Reviewerly/exHarmony huggingface dataset:
Automatic parking problem based on path planning

Research related to the problem of automatic parking of driverless passenger vehicles is of great importance, and this paper presents a series of studies on the path planning problem of automatic parking of driverless passenger vehicles. For Problem 1, three physical problems are solved: solving for the minimum turning radius, the shortest distance required, and the limiting relationship between the magnitude of curvature change on the path relative to the path length under the respective conditions. The structural Ackermann physical model involved in this driverless bus is analyzed in depth, and with the help of the differential equation system model and related physical concepts and formulas, a car motion model is established, and the minimum turning radius of 4.9947 m and the minimum distance of 5.23 m are successfully solved. The mathematical model of parking from the initial position to the specified parking space under the specified conditions is required. After analyzing the basic models of the Ackermann model, such as state equations, path smoothing and trajectory generation, the relevant mathematical models for solving the critical conditions were established and the RRT algorithm was used to build the model. Finally, the resultant model is obtained by MATLAB.
Hierarchical HOI Detection Framework Augmented by Human Interactive Intention

Human-Object Interaction (HOI) detection is a core task for human-centric visual relationship understanding. However most existing methods still face the challenge of accuracy improvement. Because there are usually too many human-object pairs and complex scenes in the image, which leads to the complexity of HOI detection task. In this paper, we innovatively introduce a multi-grained interactive intention layer to deal with human gaze and multi-level human pose information in complex human-object scenes. We construct a hierarchical architecture using macro-level spatial layout cues, mesco-level interactive intentions and micro-level appearance features of humans and objects to complement the various levels of information. Leading results on two public benchmarks validate the effectiveness of our model.
Four-wheeled Ackermann Robot Path Planning in 2D Maze Based on Q-Learning-Bezier Method

Path planning methods have been increasingly utilized in autonomous driving. However, traditional path planning methods, such as Q learning, is not suitable for wheeled vehicles. In this paper, a Q-learning-Bezier method (QLB) is proposed to address the path-planning problem of four-wheeled Ackermann robot by combining Q learning with Bezier curve to produce smooth trajectories that can be passed by Ackermann robot. Meanwhile, two methods with different numbers of optional actions, QLB4a and QLB8a are compared and constructed separately. Experiments show that both proposed methods can generate paths that can reach the end point in a random maze environment. Simulation results show that the Ackermann chassis robot can complete the whole path successfully, which reflects the effectiveness of the algorithm in this paper.
The Research and Construction of Practical Case Library of Database Based on Knowledge Graph

Using database technology and AI, it is of great significance to explore the database case for data processing and analysis of the digital transformation platform based on Knowledge graphe. Knowledge graph includes knowledge engineering, natural language processing, database, machine learning. With the development of university-enterprise cooperation in china, high-quality resources in the computer industry will be introduced into the practice of database courses, so as to create new database practice case library with new engineering characteristics. Colleges and universities have realized the training goal of talent technology under the background of new engineering by building a hierarchical database oriented talent training mode. Knowledge graph is used to process massive multi-dimensional data, which can be considered as a semantic network. The knowledge graph processing steps involve knowledge representation, knowledge acquisition, knowledge fusion, knowledge calculation and knowledge reasoning. The knowledge graph includes the processing and calculation of database data. Then, the construction of the case of the database can use the knowledge graph technology. The construction and reform of the database practice case for the new engineering and engineering education accreditation, and the strengthening of both theory and practice, have an obvious role and significance in improving the quality of talent training in the computer direction, promoting practical teaching and improving teaching quality. Based on the innovative mode of university-enterprise cooperation, this paper explores the path and direction of the construction of the case library for practical of database using knowledge graph.
Research on Blockchain Technology-based Optimization Algorithm for Precise Distribution

The article discusses supply chain optimization based on blockchain supply chain technology and genetic algorithm in the context of IoT to improve the availability of goods and the security of logistics information, improve supply chain efficiency and reduce transportation costs. First, the basic theories of supply chain technology, supply chain optimization and genetic algorithm are introduced, and supply chain optimization with collaborative logistics information management is analyzed. Then, a supply chain network optimization model based on genetic algorithm is established and the optimization results are analyzed. The optimization results show that applying blockchain technology to collaborative logistics information management system improves the overall efficiency of supply chain logistics; using genetic algorithm, the logistics distribution cost is reduced. The study is innovative in optimizing the collaborative logistics information management system in the logistics supply chain and the logistics distribution channel by using blockchain technology and genetic algorithm to improve the security and availability of logistics information. The case study shows that the optimization of supply chain logistics using blockchain technology and genetic algorithm improves efficiency, reduces costs, improves corporate governance and economic efficiency, and increases the competitiveness of the company.
Image Restoration Based on Wiener Filter and Constrained Least Square Filter

