Reviewerly/exHarmony huggingface dataset:
Research on Pedestrian Detection and Head Counting Algorithm Based on Computer Vision

This thesis focuses on the research of computer vision-based pedestrian detection and crowd counting algorithms, aiming to enhance their accuracy and robustness in practical scenarios. Firstly, for pedestrian detection, a more accurate and efficient framework is proposed by improving the Single Shot MultiBox Detection (SSD) algorithm. The optimized algorithm is validated on commonly used evaluation metrics and datasets, and is compared with other deep learning algorithms. Secondly, for crowd counting, the research is conducted based on the Multi-column Convolutional Neural Network (MCNN) algorithm. A more accurate method for crowd counting is proposed by improving the MCNN algorithm, effectively addressing the crowd counting challenges in different scenarios. Experimental results demonstrate superior performance of the improved algorithm on common evaluation metrics and datasets. Lastly, through comparative analysis of experiments, the strengths, weaknesses, and applicable scenarios of pedestrian detection and crowd counting algorithms are summarized. The research outcomes of this thesis significantly contribute to improving the effectiveness of pedestrian detection and crowd counting algorithms in practical applications.
EDnNet: An Efficient U-Shaped Image Denoising Network

Recently, a popular architecture, Transformer has garnered significant attention and success within the domain of natural language processing. However, due to its unique mechanism, the computational complexity increases exponentially with the spatial resolution of the input images. Therefore, it is not suitable for most tasks involving high-resolution image restoration. Although the receptive field of convolution is limited, the convolutional layer extracts local features by sliding small convolutional kernels on the input images. This ability allows Convolutional Neural Networks (CNNs) to capture essential local structures and texture information in images, which are crucial for image restoration. In our work, we propose a fundamental denoising block that relies on convolution, activation functions and normalization. Furthermore, we propose a denoising network named Efficient Denoising Network (EDnNet).
Brain Tumor Segmentation Based on Improved Swin-UNet

Swin UNet combines the characteristics of Swin Transformer and UNet. UNet is a classic image segmentation network with an encoder decoder structure that can provide contextual information while preserving spatial information. Swin Transformer provides a cross window attention mechanism that allows models to obtain global information on images. The core idea of Swin Transformer is to divide the image into non overlapping blocks, and then introduce a cross window attention mechanism, allowing the model to perform self attention calculations within each window to capture global and local information of the image. Swin UNet is mainly used for medical image segmentation tasks, especially for tumor segmentation in brain MRI images. A new segmentation model is proposed by combining the global information acquisition ability of Swin Transformer and the encoder decoder structure of UNet. Swin-UNet-EpA aims to improve segmentation accuracy and performance. For brain tumor segmentation tasks, the performance evaluation of the model usually uses indicators such as IoU and Dice coefficient to measure the degree of matching between the predicted results of the model and the actual labels.
Design and Construction of Machine Translation System Based on RNN Model

With the development of artificial intelligence technology, the application of deep learning algorithm makes adaptive multilingual machine translation possible. Under the conventional “encoder-decoder” framework, many machine translation models have good practical effects, but there are also obvious shortcomings. In this regard, this paper takes the field of multilingual translation as the research object, constructs a machine translation model based on the recurrent neural network (RNN), and puts forward attention mechanisms for semantic mining and long text sequence translation to optimize the practical application effect of the machine translation model. Finally, the model is integrated and sealed and deployed on the Web server, which is convenient for users to use online through remote login. The experimental results show that the RNN machine translation model with improved attention mechanism can significantly improve the translation effect under test data sets such as WMT17 and newsdev, and the BLEU value is increased by 29.33%, which effectively improves the adaptability and effectiveness of the machine translation system.
Based on the Improved YOLOv8 Pedestrian and Vehicle Detection Under Low-Visibility Conditions

To address the issue of blurred driving visibility under low-visibility weather conditions, an improved YOLOv8 (You Only Look Once) pedestrian and vehicle object detection algorithm based on a deep learning network was proposed. The algorithm utilizes DCNv2 (Deformable Convolution v2) to replace the convolution in the C2F module, enabling the network to better capture and model spatial transformations in the data. Additionally, the self-attention mechanism BiFormer is introduced to achieve a better balance, enhancing the network's focusing ability and modeling capability.To validate the accuracy and real-time performance of the model, a pedestrian and vehicle object detection dataset was constructed using real traffic scenarios. Experimental results showed that the improved YOLOv8 achieved P, R, and mAP of 94.56%, 93.28%, and 92.64 %, respectively. This represents an improvement of 3.41 %, 2.31 %, and 2.29% compared to the original YOLOv8 algorithm, effectively enhancing the detection performance for pedestrian and vehicle objects.
Enhancing the Evaluation Performance of Convolutional Neural Networks-Based Vehicle Classification Systems

