My photo when I was too young. Anyway, so thank you, guys. Really, I. Yes, I. Exactly. I look different here. So this is like maybe seven or eight years ago. Yeah. So, yeah, I'm a little bit older now. So feel free, please. If you have anything, contact me or visit me. You can even without you just pick the phone and take the phone and call me anytime. And you can visit me anytime if you have any question. If you would like to collaborate with me, even like in research work, you would like to do some research with me and your adviser. I would be more than happy to work with you and your adviser. I'm collaborating with many professors from my department and other departments. So feel free. Anything come in your mind and you'd like to discuss with me. Just come and discuss. OK, that's it. Thank you, guys. And that's it for today. Thank you, guys. And we'll see you next week. Have a good day.