Reviewerly/exHarmony huggingface dataset:
Research on Ant Colony Optimization of Oversampling Problem Based on Bootstrap and Maximum Entropy Principle

Classification of imbalanced data is an important research topic in machine learning. In unbalanced data, the minority class has significantly fewer training samples than the majority class, resulting in classification results favoring the majority class. Therefore, we propose a new algorithm for the classification of imbalanced data. The new algorithm is divided into the following three main parts. Firstly, by using the bootstrap method and the maximum entropy principle the new equilibrium data is obtained, which is compared with other oversampling algorithms. Secondly, the obtained equilibrium data are filtered by using the ant colony algorithm. The training set conducive to improving the classifier’s performance is selected. Finally, by using the filtered training set, the classifier is classified and compared with the previously proposed oversampling algorithm. By selecting eight sets of data from the UCI and KEEL databases for classification experiments, the experimental results show that the new algorithm proposed in this study outperforms existing algorithms in various evaluation metrics such as Auc, F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and Acc.
Multi-person interaction action recognition based on co-occurrence graph convolutional network

Skeleton-based human action recognition has attracted more and more attention with the accessibility of depth sensors and the development of pose estimation technology. The recognition of multi-person interaction behavior with skeleton data has wide applications in real life. However, the existing skeleton-based action recognition methods mainly focus on single-person actions, and lack the research on the semantic information of the skeleton co-occurrence relationship in the spatial dimension. Here, a two-stream co-occurrence graph convolutional network (2s-CGCN) is proposed for multi-person interactive action recognition. The topological connection structure of the proposed model establishes connections between the human limbs and the head to capture the co-occurrence feature. In addition, the coordinates and co-occurrence information of the joints are simultaneously input into the model. The experiments of mutual actions in the NTU-RGB+D dataset show the effectiveness of the proposed model.
Research On Mahjong Game Strategy Combining Hand tiles Optimization and Situation Search

Mahjong, being a classic imperfect information game, has a large playing space, a lot of hidden information, and complex game rules. It’s a fantastic research platform for artificial intelligence. This paper proposes a two-stage mahjong game strategy: in the early stages of the game, hand tile optimization based on the ShangTing distance and fundamental tile type is used to reduce the Hu distance and increase the Hu probability, and in the later stages of the game, a new type of Monte Carlo tree search algorithm is used to guide Hu selection and acquire more scores based on simulation findings. Finally, repeated comparison studies are used to test the strategy’ efficiency. The agent AgentMT was created using this approach, and it competed with the agent KnighTeam-MI01, which placed fourth in the 15th China computer gaming championship. At the end of the experimental part of the article, AgentMT and KnightTeam-MI01 are designed to play 3 sessions with 1000 games each. The experimental results show that, compared with KnighTeam-MI01, AgentMT is 53.3 points higher in the average score in a single session, 21.9 percent higher in the average time in a single session, and 3 points higher in the highest score in a single game.
Evolutionary game of banks and enterprises in digital supply chain finance driven by blockchain

In order to explore the influence of blockchain technology on supply chain financing, this article builds an evolutionary game model of banks and enterprises, which is associated with traditional supply chain finance financing and digital supply chain finance based on blockchain technology, besides, the article also analyzes the evolutionary path of subject behavior in different situations. Our research shows that, both of the two evolutionary game models have three stable evolutionary states. Through simulation, we find that under the guarantee of blockchain technology, the role of punishment mechanism and credit incentive can be better played in supply chain financing, and the supply chain ecology will be more optimized.
RUL Prediction Based on Improved LSTM Network Structure

Using the sparse idea of Highway network to design a sparse denoising LSTM network that suppresses redundant neurons to achieve more accurate residual life (RUL) prediction. Different from the idea that the traditional Highway network is sparse in time direction, this paper transforms the traditional LSTM network by designing sparse gates, and suppresses those neurons that have contributed little to the next layer in the previous layer, and highlights those nerves that contribute more. The role of the element, thereby achieving the goal of sparseness and " denoising " at the same time. When the time series is long, the prediction accuracy of RUL prediction using the sparse denoising LSTM network (Sparse Denoising LSTM, SD-LSTM) is high, and the sparse gate structure can also reduce the computational complexity to a certain extent.
Improved Path Planning Algorithm based on Fuzzy Control combining A* Artificial Potential Field Method

