Reviewerly/exHarmony huggingface dataset:
An Image Segmentation Method Based on Transformer and Multi-Scale Feature Fusion for UAV Marine Environment Monitoring

UAVs are widely used in marine environment monitoring and ecological protection, and accurate segmentation of UAV images is the basis for achieving these applications. However, the marine images taken by UAVs often have the problems of large intra-class scale changes and high inter-class similarity, resulting in a sharp decline in the performance of traditional semantic segmentation methods in this segmentation task. In order to alleviate this dilemma, we construct a new semantic segmentation network (EMFNet) combining transformer and multi-scale feature fusion to achieve accurate segmentation of UAV ocean images. For larger intra-class variations, we introduce transformer blocks made up of external attention (EA) to capture potential correlations between data samples. For the high similarity between different semantic categories, we specially construct feature fusion branches to learn diverse feature information. Inspired by camouflage object detection, in the fusion branch, we design a mixed convolutional attention (MCA) module to increase the receptive field and a dual attention fusion module (DAFM) to realize cross-level learnin. At the same time, we apply the auxiliary segmentation head to the fused features to refine the segmentation results layer by layer and discard all auxiliary segmentation headers in the inference stage to maintain real-time inference. Experimental results show that our designed EMFNet achieves 77.13% MIoU on the public dataset Cityscapes and 61.41% MIoU on our UAV-OUC-SEG dataset, respectively, and maintains a real-time inference speed of 61.6FPS.
A Hybrid Approach for Customized Skull-Stripping MR Brain Images

In high resolution MRI brain images, skullstripping procedures are typically considered to be crucial for removing non-brain tissues that provide a significant obstacle for computerized brain image segmentation and analysis methods using convolutional neural networks. Still, existing applications disregard image freedom and their rigidity makes it difficult to adapt to outside needs. In this study we propose a hybrid method which allows users to interact with the image through Matlab graphical user interface (GUI) to generate customized skullstripping (CSS) images. This application is valid for T1-weighted and T2-weighted MRI brain images.
ASAHmap: An Adaptive Chinese Handwritten Character Segmentation Algorithm for Large-Scale Ancient Handwritten Document Based on Histogram Projection and Gaussian Kernel Convolution Map

This paper presents a method for recognizing hand-written Chinese characters on ancient documents using optical character recognition (OCR). The method employs a segmentation algorithm based on histogram projection and Gaussian kernel convolution map, which accurately divides images of characters into sub-images of a single character. The algorithm was tested on a dataset of over one million Chinese handwritten characters and achieved a segmentation accuracy of over 97.75% in the best-case scenario for four categories of ancient documents. The proposed method provides a lightweight preprocessing technique for subsequent work aimed at recognizing ancient Chinese handwritten character documents using a neural network.
Panoptic Segmentation for Seasonal River Scene Based on Spatial Context Prior and DenseASPP

With the continuous development of intelligence, people's desire for the implementation of the monitoring and early warning function of drowning is becoming increasingly urgent for high-risk places such as rivers and other safety accidents. Panoptic segmentation, a fundamental technique in the field of computer vision, combines the functions of foreground detection and background semantic segmentation, allowing it to thoroughly assess the river scene and then match the algorithm requirements of intelligent early warning. However, the seasonality of the river scene, the diverse positions of foreground instances, and the haphazard distribution of background information will cause issues such as substantial changes in scene characteristics, multi-scale instance targets, and unclear boundary segmentation. To address these issues, we develop the spatial context prior module (SCPM), which increases the robustness of feature representation by emphasizing the spatial similarities and differences between similar and dissimilar pixels. In addition, a densely connected atrous spatial pyramid pooling (DenseASPP) is used to achieve multi-scale feature extraction. In the training stage, an edge feature fusion module (EFM) is proposed to fuse low-level edge features with high-level semantic information to make up for the lost edge information. Furthermore, we comply the seasonal river scene panoptic segmentation dataset (OUC-SRS-SEG), and test the proposed approaches on it. The results of experiments demonstrate the effectiveness of constructing the optimization methods. Our algorithm's PQ value is 3.28% and 3.67% greater than that of Panoptic-DeepLab and Panoptic SegFormer, respectively.
Distributed Multi-Expert Forecasting System for Non-Stationary Processes

