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In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers. [ABSTRACT FROM AUTHOR]

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test. [ABSTRACT FROM AUTHOR]

The higher the resolution of the image, the higher the satisfaction of the viewers of the image, and the super-resolution imaging has a considerable increase in research value among the fields of computer vision and image processing. In this study, the main features of low-resolution image LR are extracted mainly using deep learning super-resolution models. It learns and reconstructs the extracted features, and focuses on reconstruction-based algorithms that generate high-resolution image HR. In this paper, we investigate SRCNN and VDSR in a super-resolution algorithm model based on reconstruction. The structure and algorithm process of the SRCNN and VDSR model are briefly introduced, and the multi-channel and special form are also examined in the improved performance evaluation function, and understand the performance of each algorithm through experiments. In the experiment, an experiment was performed to compare the results of the SRCNN and VDSR models with the peak signal-to-noise ratio and image structure similarity, so that the results can be easily judged. [ABSTRACT FROM AUTHOR]

In the era of the Fourth Industrial Revolution, technological changes are being applied in various fields. Automation digitization and data management are also in the field of bills. There are more than tens of thousands of forms of bills circulating in society and bill recognition is essential for automation, digitization and data management. Currently in order to manage various bills, OCR technology is used for character recognition. In this time, we can increase the accuracy, when firstly recognize the form of the bill and secondly recognize bills. In this paper, a logo that can be used as an index to classify the form of the bill was recognized as an object. At this time, since the size of the logo is smaller than that of the entire bill, FPN was used for Small Object Detection among deep learning technologies. As a result, it was possible to reduce resource waste and increase the accuracy of OCR recognition through the proposed algorithm. [ABSTRACT FROM AUTHOR]

Since the LBP algorithm has the characteristic of local texture expression, it is possible to obtain completely different results depending on the extraction location and the size of the reference image and the sample image. In order to solve these shortcomings, in this paper, we first investigate the basic characteristics of LBP, make the size of the reference image (100X100) in order to include most of the characteristics in the image, and select a sample image (40X40) extracted from an arbitrary point. After finding the matching position in the LBP of the reference image by using the correlation test between the LBP of the reference image and the LBP of the sample image, a chi analysis method is used to find the reference image that most closely matches the sample image. [ABSTRACT FROM AUTHOR]

Information search in video streaming services such as YouTube is replacing traditional information search services. To find desired detailed information in such a video, users should repeatedly navigate several points in the video, resulting in a waste of time and network traffic. In this paper, we propose a method to assist users in searching for information in a video by using DBSCAN clustering and LSTM. Our LSTM model is trained with a dataset that consists of user search sequences and their final target points categorized by DBSCAN clustering algorithm. Then, our proposed method utilizes the trained model to suggest an expected category for the user's desired target point based on a partial search sequence that can be collected at the beginning of the search. Our experiment results show that the proposed method successfully finds user destination points with 98% accuracy and 7s of the time difference by average. [ABSTRACT FROM AUTHOR]

In this paper, we propose a virtual full-duplex relaying scheme based on data ferrying using two unmanned aerial vehicles (UAVs). By utilizing high mobility of two UAVs, the proposed relaying scheme can make each UAV communicate with a source node or a destination node when the UAV is near the source node or the destination node. The proposed relaying scheme can overcome the performance limitations of the half-duplex relaying and the implementation constraints of the full-duplex relaying. In addition, we propose an algorithm to shift the center point of two UAVs’ trajectory with consideration of inter-relay interference (IRI). We show the simulation results of our proposed trajectory’s center point shift algorithm. From the simulation results, it is shown that our proposed relaying scheme can achieve higher end-to-end spectral efficiency (SE) than the conventional static relaying scheme. [ABSTRACT FROM AUTHOR]