The rapid advancement of artificial intelligence, particularly deep learning, has spurred its integration into diverse domains. Among these, the application of deep learning in vehicle classification stands out as a vital focus within intelligent transportation systems, road traffic planning, safety warning, and driverless driving. In this research, a comprehensive approach of CNN’s evolution such as LeNet, AlexNet, and other CNN-based vehicle classification techniques are enhanced for a better performance, while also investigating R-CNN, YOLO, and related methods for vehicle detection. Evaluative datasets for these methods are highlighted. Moreover, the enhancement of the computing power of the Graphics Processing Unit (GPU) and the sharp increase in the amount of data, deep learning based on convolutional neural networks has become a research hotspot. To evaluate the accuracy and efficiency of our vehicle detection methods, RCNN and YOLO series datasets were used to evaluate these methods and an the development of CNN as the main line was introduced and use to analyze the vehicle classification methods based on LeNet, AlexNet and other CNNs. This research is pivotal for vehicle tracking, traffic management, and autonomous driving progress. In this landscape, convolutional neural networks (CNNs), self-encoder neural networks, and deep belief networks take prominence, with CNNs evolving as the most dynamic. Notably, CNN’s development has led to refined vehicle classification through models like LeNet, AlexNet, VGG, and GoogLeNet. The paper concludes by outlining prospective research directions in this domain.
Reversible Multiple Watermarking in Medical Imaging Security Applications

This paper introduces an innovative approach for high-capacity reversible watermarking, utilizing integer wavelet transform and histogram shifting techniques. This method can be applied across various scenarios, allowing for the embedding of a single watermark or multiple watermarks through both single and multi-pass embedding processes. What sets this scheme apart is its ability to achieve a capacity rate of l bit per pixel (bpp) in a single-pass operation, a feat that most reversible methods struggle to attain.By employing multi-pass embedding procedures, this scheme can even achieve higher bit rates, reaching up to 2.7 bpp in a three-pass experiment using Lena’s image as an example. One of the key strengths of this technique lies in its capacity to embed multiple watermarks, making it suitable for situations where multiple marks are required at different times, using distinct keys, or for various purposes. This property can be of great significance in medical imaging applications, where diverse information with different importance and privacy index form the to-be-embedded data. In such a case multiple watermarking schemes are of great interest.
Implementing Mass Testing Approach with Computer Vision Techniques

In medical laboratories, medical tests are taken to be analyzed for providing better treatment to patients, screening diseases in the population, and for further research on diseases. During the times of the rise of epidemics and pandemics, a mass testing approach for screening the spread of diseases in the population is used. It takes several hours for health workers to manually record the results in the database as presented during the outbreak of the COVID-19 pandemic, and this caused late notification to the infected which resulted in further spread. Based on object recognition with computer vision techniques, the OCR engine, and advanced data analysis techniques, an efficient novel approach to develop a novel deep learning model to classify images while extracting insightful information from them for further manipulation and use has been successfully achieved. The developed model contributes to designing the Bioline RDT scanner machine. The machine is designed to scan different rapid diagnosis test (RDT) types for invalid, negative, or positive results for single or many patients, and extract the corresponding patients’ IDs at a time. As the RDT types are not predefined during the development of the model, all the existing and future RDT to come can be monitored by the machine.
Unsupervised Low-light Image Enhancement with Plug-and-play Prior Modeling

Low-light image enhancement plays a very important role in computer vision. Due to the difficulty in acquiring paired samples, unsupervised learning methods have received increasing attention in recent years. The construction of the loss function plays a critical role in unsupervised methods, which often relies on the modeling of image priors. The RUAS method combines the modeling of physical imaging priors based on Retinex theory, the modeling of image geometric priors based on total variation, and the mining of priors based on network search, achieving good performance in low-light image enhancement. However, the single total variation geometric prior modeling is not suitable for preserving image color and details. In this paper, based on the RUAS algorithm, plug-and-play prior regularization terms are introduced into the loss function, and an ADMM-based unfolding optimization algorithm is designed. Numerical experiments show that the method proposed in this paper provides a flexible algorithmic framework for introducing plug-and-play priors, and the enhanced images also have better details.
A Student Engineering Competence Level Prediction Method Based on RNN-GRU and Dynamic Bayesian Network