In view of solving path planning problem of Automated Guided Vehicle (AGV), this paper proposes an A* artificial potential field method (AAPF), which can generate a safe and feasible path. The global path points of AAPF are generated by A* algorithm and saved. These points will have a gravitational force on the AGV and a repulsive force on the obstacles, and the resultant force will make the AGV go to the target point. AAPF will fall into the local minimum. This paper uses fuzzy control to give AGV an auxiliary force when the obstacle avoidance algorithm falls into the local minimum to help AGV escape from the local minimum. Experiments show that the obtained path combines the advantages of method. Compared with the traditional A* algorithm and the algorithm mentioned in the literature, AAPF can achieve a better solution in a shorter time.
A Novel Version of Sampling-based Motion Planner for Manipulation with Faster Initial Solution and Convergence

This paper presents, Anytime Fast-BIT* (AFBIT*), a sampling based, asymptotically optimal manipulation motion planner which quickly finds an initial feasible path and rapidly improves the path quality toward optimality. AFBIT* is guided by modified heuristics in task space for faster first solution. A local optimization method is adopted at the end of every round to optimize the current best path and generate the next vertex and edge for the global path while a new round begins to improve the quality of the remaining path. Simulation results suggest AFBIT* is more efficient and effective on manipulation problems than BIT* and Fast-BIT*.
Self-Attention based Siamese Neural Network recognition Model

With the rapid development of machine learning neural network technology, various architectures based on neural network emerge in endlessly. Convolutional Neural Network needs a large number of data to drive to achieve target recognition. In recent years, the Siamese Neural Network model has also been evolving in the development, and has a high recognition effect in the recognition of small samples. Its function is mainly used to measure the similarity of two samples and realize the classification of samples. In order to improve the recognition effect of Siamese Neural Network, this paper will improve the traditional CNN-based Siamese Neural Network, and integrate the self-attention mechanism to achieve better recognition efficiency of the target. After the experimental test model in 4000 data set training, the recognition accuracy can reach 95.16%.
Path planning of manipulator based on improved particle swarm optimization

Aiming at the shortcomings of traditional particle swarm optimization (PSO) in dealing with robotic arm path planning problems, such as premature and low convergence accuracy, an improved particle swarm optimization (IPSO) is proposed. While maintaining the advantage of fast convergence of traditional PSO, the algorithm dynamically adjusts the particle speed according to the evolution trend of the population to balance the global and local search of particles. This paper takes PUMA560 manipulator as the research object, and uses the envelope method to transform the collision problem into the distance problem. Finally, the algorithm is compared with other algorithms in MATLAB. The simulation results show that the algorithm can effectively avoid falling into the minimum value, and the generated path is better, which helps the robotic arm to quickly find the optimal path.
U-Select RCNN: An Effective Voxel-based 3D Object Detection Method with Feature Selection Strategy

Accurate object detection is a fundamental requirement for autonomous systems to operate in dynamic urban environments. Considering the intricacy of the environment and the occlusion of objects, various point cloud based three-dimensional (3D) object detection methods have been proposed, such as point-based or voxel-based methods. In this paper, a feature selection mechanism is proposed in a voxel-based method to generate bird’s eye view (BEV) feature maps from the original point cloud. For the 3D Backbone, the U-shaped structure and single scale feature selection module are combined. After combining high-level semantics and low-level fine-grained features, the optimized features after the BEV feature maps are applied to region of interest (RoI) refinement, so that the voxel features can better serve the subsequent 3D object detection. The experimental results on KITTI dataset show higher 3D object detection accuracy compared to the state-of-the-art 3D detection methods, which reflects the effectiveness of the proposed architecture.
A Fusion-based Enhancement Method for Low-light UAV Images

This paper focuses on the enhancement of low-light UAV images. There are some differences between low-light UAV images and general low-light images. Specifically, low-light UAV images are underexposed as a whole but contain overexposed areas produced by lamplights. In addition, these images have a larger field of vision and therefore contain more information. Based on these characteristics, we propose an enhancement method for low-light UAV images. First, we adopt two different enhancing methods, one is to improve the global brightness, the other enhances the local contrast, and then we use appropriate weights to fuse them to retain their respective advantages. Second, a new detail enhancement strategy is designed to preserve more details of these images. Finally, a brightness and chrominance optimization operation based on linear stretching is used to further optimize the enhanced images. We test the proposed method with three different datasets, including a public UAV dataset, a self-made UAV dataset and a widely used image enhancement dataset. Besides, our enhancement method is compared with several state-of-the-art enhancing methods and evaluated with two image quality assessments. All the experiments demonstrate that the proposed enhancement method is superior to others in enhancing low-light UAV images.
Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Graph Laplacian Regularization

