Reviewerly/exHarmony huggingface dataset:
Design and research of one-stop system of rural public service governance based on Big data technology

As an important content of rural governance, the modernization of rural public service governance is the need to complement the weaknesses of rural basic public service and improve the quality of public service. It is also an important basis for ensuring and improving people's livelihood and realizing rural revitalization. After clarifying the key governance objects and supply list of rural public services, the intelligent and digital construction of rural public service governance is well promoted, and the multi-subject collaborative governance of rural public service governance is realized. In this paper, an optimized Apriori algorithm is proposed for the massive data generated by the existing rural public service governance "one-stop" system. The optimized Apriori algorithm solves the problem that the efficiency of massive data mining in the system is not affected by the growth of massive data volume. The performance of the improved algorithm becomes more prominent when the support degree is less, and the advantages become more obvious.
Linear regression active learning based on IRD algorithm

Learning data samples have value and labeling data can train machine learning models. Existing active learning methods are based on supervised scenarios: initial model is trained from a small number of labeled samples, new samples are queried based on the model, and then the model is iteratively updated. Unsupervised active learning is rarely considered, that is, the best choice of initial training samples to be labeled without knowing any labeling information. In this scenario, the active learning problem is made more difficult because no label information can be utilized. For this scenario, the linear regression problem based on IRD algorithm is studied in this paper, and a new active learning method is proposed, which takes into account the three criteria of information, representativeness and diversity.
Eye Gaze Correction Using Recurrent Residual U-net-based Conditional GAN(R2A-cGAN) with AdaIN

The eye gaze correction is used to make eye contact since the different position of camera and the screen. The previous solution to the problem of eye-gaze misalignment is to generate viewpoint-transformed images using a cGAN model. By this method, the details of facial organs such as contours, teeth, and lips are blurred. We use AdaIN and Recurrent Residual Convolution Neural Network to improve the cGAN generator in order to make the details of the generated images more clear.
Research on Face Expression Recognition Based on Deep Learning

Facial expression is the most powerful, natural and universal nonverbal means for human beings to convey their emotional state and intention in the process of communication. Face expression recognition can make robots read human emotions and become more intelligent. Therefore, facial expression recognition has become one of the research hotspots in artificial intelligence, human-computer interaction and image recognition. In recent years, deep learning is a hot research direction of facial expression recognition, which can effectively improve the face recognition rate. This paper first introduces the relevant knowledge of facial expression recognition, then summarizes the commonly used deep learning algorithms and the latest research progress in facial expression recognition, and finally summarizes the main technical challenges and research difficulties in facial expression recognition.
Decorative image generation algorithm based on feature line segment synthesis

For the intelligent generation system of decorative patterns, the acquisition, drawing and entry of elements has always been a bottleneck problem. Therefore, a method of generating new pattern elements by applying the idea of comprehensive reasoning is proposed. This method can directly start from the image information of existing pattern elements and according to certain characteristic requirements. New pattern elements or original elements can be generated rapidly, thus greatly enriching the pattern element library and alleviating the problems of element acquisition, drawing and input to a certain extent.
Utilizing Relaxed A* and Adaptive Window Approach toward Hybrid Path Planning for Autonomous Mobile Robots

In recent years, demand for path planning solutions for autonomous mobile robots has increased. Particularly, challenges such as reducing execution time in large-scale environments and ensuring safety for humans have been identified. In this paper, we address these challenges by employing hybrid path planning, which combines global path planning and local path planning, and improving the global path planning aspect. Through simulation experiments, we confirmed that the proposed system reduces execution time and path length while ensuring safety.
Enhancing Age and Gender Classification through cGAN-based Data Augmentation

Age and gender classification from facial images have wide-ranging applications. Deep learning has revolutionized these tasks, outperforming traditional machine learning methods. However, existing studies have focused on individual models, leading to a performance gap and a need for alternative approaches. In this paper, we propose a data augmentation method for age and gender classification using geometric augmentation, an age transformation model, and a filtering system. Our approach utilizes a conditional Generative Adversarial Network (cGAN) with cropping and flipping techniques to augment the dataset, enhancing its diversity. We also introduce a filtering system to improve the reliability of the age and gender classification model by ensuring the accuracy of labeled synthesized data. We evaluate our method using customized VGG16 and ConvNeXt models and observe significant improvements in age and gender classification tasks.
Road Target Detection Based on Lightweight Improved YOLOv5l

