Please, Tranlsate the following English into Korean:

let's wait as usual for just a few seconds to be sure that everyone is here. Hello, hello everyone. Just for a few seconds and we will start. Hello everyone. So I think we should start. I think everyone is here. So welcome back. So this week, as you know, we will have the presentation. So we will present teams will present some teams will be to two students, some of the other teams will be just one student as usual. The teams who presented in the mid term will present in the final. And we, as I told you in the last week, we will follow the same strategy we did in mid term. So we will let you choose to who will present first. I think we should start because we will have many teams that will present this week and next week. So I would like to ask you guys here to write in the chat the order like the first team say will we will present, they will present first and then we will follow in order. So I will keep the order as you choose when you are writing in the chat. So please, I will give you like a few seconds. You can say the order of the presentations as we did in the mid term. So who will present first and then who will present next. So it will be the order will be based on the chat messages. Okay, so please write in the chat. I will present and then with my team and then based on the message, I will make the order. So let me just okay. Ihan, okay, Ihan Pin will present first. Okay, so yes, please write down your team and then we will keep the order based on the messages. Okay. Yes. Okay. I will wait. Yeah, please write that you will present only one of the team will mention I will present then I will expect that you are writing this message for you and your team member. No need to repeat. Okay. So one of the team will just say I will present. So for Ihan Pin, you can start share your screen. Yeah, you will be the first. And then after that, the next will be Kim Joo-Young will be next. Yeah, as you see in the messages, the order will be like this. I will keep this order even for next week. So please write message here. I will present and I will keep this order. Okay. So here are my points. Yes, I can hear your voice now. So only we have 10 teams to present. So is there any team did not write message here? Right. I will present. I think we have more than 10 teams. Okay, so I think we should. We should start with you and then next, maybe still some are. Thinking, okay, so you can go. Okay, so you can start. You have a team of two members, by the way, before you start, please. Sorry for this one. I already put in the iCampus the one assignment. I did one assignment today and this assignment will be until next week. Sorry, until Wednesday, midnight. Each team will upload their PPT. Okay, so after you finish, the new team will upload the PPT. One of the team will upload. Don't make two members upload. So I will see the name of this one in the PPT and I will give the grade for the two students in the PPT. So only upload the PPT. If you are two members in the team, one of the team members will upload the PPT. Okay. Okay, so you can start now Lee Hanpin. Okay, go ahead. Hello, everyone. I'm Yanbin and my teammate is Lim Seung Hoon. I have COVID, so my noise is a bit stop. I appreciate your understanding. This is my first presentation in English and I received a lot of support from my teammate while preparing for this. This is a new topic for our team. Let's start my presentation on low EIAP antenna design for tablet devices. I will proceed with the presentation in the order you see on the screen. First, I will explain what EIAP is and why low EIAP is necessary. Next, I will briefly explain how antennas are placed in mobile devices and why EIAP is a problem in tablet. Finally, I will talk about how I am working on finding a solution. All antennas have directivity. High directivity means that the antenna can communicate well or transmit and receive signal effectively in a specific direction, but it does not perform as well in other directions. EIAP is a measure of the power radiated by an antenna system in a specific direction. EIAP includes the power value in the antenna's directivity. For antennas used in radar, they are designed to have high EIAP for accuracy in the desired direction. However, in mobile devices, EIAP needs to be low to ensure good communication regardless of the base station's direction. Additionally, high EIAP can interfere with other devices using similar frequency. For these reasons, EIAP limits set by F3C. The F3C is the regulatory body in the United States responsible for managing communication regulation to reduce unnecessary signal interference and ensure communication quality. The table shows the F3C's EIAP limits for various LT bands. Specifically, LT band 30 and LT band 48 require even lower EIAP because they are used in radio hour satellite system. The EIAP value is calculated by adding the output power of the communication chip and the gain of the antenna. Antenna gain is divided into antenna efficiency and connectivity. To provide stable data communication, ELLIC communication companies require specification for TRP. TRP is the total targeted power by the antenna. It is calculated by adding the transmit power and antenna efficiency. In the case of LT band 30 and LT band 48 in the table, it meets the TRP specification required by telecommunications companies but fails to meet the EIAP F3C specification. To provide excellent data communication and base EIAP regulation, we need to lower antenna selectivity. Next, I will explain how antennas are integrated into mobile devices. In the early mobile phones, you could easily find the antenna. However, as technology has evolved so has the design of smartphones, today's minimalist design has hidden these many components so that most people do not know where the antenna is. Briefly regarding antennas in mobile devices, except for NFC, WPT, and BWV, all LT antennas use metal frame at the top and bottom. If you look at a Galaxy or iPhone, from the top or bottom, you will see a gap in the metal. This is not just for design, it is to divide the antennas. Through the designs of smartphones and tablets deeper, their antenna placement and shape are similar. However, even with the same antenna design, larger mobile devices like tablet or notebook have higher directivity issue than smartphone. In antenna, larger metal parts are called the ground. When the ground size increases while keeping the same antenna design, the antenna's directivity increases. That's why devices with larger ground have more problems with antenna directivity. Even if the same metal is used for antennas, they are categorized into different types based on how their feet are grounded. When the type of antenna changes, the electric field and magnetic field also change, resulting in entirely different characteristics. Each antenna type has its advantages and disadvantages, but in terms of directivity, we can see that IFA type antennas tend to have relatively lower directivity. By changing the antenna's position and checking the color flow, it is observed that when the antenna is placed in the center, there is no color flow at the bottom, resulting in lower directivity. It seems to have the same