Reviewerly/exHarmony huggingface dataset:
Monte Carlo oversampling algorithm based on Bootstrap method

The problem of imbalanced data has been a hot problem in the field of data mining, and the problem of classification accuracy is low due to the tendency of imbalanced data classification to minority class. In this paper, we propose a new Monte Carlo sampling algorithm based on Bootstrap method. Firstly, the empirical distribution function of the minority class data is first obtained by the Bootstrap method, and then the distribution function is interpolated to obtain the probability density function. In addition, the weights of the new data are determined according to the probability density function of the minority class data. We use the Monte Carlo algorithm to oversample minority class data to make imbalanced data comes into balanced data. Finally, six sets of data are selected from UCI and KEEL databases for comparative study, and the decision tree classifier is selected for classification. Compared with the proposed algorithm in the past, the new algorithm is more feasible through experimental analysis.
GIB-RRT: a gamma interpolation bidirectional RRT algorithm

In a complex environment, when B-RRT and RRT* algorithms are used for path planning, there will be problems such as long planned paths, large number of iterations, low sampling efficiency and long search time. To solve these problems, this paper proposes a gamma interpolation bidirectional RRT algorithm––GIB-RRT. First, the algorithm uses a bidirectional search strategy to expand two random trees simultaneously to speed up the convergence speed. In the expansion process, an adaptive goal biasing strategy is introduced to improve the sampling efficiency, and the probability of expansion to the respective target point is continuously changed according to the number of collision detection failures. After the initial path is obtained, a greedy pruning algorithm is used to simplify the path points and reduce the path cost. An optimisation method of Gamma interpolation is then devised for the simplified path and combined with cubic uniform B-spline curve to generate shorter and smoothly executable path. The proposed algorithm is compared with B-RRT*, IB-RRT* and B-RRT in different complex environments in simulation experiment, and the results show that the proposed algorithm has better search efficiency and is able to obtain optimal path in the least time and with the most stable efficiency.
Low-light image enhancement based on Transformer and CNN architecture

Affected by low photon counts and noise, images captured in low-light environments commonly have low signal-to-noise ratios, details losing, low contrast and other artifacts. Low-light image enhancement which restores clear images from low-light images with unsatisfactory quality is challenging. In this paper, we propose a strong baseline model based on transformer and CNN architecture for low light image enhancement (TrCLLE). In TrCLLE, the advantages of transformer in effectively modeling long-distance dependence and convolutional neural network in modeling local features through inductive bias are integrated. TrCLLE consists of three parts: shallow feature extraction module, deep feature extraction module and high-quality image reconstruction module. At last, the factors affecting performance are analyzed in detail and the experimental results demonstrate that the proposed method achieves enhanced images with higher quality compared to other popular approaches.
Cross-domain recommendation of overlapping users based on self-attention graph convolution network

The cold start and data sparsity problems of the recommendation system can be effectively alleviated by cross-domain recommendation. It is possible to achieve good results by combining information from several knowledge-rich domain sources to recommend users with less information in the target domain . In other cross-domain recommendations, user and domain item information are embedded separately, and messages are transmitted through information sharing or potential feature mapping. A similarity relationship between items between domains and a relationship between users' preferences for items between domains are not considered in the transmission process. This paper proposes Cross-domain recommendation of overlapping users based on self-attention graph convolution network(OUAG). We use the graph convolution neural network to extract characteristics of embedded users and items, the attention mechanism dynamically assigns weights, and captures the higher-order user preferences in the graph through the propagation layer. A large number of experiments on two real-world data sets show that our model performs better than the baseline algorithm.
TripleGAN: Multi-Adversarial Network Domain Adaptation for Satellite Image Segmentation

Satellite image segmentation has become an important auxiliary tool for decision-making in various fields, including city planning, building extraction, traffic control, and environmental protection. However, for satellite images, there is a significant challenge that the datasets are often small and lacked of label. To address this issue, we propose a novel framework called the Triple Generative Adaptive Networks (TripleGAN), which employs domain adaptation to transfer models from labeled datasets to unlabeled ones.TripleGAN is a deep neural network consisting of one segmentation module and three discriminators to reduce the domain gap between labeled and unlabeled datasets. By jointly training three GANs, our model achieves a significantly better performance in handling different data distributions. In experiments on IS-PRS public datasets, TripleGAN outperforms the state-of-the-art model, demonstrating its advantages and providing a promising multi adversarial learning scheme for future researches.
Multi Aspect Attention Online Integrated Distillation For Few-shot Learning

