Reviewerly/exHarmony huggingface dataset:
A Hybrid CNN Compression Approach for Hardware Acceleration

Convolutional neural networks (CNNs) have achieved remarkable achievements in the fields of artificial intelligence (AI). A large volume of weights hinder the deployment of CNNs on embedded devices. The compression of CNNs can reduce the volume of weights and facilitate the hardware acceleration. In this paper, a hybrid CNN compression approach for hardware acceleration is proposed. The approach is divided into pruning phase and quantization phase. In the first phase of pruning, we use filter pruning for the convolutional (Conv) layers with complicated computing and weight pruning for the fully connected (FC) layers with the large volume of weights. In the second phase of quantization, weights bit-widths are set to 16-bit for the Conv layers and 8-bit for the FC layers. Based on experimental verification, for VGG16, 93.05% of weights are pruned and the model is compressed by 37.88 × with 1.17% accuracy loss. For ResNet18, the model is compressed by 7.27× with 0.47% accuracy loss. Experimental results show that the proposed approach can effectively reduce computational complexity and memory overhead for the implementation of CNN hardware accelerators.
Analysis of Influence of Noise Interference on Frequency Measurement Results of IFM Receiver

With the rapid development of electronic information technology, the electromagnetic environment of modern battlefield is becoming more and more complex. Due to the characteristics of high sensitivity and wide frequency band reception, the IFM receiver can easily detect the interference noise while detecting the target signal. However, due to the poor ability of the IFM receiver to process simultaneous arrival signals, when the receiver receives both the interference noise and the target signal at the same time, the interference noise will definitely have a serious impact on the frequency measurement results. Aiming at this problem, this paper quantitatively analyzes the influence of noise interference on the frequency measurement results of the IFM receiver. The simulation results show that the RMSE of the frequency measurement of the IFM receiver increases with the frequency difference between the interfering noise and the target signal. Moreover, the larger the jamming to signal ratio at the input end of the IFM receiver, the larger the RMSE of the frequency measurement.
Next Generation Access Network Based on Hyperchaotic Spread Spectrum Sequences

The research focus of the next-generation optical access networks is spread-spectrum communication. However, finding a high-performing pseudo-random address code is challenging in spread spectrum communication. The conventional address code has shortcomings such as insufficient numbers and being easily cracked. The development of chaos theory has promoted the research of chaotic sequences as a spread spectrum address code. In this paper, an optimal sequence combining Chebyshev mapping and Henon mapping is proposed and constructed. The initial sensitivity and correlation characteristics of the optimal sequence are therefore evaluated. Finally, the BER of the system is simulated and tested, and it is determined that the optimal sequence is suitable for the optical access network system of the next generation.
Research on Intelligent Sorting System

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through end control. The acquired image is enhanced and edge detected. Aiming at the problem that the deep learning algorithm takes a long time, The traditional image processing algorithm and deep learning target detection algorithm are organically combined to reduce the use delay of image processing in the system. Because the training of deep learning model requires a large number of data sets, but the amount of data in manufacturing factories is small, and data labeling is time-consuming and laborious, YOLO v3 algorithm is used to improve the efficiency of training model under a small number of labeled data sets. In the algorithm of position, the centroid is extracted as the positioning reference coordinate, the kinematics model of the manipulator is established. and sent to STM32 through the serial port. Then the STM32 completes the control command to grasp and place the workpiece and complete the sorting task. The experimental results show that the system meets the design requirements.
Attribute-Based Keyword Searchable Encryption Scheme for Multi-authority in Cloud Storage

Based on the problem of the key escrow problem of one single authority in cloud storage and the low search efficiency when users search the required data, a multi-authority attribute-based keyword searchable encryption scheme was proposed. The proposed method adopted distributed key generation technology and was integrated to realize the threshold generation mechanism of the user's private key, and the right of each authority was weakened. In addition, the attribute-based keyword searchable encryption method based on ciphertext structure provided a fine-grained access control function for encrypted data and, at the same time, used aggregation technology to effectively improve the search efficiency of users. The experimental result shows that the proposed scheme can ensure the privacy of users and access policies and has low computational, which is more suitable for resource-constrained devices in cloud storage.
Identification of Active Jamming Based on Swin Transformer Model and Splitting Features

