Reviewerly/exHarmony huggingface dataset:
LPGL: A Model Applicable to Data Augmentation Algorithms for Depression Detection

The clinical detection of depression generally judges depression and the degree of depression by scoring according to the depression scale. For doctors, it is time-consuming to perform the same scale screening for each patient, and the program based on computer-aided diagnosis has practical significance. At present, large-scale neural networks often fall into the dilemma of insufficient training data labeling in practical applications. We propose an algorithm based on label propagation that combines semi-supervised learning with graph structure learning (LPGL). The LPGL algorithm can simultaneously utilize three types of data: labeled data, unlabeled data, and data enhancement, so that the trained classifier is better than the training data and outperforms than other few-shot learning methods when labels are insufficient. This article uses the AVEC 2013 dataset to verify the superiority of the LPGL algorithm in terms of accuracy. Early diagnosis of depression is also conducive to early medical intervention, which will be of great help in alleviating depression.
Graph Convolutional Extreme Learning Machine Autoencoder for Graph Embedding

The purpose of graph embedding is to encode the known node features and topological information of graph into low-dimensional embeddings for further downstream learning tasks. Graph autoencoders can aggregate graph topology and node features, but it is highly dependent on the gradient descent optimizer with a large iterative learning time, and susceptible to local optimal solutions. Thus, we propose Graph Convolutional Extreme Learning Machine Autoencoder. To address the limitation that the extreme learning machine autoencoder cannot use topological information, the graph convolution operation is introduced between the input layer and the hidden layer to improve the representation ability of the graph embedding obtained. Experiments of link prediction and node classification on 5 real datasets show that our method is effective.
Virtual exhibition image stitching fusion method based on light compensation and improved optimal stitching lines

To address the problems of uneven quality of the panoramic image in the case of constructing a virtual exhibition hall and the existence of uneven lighting and color differences at the stitching seams, an optimal stitching line multi-scale image stitching fusion method using lighting compensation and new energy function is proposed. First, the method uses gamma correction for global luminance compensation of the image to be stitched, where the coefficient of gamma correction is the ratio of the sum of its luminance components in HSV (Hue, Saturation, Value) color space for each of the overlapping regions, secondly, the saturation component of HSV color space is used instead of the original color energy function to obtain the best stitching mask map according to the best stitch search criterion, and finally, the Laplace image pyramid method is used to Finally, the images to be stitched are stitched and fused according to the mask map using the Laplace image pyramid method in different scale-spaces. The results show that the method mitigates the visual fragmentation near the stitching seams due to uneven illumination and color differences in the case of constructing a virtual exhibition hall, and has a better stitching fusion effect.
Bayesian Filter Pruning for Deep Convolutional Neural Network Compression

Network pruning has been demonstrated as a feasible approach in reducing model complexity and accelerating the process of inference, which make it possible to deploy deep neural network in resource-limited devices. Many previous works on network pruning consider the magnitude of parameters or other intrinsic properties in point-estimates based network as the criterion of module selection, which are incapable of estimating uncertainty of parameters. In this paper, we propose a novel Bayesian filter pruning method, which leverages the advantage of Bayesian Deep Learning (BDL), by exploring the properties of distribution in weight. The proposed method removes redundant filters from a Bayesian network by a criterion of the proposed Signal to Noise Ratio (SNR) that combines properties of importance with uncertainty of filters. Experimental results on two benchmark datasets show the efficiency of our method in maintaining balance between compression and acceleration.
Mask Wearing Detection System Based On YOLO And Raspberry Pi

To identify mask wearing quickly and automatically in public places is particularly important for epidemic prevention and control. In this paper, we present a real-time mask wearing detection algorithm based on improved YOLOv5s, which speeds up the reasoning speed by 5~10% and achieves a detection accuracy of more than 96%. The proposed algorithm can be easily deployed in Raspberry PI. We also design a Web-based mask wearing detection system consisting of two parts: the cloud subsystem and the edge subsystem. The cloud part mainly realizes data storage, model training, equipment monitoring, big data visualization and other functions. The edge part uses Raspberry Pie as the core deployment equipment to complete data collection, model reasoning, information early warning and other functions. Our system features the advantages of high real-time, low cost and low network traffic. It can be widely deployed in the supermarket, parks, intelligent light poles and other open scenes, resulting in greater practical application value.
Individual Identification of OFDM Signal Radiation Source Based on Depth Adaptive Wavelet Network