The paradigm of Internet-of-things(IoT) systems is changing from a cloud-based system to an edge-based system to solve delays caused by network congestion, server overload and security issues due to data transmission. However, edge-based IoT systems have fatal weaknesses such as lack of performance and flexibility due to various limitations. To improve performance, application-specific hardware can be implemented in the edge device, but performance cannot be improved except for specific applications due to a fixed function. This paper introduces a edge-centric metamorphic IoT(mIoT) platform that can use a variety of hardware through on-demand partial reconfiguration despite the limited hardware resources of the edge device, so we can increase the performance and flexibility of the edge device. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to previous studies in which the reconfiguration algorithm was executed on the server. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm to remove AWGN by considering the characteristics of noise present in the image. The proposed algorithm uses block matching to calculate the output, and calculates an estimate by determining the similarity between the center mask and the matching mask. The output of the filter is calculated by adding or subtracting the estimated value and the input pixel value, and weighting is given according to the standard deviation of the center mask and the noise constant to obtain the final output. In order to evaluate the proposed algorithm, the simulation was performed in comparison with the existing methods, and analyzed through the enlarged image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves important characteristics of the image, and shows the performance of removing noise efficiently. [ABSTRACT FROM AUTHOR]

Recently, the smart video surveillance research, which has been receiving increasing attention, has mainly focused on the intruder detection and tracking, and abandoned object detection. On the other hand, research on real-time detection of stolen objects is relatively insufficient compared to its importance. Considering various smart surveillance video application environments, this paper presents two different types of stolen object detection algorithms. We first propose an algorithm that detects theft of statically and dynamically registered surveillance objects using a dual background subtraction model. In addition, we propose another algorithm that detects theft of general surveillance objects by applying the dual background subtraction model and Mask R-CNN-based object segmentation technology. The former algorithm can provide economical theft detection service for pre-registered surveillance objects in low computational power environments, and the latter algorithm can be applied to the theft detection of a wider range of general surveillance objects in environments capable of providing sufficient computational power. [ABSTRACT FROM AUTHOR]

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%. [ABSTRACT FROM AUTHOR]

In a map, placing the labels, corresponding to names or explanations of specific features, is called map labeling. In this paper, n points on a line are given, and placing rectangular labels for the points is considered. Particularly, the labels have a same height and their lower sides lie on a straight line in the upper of the line on which the given points are. The points and the labels are connected by polygonal lines, which are called leader lines. The leader lines are classified into straight leader lines and bended leader lines, where the straight leader line consists of only the vertical line and the bended leader line consists of vertical, horizontal, vertical lines. The problem is placing the labels to minimize the number of bended leader lines, and we propose an O(nlogn) -time algorithm, which improves the O(n²)-time algorithm previously provided in [13]. [ABSTRACT FROM AUTHOR]

With the establishment of the railway integrated radio network (LTE-R) environment, radio-based train control transmission and reception and various forms of service are provided. The smooth delivery of these services requires improved performance in a highly reliable and available wireless environment. This paper measured the LTE-R radio communication environment to improve radio communication performance of railway integrated wireless network reliability and availability, analyzed the results, and established the wireless environment model. Based on the built-up model, we also proposed an improved radio-access algorithm to control trains for improved reliability, suggesting a way to improve stability for handover that occur during open-air operation, and proposed an algorithm for frequency auto-heating to improve availability. For simulation, data were collected from the Korea Rail Network Authority (Daejeon), Manjong-Gangneung KTX route, which can measure the actual data of LTE-R wireless environment, and the results of the simulation show performance improvement through algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a low cost hardware message passing engine of enhanced atomic pipelined broadcast based on processing node status. In this algorithm, the previous atomic pipelined broadcast algorithm is modified to reduce the waiting time until next broadcast communication. For this, the processor change the transmission order of processing nodes based on the nodes’ communication channel. Also, the hardware message passing engine architecture of the proposed algorithm is modified to be adopted to multi-core processor. The synthesized logic area of the proposed hardware message passing engine was reduced by about 16%, compared by the pre-existing hardware message passing engine. [ABSTRACT FROM AUTHOR]

Vessels or aircraft should be loaded with containers or cargo to maintain weight balance in order to be stable when navigating the route. The container loading algorithm is known as the NP problem and several heuristic methods have been studied. Containers can be characterized by the uniform volume and weight, which makes it easier to find an optimal loading method. In this paper, we propose an algorithm for weight balance when the volume and weight of an object are uniform. It is assumed that the loading space has a special structure of m * n mesh (where m and n are both odd). In this case, we designed a greedy algorithm and proved that the algorithm is optimal in that it can always find a loading position that maintains a weight balance regardless of the number of objects. Our algorithm can be used in many engineering problems, such as loading algorithms and load balancing problems. [ABSTRACT FROM AUTHOR]

