Reviewerly/exHarmony huggingface dataset:
MATLAB-Based App for Handwritten Numeral Recognition

Handwritten numeral recognition (HNR) is widely used in various industries and is essential to the research and development of optical character recognition (OCR) technology. Given that most of the current HNR algorithms require high-quality input images rather than naturally taken photographs, a MATLAB-based application with a graphical user interface (GUI) is designed for pre-processing and segmenting photographs of handwritten numerals and then recognizing the numerals. For the various conditions that a naturally taken a photograph may have in practice, the application can perform denoising, perspective correction, and binarization of the original image, as well as removing horizontal lines, grids, or boxes from the background. The application can then segment and recognize every numeral from the processed image. It has been experimentally verified that the application can satisfactorily pre-process the original image while providing high accuracy in recognizing numerals.
A fisheye distortion correction method based on deep learning

As machine vision involves many fields such as photogrammetry, national defense and military, and security surveillance, fisheye cameras have also been widely used as an ultra-wide-angle lens. Different from ordinary cameras, fisheye cameras can obtain a wider viewing angle, but their imaging laws will produce nonlinear distortion, which needs to be corrected to achieve better visual effects. In this paper, a fisheye distortion correction method based on deep learning is proposed. According to the parameter division model, the distortion dataset is synthesized, the neural network is constructed, and the parameter model is trained. Finally, the fisheye image is corrected by extracting the distortion features and estimating the distortion parameters. Image enhancement. This method overcomes the limitation of traditional correction methods that rely heavily on imaging model or camera calibration, benefits from the learning features of neural networks, and has better universality; The results show that this method has better performance.
Self-attention Pyramidal Convolutional Network for Weakly-supervised Video Anomaly Detection

Video anomaly detection refers to detecting and recognising abnormal performance in videos that deviate from normal behaviour. The anomaly detection performance in weakly supervised video anomaly detection degrades due to the lack of attention to temporal information in the video features extracted by the pre-trained network. To address this problem, we propose a weakly supervised video anomaly detection method based on a self-attention pyramidal convolutional network (SAP-net), which includes a redesigned multi-scale module with a self-attention mechanism. Experimental results show the SAP-net outperforms the state-of-the-art method in the UCF-Crime dataset.
Remote Sensing Image Registration Via Feature Fusion and Enhanced Matching

Image registration is a basic problem in image analysis and image processing. Image registration has important applications in aerial image fusion, pattern recognition, three-dimensional reconstruction and other fields. Aiming at the problem of low registration accuracy and mismatching in remote sensing image registration, this paper proposes to use the Involution kernel to improve the ResNext network in the feature extraction stage and combines the SPANet attention mechanism with an improved ResNext network to improve the feature extraction ability of the network. In the feature matching stage, an enhanced matching method is proposed, which uses cross-correlation and nearest neighbor to second nearest neighbor ratio to filter out mismatched points to cope with complex images and background interference. The experimental results show that the proposed algorithm can achieve superior results in a variety of indexes compared with other algorithms, which proves that the proposed algorithm is effective.
Key Phrase Extraction based on Pre-trained Language Models

With the explosion of information and a large amount of data appearing every moment, it is a meaningful task to quickly find the information people want to know in a large amount of text and to present long texts in a streamlined form. Key phrase extraction, which aims to extract from documents a collection of key phrases that express the topic and content of the document, is important for text processing tasks such as information retrieval and document classification and can provide readers with a more comprehensive overview of the topic. We use two types of pre-trained language models for key phrase extraction, namely DeBERTa and RoBERTa, which are first pre-trained on the dataset and then fine-tuned, and the experimental results of these models proved that DeBERTa-V3-Large has reached an F1 score of 0.8925, which is the best result among these models.
Design and implementation of a semantic retrieval system based on document understanding

In the urrent information environment, unstructured text is an important part of all kinds of information, and it has become an urgent problem in the current information service field to extract the required information from text data for user information needs. Based on semantic retrieval and an extractive document reading comprehension model, this paper investigates how to quickly and effectively extract the required answer information from a large document library based on user questions, and constructs a semantic retrieval system based on document comprehension. It is important to help users get the required information quickly and effectively and improve the efficiency of information services in the current massive information environment. The experiments show that the system can quickly and precisely locate the answers to the user's questions and help the user to obtain the required information quickly and effectively.
Unsupervised Sentiment and Style Transfer from Massive Texts