To achieve effective feedback and proficiency tracking of students in contemporary educational settings, we propose a methodology that integrates the RNN-GRU model and dynamic Bayesian networks for predicting students’ engineering competence level based on their online learning behaviors. The approach employed utilizes the RNN-GRU model to analyze students’ progression in algorithm development skills and algorithm implementation ability, resulting in the generation of a historical record of their learning features. These features are then employed to train a dynamic Bayesian network model, facilitating the prediction of students’ engineering competence level. After the amount of data reaches a certain amount, it can compare the previous models such as RNN and LSTM to obtain higher accuracy.
A Novel Approach for Self-Supervised Denoising

Amidst the swift progression of neural networks, numerous strategies have been put forward to address the issue of image denoising under the strong supervision of large-scale datasets. Leveraging self-supervised blind-spot networks to tackle spatially correlated noise in real-world scenarios presents a significant and daunting obstacle. In this paper, we introduce an innovative method for denoising based on multi-branch blind-spot network with PD-random replace refinement (MBPDR3) to overcome information loss. Extensive research has shown that the proposed method is substantially superior to other self-supervised methods, and the efficacy of the proposed approach is demonstrated through extensive experimentation, yielding superior performance on both synthetic and real datasets.
A Discriminant Face Recognition Algorithm Based on Improved Softmax Loss Algorithm

Traditional Softmax loss algorithm has only separability for features algorithm. This study proposed an improved Softmax loss algorithm to recognize facial features. The algorithm first applies an intra class cosine similarity loss between the features and weight vectors based on the Softmax loss feature distribution, making the intra class more compact and separating the classes as much as possible; then, on the basis of Softmax loss, we use normalized features to better simulate low-quality facial images, and reduce category imbalance by normalizing weights to ensure consistency with the cosine similarity measurement during testing; finally, the joint normalization of Softmax loss and intra class cosine similarity loss were fine-tuned on the pre trained model. The algorithm achieved recognition rates of >98% and >93% on the face recognition benchmark test sets LFW (labeled faces in the wild) and YTF (YouTube face database), respectively. The experimental results showed that in large-scale face recognition, the algorithm improved the discriminability of features, Enhanced generalization ability of the model can effectively improve facial recognition rate.
Improved Density Peak Clustering Algorithm to Optimize Financial Decision Support System

To improve the performance of financial decision support systems. This article proposes a density peak clustering algorithm for deep analysis of financial data. Establish a density peak clustering financial analysis model by extracting features from financial data samples. By calculating the distance between each node and each cluster center point, the sample category is determined based on the set distance threshold. In order to prevent unreasonable distance threshold setting from causing significant disturbance to clustering results, a fruit fly optimization algorithm is introduced to optimize and improve the distance threshold parameters. By continuously updating and optimizing the taste concentration of the fruit fly population, the optimal fitness individual is obtained. The density peak clustering algorithm optimized by the fruit fly optimization algorithm is used to complete clustering analysis and obtain financial data clustering results. The experimental results show that by reasonably setting parameters such as the range of odor change rate in the fruit fly optimization algorithm, the clustering accuracy of all 6 industry samples exceeded 0.9. Compared to commonly used financial data analysis clustering algorithms, the density peak clustering algorithm improved by the fruit fly optimization algorithm can achieve higher clustering accuracy and stability.
A Self-Distillation Assisted ResNet-KL Image Classification Network

Traditional ResNet models suffer from large model size and high computational complexity. In this study, we propose a self-distillation assisted ResNet-KL image classification method to address the low accuracy and efficiency issues in image classification tasks.Firstly,we introduce depthwise separable convolutions to the ResNet network and enhance the model’s classification performance by improving the design of activation functions, using TReLU instead of traditional ReLU. Secondly,we enhance the model’s perception of features at different scales by incorporating multi-scale convolutions for the fusion of residual layers and attention mechanism layers. To reduce the model’s parameter count, we combine feature distillation with logic distillation and optimize the model layer by layer through selfdistillation, while applying pruning techniques multiple times to reduce its size. Finally, To assess the efficacy of our methodology, we conduct experimental evaluations on public datasets CIFAR-10, CIFAR-100, and STL-10. The results show that the improved ResNet-KL network achieves an accuracy improvement of 1.65%, 2.72%, and 0.36% compared to traditional ResNet models on these datasets, respectively. Our method obtains better classification performance with the same computational resources, making it promising for applications in tasks such as object classification.
Improved A-RAFT Consensus Algorithm Based on SHA256 Encryption Algorithm

