Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods.

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many- objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems.

The four wing unmanned aerial vehicle owns the characteristics of small size, light weight, convenient operation and well stability. But it is easily disturbed by external environmental factors during flight with these disadvantages of short endurance and poor attitude solving ability. For solving these problems, a microprocessor based on STM32 chip is designed and the overall development is completed by the resources such as built-in timer and multi-function mode general-purpose input/output provided by the master micro controller unit, together with radio receiver, attitude meter, barometer, electronic speed control and other devices. The unmanned aerial vehicle can be remotely controlled and send radio waves to its corresponding receiver, control the analog level change of its corresponding channel pins. The master control chip can analyze and process the data to send multiple sets pulse signals of pulse width modulation to each electronic speed control. Then the electronic speed control will transform different pulse signals into different sizes of current value to drive the motor located in each direction of the frame to generate different rotational speed and generate lift force. To control the body of the unmanned aerial vehicle, so as to achieve the operator's requirements for attitude control, the PID controller based on Kalman filter is used to achieve quick response time and control accuracy. Test results show that the design is feasible.

Technologies for disaster management are highly sought areas for research and commercial deployment. Landslides, Flood, cyclones, earthquakes, forest fires and road/train accidents are some causes of disasters. Capturing video and accessing data in real time from the disaster site can help first responders make split second decisions which may save human lives and valuable resource destructions. In this context the communication technologies performing the task should have high bandwidth and low latency which only 5G can deliver. But unfortunately in India, deployment of the 5G mobile communication systems is yet to give a shape and again in remote areas unavailability of 4G signals is still severe. In this situation the authors have proposed, simulated and experimented a 4G-5G communication scheme where from the disaster site the signals will be transmitted by a 5G terminal to a nearby 4G-5G gateway installed in a mobile vehicle. The received 5G signal will be further relayed by the 4G-5G gateway to the fixed 4G base station for onward transmission towards the disaster management station for decision making, deployment and relief monitoring. The 4G-5G gateway acts as a relay and converter of 5G signal to 4G signal and vice versa. This relayed system can be further mounted on a vehicle mounted relay (VMR) as proposed by 3GPP in Release 18. The scheme is also in the same line of context with Verizon's, "Tactical Humanitarian Operations Response" (THOR) vehicle concept. The performance of the link is studied in different channel conditions, the throughput achieved is superb. The authors have implemented the above mentioned system towards smart campus networking and monitoring landslides activities which are common in their regions.

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.

The state-of-the-art video-based point cloud compression(V-PCC) has a high efficiency of compressing 3D point cloud by projecting points onto 2D images. These images are then padded and compressed by High-Efficiency Video Coding(HEVC). Pixels in padded 2D images are classified into three groups including origin pixels, padded pixels and unoccupied pixels. Origin pixels are generated from projection of 3D point cloud. Padded pixels and unoccupied pixels are generated by copying values from origin pixels during image padding. For padded pixels, they are reconstructed to 3D space during geometry reconstruction as well as origin pixels. For unoccupied pixels, they are not reconstructed. The rate distortion optimization(RDO) used in HEVC is mainly aimed at keeping the balance between video distortion and video bitrates. However, traditional RDO is unreliable for padded pixels and unoccupied pixels, which leads to significant waste of bits in geometry reconstruction. In this paper, we propose a new RDO scheme which takes 3D-Distortion into account instead of traditional video distortion for padded pixels and unoccupied pixels. Firstly, these pixels are classified based on the occupancy map. Secondly, different strategies are applied to these pixels to calculate their 3D-Distortions. Finally, the obtained 3D-Distortions replace the sum square error(SSE) during the full RDO process in intra prediction and inter prediction. The proposed method is applied to geometry frames. Experimental results show that the proposed algorithm achieves an average of 31.41% and 6.14% bitrate saving for D1 metric in Random Access setting and All Intra setting on geometry videos compared with V-PCC anchor.

Traditional cloud computing faces some challenges such as huge energy consumption, network delay and single point of failure. Edge computing is a typical distributed processing platform which includes multiple edge servers closer to the users, thus is more robust and can provide real-time computing services. Although outsourcing data to edge servers can bring great convenience, it also brings serious security threats. In order to provide image retrieval while ensuring users' data privacy, a privacy preserving image retrieval scheme in edge environment is proposed. Considering the distributed characteristics of edge computing environment and the requirement for lightweight computing, we present a privacy-preserving image retrieval scheme in edge computing environment, which two or more "honest but curious" servers retrieve the image quickly and accurately without divulging the image content. Compared with other traditional schemes, the scheme consumes less computing resources and has higher computing efficiency, which is more suitable for resource-constrained edge computing environment. Experimental results show the algorithm has high security, retrieval accuracy and efficiency.