effect as making an antenna on a smaller ground. In radar system, multiple antennas are placed and the same phase signals are input to all antennas to increase the EIRP. I'm currently researching if I can do the opposite by inputting signals with different phase into the antenna. I am studying if it is possible to further reduce EIRP. That concludes my presentation. Thank you for your attention. Thank you for your presentation. This is your research field? Yes, this is my research topic. Interesting. It's an interesting topic. Any questions for the presenter? Yes, okay. I have just one question here. If you go back to the previous slide, so the best position in the case of, you said that in the tablet situation where the space is big, the best position is to put the antenna in the middle for the directive, for the best efficient position for the antenna. Is the one in the middle? So what is the best position based on research so far about the antenna position in the case of tablet? In the middle? I think your voice is not, you don't have, you are muted. So can you? Yes, you can speak now. Yes, middle position is the best for relativity. But many antennas are placed in the side. Yes. I see. So now your research is to find best antenna design or best antenna position. What is your exact research in this field? You are muted. I want to find new design. Ah, new design. Totally new design. Yes, okay. Okay, thank you. Good luck with your research. It's interesting topic and wish you all the best. Okay, thank you. So you can unshare your presentation. And now let's go to the next presentation with Kim Joo-hyun. Please share your screen. Yes. Okay. Hello, could you hear me? Yes. Can you please raise your voice and yeah, you can. I will think I need to increase the voice here. Okay. Okay. Yes, you can. You can start. Hello, everyone. I'm <PRESIDIO_ANONYMIZED_PERSON>. Today's presentation is titled, It's Time for Tech AVF, Android Virtualization Framework. The title is a bit aggressive, but the content is not. Let's get started. Before we begin, we talked about the advancement in mobile services and the importance of securing mobile devices in our last midterm presentation. We've briefly looked at the TrustZone and AVF, and we talked about the idea of collaboration models to maximize their strengths for UI security. However, we left out a lot of details, such as why AVF came into the mobile market and what makes it special. Here's the outline of my presentation today. We've already briefly reviewed our previous presentation. Now as your background, we will quickly review why AVF isn't necessary and explore the role and capabilities of PKVM within AVF. Additionally, we will also take a quick look at confidential VVM's, which offer a similar isolation environment. Afterwards, I will question whether AVF's security has been proven and argue that it's time to attack. This slide is from the past presentation. This slide shows an isolated execution environment and talks about flexibility and compatibility as a key benefit of AVF. However, it omits the story of why AVF came into the market. So let's start with that first. I think this is not the only point, but I think the most important one is to overcome the design limitation of TrustZone. As described, TrustZone splits the execution environment into two worlds, the normal world and the secure world. Then the secure world is granted more privileges. This design choice shows that the secure world can access the memory of the normal world. As shown in the figure, the mobile normal world does not have access to the secure world's memory, but the secure world has access to the normal world's memory. As a result, many attacks are being attempted because one successful attack on the secure world could compromise the entire device. As more services rely on the secure world, the number of vulnerabilities is likely to increase. In conclusion, AVF provides a mutually distrusted execution environment. That is, the host cannot access the guest's memory, and the guest also cannot access the host's or other guest's memory. This mitigates the problem of a more privileged TrustZone. This means that an attack on one execution environment cannot take over the entire system. Let's move to PKVM. PKVM is the most important component of AVF. The mutual distrusted execution environment is a key feature of AVF. It is provided by PKVM. PKVM is an extension to the RINSCAPED VM hypervisor that has the ability to restrict access to a guest's VM when creating a protected virtual machine called PVM. PKVM tightly isolates the memory access between Android and PVM. Simply put, even if the Android or VM is compromised, PKVM can still maintain the integrity of executable code and the confidentiality of PVM's asset. Because of this strong isolation and protection, PKVM has been adapted as a deeper type of phyasor over Android AVF. I think we need to dig a little deeper into how PKVM implements this isolation and protection. The key is the ARM architecture. The ARM architecture has a two-staged MMU structure, which allows for more fine-grained management of memory access and protection. The PKVM leverages this MMU unit to protect from illegal memory access. First, stage 1 MMU is controlled by ER1, the kernel, and it is responsible for managing the user space and the kernel's virtual address space. At this stage, virtual addresses are translated into intermediate physical addresses. Second, stage 2 MMU is controlled by ER2, the hypervisor, I mean the PKVM. It is responsible for translating the intermediate physical addresses generated by the stage 1 MMU into the final physical address. This process ensures that guest VM does not have access beyond the memory regions allocated to them. This two-staged memory translation process maintains a clear separation between VMs. And I will briefly introduce confidential VMs powered by AMD SEV and Intel TDX, which have similar goals to PKVM. Those are providing strong isolation mechanisms to protect the memory and the state of virtual machines. However, while PKVM is primarily optimized for mobile environment, AMD SEV and Intel TDX are focused on server and cloud environment. Okay, we check the background briefly. As I mentioned before, I will argue that it's time to attack AVF. It's for hardening of AVF. The reason I'm arguing that now is the time to attack is because this is a fairly new feature on the market. Only few fixed phones are supported and the API is not yet opened to third-party application developers. And compared to the number of papers on third-gen, only very small number of papers have been published for AVF or PKVM. So I believe that it is not researched fully. And similar tests have existed in a similar security model. Heckler and VC attacked the interrupt handling mechanism in CBM by injecting malicious interruptions. So I argue that we should try to exploit vulnerabilities in the interrupt handling mechanism as a faster attack model. Okay, but someone could argue that it lacks impact because it's still early in its launch and there are few supporting models. But Google is going to explode it. Therefore, I would argue that now is the time to attack it. But we need to succeed in attacking. And we also need to find