In the process of image acquisition, the final acquired image is always accompanied by dynamic blurring and noise. This article describes the image degradation process as a degradation system. The original image function is first convolved with Point Spread Function (PSF) to simulate the motion blur process, and then Gaussian white noise is added to obtain the final degraded image. For degraded images, this article discusses how to use the Wiener filter and constrained least square filter to restore the image. The Wiener filter uses the minimum mean square error as a criterion for recovery while the least square filter uses the least square criterion for recovery. On the premise of accurately grasping the degradation function and the statistical information of the original picture and noise, the Wiener filter can have a better restoration effect. When only the information about the mean value and variance of the noise is grasped, the constrained least square filtering method can be used to obtain a better restoration effect.
Improved facial expression recognition algorithm based on convolutional neural network

It is fascinating to hear about the research conducted on facial expression recognition algorithms and its significance in the era of intelligence, especially with the development of the internet and IoT. The use of deep learning method and neural network VGG to study facial expressions sounds promising. It is encouraging to know that the network structure was improved and pooled using Soft pool, thereby enhancing the extraction of facial expression features. The integration of an attention mechanism to improve the ability of the network to recognize facial expressions appears to be a valuable addition to the research. it is impressive that the recognition rate of the improved network on the face expression dataset RAF-DB reached 86.1%, exceeding that of the original VGG network on this data set. this outcome suggests that the changed network is effective for facial expression recognition. thank you for sharing this information.
2D Maze Path Planning Based on Q-Learning and Bezier Curve

Q-Learning is widely used in path planning situations, while the trajectories it generates usually have abrupt bends, bending points, and sharp endpoints. A modified Q- Learning algorithm and Bezier curve are introduced in this study to improve trajectory smoothing, bringing the resulting trajectory closer to the routes of real-world vehicles. First, different mazes with the same start point and end point are created. Second, Q-Learning and modified Q-Learning are applied to path planning. Lastly, the path smoothed by the Bezier curve is imported into the simulation environment to verify its validity. The results demonstrate the effectiveness of the above methods in enhancing path smoothness and shortening path length.
Based on Sememe Knowledge Enhanced Network for Paraphrase Identification

Paraphrase Identification(PI) aims to judge whether the semantics of two sentences are the same, and the semantic representation of sentences is the basis of paraphrase identification. Most of the existing paraphrase identification methods use pre-trained word vectors as model input. This pre-trained word vector ignores the common polysemy and synonyms in Chinese. In order to improve the accuracy of paraphrase identification on the datasets LCQMC and BQ, we use sememe knowledge in HowNet is used to enhance the semantic representation effect of polysemous words and synonyms in paraphrase identification, thereby improving the accuracy index of paraphrase identification on the dataset. In the text representation stage, two sememe knowledge representation methods are used to enhance the semantic representation effect in the paraphrase identification task. In the semantic distance measurement stage, Euclidean distance, Manhattan distance and cosine distance are used to measure the semantic relationship between sentences. The model we proposed has been verified on two Chinese datasets LCQMC and BQ, and the ablation experiment and experimental analysis of the representation of sememe knowledge have been carried out. The results show that the accuracy of the model is about 1% higher than that of the typical model. At the same time, the introduction of sememe Knowledge can enhance the semantic representation of polysemous words and synonyms, thereby improving the accuracy of paraphrase recognition tasks.
Multi-Label Web Service Classification Using Neural Networks

With the development of the Internet, web services have played an increasingly important role in the software industry in recent years, and the increasing number of web services has brought about problems such as increased difficulty in obtaining labels. Therefore, finding a technique that can automatically match and recommend the most accurate labels quickly and accurately based on the service descriptions published by service providers has become a hot issue in the field of service computing. The current web service classification methods mainly focus on the main labels of web services, and there is no good method to classify web services with multiple labels. In this paper for the problem of multi-label classification of web services, we propose a multi-label classification model BCLAS for web services based on label embedding and attention. The BCLAS model uses BERT to generate word vector sequences of service descriptions and labels, and uses stacking networks of CNN and Bi-LSTM to extract semantic features in service descriptions, after which the obtained semantic features and labels are jointly learned in the embedding space, the learned attention is weighted to the final web service description text sequence and label representation, the feature vector representation of the optimized text, the labels are also learned to different text feature representations, and finally the classifier classifies web services using the weighted label embedding representation.
Big Data Classification Model and Algorithm Based on Double Quantum Particle Swarm Optimization

