Reviewerly/exHarmony huggingface dataset:
Arabic handwritten digit classification without gradients: Pseudoinverse Learners

The topic of handwritten digit recognition (HDR) has drawn more and more attention in recent years. The biggest drawback of HDR is the lack of an efficient model that can categorise the handwritten numbers that users present via digital devices. Various methods have been developed to enhance HDR in Arabic, using on sophisticated deep learning techniques such convolution neural networks (CNNs), which are learning using a backpropagation algorithm that has many drawbacks, including: 1) Local minima possibility, 2) long learning time, 3) Non-assured convergence, 4) Selective learning data and 5) Black box: the inner mapping techniques of the BP are remaining unclear and not grasped. To overcome these limitations, this paper introduces the use of pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE is not a gradient descent strategy; however, it is not required to specify the learning rate or suggest the quantity of hidden layers and the drawback of gradient vanishing. According to experimental findings, the introduced technique improves test accuracy while maximising computational efficiency.
Analysis of Research Status and Development Trend of Industrial Robot Based on Knowledge Map

In recent years, the literature in the field of industrial robot research has increased significantly, but these literatures have not been systematically and quantitatively summarized. Based on the scientific knowledge map, this paper uses China National Knowledge Infrastructure (CNKI) database and Web of Science (WOS) core database 1990-2022 industrial robot related literature as the data base, and uses CiteSpace literature visualization software to analyze the countries, institutions, keywords, etc. of the extracted literature, so as to obtain the corresponding visual knowledge map. Through the interpretation of the visual knowledge map, researchers in the field of industrial robot research can have a more overall and macro grasp of the research history, research status, development trend, etc. in this field.
Joint Distribution and Class-based Data Augmentation for Wildlife Detection

Data augmentation is of great importance to alleviate the insufficiency of training samples, and further improve wildlife detection accuracy. However, current data augmentation methods tend to augment all kinds of samples equally, ignoring the problem of uneven distribution of the number and size of all kinds of samples in wildlife detection datasets, resulting in poor generalization of the model. To address this problem, this paper proposes a joint distribution and class-based data augmentation method for wildlife detection. In this method, diverse rather than universal data augmentation methods are introduced for different classes with a small proportion. This makes the distributions of different classes more balanced. Therefore, each class even with a small number of samples gets good training as well. To evaluate the effectiveness of the proposed method, extensive comparative experiments are conducted. Experimental results show the superiority of our proposed method. Specifically, the detection accuracy of Faster RCNN with Swin Transformer as the backbone network is improved by 0.8% to 96.2% after data augmentation with our method.
Knowledge Base Personalized Updation Based on User Profile

Knowledge graphs are a hot topic in the field of artificial intelligence. From the beginning of Google search to today’s chatbots, big data risk control, securities investment, intelligent medical care, adaptive education and recommendation systems, all are related to knowledge graphs. The popularity of knowledge graphs in the technology field is also increasing year by year. The construction of a knowledge graph mainly involves creating the mirror image of the encyclopedia knowledge base at a certain timestamp. Currently, information is explored so fast that this knowledge will soon become out of date.
An End-to-End Model for Printed Uyghur Text Recognition

The recognition method based on the end-to-end model has a wide range of applications in Chinese and English text recognition, but there are few researches of Uygur text recognition. Using the end-to-end method can effectively avoid the wrong recognition problem caused by the mis-segmentation of Uyghur letters. Based on the Transformer model, we propose a model named EfficientNet-Transformer for printed Uyghur text recognition. By replacing the SE attention of EfficientNet with Triplet attention, the computing ability of the network for spatial and channel attention is improved. The encoder of the original Transformer model is replaced by an improved EfficientNet, which makes the model simpler and fewer parameters. The dataset is expanded by synthesizing words through Uyghur syllable rules, and comparative experiments are carried out on this dataset by using our model with other’s RNN-based model. Experiments show this model is superior to others models in Character error rate, recognition speed and space occupation.
Performance Analysis on Vibe Detection of Moving Object

Moving object detection is an important research hotspot in the field of image processing, which is the basis of object classification and object tracking. On the one hand, moving object detection should adapt to the changes of environment and itself, on the other hand, it should take into account the accuracy and real-time of detection. ViBe algorithm is a simple and fast algorithm in moving object detection methods. It has no specific requirements for video format, color space, and background relationship. It only needs a small number of sample values to cover the background samples, and has both accuracy and computational efficiency. How to choose parameters is the key problem to determine the performance of ViBe algorithm. This paper mainly studies the ViBe detection method for moving objects, and optimizes the main parameters of the ViBe algorithm through simulation. Firstly, the sample radius is selected according to the target resolution; Secondly, the updating factor of time parameters and the number of samples in the pixel model are optimized; Finally, the minimum matching number is selected to constrain the number of matching samples to classify new pixels. The experimental results show that the method has good performance in moving object detection and can retain the integrity of moving objects.
Design of Image Recognition Monitoring System of Hydrological Monitoring Station Based on Edge Intelligence

