Inpainting has been continuously studied in the field of computer vision.As artificial intelligence technology developed,deep learning technology was introduced in inpainting research,helping to improve performance.Cu...Inpainting has been continuously studied in the field of computer vision.As artificial intelligence technology developed,deep learning technology was introduced in inpainting research,helping to improve performance.Currently,the input target of an inpainting algorithm using deep learning has been studied from a single image to a video.However,deep learning-based inpainting technology for panoramic images has not been actively studied.We propose a 360-degree panoramic image inpainting method using generative adversarial networks(GANs).The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format,which has relatively little distortion and uses it as a training network.Since the cube map format is used,the correlation of the six sides of the cube map should be considered.Therefore,all faces of the cube map are used as input for the whole discriminative network,and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image.The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.展开更多
In this paper,we investigate video quality enhancement using computation offloading to the mobile cloud computing(MCC)environment.Our objective is to reduce the computational complexity required to covert a low-resolu...In this paper,we investigate video quality enhancement using computation offloading to the mobile cloud computing(MCC)environment.Our objective is to reduce the computational complexity required to covert a low-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication costs.To do so,we propose an energy-efficient computation offloading framework for video streaming services in a MCC over the fifth generation(5G)cellular networks.In the proposed framework,the mobile client offloads the computational burden for the video enhancement to the cloud,which renders the side information needed to enhance video without requiring much computation by the client.The cloud detects edges from the upsampled ultra-high-resolution video(UHD)and then compresses and transmits them as side information with the original low-resolution video(e.g.,full HD).Finally,the mobile client decodes the received content and integrates the SI and original content,which produces a high-quality video.In our extensive simulation experiments,we observed that the amount of computation needed to construct a UHD video in the client is 50%-60% lower than that required to decode UHD video compressed by legacy video encoding algorithms.Moreover,the bandwidth required to transmit a full HD video and its side information is around 70% lower than that required for a normal UHD video.The subjective quality of the enhanced UHD is similar to that of the original UHD video even though the client pays lower communication costs with reduced computing power.展开更多
With the explosive advancements in wireless communications and digital electronics,some tiny devices,sensors,became a part of our daily life in numerous elds.Wireless sensor networks(WSNs)is composed of tiny sensor de...With the explosive advancements in wireless communications and digital electronics,some tiny devices,sensors,became a part of our daily life in numerous elds.Wireless sensor networks(WSNs)is composed of tiny sensor devices.WSNs have emerged as a key technology enabling the realization of the Internet of Things(IoT).In particular,the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities,smart homes,smart healthcare,security applications,environmental monitoring,etc.However,the limitations of energy,communication range,and computational resources are bottlenecks to the widespread applications of this technology.In order to tackle these issues,in this paper,we propose an Energy-efcient Transmission Range Optimized Model for IoT(ETROMI),which can optimize the transmission range of the sensor nodes to curb the hot-spot problem occurring in multi-hop communication.In particular,we maximize the transmission range by employing linear programming to alleviate the sensor nodes’energy consumption and considerably enhance the network longevity compared to that achievable using state-of-the-art algorithms.Through extensive simulation results,we demonstrate the superiority of the proposed model.ETROMI is expected to be extensively used for various smart city,smart home,and smart healthcare applications in which the transmission range of the sensor nodes is a key concern.展开更多
Detecting malicious Uniform Resource Locators(URLs)is crucially important to prevent attackers from committing cybercrimes.Recent researches have investigated the role of machine learning(ML)models to detect malicious...Detecting malicious Uniform Resource Locators(URLs)is crucially important to prevent attackers from committing cybercrimes.Recent researches have investigated the role of machine learning(ML)models to detect malicious URLs.By using ML algorithms,rst,the features of URLs are extracted,and then different ML models are trained.The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL.Therefore,deep learning(DL)models are used to solve these issues since they are able to perform featureless detection.Furthermore,DL models give better accuracy and generalization to newly designed URLs;however,the results of our study show that these models,such as any other DL models,can be susceptible to adversarial attacks.In this paper,we examine the robustness of these models and demonstrate the importance of considering this susceptibility before applying such detection systems in real-world solutions.We propose and demonstrate a black-box attack based on scoring functions with greedy search for the minimum number of perturbations leading to a misclassication.The attack is examined against different types of convolutional neural networks(CNN)-based URL classiers and it causes a tangible decrease in the accuracy with more than 56%reduction in the accuracy of the best classier(among the selected classiers for this work).Moreover,adversarial training shows promising results in reducing the inuence of the attack on the robustness of the model to less than 7%on average.展开更多