The RAFT algorithm is one of the core technologies of blockchain. However, the strong leader strategy adopted by the RAFT algorithm can bring a fatal blow to the whole system when the leader node engages in malicious behavior, and its performance gradually decreases as the scale of the system continues to expand. In order to solve these problems, this paper adds a storage node to the original three nodes on the basis of the RAFT algorithm, reducing the choice of leader nodes, and the storage node is responsible for storing data, while other nodes only need to store data hashes, greatly improving the efficiency of the algorithm. And to solve the Byzantine problem of RAFT algorithm, a searchable encryption scheme based on SHA256 algorithm is used to ensure that when the leader node makes a mistake, the system can conduct a new leader node election in time, so that the A-RAFT algorithm can improve the efficiency of the system operation and ensure that the system can operate properly.
The Use of AR Technology in Retail Promotion: an Empirical Study of Innovative Forms and Customer Interaction

In recent years, virtual reality (VR) and augmented reality (AR) technologies have been widely used in many fields such as education and healthcare. Compared to traditional retail stores, the existing shopping experience has obvious shortcomings in terms of immersion and interactivity. This study proposes a smart retail store program design based on the Unity platform, which aims to enhance consumers’ shopping experience by displaying product information and conducting virtual sales. Applying this design, consumers will be able to obtain end-to-end shopping services in a virtual shopping environment, combined with a real context. This report details the design process and technical details. The results show that the AR intelligent retail solution provides an intelligent reference for the retail industry, helping to optimize the customer experience and provide more refined services.
A Course Recommendation System Based on Collaborative Filtering Algorithm

With the development of information technology and the spread of online education, people have gradually moved from an era of information scarcity to an era of information overload. In order to solve the problem of how match massive course resources with user needs. This study designs a course recommendation system based on collaborative filtering algorithm. We utilized nearly 200000 learning record data and over 40000 user data from the education platform, and constructed an item similarity matrix, personalized user recommendations were made based on the matrix. Subsequently, the user-based recommendation model, UserCF, and the object-based recommendation model, ItemCF, were proposed. We further proposed the multi process idea to optimize the training efficiency of the model. The experimental results showed that this method can efficiently and accurately recommend online courses on educational platforms, with the recall rate is 18%, with accuracy and coverage rates of21% and 73%, respectively. Our algorithm not only maintains a balance between accuracy and recall, but also has good coverage, achieving personalized recommendations for designated users.
A Method for Depth Camera Calibration Based on Motion Capture System

With the rapid development of 3D computer vision technology, depth cameras have been widely used. Depth camera calibration is mainly constrained by two aspects: (1) alignment of depth data, and (2) distortion in RGB images. Under the condition of unknown camera intrinsic parameters, a method for RGBD commercial camera calibration is studied based on an optical motion capture system. The proposed approach utilizes the projection from the motion capture space to the depth camera’s field of view, establishing a coupled parameter model for the camera’s CCD photosensitive elements to improve the calibration of the depth camera. Experimental results demonstrate a 33% reduction in errors in the central region of the camera’s field of view compared to commonly Least Squares (LS) calibration methods. Moreover, the method exhibits faster computation speed, enabling efficient real-time calibration of depth cameras with unknown parameters in practice.
ORFPPrediction: Machine Learning Based Online Recruitment Fraud Probability Prediction

In response to the issue of inadequate public datasets concerning imbalances in Internet recruitment scams, this paper constructs an imbalanced experimental dataset, labels it, and proposes an ensemble learning method based on model stacking. This method enhances the prediction accuracy of the original model built with Random Forest. Initially, the method employs label encoding and TF-IDF (Term Frequency- Inverse Document Frequency) to preprocess structured and unstructured data respectively. Then, it utilizes Truncated SVD (Singular Value Decomposition) to reduce the dimensionality of the sparse matrix generated by feature extraction from TF-IDF. Subsequently, decision trees, random forests, and Light Gradient Boosting Machine are utilized as base models, with the random forest employed as the meta-model to integrate the predictions of the base models. This paper presents a comparison of the experimental results from seven machine learning models based on the self-constructed experimental dataset, with model stacking implemented on the random forest that yielded the best experimental result. Through model stacking enhancement of the machine learning models, compared to the random forest model that performed best on the self-constructed experimental dataset, an accuracy of 94.85% was achieved, marking a 7.33% improvement. The experimental results hold significant value and practical implications in understanding and resolving the probability risk issue of recruitment fraud.