To solve the problem of information security hidden danger when using consumer electronic products for wireless communication, an individual identification method of OFDM signal radiation source based on a deep adaptive wavelet network is proposed. Firstly, a mathematical model is established to generate different fine features between subcarriers of OFDM signal transmitter. Then, I/Q channel signals are input into the network. In the process of network training, the lifting wavelet transform process is implemented adaptively to generate multiple signal segments with different time-frequency domain resolutions. Finally, multiple OFDM radiation sources are classified and identified by a classifier. The experiment results show that, under the condition of a signal-to-noise ratio of 30dB, the accuracy of end-to-end identification of five OFDM transmitters by using the deep adaptive wavelet network can reach 95.6%, and the anti-noise performance and parameter number of the network are better than the traditional convolutional neural network.
Research on a Algorithm of Medicine Box Tracing Code Location

Aiming at the identification problem of medicine box traceability code, according to the principle that the relative position of each character in the medicine box remains unchanged, this paper proposes an efficient and accurate barcode identification method based on barcode neighborhood template matching algorithm. First, create a variable shape template of fixed elements in the kit, and then obtain the edge of the variable model, so as to determine the transformation matrix parameters of the collected image. Through geometric transformation, complete the positioning of the kit traceability code, and finally achieve the extraction of the traceability code. In order to verify the accuracy of the proposed algorithm, a test platform is built to verify the application of the proposed method. The experimental results show that the recognition rate of different kit samples is more than 99.4%, the algorithm has good robustness, high positioning accuracy, and fast recognition speed, which provides a new solution for the identification of kit traceability code on the production line, and has high practical significance.
Data fusion algorithm in radar network

Radar networking mostly solves the multipath problem in the interference environment. It fuses the target trajectories between different radars so that the target track can be completely present in radar networking. Moreover, this paper proposes fusing 360° radar networking targets for the target trajectories left by different radars are analyzed and processed, and the trajectories of different radars are judged to be the same target data. Through the actual measurement and analysis, the root means the square error is 0.022, which can determine the trajectory between different radars as the trajectory of the same target.
Instance Segmentation Combined CMT and Swin Transformer in Driving Scenes

CNN and Transformer have been used widely through computer vision problems, including object detection and instance segmentation. But usually, CNN and Transformer are utilized independently. Recently, a new method called CMT has combined the advantages of both. It applies convolution to mitigate the computation overhead. In this work, we combine the advantages of CMT and swin transformer to enrich feature extraction. And build a framework that used the new backbone to achieve instance segmentation. Finally, we have done experiments in driving scenes and achieved good results.
Analysis of Integrated Learning Methods in Computer Aided Translation Software

In the era of intelligence today, computer translation has been equipped with the ability of self-memorization and self-learning. Coupled with continuous manual intervention and iterative optimization, the accuracy rate of many contents in computer translation has reached as high as 90%. Therefore, computer-aided translation software plays a crucial role in the improvement of translation efficiency. Ensemble learning is a machine learning method that combines multiple learners to make collaborative decisions. It can effectively integrate the probability distribution predicted by multiple models in the process of machine translation task inference, so as to improve the accuracy of translation system. It is also the focus of this paper.
Chinese Short Text Matching Model Based on WoBERT Word Embedding Representation and Priori Knowledge

Chinese short text matching is an important task in natural language processing. It is widely used in information retrieval, question and answer systems and dialogue systems. A model (WHCR) based on WoBERT word embedding representation and priori knowledge is proposed to address the problems that traditional Chinese short text matching models cannot solve multiple meanings of words, poor extraction of similar text features and insufficient extraction of granularity information. The pre-training model WoBERT is introduced instead of traditional Word2Vec, which combines contextual information in word vector representation. It introduces OpenHowNet as priori knowledge to guide word-sense interactions, and one-dimensional convolution to extract multi-granularity information, so that the model can obtain multi-level semantic information. Finally, for the problem of inconsistent training and inference due to random deactivation of neurons by dropout, R-drop is introduced to improve the robustness of the model. The model achieves good results on the Chinese datasets BQ and BZQMC, with Acc, AUC and F1-score reaching 85.57%, 92.68%, 85.44% and 84.04%, 93.19%, 85.71% respectively.
An Byzantine fault-tolerant consensus algorithm based on MuSig