In the current stage of autonomous driving technology, road target detection is a key problem for performing related operations. However, the most of the target detection models are not suitable or difficult to be applied in practical real-time road target detection due to the problems of the large parameter computation and the long discriminative time. To address this series of issues, In this paper, we propose a method based on the lightweight improvement of YOLOv5l used into the target detection of road target. Firstly, the Slim-Neck structure is introduced into the original model's Neck to decrease the volume of operations and increase the accuracy of the original model; secondly, the framework which is used in the backbone network of YOLOv5l is altered by the PP-LCNet network structure to reduce the large volume of operations required by the model in the feature extraction of images and substantially increase the operating speed of the model; Finally, inspired by RFBNet, the SPPF layer of the original model is replaced by the RFB structure in RFBNet to prevent the loss of model accuracy in the case of a large reduction of floating point operations and to increase the original model's detection accuracy. According to the experimental results, the improved YOLOv5l model is compared with the original YOLOv5l model, the size of model is reduced to 43.33% of the original size, the operation parameters of model are reduced to 43.95% of the original size, the floating point operations of model are reduced to 28.23% of the original size, and the accuracy of model is improved by 2.46% to 95.77%. The result of experiments shows that the model which is proposed in this paper based on the lightweight improved YOLOv5l can be more efficiently and accurately applied in the practical scenarios for road target detection.
Study on Medical Entity Recognition in Chinese academic articles on Hypertension

The research results of Chinese Medical academic articles are more accurate and representative than other medical texts. This paper studies the extraction method of medical entity recognition in Chinese academic articles on hypertension. This paper proposes a BiLSTM-CRF (Bidirectional Long Short-Term Memory and Conditional Random Fields) model blended with TFE (Text Feature Embedding) and BERT (Bidirectional Encoder Representation from Transformers) for Chinese medical entity recognition. BERT model was used for text pre-processing while text features of Chinese medical academic articles were obtained, and then the annotation and classification of medical entity were completed via three models. Compared with the BiLSTM-CRF model and BERT-BiLSTM-CRF model, the recognition effect of the BERT-TFE-BiLSTM-CRF model proposed in this study was better than the other two models and the F1 value of model was 6.9% and 3.3% higher respectively. The BERT-TFE-BiLSTM-CRF model can effectively recognize medical entity in Chinese academic articles on hypertension.
Transaction Database Encryption Technology based on Blockchain Technology

In order to improve the security and reliability of digital asset transaction management, this paper summarizes the current data transaction mode and believes that the ability of data transaction platform to retain transaction data has become a potential threat to both sides of the transaction. A transaction database encryption technology based on blockchain technology and open source and open mechanism is proposed. The whole technology system includes public area, member area and node equipment, and a transaction platform service system is built in the third party. Take the user credit information sharing data standard as an example. It realizes such functions as membership authentication, submission of data index, blockchain generation of shared data index, information query and access, and information supervision. Through the verification of the transaction database encryption technology in this paper, it is found that it can effectively resist malicious tampering and network anomalies, and ensure the security of transaction information data. Combining the test results, it is found that the leader node can trigger an automatic election at each outage, with a success rate of 100% and the average time of about 14.5s.
Lightweight classification method of human action in long video of basketball competition

Aiming at the problem that the current lightweight classification effect of human motion is poor, this paper puts forward the lightweight classification method of human motion in the long video of basketball sports competition, collects and recognizes the characteristics of human motion in the long video of basketball sports competition combined with the principle of neural network, and further uses the deep learning model to lightweight classify the motion characteristics. Finally, it is confirmed by experiments, The lightweight classification method of human action in long video of basketball sports competition has high practicability and accuracy. It can better know that basketball players carry out sports training and fully meet the research requirements.
Research on Text Mechanism of the Legal Protection of Personal Information Based on Big Data

With the continuous advancement of urban information construction, urban applications are increasingly rich, and various business applications have evolved from "single application" to "network application" and from "deep application" to "intelligent application". Information resources are highly shared and deeply integrated. All kinds of information networks carry a large number of important data such as population information, transaction information and order information. The development of information technology has brought a series of new legal issues. Although the legal construction of personal information is constantly improving, there are still many problems. This paper firstly introduces the status quo of network user privacy protection under the background of big data, analyzes the challenges faced by privacy protection at present, and analyzes effective measures for user privacy protection from two aspects of technology and countermeasures.
Implementation of data mining algorithm based on Cluster analysis for sports training technique and tactics analysis