For the traditional big data classification models and algorithms that have the drawback of long processing time, the use of intelligent algorithms to model identification of multivariable systems has become a mainstream research direction. Discussion This paper proposes an efficient solution for data-parallel optimized computing. The problems in the identification of multi-variable systems that integrate historical big data and intelligent algorithms are analyzed, and the impact of input variables on output is quantified. Build a big data classification model, mine valid data points, arrange data combination methods, divide data local connection methods, obtain local node micro-cluster data, calculate redundant data and invalid data increment values in node data groups, and reconstruct central node sample algorithm, adjust the integrated data classification strategy, optimize and update the data integration classification method. Design the experiment, simulate the experimental environment, and verify that the proposed model calculation method can shorten the data processing time in practical application, which has practical research value. Through the design of the overall process of the algorithm, data classification to form a data set, and then describe the steps of the algorithm, and finally verify the feasibility of the algorithm through simulation experiments, which can not only greatly reduce the communication cost between network nodes, but also greatly improve the global mining accuracy and improve the query efficiency in distributed data, It has good utilization value in the analysis of massive data information.
Six Transformations and Their Relationships in Digital Signal Processing

An important content in the course of digital signal processing is the analysis of the time domain and frequency domain transformation of the system, which involves several important transformations, including the Fourier transform (FT) of continuous non-periodic signals, Fourier Series for Continuous Periodic Signals (FS), Discrete Time Fourier Transform (DTFT), Z Transform (ZT) of Discrete Signals, Fourier Series (DFS) of Discrete Periodic Signals, and Fast Fourier Transform (FFT),. The definitions and relationships of these transformations often confuse beginners. In response to this problem, this paper narrates the definitions of the six major transformations, and expounds the relationship of various transformations.
Photovoltaic Panel Fault Detection Based on Improved Mask R-CNN

Solar photovoltaic panels are widely recognized as a clean energy generation device, and their quality and efficiency are becoming increasingly important for power generation. However, due to the harsh environmental conditions that PV panels are exposed to, such as high winds, heavy rain, and high temperatures, as well as damage during manufacturing and transportation, various types of failures can occur. These failures can lead to a reduction in the performance of the PV panels, affecting the efficiency and lifespan of the electricity generated. Therefore, it is crucial to detect and monitor PV panels for faults. The traditional target detection method has limitations, including low recognition accuracy and slow detection speed. This paper suggests an improved Mask R-CNN-based intelligent detection method for PV panel faults. The FPN in the Mask R-CNN model is improved to BiFPN to better reflect the original image information. In the later Mask stage, the channel attention mechanism is added to help the model focus more effectively and quickly on important areas of the image. Experiments show that the model can be applied to a variety of scenarios and effectively solves the problems of missed detection and false detection.
Research on UAV path planning based on improved particle swarm algorithm with inertia weight

For standard particle swarm algorithm in track planning, there are search depth convergence slow, local search ability is weak shortcomings, tthis paper studies a based on the inertial weight of nonlinear improvement strategy particle swarm algorithm for drone path planning, using logarithmic function to control inertial weight change, and add Gaussian random adjustment in the process of evolution to adjust the inertia weight, enhance the search ability of the algorithm, to enhance the stability of the path planning, improve the ability to get better path solution.
Improved A-star Method for Collision Avoidance and Path Smoothing

The A-Star algorithm is one type of typical and useful method in the field of path planning and has been used in many fields. However, the conventional A-Star algorithm just takes the shortest path as the optimal path generation condition and does not consider the actual morphology of the robot. At the same time, the path search efficiency can be further improved. In this study, the problems with the A-Star algorithm above are improved. Firstly, changing the evaluation function of the traditional A-Star algorithm make it more effective. Then, the search directions are changed from 8 to 5. The orthodox A-Star algorithm has eight directions for exploration, which is not always efficient, so the search directions are adjusted to five to improve its efficiency. Finally, avoid oblique penetrations of the obstacle vertex to avoid the collision. The size of objects is usually neglected in the traditional A-Star algorithm. The route produced by the new method avoids moving too close to obstacles. The result of the experiment proves the effectiveness of the improved method. It improves the traditional A-Star algorithm so that the generated paths avoid sticking too close to the obstacle or even colliding with it.
Improvement of semantic segmentation algorithm based on ResNeSt network

