Reviewerly/exHarmony huggingface dataset:
Shot-Based Content Adaptive Robust Digital Video Watermarking Algorithm Against Camera Recording

Digital video is one of the most important media in current communication. It is very popular for its rich content and vivid forms. With a rapid development of the Internet, the progress of computer technology makes communication more convenient, and also facilitated infringers' illegal and pirated acts. The copyright protection of digital video has become an urgent problem to be solved. Camera recording is a serious threat to the security of video content. The camera recording is not a single attack, but a combined attack in the spatio-temporal domain. It is difficult for a video watermarking algorithm to be robust to combined attacks in the spatio-temporal domain and satisfies the content adaptation. In this paper, we proposed a video watermarking algorithm based on the temporal domain, which takes the video shot as an embedding unit. We introduce a new metric called chrominance standard deviation (ST) to represent the changes in chrominance amplitude within the shots. According to the chrominance characteristics of the video, the embedding strength and period are determined adaptively, and the watermark is embedded in every frame of a shot. In the watermark extraction, we introduce geometric correction and color correction modules to correct the distorted video. The experimental results show that the proposed algorithm has high robustness not only to various single common attacks but also to mobile phone camera recording, while satisfying the imperceptibility of the watermark.
Deep Learning-Based Joint Modulation and Coding Scheme Recognition for 5G New Radio Protocols

Blind detection of signals is a crucial technique in the 5G/B5G wireless communication systems, especially for the cognitive spectrum radio network, where the parameters of the transmit signals working on the free spectrum can not be known by the receiver. Following the 5G New Radio (NR) protocols, we propose a joint modulation and coding scheme (M-CS) recognition framework based on the supervised learning architecture and the given candidate set of the LDPC encoder. Specifically, the framework is composed of two cascaded modules. Firstly, the type of digital modulation according to the SG NR protocols is recognized blindly based on the proposed Res-Inception convolutional neural network (RICNN). Then, the low-density parity check (LDPC) coding scheme implemented under various bitrates is identified by exhaustively searching the validation candidate to maximize the corresponding average log-likelihood ratio (ALLR). Numerical results show the effectiveness of our proposed blind recognition framework, especially for the practical 5G NR protocols. Moreover, it is demonstrated that our proposed method can guarantee the robustness of the recognition under various channel fading model scenarios.
Machine Learning Based Solar Power Forecasting Techniques: Analysis and Comparison

Because of global warming and the overconsumption of traditional resources, it is critical to consider alternative forms of energy sources in order to have clean and adequate energy for long-term growth. Solar photovoltaic (PV) power generation is essential to reducing power demand shortages and supplying clean energy to smart grids. Due to the intermittent and unpredictable nature of solar PV-generated power, an accurate approach for PV power forecasting is required. However, because of the rise of big data and machine learning (ML), forecasting is now a realistic solution. This paper analyses and compares different advanced ML-based models for PV power forecasting. Moreover, a data split technique for time series data with a small period has been proposed for better regularization. The analytical findings revealed that the suggested long short-term memory and gated recurrent unit models outperformed other models, including the artificial neural network model. The ML-based models are compared with the benchmark persistence forecast technique.
A Resource Allocation Scheme for Independent Core Transmission of Quantum Key Distribution

To ensure the security of optical networks in the future, integrating quantum key distribution (QKD) with optical networks is one of the solutions. Moreover, the optical network based on multi-core fiber (MCF) is also one of the ways to improve the transmission capacity. Therefore, we study the integration of QKD into MCF-enabled optical networks. Aiming at the resource allocation problem of MCF-enabled wavelength division multiplexing optical networks, this paper proposes a resource allocation scheme which is suitable for independent-core-transmission of classical signals and quantum signals. For core resource, we propose the alternate core allocation algorithm. For wavelength resource, we propose the iteration-based wavelength allocation algorithm. The overall scheme uses the proposed two algorithms to first allocate the core and then allocate the wavelength. The simulation results show that the proposed scheme has a great improvement in secret key rate (SKR) compared with the benchmark scheme on the regular hexagonal 7-core fiber and 19-core fiber. And the proposed scheme still has good performance under long transmission distance and high classical signal power.
Background Knowledge Aware Semantic Coding Model Selection

Semantic communication is deemed to break Shannon channel capacity by transmitting extracted semantics rather than all binary bits. One critical challenge in semantic communication system is how to select a matching semantic coding model (SCM) in light of complicated source information, diversified user background knowledge (BK) and dynamic wireless channel. In this paper, we mathematically model the relationship among different BKs by using graph theory, and introduce a metric to evaluate SCMs performance as per BK relationships. Then, we propose a Background knowledge Aware SCM SElection (BASE) scheme, where a deep learning algorithm is exploited to accurately predict SCM performance in context of the modeled BK, guiding the SCM selection. Numerical simulation results show that the BASE has superiorities in information recovery accuracy along with the probability of selecting the optimal SCM when compared with other benchmarks.
Millimeter-Wave Radar Target Detection Based on Inter-frame DBSCAN Clustering