Few-shot learning is to identify new visual patterns with only a small number of labeled samples in each class. We propose an online integrated distillation extraction method KDCP (Multi Aspect Attention Online Integrated Distillation) based upon a multi-attention mechanism. The parallel attention mechanism is used to construct the sub-network branch part to mine the semantic information of the image multi-aspect. The feature distribution obtained from different attention angles is integrated as a teacher network. Let the integrated teacher probability distill the probability of different attention predictions so that the child-student network continuously learns the implicit essential point relationship. Through a major number of experiments on commonly used benchmark datasets, it is proved that the proposed method is superior to the most advanced method, and it is confirmed that multi-aspect online knowledge distillation can clearly impress the accuracy of the network.
A YOLOv4-tiny Based Moving Object Detection Method for Embedded Vision System

YOLO nets have been widely used in the field of object detection,but the nets are designed for specific object detection in single image without motion information.They are difficult to be applied in embedded vision system directly to do the task of moving object detection.In this paper, combining the YOLO net and inter-frame motion detect,a fast moving object detection method is proposed for embedded vision system.First of all,YOLOv4-tiny is applied to detect objects in sequence images; then,by the the confirmation of inter-frame motion information,every detected object is judged to be moving object or not,the moving objects are saved as final result. Experiments show that the algorithm can detect moving objects in real time and accurately.
Channel Estimation Algorithm Based on Deep Learning for OFDM System

In the process of channel estimation in orthogonal frequency division multiplexing (OFDM) systems, reliable channel estimation is the key task to achieve high data rate. The traditional channel interpolation algorithm is based on the assumption that the estimated values near the pilot are correlated. When the channel characteristics are discontinuous due to the time-varying and frequency-variable characteristics of the wireless channel, the estimated results will not be ideal. To solve this problem, this paper introduces a super-resolution reconstruction model, ESRGAN, to replace the interpolation processing in channel estimation. First, the estimated value is obtained by the least squares method (LS) at the pilot, and then the estimated value is compared to the pixel points in the low-resolution image. First, the channel characteristics are extracted through the convolution network, and then the mapping relationship is learned through multiple residual networks, and then amplified by the upper sampling layer. Finally, the estimation effect is continuously judged and improved through the discrimination network. The simulation results show that the channel estimation based on ESRGAN is better than the traditional LS algorithm and the same type of SRCNN model. And even if the model has fewer pilots, it also achieves excellent MSE performance, which shows great application potential in spectrum saving.
Improved tentacle-based method based on approximate analysis for entry flight

Aiming at the no-fly zone constraint in the process of aircraft gliding reentry, this paper presents an improved strategy for tentacle-based detection. The turning radius and reference pitch angle of the aircraft can be obtained by approximate analytical solution and then the waypoint can be obtained according to geometric relationship. Next tentacle-based method is used to correct the trajectory to meet the flight requirements. By approximate analytical solution programming, the number of tentacles required can be reduced, and the influence of integration process on the calculation efficiency can be reduced, thus the calculation time can be shortened effectively. After the no-fly zone is bypassed, height planning is performed to meet the terminal height constraints. The simulation results show that this method can effectively avoid the no-fly zone and satisfy various constraints of gliding reentry process, and The calculation time is shortened to 22.24%.
An Improved Snake Optimization Algorithm and Its Application in Manipulator Trajectory Planning

Manipulator is a kind of widely used mechanical equipment, which has important applications in industry, agriculture, medical and other fields. Manipulator trajectory planning has always been a hot topic in manipulator research. In this paper, an improved snake optimization algorithm is proposed for robot manipulator trajectory planning in complex scenes. Firstly, the mathematical model of trajectory planning problem is constructed and a new cost function with constraint penalty term is proposed. Secondly, the updating mechanism of SO algorithm is improved and a new chaotic local search method is proposed to enhance the search ability and convergence speed of the algorithm. Finally, simulation experiments are carried out in multiple complex environments and compared with traditional algorithms. Experimental results show that ISO algorithm has better performance and stability in trajectory planning.
AGKE: Improve Knowledge Graph Embedding by Adaptively Generating Kernel

