Reviewerly/exHarmony huggingface dataset:
Visual Analysis of Computer Security Data Using Artificial Neural Networks

Artificial neural networks are used in various fields of human activity, including information and computer security, for example, in biometric systems such as face and fingerprint recognition. However, in the field of visual analysis of security incident data, it is assumed that the visualization is intended for the human operator since the person makes the final decision. This paper investigates whether computer vision systems can perform the tasks of a human operator during visual data analysis. To study the question posed, the paper proposes a model architecture for classifying visualizations of traffic with anomalies and normal traffic. Experiments were also carried out using a neural network and involving subjects. Experiments show that a trained neural network can recognize anomalies in images with visualization of security events and, in some cases, even better than a human.
Research on Multi-scale Feature Fusion Method for Target Detection Based on IN-FPN

In order to alleviate the problem of insufficient multi-scale target feature information in object detection, inspired by BiFPN network, an inverted N-type FPN structure (IN-FPN) is proposed in the Neck part of the network model, which optimizes the multi-scale feature fusion structure of the network, has a hierarchical structure with lateral connections, and fuses features twice from top to bottom and once from bottom to top. The multi-scale features of the object can be fully integrated and mutually promoted. At the same time, considering the different contributions of different scales in feature fusion, an adaptive learning weight $\omega_{i}$ is added to each scale. In addition, in order to solve the problem of network degradation and further improve the network performance, the path containing Block module is added with reference to the residual network structure. Experimental results show that the AP values of the proposed method reach 53.02% and 25.21% on COCO2017 and VisDrone2019 datasets, respectively, which are better than those of the benchmark model, and verify the effectiveness of the proposed method.
VulSAT: Source Code Vulnerability Detection Scheme Based on SAT Structure

The number of software vulnerabilities have increased rapidly, and their forms have shown the characteristics of complexity and diversity, which has brought severe challenges to software systems. Deep learning can automatically learn the features of code and is widely used in source code vulnerability detection. Some studies treat codes as text sequences, ignoring the structural relationship between lines of code. Other studies extract the code as a graph structure, but ignore the order relationship of code lines and do not make full use of the dependencies between code lines. This paper proposes a C/C++ source code vulnerability detection scheme based on SAT (Structure-Aware Transformer) structure. It first standardizes the source code, then extracts the PDG (program dependency graph) representation of source code, next convert each node into a vector representation using sentence embedding, which retains the dependencies between nodes. Finally input it into the SAT architecture model for training, and judge whether the input program contains vulnerabilities according to the output results. We compared VulSAT with a variety of vulnerability detection methods (i.e., Devign, SySeVR, VulCNN, Checkmarx, VulDeePecker), and selected C/C++ functions in the SARD vulnerability dataset as experimental objects. The performance of accuracy rate and false positive rate has been improved to a certain extent.
A Two-Stage Adaptive Algorithm for Tracking Multi-mode Maneuvering Targets

This paper proposes an adaptive algorithm for tracking multi-mode maneuvering targets using bearing and frequency measurements in two stages: tracking establishment and tracking maintenance. The algorithm uses a three-model structure with ceiling, flooring and adaptive models to handle different levels of target maneuvers. The posterior probability of each model is calculated after one update and the adaptive model can adjust system noise coefficient according to it. The final estimation is a data fusion of the results from the three models. By testing on a simulated scenario where the target performs three consecutive turns with increasing curvature, it is proved that the algorithm can effectively track the target under various situations.
Broad Learning System Based on Triangular Fuzzy Membership Function

Broad learning system (BLS) has become a popular neural network model due to its fast learning speed and excellent performance in a wide range of applications in recent years. However, BLS attributes the same importance to all samples, which results in a degraded accuracy for imbalanced data classification problems. To this end, in this paper, we propose a broad learning system based on triangular fuzzy membership function(TBLS). Specifically, we use the triangular fuzzy membership function to calculate the membership degree of each sample, and the membership matrix is added to the BLS to calculate the weights by pseudo-inverse, which can redistribute the contribution of samples to different classes and thus improve the classification accuracy. Numerical simulations on several benchmark datasets show that TBLS proposed in this paper greatly outperforms the original BLS and several other counterparts in imbalanced binary classification problems.
Multi-Branch Interaction Network for Low-light Image Enhancement