This paper explores the challenge of predicting complex dynamic systems that exhibit oscillatory non-periodic processes and non-stationary random components. Such systems are prevalent in gas-dynamic and thermodynamic media, as well as in economics and social psychology. The paper proposes a distributed two-level computational scheme with independent intellectual agents based on diverse predictive algorithms and a Bayesian supervisor to create a final forecast. The implementation of a distributed system with a Bayesian supervisor can improve the forecast's overall quality by approximately 8.5% compared to a linear extrapolator and 15.7% compared to a quadratic extrapolator. Results show the potential of this system, but the proposed model has limitations and requires the utilization of more advanced predictor agents and interagent communication technologies.
CNXResNet: A Light-weight Backbone based on PP-YOLOE for Drone-captured Scenarios

Aerial target recognition has important applications in traffic monitoring and forest fire, etc. which have been significantly developed with deep convolutional neural networks. However, aerial target recognition is currently facing several challenges, among which the method for small target recognition is much behind practical demands, even though many models such as feature pyramid network (FPN) and U-Net have been proposed for this purpose. YOLO and its variant versions are the most successful models to detect and recognize small- and largescale targets. PP-YOLOE is one excellent model among YOLO versions, which combines FPN and efficient squeeze-excitation (ESE) to solve large-scale object detection and recognition. However, these existing methods have not demonstrated their good performance in small object detection and recognition. In this paper, We propose a new backbone network called CNXResNet using the backbone architecture of PP-YOLOE with ConvNext V2 Block as the core module and further introduce the style translation idea into the PP-YOLOE model to improve the detection and recognition performance of the small objects. Our experiments on the VisDrone2019 dataset show that the improved model performs better but has fewer parameters. At an input scale of $640 \times 640$, the improved model improves 2.9% mAP@.5 over PP-YOLOE-l, on the small target detection, the mAP is improved by 3.1%.
Image Super-Resolution Algorithm Based UAV Forensics Method

The continuous development of modern intelligent drone technology has brought a lot of convenience to our life and work. Drones have the advantages of small size and sensitive operation, which can help people to complete many difficult or dangerous work, especially in police-related work shows strong advantages, can meet the evolving needs of investigation and evidence collection, such as aerial investigation, search and rescue and on-site forensics. At the scene of the crime, police officers can operate drones to take aerial images of the scene, these images are important data information for on-site analysis and investigation and forensics. In practice, due to the impact of hardware and environmental factors, the images obtained by drones sometimes appear distorted, blurred and low resolution, making police officers face practical difficulties in further investigation and forensics work. In order to effectively solve the image problem in UAV forensics, we describe an automatic image processing framework based on image resolution reconstruction to help police officers carry out UAV image investigation and forensics work.
Spammy Names Detection in Pashto Language to Prevent Fake Accounts Creation on Social Media

With the widespread adoption of online social networks (OSNs), the proliferation of fake accounts has become a significant concern. For malicious users, fake accounts are their preferred means to commit fraud, send spam, and abuse the system. Detecting fake profiles after account registration can also be valuable, while unlawful activities can be carried out very quickly, and it would be more beneficial to identify fakers at the early stage before the first abuse occurs. Additionally, some social media platforms are based on the assumption that users’ online information reflects their true identity. On such platforms, if a user fills his name field with random strings violating their terms of service. This paper presents a technique for detecting spammy names associated with fake profiles before any additional user information or history is available. The proposed approach involves analyzing the name field for patterns commonly found in fake accounts. To achieve this, we have developed a supervised machine learning model to discriminate between valid and spammy names. The model is trained on a labeled dataset of 100K instances (words and phrases), manually labeled for two categories, “valid names” and “spammy strings”. For classification, we examined several Machine Learning algorithms, including Naïve Bayes (NB), k-Nearest Neighbor (KNN), SVM, Linear Regression, and Decision Trees. Experimental results show that the Naïve Bayes algorithm performs the best and yields an F1-score of 94.1% with an accuracy of 95.3%.
Person Re-Identification Based on Multi-Spectrum Information Aggregation Transformers

Convolutional Neural Networks (CNNs) have achieved remarkable success in various image-related tasks. However, CNNs' chunking approach of sliding windows over pixels hinders its ability to learn sequential semantic information. To address this issue, researchers have proposed models that integrate Transformers to aggregate features across different resolutions, taking advantage of the Transformer's strengths. Hierarchical Aggregation Transformer (HAT) is one such model that integrates low-level and high-level details, showing a great effect on person re-identification. Based on HAT, we propose the Multi-Spectrum Information Aggregation Transformers (MIAT), which enhances the low-level structural information captured by CNNs through a frequency channel attention module that extracts multi-spectral features. By leveraging discrete cosine transform (DCT), more detailed features can be learned to improve person re-identification, enabling better discernment of distinguishing features between different individuals. Through extensive experimentation, we validate our model's performance on two widely employed person re-identification datasets and demonstrate its superiority over existing advanced methods.
Attention-Based Fusion Factor to FPN for Small Object Detection