Viola & Jones' object detection algorithm is a very good algorithm for the face component(FC) detection, but there are still problems such as duplicate detection, false detection and non-detection due to parameter setting. This paper proposes an improved FC detection algorithm that applies the variable parameter to reduce non-detection and the verification to reduce duplicate detection and false detection to the Viola & Jones' algorithm. The proposed algorithm reduces the non-detection by changing the parameter value of the Viola & Jones' algorithm until the potential valid FCs are detected, and eliminates the duplicate detection and the false detection by using the verification that evaluates size, position, and uniqueness of the detected FCs. Simulation results show that the proposed algorithm includes valid FCs in the detected objects and then detects only the valid FCs by removing invalid FCs from them. [ABSTRACT FROM AUTHOR]

Today, due to the large amount of data duplication caused by the increase in user data, various deduplication studies have been conducted. However, research on personal storage is relatively poor. Personal storage, unlike high-performance computers, needs to perform deduplication while reducing CPU and memory resource usage. In this paper, we propose an adaptive algorithm that selectively applies fixed size chunking (FSC) and whole file chunking (WFH) according to the file type and size in order to maintain the deduplication rate and reduce the load in personal storage. We propose an algorithm for minimization. The experimental results show that the proposed file system has more than 1.3 times slower at first write operation but less than 3 times reducing in memory usage compare to LessFS and it is 2.5 times faster at rewrite operation. [ABSTRACT FROM AUTHOR]

Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results. [ABSTRACT FROM AUTHOR]

The recent software education in middle schools is focused on physical computing, unplugged learning and pilot training. However, they are struggling in many ways, including cost, inducement of interest, motivation, and concentration. Also, the lack of systematic classroom design could make negative effect to students' understanding of classes or academic performance. In this paper, we intend to study the method of algorithm education using Chatbot system, which will increase efficiency of software education, with less burdensome in terms of cost, and also could be able to used as an assist tool in various classes. In class scenarios that require the understanding of coding mechanisms such as function application, algorithm design, and program coding, students can learn by themselves through the Chatbot system, which has a positive effect on student learning. [ABSTRACT FROM AUTHOR]

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive-perturbation-aided opportunistic precoding (APOP) scheme for multiple-input multiple-output (MIMO) systems. To update a precoding matrix in MIMO systems, the proposed algorithm produces a random perturbation in each time slot. Then the additional adaptive perturbation is also applied, which depends on the reports of achievable data-rates from users. If the prior random perturbation increased the data rate, the adaptive perturbation is set to be the same as the prior random perturbation, otherwise the negative value of the prior random perturbation is applied for adaptive perturbation. Furthermore, to enhance the achievable data rates, the information on the stored precoding matrices in the memory as well as the currently generated precoding matrix is used for scheduling. Simulation results show that compared to conventional opportunistic precoding schemes, higher data rates are achieved by the proposed APOP scheme, especially when there are a relatively small number of users. [ABSTRACT FROM AUTHOR]

With the increase in there are smart devices penetration around the world, services related to exercise checks are attracting attention. However, there is existing exercise amount measurement service does not use the altitude information, or because the use of an algorithm that does not corrected the GPS altitude error is not accurate movement distance provided have a problem. Therefore, in this paper, to improve the existing problems, Kalman-filter-based user movement distance measurement algorithm is designed and implementation of improved by using the Kalman-filter based GPS and barometric altimeter sensor fusion algorithm to improve the altitude value the accuracy and of calculate the coordinate plane distance. As a result of comparing the designed and implementation of algorithm with the existing algorithms, it is confirmed that the proposed algorithm improves the accuracy by about 2.17%. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile communication systems, various mobile convergence services are appearing and data traffic is exploding accordingly. Because the number of base stations to support these surging devices is also increasing, from a network provider's point of view, reducing energy consumption through these mobile communication networks is one of the most important issues. Therefore, in this paper, we apply the DBSCAN (density-based spatial clustering of applications with noise) algorithm, one of the representative user-density based clustering algorithms, in order to extract the dense area with user density and apply the thinning process to each extracted sub-network to efficiently control the mode of the base stations. Extensive simulations show that the proposed algorithm has better performance results than the conventional algorithms with respect to area throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