True color information and texture details are challenging to capture while taking pictures in low light. Taking that into consideration, we propose a multi-branch interaction network (MBIN). Specifically, our method combines the theory of CNN and transformer to obtain abundant color information with two branches respectively. Additionally, a channel attention module is designed to obtain more important details and address the issue of gradient fragmentation and gradient dispersion. Ultimately, the final output images are generated after a fusion module which takes the results of the mentioned two branches into consideration. Compared to other state-of-the-art methods, extensive experiments show the result that MBIN performs better than them when it comes to obtain color information and restore the real-world images.
Text Detection and Recognition from Piping and Instrumentation Diagrams

Piping and Instrumentation Diagrams (P&IDs) are essential engineering documents used to design and operate industrial processes. They consist of graphical symbols and text annotations that describe the equipment, piping, and control systems within a process. Text recognition and detection in P&IDs is an important task as it enables automatic information extraction and analysis, which can help improve the efficiency of industrial processes. In this paper, we present a novel approach of text detection and recognition in P&IDs using deep learning techniques. Our proposed method consists of two stages: text detection using the deep learning-based algorithm EAST and text recognition using EasyOCR. We evaluate our approach on a P&IDs dataset. Experimental results demonstrate the performance of our approach in detecting and recognizing text from P&IDs.
An Object Recognition Procedure Based on the Image with the Specified Color Features in the Aquatic Environment

The article considers an approach to recognize an underwater object from its image obtained with the help of a technical vision system. The color of the object is used as a feature to distinguish the object in the image. The detection problem is solved for an AUV, which follows a trajectory within a monitoring mission to detect and classify an object with known characteristics. Based on the proposed method, a program has been developed to provide the AUV with an image of the detected object and its characteristics. The method is based on the analysis after transcoding from RGB to HSV. According to the experimental data, the brightness interval of the image, which is considered to be not corrupted and in which it is possible to detect objects, is determined.
Cooperative Ultrasonic Odometry of Autonomous Mobile Robots for Grouping Navigation

This paper considers a method for local positioning of a group of mobile robots equipped with ultrasonic beacons. The described alternate movement algorithm allows a group of four wheeled robots to move on a flat surface with centimeter accuracy. While one robot is moving, the others act as fiducial beacons for it. The positioning problem is solved based on trilateration and numerical optimization. The overall trajectory is obtained by integrating the steps of each mobile robot. The leader communicates with all participants, receives the necessary data from them and organizes the grouping movement. The proposed approach can be used for autonomous cooperative navigation in the absence of other positioning systems.
AutoExploit: Automatic Exploit Generation with Security Mitigation

The rapid growth of program complexity and the development of security mitigation for program have brought serious challenges to program security research. It is more difficult to evaluate program security efficiently by analyzing vulnerabilities manually. Therefore, an automatic method of detecting vulnerabilities and generating exploit is in critical demand. This paper proposes AutoExploit, which is an automatic scheme to detect various vulnerabilities and generate exploit in binary programs after bypassing the security mitigation. The method detects potential vulnerabilities including format string vulnerability and buffer overflow vulnerability using symbolic execution and dynamic analysis at the dangerous functions. For the potential buffer overflow vulnerability, this method generates exploit automatically by constructing constraints expressions and solving constraints after bypassing the security mitigation through code reuse. The effectiveness of our method is demonstrated using the datasets from CTF competition.
Flexible Autoencoder for Orthogonal Chirp Division Multiplexing Communications

This paper introduces a flexible autoencoder (AE) designed for orthogonal chirp division multiplexing (OCDM). The AE is based on a deep neural network (DNN) and a flexible structure of OCDM, which can accommodate various structures for, such as orthogonal frequency division multiplexing, chirp spread spectrum, affine frequency division multiplexing etc. The AE is designed by jointly training a mapper and a demapper made up of a DNN in the flexible structure. Comparing with traditional M-ary quadrature amplitude modulation (MQAM) systems, the trained AE shows different shapes of constellation for additive white Gaussian noise channels and outperforms the traditional MQAMs in bit error rates.
Region-Aware Multiscale Aggregation Networks for Crowd Counting