solutions to depend against attacks together. And another point is that the PKVM is not single, not only hypervisor for AVF. Qualcomm's Ku-Nya uses a different interrupt handling mechanism than PKVM and also uses it for AVF. So we need to look at it for different attacks. To conclude, we briefly leave the background and the key features of AVF and discussed it of the capabilities based on attack success of a similar protection model. And I argued for the need to validate security through attacks based on the potential or possibilities for future market expansion. Okay, thank you. I think time is over. Okay. Thank you again. Yes, thank you. Yeah, thank you for your presentation. Any question from the audience for the presenter? Okay, I have a question. So yeah, I agree with you that if it's possible to attack, then in this case, why not? Even if it's still in the early stage of applying this mechanism of the hypervisors and the visualizations that they apply. But I just want to ask, your attack will be on the hypervisor itself or you will be attacking above the hypervisor? I will try to find the attack model of hypervisor. Actually, the interrupt handling model, the PKVM delegates their interrupt to host VM. So I tried to find the attack model of this mechanism. I think you should be careful about the assumption when you are doing that, like what is the capability of the attacker? Because you know, this is a low level when you are talking about hypervisor, you are talking about low level hardware. So maybe you need to take care of the assumptions. Are your assumptions for the attack are realistic or not realistic? Okay, so when you say that I will attack assuming that the attacker has this power and the attacker is doing this one. So you need to know what is that? So something we call it in security, the threat model. You know the threat model? Yes, I heard that. Yes, you should be careful when you are proposing your attack is what is the threat model? You should clearly say what are the capabilities of the attacker and what is the knowledge that attacker need to have to make the attack? Did you succeed in any attack not published yet or not yet? Actually, not yet. So I need to find the solution to attack and need to define the threat model also. Exactly. I think you should find the attack. Once you find the attack, then you will define the threat model. Okay. And then after you define the threat model, you need to see that this threat model is realistic or not realistic. This threat model could be possible. I mean, could the attacker have this kind of capabilities and this kind of knowledge and can do this one a realistic scenario or not? If not, then still the attack is okay. But you, I mean, when someone check your attack, they need to know explicitly what the attacker knowledge and what is the attacker threat model? Even if it's, you know, in security, we have a lot of attacks which are with the threat model, which could not be for now realistic, but maybe in the future will be realistic. But that's okay. But you should define the threat model once you know you find the attack. Okay. Okay, thank you. Okay. Yes. Okay. Good luck. It's interesting topic. And usually security in Android system, the security is very important and will, if you have some attacks succeeded, it will be have huge impact in the future because, you know, the Android operating system is applied for many mobile devices and many small devices. Okay. Good luck. Any question again for the presenter? Okay, thank you. And let's move on. You can unshare your screen and we can move on to the next presenter. So for the next presenter will be Kwan Young Pui. So please share your screen. And yeah, let's start. Yes, you can start. You are alone. Okay, yes. I cannot hear you. So I don't know if you speak or not. Hello. Yes, I can hear you now. Okay, go ahead. Okay. Hello everyone. I am <PRESIDIO_ANONYMIZED_PERSON>, software engineer at Samsung Research, Samsung Electronics. Also, I'm currently a student master degree at TMC SKSKU. In this section, I will briefly introduce code review automation research period. The main goal of code review are to increase readability, code readability and bind and fix program bugs in order to improve quality of software code. It additionally performs a critical role in software engineering culture since they are commonly used in code reviews as education methods to share knowledge among team members. Major IT company also have code review as an essential activity. For instance, Google has a code review process and generates millions of code review annually. Microsoft also 79% of developers participated in code review in daily. For code review, our essential activity in software development process, they require substantial time and effort for both code author and reviewers. So we need code review automation. Previously and currently, the industry area has achieved total results by automating the code review process with results from the code analysis tool, like Stack Analyzer. Recently, there has been a lot of research conducted by using AI model to generate code review comment, like LLM. First, I should comment on examples generated by using code analysis tools. This is something I talked about in the mid-term presentation. Let me say it again. This is an example of test coverage service. At GitHub, generate commented a Samsung Retina case, which shows the line coverage for both total line and change line. Uncover line is orange line and cover line is green line to visualize to reviewer. Also, this figure shows comments generated into GALI code review system in Samsung Retina case, which contains potential defects in the mid-case. As you know, the potential defects automatically detect a potential error through static code analysis without running the target program. Also, this figure shows generated into Google GALI review system case called Critique, which contains syntax checking in the review request. With the rise of deep learning in software engineering field, researchers started addressing more challenging general test aims at imitating human developers. There have been studies using language models based on transformers architecture, such as T-PIPE or POT. Table 1 shows a pre-policed summary of this study. All components in this table are typed here of the software engineering research field. Among these, Microsoft released code review, which used deep trunk as the state of the art for applying nine different languages. This is OTA model. Recently, in the event of large language models, such as OpineAS, Chachapiti, Ojibiti, and Betas, LAMA, this LLM helps demonstrate the ability to solve problems using in-case learning. We propose an idea to enhance the quality of automatically generated code review by providing LLM with information aligned with code review comment through in-case learning. This figure illustrates our idea. We propose AI-based code review board that, like Human Reviewer, generates code review comment at a pre-case stage. Using a few-side learning approach, we supplied the LLM sustainable context knowledge, such as a code section or functional list, or maybe developer information or project information, together with the adaptive pro-port to optimize its code