This paper proposes an improved semantic segmentation algorithm based on the split attention network resnest. The original icnet semantic segmentation model has some defects in the segmentation of foreground objects. In view of these problems, we optimized the icnet model with resnest network and CBAM convolution attention module. On the one hand, we replace the backbone network in the original model with resnest network, which enhances the feature extraction capability of the model. On the other hand, we introduce the CBAM convolution attention module to improve the segmentation ability of the model to the target in the foreground region. Without affecting the inference rate of the original model, the training effect of the improved idea proposed in this paper on the cityscapes open data set is improved separately from 71.0% to 71.3% and 71.9%. Respectively compared with the MIoU of the baseline model, the MIoU of the icnet model combined with the resnest backbone network and the CBAM convolution attention module on the cityscapes open data set is increased from 71.0% to 72.1%, and the segmentation effect of the foreground area target is significantly optimized in the visualization images, which has certain positive significance for the application of the semantic segmentation algorithm in the field of unmanned driving.
Survey on Neural Networks in Natural Language Processing

Neural Networks can learn natural language representations within levels of abstraction, and have recently shown much promise for Natural Language Processing (NLP) applications. Kinds of Neural Network Language Models (NNLMs) have gained a lot of success by predicting the next word given the other words in a context. These models have improved the state-of-the-art in word sense disambiguation, question answering, web search and many other domains in semantic parsing. Compared with "bag of word", Word Embedding has brought about breakthroughs in representations of words, sentences, or even documents. Recurrent Neural Network Language Models (RNNLMs) have shone light. They can learn and store long-memory knowledge that would help extracting better representations for long documents.
Human resource recommendation algorithm based on convolutional neural network

Among various kinds of image recognition, the Chinese character recognition has a very wide range of application prospect and practical value, for example, can be used in sorting, license plate recognition, billboard recognition, identity CARDS, auxiliary blind people read scene, can realize automatic recognition, reduce artificial operation, save time and manpower cost, convenient people's life. Deep learning mainly builds a neural network model with multiple hidden layers. In this paper, a large number of training samples are used to learn more useful features, so as to improve the prediction and classification accuracy of the network model. As an important network model of deep learning, convolutional neural network has the characteristics of hierarchical structure, weight sharing, regional local perception, feature extraction and global training combined with classification process, etc., and has been widely applied in the field of image recognition. In particular, deep convolutional neural network is currently a research hotspot. It is of great application value to study its own and its application in the identification of different samples.
A Cloud-based Photographing System with Adaptive Image Quality Promotion

With the development of science and technology, we have now entered the information society. The traditional ID photo shooting system needs to go to a photo studio to take pictures. The relatively expensive price and long waiting time are no longer suitable for today's development needs. Although there are already some self-service ID photo shooting systems that have overcome the shortcomings of traditional ID photo shooting, it is not convenient to use in many scenarios due to the lack of cloud platform services. This paper proposes a new ID photo shooting system. Users can take pictures through the WeChat applet, detect and modify the source image, and upload the processed image to the cloud, which greatly simplifies the user's time cost and realizes Automatic source image authentication, posture detection and standard certificate generation provide an efficient and fast system for ID card production units. After simulation tests, the system can effectively and reliably capture and process ID photos.
Research on the star algorithm for safe path planning

The conventional A star algorithm is one of the most effective direct search methods to solve the shortest path in the static map. However, to keep the path as short as possible, the best path it seeks tends to be close to obstacles, which has defects for some medical and service robots that need to keep a safe distance from obstacles. In this paper, based on the conventional A star algorithm, the computational form of its heuristic function is adjusted so that the search efficiency is improved by nearly 80%. Meanwhile, the obstacles are extended during calculation so that they are kept in line with the obstacles. Experiments show that the mentioned method can improve computational efficiency while ensuring that the generated path and obstacles are within the safe range. The results of this paper may be conducive to the application of the algorithm in practical situations.