Reviewerly/exHarmony huggingface dataset:
Multi-Agent Reinforcement Learning With Distributed Targeted Multi-Agent Communication

Centralized training distributed execution (CTDE) in multi-agent reinforcement learning (MARL) is a commonly used application paradigm. This paradigm usually assumes that the global state of the environment can be obtained during training, which is often difficult to satisfy in various scenarios due to constraints such as data transfer and processing power. Fully distributed multi-agent reinforcement learning algorithms do not depend on the knowledge of global state, with each agent trained independently and treating the remaining agents as part of the environment. However, applying single-agent algorithms to multi-agent systems faces the problem of non-smoothness of the environment and difficulty in forming effective collaborative strategies. In this paper, we propose a new method, Distributed Targeted Multi-Agent Communication (DTMAC), which makes each agent generate messages and pass them to other agents, explicitly enhancing the collaboration among individual agent and facilitating the formation of collaborative strategies. Experiments are given to illustrate the effectiveness of the method.
Research on Movie Recommendation System Based on Multimodal Fusion

Using the traditional collaborative filtering method to recommend often ignores the characteristics of the film itself. A movie recommendation system based on multimodal fusion is proposed by integrating the movie poster style, movie ID and other contents, and taking the fused feature information as the supplement of collaborative filtering recommendation. Feature extraction is mainly carried out for movie ID, movie genre, movie title, movie poster, etc., and fusion is carried out through convolutional network. Finally, based on the MovieNet dataset, the model is used for experiments, and the experimental results prove that the proposed method is superior to other methods.
Multi-stage Feature Interaction Network for Masked Visible-thermal Person Re-identification

Visible-thermal person re-identification (VT-ReID) aims to match person images between visible and thermal modalities. Existing methods have achieved desirable performance based on holistic person images. However, they cannot effectively cope with the information loss issue due to occlusion in the imaging process in complex scenes. In this paper, we propose a multi-stage feature interaction network (MSFIN) to learn features from the body and background regions respectively. First, a multi-stage feature fusion learning (MFL) module enhances semantic information by aggregating multi-dimensional features to mitigate adverse effect of occlusion. Second, a multi-scale attention (MSA) module is designed through receptive field scaling to effectively highlight the pixels of non-masked regions. Experiments on SYSU-MM01 and RegDB with occlusion demonstrate that our approach outperforms the state-of-the-art VT-ReID methods.
Multi-UAV collaborative task allocation method based on LGMPA algorithm

The multi-UAV task allocation problem is a multi-constrained combined optimization problem. To obtain the best benefit of the problem, this paper establishes a task allocation model that matches the battlefield environment. And the path planning between UAV and target is completed simultaneously. The real flight distance is used to replace the straight-line distance to realize the tight coupling of task allocation and path planning. A modified marine predator algorithm is proposed firstly in this paper to better solve the task allocation model. The exploitation and exploration ability of the algorithm is enhanced by using a logarithmic spiral strategy and a Gaussian distribution estimation strategy. The improved algorithm is then discretized and applied to solve the multi-UAV task allocation model. The simulation results show that the use of the real flight distance instead of the straight-line distance can plan a more reasonable task execution sequence and get a better combat profit under the consideration of the real terrain and various types of threats. The improved algorithm can solve the model stably and effectively, with better solution accuracy and faster convergence speed.
Multi-stage Feature Fusion Network for Edge Detection of Dunhuang Murals

Dunhuang murals have generally suffered serious damage due to natural disasters and human factors. As a result, the conservation and restoration of Dunhuang murals are becoming increasingly urgent, in which the line drawings of Dunhuang murals are an important work in the restoration of Dunhuang murals. The line drawings of the Dunhuang murals can be regarded as an image edge detection task. Although many CNN-based edge detection methods have made significant progress, they still produce thick edges and non-edges in the Dunhuang murals dataset. To solve this problem, we propose a novel edge detection method for generating line drawings of Dunhuang murals. Firstly, a novel loss function is proposed for edge detection, which uses focal Tversky loss to effectively suppress the background pixels near the edge pixels and allows CNN to produce sharp edges. Moreover, the proposed network introduces the dilated convolution modules and the spatial attention modules in each side output features to fully learn hierarchical features and obtain richer scale features. Finally, the results of experiments conducted on the BIPED dataset and the Dunhuang murals dataset show that the proposed method can generate richer and sharper edge maps.
Maneuvering Target Tracking Based on An Adaptive Interactive Multiple Model Box Particle Filter Under Interval Measurement