In the safety monitoring of hydrological monitoring stations, the target detection algorithm can be used to avoid unnecessary trouble caused by artificial supervision through intelligent monitoring. However, the accuracy of target recognition and time - delay is always a contradiction in security monitoring. SSD target detection algorithm is the latest target recognition algorithm after Faster RCNN and YOLOv1 algorithm, combining the advantages of both. The algorithm is faster than the fast RCNN algorithm and has higher accuracy than the YOLOv1 algorithm. In this paper, a PSO intelligent algorithm based on hyperlight fast generic face detection and 1mb network convergence is proposed, called PSO-1MB. The device is deployed in or near the hydrologic monitoring station on the edge node server for calculation and processing. In this paper, the test whether the staff wear a hard hat as an example, using the Pytorch environmental framework, experiment simulation. Experimental results show that this model and algorithm can detect helmet features more accurately and quickly, and can better meet the engineering requirements.
Research on Collision Avoidance Path Planning of Dual Manipulator Robot Based on Fusion Algorithm

When a dual manipulator robot performs handling operations in a complex space, it is important to plan a collision avoidance path to the target point quickly and accurately. In order to solve the problems of local minimum and high sampling randomness of traditional path planning algorithms, a new fusion algorithm is proposed for global path planning of a dual manipulator robot. First, an improved artificial potential field (IAPF) method is mentioned for path planning in the start and target point areas. Then, for the local minimum problem, an improved RRT algorithm (IRRT) based on the ε-greedy sampling target biasing strategy and repeated iterative update strategy is fused to reduce the randomness of random tree growth, and explore an optimal path that grows toward the target as much as possible for jumping out of the local minimum area. The URDF model file of the dual manipulator robot is created, and simulation experiments of path planning are conducted based on the Rviz visualization tool under Ros system, which proves the effectiveness of the fusion algorithm.
Dynamic Reversible Data Hiding for Edge Contrast Enhancement of Medical Image

Reversible data hiding (RDH) for medical image contrast enhancement is designed to effectively improve the quality of medical images to help doctors make correct diagnosis, while addressing issues of privacy protection and image content integrity. In this paper, we propose a new RDH method for medical image contrast enhancement. To enhance the edge contour of medical image, we employ the superpixel segmentation to identify region of interest (ROI), and then improve the region contrast to facilitate the diagnosis. A new histogram modification is proposed to achieve a local histogram equalization effect. Two adjacent bins with the largest difference in number are selected for expansion, in order to spread the histogram evenly as much as possible. In addition, the histogram modification is adaptive to the expansion bins by using the multiple modification manner, and can spread out the highly populated bins more evenly. Experimental results verify that, compared with the existing typical methods, the proposed method can better improve the medical image quality after data embedding in terms of contrast.
Asynchronous Deep Federated Learning Method Based on Local Trainer Data Pollution

The essence of federated learning is that each trainer cooperates with other trainers to achieve strong robustness and high prediction accuracy on the premise of ensuring their own private data security. When some local trainers’ data are polluted, it may cause systemic disasters. How to efficiently detect abnormal servers and dynamically manage abnormal local trainers is currently a challenging research direction. Based on this, this paper proposes the N batch RCF abnormal client federated detection algorithm based on normalized gradient. RCF detects the client abnormal gradient and records it in the abnormal gradient queue. If the N consecutive batch gradients uploaded by the client are abnormal, the communication with the global server is terminated. When the upload gradient does not meet the communication termination conditions, the communication is resumed; extracts sample features based on heterogeneous multi-model fusion to fully learn the spatial features of sample features.
Hard Contrastive Learning for Video Captioning