This paper proposes a new Novel Global K-meansClustering Algorithm (NGKCA). This algorithm uses NJW algorithm of spectral clustering to reduce and normalize the data, and optimizes the global Kmeans algorithm based on the fast global convergence of PSO. The proposed algorithm overcomes the shortcomings of the detection rate and stability of the classical clustering algorithm and is suitable for solving the clustering problem in the environment of big data. NGKCA clustering algorithm consists of four stages: Firstly, the spectral clustering NJw algorithm is used for column reduction and data normalization of the large data set. Secondly, the particle swarm optimization algorithm, which is insensitive to the initial value, is introduced for row dimension reduction of the data set so as to select the temporary clustering center set. Then, the optimal clustering center set is clustered by the global Kmeans algorithm to obtain the clustering center point. Finally, the particle swarm optimization algorithm is used to adjust the clustering center point and obtain the final clustering division.
Object recognition system of sports equipment based on convolutional neural network

Based on the performance characteristics of DSP and convolutional neural network technology, this paper tries to solve the shortcomings of traditional image recognition technology such as slow speed, low precision and high equipment requirements, and realizes the operation of convolutional neural network framework on embedded devices. According to the principle of similar scale, these public image data are screened and classified, and individual images that affect the classification are eliminated. The data set is expanded by reflection and noise, etc., and the effect is tested by experimental network training. The final data set is formed (the training set contains 53,000 pictures, and the test set contains 21000 pictures). Understand the performance of different network frameworks and network models in the field of image recognition, combined with the characteristics of embedded devices to be used, determine the appropriate network for model training. The transfer learning strategy is adopted to improve the training speed and efficiency, and finally a more effective recognition model is obtained.
Research on Recommendation Algorithm of Graduate Group Based on GGRBNMF

In recent years, with the rapid development of media technology, the phenomenon of group activities of college graduates has gradually increased, and the group recommendation system has gradually attracted attention. The existing group recommendation system tends to treat different members as homogenous objects, ignoring the relationship between the professional background of members and the inherent attributes of the project, and failing to solve the preference conflict problem in the integration process. Therefore, a recommendation algorithm of graduate group based on non-negative matrix decomposition is proposed. The group grading information is decomposed into graduate matrix and project matrix through non-negative matrix decomposition The membership matrix and the member professionalism matrix were calculated by using the membership and expertise weights respectively, and the group preference model was constructed by obtaining the contributions of each member in different projects. Experimental results show that the proposed algorithm still has high recommendation accuracy under the condition of group size and intra-group similarity.
Metaheuristic Optimized Extreme Gradient Boosting for Solar Flare Prediction

Intense electromagnetic activity on the Sun’s surface can lead to events known as solar flares that often lead to mass ejections and other solar events. Sufficiently powerful solar events can make it to the Earth and interfere with telecommunication systems. Notably energetic events can even affect satellite and ground communication infrastructure potentially causing massive damage. Extensive monitoring systems in combination with robust forecasting techniques can be used as an early warning system to minimize potential damage. This work proposes a method based on extreme gradient boosting (XGBoost) applied to solar flare classification. As the performance of XGBoost heavily relies on adequate hyperparameter selection, a modified metaheuristic algorithm is also introduced to tune network hyperparameters. The introduced approach has been evaluated on a real-world dataset and a comparative analysis has been carried out against several contemporary algorithms tackling the same task under identical conditions. The introduced approach has attained promising outcomes.
AGVO: Adaptive Geometry-Based Velocity Obstacle for Heterogenous UAVs Collision Avoidance in UTM

We introduce Adaptive Geometry-based Velocity Obstacle(AGVO), a novel approach for collision avoidance in Un manned Traffic Management(UTM) scenarios with heterogenous UAVs, namely fixed-wing and quadrotor UAVs. Our approach allows collision avoidance while maintaining a minimum safe distance from other vehicles using state information from UTM in the case of heterogeneous UAVs with different collision-avoidance algorithms. To enable collision avoidance for heterogeneous UAVs with different collision avoidance algorithms, we have modified the Velocity Obstacle algorithm to account for the relative speed of all the surrounding UAVs when searching for collision avoidance velocity. We evaluate our algorithm's effectiveness against other State-of-the-art online planners for non-reciprocal and heterogeneous collision avoidance in different mission scenarios with varying numbers of UAVs.
Data Visualization Optimization of Force-Oriented Layout Algorithm