review. This is the end of my presentation. Thank you. Any questions? I think you should keep this slide. Any questions to the presenter? So, in this one, this is the one you proposed. This is the way that you would like to propose your idea. Yes. And are you going, because I see here a few short data, are you going to fine-tune some LLM for the code review? So, are you going, because you said that LLMs are recently used for the code review automation, are you going to fine-tune these LLMs with some data set? Because you said a few short data here. I'm not sure fine-tune is available because the completion part is needed. So, just influence with few-side data. I think it is available. So, then in this case, what do you mean by few-shot data here? For what you are using this few-shot data? When code review, in this case, code review using code just needs code dip only. Ah, okay. This stage, but I will provide more code and related functions and related positive information and also similar code comment is needed. That is in context learning data. I see. Did you start working on this kind of thing to check if you can like use a chat GPT or something for the code review or not yet? I started this month. Okay. This is example of chat depot. Yes. This is sort of real developer's comment. Yes. This is prompt part. GPT answer is correct, but it's too long. I see. Yes. I will research it. Maybe you can do some prompt engineering to make this one more compact answer. Yes. Yes. Yes. Okay. Yeah. This is interesting, but you need to, you have some data sets to validate that chat GPT is working well. Do you have some, you need some validation data sets of some prompts and some, sorry, some codes and their review. Do you have this kind of thing? I will use this data set. I see. I see. I see. Okay. Yes. Yeah. This is looks interesting. Good luck with your research. Any question? Okay. Good luck. Good luck with your research. Thank you. Yes. Thank you. Okay. So I think should we start with a new one or okay. Let's take a early break and let's back at two five five because we and who will present next? Okay. Let me just see what four percent next. John G. I think we'll present next, but we'll have a break until two fifty five. So we will back after two minutes just to let you everyone take risks before we start the next presentation. So let's back at two fifty five after ten minutes. Thank you. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. You. Hello, guys. Welcome back. So let's start with the room tune. So please share your screen and. Let's just start the second part of our day presentations. Okay. All right. Hello. Can you hear us? Yes, I can hear you now. Okay. Okay. Can you see my presentation? Yes, I can see now. Yes. Go ahead. Hello, everyone. My name is <PRESIDIO_ANONYMIZED_PERSON> and together with Jie Up Choi. We will explain the display technology in detail. I will give the first presentation and then he will, my teammate will present next. So actually now I'm focusing on micro LED pixel circuit and my teammate is focused on device physics of light sources. However, the purpose of these two studies are saying improving the color shift by the characteristics of light sources. Okay, I will explain about micro LED first. Fin film transistor technology is crucial for all kinds displays, including micro LED. Each pixel in a micro LED panel requires a TFT pixel circuit for proper operation. The TFTs are built on a glass substrate and they store the voltage data through capacitors. The source current is responsible for driving each micro LED pixels, but there's a challenge here. Unlike crystalline silicon, as I was saying before, the TFTs are built in glass substrate. So the threshold voltage of TFT varies much heavier than silicon substrate. So that means each pixel might have a different current leading to non uniformity issue. Okay. The left picture is a pixel circuit in the panel. The capacitor in each circuit stores the voltage data needed for operation. And then TFT acts like switch. It can be converted like right picture. The current for micro LED is made by the drain current of a TFT. Actually, as we learned in our undergraduate semiconductor engineering course, the drain current is proportional to the scale of the threshold voltage. Here, a problem arises. The graph on the left shows the variation in drain current with respect to drain voltage for different TFTs. And as you can see, each curve is different. Moreover, the graph on the right illustrates the variation in VTH according to the TFT manufacturing process, which also shows fluctuations. These various variations lead to deviations in the TFT current, which result in deviation in the current driving the deviation in the current. If so, you can see non uniformity display like the picture. To fix this problem, threshold voltage variation must be measured and compensated. So how are threshold voltage variation measured and compensated? The blow circuit is the pixel circuit, which has compensation part for threshold voltage variation. When the V data flows to the source of driving TFT, the blue arrow and the drain and gate of the driving TFT are connected, the yellow arrow, the driving TFT turns on. It flows current to the storage capacitor. As a result, the storage capacitor stores until the voltage is V data plus VTH. And then as you can see the equation on the right, it will cancel the VTH turn, allowing the driving TFT to supply stable current to micro LED without the influence of VTH variation. Unlike organic LED or OLED, micro LED has different electrical characteristics. One critical observation is the color shift due to the size of the pixels. And at higher current densities, a red wavelength shift occurs. That means if micro LED is driven by PAM driving method, the wavelength shift will occur and it leads to immediate distortion. That's why PWM driving method is introduced. Here I compare two different pixel circuits. First amplitude modulation, we can call it PAM, and the first with modulation, we can call it PWM. PAM controls brightness by varying the amplitude of the current. On the other hand, PWM controls the duration of the current pulse. Actually, PAM pixel circuit needs only one driving TFTs usually. However, PWM pixel circuit needs at least two driving TFTs. One is for controlling the current of micro LED and another is for controlling the duration of emission time. That means the size of pixel circuit is more than twice compared to PAM. It could cause low yield in panel production and high power consumption. That's first problem. The second problem is a slow transient time. To control emission time, the new signal is needed. Usually we call it sweep signal. It is kind of a ramp signal and it decreases or increases linearly during the emission time as shown in the timing diagram on the right. In the circuit on the left, the sweep signal turns on the T3 TFT and turn off the T5 TFT. Finally, stopping the current supplied to the micro LED. However, as I mentioned earlier, the sweep signal is very slow, which means it cannot quickly change the state of T3. If you look at the bottom right, it shows the change in drain current as a function of a gate voltage. Since the gate voltage changes slowly due