Maximum likelihood estimation has been widely adopted along with the encoder-decoder framework for video captioning. However, it ignores the structure of sentences and restrains the diversity and distinction of generated captions. To address this issue, we propose a hard contrastive learning (HCL) method for video captioning. Specifically, built on the encoder-decoder framework, we introduce mismatched <video, caption> pairs to learn a reference distribution of video descriptions. The target model on the matched pairs is learned on top the reference model, which improves the distinctiveness of generated captions. In addition, we further boost the distinctiveness of the captions by developing a hard mining technique to select the hardest mismatched pairs within the contrastive learning framework. Finally, the relationships among multiple relevant captions for each video is consider to encourage the diversity of generated captions. The proposed method generates high quality captions which effectively capture the specialties in individual videos. Extensive experiments on two benchmark datasets, i.e., MSVD and MSR-VTT, show that our approach outperforms state-of-the-art methods.
Speech Recognition Model of Civil Aviation Radiotelephony Communication Based on Improved Conformer

Radiotelephony communication has a special grammatical structure and pronunciation, and it is difficult to apply the model of generic speech recognition directly to the field of radiotelephony communication. We propose a Conv1DSlide-Conformer model for speech recognition of radiotelephony communication. The sliding-window attention mechanism is used instead of the self-attention mechanism to improve the decoding speed of the model and increase the adaptability of the model to radiotelephony communication. The convolutional module is used instead of the feedforward neural network module to make the encoder focus more on local information. The improved Conformer model processes the FBANK features of radiotelephony communication and can extract high-dimensional features that better fit the characteristics of radiotelephony communication. The use of concatenated temporal classification (CTC) combined with a data augmentation strategy assists training to speed up convergence during model training and reduce the complexity of model training. Decoding is assisted by CTC and language models to improve the performance of speech recognition. The experimental results show that the improved Conformer speech recognition model in this paper reduces the word error rate to 8.1% and 7.8% on the actual Chinese radiotelephony communication speech dataset.
Research and Development Trend of Knowledge Graph Construction Technology

Taking the construction of knowledge atlas as the main line, this paper introduces the concept definition, architecture and construction technology of knowledge atlas, discusses the problems and challenges in the iterative process of constructing knowledge atlas, and finally looks forward to the possible research directions in the future. In order to help interested readers fully understand and understand the technology.
The Generation Method of Simulation Scenario Sample Space Based on Sensitivity Analysis of Meta-model

To ensure the feasibility and effectiveness of exploratory simulation experiments, it is necessary to take the simulation scenario sample space with acceptable scale and typical representative as input. In this paper, a method of generating simulation scenario sample space combining qualitative and quantitative analysis is proposed. This method constructs a machine learning meta-model based on simulation pre-experiment, and screens the key experimental factors based on sensitivity analysis of meta-model to determine the factor levels. Finally, the space is sampled and compressed to complete the generation of the hypothetical sample space.
Improved Gray Wolf Optimization (GWO) Algorithm for Path Planning

As we all known that existing optimization algorithms for path planning are easy to arrive local optimum state. An improved gray wolf optimization (GWO) algorithm is proposed for path planning application. GWO initializes the population randomly, which will cause poor population diversity. The searching mechanism of GWO will slow down the convergence rate and arrive the local optimal state. For the shortcomings of GWO, the initial population, searching mechanism and convergence factor have been improved. Firstly, the chaos mapping strategy is introduced to initialize the population for avoiding the uneven initial distribution of wolves individuals. Secondly, by using an adaptive solution to the convergence factor, the problem of convergence rate of GWO is improved. Finally, the weighted algorithm is presented to update the individual positions. In the paper, six classical functions are used to simulate and test particle swarm optimization (PSO), GWO and improved GWO algorithm. The results show that the improved GWO has better optimization ability and stability. The improved GWO is utilized to the path planning application with 3D non-grid map scene. The simulation results show that the proposed improved GWO for path planning achieve better performance.
HLA: Harmonized Label Assigner for Two-stage Oriented Object Detection

The existing state-of-the-arts two-stage oriented object detectors have no significant improvement in the label assignment strategies, and the most widely-used one is the so-called Max IoU Assigner (MIA). In this paper, we first illustrate that MIA may cause matching conflicts in some cases, hinder the matching of ground-truth (GT) boxes with high-quality samples, which is extremely harmful to the training process. After that, we propose a Harmonized Label Assigner (HLA) for the oriented RPN, which can automatically harmonize the assignment priority of each GT box according to the corresponding number of candidate samples, solve the matching conflicts, and improve the detection accuracy of the two-stage oriented detectors. Finally, we implement the proposed HLA on Oriented R-CNN and conduct sufficient experiments on two public datasets (MAR20 and HRSC2016). Without tricks, our HLA significantly improves the detection accuracy of the detector to 83.97% mAP (on MAR20) and 90.42% mAP (on HRSC2016), respectively.
Dual-semantic Graph Similarity Learning for Image-text Matching