360 video streaming services over the network are becoming popular. In particular, it is easy to experience 360 video through the already popular smartphone. However, due to the nature of 360 video, it is difficult to...360 video streaming services over the network are becoming popular. In particular, it is easy to experience 360 video through the already popular smartphone. However, due to the nature of 360 video, it is difficult to provide stable streaming service in general network environment because the size of data to send is larger than that of conventional video. Also, the real user's viewing area is very small compared to the sending amount. In this paper, we propose a system that can provide high quality 360 video streaming services to the users more efficiently in the cloud. In particular, we propose a streaming system focused on using a head mount display (HMD).展开更多
In order to improve the user’s satisfaction with the augmented reality (AR) technology and the accuracy of the service, it is important to obtain the exact position of the user. Frequently used techniques for finding...In order to improve the user’s satisfaction with the augmented reality (AR) technology and the accuracy of the service, it is important to obtain the exact position of the user. Frequently used techniques for finding outdoors locations is the global positioning system (GPS), which is less accurate indoors. Therefore, an indoor position is measured by comparing the reception level about access point (AP) signal of wireless fidelity (Wi-Fi) or using bluetooth low energy (BLE) tags. However, Wi-Fi and Bluetooth require additional hardware installation. In this paper, the proposed method of estimating the user’s position uses an indoor image and indoor coordinate map without additional hardware installation. The indoor image has several feature points extracted from fixed objects. By matching the feature points with the feature points of the user image, we can obtain the position of the user on the Indoor map by obtaining six or more pixel coordinates from the user image and solving the solution using the perspective projection formula. The experimental results show that the user position can be obtained more accurately in the indoor environment by using only the software without additional hardware installation.展开更多
基金Korea Electric Power Corporation(Grant No.R18XA02).
文摘Inpainting has been continuously studied in the field of computer vision.As artificial intelligence technology developed,deep learning technology was introduced in inpainting research,helping to improve performance.Currently,the input target of an inpainting algorithm using deep learning has been studied from a single image to a video.However,deep learning-based inpainting technology for panoramic images has not been actively studied.We propose a 360-degree panoramic image inpainting method using generative adversarial networks(GANs).The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format,which has relatively little distortion and uses it as a training network.Since the cube map format is used,the correlation of the six sides of the cube map should be considered.Therefore,all faces of the cube map are used as input for the whole discriminative network,and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image.The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.
文摘In this paper,we investigate video quality enhancement using computation offloading to the mobile cloud computing(MCC)environment.Our objective is to reduce the computational complexity required to covert a low-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication costs.To do so,we propose an energy-efficient computation offloading framework for video streaming services in a MCC over the fifth generation(5G)cellular networks.In the proposed framework,the mobile client offloads the computational burden for the video enhancement to the cloud,which renders the side information needed to enhance video without requiring much computation by the client.The cloud detects edges from the upsampled ultra-high-resolution video(UHD)and then compresses and transmits them as side information with the original low-resolution video(e.g.,full HD).Finally,the mobile client decodes the received content and integrates the SI and original content,which produces a high-quality video.In our extensive simulation experiments,we observed that the amount of computation needed to construct a UHD video in the client is 50%-60% lower than that required to decode UHD video compressed by legacy video encoding algorithms.Moreover,the bandwidth required to transmit a full HD video and its side information is around 70% lower than that required for a normal UHD video.The subjective quality of the enhanced UHD is similar to that of the original UHD video even though the client pays lower communication costs with reduced computing power.