to the sweep signal, the current also changes slowly. This result in T5 being turned off slowly then makes the long falling time. Moreover, as shown in the bottom left table, the electron mobility in TFT is much lower, about 1 15th of that in CMOS. It makes the problem worse. This graph is the simulation results of PWM pixel circuit using sweep signal. I simulated this pixel circuit under 8 bit ranging from 60 to 255 grayscale. You can see 60 grayscale cannot maintain constant current. If it goes below 60 grayscale levels, it will not be able to maintain peak current at all and it leads to emission distortion. To solve this issue, recent advancements have introduced the use of inverters in PWM circuits, which is motivated from SRAM structure to shorten the falling time of the emission period. I am currently researching a novel pixel circuit to improve these two issues. That's the end of my presentation. Your team mate will present next? We'll present next. Yes. Hello, do you hear me? Yes. Hello, my name is <PRESIDIO_ANONYMIZED_PERSON>. I will continue the presentation. I'd like to talk about why current distortion happens in LCD products that use quantum dot enhancement theorem and suggest a way to fix it. This is also the topic of my current research. First, let me explain what quantum dot enhancement theorem is and how it works. And then I will talk about why it can cause color distortion. QDEF, as shown in figure one, is a film that appears yellow. As you can see in figure two, it is placed between the bank light unit and the liquid crystal. Improving the display's color accuracy and brightness. Quantum dots are tiny nanoparticles that can absorb light and emit light at specific wavelengths. QDEF is a film made with these quantum dots. Typically, a blue LED is used as the light source and QDEF converts this blue light into red and green using specific quantum dots. Quantum dots help convert the light with very high purity. The combined and purified white light then passes through a color filter, showing clear and pure colors on the screen. Because of this, displays with QDEF can support a wider color gamut than traditional LCDs. This process is illustrated in figure three. Next, I will explain how QDEF causes color distortion. As shown in the diagram, light from the blue LED is converted into different colors when it meets the quantum dots and then spreads out in all directions. Some of this light bounces off the reflector behind it and leaks out. This leaked light overlaps with the light coming from the original light source, making the color conversion too strong and resulting in distorted colors being displayed. Now I will explain the color correction process I'm planning to try. First, I measure the light profile from the display to get information about how much and where the color distortion occurs. This light profile changes depending on the intensity of the light sources, and the amount of color distortion can vary. By analyzing the measured light profile, we can find the color coordinate at different positions. Analyzing these coordinates shows how much the color shift depending on the distance from the light source. We can use this information to create a map showing where and how much the color is distorted, as shown in figure five. To do it simply by setting reference points and correction points, as shown in figure six, and comparing their color coordinates, we can figure out how much correction is needed. Since the display uses an RGB signal system, we need to do some color space conversion. The best color space for comparing color distortion is a CIE lab, which consists of hue, chroma, and lightness components. In this case, we focus on hue and chroma for color comparison. Lightness is also important, but since this research is about color correction, it's less relevant here. By calculating the A-star and B-star values at both the reference and correction points, we can see how much the colors differ. If we adjust the values at the correction point to match the reference point, the colors will be the same. Finally, after making these adjustments, we convert the corrected L-star, A-star, and B-star values back to RGB to determine the right RGB output at that position. These data processing and correction are done by the display device's main SoC. However, it's difficult to measure the light profile and do this process every time. So, if we collect data for different cases and store it in a lookup table, the correction process can be done faster and more easily in the actual display device. The lookup table will contain information on how much correction is needed based on the distance, intensity, and the RGB data over the light sources. This allows the device to control the signal output at the correction point based on the input RGB signal and the current light source. That's the overview of my research. It's not final yet, and I'm still working on refining and adjusting it as needed. Thank you. Yes, thank you. Thank you both for your presentation. So, any questions for the team here? So, both of you are working on the… There's some questions? Yes. Okay, so I have just for the light profiling. So, the light source that have the issue for the display quality of the color, that light is coming from where exactly? Is it coming from the surrounding environment of the screen or is it coming from the screen itself? From where the source of light that you would like to profile? The light profile is coming from the light source, blue LED, and the problem is made by the reflected light around the… When this painting, you can see the green arrow reflected, right? Yes. It is bounced off to adjust the position, and the light is overlapped. Yes, but that source of light is coming from the source. That source of light depends on what exactly? So, to make it… to create a profile for it, it depends a lot on the displayed contents or… I mean that light depends on what? It's contents. Contents can determine the signal strength. I see. But I'm just wondering because when you are saying that you would like to make a profiling, light profiling, I think this one will be… depends on the contents that displayed on the screen. And in this case, it will be not easy or it's a little bit difficult to make the profile in this way. So, you start? Not yet. Yes, I think it will be a little bit difficult. So… Yes, I need very much time to prepare this algorithm. Exactly, yes. You need to design the algorithm or if it's possible to… because building and designing a profile needs you to display many contents and see how the light will change, or if you could not figure out this one, then you can train some… I don't know if AI could be doing anything here, but think about AI. Think about AI that can generate the profile for you. Because nowadays, we are also using AI for profiling. To… like you feed some data and the AI will find a suitable profile, depends on the input data. Okay, so you can train the AI, simple AI model on contents or different emission lights and then it will automatically generate for you the profile. So, think about this one or check if someone did this one before, maybe it could help here. Okay, thank you. Yeah, I wish you guys a piece of luck. It's not easy for both