Image-text matching has received increasing attention because it enables the interaction between vision and language. Existing approaches have two limitations. First, most existing methods only pay attention to learning paired samples, ignoring the similar semantic information in the same modality. Second, the current methods lack interaction between local and global features, resulting in the mismatch of certain image regions or words due to the lack of global information. To solve the above problems, we propose a new dual semantic graph similarity learning (DSGSL) network, which consists of a feature enhancement module for learning compact features and a feature alignment module that learns the relations between global and local features. In the feature enhancement module, similar samples are processed as a graph, and a graph convolutional network is used to extract similar features to reconstruct the global feature representation. In addition, we use a gated fusion network to obtain discriminative sample representations by selecting salient features from other modalities and filtering out insignificant information. In the feature alignment module, we construct a dual semantic graph for every sample to learn the association between local features and global features. Numerous experiments on MS-COCO and Flicr30K have shown that our approach reaches the most advanced performance.
A Graph Neural Network with Type-Feature Attention for Node Classification on Heterogeneous Graphs

Heterogeneous graphs are emerging as a prevalent form of data representation to capture complex structures and different relationships between a set of different types of objects in diverse disciplines. Node classification on heterogeneous graphs is a basic and critical task that remains unaddressed until the present day. Graph Neural Network is a powerful tool and has demonstrated remarkable performance in various tasks on graphs. However, most existing graph neural networks are based on the homophily assumption, which may be unsuitable for heterogeneous graphs. In this paper, we propose a graph neural network with type-feature attention mechanism to solve the problem of node classification on heterogeneous graphs. As a heterogeneous graph is composed of a group of edges between different types of nodes, it is reasonable to assume that each type of edge plays a different role in message propagation with different importance. An attention mechanism that considers the edge type and the features of the end nodes of the corresponding edge is built and incorporated into the process of message propagation of the graph neural network, by which the different heterogeneous information of nodes and edges is used jointly in solving the problem of node classification. We evaluate the proposed method on two public real-world heterogeneous graphs and the experimental results demonstrate the effectiveness of the proposed method.
A 3-Layer Deep Learning Model for Predicting Breast Cancer Disease

Breast cancer is the major form of cancer attacking Malaysian women. Almost one over 20 women in Malaysia has been diagnosed to have this disease. Breast cancer is also called as malignant (cancerous) tumor initiating from the cells of the breast. Breast cancer disease is shown to contribute a high rate of mortality for women in worldwide. Thus, a mechanism for an early detection and prediction is of a great concern. The Machine Learning (ML) and Deep Learning (DL) approaches are shown to have a significant impact on the process of breast cancer prediction for an early awareness. Both machine learning and deep learning have become a research hotspot due to the highest prediction accuracy percentage have been reported. As for deep learning, it derives from a machine learning technique, which is neural network. This research attempts to explore on deep neural network or also called deepnets. How well the neural network will be used on how many layers of the neural network can contribute to the highest percentage of prediction accuracy. This paper presents the results of breast cancer prediction prior to adopting a 3-layer deep learning approach. We went through a 3-layer deep neural network in this research on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. The results of the experimentation reveal the significant prediction accuracy of approximately 0.9809 as the indication of our proposed deep learning approach has given a significant contribution in breast cancer disease.
Research on Application of Personalized Recommendation Technology in Adaptive Learning System Based on Java Programming

This paper taking the course of Java programming as an example, an adaptive learning question bank system based on personalized recommendation is realized by using collaborative filtering recommendation algorithm. Personalized test exercises that are suitable for students' own curriculum learning status can help students improve their learning efficiency and effectively promote the improvement and progress of students' academic achievements. At the same time, the application of this system can provide a reference basis for other adaptive learning systems based on personalized recommendation.
Fast Route Planning on DEM Big Data with Cost Constraint Sampling

The current method of route planning on digital terrain model with no road network mainly adopts the algorithm of Dijkstra and A*. When the target area is large and the data resolution is high, the planning calculation will take a long time. In this paper, a fast route planning method for no road area based on cost constraint sampling is proposed. The Bresenham algorithm is used to design the cost calculation mode between sampling points, which solves the problem that the method cannot calculate the cost between non adjacent grid points on the digital elevation model, making it possible to obtain the routes based on sampling on the digital elevation model. At the same time, the sampling function is optimized in combination with the path traffic cost to improve the utilization of sampling points and reduce the computational scale of route planning, which greatly improve the speed of route planning. Experiments show that the proposed methods get the routes quickly, and has better route quality than the traditional uniform sampling PRM method. The proposed method has greater advantages in the rapid rough route planning in the area without road network.