Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

In cut throat competition, organizations all over the world try to utilize the immense opportunities the Internet has to offer in almost all of their operations. Today, Social networking sites (SNSs) provide utilities designed to help companies to recruit right personnel. Considering the implication of social media for recruitment at infancy stage in Pakistan and lack of studies related to it, this study aims to investigate the role of different SNSs qualities (Easily navigate, Secure process, Eminence Proficiency, Candidate's Attraction and Network Expedition) in effective E-recruitment (EER). Data were collected through structured questionnaire from employees and managers of major telecom companies of Pakistan. Finally, the result of 355 returned and valid questionnaire with 55% response rate show that the relationship between SNSs qualities and EER is significant. Moreover, results also prove that EER is better than the tradition recruitment and a SNSs comparison show that Facebook is more effective than LinkedIn for EER. The results of this study will help Pakistani companies to develop a successful e-HRM and EER strategy in the current scenario.

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

Distributive desynchronization algorithms based on pulse-coupled oscillator (PCO) models have been proposed for achieving collision-free wireless transmissions. These algorithms do not depend on a global clock or infrastructure overheads. Moreover, they gradually converge to fair time-division multiple access (TDMA) scheduling by broadcasting a periodic pulse signal (called a 'firing') and adjusting the next firing time based on firings from other nodes. The time required to achieve constant spacing between phase neighbors is estimated in a closed form or via stochastic modeling. However, because these algorithms cannot guarantee the completion of desynchronization in a short and bounded timeframe, they are not practical. Motivated by the limitations of these methods, we propose a practical solution called PD-DESYNC that provides a short and deterministic convergence time using a flag firing to indicate the beginning of a cycle. We demonstrate that the proposed method guarantees the completion of desynchronization within three cycles, regardless of the number of nodes. Through extensive simulations and experiments, we confirm that PD-DESYNC not only outperforms other algorithms in terms of convergence time but also is a practical solution.

The modern mobile devices are typically equipped with multiple network interfaces, e.g., 4G LTE, Wi-Fi, Bluetooth, but the current implementation of TCP can support only a single path at the same time. The Multipath TCP (MPTCP) leverages the multipath feature and provides (i) robust connection by utilizing another interface if the current connection is lost and (ii) higher throughput than single path TCP by simultaneously leveraging multiple network paths. However, if the performance between the multiple paths are significantly diverse, the receiver may have to wait for packets from the slower path, causing reordering and buffering problems. To solve this problem, previous MPTCP schedulers mainly focused on predicting the latency of the path beforehand. Recent studies, however, have shown that the path latency varies by a large margin over time, thus the MPTCP scheduler may wrongly predict the path latency, causing performance degradation. In this paper, we propose a new MPTCP scheduler called, choose fastest subflow (CFS) scheduler to solve this problem. Rather than predicting the path latency, CFS utilizes the characteristics of these paths to reduce the overall flow completion time by redundantly sending the last part of the flow to both paths. We compare the performance through real testbed experiments that implements CFS. The experimental results on both synthetic packet generation and actual Web page requests, show that CFS consistently outperforms the previous proposals in all cases.

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to $[1,1,{\cdots},1]^T$. The solutions of all the mapping matrices are integrated into an overall objective function, which uses ${\ell}_{2,1}$-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.

This paper proposes a novel method for locating objects in real space from a single remote image and measuring actual distances between them by automatic detection and perspective transformation. The dimensions of the real space are known in advance. First, the corner points of the interested region are detected from an image using deep learning. Then, based on the corner points, the region of interest (ROI) is extracted and made proportional to real space by applying warp-perspective transformation. Finally, the objects are detected and mapped to the real-world location. Removing distortion from the image using camera calibration improves the accuracy in most of the cases. The deep learning framework Darknet is used for detection, and necessary modifications are made to integrate perspective transformation, camera calibration, un-distortion, etc. Experiments are performed with two types of cameras, one with barrel and the other with pincushion distortions. The results show that the difference between calculated distances and measured on real space with measurement tapes are very small; approximately 1 cm on an average. Furthermore, automatic corner detection allows the system to be used with any type of camera that has a fixed pose or in motion; using more points significantly enhances the accuracy of real-world mapping even without camera calibration. Perspective transformation also increases the object detection efficiency by making unified sizes of all objects.

Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.

In this paper, we present an approach for detecting slide transitions in lecture videos by introducing the spatio-temporal residual networks. Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find keyframes where slide content changes. Since temporal dependency among video frames is important for detecting slide changes, 3D Convolutional Networks has been regarded as an efficient approach to learn the spatio-temporal features in videos. However, 3D ConvNet will cost much training time and need lots of memory. Hence, we utilize ResNet to ease the training of network, which is easy to optimize. Consequently, we present a novel ConvNet architecture based on 3D ConvNet and ResNet for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better accuracy than other slide progression detection approaches.

