• KIPS Transactions on Computer and Communication Systems
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• v.11 no.1
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• pp.35-42
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• 2022

Visible light communication (VLC) is a method of transmitting data through LED blinking and is vulnerable to eavesdropping because the illumination affects the wide range of area. IEEE standard 802.15.7 defines On-Off Keying (OOK), Variable Pulse Position Modulation (VPPM), and Color Shift Keying (CSK) as modulation. In this paper, we propose an encryption method in VPPM for secure communication. The VPPM uses an encoding method called 4B6B where 16 different outputs are represented with 6-bit. This paper extends the number of outputs to 20, to add complexity while not violating the 4B6B generation conditions. Then each entry in the extended 4B6B table is scrambled using vigenère cipher. The probability of decrypting each 6-bit data is $\frac{1}{20}$. Eavesdropper should perform $\sum\limits_{k=1}^{n}20^k$ number of different trials to decrypt the message if the number of keys is n. The proposed method can be applied to OOK of PHY II and CSK of PHY III. We further discuss the secure encoding that can be used in OOK and CSK without performance degradation.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.1-10
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• 2022

Deep neural networks are widely used to solve various problems. In a fully connected neural network, the nonlinear activation function is a function that nonlinearly transforms the input value and outputs it. The nonlinear activation function plays an important role in solving the nonlinear problem, and various nonlinear activation functions have been studied. In this study, we propose a combined parametric activation function that can improve the performance of a fully connected neural network. Combined parametric activation functions can be created by simply adding parametric activation functions. The parametric activation function is a function that can be optimized in the direction of minimizing the loss function by applying a parameter that converts the scale and location of the activation function according to the input data. By combining the parametric activation functions, more diverse nonlinear intervals can be created, and the parameters of the parametric activation functions can be optimized in the direction of minimizing the loss function. The performance of the combined parametric activation function was tested through the MNIST classification problem and the Fashion MNIST classification problem, and as a result, it was confirmed that it has better performance than the existing nonlinear activation function and parametric activation function.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.439-448
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• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.297-304
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• 2021

Commercial obfuscation tools (protectors) aim to create difficulties in analyzing the operation process of software by applying obfuscation techniques and Anti-reversing techniques that delay and interrupt the analysis of programs in software reverse engineering process. In particular, in case of virtualization detection and anti-debugging functions, the analysis tool exits the normal execution flow and terminates the program. In this paper, we analyze Anti-reversing techniques of executables with Debugger Detection and Viralization Tools Detection options through VMProtect 3.5.0, one of the commercial obfuscation tools (protector), and address bypass methods using Pin. In addition, we predicted the location of the applied obfuscation technique by finding out a specific program termination routine through API analysis since there is a problem that the program is terminated by the Anti-VM technology and the Anti-DBI technology and drew up the algorithm flowchart for bypassing the Anti-reversing techniques. Considering compatibility problems and changes in techniques from differences in versions of the software used in experiment, it was confirmed that the bypass was successful by writing the pin automation bypass code in the latest version of the software (VMProtect, Windows, Pin) and conducting the experiment. By improving the proposed analysis method, it is possible to analyze the Anti-reversing method of the obfuscation tool for which the method is not presented so far and find a bypass method.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.409-418
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• 2022

In this paper, we propose SAAnnot-C3Pap, a semi-automatic annotation method for obtaining ground truth of a player's posture. In order to obtain ground truth about the two-dimensional joint position in the existing music domain, openpose, a two-dimensional posture estimation method, was used or manually labeled. However, automatic annotation methods such as the existing openpose have the disadvantages of showing inaccurate results even though they are fast. Therefore, this paper proposes SAAnnot-C3Pap, a semi-automated annotation method that is a compromise between the two. The proposed approach consists of three main steps: extracting postures using openpose, correcting the parts with errors among the extracted parts using supervisely, and then analyzing the results of openpose and supervisely. Perform the synchronization process. Through the proposed method, it was possible to correct the incorrect 2D joint position detection result that occurred in the openpose, solve the problem of detecting two or more people, and obtain the ground truth in the playing posture. In the experiment, we compare and analyze the results of the semi-automated annotation method openpose and the SAAnnot-C3Pap proposed in this paper. As a result of comparison, the proposed method showed improvement of posture information incorrectly collected through openpose.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.345-352
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• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• The Korean Journal of Franchise Management
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• v.13 no.3
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• pp.17-30
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• 2022

Purpose: Recent emergence of diverse businesses in the distribution industry has led small and medium-sized retailers and their distribution logistics centers to face difficulties. Transactions between companies are connected within a supply chain, and the companies have relationships in the form of a supplier and a buyer. Therefore, it is important to identify causes of problems among companies through supply chain and strategic partnerships, thus developing optimal management plans and maximizing performances of companies. This study proposes that sustainable supply chain management consists of product quality, price quality, distribution quality, and promotion quality based on stakeholder theory and resource-based view. This study examined the impacts of sustainable chain management factors on satisfaction and win-win cooperation. Research design, data, and methodology: In the proposed model, satisfaction plays a mediating role in the relationship between sustainable chain management and win-win cooperation. The data were collected from 245 owners who use small and medium-sized distribution logistics center and analyzed using 2SLS (two-stage least square) with SPSS 28.0. Exploratory factor analysis and correlation analysis were used to assess the validity and reliability of constructs. Results: The findings are as follows. In the case of the total and Nadeulgage samples, product, price, and distribution quality had a significant positive effect on satisfaction, but in the case of Neighborhood super, product and price quality have a significant positive effect on satisfaction. Satisfaction has a significant positive effect on win-win cooperation in the overall, Nadeulgage, and Neighborhood super. Satisfaction plays a partial or full mediating role in the case of total, Nadeulgage, Neighborhood super. Conclusions: This study emphasized the need for sustainable supply chain management of small and medium-sized distribution logistics centers by examining the relationship between small and medium-sized distribution logistics centers and chain stores. It was found that store satisfaction plays an important role in the win-win cooperation between small and medium-sized distribution logistics centers and chain stores. Small and medium-sized distribution logistics centers can maximize product quality, price quality, distribution quality, and promotion quality by understanding the effect of chain store-related satisfaction and win-win cooperation on chain stores.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.