With the continuous development of Digital Radio Frequency Memory (DRFM) technology, radar working condition is seriously threatened by various activate jamming, echo of true target will be mixed or covered by jamming. In this condition, splitting features extracted by modulating splitting code into the process of pulse compression present greatly difference between true target and jamming, and then this paper proposes a jamming identification method based on splitting feature and Swin Transformer (shifted window Transformer) neural network which can effectively distinguish the typical jamming, achieve classification task, and improve detection performance and recognition accuracy. Finally, the verification result of measured data shows that true target and jamming can be recognized perfectly.
Joint Calibration of Binocular-ToF Camera and Docking Ring Correction

This paper presents the joint calibration of binocular and TOF and a simple application: docking ring correction. In the first part, a new joint calibration algorithm was proposed. Firstly, replace the traditional checkerboard calibration plate with a circular calibration plate. Secondly, the 2D-3D mapping is done via the TOF camera intensity map. The calibration results demonstrate the accuracy of the proposed method. In the second part, we first perform the extraction, reconstruction and correction of the docking ring and a scheme on how to obtain the true value of the normal vector of the docking ring was proposed. We hope that the content of this paper can provide a certain reference value for multi-sensor data fusion and other fields.
A Defense Method Against Persistent Fault Attack

Persistent fault attack is a powerful attack technology to recover key information by using persistent fault and statistical methods. It can be applied to the key recovery realized by block cipher look-up table. Its biggest advantage is that the key information can be recovered only by one failure injection, and persistent fault attack can be applied to the classic block cipher protection implementation such as detection technology and mask technology. Nevertheless, the classic fault attack protection technology still improves the difficulty of persistent fault attack. The detection and infection technologies increase the number of ciphertext required to extract the correct key by a constant multiple, which will hinder the attack in the actual scene. Real time health detection of S-box is an effective means to prevent persistent fault attacks. Once the injected fault of S-box is detected, reset, restart or refuse to continue encryption. However, this defense strategy is not effective against multiple persistent fault attacks without changing the secret key. Persistent fault attack makes full use of the double mapping property of S-box, so health detection based on the double mapping property of S-box is an efficient protection method. Compared with the traditional detection method, the 255 times XOR method proposed in this paper has low cost and high efficiency, and the single byte modification of S-box after inspection can effectively mislead the encrypted data obtained by the attacker, even if this data is extremely useful to attackers. The research results of this paper show that the proposed defense scheme can effectively resist three secret key analysis strategies of persistent fault attack.
Optimal Software Distribution and Allocation Strategies for Improved Datacenter Resource Utilization

In this paper, we propose software distribution and allocation strategies to improve the resource utilization for distributed military command centers. For the software distribution strategy, software are divided into multiple slices and simultaneously transmitted along different paths in parallel, with other command centers as cache nodes for shared and collaborated data transit. For the software allocation strategy, we adopt an adapted integer multi-knapsack model to allocate software groups on different servers to utilize the server resource in a load-balanced way for enhanced system stability. The simulation results demonstrate that our distribution strategy achieves 90% time reduction, and software allocation strategy increases 25% resource utilization.
Multi-person Identification and Localization Algorithm Using RGB-D Image Segmentation

In recent years, with the development of smart phones and intelligent monitoring systems, visual object detection and localization methods have been widely used. Since RGB-D based person identification and localization technology have achieved better performances, an algorithm based on deep learning and image segmentation is proposed in this paper. First, the raw images are preprocessed. Then, image segmentations are performed according to the results of person detecting by YOLOv3 network. For multi-person identification, the Haar cascade classifier is used to determine regions of face on the segmented color images and the Eigen Face method is used for classification. For person localization, the convolutional neural network is preformed regression learning based on the depth images of a single person. Experiments results show that the proposed algorithm performs better than other existing approaches.
An Abnormal Traffic Detection Based on Attention-Guided Bidirectional GRU

With the development of network technology and the diversification of network applications, network environment is becoming increasingly complex which leads to various network attacks. Malicious traffic, such as DDOS and infiltration attacks, may degrade the network quality, or even block the network directly. Now, traditional network security methods are difficult to meet the needs for network security and information security. Due to the high recognition accuracy and strong generalization of deep neural network (DNN), deep learning based abnormal traffic detection methods have become a research hotspot. However, there are obvious problems containing high false positive rate poor robustness. We propose a novel abnormal traffic detection method based on attention-guided bidirectional gated recurrent unit (Att-BiGRU). Different from the traditional feature extraction methods in DNNs, we use stacked sparse Auto-encoder (SSAE) to extract traffic features for interference factors filtering. The extracted features can reproduce the original input samples as much as possible which enhances the representation of traffic features and effectively improves the robustness of anomaly detection traffic. Then, attention mechanism was introduced to optimize the traffic features. Extensive experiments on UNSW-NB15 show that the proposed scheme has the impressive performance. Compared with recent abnormal traffic detection algorithms, the proposed scheme achieves better results with 89.67% accuracy and 4.2% FAR.
NB-Radar: An Effective Method for Detecting Local Network Behaviors