Scene complexity and scale variation have long been recognized as common problems in crowd counting. Although deep learning has a good performance in crowd counting, it is still challenging to accurately perform the crowd counting task in scenes with large-scale variations or sheer complexity, and a few objects are often mistaken for people. To address these difficulties, we propose a Region-Aware Multiscale Aggregation Network (RMANet) for crowd counting. On the whole, we design a Fine-Grained Module (FGM). Specifically, FGM consists of an adaptive scale pyramid module (ASPM) and a weight recalibration fusion module (WRFM). ASPM is used to extract contextual features within different regions. WRFM adopts region-aware attention (RAA) to further calibrate the feature weights for different regions. Furthermore, the Multi-scale Aggregation Regression Head Module (MARHM) is introduced to make full use of multi-scale and contextual features extracted from images to generate high-quality crowd density maps and perform accurate count estimation. We implemented extensive experiments on three different challenging datasets, i.e., Shanghai Tech, UCF_CC_50, and UCF-QNRF, and the results demonstrate the effectiveness of the method.
A Two-Step Algorithm for Denoising Peach Tree Leaf Images

The study aims to compare the effectiveness of different denoising algorithms and filters for reducing noise in real digital images with mixed Gaussian and Poisson noise, specifically in images of peach tree leaves,and propose the solution. The general methods used in the study include a comprehensive analysis of eleven diverse noise models and twenty-three distinct denoising filters, the proposal of a novel metric function based on MSE, PSNR, and SSIM to evaluate their performance, and the proposal of a two-step procedure for reducing noise in real images using the optimal filters: the bilateral filter and the inverse harmonic mean filter. The results demonstrate the effectiveness and viability of the proposed approach, with the two-step denoising procedure achieving a denoising outcome of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f=0.9449$</tex> for the artificial image and successfully reducing noise in the real images of peach tree leaves while preserving the details and structure.
Low-Light Image Enhancement Using QRCP Decomposition in HSV Space

The low-light image enhancement is a challenging task in the field of image processing. Based on the fact that the light of the image in hue, saturation and value (HSV) color space is mainly focused on its channel value (V), and energy coefficients of orthogonal triangular with column pivoting (QRCP) can describe light of the image. In this paper, we proposed to enhance the channel V in the HSV space of low-light images with the QRCP matrix decomposition algorithm, and the contrast limited adaptive histogram equalization (CLAHE) is performed on the exponential version of the processed channel V, which is termed as HSVQRCP-CLAHE. First, we convert the image from the red, green and blue (RGB) color space to the HSV color space and the logarithmic version of channel V is decomposed by QRCP. Then, the QRCP energy coefficients of the logarithmic version of channel V are normalized to have unit L2-norm. Finally, CLAHE is performed on the exponential version of the processed channel V. The channel H, channel S and processed channel V are used to construct the enhanced image by HSV to RGB transformation. The experimental results on ExDark, BDD 100K and SICE databases show the proposed method not only significantly improves the visibility and contrast, but also outperforms other methods.
Speaker Gender Classification and Error Analysis Using Deep Learning and YOHO Corpus

Nowadays, digital speech processing has become a required field due to the evaluation of communication technology. Speech signals for any speaker hold meaningful information such as identity, gender, and age. Research into the gender and age of speakers has shown remarkable results in using deep learning architectures to capture the time and frequency pattern of the speech signal. Deep learning confers researchers the motivation to investigate speaker classification methods more deeply. In this work, we develop a speaker gender classification system using Bidirectional Long Short-Term Memory (BLSTM). The developed system is based on the prosodic features of the speech signal, such as speech fundamental frequency and log energy of the signal and their derivations. The considered prosodic features of the system give a novel method in the gender classification system. The system adapts using the public YOHO speech dataset and gives an overall accuracy of 98.59%, with 99.39% and 95.96% average accuracy for males and females, respectively. Also, human being perceptual classification is considered for critically missed speakers. This is helpful in order to uncover reasons for high errors for the purpose of increasing the robustness of the system regarding features selection and system tune up.
SmartWasteCloud: An Intelligent Waste Management System Based on IoT and Neural Networks

Municipal waste is a major environmental problem. It is well known that recycling can reduce the amount of general municipal waste. A major challenge is to increase the amount of recycled material in the waste. Computer technology allows us to operate a complex reward system linked to an embedded system for waste collection. An embedded system controlled by artificial intelligence helps to support a decision about the type of waste. This paper presents the design methodology, architecture, and initial results of the intelligent waste management application that uses a neural network to detect the type of waste. To achieve the best possible results with a neural network on collected data, we conducted experiments on ResNet and VGG neural network architectures of different depths using the transfer learning technique. The results show that the depth and complexity of the architecture were not critical for the accuracy of the neural network on our dataset. The best resulting neural network was used as the basis for waste-type decision-making. With this system, we expect a drastic reduction in incorrect waste disposal.
Radar Intra-Pulse Signal Modulation Classification Based on Omni-Dimensional Dynamic Convolution