In view of the disadvantages of PBFT consensus algorithm such as large traffic and complicated view change, a Byzantine fault-tolerant consensus algorithm based on MuSig, namely MSBFT consensus algorithm, is proposed. The algorithm adopts multi-signature MuSig and pipeline idea, and optimizes the consensus process, view change protocol and checkpoint protocol. The communication complexity of the consensus process, view change protocol, and check point protocol in MSBFT is reduced from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), and O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) in PBFT to O(n) respectively. Experimental results show that compared with ABFT, PBFT and SBFT, the consensus efficiency and throughput of MSBFT algorithm are greatly improved.
Human Table Tennis Actions Recognition and Evaluation Method Based on Skeleton Extraction

With the rise of computer vision, it is becoming more and more important to accurately recognize and evaluate human actions. However, the complexity, intraclass differences, and viewing angle changes of human actions significantly impact the accuracy of identification human actions. This paper proposed an action recognition and evaluation method based on skeleton information extraction. Briefly, We use Lightweight OpenPose to extract the key points of human skeleton and perform processing work, including video data cutting, deleting some key points, supplementing missing key points, filtering processing, feature extraction, etc. Through an in-depth exploration of related theoretical technologies, we proposed a model for recognition and evaluation of human table tennis actions with an ordinary camera. The support vector machine algorithm (SVM) classification model is used to identify table tennis actions in real-time. Then the dynamic time regularization (DTW) algorithm calculates the similarity of each human skeleton key point in the action sequence. The low-scoring bone key points are marked to evaluate the human table tennis action in real time. The results show that a recognition rate of more than 95% is achieved in the test set, which proves the method's effectiveness. In addition, we compared the results with previous work using inertial sensors for action recognition, which shows our method can preserve the same accuracy with a much lower cost of implementation.
Study on human pose estimation based on channel and spatial attention

Accurate pose estimation is crucial for understanding human behavior in images or videos. Given an RGB image, we want to be able to accurately locate some important keypoints on the body. Understanding human pose and body structure is important for high-level tasks such as human-computer interaction. Human pose estimation usually has problems such as low discrimination between human body and background, and human pose estimation based on HRnet network does not make full use of important feature information. To solve these problems, a human pose estimation method MCSA-hrnet (Multi-scale Channel and Spatial Attention) based on multi-scale channel and spatial attention is improved by using channel attention mechanism and spatial attention mechanism. Starting from the channel domain and spatial domain, MCSA-HRnet integrates the multi-level attention mechanism into the high-resolution network structure, and designs the channel attention block and spatial attention block. This enables the network to focus on the regions of the image that are highly associated with the human body and not on other regions. MCSA-HRnet uses 1×1 convolutions for information extraction in the core part of the ca block (channel attention block) and parallel <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{3\mathrm{x}3}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{5\mathrm{x}5}$</tex> convolutions in the sa block (spatial attention block). Different sizes of parallel convolutions can derive spatial attention maps of different scales, which makes the ability of the network to distinguish human features from background features more significant. Thus, the human body region and its key points can be accurately located. The improved method is verified on COCO keypoint dataset, and the results show that MCSA-HRnet can effectively improve the accuracy of human pose estimation joint point localization.
Application of Vehicle Tracking Algorithm in Bridge Load Monitoring

The current traffic monitoring system adopts the centralized cloud computing mode, which brings pressure on the computing load and network bandwidth of the cloud computing center. The edge computing platform based on edge devices can effectively solve the above problems by migrating some computing tasks to edge devices for execution. Vehicle detection and tracking is the core technology of intelligent traffic monitoring system, but its operation on edge devices will be limited by storage memory and computing speed. Therefore, it is necessary to simplify the network model to adapt to the operation on edge devices. This paper proposes a vehicle tracking algorithm based on YOLOX and DeepSORT and deploys it on the edge device Jetson Nano. Our experiment shows that the proposed method can meet the real-time requirements of edge devices.
Robot Path Planning Based on Grid Map Using Improved Ant Colony Algorithm

For the problems of slow convergence and easy to fall into local optimum of traditional ant colony algorithm (ACO) for robot path planning, An improved ant colony algorithm (IACO)based on grid map is proposed in this paper. Firstly, in order to improve the positive feedback ability of pheromone in the later period, an adaptive adjustment factor is introduced into the heuristic function. Secondly, the rotation function is introduced into the pheromone state transition probability to balance the relationship between path length and angle. Finally, in order to ensure the quality of participating pheromone update nodes, local optimization strategies are designed based on cross optimization and redundant point deletion, and different quality paths are updated with pheromone difference mechanism to achieve convergence of high-quality nodes. The experimental results show that IACO can make the robot obtain the global optimal path, and it will have good stability and environmental adaptability.
The Research on Traffic Sign Recognition Algorithm Based on Improved YOLOv5 Model