The last decade has witnessed the increasingly rapid advances of high-speed network for both of the infrastructures and applications. Especially the booming of network applications constantly produces various and complicated network behaviors, which calls more complicated measurements with high accuracy and lower latency. In this paper, we propose a novel method, namely, NB-Radar (Network Behavior Radar), to take snapshots for the target network environment as network situation awareness result. The major objective of NB-Radar is to accelerate the measurement and to facilitate the perception, as a result, the capabilities of the network operation and maintenance personnel will be improved. Compared to studies that mostly focus on flow level analysis, NB-Radar simply operates optimized time-frequency analysis at traffic level (with no flow-grouping), making it possible to work on high-speed network environments with lower computational capability. In addition, in order to verify the performance of NB-Radar, we execute a set of validating experiments, and results show that NB-Radar is promising for network behavior perception.
Improved Hierarchical Architecture for Graph Classification

The existing methods of graph classification often do not consider the information of the graph from multiple perspectives, nor do they dynamically fuse the relevant information scores of nodes, which will lead to incomplete graph structure information. Additionally, after sending the coarse graphs obtained from the pooling layer to the readout layer, the graph representations output by the readout layer is sent to the linear layer directly, which will cause the final generated graph representations containing redundant information. In response to the above issues, two improvements are proposed in this paper: (1) Consider the graph structure from multiple perspectives so that the second-order neighbor information is introduced. Thereby the structural relationship among neighbor nodes can be fully used to strengthen the integrity of graph structure. After that, the structure and feature information is dynamically fused. (2) Analyze the importance of each coarse graph obtained from graph pooling via attention and then designate weights according to their importance. Eventually, the more representative graphs are obtained. The results of the experiments on four different datasets reveal that this fashion can achieve better performance in graph classification tasks.
Research on Image Defogging Algorithm for Power Monitoring

With the continuous expansion of power grid scale, smart grid has become the mainstream of power monitoring. Bad weather conditions such as smog will lead to the deterioration of the quality of captured images, which will further restrict the development of smart grid. In this paper, an image defogging algorithm is proposed according to the characteristics of power monitoring images. According to the characteristics of image regions, the image is divided into sky regions and non sky regions. For non sky regions, an improved dark channel prior algorithm is used to solve the problem of poor detail processing. For sky regions, a large number of experiments have observed that there is a linear relationship between the minimum channels of fog free images and fog images. It improves the poor segmentation effect of the conventional algorithm in the sky area, and applies the synthetic projection rate and atmospheric light to the defogging of the power monitoring image. Compared with other defogging methods, its objective indicators are improved, and the overall visual effect of the defogging image is good.
Deepfake Video Detection Based on Convolutional Neural Networks

The increasing use of mobile camera technology and the growth of social media using and sharing have made the generation and publishing of digital videos more suitable than ever before. However, the manipulation and fabrication of videos have decreased in recent years, because of machine learning and computer vision techniques. This study uses the detection method that applies by comparing the areas of the generated face and their surrounding areas with the Convolutional Neural Network (CNN) Model. The model was applied to the DFDC dataset with different 60 clips for real and fake. The methodology of this work passes through three stages, the first stage pass through preprocessing to convert each video into frames and detect the face in each frame and then be cropped by Haar Cascade function; in the feature's extraction stage the ResNet-50 is applied as the feature extraction model. In the last stage, the CNN classifier for detecting whether the image is fake or real. From the experiment, The Deepfake detection method detects the fake face in the video, where the detection accuracy was achieved at 98%.
Privacy Preserving Electronic Scoring Scheme based on CKKS