In this work, the omni-dimensional dynamic convolution (ODConv) layer based network (OD-CNN) with focal loss function is applied to the radar intra-pulse signal modulation classification, which greatly improves the classification accuracy. Compared to the convolution layer, the ODConv layer employs a novel multi-dimensional attention mechanism to learn four types of attentions along four dimensions of the kernel space in a parallel manner, which further improves the feature mining ability of the model. In order to illustrate the superior modulation classification ability of the proposed model, it is compared with the other three CNN based models. Simulation results demonstrate that the proposed model not only has the highest classification accuracy, but also uses the least number of training parameters and floating point operations (FLOPs).
Human Action Recognition Based on Deep Neural Networks

To better handle human action data of text type, an accurate and fast human action recognition scheme is proposed in this paper. The scheme fine-tunes two classical deep neural network (DNN) models (ResNet-50, EfficientNet-B3) to recognize different actions by processing text-type data of 19 human actions. The method’s feasibility is demonstrated by several sets of experimental data showing that the highest 98.98% Top-l ACC is achieved on the noise-free validation set. The dataset containing Gaussian noise achieved 97.81% and 97.50% Top-l ACC on the validation and test sets, respectively, proving the excellent generalization performance of the method. Applying dropout techniques with different ratios to the fully connected layer of the model, the method is proved to have an excellent ability to prevent overfitting through diverse experiments introduced on the Gaussian noise validation set and test set.
Multi-Scale Enhancement and Sharpening Method for Visible Light Images in Underground Coal Mines

The dusty and fog may cause inefficient production and safety hazard for staffs on the underground of coal mine. According to this characteristic, the visible image sharpening method is studied in our work. In fact, some known methods cannot fit on the underground of coal mine environment. Moreover, model complexity needs to be reduced to help real-time processing. Hereby, we provide a sharpening method based on multi-scale enhancement in this paper. The end-to-end Gated Context Aggregation Network (GCANet) is the baseline of the proposed one. Moreover, the multi-scale feature enhancement strategy is the key point to capture and fuse different level information. To reduce overall computational complexity of the model, a lightweight octave convolution is used to replace the classical convolution operation. In experiment part, the public dataset, i.e., RESIDE, and the measured underground coal mine dataset, i.e., Mine-dehaze, are sharpened by the proposed method. In addition, the sharpening result is compared with ones by some known methods, which can reveal the superiority of the proposed one.
Facial Expression Recognition Based on the Combination of Landmarks and Texture Features

Facial expression recognition plays an important role in understanding people’s emotion and social interaction. It is meaningful to design method to recognize facial expression automatically. It can be applied to many situations, such as emotional state recognition, monitoring the status of students in class, monitoring the status of drivers, online recommendation system and so on. This paper proposes a facial expression recognition method based on the combination of landmarks and local texture patterns. First, find out the Landmarks. Next, the facial components are extracted from the facial image. The local texture feature is extracted from facial components. In order to combine these features for facial expression recognition, we have carried out data normalization, redundancy removal, and dimension reduction. The experimental results on the JAFFE dataset and CK+dataset show that the proposed facial expression recognition method based on the combination of landmarks and the local texture patterns is effective.
Vehicle Re-Identification for Intelligent Transport System Using DOLGNet Attention Model

Vehicle re-identification is an important task that involves identifying a specific vehicle from multiple images in computer vision. In recent years, deep learning-based approaches have shown promising results in this field. However, due to the significant variations in vehicle appearances caused by different viewpoints, occlusions, and lighting conditions, it remains a challenging problem. This paper proposed a Deep Orthogonal Local and Global Network (DOLGNet-Attention) for vehicle re-identification, which uses ResNet as the backbone and integrates self-attention and dilated convolution layers to effectively capture local and global features. Our approach combines local and global features by incorporating self-attention and dilated convolution layers into the network architecture. The self-attention mechanism focuses on relevant components within the local feature maps. At the same time, the dilated convolution layers capture information from multiple scales, which enhances the network's ability to handle variations in vehicle appearance. The results show that DOLGNet-Attention achieves top performance in terms of accuracy and robustness. Testing and training accuracy are done to determine the accuracy of the model. It determines image matches and non-match images compared to query images. Similarity values greater than 90% indicate that the image is similar to the query and the vehicle is re-identified. The DOLGNet-Attention model represents a significant advancement in vehicle re-identification, with the potential to improve the accuracy and efficiency of applications such as traffic management, surveillance, and security.