Key establishment method based on channel reciprocity for time division duplex (TDD) system has earned a vital consideration in the majority of recent research. While most of the cellular systems rely on frequency division duplex (FDD) systems, especially the 5G network, which is not characterized by the channel reciprocity feature. This paper realizes the generation of a group secret key for multi-terminals communicated through a wireless network in FDD mode, by utilizing the nature of the physical layer for the wireless links between them. I consider a new group key generation approach, which using bitwise XOR with a modified pairwise secret key generation approach not based on the channel reciprocity feature. Precisely, this multi-node secret key agreement technique designed for three wireless network topologies: 1) the triangle topology, 2) the multi-terminal star topology, and 3) the multi-node chain topology. Three multi-node secret key agreement protocols suggest for these wireless communication topologies in FDD mode, respectively. I determine the upper bound for the generation rate of the secret key shared among multi-node, for the three multi-terminals topologies, and give numerical cases to expose the achievement of my offered technique.

ACORN is an authenticated encryption algorithm proposed as a candidate in the currently ongoing CAESAR competition. ACORN has a good performance on security and efficiency which has been a third-round candidate. This paper mainly concentrates on the security of ACORN under the forgery attack and the non-repudiation of ACORN. Firstly, we analyze the differential properties of the feedback function in ACRON are analyzed. By taking advantage of these properties, the forgery attacks on round-reduced ACORN are proposed with a success probability higher than $2^{-128}$ when the number of finalization rounds is less than 87. Moreover, the non-repudiation of ACRON in the nonce-reuse setting is analyzed. The known collision can be used to deny the authenticated message with probability $2^{-120}$. This paper demonstrates that ACORN cannot generate the non-repudiation completely. We believe it is an undesirable property indeed.

Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.

Proof of Work (PoW) based blockchains have limitations in throughput, time consumption, and energy efficiency. In these systems, a miner will consume significant time and resources to obtain a reward for contributing to the blockchain. To overcome these limitations, recent research on blockchains are focused on accelerating the speed, scalability, and enhancing the security level. By enhancing specific procedures of blockchain system, the level of data integrity supported by the blockchain can become more robust, and efficient. In this paper, a new blockchain consensus model based on the Bryllite Consensus Protocol (BCP) is proposed to support a hyper-connected massively multiplayer online game (MMOG) ecosystem. The BCP scheme enables users to participate directly in new consensus processes through a Proof of Participation (PoP) algorithm. In this model, the consensus algorithm has a simpler form while maintaining high security level. In addition, because the BCP scheme gives users an equal chance to make a contribution to the blockchain, rewards are distributed in an equal fashion, which motivates user participation. The analysis of the proposed scheme is applied to the Bryllite consortium blockchain system (homed in Hong Kong), which is a new blockchain network developed for international game industries, gamers, and game events.

Workflow process mining is becoming a more and more valuable activity in workflow-supported enterprises, and through which it is possible to achieve the high levels of qualitative business goals in terms of improving the effectiveness and efficiency of the workflow-supported information systems, increasing their operational performances, reducing their completion times with minimizing redundancy times, and saving their managerial costs. One of the critical challenges in the workflow process mining activity is to devise a reasonable approach to discover and recognize the bottleneck points of workflow process models from their enactment event histories. We have intuitively realized the fact that the iterative process pattern of redo-activities ought to have the high possibility of becoming a bottleneck point of a workflow process model. Hence, we, in this paper, propose an algorithmic approach and its implementation to discover the redo-activities and their performers' involvements patterns from workflow process enactment event logs. Additionally, we carry out a series of experimental analyses by applying the implemented algorithm to four datasets of workflow process enactment event logs released from the BPI Challenges. Finally, those discovered redo-activities and their performers' involvements patterns are visualized in a graphical form of information control nets as well as a tabular form of the involvement percentages, respectively.

Autonomous driving technology is divided into 0~5 levels. Of these, Level 5 is a fully autonomous vehicle that does not require a person to drive at all. The automobile industry has been trying to develop Level 5 to satisfy safety, but commercialization has not yet been achieved. In order to commercialize autonomous unmanned vehicles, there are several problems to be solved for driving safety. To solve one of these, this paper proposes 'A Deep Learning Part-diagnosis Platform(DLPP) based on an In-vehicle On-board gateway for an Autonomous Vehicle' that diagnoses not only the parts of a vehicle and the sensors belonging to the parts, but also the influence upon other parts when a certain fault happens. The DLPP consists of an In-vehicle On-board gateway(IOG) and a Part Self-diagnosis Module(PSM). Though an existing vehicle gateway was used for the translation of messages happening in a vehicle, the IOG not only has the translation function of an existing gateway but also judges whether a fault happened in a sensor or parts by using a Loopback. The payloads which are used to judge a sensor as normal in the IOG is transferred to the PSM for self-diagnosis. The Part Self-diagnosis Module(PSM) diagnoses parts itself by using the payloads transferred from the IOG. Because the PSM is designed based on an LSTM algorithm, it diagnoses a vehicle's fault by considering the correlation between previous diagnosis result and current measured parts data.