FPN-based detectors have made notable progress in general object detection, but the performance and efficiency of detecting small objects are far from satisfactory. Even through FPN can fuse semantic information from deep layers to shallow layers, it also brings noise containing spatial information of large objects to shallow layers. To address this challenge, we propose an attention-based fusion factor (AFF) consisting of two components, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\alpha}-\mathbf{CBAM}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\alpha}-\mathbf{Query}$</tex> , to enhance the semantic information of small objects in the fusion process. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\alpha}-\mathbf{CBAM}$</tex> can be used to learn the vertical fusion factor of the FPN to control the information passed from deep to shallow layers. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\alpha}-\mathbf{Query}$</tex> acts as an fusion factor to control the horizontal transfer impact of FPN. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\alpha}-\mathbf{Query}$</tex> first predicts the coarse locations of small objects in low-resolution feature maps and then increases their weights in high-resolution feature maps. Ablation studies on the publicly available dataset PASCAL VOC are conducted to demonstrate the effectiveness of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\alpha}-\mathbf{CBAM}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\alpha}-\mathbf{Query}$</tex> . Moreover, experimental results on the datasets MS COCO and VisDrone show that the baseline model with additional AFF evidently outperforms itself in almost all the related metrics.
Multi-vehicle Speed Measurement Based on Monitoring Video Using Improved Multi-object Tracking Algorithm

Due to the increasing demand for multi-vehicle speed measurement on urban roads, as well as the cost and environmental constraints of using laser and ultrasonic speed measurement methods, we propose an accurate multi-vehicle speed measurement method based on urban viaduct monitoring video without additional equipment and labels. This paper first adopts an improved multi-object tracking (MOT) algorithm to achieve high-precision tracking of multi-vehicles in urban viaduct monitoring videos. Then, the mapping of the image space to the actual space is realized by using the novel space transformation method designed independently to complete the measurement of the actual vehicle speed. At the same time, the Kalman Filter and Polynomial Fitting are employed to eliminate the errors in the speed measurement process and further improve the measurement accuracy. Finally, the effectiveness of the method was demonstrated by its application to an urban viaduct in Shanghai, China. The velocity measurement error in straight and curved scenes can be controlled within 0.5km/h, and 1km/h, respectively. The final velocity curve can accurately reflect changes in vehicle speed.
A quantum feature selection framework via ground state preparation

Abstract Traditional feature selection methods face the challenges of increasing time complexity and local optima. In previous works, many classical feature selection methods were accelerated through quantum algorithms. However, these approaches still inherit the constraints of these classical methods as they do not address the issue of local minima. Here, we propose a novel quantum feature selection framework based on the classifier’s result, which utilizes Hamiltonian encoding and a ground state preparation algorithm. Numerical experiments are conducted on real-world datasets from the finance and medicine domains. Moreover, the results demonstrate that the proposed method produces the same or better classification accuracy on the classifier than the original data without feature selection. Overall, our approach presents a promising solution to feature selection using quantum computing.
Improving the Reliability of Data Transmission Embedded in Images over a Noisy Communication Channel

A method for improving reliability of data transmission embedded in images through the noise channel is proposed. The steganography method is based on inverting part of the bits of the embedded image according to a certain rule and adding them by mask to the lower bits of the container image. In order to preserve the integrity of the transmitted data at low signal-to-noise ratios, we propose using two algorithms: two-dimensional nonlinear and median filtering. The nonlinear filtering algorithm effectively restores images distorted by white Gaussian noise due to their statistical properties. The median filtering algorithm improves the visual quality of the original and embedded images by eliminating artifacts like impulse noise. The proposed method of embedding data in the container image ensures the invisibility of the hidden image. The quality indicators of the restored images are PSNR=57.4 dB, SSIM=0.999 with an embedding volume of 0.25 bits/pixel. The method restores the original and embedded data for signal–to-noise ratios up to - 6dB.
Online Geometry Feature Tracker by Adaptive Cascade