Hyperspectral images are often degraded by various types of noise. It is important to choose appropriate image priors for the underdetermined imaging denoising problems. Graph Laplacian regularizer is one popular prior which can reveal the inherent relationship between signals and exhibits desirable PWS-preserving properties. In this paper, we combine the Graph Singal Processing theory (GSP) with HSI low-rank-based denoising methods. We classify the noise on HSI into three types, and treat the clean image and different types of noise as independent components. The simulated and real-data experiments demonstrate that the proposed denoising methods outperforms many of the mainstream methods in both the quantitative evaluation indexes and visual effects.
RGB-D Saliency Detection based on Cross-Modal and Multi-scale Feature Fusion

The mainstream algorithm brings noise from the depth map, and the high-level features are diluted in the fusion process. To address these problems, RGB-D saliency detection based on Cross-Modal and Multi-Scale Feature Fusion is proposed. In this paper, we propose Cross-Modal Feature Fusion Module(CMFFM) to fuse the RGB image with semantic feature advantages and the depth map with position feature advantages. Based on this, CMFFM effectively suppress the interference of noise in the depth map. Furthermore, Multi-Scale Residual Channel Attention Feature Fusion Module(MRCAFFM) is proposed to fuse the high-level and low-level features level-by-level, which enriches the expression of high-level semantic features and enhances the capability of feature selection. Finally, experimental results on four benchmark datasets show that the comprehensive performance of the algorithm is better than the compared algorithms.
A Calibration System of Intelligent Driving Vehicle Mounted Scene Projection Camera Based on Zhang Zhengyou Calibration Method

This paper proposes a calibration system for intelligent driving vehicle mounted scene projection camera based on Zhang Zhengyou calibration method. Among them, the key of the calibration system is to correct the side cutting angle, the pitch angle and the yaw angle between the checkerboard calibration board and the vehicle to be calibrated. First of all, this article needs to build the entire calibration system platform and obtain the internal parameter matrix of the vehicle mounted projection camera based on the Zhang Zhengyou calibration method. Second, this article realizes the correction of the side cutting angle, the pitch angle and the yaw angle between the checkerboard calibration board and the vehicle to be calibrated based on physical methods. Then, based on Zhang Zhengyou calibration method, the calculation of the rotation matrix and the translation vector between the two cameras is realized. Finally, this paper verifies the effectiveness of the method proposed in this paper from both qualitative and quantitative aspects. The experimental results show that the method proposed in this paper can effectively achieve the calibration of the vehicle mounted scene projection camera.
Application and optimization of the UCT algorithm in Einstein würfelt nicht!

Based on the characteristics of Einstein würfelt nicht!(EWN), this paper puts forward the UCT algorithm applied to EWN, and on its basis, an optimized UCT algorithm with the evaluation function and the dynamic searching rounds is proposed. The evaluation function is used to better evaluate the situation, and the dynamic searching rounds is used to reduce the average cost per game by a decay function. By playing multiple games with the UCT algorithm, the optimized UCT algorithm proved to be effective in improving the strength of the EWN game system.
Research on Collaborative Governance and Development Countermeasures of Intelligent Elderly Care in Beijing-Tianjin-Hebei Communities Driven by Blockchain Technology

China’s elderly care is facing new development trends and some new characteristics. Community intelligent elderly care collaborative governance can promote the high-quality comprehensive development of Beijing, Tianjin and Hebei. At present, blockchain is setting off an upsurge of innovation all over the world, and China has also put forward an innovative development strategy with blockchain as the core technology. In this context, it is of great significance to promote the coordinated development of Beijing, Tianjin and Hebei by building a collaborative governance mechanism for smart elderly care in Beijing, Tianjin and Hebei communities based on blockchain technology, creating a good blockchain innovation environment and promoting the high-quality development of community elderly care.
An Interval Particle Swarm Optimization Algorithm for Solving Multimodal Optimization Problem

For the interval particle swarm optimization (IPSO) algorithm, we found that when the target of IPSO to be optimized is a multimodal function, the IPSO is easy to fall into a local optimal solution and it is difficult to find multiple optimal solutions. Therefore, this paper proposes a new IPSO for solving multimodal optimization problems, i.e., niching IPSO (NIPSO). First, the niching technology is studied and the PSO topology is analyzed. In order to improve the ability of the algorithm to jump out of the local optimal solution, the Ring topology is used to construct the particle swarm niching. Then based on the niching idea, a dual file mechanism is proposed to maintain multiple optimal solutions through individual best file PBA and neighborhood best file NBA. Finally, several multimodal functions are selected to simulate the algorithm proposed in this paper, and the effectiveness of the algorithm is verified.
Rough K-prototypes Clustering Algorithm Based on OTC Similarity and Between-Cluster Information for Mixed Data