We have developed composite measures of supercomputer technology, reflecting various factors of supercomputers using Martino's scoring model. CPUs, accelerators, memory, interconnection networks, and power consumption are chosen as factors of the model. The weight values of the factors are derived based on a survey of 129 domestic and international experts. The measured values are then standardized to integrate measurement units of the factors in the model. This model has been applied to 50 supercomputers, and rank correlation analysis was performed using representative measures. As a consequence, the ranking drastically changes except for the 1st and 2nd supercomputers on the TOP500. In addition, the characteristics of memory and interconnection networks influence the ranking, and the results demonstrate that the proposed model has low correlations with HPL and HPCG but a high correlation with Green500. This indicates that power consumption is an important factor that has a significant effect on the measures of supercomputer technology. In addition, it is determined that the differences between the HPL ranking and the proposed model ranking are influenced by power consumption, CPU theoretical peak performance, and main memory bandwidth in order of significance. In conclusion, the composite measures proposed in this study are more suitable for comprehensively describing supercomputer technology than existing performance measures. The findings of this study are expected to support decision making related to management and policy in the procurement and operation of supercomputers.

Borescopes facilitate the inspection of areas inside machines and systems that are not directly accessible for visual inspection. They offer real-time, up-close access to confined and hard-to-access spaces without having to dismantle or destructure the object under inspection. Borescopes are ideal instruments for routine maintenance, quality inspection and monitoring of systems and structures. The main application being fault or defect detection, it is useful to have measuring capability to quantify object dimensions in a target area. High-end borescopes use multi-optic solutions to provide measurement information of viewed objects. Multi-optic solutions can provide accurate measurements at the expense of structural complexity and cost increase. Measuring functionality is often unavailable in low-end, single camera borescopes. In this paper, a single camera measurement solution that enables the size estimation of viewed objects is proposed. The proposed solution computes and overlays a scaled grid of known spacing value over the screen view, enabling the human inspector to estimate the size of the objects in view. The proposed method provides a simple means of measurement that is applicable to low-end borescopes with no built-in measurement capability.

IEC 62304 is a standard for the medical device software lifecycle. Developers must develop software that complies with all specifications in the standard for licensing. However, because the standard contains not only a large number of specifications, but also domain-specific information and association relationships between specifications, it requires considerable effort and time for developers to understand and interpret the standard. To support developers, this paper presents a method for extracting the contents of the IEC 62304 standard as a goal model, which is the core methodologies of requirements engineering. The proposed method analyzes the grammar of the standard to robustly extract complex structures and various information from standard specifications and define rules that extract goals and links from syntactic element units. We validated the actual extraction process for the standard document experimentally. Based on the extracted goal model, developers can intuitively and efficiently comply with the standard and track specific information within the medical software and standard domains.

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

In this paper, a design and implementation for direct deal distribution platform is proposed to bypass the complex traditional distribution structure of agricultural market, as one of the fields where distribution patterns have changed. In the case of domestic agricultural distribution, demand and supply are unstable since the sales market is excessively concentrated in the designated wholesale market. Besides sales must go through multiple stages of distribution leading to problems in freshness and stability of agricultural products and downward pressure on profit margins for producers. To solve the above mentioned issues, we propose a cloud service convergence direct deal distribution platform based on asynchronous-driven Node.js. The proposed platform can facilitate a variety of direct trading functions and also access to visualization information related to agricultural products, which may increase user confidence at an intermediary-free direct transactions platform. First, we describe the requirements of intermediary-free direct transactions of agricultural products and transaction entities. Next the database structure and transaction functions are designed and then implemented according to those requirements. Finally, an API based cloud convergence service structure is designed to provide the analyzed information to ensure a trustworthy system.

In this paper, we present our proposed magnetic induction based wireless communication system. The proposed system is designed to perform communication as well as distance measurement in underground environments. In order to improve the communication quality, we propose and implement the adaptive channel compensation technique. Based on the fact that the channel may be fast time-varying, we keep track of the channel status each time the data is received and accordingly compensate the channel coefficient for any change in the channel status. By using the proposed compensation technique, the developed platform can reliably communicate over distances of 10m while the packet error rate is being maintained under 5%. We also implement the distance measurement block that is useful for various applications that should promptly estimate the location of nearby nodes in communication. The distance between two nodes in communication is estimated by generating a table describing pairs of the magnetic signal strength and the corresponding distance. The experiment result shows that the platform can estimate the distance of a node located within 10m range with the measurement error less than 50cm.