As data sharing increases explosively, such information encoded in QR code is completely public as private messages are not securely protected. This paper proposes a new 'PROMISE' framework for hiding information based on the QR code projection matrix by using image segmentation without modifying the essential QR code characteristics. Projection matrix mapping, matrix scrambling, fusion image segmentation and steganography with SEL(secret embedding logic) are part of the PROMISE framework. The QR code could be mapped to determine the segmentation site of the fusion image as a binary information matrix. To further protect the site information, matrix scrambling could be adopted after the mapping phase. Image segmentation is then performed on the fusion image and the SEL module is applied to embed the secret message into the fusion image. Matrix transformation and SEL parameters should be uploaded to the server as the secret key for authorized users to decode the private message. And it was possible to further obtain the private message hidden by the framework we proposed. Experimental findings show that when compared to some traditional information hiding methods, better anti-detection performance, greater secret key space and lower complexity could be obtained in our work.

Logs play an important role in mastering the health of the system, experienced operation and maintenance engineer can judge which part of the system has a problem by checking the logs. In recent years, many system architectures have changed from single application to distributed application, which leads to a very huge number of logs in the system and manually check the logs to find system errors impractically. To solve the above problems, we propose a method based on Message Middleware and ATT-GRU (Attention Gate Recurrent Unit) to detect the logs anomaly of distributed systems. The works of this paper mainly include two aspects: (1) We design a high-performance distributed logs collection architecture to complete the logs collection of the distributed system. (2)We improve the existing GRU by introducing the attention mechanism to weight the key parts of the logs sequence, which can improve the training efficiency and recognition accuracy of the model to a certain extent. The results of experiments show that our method has better superiority and reliability.

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

3D human pose estimation is widely applied in various fields, including action recognition, sports analysis, and human-computer interaction. 3D human pose estimation has achieved significant progress with the introduction of convolutional neural network (CNN). Recently, several researches have proposed the use of multiview approaches to avoid occlusions in single-view approaches. However, as the number of cameras increases, a 3D pose estimation system relying on a CNN may lack in computational resources. In addition, when a single host system uses multiple cameras, the data transition speed becomes inadequate owing to bandwidth limitations. To address this problem, we propose a distributed real-time 3D pose estimation framework based on asynchronous multiple cameras. The proposed framework comprises a central server and multiple edge devices. Each multiple-edge device estimates a 2D human pose from its view and sendsit to the central server. Subsequently, the central server synchronizes the received 2D human pose data based on the timestamps. Finally, the central server reconstructs a 3D human pose using geometrical triangulation. We demonstrate that the proposed framework increases the percentage of detected joints and successfully estimates 3D human poses in real-time.

Machine learning is widely used in various academic fields, and recently it has been actively applied in the medical research. In the medical field, machine learning is used in a variety of ways, such as speeding up diagnosis, discovering new biomarkers, or discovering latent traits of a disease. In the respiratory field, a relative regional air volume change (RRAVC) map based on quantitative inspiratory and expiratory computed tomography (CT) imaging can be used as a useful functional imaging biomarker for characterizing regional ventilation. In this study, we seek to predict RRAVC using various regular machine learning models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP). We experimentally show that MLP performs best, followed by XGBoost. We also propose several relative coordinate systems to minimize intersubjective variability. We confirm a significant experimental performance improvement when we apply a subject's relative proportion coordinates over conventional absolute coordinates.

With the emergence of advanced encryption technologies such as Quantum Cryptography and Full Disk Encryption, an era of strengthening information security has begun. Users respond positively to the advancement of privacy-enhancing technology, on the other hand, investigative agencies have difficulty unveiling the actual truth as they fail to decrypt devices. In particular, unlike past ciphers, encryption methods using biometric information such as fingerprints, iris, and faces have become common and have faced technical limitations in collecting digital evidence. Accordingly, normative solutions have emerged as a major issue. The United States enacted the CLOUD Act with the legal mechanism of 'Contempt of court' and in 2016, the United Kingdom substantiated the Compelled Decryption through the Investigatory Powers Act (IPA). However, it is difficult to enforce Compelled Decryption on individuals in Korea because Korean is highly sensitive to personal information. Therefore, in this paper, we sought a method of introducing a Compelled Decryption that does not contradict the people's legal sentiment through a perception survey of 95 people on the Compelled Decryption. We tried to compare and review the Budapest Convention with major overseas laws such as the United States and the United Kingdom, and to suggest a direction of legislation acceptable to the people in ways to minimize infringement of privacy. We hope that this study will be an effective legal response plan for law enforcement agencies that can normatively overcome the technical limitations of decoding.