of you, I think, this topic, but I wish you all luck. Any questions for the presenters again? Okay, thank you guys. And well, but I just I have a note here is that you are, both of you, I noticed you are adding a lot of contents in one slide, especially for the first presentation, the first part of the presentation. You should try, especially I noticed that the tables, you put like four figures in one slide, this will make the audience hard to see, I mean the contents of the figures. So, I would recommend you next time when you present to reduce the amount of figures and tables in one slide. So, even the slides, the tables in the slides are very hard to read because they are, there are four figures and tables in one slide. So, try to avoid this one in your next future presentations, reduce the amount of contents in one slide. Okay, this is just notes for you. Okay. Okay, thank you. Okay, let's go to the next, Jang Moon-Joon, I think will be next. Is there, by the way, until Jang Moon-Joon prepared their presentation, shared their slides, is there anyone that not put his name to present next? Because we need to know all the teams who will present. So, only we have 10 teams, I think we have more than 10 teams to present. Anyway, so if you did not put your name in the chat that you will present, you should write down, I'm going to present next, if you did not write. I mean if your team did not write. Okay, go ahead. Yes, go ahead. Start for mixing, you can start the presentation. Do you hear me well? Yes, okay, go ahead. Nice to meet you. Hello everyone, my name is <PRESIDIO_ANONYMIZED_PERSON> and my teammate is Yeon Jin Woo. I'm a software engineer at Samsung Electronics working in the connectivity R&D group within the Mobile Experience Division. Today, I'd like to introduce you to the Wi-Fi protocols used in the Android operating system. I'm not sure if you guys are interested or not. Anyway, first of all, the content of this presentation will cover the following. First one is introduction. I will tell you the wireless protocols used in Android, an overview of the various Wi-Fi drive protocols, the importances of device-to-device called D2D communication, and the different D2D communication services. After introducing the four main Wi-Fi protocols, I will conclude the presentation. Let me see. So let's start with the fact that there are numerous wireless protocols in use today. As you can see in the image, there are many protocols such as Wi-Fi, Bluetooth, NFC, LoRa, Zippy, and so on. But what kinds of wireless protocols are used in Android OS? Do you know that? As shown here, Android uses cellular networks, Wi-Fi, Bluetooth, NFC, UWB, and etc. Today, I will focus on Wi-Fi and explain its drive protocols from the perspective of D2D communication. So why is Wi-Fi used in D2D communication? When compared to Bluetooth, which is another widely used wireless technology, Wi-Fi is significantly faster. However, since Wi-Fi consumes more power than Bluetooth, it is better suited for services that require high data transfer speeds. There are several drive Wi-Fi protocols. Among them, Android uses four protocols, Wi-Fi Staying, Wi-Fi Hospital, Wi-Fi Direct, and Wi-Fi Aware. And out of all four can be used for D2D communication, Wi-Fi Direct and Wi-Fi Aware are the most suitable for this purpose. Next, I will explain why D2D communication is important. You might wonder why not just send everything over the internet instead of using D2D communication. There are some reasons. Personally, because it doesn't require a network, it doesn't consume data from your mobile plan. This means that devices like tablets without a SIM card can use D2D communication anytime. Secondly, it offers low latency as data doesn't have to pass through a base station. Additionally, it generally provides faster transfer speeds than cellular networks. Also, it has wider coverage because it can be used in poor cellular coverage areas. So what are some common services that utilize Wi-Fi D2D communication? These include file sharing services, screen mirroring services, migration services, and so on. Now let's dive into the details of each protocol. First up is Wi-Fi STA. Wi-Fi STA refers to Wi-Fi Station Mode, which is the mode most people are familiar with when connecting to a router to access the internet. If devices are connected to the same router, it can also be used for D2D communication. This can happen through the Wi-Fi access point, like from here to there. Or directly via a protocol called TDLS, which bypasses the access point, like this. However, in the real world, it can be difficult to ensure that both devices are connected to the same router. For instance, if the devices are connected to different APs, or to the same IP but on different frequency bands, like this, using Wi-Fi STA may not be feasible. Next, I will introduce Wi-Fi hotspot. Wi-Fi hotspot is something most of you are already familiar with. The device that turns on the hotspot functions acting as a host, like a router. It is mainly used for sharing a mobile device's internet connection. This protocol can also be used for D2D communication. However, since a guest device connects to a host device's Wi-Fi, it can't connect to another Wi-Fi network simultaneously at the same time. So it is potentially leading to data charges for the mobile network usage. Therefore, Wi-Fi hotspot is generally not used for D2D communication and is mainly reserved for sharing the internet. Next protocol, I will introduce Wi-Fi direct and Wi-Fi AWARE, which are specifically designed for D2D communication. Wi-Fi direct is a star-quality network structure similar to Wi-Fi with a host and guest. Even when connected via Wi-Fi direct, both the host and guest can still use the Wi-Fi access point. Wi-Fi direct is widely supported not only by mobile devices but also by G and RF generators and other devices from Samsung as well as other manufacturers. Unlike Wi-Fi state or hotspot, Wi-Fi direct does not have strong restrictions making it the most widely used D2D protocol. Finally, Wi-Fi AWARE is a relatively new protocol that has only recently been commercialized. While it shares a lot of similarities with Wi-Fi direct, Wi-Fi AWARE operates on a mesh topology rather than a star topology. Comparing star and mesh topologies, a star topology is a centralized network which can lead to traffic congestion. Like this, if the central device falls down, all connections are lost. On the other hand, a mesh topology remains functional even if one device disappears, allowing the remaining devices to continue communicating. This makes it more stable. Compared to Wi-Fi direct, Wi-Fi AWARE offers various advantages such as connection stability and lower power consumption. However, it has some drawbacks. The structure is more complex and there are fewer devices that support it, leading to lower compatibility. Among Samsung products, it is currently only available on mobile devices and even then only on mid- to high-end products. Now to wrap things up to conclude. D2D wireless communication is helping to network usage and enhance user experiences. Android mainly uses Wi-Fi as D2D communication services. There are four types of Wi-Fi-derived protocols. Wi-Fi direct and Wi-Fi AWARE are the main