High-resolution millimeter wave radar sensor plays an important role in the process of detecting and classifying targets in complex environments. To process the radar data, clustering is often used to group the measured data. However, due to the different numbers and densities of available target points in different scenarios, data with high sparsity characteristics can cause incomplete target detection in the process of classifying targets, which is very challenging. Therefore, this study introduces a target detection method based on inter-frame DBSCAN clustering. In order to improve the accuracy of target detection, we employ a modified DBSCAN algorithm to cluster the inter-frame data by fusing Doppler features. The method uses a multi-frame merging process to improve the single-frame clustering accuracy, and uses frame sequence features to solve the multi-target noise problem. The effectiveness of the proposed method is verified using real-world dataset, as the clustering accuracy is improved compared to other methods, indicating the advantages of the proposed method.
The Appropriate Image Enhancement Method for Underwater Object Detection

Underwater datasets usually have blurred objects, low contrast, and color distortion, which seriously restrict the performance of the target detector on it. It is very important to use data enhancement methods to improve the original dataset. In this paper 7 target detection models are trained on Trash-ICRA19 underwater datasets, namely Faster-RCNN, SSD, YOLO V1-V5. Through comparison, it is found that YOLOV5 has the best performance with 79.7 AP and 138.89 FPS. Then three methods about data enhancement are adopted to improve the UDD underwater datasets, namely Underwater Dark Channel Prior(UDCP), Contrast-Limited Adaptive Histogram Equalization(CLAHE), and Relative Global Histogram Stretching(RGHS). Comprehensive comparison found that CLAHE and RGHS can have a better recovery effect. Finally, the YOLOV5 model is re-trained on the enhanced UDD underwater dataset. Compared with visual results, these enhancement methods can make the detector better distinguish between objects and backgrounds.
A Novel Chaotic map to improve security Image Based on Mandelbrot Fractals

Image encryption has recently attracted a lot of scientists and researchers recently as it's an important information security field. Nevertheless, a number of researches were carried out using various techniques, new and practical algorithms were proposed to enhance secure image encryption schemes. Nowadays, chaotic approaches are used in many different fields, including image encryption and cryptosystem design. Digital image encryption techniques depending on chaotic approaches are new. The given approach for encrypting images makes advantage of random chaotic sequences and it can be quick and very secure. This study proposes a new (eight-dimension chaotic cat map). An 8D chaotic cat map which boosts encryption security and efficiency by encoding pixel intensity values using a lookup table. This method is resistant to recognized plain text attacks, differential and statistical attacks, and differential attacks. The base for the map is the chaotic approach, yet the base for the cryptographic algorithm is discrete math. As a result, it offers more rapid and secure methods of data protection. The image encryption, accompanied with proposal's generated key uses fractal geometry's Mandelbrot set and XOR with the image to boost security. The results that obtained from compute several metrics where the average of NPCR (Number of Pixels Change Rate) (99.758333) the value close to 100% and UACI (Unified Average Changing Intensity) (33.2783333) this value near 33% to against differential attacks additional average of Entropy (7.77048333) and the range of PSNR (0.173-0.149) this value and MES (55.7-56.37) proposed scheme has low PSNR values and high MSE. Our findings demonstrat that the suggested approach has an extremely low correlation coefficient and good security level, Moreover, they reflect that this method is good for the actual image encryption.
SQL Injection Detection Using 2D-Convolutional Neural Networks (2D-CNN)

It is common for modern multi-tier applications to use highly performant databases to manage and store business data. These systems are attractive to hackers because they contain essential data, and great care must be taken to prevent unauthorized access to the database layer. SQL Injection attacks (SQLIA) are one of the most common web applications (WA) threats. SQL injection, more often known as SQL Injection (SQLI), is a different attack method that involves injecting malicious SQL code into a database. Thanks to deep learning algorithms, the classification can be performed without manual feature extraction. It has been proposed a Specially Designed two-dimensional convolutional neural network (2D-CNN) architecture built using python. Further, the proposed 2D-CNN can be extracted automatic features from numerical data. Here, binary classify (normal and malicious queries) was applied. While this dataset is one-dimensional text, these are transformed into the two-dimensional matrix with word embedding (Skip-Gram Model) technique taking both context and syntax information from SQL into consideration for extracting low-dimensional vector representations of words and feature engineering using a neural network. Technically, this process would increase the classification accuracy detection of SQLIA with high harmony. The dataset from Kaggle.com, which includes all major types of SQL attacks, was used to test the proposed Model. The proposed model 2D-CNN accuracy is 0.9966%, while the loss is 0.0178%, based on a dataset. Deep learning algorithms and state-of-the-art classification models are compared for performance. In terms of accuracy and loss, the proposed model outperforms comparable models, according to the findings of our testing.
A Survey of SQL Injection Attacks, Their Methods, and Prevention Techniques