Described in this paper is a design of hardware accelerator for implementing public-key cryptographic protocols (PKCPs) based on Elliptic Curve Cryptography (ECC) and RSA. It supports five elliptic curves (ECs) over GF(p) and three key lengths of RSA that are defined by NIST standard. It was designed to support four point operations over ECs and six modular arithmetic operations, making it suitable for hardware implementation of ECC- and RSA-based PKCPs. In order to achieve small-area implementation, a finite field arithmetic circuit was designed with 32-bit data-path, and it adopted word-based Montgomery multiplication algorithm, the Jacobian coordinate system for EC point operations, and the Fermat's little theorem for modular multiplicative inverse. The hardware operation was verified with FPGA device by implementing EC-DH key exchange protocol and RSA operations. It occupied 20,800 gate equivalents and 28 kbits of RAM at 50 MHz clock frequency with 180-nm CMOS cell library, and 1,503 slices and 2 BRAMs in Virtex-5 FPGA device. [ABSTRACT FROM AUTHOR]

This paper proposes a method for measuring fine dust concentration through digital processing of images captured by only existing CCTVs without additional equipment. This image processing algorithm consists of noise reduction, edge sharpening, ROI setting, edge strength calculation, and correction through HSV conversion. This algorithm is implemented using the C ++ OpenCV library. The algorithm was applied to CCTV images captured over a month. The edge strength values calculated for the ROI region are found to be closely related to the fine dust concentration data. To infer the correlation between the two types fo data, a trend line in the form of a power equation is established using MATLAB. The number of data points deviating from the trend line accounts for around 12.5%. Therefore, the overall accuracy is about 87.5%. [ABSTRACT FROM AUTHOR]

Histogram H is an x-monotone rectilinear polygon with a horizontal edge, called by a base, connecting the leftmost vertical edge and the rightmost vertical edge. Here the rectilinear polygon is a polygon with only horizontal and vertical edges and the x-monotone polygon P is a polygon in which every line orthogonal to the x-axis intersects P at most twice. Walking counterclockwise on the boundary of a histogram H yields a sequence of left turns and right turns at its vertices. Conversely, a given sequence of the turns at the vertices can be realized by a histogram. In this paper, we consider the problem of finding a histogram to realize a given turn sequence. Particularly, we will find the histograms to minimize its area and its bounding box. It will be shown that both of the problems can be solved by linear time algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning-based scheduling scheme for user selection in multi-user multi-antenna networks which is considered one of key technologies for the next generation mobile communication systems. We obtained 90,000 data samples from the conventional optimal scheme to train the proposed neural network and verified the trained neural network to check if the trained neural network is over-fitted. Although the proposed neural network-based scheduling algorithm requires considerable complexity and time for training in the initial stage, it does not cause any extra complexity once it has been trained successfully. On the other hand, the conventional optimal scheme continuously requires the same complexity of computations for every scheduling. According to extensive computer-simulations, the proposed deep learning-based scheduling algorithm yields about 88~96% average sum-rates of the conventional scheme for SNRs lower than 10dB, while it can achieve optimal average sum-rates for SNRs higher than 10dB. [ABSTRACT FROM AUTHOR]

In this paper, I propose an modified back-off algorithm to improve the fairness for slotted ALOHA sensor networks. In hierarchical networks, the performance degradation of a specific node can cause degradation of the overall network performance in case the data transmitted by lower nodes is needed to be synthesized and processed by an upper node. Therefore it is important to ensure the fairness of transmission performance to all nodes. The proposed scheme choose a back-off time of a node considering the previous transmission results as well as the current transmission result. Moreover a node that failed to transmit consecutively is given gradually shorter back-off time but a node that is success to transmit consecutively is given gradually longer back-off time. Through simulations, I compare and analyze the performance of the proposed scheme with the binary exponential back-off algorithm(BEB). The results show that the proposed scheme reduces the throughput slightly compared to BEB but improves the fairness significantly. [ABSTRACT FROM AUTHOR]

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments. [ABSTRACT FROM AUTHOR]

