Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
When aWindows-based system is used for an exceedingly long time,its performance degrades,and the error occurrence rate tends to increase.This is generally called system aging.To investigate the reasons for system agin...When aWindows-based system is used for an exceedingly long time,its performance degrades,and the error occurrence rate tends to increase.This is generally called system aging.To investigate the reasons for system aging,various studies have been conducted within the range of the operating system kernel to the user application.However,finding an accurate reason for system performance degradation remains challenging research topic.In this study,system monitoring was conducted by dividing a system into‘before software installation,’‘after software installation,’and‘after software removal.’We confirmed that when a software installed in a system is removed,various system elements,such as storage and memory,are not restored to the level prior to the software installation.Consequently,we established a hypothesis regarding the performance degradation of a computer system owing to repeated software installation/removal operations,investigated the correlation between system aging and repeated software installation/removal operations,and proposed a system aging analysis framework for analyzing the reason behind system aging.In the proposed system aging analysis framework,we aim to forcibly age a Windows-based system by repeating the software installation/removal operation by utilizing the system forced aging module.The framework identifies the elements affecting system performance through a differential data analysis of the system time-series data extracted by the system performance extraction and system component snapshot modules.Consequently,the aging analysis framework presented in this study is expected to be effectively utilized as an index for studying system aging.展开更多
Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional ...Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.展开更多
One of the latest technologies enabling remote control,operational efficiency upgrades,and real-time big-data monitoring in an industrial control system(ICS)is the IIoT-Cloud ICS,which integrates the Industrial Intern...One of the latest technologies enabling remote control,operational efficiency upgrades,and real-time big-data monitoring in an industrial control system(ICS)is the IIoT-Cloud ICS,which integrates the Industrial Internet of Things(IIoT)and the cloud into the ICS.Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction,efficiency improvement,and real-time monitoring,the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world.An adversary can collect information regarding senders,recipients,and prime-time slots through traffic analysis and use it as a linchpin for the next attack,posing a potential threat to the ICS.To address this problem,we designed a network traffic obfuscation system(NTOS)for the IIoT-Cloud ICS,based on the requirements derived from the ICS characteristics and limitations of existing NTOS models.As a strategy to solve this problem wherein a decrease in the traffic volume facilitates traffic analysis or reduces the packet transmission speed,we proposed an NTOS based on packet scrambling,wherein a packet is split into multiple pieces before transmission,thus obfuscating network analysis.To minimize the ICS modification and downtime,the proposed NTOS was designed using an agentbased model.In addition,for the ICS network traffic analyzer to operate normally in an environment wherein the NTOS is applied,a rule-based NTOS was adopted such that the actual traffic flow is known only to the device that is aware of the rule and is blocked for attackers.The experimental results verified that the same time requested for response and level of difficulty of analysis were maintained by the application of an NTOS based on packet scrambling,even when the number of requests received by the server per second was reduced.The network traffic analyzer of the ICS can capture the packet flow by using the pre-communicated NTOS rule.In addition,by designing an NTOS using an agent-based model,the impact on the ICS was minimized such that the system could be applied with short downtime.展开更多
There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve the...There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve these problems.This model consists of three processes.The first process is the dynamic symmetric key generation.The second one is the compression process,which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map.The last process is the symmetric key distribution.The symmetric key is encrypted twice using Rivest-Shamir-Adleman(RSA)to provide both authentication and confidentiality.Then,it is inserted into the cipher image using the End of File(EoF)method.The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key.Hence,the image data is safe from ciphertext-only attacks.This model is fast and able to withstand entropy attacks,statistical attacks,differential attacks,and brute-force attacks.展开更多
This paper reports the continuous wave （CW） and Q-switched operation of a diode pumped KGd （WO4）： Nd （Nd：KGW） slab laser with a comer pumped geometry at the wavelength of 1067 nm. With an optical conversion ef...This paper reports the continuous wave （CW） and Q-switched operation of a diode pumped KGd （WO4）： Nd （Nd：KGW） slab laser with a comer pumped geometry at the wavelength of 1067 nm. With an optical conversion efficiency of 38% and 34%, average powers of 23 and 20 W in CW and Q-switched modes were achieved respec- tively. The maximum pulse energy of 27 mJ was observed with a repetition rate of 840 Hz.展开更多