The vast majority of web applications' databases are vulnerable to SQL Query Injection Attacks, which let clients directly insert sensitive data. They carry out their operations by inserting nefarious SQL Injection Query codes into the client-side web API, which allows them to retrieve all the confidential and sensitive data from the database. SQL injection is a technique where web attackers post the malicious SQL injection Query, occupying the full admin login access of the web database, for malicious Input data modifications or deletion of the existing user User's Information. The goal of this technique is to change the structure and behavior of the query that the computer programmer has proposed. The examination of SQL injections, which target a Web application's front end to get access to its back-end database, It will address the implications, categorization, and techniques of these attacks. Then, two tactics have chosen to defend the database against SQL injection assaults. As a consequence, the design and development of any new SQL injection attacks for web application may use this work as a guide and reference
Channel Estimation of mmWave Massive MIMO System Based on Manifold Learning

In this paper, a channel estimation algorithm for millimeter wave communication system based on Manifold Learning Extreme Learning Machine (ML-ELM) is proposed. Particularly, the proposed algorithm uses the manifold learning to reduce the characteristic dimension of the received signal, such that the dimension of the received signal is reduced. Then, a one-shot training Extreme Learning Machine (ELM) is used to estimate the Channel State Information (CSI). Due to the low-dimensional date, the ELM can be better trained such that the channel is well estimated. Moreover, the computational complexity can also be reduced. Simulation results show that compared with LS, MMSE and Deep Neural Network (DNN) algorithms, the proposed algorithm provide a better performance, which meets the real-time requirements of the communication system.
Research on GA-KNN Image Classification Algorithm

This paper studies KNN algorithm and analyzes the factors that affect the accuracy of image classification. Then the algorithm is improved by optimizing the selection strategy of K value in traditional KNN model using the characteristics of genetic algorithm, which is survival of the fittest and survival of the fittest. The Fasion-MNIST dataset is selected to extract the gray pixel value of the image. KNN algorithm model and GA-KNN algorithm model have been trained, and the classification accuracy of the latter is improved by nearly 10%, making the image classification effect better.
Bird Movement Recognition Research Based on YOLOv4 Model

Taking the basic daily behavior of birds as the research object of this paper, the deep learning object detection algorithm is used to identify and detect the behavior and behavior of birds. Using the YOLOv4 model to extract features to complete the recognition of bird behavior and action and the location of birds, this study first crawled target images from the computer vision website and the China Bird Network, Pass the bird action image into the model after processing, and used the CSPdarknet network for feature extraction. Feature enhancement processing is carried out using PANet and SPP to expand the sensory field of view. In order to make the research results better applied to reality Finally, positioning and classification are implemented through the header layer. The yolo family of algorithms utilizes an end-to-end idea to revert to boundary locations and classifications using only a one-time feature extraction. This enables rapid capture and identification of bird targets, as well as real-time monitoring through OpenCV invocation of monitoring devices. The web front-end system is set up using the flask framework, and the results of model detection and recognition are passed to the backend to obtain more training data and optimize the model. The research shows that the yolov4 object detection and recognition algorithm can accurately capture the position of birds in images and videos and identify their behavior. The research of bird behavior and motion recognition is the exploration and prospect of deep learning algorithms in the biological field, which provides a feasible intelligent solution for the modernization of ecological civilization.
Influence of Quantum Computing on IoT Using Modern Algorithms

Even while the Internet of Things (IoT) already affects our day-to-day activities, its future relevance and potential for transformation remain untapped. Security issues with present communications technology must be solved before secure end-to-end connection between services can be achieved. Internet of Things (IoT) is here to stay. In the next years, it will become an integral part of our everyday life. There is a good chance that sensor-based networks can recognize it as a direct service provider in our surroundings. Even if it's just in the form of a value-added service delivered through cellular networks, it's going to be helpful nevertheless. It is, nevertheless, subject to a wide range of security threats. The current level of security is insufficient for IoT applications in the future. A secure cryptosystem is needed for the Internet of Things. quantum-based security has seen a surge of attention recently. Additional quantum key distribution systems and network services are now supported by this solution. As a result of the quantum computing's unrivaled security level, it has become more popular in recent years. Because every measurement must affect the state of the quantum bit being sent, quantum physics dictates that this must be the case. Regardless of whether the sender or recipient is aware of the change, it is clear. It is thus no longer possible to listen passively. Polarized photons may encode a string of bits using protocols like BB84. Secure cryptographic keys may be generated over an unsafe channel utilizing various key distillation procedures.
Improving the Clustering Performance of the K-Means Algorithm for Non-linear Clusters