In the iterative updating process of cluster center and partition matrix, most of the clustering algorithms for mixed data mainly consider the impact of the information within the cluster, but ignore the information between clusters, which results in low cluster separation. A rough clustering algorithm for mixed data based on the frequency division information between clusters is proposed. In the process of measuring the similarity between data objects and clusters, the unified object-cluster (OTC) similarity of mixed attribute data is used to avoid the transformation of classification attributes and numerical attributes and the parameter adjustment in the traditional clustering algorithms for mixed attribute data. The inter-cluster frequency division information is added in the iteration process of algorithm to ensure that the clustering result has high intra-cluster compactness and inter-cluster separation. Several groups of comparative experiments verified the effectiveness of the proposed algorithm.
HEFT-Dynamic Scheduling Algorithm in Workflow Scheduling

Cloud computing is a solution that can provide remote computing today. It provides users with a high-performance computing resource that can be used at any time. When users are dealing with large tasks, if the task scheduling algorithm is not scientific and reasonable, it will cause unnecessary energy waste in cloud computing. This paper proposes a new scheduling algorithm based on the currently widely used scheduling algorithm HEFT, which aims to save scheduling time in task scheduling. Comparing the optimized algorithm with the benchmark algorithm, the results show that it can also be completed within the deadline, but it can save up to 24% of energy consumption.
Optimization-Based Motion Planning for Autonomous Parking Considering Dynamic Obstacle: A Hierarchical Framework

We present a hierarchical framework based on graph search and model predictive control (MPC) for electric autonomous vehicle (EAV) parking maneuver in tight-environment. At high-level, only static obstacles are considered, and the scenario-based hybrid A* (SHA*), which is faster than the traditional hybrid A*, is designed to provide an initial guess (also known as a global path) for the parking task. To extract the velocity and acceleration profile from an initial guess, an optimal control problem (OCP) is built. In low-level, a NMPC-based strategy is used to avoid dynamic obstacles (also konwn as local planning). The efficacy of SHA* is evaluated through 148 different simulation schemes and the proposed hierarchical parking framework is demonstrated through a real-time parallel parking simulation.
Knowledge Graph Based Recommendation Algorithm for Educational Resource

Connecting the educational resources of specific disciplines to form a knowledge network is a key step in the intellectualization of education and research. Based on a knowledge graph of power grid technology education resources of 42510 literature and information in China National Knowledge Infrastructure (CNKI) in our former research, a recommendation algorithm is proposed in this paper. The algorithm aims at the problem of data sparsity and cold start of traditional recommendation algorithm, which adopts hybrid similarity fusion to quickly match core resources and improve the pertinence and efficiency of education and research. The work in this paper show that the integration of knowledge graph technology and academic literature database can provide scientific and intelligent data analysis methods for discipline education and research.
Mobile learning in Chinese higher education: student perspectives, advantages and challenges

The focus of this study was to investigate the attitudes of Chinese university students towards mobile learning and their perceived advantages and disadvantages of mobile learning. A random sample of 104 students from a public university was asked to answer a questionnaire on mobile learning. The questionnaire was divided into two parts: closed-ended questions and open-ended questions. The closed-ended questions were based on a Likert scale and were about the students' attitudes towards mobile learning. The open-ended questions were about the advantages and challenges of mobile learning. First the researcher conducted a descriptive analysis of the data collected through the Likert scale, which was carried out using SPSS(Statistical Product and Service Solutions). In addition, this study also conducted a thematic analysis of open-ended questions on the benefits and problems of mobile learning using NVIVO software. The results of the survey showed that the majority of students were more positive about the effectiveness of mobile learning, while most of them also felt that mobile learning was accompanied by additional distractions. In addition, the responses to the open-ended questions collected showed that the advantages of mobile learning as perceived by the students included convenience, environmental friendliness, rich learning resources, facilitation of communication, educational equality and accessibility, ease of review, personalized learning and facilitation of time management. Additional challenges to mobile learning include: distractions, threats to privacy, health issues, device failure, high costs, difficulty in replacing writing, poor supervision, uneven information on the internet and software adaptability. This study has important implications for future policy makers or headmasters to further improve the effectiveness of mobile learning for university students. Future research should adopt a larger sample size methodology to provide more detailed and comprehensive survey data.