Knowledge graph embedding is an important part of knowledge graph research field, and it is the basis of knowledge acquisition and downstream tasks. It enables efficient representation of knowledge by mapping entities and relations into low-dimensional vector spaces. In recent studies, neural network methods for encoding semantic matching have achieved remarkable predictive performance. It brings entities and relations into deep neural networks and computes semantic matching scores. However, some existing methods have weak feature interactions, complex model structures, low generalization ability, and are effective only for specific datasets. Therefore, we propose an improved method for knowledge graph embedding. The model enhances feature interactions by adding an involution module, which adaptively generates convolution kernel parameters based on the input information. The model structure is simple and easy to implement. Our approach is evaluated on several public benchmark datasets. Its evaluation metrics achieve better performance compared to some state-of-the-art models, and the results prove that it is effective.
Ladle Collision Detection Algorithm Based on Three-dimensional Point Cloud Data

To solve the problem of collision detection between the ladle and the object in the workshop during crane movement, a fast collision detection algorithm is proposed based on large-scale 3D point cloud data. The implementation of the algorithm is divided into three stages: model building, point cloud data reading, and collision detection. Firstly, the physical model of the ladle is reasonably abstracted into collision detection points in five directions, and the detection range is set according to the actual application situation, simplifying the collision detection problem without affecting the algorithm’s accuracy. Secondly, according to the distribution characteristics of data points in the point cloud file, the large-scale point cloud data file is preprocessed to generate an index table, which is used to optimize data reading and reduce unnecessary I/O collision detection operations. Finally, to meet the algorithm’s real-time requirements in the production process, the algorithm is optimized using multi-threaded parallel processing technology. Experiments show that the multi-threaded parallel algorithm works much better in real time than the single-threaded algorithm and can improve the efficiency of collision detection by a large amount.
An automatic inspection system of work orders based on text classification

The number of rectification work orders handled by electric power system departments each year is as high as tens of millions, with each work order consisting of hundreds of Chinese characters on average. Inspection work is both heavy and difficult, so it is crucial and urgent to research automated inspection of rectification work orders. In this paper, we utilize the Transformer model as the foundation for building an automatic inspection system for rectification work orders. By analyzing the characteristics of rectification work orders, we propose using comprehensive position encoding and sparse attention in the system. Comprehensive position encoding allows the model to obtain both relative and absolute position information between tokens, while sparse attention enables the model to focus on key tokens and avoid irrelevant information. Our system first processes the text using a self-designed stop words list and data enhancement method. We then use an improved model to extract semantic features, followed by classifying the rectification work orders into two categories to obtain inspection results. The experimental results show that our system can automatically inspect rectification work orders with an accuracy rate of 95.41%.
Pointer-Type Instrument Recognition Based on Improved U<sup>2</sup>-net

The reading and recognition technology of pointer-type instruments have laid the foundation for the realization of intelligent industry. The existing image segmentation method has achieved good results in the reading and recognition stage, but the problem that the low recognition accuracy of pointer and scale in the actual complicated scene is inevitable. As a result, an image segmentation method based on the modified U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net is proposed to improve the recognition accuracy of strip scale and pointer. Firstly, this method presents a SPRSU (Strip Pooling Residual U-block) module to add the attention of strip objects during the process of pointer-type recognition. The recognition accuracy of strip-shaped objects in the segmentation process is considerably improved by adding a strip pooling layer to the residual joint. Secondly, the focal loss function is used to solve the problem of uneven distribution of image samples in the process of instrument segmentation. The experimental results suggests that the proposed method increases pointer and scale recognition accuracy in complicated scenarios.
Intent Recognition and Slot Filling Joint Model for Question-Answering of Hazardous Chemical Knowledge

A question-answering system that provides accurate knowledge about hazardous chemicals is essential for their production and management. The system’s ability to accurately recognize intent and slot in the relevant information in the question sentence is critical to ensuring semantic accuracy in the answers. In response to a field-specific issue that slot information in hazardous chemical questions may impede accurate intent recognition, this paper proposes the CJIRSF-Bert model. This model leverages the location and intent information of slots to strengthen the relationship between key slots and intents. To further enhance intent recognition accuracy, we create a hazardous chemical question dataset through manual labeling. Experimental verification demonstrates that the CJIRSF-Bert model achieves a semantic frame accuracy rate of 91.6% on the dataset, surpassing the Joint-Bert model’s performance by 1.4%. This model effectively addresses the challenges of intent recognition and slot filling in hazardous chemical question-answering systems.
A simple distributed fuzzy c-means clustering method via the technique of Map-Reduce