Aiming at the shortcomings of model switching and tracking accuracy of the existing interactive multiple model box particle filter (IMMBPF) in the interval measurement target tracking, an adaptive IMMBPF (AIMMBPF) algorithm is proposed, which uses posteriori information to construct a correction factor and modifies the Markov probability transition matrix. The probability of matching model increases rapidly, and the influence of mismatching model can be reduced. The simulation results show that the AIMMBPF algorithm has better model matching degree and target tracking accuracy than the original IMMBPF in the application of interval measurement maneuvering target tracking.
Research and Implementation of Production Safety Accident Description Element Extraction

Accident description element extraction is an important basis for building accident knowledge base, and its purpose is to extract the key accident description features from the text. Previous element extraction usually uses multi-label classification methods for modeling, and the model mainly relies on text features, ignoring domain-specific semantic information. At the same time, due to the lack of large-scale annotated data corpus in production safety accident texts, it is difficult to solve this problem by traditional supervised learning methods. To address the above problems, we propose an accident description element extraction method based on a few-shot Named Entity Recognition (NER) framework, define seven description element categories reflecting basic accident information, and build a few-shot element extraction model based on transfer learning — RPBMC model, which introduces a RoBERTa-WWM pre-trained language model adapted to Chinese datasets; we further design a Partial Least Squares (PLS) domain word mapping layer based on feature transformation to enhance the word vector generalization ability. And we combine the Multi-Head Self-Attention (MHSA) mechanism to learn more fine-grained semantic information. The experimental results show that the model effectively completes the element extraction with an overall F1 value of 91.34%.
A Study of Automatic Positioning Control Based on Vision Servo Control

The traditional automatic crane positioning method has low accuracy, poor stability and cannot form a true closed-loop control, and the existing crane visual positioning method generally suffers from poor anti-disturbance performance and difficulty in obtaining visual Jacobi matrix parameters. In order to solve the problem of external disturbance affecting the accuracy and stability of visual servo positioning, a mathematical model of the position posture of the crane end-effector under the disturbance condition is established, and a visual servo disturbance suppression method based on a self-anti-disturbance controller is proposed. According to the image projection characteristics, the equation of the differential relationship between the Jacobi matrix parameters and image features is derived, the adaptive update rate of the estimated Jacobi matrix parameters is designed, and the closed-loop dynamics equation is established. According to the image characteristic error, the Liapunov function is constructed and the proof of system stability is given. Simulation and experimental analysis are conducted, and the results show that the position and velocity profiles converge to zero values when the visual error converges, indicating that the method in this paper still has satisfactory positioning accuracy under visual uncertainty and constant, transient perturbations. The analysis of the comparison results with other control methods shows that the method in this paper can speed up the convergence of the visual error and has better stability while ensuring the positioning accuracy of the visual servo system, therefore, it is suitable for the automatic positioning visual servo system of cranes for hoisting.
Cooperative collision avoidance and obstacle avoidance path planning for multiple unmanned underwater vehicles

To address the problem of collision avoidance and obstacle avoidance of multiple unmanned underwater vehicles (UUVs) in complex environments, a dynamic path planning method based on the velocity obstacle is proposed. This method constructs a three-dimensional model of UUV and other dynamic obstacles when UUV needs to avoid obstacles and establishes a velocity obstacle cone according to the velocity obstacle method. A feasible velocity outside the obstacle cone in the UUV velocity set for collision avoidance. To address the problem of path oscillation of the traditional velocity obstacle, reciprocal velocity obstacles are adopted so that each UUV can reach the target point safely. Simulation results show that this method can make multiple UUVs reach their respective target areas safely and quickly.
Vector map drawing method for path planning in automatic driving

Vector map is an important part of path planning. As an open source autopilot software, Autoware provides a learning platform for autopilot practitioners. However, the drawing process of vector maps tends to be commercialized,at the same time, the drawing of vector maps requires manual point drawing based on 3D point cloud maps and takes a lot of time. On the basis of the autoware autopilot software platform, this paper designs a network connected online tool AutoCar, which optimizes the drawing and loading process of vector map in path planning. The experimental results show that under the condition of using traditional methods to complete the same global path planning, the path completion time and cost of this paper are greatly reduced, and the accuracy and convenience are also steadily improved.
Random Mask Slice Stitching(RMSS): A Data Augmentation for Depth Estimation