Electronic voting (e-voting) is more economical and efficient than traditional voting, however, it also faces the challenges of privacy and security. Existing e-voting schemes take advantage of homomorphic encryption to solve the privacy and security issues for {0,1} type e-voting applications. Unfortunately, existing schemes cannot meet the requirements in (real number) score-type e-voting applications, e.g., in competitive competitions or satisfaction surveys. This paper focuses on solving the privacy and security issues for score-type e-voting applications based on cryptographic technology. In order to solve such issues, we propose an electronic scoring scheme based on the CKKS(Cheon-Kim-Kim-Song) homomorphic encryption algorithm. It supports floating-point computing for exploiting homomorphic encryption in scoring and evaluating procedures. In addition, the security analysis demonstrates that the proposal achieves data privacy and security protection. Furthermore, we design an electronic scoring experiment based on the CKKS scheme in the library of SEAL. The experimental results indicate that it only takes 0.42 seconds to complete the scoring evaluation for 5000 people. Finally, the comparison results show that the proposal is more efficient, flexible, and practical.
Literature Survey: Biometric Cryptosystems Based on Fingerprint Processing Techniques

A significant portion of today access controls rely on biometrics for authentication. Biometrics, for instance, is utilized on commonplace devices like cellphones and computers as a means of authentication. Biometric cryptosystems are a relatively new form of technology that utilizes a user's unique biometric characteristics to either create or access a cryptographic key. In other words, biometric cryptosystems offer means for releasing keys in response to biometric authentication. This paper presents a comprehensive overview of biometric cryptosystems that covering different approaches like fuzzy commitment, fuzzy vault, and convolutional neural networks CNN. This research depicts a literal survey which is discussing fingerprint based biometric cryptosystems issue. Biometric cryptosystems are also discussed briefly as well as cancellable biometrics. The research discusses cryptographic key binding at length, including the dissimilar approaches associated to key binding and how cryptographic keys are generated. The research also make a comparison of performance metrics to evaluate the biometric systems and to explain the relevant terms such as FAR, FRR and EER.
Multi-layer Security for Color Image Based on Five-dimension Chaotic System and Image Steganography Algorithm

The most well-known data hiding technique is steganography and the potential for exploiting them in security computing Government and corporate communications. This technology allows for a variety of digital covers to be used without attracting attention to the fact that information is included in the digital material. Key space and security are two fundamental aspects of steganography, in general, it has a direct impact on the proposed method. A chaotic system is utilized in many steganography techniques as an extra layer of protection and to expand the key space in the proposed system. The most essential reason for employing chaotic systems in the coding process is their control parameters and sensitivity to primary conditions, which is one of the most significant aspects of these systems. A new hiding strategy based on a new five-dimensional hyperchaotic system has been proposed in this research with the key space 2651. This system had two stages the first stage is a scramble process, in which a random sequence has been generated from the system's equations to scramble the secret image by the new location, and the second is the XOR process between the second sequence generated from the proposed system and the scrambled secret image from the previous stage and obtain the encrypted secret image, after that the encrypted secret image is then hidden using discrete wavelet transform(DWT) and Discrete cosine transform(DCT) techniques. The experimental results have been demonstrated that the proposed system's performance is efficient in terms of security, robustness, and resistance to statistical analysis attacks. So, The PSNR of embedding phase reaching up to (56.958) dB, MSE is near to (0), and SSIM is near to (1). On the other hand, secret image is recovered with PSNR reaching $(\infty)$, MSE being (0), and SSIM being (1).
Effect of Several Parameters on Deep Multi-Layer CNN Model for Face Recognition

Face recognition is the challenge of recognizing and trying to verify people in these systems, and it has been one of the challenges in computer vision for decades until recently due to the numerous variables that influence the accuracy and detection speed, such as the number of people in sense, the quantity Ff cameras linked to the control system, varying light, if obstructed with accessories, facial hair, clarity of the face and its angle from the camera's point of view, and some other aspects. Deep learning methods can utilize very large datasets of faces to learn rich and compact characterizations of faces, permitting modern models to implement as well as, and eventually outperform, human face recognition capabilities. In this paper, we propose a surveillance system-based deep neural network architecture for developing an end-to-end facial recognition system that includes collecting real-time data (human faces), preprocessing, model training, and hyperparameter optimization. The model is intended to perform three functions: recognize the identity of people in sense, improve the accuracy of recognize detection, and increase speed. Different training parameters have been investigated, including kernel size, activation function, and optimizer type. The result is that the best kernel size is ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times 3$</tex> ), the best activation function is “SoftMax”, and the best optimization function is “Adamax”. By using these parameters, the model achieves 98.91% accuracy, 99.10% precision, 98.91% recall and 98.91% f1-score when using the Extended Yale B database and 100% for all metrics when used for the proposed dataset.