K-means clustering is known to be the most traditional approach in machine learning. It's been put to a lot of different uses. However, it has difficulty with initialization and performs poorly for non-linear clusters. Several approaches have been offered in the literature to circumvent these restrictions. Kernel K-means (KK-M) is a type of K-means that falls under this group. In this paper, a two-stepped approach is developed to increase the clustering performance of the K-means algorithm. A transformation procedure is applied in the first step where the low-dimensional input space is transferred to a high-dimensional feature space. To this end, the hidden layer of a Radial basis function (RBF) network is used. The typical K-means method is used in the second part of our approach. We offer experimental results comparing the KK-M on simulated data sets to assess the correctness of the suggested approach. The results of the experiments show the efficiency of the proposed method. The clustering accuracy attained is higher than that of the KK-M algorithm. We also applied the proposed clustering algorithm on image segmentation application. A series of segmentation results were given accordingly.
Research on insulator detection algorithm of YOLOv5 fused with attention mechanism

In high-voltage transmission lines, the status of insulators needs to be monitored regularly to prevent fault, and the detection of insulators is more difficult under mist conditions. In this paper, an improved lightweight YOLOV5 algorithm is proposed to achieve defect detection of insulators. First of all, the MSR algorithm is incorporated in the YOLO model to achieve the anti-fogging effect, and in order to better detect the insulator, the CBAM attention module is introduced in the Backbone part of the YOLOv5 model. Experiments show that compared with the original model, the improved YOLOv5s-CBAM detection accuracy has been improved, and the mAP value has reached 98.4%, which can better complete the insulator detection task.
Object Detection System Based on Faster R-CNN

Aiming at the low efficiency of image target detection in cloud computing mode, a target detection system suitable for edge devices is designed. First, the system selects Faster R-CNN in the deep learning algorithm as the target detection and recognition model, and trims the network feature extraction layer through the residual module. Second, a proposal region extraction sub-network with adjustable anchor boxes is used to obtain proposal regions more quickly by setting a convolutional sliding window of reasonable size. Finally, a complete target detection system is built using hardware such as Raspberry Pi development board and Intel neural computing stick. The experimental results on the KITTI dataset show that the system achieves good detection results, and achieves a faster recognition speed without reducing the target detection accuracy, which can meet the real-time requirements of offline work.
ASPP-UNet: A New Semantic Segmentation Algorithm for Thyroid Nodule Ultrasonic Image

Thyroid nodule ultrasound image has serious noise, low contrast between different tissues, blurred boundary and irregular shape in malignant lesions. However, existing segmentation algorithms of thyroid nodule ultrasound image often have problems of coarse edge and inaccurate segmentation of small nodule. In order to segment thyroid nodules from ultrasound images more precisely, this paper proposes a new deep learning algorithm, ASPP- UN et, to achieve precise semantic segmentation of thyroid nodules from ultrasound images. In the final phase encoding ASPP module is introduced, using different void rate of atrous convolution for multi-scale features, the next sampling by using convolution instead of pooling, which can reduce the resolution of characteristic figure, can retain more detail characteristics, and use the binary cross entropy loss and damage Dice coefficient as a comprehensive loss, improve data imbalance. In the experiment, the algorithm of FeN, FusionNet, U-Net and ResUNet was repeated and compared. The experimental results showed that the ASPP-UNet model could segment thyroid nodules more precision, and the fraction of F1 score was increased from 81.7% of the original U-Net model to 90.2%. The precision was improved to 87.9%.
Image processing technology based on computer vision algorithm

Under the background of information age, image processing technology is a commonly used technology in all walks of life. In order to improve the image correction effect, it can be processed based on computer vision algorithm. This paper introduces the image categories and technical characteristics of computer vision algorithm and image processing technology, discusses the image processing technology based on computer vision algorithm, including computer vision display system and image distortion correction two parts, the effect is good, has high application value. In the past, due to the lack of technological development, objects in the traditional two-dimensional environment can only display the side projection, relatively single, in order to adapt to social progress, technology will continue to develop, so three-dimensional three-dimensional picture emerged at the historic moment, a new display technology appeared. In order to obtain the ideal visual effect, technicians need to implement image processing technology on the basis of computer vision algorithm, express the actual coordinates of objects in three-dimensional space with three-dimensional voxels, and correct the distorted image caused by projection. Compared with the traditional BP neural network, the image processing technology based on computer vision algorithm obviously has more advantages and higher accuracy.