Unsupervised style transfer aims to transfer the intrinsic style of text while preserving its content without parallel datasets. Many sophisticated methods using reinforcement learning and neural networks have been developed to address this problem, however, their performance is not very ideal yet. We observe that given massive unpaired texts, there would exist high-quality sentence pairs that have similar style-independent content but different style words. Inspiring by this observation, in this paper, we propose a simple yet effective method without any neural network. Specifically, we consider both embedding similarity and BLEU score to locate similar sentences of different styles for a pseudo-parallel dataset construction. From this pseudo-parallel dataset, we distill the style words and align them into pairs based on statistical signals. We further refine our pseudo-parallel dataset by ignoring the identified style words during similarity calculation. After the style word pairs converged, we put them together as a lookup table to recognize and replace style words for style transfer. Extensive experiments demonstrate that our method is effective in different style transferring settings, such as sentiment and formality, outperforming state-of-the-art methods.
Design and Implementation of Course Review System

Based on the guiding of “Internet Plus Education”, this paper researches the main stream technical framework of website development, designs and implements a course review system. Its implementation is divided into two parts: the main website and the admin management. The main website includes searching information, registering, and reviewing courses, modifying user information, viewing personal favorites, comments and notices; the admin management includes user information, course information, comment information, collection information, review information, and notice information management. The system adopts four-layer (View- Controller-Service-Infrastructure) architecture, mainlyuses Spring Boot and Mybatis-Plus, integrates Elastic Search and Redis. The application of these technologies can reduce construction and maintenance costs, improve the user access experience inconcurrent scenarios and enhance the search accuracy and performance.
Color Search: An efficient region proposal generation method

Inspired by the process of searching for targets by the human eyes, our proposed target candidate region generation method focuses on the color information of the target. Current mainstream target detection algorithms often incorporate an Region Proposal Network (RPN) component for finding out the possible locations and sizes of targets, which facilitates the training and optimization of the backbone network. The RPN network uses more convolutional layers, which makes it impossible to be applied in embedded platforms that require high real-time performance and does not take full advantage of the target's a priori information. In this paper, We convert the image from RGB color model to HSV model and reduce dimensionality to form a color set. After quantizing the color set, we can obtain the Q-HSV of the image by which to find out the possible locations of the target efficiently. With this method, we can find out the possible areas of the target using only one RGB template of the target, and it is not affected by target viewpoint changes, shape changes, etc. Our approach can significantly reduce the image data to be processed by recognition and detection algorithms by more than 80%.
VDDT: Improving Vessel Detection with Deformable Transfomer

Vessel detection has received wide attention in object detection, and the recently proposed DETR has successfully achieved true end-to-end object detection and has shown good performance. However, DETR is not sensitive to detect small objects, resulting in its unsatisfactory performance in vessel detection. In this paper, we use Deformable DETR as the baseline model and modify it on top of that. Firstly, we add reference point information to object queries to make the features learned by object queries richer to improve the performance of the detector. Secondly, we use multi-layer perceptron instead of multi-head self-attention to reduce the computational effort of the decoder. In addition, we collected 85 videos annotated with 4563 images and used these images to make a vessel dataset. The experimental data on our vessel dataset shows that VDDT performs better compared to the baseline.
Depth-aware Trimodal Network for RGB-T Salient Object Dection

RGB-T salient object detection (SOD) aims to locate and segment the most attractive object in color-thermal image pairs. Depth information has been proved beneficial in RGB-D salient object detection during recent years. To take advantage of the structural information contained in the depth map, we build a depth-aware trimodal network (DATNet) to introduce depth modal into RGB-T SOD. First, we generate depth maps from RGB frames through existing monocular depth estimation methods, depth stream constitutes the third input to our DATNet. We use three independent transformer backbone net to extract hierarchical features. Our multimodal interaction module (MIM) is designed to achieve three modal feature enhancement and complementation. Notably, we treat the three modals unequally. Multimodal enhanced fusion module (MEFM) aims to refine the features after MIM and fuse them in stages. Finally, we use multiple supervisions in progressive decoding to obtain high-quality saliency maps. Comprehensive experiments on three public RGB-T SOD datasets show that the proposed network surpasses 8 state-of-the-art RGB-T SOD methods in terms of five metrics.
Dual Guidance Of Optical Flow And Decoupled Attention For Infrared Video Object Detection Network

Infrared video object detection is a task to detect moving objects in infrared video. The traditional object detection algorithm often ignores the motion information of the target, and often miss detection and false detection of small objects and objects in complex backgrounds.
A Prompt-based Few-shot Machine Reading Comprehension Model for Intelligent Bridge Management