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
基金This work was supported by the ICT R&D program of MSIT/IITP(Project No.2021-0-01816,A Research on Core Technology of Autonomous Twins for Metaverse,10%)National Research Foundation of Korea(NRF)(Project No.NRF-2020R1A2C4002737,90%)grants funded by the Korean government。
文摘When aWindows-based system is used for an exceedingly long time,its performance degrades,and the error occurrence rate tends to increase.This is generally called system aging.To investigate the reasons for system aging,various studies have been conducted within the range of the operating system kernel to the user application.However,finding an accurate reason for system performance degradation remains challenging research topic.In this study,system monitoring was conducted by dividing a system into‘before software installation,’‘after software installation,’and‘after software removal.’We confirmed that when a software installed in a system is removed,various system elements,such as storage and memory,are not restored to the level prior to the software installation.Consequently,we established a hypothesis regarding the performance degradation of a computer system owing to repeated software installation/removal operations,investigated the correlation between system aging and repeated software installation/removal operations,and proposed a system aging analysis framework for analyzing the reason behind system aging.In the proposed system aging analysis framework,we aim to forcibly age a Windows-based system by repeating the software installation/removal operation by utilizing the system forced aging module.The framework identifies the elements affecting system performance through a differential data analysis of the system time-series data extracted by the system performance extraction and system component snapshot modules.Consequently,the aging analysis framework presented in this study is expected to be effectively utilized as an index for studying system aging.
基金Princess Nourah bint Abdulrahman University Riyadh,Saudi Arabia with Researchers Supporting Project Number:PNURSP2024R234.
文摘Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.
基金This work was supported by the Defense Acquisition Program Administration and Agency for Defense Development under the contract UD210029TD.
文摘One of the latest technologies enabling remote control,operational efficiency upgrades,and real-time big-data monitoring in an industrial control system(ICS)is the IIoT-Cloud ICS,which integrates the Industrial Internet of Things(IIoT)and the cloud into the ICS.Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction,efficiency improvement,and real-time monitoring,the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world.An adversary can collect information regarding senders,recipients,and prime-time slots through traffic analysis and use it as a linchpin for the next attack,posing a potential threat to the ICS.To address this problem,we designed a network traffic obfuscation system(NTOS)for the IIoT-Cloud ICS,based on the requirements derived from the ICS characteristics and limitations of existing NTOS models.As a strategy to solve this problem wherein a decrease in the traffic volume facilitates traffic analysis or reduces the packet transmission speed,we proposed an NTOS based on packet scrambling,wherein a packet is split into multiple pieces before transmission,thus obfuscating network analysis.To minimize the ICS modification and downtime,the proposed NTOS was designed using an agentbased model.In addition,for the ICS network traffic analyzer to operate normally in an environment wherein the NTOS is applied,a rule-based NTOS was adopted such that the actual traffic flow is known only to the device that is aware of the rule and is blocked for attackers.The experimental results verified that the same time requested for response and level of difficulty of analysis were maintained by the application of an NTOS based on packet scrambling,even when the number of requests received by the server per second was reduced.The network traffic analyzer of the ICS can capture the packet flow by using the pre-communicated NTOS rule.In addition,by designing an NTOS using an agent-based model,the impact on the ICS was minimized such that the system could be applied with short downtime.
文摘There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve these problems.This model consists of three processes.The first process is the dynamic symmetric key generation.The second one is the compression process,which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map.The last process is the symmetric key distribution.The symmetric key is encrypted twice using Rivest-Shamir-Adleman(RSA)to provide both authentication and confidentiality.Then,it is inserted into the cipher image using the End of File(EoF)method.The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key.Hence,the image data is safe from ciphertext-only attacks.This model is fast and able to withstand entropy attacks,statistical attacks,differential attacks,and brute-force attacks.
文摘This paper reports the continuous wave （CW） and Q-switched operation of a diode pumped KGd （WO4）： Nd （Nd：KGW） slab laser with a comer pumped geometry at the wavelength of 1067 nm. With an optical conversion efficiency of 38% and 34%, average powers of 23 and 20 W in CW and Q-switched modes were achieved respec- tively. The maximum pulse energy of 27 mJ was observed with a repetition rate of 840 Hz.