This study is a demand research for the selection of public safety science and technology equipment and suggests an empirical research method. The technology demand survey is the beginning of the selection of innovative technology. And it is the basis of collecting information required for the technology required in the market and helping to apply it to the field. The demand survey for police science and technology can reduce the uncertainty of crime prevention and help the smooth implementation of security policies. However, in Korea, adoption of security science and technology equipment was centered on social issues or researchers' opinions rather than the demands of field users. Until, there was no research has been conducted on the demands of field police officers for selection of security science and technology equipment in Korea. Also, there was no preferential study for the demand for security science and technology equipment. Therefore, this study proposes a methodology that can systematically identify the needs for the technology and equipment of field experts suitable for the public security situation for the selection of security science and technology equipment. Specifically, we propose a sample design for a technology classification system and a survey tool for technology awareness and satisfaction. It is expected that this tool will provide a classification system for security science and technology equipment selected for the Korean police and will help determine the priority of equipment suitable for the field.

As mobile forensics has emerged as an essential technique, the demand for technology development, education and training is increasing, wherein images are used. Academic societies in South Korea and national institutions in the US and the UK are leading the Mobile Forensic Image development. However, compared with disks, images developed in a mobile environment are few cases and have less active research, causing a waste of time, money, and manpower. Mobile Forensic Images are also difficult to trust owing to insufficient verification processes. Additionally, in South Korea, there are legal issues involving the Telecommunications Business Act and the Act on the Protection and Use of Location Information. Therefore, in this study, we requested a review of a standard model for the development of Mobile Forensic Image from experts and designed an 11-step development model. The steps of the model are as follows: a. setting of design directions, b. scenario design, c. selection of analysis techniques, d. review of legal issues, e. creation of virtual information, f. configuring system settings, g. performing imaging as per scenarios, h. Developing a checklist, i. internal verification, j. external verification, and k. confirmation of validity. Finally, we identified the differences between the mobile and disk environments and discussed the institutional efforts of South Korea. This study will also provide a guideline for the development of professional quality verification and proficiency tests as well as technology and talent-nurturing tools. We propose a method that can be used as a guide to secure pan-national trust in forensic examiners and tools. We expect this study to strengthen the mobile forensics capabilities of forensic examiners and researchers. This research will be used for the verification and evaluation of individuals and institutions, contributing to national security, eventually.

There are still outbreaks of COVID-19 across the world. Ships increase the risk of worldwide transmission of the virus. Close contact tracing remains as an effective method of reducing the risk of virus transmission. Therefore, close contact tracing in ship environments becomes a research topic. Exposure Notifications API (Application Programming Interface) can be used to determine the encountered location points of close contacts on ships. Location points of close contact are estimated by the encountered location points. Risky areas in ships can be calculated based on the encountered location points. The tracking of close contacts is possible with Bluetooth technology without the Internet. The Bluetooth signal can be used to judge the proximity among detecting devices by using the feature that Bluetooth has a strong signal at close range. This Bluetooth feature makes it possible to trace close contacts in ship environments. In this paper, we propose a method for close contact tracing and showing the risky area in a ship environment by combining beacon and Exposure Notification API using Bluetooth technology. This method does not require an Internet connection for tracing close contacts and can protect the personal information of close contacts.

As the paradigm of future vehicles changes, the interest in automotive semiconductor, which plays a key role in realizing this, is increasing. Automotive semiconductors are the technology with very high entry barriers that require a lot of effort and time because it must secure technology readiness level and also consider safety and reliability. In this technology field, it is very important to develop new businesses and create opportunities through technology trend analysis. However, systematic analysis and application of automotive semiconductor technology trends are currently lacking. In this paper, U.S. registered patent documents related to automotive semiconductor were collected and investigated based on the patent's IPC. The main technology of automotive semiconductor was analyzed through topic modeling, and the technology path such as emerging technology was investigated through cosine similarity. We identified that those emerging technologies such as driving control for vehicle and AI service appeared. We observed that as time passed, both convergence and independence of automotive semiconductor technology proceeded simultaneously.