This work aims to summarize some existing data augmentation methods in the field of depth estimation, and propose a depth estimation data augmentation method suitable for both supervised learning and self-supervised learning. In the training task of computer vision, data augmentation is very significant to improve the accuracy and robustness of the model. However, for depth estimation tasks, data augmentation is not as common as detection or segmentation tasks. Although some data enhancement methods have been proposed in recent years, these data enhancement methods are not effective in different datasets or in supervised learning modes and unsupervised learning modes. Therefore, the data augmentation methods proposed in recent years are sorted out in this paper, mainly for two challenging datasets NYU-Depth-v2 and KITTI. In this paper we propose a simple and effective data augmentation method Random Mask Slice Stitching(RMSS) that can be used in supervised and self-supervised tasks and outperforms existing methods.
Arbitrary-scale dense light field reconstruction with meta-learning

Light field (LF) suffers from great data volume, and an effective approach to cope with this problem is to reconstruct dense LFs from sparse ones. However, the existing LF reconstruction methods are performed with given and fixed up-scaling factors and thus lost the flexibility and the ability of adapting to different applications. In this paper, we propose to reconstruct dense LF with meta-learning. The framework consists of two inputs, e.g. the sparse light field and the angular coordinate of the desired viewpoint, and three modules: feature extraction module that extract sparse LF features, weight prediction module that predicts view-adaptive weights, and view synthesis module that exactly generates virtual new views. Especially, different from conventional work, the proposed framework leverages meta-learning that learns view-adaptive synthesis parameters, so that it can be used to render arbitrary viewpoints and reconstruct dense LF with arbitrary up-scaling factors. The proposed framework is verified with extensive experiments, which demonstrate that it can generate arbitrary views and reconstruct dense LFs with high quality and high efficiency that outperforms the state-of-the-art methods.
The Effectiveness of Scrabble Games Towards Students’ English Vocabulary Mastery at Junior High School Number 2 Jambi City

The purpose of this study was to find out the effectiveness of the scrabble game on students’ vocabulary mastery Junior High School Number 2 Jambi City. This research was conducted at Junior High School Number 2 Jambi City. In this research, the researcher use scrabble game as a media to increase students’ vocabulary mastery. The research aims to determine whether or not the scrabble game is effective in improving students' vocabulary mastery and tolearn about the students' responses to the scrabble game. The experimental design was used in this study. The study's subjects were ninth-grade students at Jambi City Junior High School Number 2. As a sample, the researcher attended two classes with 30 students each. The first class served as an experimental class with a scrabble game, while the second served as a control class without a scrabble game.A pretest and a posttest were used to collect the data. The scores before treatment were determined by the pretest score, and the scores after treatment were determined by the posttest score. The results of the pretest and posttest scores were analyzed using the t-test formula to determine whether or not the scrabble game is effective. The t-test was used to analyze data in this study using the Statistical Package for the Social Sciences (SPSS) for Windows version 25 program. The findings of this study revealed that the T-test was greater than the T-table (7.224 &gt; 2.045) and the sig (2 tailed) result was less than the level of significance (0.00 &lt; 0.05), implying that H1 was accepted while H0 was rejected. According to the results of the students, there was a significant improvement from the pre-test to the post-test, which was analyzed using SPSS. According to the explanation above, it is possible to conclude that scrabble games improve students' English vocabulary at Junior High School Number 2 Jambi City.
A new Graph Pooling Method based on Topology and Attribute Features in Graph Neural Networks

Graph neural networks have shown remarkable performance in graph-structured data. However, current research mainly focuses on designing graph convolution operations, while the pooling operation, which is crucial for graph classification tasks, has received insufficient attention. Existing graph pooling methods suffer from the problem of losing graph topology information, resulting in insufficient node feature information mining. Moreover, they only utilize first-order statistics and fail to utilize second-order statistics. In this work, we propose a novel pooling method consisting of two parts. First, we select important nodes based on both attribute and topology features, and then use these nodes to form a pooling subgraph that preserves rich features. Second, we implement a second-order pool to retain higher-order features, which can encode the feature correlation and topology information of all nodes. Our proposed pooling module can be integrated with GCN layers to form a hierarchical pooling structure for graph classification tasks. Experimental results on benchmark datasets demonstrate the superiority of our method.
Entity Recognition for Military Situation Awareness Knowledge Graph with Wikipedia Data

Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a novel method, BERTATT_POSBiLSTMLSTMCRF, which is based on the traditional entity recognition model BERT_BiLSTM_CRF. The local location information and the impact of the entity's position in the sentence on the entity recognition task are both fully considered by introducing the attention mechanism. Additionally, an LSTM layer is added after the BiLSTM layer to deal with long-distance label dependencies while improving the model's ability to recognize long entities. Comparative experiments demonstrate that the improved model proposed in this paper is effective in entity recognition with Wikipedia data.
Students' Writing Skills in Descriptive Text by Using Animated Movies in Tenth Grade Students of SMA N 9 Jambi City

Writing skills is the ability to express ideas or ideas into an essay. The purpose of this study was to determine the ability of FASE E1 SAJ class students at SMA N 9 Jambi City in writing descriptive text using animated movies. This research uses qualitative as the research design and descriptive as the research method. researchers used tests given to students as research instruments, then the results of these tests were assessed and analyzed. The population is class phase students at SMA N 9 Jambi city for the 2022/2023 academic year and the sample is 34 students in class fase E1 saj. the researcher uses a score rubric to help assess students' grades and written tests to obtain data. Based on research findings, indicators of students' writing abilities, namely content (63,23), organization (66,23), grammar (65,23), vocabulary (63,73), mechanics (76,47) are at a great level. In addition, students' scores in writing descriptive texts have an average score (66,91).
Multimodal data consistency anomaly detection based on sparse subspace clustering

The consistency anomaly of multimodal data during transmission is a challenge faced by social networks, intelligent monitoring, and other applications. These existing research methods of multimodal data consistency are based on supervised training and learning, which require a large amount of training data and high computing power. To overcome the problem, a multimodal consistency anomaly detection algorithm based on sparse subspace clustering is proposed in this paper. First, to solve the problem that the performance of existing multimodal anomaly detection algorithms is usually highly correlated with the amount of anomalous data and has a limited application, a sparse representation matrix is obtained by using the sparse subspace clustering algorithm combined with correlation entropy for each data modality. Then, a joint sparse representation matrix of multimodal data is obtained by combining all sparse representation matrices, and the highdimensional multimodal data are mapped into the lowdimensional joint space. Next, the above matrices are used to cluster the multimodal data into their respective categories using spectral clustering methods to detect the consistent anomalous data. It is noted that the proposed algorithm is based on the unsupervised learning mode, which can reduce the training set data and decrease the model complexity, and then introduces the correlation entropy into subspace clustering for multimodal data processing, which improves the robustness of the algorithm and can effectively detect the consistency anomalous data. Finally, compared with five typical clustering algorithms, experimental results show that the proposed algorithm can improve the detection accuracy of 0.53%~4.88% and the normalized mutual information performance of 3.67%~17.22% for different degrees of the inconsistent multimodal data.
The Effect of Using Pictionary Game Towards Students Vocabulary Mastery at Tenth Grade Student of Senior High School 8 Jambi

The researcher was conducted based on the problem the students in learning English especially in vocabulary mastery. The researcher said that inthis study he used two classes. Researchers use this test as a tool to obtain data consisting of vocabulary tests. The test used is a multiple-choice test designed to identify the impact of the use of picture games on students' vocabulary. These test results are further analyzed for the impact of picture play on increasing students' vocabulary. Researchers calculated the data using quantitative analysis. Researchers tested the hypothesis using a t-test formulation. The survey results showed that the average score of the experimental class was 55.33 before applying the picture play technique. The control class average score was 56.88. On the other hand, after applying the picture game technique, the test class average is 64.77. The control class average score was 62.77. The researchers found t-test results with t0 higher than tt - t0 was 5.835 and tt was 1.669 (5.835 &gt; 1.669). This means that Ha was accepted and H0 was rejected. The conclusion of this study is that there was a significant effect of picture play techniques on her 10th grade vocabulary skills in Senior High School 8 Jambi.
Design and Implementation of Alignment Device for Proximity Lithography System

For the "jammed" core lithography process in the domestic chip manufacturing field, in order to improve its alignment technology maturity and equipment stability, this paper carried out hardware improvement and software innovation respectively from the two stages of pre-alignment and precision alignment. Furthermore, a high precision and high efficiency platform has been achieved. A combination of Fourier function fitting and least square method was realized to complete the positioning of wafer notch and center. Besides, combined with affine change technology, the feature points extracted by SIFT algorithm were screened by random sampling consistency algorithm, and then the homography matrix is deduced to directly calculate the position deviation and angle deviation, which are fed back to the motion control system for accurate control. Through many experiments, the pre-alignment time is less than 5s and the precision reaches 0.3μm.