D2D Wi-Fi protocols used. QuickShare, AirDrop, and other many services use Wi-Fi D2D protocols to transport data faster. Thank you for your attention. If you have any questions, please tell me. Thank you for your presentation. Any questions for the presenter? Can you please back again to the Wi-Fi AWARE advantage table? Yeah, for the table. The table, I mean, just the T. No, next. Yeah, so why here the power consumption is low? Why do you think Wi-Fi AWARE power consumption is low? Because in the Wi-Fi direct, there is need to, it is need at main centralized access point. Access point, yes. Access point is needed. So operating at this point need more power consumption. So compared to Wi-Fi AWARE, it does not need to consume, spend more power. So it is... Okay, yeah, it's clear. So why you think, is there, by the way, is there any products nowadays supported this Wi-Fi AWARE already in the market? Yes, there are some mobile devices like Galaxy S series and all the public series and so on. So is it this one? Yeah. You said continue. I thought you are finished. Yes, continue. However, why? Yeah, nowadays we are launching many devices, including Wi-Fi AWARE technology. I just want to ask, is the Wi-Fi AWARE because they did not use this one before. I know about Wi-Fi direct. So at Wi-Fi AWARE, does it mean nowadays there are some prompt to connect to Near-Pi devices? We call Near-Pi devices and they are connected using Wi-Fi AWARE? Or how we can use this service of Wi-Fi AWARE? For example, if I have Galaxy, yeah, Galaxy News series and how this one is available, do you have any example of how to use this one or any case where the devices are using Wi-Fi AWARE? Okay, Wi-Fi using Android AWARE. Let me see. You can check the API items at Google Android API references. You can check the references through that document. You mean for applications to activate Wi-Fi AWARE for their software, right? Yeah, yeah. Okay, I see. I'm not familiar with this one yet, but good that you get for us. Are you doing research on this one or what exactly are you doing? Actually, my research topic has changed recently. So I decided to present this topic because those are related to my job, but research. I see. I see. Thank you. Yes. Any questions here for the presenter? Okay, that's it. Thank you. Thank you for your presentation and let's now move to the next presenter. So for next will be, I think, Isamu will be next. Yes, you can share your screen. Okay. Hello. Yes, we can hear you. Okay, thank you. Hello, everyone. I'm <PRESIDIO_ANONYMIZED_PERSON> and my teammate is Inte-chan. Today we will be presenting on the same topic as the previous presentation. We will start with a brief review of our previous presentation, followed by a technical discussion on two cases of using LLMs in software engineering and security for LLM. In our last presentation, we introduced what LLMs are. LLM stands for Large Language Model, which is an AI model trained on a large amount of data. These models can understand human language and response in a human-like manner, and in the moniker of Generated AI. Recently, notable examples of LLMs include GPT, Gemini, and Llama. Promenade research area leveraging LLMs in software engineering include code generation, software testing, and test case generation. And major challenges in the security domain include privacy, toxicity, trustfulness, copyright, fairness, and jailbreaking. So far, our brief summary of the previous presentation. Today we will focus on software engineering research utilizing LLMs, specifically examining the case of autonomous software testing and privacy challenges in LLM with technical perspective. First, autonomous software testing. Before the advance of LLMs, the traditional workflow of autonomous software testing followed the structure in the figure. Initially, the current screen information from a mobile device is dubbed and abstracted into form, comprehensible to the tool, resulting in a graphic model. After that, using an appropriate exploration strategy, the tool then decided which event to inject. Key research in this area has focused on UI abstraction and explore strategies to determine how well the tool can recognize screen states and find more activities or pages. With the integration of LLMs, autonomous software testing has evolved to multi-traditional abstraction, GUI model, and exploration strategies into LLM components. Detailed examination of components for autonomous software testing reveals that the GUI context includes app information, page UI information, visual information, is conveyed in the structured format, along with exploration strategies posed as operation questions to the LLM. This integration allows us to bypass many steps required in previous research, leading to more efficient studies. The figure on the left compares the performance of GPT Droid against existing state-of-the-art tools. Over a one-hour testing period, GPT Droid demonstrates significantly enhanced performance. However, this research also highlights clear limitations, primarily the dependence of the performance of the LLM model used. Spurious results are achieved with high-performance LLM models, whereas lower performance can hinder outcomes. This remains a challenge for future research to address. So far, I have presented about autonomous software testing. I will now hand over to Inejung, who will discuss LLM privacy. Can you hear me? Yes, I can hear you. Go ahead. Hello, my name is Inejung and I am in charge of LLM privacy. As I mentioned in the previous presentation, current LLMs are facing several challenges from a security perspective. In this presentation, I will leave the benchmark test for these challenges and then delve into the fly-watch issue. First, in the previous presentation, I briefly explained why toxicity through splints and fly-watch end are problematic in LLM. To demonstrate and resolve these issues, many researchers are studying benchmark data sets. For toxicity, there are benchmark data sets related to crime and hate speech. These are used to clarify whether the LLM actually makes such statements. Of course, even humans can easily judge whether a statement is a toxic or not. Based on this data, meta has a digital model called Ramakart, which includes some privacy-related class or primary focus on toxicity. Third place, measure whether the LLM can be true, which again is something that humans can easily judge as true or false. Egojim-Fen includes asking about the earth location, primary global right, or the capital of plants. But what about Trebuche? Trebuche is a multi-brother concept and the role surrounding it very greatly from country to country. However, research and benchmark data on this topic are still limited. Today, I would like to discuss in-depth the type of privacy-related market tests and disadvantages. Operative privacy-related market data set is the unknown data set. This data set consists of an email from a company that actually went bankrupt in 2001. During the bankrupt investigation, the data was accidentally made public. It is now used for research purposes. Because the data set includes your email format, it contains extra personal information such as email address and name. For this reason, the unknown data set is widely