Bridge inspection reports are an important data source in the bridge management process, and they contain a large amount of fine-grained information. However, the research on machine reading comprehension (MRC) methods for this field is insufficient, and annotating large scale domain-specific corpus is time-consuming. This paper presented a novel prompt-based few-shot MRC approach for intelligent bridge management. The proposed model uses the pretrained model MacBERT as backbone. The prompt templates are designed based on some domain-specific heuristic rules. The experimental results show that our model outperforms the baseline models in different few-shot settings. The proposed model can provide technical support for the construction of automatic question answering system in the field of bridge management.
Transformer-based Iterative Update Stereo Matching Network

Feature extraction is a crucial part of the stereo matching algorithm based on deep learning. The existing stereo matching algorithms have poor matching effects on smaller objects in the background and low-texture areas, which leads to the decrease of disparity estimation accuracy. To improve the accuracy of disparity estimates, we propose TUNet, a Transformer-based iterative update stereo matching network in this paper. After the feature extraction module, the attention mechanism and positional encoding of the Transformer algorithm are added to the network, which enables the transformed features to better combine global context information. In the disparity calculation stage, the improved Gate Recurrent Unit (GRU) module in Recurrent All-Pairs Field Transforms for Optical Flow (RAFT) algorithm is added. In addition, the correlation of each feature is calculated at various resolutions. With correlation lookup and disparity update at the highest resolution and the final disparity map is obtained by GRU cross-up update. Our results show higher accuracy and dense disparity. The matching effect of smaller objects in the background or low-texture areas is improved, as is overall disparity accuracy. The proposed method is trained in ETH3D, Middlebury, and KITTI 2015 datasets. The proposed method minimizes the rate of mismatching and has some robustness when compared to earlier stereo matching algorithms.
Automatic generation of SysML requirement models based on Chinese natural language requirements

In the face of increasingly complex systems engineering R&D, Model-Based Systems Engineering (MBSE) is gradually replacing Traditional Systems Engineering, producing a significant shift in modelling language, modelling thinking and modelling tools. And SysML, as a common modelling language for MBSE practitioners, has many irreplaceable advantages. Requirements diagrams are the primary medium of SysML for conveying traceability between requirements and from requirements to structure and behaviour in a system model. Today, how to automate the construction of SysML requirements models is a significant challenge. This paper proposes an automatic SysML model generation method based on Chinese free-text requirements using natural language processing. Firstly, perform word segmentation and POS Tagging on the requirement text to construct a domain lexicon, extract effective requirements by combining the domain lexicon and regular expressions, and then mark the effective requirements with semantic roles, and determine the requirements elements through established rules, which are ultimately converted into SysML requirements diagrams.
Detection on chemical fiber silk detects by deep learning

There are many surface defects which are difficult to detect manually in the process of chemical fiber silk production. In order to realize the intelligent detection on these defects and improve detection accuracy, an improved Faster RCNN algorithm was proposed. Firstly, the deformable convolution model was added to the backbone feature extraction network to improve the adaptability of the network to different defect features. Secondly, the Feature Pyramid Network was replaced by Recursive Feature Pyramid structure to extract features twice. Finally, the Loss function was improved, and RS Loss function was used to replace the original classification loss function to solve the problem caused by imbalanced sample categories. Experiment result shows that the mAP value calculated by the improved model is 84.7%, which is 4.3% higher than original Faster RCNN model. The improved model can meet the requirements of intelligent detection on chemical fiber silk defects in practical production and processing.
Application of the perspective based on virtual reality technology to relieve anxiety in practice

Virtual reality technology is a new comprehensive technology with computer technology as the core. It makes comprehensive use of computer 3D graphics technology, simulation technology, sensing technology and display technology to generate almost real visual, auditory, tactile and sensory 3D models. The goal is to create a highly immersive and highly immersive non-realistic environment. With the help of computer programming, virtual reality technology can produce virtual scenes and related stimuli needed to relieve anxiety, with immersive, interactive and present-thinking ability. It can break through the limitation of traditional anxiety treatment and make the treatment process operable. This paper reviews the application of virtual reality technology in anxiety relief, chatbot generation model based on Seq2seq, equipment requirements and mechanisms. It has unique advantages over traditional therapies, but it also has some limitations. Future developments should consider technological innovation and standardization of treatment options.
YOLOX-EC: A Pedestrian and Vehicle Detection Algorithm in Automatic Driving Scenes