The paradigm of tracking-by-detection is one of the most popular approaches in multi-object tracking. However, it can be observed that DeepSORT, the prevailing multi-object tracker, still has some deficiencies in practical applications. The matching cascade method used in DeepSORT not only fails to effectively utilize the temporal differences among tracklets but also overlooks their spatial correlations, which can easily lead to ID switches and erroneous associations among multiple objects. Additionally, methods that rely on visual cues often suffer from slow running speeds. To tackle the mentioned problems, our paper demonstrates a new tracker based on a simple motion feature DIoU to alleviate the ID switches problem caused by occlusions. We introduce an Adaptive Cascade module comprising Pseudo-NMS and Gapping Track Age, which considers the spatial correlations and temporal differences among tracklets, thereby reducing the difficulty of retrieving occluded targets. Even with the simple DIoU feature, our approach can successfully retrieve targets from occluded objects. Experiments demonstrate that our tracker effectively improves the model performance and reduces ID switches. Our tracker achieves superior tracking accuracy and speed balance compared to DeepSORT, achieving 77.3% MOTA and 81.3% IDFI on the MOT2017 validation set with 130 FPS running speed.
Deep Learning-Based Scene Text Image Super-Resolution Methods: A Survey

Text recognition techniques have yielded positive results in identifying clear text images. However, recognition of low-resolution or fuzzy text may lead to a significant degradation of performance. The main challenge in recognizing fuzzy text lies in the absence of detailed information pertaining to the text. Unfortunately, low-resolution text images are often encountered in natural scenes, such as distant images captured by mobile phones, pictures taken under severe lighting, or in the presence of rain or fog. With the recent developments in deep learning, an increasing number of researchers have focused on computer vision, leading to greater exposure of super-resolution techniques. This paper aims to compare various models of scene text image super-resolution and elucidates on its evolution in recent years.
Task Sequence Filling for Multi-function Radar via BERT Model

The received multi-function radar (MFR) pulse signals usually are partially lost in the complex electromagnetic environment (e.g., multiple radiation sources scenario, low signal noise ratio (SNR) scenario), making it difficult for the reconnaissance team to correctly sense the MFR task sequence which further affects the accuracy of electronic intelligence information. To this end, this paper develops a MFR task filling method based on the BERT model in the presence of complex electromagnetic environment. More precisely, a MFR hierarchical model is established which consists of waveform unit, radar task and work mode. Exploiting the radar knowledge database, the intercepted pulse sequence is mapped to the radar task sequence, and the BERT model is further introduced to fill the radar task sequence. The simulation results show that the proposed methodology achieves relatively high filling accuracy in different working modes.
Fast Pre-Training Kernel Estimation Network for ISAR Space Target Image Deblurring

In inverse synthetic aperture radar (ISAR) imaging applications, the lack of image clarity often limits their usage in down-streaming tasks such as recognition and component judgment. The super-resolution of ISAR images after imaging needs to consider the uncertainty of the blur kernel and cannot simply use traditional super-resolution (SR) methods. Moreover, ISAR images are scarce, expensive to obtain, and pre-training based on a large number of images is challenging. Therefore, this paper proposes a Single-Image Super-Resolution (SISR) method with generalization ability for different blur kernels, which does not rely on a large amount of image data training for ISAR image super-resolution. A large number of experiments show that the proposed method is superior to the traditional Bicubic algorithm and learning-based DIP, Double-DIP methods, and achieves significantly better performance with less pre-training time.
Automatic Sketch Generation for Person Recognition

In recent years, technology is advancing while criminal activity is also increasing in crowded public spaces such as airports, railway stations and meetings. Biometrics plays a significantly important role in person reidentification, it requires a long time for data collection and processing. The demerits of biometrics can be rectified using sketch recognition. Sketching is an art used to identify a person. This work analyzed sketches of person identification and recognition using machine learning classifiers. Experimental results show smaller the stroke line period, the greater the recognition rate. The stroke line period with 4 and 7 have experimented for accurate recognition.
Detection of Vulnerabilities in the Perimeter of the ICS Network Infrastructure Using TCP/IP Protocols

The use of vulnerability scanners, as a rule, is aimed at finding vulnerabilities in the network infrastructure of industrial networks for their subsequent elimination. In automated mode, scanners implement several targeted malicious actions to evaluate the response of protection systems. As a rule, common scanners are aimed at checking protection against effective attacking influences, while checking for protection against simple but working attacks is not carried out. The aim of the study is to develop a tool for scanning vulnerabilities in the ICS network infrastructure that implements "simple" attacks. The tool presented in the work generates 4 groups of influences using standard means of network exchange via TCP/IP protocols. The results of experiments on a laboratory stand showed that a specialized industrial network protection tool could not detect some "simple" malicious effects. This indicates the need to protect the network infrastructure of APCS from "simple" malicious influences when building information security systems.