used in many NPD such areas. It is also included in LLM testing to verify whether the model has been trained on the data and to test for potentially privacy breaks. The following is a data set used to evaluate the discovery of personal information leakage. This data set is based on unknown data mentioned earlier. Whether a pack of personal information was generated and merged with the name included in the unknown data. The data set supports the total of nine times of PII. And if this information were actually leaked, the risk would be very high. However, as you can see in the example below, LLM is quality as follows. This can also be viewed as a PII problem, better options are provided. And the correct answer is selected. Since the disconcerned personal information evaluating the LLM response to such quality cannot be considered entirely negative. However, more fundamental assessment of personal information leakage seems necessary. The following are the limitations I observed while reviewing this data. The most critical issue in PII-BOSS is the ability to identify individuals. The existing benchmarks will be sufficient to archive this important goal. As a result, we concluded that there is a need for research into new benchmarks specifically designed to test this issue within LLM. In conclusion, we have briefly explored autonomous software testing using LLM. And the security issue related to this LLM. While LLM are being utilized in many downstream tasks, including software testing, they are also facing numerous security challenges. Therefore, I would like to emphasize this support research to create a gap between usability and security in LLM. This concludes my presentation. Thank you. Yes, thank you. Any questions for the presenter? I can go back just one slide back. I have a question. So how your investigations about the privacy of LLM will help you for the task of autonomous software testing? So is there any software leakage will be happening in the autonomous software testing in the case of using LLM? I would like to see the clear relation about your investigation about the privacy of LLM and autonomous software testing. This is not clear for me. Or you are just only investigating the privacy of LLM and that's it. Or you would like to connect this one with autonomous software testing. Is my question clear? I just want to know what's the relation between LLM privacy and autonomous software testing. That's it. This is what I want to know. Yes, but I think now there is a specific case about the related LLM privacy with autonomous testing. That it has also potential privacy issues. But you did not clarify this one in your presentation, which is not good. You have to clarify this one. Because audience when they are listening, they need to know like what kind of things could be there. Yeah. That's right. How about your teammates? We want to summarize more normally like this. Yes. Can you go? Yes, exactly. Because this is not clear for me. Also, can you go back in the slides just when you're talking about truthness? Going back again one more about truthness of the, yeah here, toxicity and truthfulness of the LLM. You said that this one is easy to or it's okay to verify and shake, but privacy is a concern. But actually, until now, because I'm working in this field, toxicity and also hallucination or saying the truth through or not say the truth are also not easy to judge for human. So many times, even me, I tested this one by myself. It's not easy to, for example, for hallucinations to know that if LLM is saying the truth or is just hallucinate, say some because nowadays, many of the recent LLM models are saying the information in a way which is very convincing, very convincing for human. And human will think that he's the model say the truth, but it's not saying the truth. It's just a fake. It's a fake or made up story or made up information, which is not easy to judge for human. So there is no, even for human, it's not easy to judge if things are the truth, saying the truth or the LLM is not saying the truth. Okay. And even for toxicity, that a human is you can judge if the information is, this is not also, if judge this is a hate speech or not, or even we can, LLM models nowadays can, we can go around the guard of the LLM and make the LLM make, human can prompt the LLM and make it say hate speech, even with the existence of the guard systems. So if there is a guard that prevent hate speech and the crime information, still human can go around this one and make it say hate speech and also make a crime. So these two problems are not easy, not easy. So when you are saying you're easy, those are not easy yet. We did not solve these two problems. Still we are working on research, on research. We are still working on this one. Okay. So just, I want to let others who are listening to you that this is not solved yet. We still work on it with the privacy that you already mentioned. Privacy is your right. Privacy is still a very serious problem in LLM. And even when we are prompting LLM, we could know what is the data used by LLM for training and which is a privacy issue. It's a very critical privacy issue. But the only thing that I couldn't connect with is what's the relation between LLM and autonomous software testing for privacy things. Yeah. So I don't know. Can you answer this question or you cannot for now? Can you explain one more? I can understand. Ah, you cannot understand my question? Yeah, yes. My question in a very simple way. When you are talking about LLM privacy, what is the relation between LLM privacy and autonomous software testing? That's it. What's the relationship between them? Actually, we... In another way, in another sentence, why you mentioned LLM privacy when you are talking about autonomous software testing? What is the relationship between them? Actually, my teammate and me, our research focus on the different topic. So we wanted to share about each topic about LLM in software engineering and security on LLM. So we wanted to focus on each topic. Okay, so you merged two topics together. Yes, right. Okay, I see. I got the points. Okay. Yeah, but for the people who are listening, they thought that you are trying to... There is an issue with autonomous software testing and LLM privacy, but you're just trying to merge both topics together. That's it. Yeah, that's right. Okay. Thank you. Yeah. Thank you guys for the presentation. Any questions for the presenters again? Okay, that's it. Okay, thank you. Thank you guys. Yeah, so I think we should stop here today. We got around six presentations. We have five more based on what I see here. There are five more presentations that we will do next. That means next time, hopefully we can finish earlier. So we will continue next presentation from Aji Hyun. So we will do the same order you see here. We'll present next time. So that's it for today. And we will see you on Wednesday to continue the presentations of all teams. In the meanwhile, you can upload for the teams who presented, finished the presentation. You can upload your PPT to the assignment that I open in iCampus. So please upload your presentation to the assignment I open today on iCampus. Thank you guys. And we'll see you on Wednesday. Thank you, everyone.