It is particularly significant that autonomous vehicles are able to accurately detect the dynamic information of pedestrians and vehicles in complex and changeable traffic scenes. However, the target detection is difficult in the automatic driving scene because the target scale varies drastically and there is mutual occlusion between targets. In order to solve the issues, we propose a pedestrian and vehicle detection algorithm that is named YOLOX-EC. Firstly, we design the Efficient Attention Module (EAM) according to the ideas of Efficient Channel Attention Network (ECANet) and Convolutional Block Attention Module (CBAM), and introduce it into backbone of YOLOX to enhance the feature extraction ability of network; Secondly, we use Content-aware reassembly of features(Carafe) to enlarge the receptive field of upsampling features and enrich the semantic information of the features; Finally, we change the bounding box regression loss to Efficient Intersection over Union Loss (EIoU Loss) for improving the regression accuracy. The experimental results on KITTI dataset show that compared with YOLOX, mean Average Precision (mAP) of the YOLOX-EC is increased by 2.8%, and the reasoning speed still keeps the real-time requirement of automatic driving platform.
Face recognition using feature dimension decrease arithmetic∗

DR (Dimensional Decrease) arithmetic is a tool. Initially, DR arithmetic was used to solve problems in computer vision. Here, DR arithmetic is used to decrease the number of features. Contrast among various DR arithmetics is executed to face discrimination. Contrast among various DR arithmetics is executed to face discrimination. Tall order of dimension face pictures is low order of dimension manifolds in nature. The manifold learning arithmetic can decrease the order of dimension by digging the essential structure of the data, draw the inherent features of tall order of dimension face image data, and find the low order of dimension embedded manifold equivalent to the tall order of dimension original data. Contrast among LLE, Isomap arithmetic and modified Isomap arithmetic are performed to face discrimination. The outcomes of the test are gratifying, which it makes clear that DR arithmetic can settle face discrimination problems well.
Sequence Recognition of Scene Text Based on CRNN and CTPN Models

Image-based sequence recognition has lately emerged as a prominent study subject in the science of computer vision, while text detection and identification in natural situations has emerged as an active research field. Based on scene text data, this paper addresses the theory of deep learning-based CRNN and CTPN models and the process of processing text. Using CRNN, text recognition can be turned into a time-dependent sequence learning issue, which is commonly employed for indeterminate-length text sequences. Contextual relationships between text images are learned using BLSTM and CTC, thus effectively improving text recognition accuracy and making the model more robust. It also excels in text recognition tests for wordless and lexical-based scenes, as it is not constrained by any predefined language. It produces a more efficient, but smaller, model that is more suited to real-world settings. CRNN recognition accuracy is lower for short texts with large morphological changes, such as artistic words, or texts with large changes in natural scenes. Because of the Anchor setting, CTPN can only detect horizontally distributed text, but a small improvement can detect vertical text by adding horizontal Anchor. As a result of the limitations of the framework, the irregularly inclined text can be detected very broadly.
Use Edge Detection Method and Matlab for Time Measurement in Athletics Field

Even though the technology of real-time recording in the Athletics field is highly developed, it is still not easy for people to calculate the time from a recorded video. People can only estimate the time with their eyes if a randomly chosen video is given. So in this paper, the method will be introduced to provide a fundamental choice to solve such a problem. The process will be based on the Matlab program as it is a convenient tool providing an essential technique for image processing. For the detail of this method, the Edge Detection method and Hough Transform are used to locate the edge of athletes and tracks to find out the time score from a running race video. Even though the result may have an error in about 0.03 seconds, it is much more precise than calculating from the human eyes and, as a result, can have practical use with further development.
Image Restoration Using Multi-Stage Progressive Encoder-Decoder Network With Attention and Transfer Learning (MSP-ATL)

Convolutional neural network (CNN) is widely used in the field of image restoration, however, most existing CNN based image restoration methods only focus on a part of image restoration without considering the relationship between image deblurring, image de-raining and image denoising. In addition, training a neural network model for different image restoration tasks requires a large amount of training data, plenty of hardware overheads and a great deal of time. In order to reduce resource consumption and improve the generality of the model, this paper proposes an image restoration algorithm using multistage progressive encoder-decoder network with attention and transfer learning (MSP-ATL). First of all, we design a multi-stage progressive encoder-decoder network with attention mechanisms, which does not require down-sampling or fragmentation of the input, and can retain the overall information of the image. Secondly, following the idea of coarse-to-fine which is widely used in the field of image restoration, we propose a multistage progressive loss function to recover images from coarse to fine by cooperating with the multi-stage network structure. Finally, using transfer learning, the obtained deblurring model can be transferred to image de-raining and image denoising tasks with less training data and simple training process. Extensive experimental results on commonly used datasets demonstrate the efficiency and effectiveness of the proposed method.