The clustering problems are one of the fundamental data analysis tasks in machine learning and data mining. However, many traditional clustering methods cannot directly deal with large-scale data sets as the restriction of memory. In this paper, we develop a simple distributed fuzzy c-means clustering method via the technique of Map-Reduce for large-scale data sets. First, a distributed data dividing method is designed to randomly partition the original data set to several data blocks. Then, we establish a data center determining method through applying fuzzy c-means clustering algorithm in the framework of Map-Reduce. Finally, a distributed clustering method is proposed according to the distance of between each sample and the nearest center. The experimental studies verify the feasibility in clustering accuracy and the parallelism in clustering time.
An improved YOLOv7 method for vehicle detection in traffic scenes

Vehicle target detection is a key task in intelligent traffic management and has important significance for the construction of intelligent transportation systems. To address the problem of the adaptability of target detection algorithms in actual traffic scenes, a vehicle target detection algorithm based on the improvement of YOLOv7 is proposed. Firstly, the original backbone network is replaced with the MobileNetV3 network with a depth separable convolution structure, which reduces the model parameters and computational complexity. Secondly, the BiFPN structure is introduced in the feature fusion module to combine the position information of low-level feature maps with the semantic information of high-level feature maps. Finally, the Focal-Loss EIOU is used as the positioning loss function to accelerate the convergence speed of the predicted box. Based on the UA-DETRAC dataset, the best improvement strategy is determined through comparative experiments, and the superiority of the combination of improvement strategies is verified through ablation experiments. The experimental results show that the improved YOLOv7 algorithm has an average precision of 93.1%, which is 6.6% higher than the original YOLOv7, and the frame rate reaches 41, meeting the real-time requirements.
Mask detection based on R2-yolov5

Properly wearing masks in specific situations can effectively inhibit virus transmission, while manual detection can be labor-intensive and inefficient. To solve this problem, a target detection algorithm based on R2-yolov5 is proposed. Based on yolov5, the residual module bottleneck in the unique C3 structure is replaced by the Res2Net residual module and optimized in terms of channels and activation functions, and CBAM module is introduced to improve the detection effect. Through experiments, the map of R2-yolov5 proposed in this paper can reach 88.9% tested on the mask dataset, which is 2.4% better than the yolov5 network, and there is a significant improvement in the detection effect.
Multimodal Fusion for Human Action Recognition via Spatial Transformer

The recent generation of Microsoft Kinect cameras capture a range of multimodal signals providing RGB video, depth sequences, and skeleton information, making it an option to achieve enhanced HAR performance by fusing different data models. However, most existing fusion methods simply fuse multimodal features with a large amount of background noise, ignoring the underlying semantics between different models, resulting in a lack of accuracy. In this work, we propose a bilinear pooling and attention network (BPAN) fusion mechanism based on spatial transformers for human action recognition. This work improves the recognition accuracy in the following aspects.1) An effective two-stream feature pooling and fusion mechanism is proposed. RGB frames and skeletons are fused to enhance the spatiotemporal feature representation. 2) A spatial visual transformer is proposed and the framework is evaluated based on two widely used video action benchmarks, and the proposed method exhibits more favorable performance compared to the existing state-of-the-art methods.
Self-supervised learning based multi-modal intra-hour irradiance forecast

Multi-modal short-term irradiance forecast models usually fuse multi-source heterogeneous variables to predict future irrdiance and ground-based cloud images is one of the important modal. The dynamic information of clouds is shown to improve the models performance. However, such methods usually use original images directly for prediction, which decreases the efficiency and practicality of the training process. Therefore, a self-supervised learning method is proposed to learn the features of ground-based cloud images and meteorological factors. The obtained representations of exogenous variables are then used as the input for multi-modal short-term irradiance forecast. This method is validated on an open access dataset, and the results demonstrate that the self-supervised based approach outperforms other classical forecast models. Meanwhile, the images are compressed into vectors, which not only saves storage space but also reduces the training time of the prediction model significantly. In addition, the model achieves good transferability for ground-based cloud images with different qualities.