The efficiency and accuracy of handwriting measurement could be improved by adopting digital image processing. This study developed a computer-based Korean Handwriting Assessment tool. Second graders participated in this study by performing writing tasks of consonants, vowels, words, and sentences. We extracted boundary parameters for each letter using digital image processing and calculated the variables of size, size coefficient of variation (CV), misalignment, inter-letter space, inter-word space, and ratio of inter-letter space to inter-word space. Children were also administered traditional handwriting and visuomotor tests. Digital variables from image processing were correlated with these previous tests. Using these correlations, we established a three-point scoring system that computed test scores for each variable. We analyzed inter-rater reliability between the computer rater and human rater and test-retest reliability between the first and second performances. The validity was examined by analyzing the relationship between the Korean Handwriting Assessment and previous handwriting and visuomotor tests. We suggested the Korean Handwriting Assessment to measure size, size consistency, misalignment, inter-letter space, inter-word space, and space ratio using digital image processing. This Korean Handwriting Assessment tool proved to have reliability and validity. It is expected to be useful for assessing children's handwriting.

This paper proposes an image generation method using a Multi Discriminator Generative Adversarial Net (MDGAN) as a next generation 2D game sprite creation technique. The proposed GAN is an Autoencoder-based model that receives three areas of information-color, shape, and animation, and combines them into new images. This model consists of two encoders that extract color and shape from each image, and a decoder that takes all the values of each encoder and generates an animated image. We also suggest an image processing technique during the learning process to remove the noise of the generated images. The resulting images show that 2D sprites in games can be generated by independently learning the three image attributes of shape, color, and animation. The proposed system can increase the productivity of massive 2D image modification work during the game development process. The experimental results demonstrate that our MDGAN can be used for 2D image sprite generation and modification work with little manual cost.

This study presents a reference model (RM) and the architecture of a cognitive health advisor (CHA) that integrates information with ambient intelligence. By controlling the information using the CHA platform, the reference model can provide various ambient intelligent solutions to a user. Herein, a novel approach to a CHA RM based on evolutional cyber-physical systems is proposed. The objective of the CHA RM is to improve personal health by managing data integration from many devices as well as conduct a new feedback cycle, which includes training and consulting to improve quality of life. The RM can provide an overview of the basis for implementing concrete software architectures. The proposed RM provides a standardized clarification for developers and service designers in the design and implementation process. The CHA RM provides a new approach to developing a digital healthcare model that includes integrated systems, subsystems, and components. New features for chatbots and feedback functions set the position of the conversational interface system to improve human health by integrating information, analytics, and decisions and feedback as an advisor on the CHA platform.

Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.

With the development of video-related applications, media content has increased dramatically through applications. There is a substantial amount of near-duplicate videos (NDVs) among Internet videos, thus NDVR is important for eliminating near-duplicates from web video searches. This paper proposes a novel NDVR system that supports large-scale retrieval and contributes to the efficient and accurate retrieval performance. For this, we extracted keyframes from each video at regular intervals and then extracted both commonly used features (LBP and HSV) and new image features from each keyframe. A recent study introduced a new image feature that can provide more robust information than existing features even if there are geometric changes to and complex editing of images. We convert a vector set that consists of the extracted features to binary code through a set of hash functions so that the similarity comparison can be more efficient as similar videos are more likely to map into the same buckets. Lastly, we calculate similarity to search for NDVs; we examine the effectiveness of the NDVR system and compare this against previous NDVR systems using the public video collections CC_WEB_VIDEO. The proposed NDVR system's performance is very promising compared to previous NDVR systems.

The present study investigated the effects of work satisfaction on quality of life for 119 paramedics, the Korean equivalent of 911 paramedics. Data collection began in July 2017 and lasted for six months. Data was collected from 410 119 paramedics of the J province and the G metropolitan area who consented to study participation. The results showed that the higher the job satisfaction of the participants, the higher the quality of life (r = 0.508, p = 0.008). The factors affecting the quality of life were leisure activity, subjective health, and job satisfaction. For 119 paramedics to improve their quality of life, they need to relieve work stress and fatigue through leisure activities, and recover and maintain their health through sufficient rest. Moreover, returning to work in a healthy state likely leads to increased job satisfaction, and the quality of life will increase accordingly. Therefore, 119 paramedics should be permitted to have plenty of leisure time outside their work hours and should be able to maintain health through periodic health checks.