The traditional force-directed layout algorithm has a large variation in effect after data visualization, which affects the display effect after data visualization. Accordingly, an artificial bee colony algorithm is proposed for iterative parameter optimization of the force-oriented layout algorithm. In this paper, the optimal node deviation and the optimal distribution distance are used as parameter sets to improve the traditional force-oriented layout algorithm using iterative bee colony optimization. The improved algorithm follows the node deviations and distribution distances in the aesthetic criterion and can obtain better visualization results. Experiments show that the improved algorithm provides better visualization and the layout effect is more in line with the aesthetic criteria.
Research on educational big data analysis based on Hive and Spark

With the increasing number of students in colleges and universities, a large amount of educational big data has been accumulated in daily teaching work. How to make full use of educational big data to serve education and teaching has theoretical and practical significance. In order to mine the hidden value of educational big data and improve teaching quality, this paper processes and analyzes educational big data based on Hive data warehouse technology. First, the educational big data is collected from the Oracle database to the data warehouse through the Sqoop tool, and the analysis results are obtained through data cleaning, data transformation, data statistics and other steps. Hive and Spark are used in data analysis and statistics. The analysis results are stored to the MySQL and Redis, and finally the analysis results are visualized through the front-end and back-end separation technology.
A Novel Image Super-Resolution Approach for Industrial Product Visual Enhancement

This study proposes a novel approach for image super-resolution (SR) that combines deep learning algorithms, adaptive multi-path structures, and classification models. The goal is to enhance industrial product details, improve visual quality, and achieve better quantitative metrics such as PSNR and SSIM. The proposed multi-path super-resolution algorithm utilizes a big-size convolution kernel and residual network to extract features at different scales, enabling the capture and enhancement of fine details in low-resolution images. Two different loss functions are incorporated to improve the visual quality and fidelity of the SR images. Furthermore, the integration of a super-resolution model with a ResNet-18 classification model enhances image clarity, detail retention, and overall performance,. The experimental results demonstrate that our proposed super-resolution (SR) algorithm outperforms several typical methods. Additionally, incorporating the ResNet-18 classification model improves the performance of the model on the NEU-CLS dataset, achieving higher accuracy, recall, precision, and F1-score compared to the original dataset.
Breakthroughs and Challenges of Machine Translation in the Era of Artificial Intelligence

ChatGPT innovatively integrates the existing artificial intelligence methods and techniques, while the innovation of artificial intelligence has propelled the further development of neural machine translation. Each critical stage that machine translation progress has gone through, from rule-based machine translation to statistical machine translation to neural machine translation, has made great breakthroughs, but also faces challenges.
An improved K-means clustering algorithm based on spark framework

The K-means algorithm is a common clustering algorithm for data mining, which has a slow iteration speed when dealing with large-scale data, and the initial cluster center selection also has a large impact on the clustering results. Therefore, this paper proposes an improved K-means algorithm based on the spark framework, and the performance of the algorithm is experimentally verified. The experimental results show that the improved K-means algorithm based on the Spark framework has good clustering effect and accuracy.
Research on Learning and Growth Information Management of College Students based on Blockchain Technology

Most of the existing university information systems are based on the traditional B/S structure and Web technology, and the functions are concentrated on the central server, and the authority is too centralized, which may cause unauthorized tampering of data and lead to untrusted data. This paper proposes a scheme that uses Ethereum as the underlying architecture of the blockchain, puts the learning and growth information of students on the chain, and ensures the data consistency of all nodes on the chain through the consensus mechanism. However, when the number of nodes in the network increases, the PBFT consensus algorithm used in the blockchain will limit the scalability of the system. In this scheme, the threshold signature and Merkle tree are used in the PBFT algorithm to improve the performance of PBFT and optimize the process of the consensus mechanism.
Research on Node Collaborative Storage Method for Consortium Chain Ledger Data

Blockchain as a technology for solving trust issues has received increasing attention, especially for consortium chains that combine the advantages of public and private chains, which have become very popular. However, as consortium chain applications deepen, ledger data expands rapidly, and general users find it difficult to provide hardware support for such large capacity storage. This leads to fewer high-intensity nodes that can participate in maintaining the consortium chain, gradually centralizing the consortium chain ledger. At the same time, as the scale of the consortium chain expands, the requirements for the concurrency of the consortium chain are also increasing, and the storage scalability of the consortium chain has become a bottleneck for the future development of the consortium chain. This article proposes a collaborative storage method for consortium chain ledgers, which aims to address the shortcomings of traditional consortium chains in ledger storage modes.