Traffic sign detection is an important research direction in target detection. At present, the detection problem of small target traffic signs exists, therefore, a traffic sign detection algorithm based on YOLOv5s model is constructed, and some important improvements are proposed to solve the small target detection problem. In order to ensure lightweight, YOLOv5s model is selected, and a small target prediction head is added to detect small targets. Fuse module is proposed to supplement shallow information to the backbone network to increase the ability of small target detection. BIFPN idea is used and improved to solve the problems of network depth degradation and shallow information deficiency. Finally, the Loss function is improved, and Varifocal Loss function is used to improve the problem of unbalanced positive and negative samples. The experimental results show that the detection effect of the proposed algorithm is increased by 7.7% compared with the original algorithm, and the experimental results on multiple datasets show obvious improvement effect. This is a lightweight model that works well in the field of traffic sign detection.
Source-free Domain Adaptation via Multicentric Prototype for Alzheimer's Disease Detection

The use of deep learning and transfer learning techniques for the early diagnosis of Alzheimer's Disease (AD) is of great significance for delaying its development. In the real world, due to different scanners, scanning protocols, and subject cohorts, structural Magnetic Resonance Imaging (MRI) often has the problem of domain shift. Conventional Domain Adaption (DA) methods need to access both source domain and target domain for feature alignment to achieve the generalization in target domain. However, medical image data usually need to concern data privacy and security, the source domain always cannot be accessed. Based on the above situation, we propose a Source-Free Domain Adaptation (SFDA) framework for AD detection. Firstly, we design a feature extraction module combining the advantages of CNN and Transformer, then we use the class-balanced multicentric prototype method to obtain robust pseudo labels. Finally, noise-robust loss function which based on Determinant based Mutual Information (DMI) is used to optimize the model. On the ADNI dataset, our method achieved 90.79%, 75.00% and 80.13% accuracy on the AD vs. CN, AD vs. MCI and MCI vs. CN tasks, respectively. Compared with the supervised learning methods, DA methods which can access to source domain and SFDA methods, our method achieves competitive results.
Design and acceleration of driving behavior detection model

Traditional driving behavior recognition algorithms based on vehicle motion features and machine learning have low recognition accuracy and poor flexibility under complex background. In this paper, a driving behavior detection model implementation and acceleration method based on domestic chip platform RV1126 is proposed. Firstly, the YOLOv5s model operator was adjusted and adapted to replace the Focus operator and the large-size Maxpool operator in the network, so that the model could deploy the chip platform. Then, in order to solve the problem of decreasing the running accuracy of quantization model board and low detection frame rate, the method of adaptive image scaling and filling and multi-thread binding CPU to complete the detection task concurrently is used to optimize and accelerate. The experimental results show that the proposed method can effectively improve the speed and accuracy of the end-side deep learning detection model.
Research on hand gesture recognition based on millimeter wave radar

Gesture recognition is an important research direction in the new generation of Human Computer Interaction (HCI) technology, which has a wide range of application prospects in automotive collision avoidance, medical care, and entertainment. Radar-based gesture recognition technology is not affected by site and environmental factors, has the advantages of being independent of light intensity and easy to place. In this paper, we use millimeter wave radar to collect seven defined hand gesture signals, and use signal processing algorithms such as Fourier transform to extract the distance and Doppler information of hand gesture signals according to the radar principle, and accumulate them in the time domain to form a distance-time map and a Doppler-time map, and use the two feature maps as the input of the residual network for recognition and classification, respectively. To improve the gesture recognition accuracy the two feature maps are fused to obtain a new feature fusion map, which is then put into the residual network, and the experimental results show that the recognition accuracy of the seven gestures is 98.58%.
Research on Multi-object Tracking Algorithm Based on Person Recognition and Detection Fusion

To solve the problem of poor tracking performance in complex scenes such as scale change and frequent occlusion, a multi object tracking method based on the fusion of person detection and person recognition is proposed. Firstly, the improved DLA-34 network is used as the backbone network, and the residual block is improved to improve the object detection accuracy; Then a Re-ID module based on time graph convolution neural network is designed to effectively solve the problem of person identity switching and tracking object loss caused by occlusion and small object in video. Finally, the effectiveness of the algorithm is verified on MOT16 and MOT17 datasets, and the accuracy and tracking accuracy of multi- object tracking are improved. The experimental results show that the network has good detection and tracking effects.