Modern society is deepening people's interest in healthy life. Therefore, the development of research and service related to health is proceeding. In the IT field, healthcare systems using information in the medical field are being developed. Most systems utilize PC and smart TV or use smart phones to provide users with information and movement-related information in the healthcare field. However, algorithms and systems that accurately analyze user 's body information are in development stage. To solve this problem, this paper proposes a system that provides information to the user by analyzing the BMI (Body Mass Index) index and BMR (Basal Metabolic Rate) of the user. The information provided to the user suggests appropriate exercise intensity, appropriate level of exercise, and fitness equipment that are appropriate for the user's body, unlike conventional fitness systems that detect motion and show the calories consumed. In addition, the user can perform efficient exercise based on the recommended information. [ABSTRACT FROM AUTHOR]

In this letter, we investigate a beam placement problem to determine the optimal beam center position that maximizes the minimum transmission rate of users within a spot beam for a multicast transmission in low-Earth-orbit satellite communications. We make a system model for satellite beam placement and formulate an optimization problem that maximize the minimum transmission rate of ground nodes (GNs). Then, we propose a gradient ascent algorithm to calculate the optimal beam center position. The simulation results show that the proposed scheme optimally determines the position of the beam center and maximizes the minimum rate of GNs according to various satellite parameters. [ABSTRACT FROM AUTHOR]

In the metaverse environment, avatars perform various roles as alternative representations of individuals. Avatars can be expressed from the perspectives of enhancing immersion and providing flexible security measures within the metaverse space. This study aims to adjust the representation of avatar details based on the avatar's role in the virtual space. By tuning the hyper-parameters(such as Random Seed, Style Embedding Vector, Batch Size, Number and size of layers in the generator network, Number and size of layers in the discriminator network) of the styleGAN algorithm, a system is designed where the level of immersion, privacy preservation, and data exchange on the network can be controlled to automatically adapt the level of detail in the avatar. [ABSTRACT FROM AUTHOR]

This paper describes a design of crypto-processor that supports multiple block cipher algorithms of PRESENT, ARIA, and AES. The crypto-processor integrates three cores that are PRmo (PRESENT with mode of operation), AR_AS (ARIA_AES), and AES-16b. The PRmo core implementing 64-bit block cipher PRESENT supports key length 80-bit and 128-bit, and four modes of operation including ECB, CBC, OFB, and CTR. The AR_AS core supporting key length 128-bit and 256-bit integrates two 128-bit block ciphers ARIA and AES into a single data-path by utilizing resource sharing technique. The AES-16b core supporting key length 128-bit implements AES with a reduced data-path of 16-bit for minimizing hardware. Each crypto-core contains its own on-the-fly key scheduler, and consecutive blocks of plaintext/ciphertext can be processed without reloading key. The crypto-processor was verified by FPGA implementation. The crypto-processor implemented with a 0.18㎛ CMOS cell library occupies 54,500 gate equivalents (GEs), and it can operate with 55 MHz clock frequency. [ABSTRACT FROM AUTHOR]

The news articles collected with keyword “non faceto-face” were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-faceto-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing. [ABSTRACT FROM AUTHOR]

In order to overcome the limitations of the instructor-centered lecture-style teaching method, recently, flipped learning, a learner-centered teaching method, has been widely introduced. However, despite the many advantages of flipped learning, there is a problem that students cannot solve questions that arise during prior learning in real time. Therefore, in order to solve this problem, we developed DBbot, an assistant chatbot for database design course managed in the flipped learning method. The DBBot is composed of a chatbot app for learners and a chatbot management app for instructors. Also, it’s implemented so that questions that instructors can anticipate in advance, such as questions related to class operation and every semester repeated questions related to learning content, can be answered using Google's DialogFlow. It’s implemented so that questions that the instructor cannot predict in advance, such as questions related to team projects, can be answered using the question/answer DB and the BM25 algorithm, which is a similarity comparison algorithm. [ABSTRACT FROM AUTHOR]

We study the problem of sampling a subset of nodes of graphs for bandlimited graph signals such that the signal values on the sampled nodes provide the most information in order to reconstruct the original graph signal. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound of the reconstruction error to reduce the complexity of the selection process. We further simplify the upper bound by applying useful approximations to propose a low-weight greedy selection process that is iteratively conducted to find a suboptimal sampling set. Through the extensive experiments for various graphs, we inspect the performance of the proposed algorithm by comparing with different sampling set selection methods and show that the proposed technique runs fast while preserving a competitive reconstruction performance, yielding a practical solution to real-time applications. [ABSTRACT FROM AUTHOR]