基金supported by Korea Electric Power Corporation(Grant Number:R18XA02)。
文摘With the explosive advancements in wireless communications and digital electronics,some tiny devices,sensors,became a part of our daily life in numerous elds.Wireless sensor networks(WSNs)is composed of tiny sensor devices.WSNs have emerged as a key technology enabling the realization of the Internet of Things(IoT).In particular,the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities,smart homes,smart healthcare,security applications,environmental monitoring,etc.However,the limitations of energy,communication range,and computational resources are bottlenecks to the widespread applications of this technology.In order to tackle these issues,in this paper,we propose an Energy-efcient Transmission Range Optimized Model for IoT(ETROMI),which can optimize the transmission range of the sensor nodes to curb the hot-spot problem occurring in multi-hop communication.In particular,we maximize the transmission range by employing linear programming to alleviate the sensor nodes’energy consumption and considerably enhance the network longevity compared to that achievable using state-of-the-art algorithms.Through extensive simulation results,we demonstrate the superiority of the proposed model.ETROMI is expected to be extensively used for various smart city,smart home,and smart healthcare applications in which the transmission range of the sensor nodes is a key concern.
基金supported by Korea Electric Power Corporation(Grant Number:R18XA02).
文摘Detecting malicious Uniform Resource Locators(URLs)is crucially important to prevent attackers from committing cybercrimes.Recent researches have investigated the role of machine learning(ML)models to detect malicious URLs.By using ML algorithms,rst,the features of URLs are extracted,and then different ML models are trained.The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL.Therefore,deep learning(DL)models are used to solve these issues since they are able to perform featureless detection.Furthermore,DL models give better accuracy and generalization to newly designed URLs;however,the results of our study show that these models,such as any other DL models,can be susceptible to adversarial attacks.In this paper,we examine the robustness of these models and demonstrate the importance of considering this susceptibility before applying such detection systems in real-world solutions.We propose and demonstrate a black-box attack based on scoring functions with greedy search for the minimum number of perturbations leading to a misclassication.The attack is examined against different types of convolutional neural networks(CNN)-based URL classiers and it causes a tangible decrease in the accuracy with more than 56%reduction in the accuracy of the best classier(among the selected classiers for this work).Moreover,adversarial training shows promising results in reducing the inuence of the attack on the robustness of the model to less than 7%on average.
文摘360 video streaming services over the network are becoming popular. In particular, it is easy to experience 360 video through the already popular smartphone. However, due to the nature of 360 video, it is difficult to provide stable streaming service in general network environment because the size of data to send is larger than that of conventional video. Also, the real user's viewing area is very small compared to the sending amount. In this paper, we propose a system that can provide high quality 360 video streaming services to the users more efficiently in the cloud. In particular, we propose a streaming system focused on using a head mount display (HMD).
文摘In order to improve the user’s satisfaction with the augmented reality (AR) technology and the accuracy of the service, it is important to obtain the exact position of the user. Frequently used techniques for finding outdoors locations is the global positioning system (GPS), which is less accurate indoors. Therefore, an indoor position is measured by comparing the reception level about access point (AP) signal of wireless fidelity (Wi-Fi) or using bluetooth low energy (BLE) tags. However, Wi-Fi and Bluetooth require additional hardware installation. In this paper, the proposed method of estimating the user’s position uses an indoor image and indoor coordinate map without additional hardware installation. The indoor image has several feature points extracted from fixed objects. By matching the feature points with the feature points of the user image, we can obtain the position of the user on the Indoor map by obtaining six or more pixel coordinates from the user image and solving the solution using the perspective projection formula. The experimental results show that the user position can be obtained more accurately in the indoor environment by using only the software without additional hardware installation.
