Reviewerly/exHarmony huggingface dataset:
Application of Deep Reinforcement Learning in Guandan Game

In recent years, imperfect information game has become an important touchstone to test the level of artificial intelligence. There are many imperfect information game scenarios in the real-world, such as economic transactions, military games, automatic driving. Therefore, the study of imperfect information game problems has very important practical significance. Guandan is a type of imperfect information card game with four players which are divided into two teams. The mass hidden information in the Guandan game leads to a high-dimensional game state. Reinforcement learning algorithm has efficient ability in strategy search of computer games. But it cannot converge under the condition of imperfect information and high-dimensional state space which caused by Guandan Game. According to these problems, this paper introduces the Proximal Policy Optimization (PPO) algorithm based on deep reinforcement learning to solve the problem of imperfect information, high-dimensional state space, and action space. It enables the agent to perceive high-dimensional information and makes decisions according to the acquisition information. The experiment result shows that the decision model based on the Proximal Policy Optimization algorithm is better than the intelligence level of the Policy Gradient algorithm and A2C algorithm, which proves that the system has a self-learning, ability to improve the game level of Guandan.
Optimal Sensor Fault-Tolerant Deployment for Cost Minimization in Target-based Heterogeneous Wireless Sensor Networks

Coverage and connectivity are two fundamental issues in target-based wireless sensor networks (WSNs). Considerable work has been carried out on the issues of how to maintain coverage and connectivity simultaneously in homogeneous sensor networks. In some applications, sensor nodes of multiple types with different sensing ranges, communication ranges, and prices are available for optimally organizing a network. In this paper, the problem of how to deploy such heterogeneous WSNs into predefined positions with k-coverage to all targets and c-connectivity to each sensor node under the minimum-cost constraint is addressed. First, we prove its NP-completeness. Then, this problem is formulated as an integer linear programming model. To solve this problem, we proposed an enhanced coral reef optimization algorithm (CRO). The enhanced version of CRO is named ECRO. The improvements of the ECRO lie in four aspects. First, the operator of immigration in the biogeography-based optimization algorithm is introduced into the brooding procedure of the coral larvae formation to conserve the excellent population solutions. Second, a chaotic differential mutation operator is adopted in the brooding procedure of ECRO to enhance the diversity of the descent and the optimization ability for the coral reef. Third, a random opposition-learning strategy is proposed to improve the performance of the worst individual of the population. Moreover, the reuse of excellent decision variables in the solution is proposed to improve the decision variables of other individuals. Extensive simulation experiments are performed to validate the proposed ECRO. The results show that the proposed ECRO outperforms the compared algorithms, which demonstrates the superiority of the proposed ECRO algorithm.
EnsembleCard : A Strategy Ensemble Bot For Two-Player No-Limit Texas Hold’em Poker

Nash Equilibrium is a powerful paradigm to create advanced AI in two-player zero-sum setting, which has a great advantage in getting a statistically significant result. However, playing millions of matches is time-consuming and even more problematic when human player participate in. Single paradigm is hard to hold absolute advantages in this real-world setting because of uncertainty in games. In this paper, we proposed a robust strategy ensemble framework in decision-making tasks, which is useful for real-world settings and has dominant advantage in comparison to single paradigm-based algorithm. We create a strong poker AI with two base decision-makers(including NE searching-base and rule-based), where we improve DeepStack and unlock the potential of CFVnet for creating a high-performance AI. Finally, we applied these approaches to build a poker AI for two-player no-limit Texas Hold’em and won the silver medal of the 2021 Chinese computer poker tournament. And additional experimental results also proved the effectiveness of strategy ensemble framework.
Research on Opinion Mining for Sentiment Classification of Micro-blog Text Based on DeBERTa

Aiming at the problems that the sentimental information contained in micro-blog text is not fully represented and the sentiment classification is not accurate enough, this paper proposes a sentiment classification model of micro-blog text based on DeBERTa combined with BiGRU network and attention mechanism. Firstly, the text is represented by word vector through the pre-trained language model DeBerta. Then, it is input into BiGRU network to further extract context semantic information, and then through attention mechanism to assign corresponding weights to text features. Finally, the text is classified by the output layer. This paper selects the dataset of "sentiment recognition of netizens during the epidemic" competition held by DataFountain platform in 2020. The experimental results show that sentiment classification effect of the model proposed in this paper is better, which verifies the effectiveness of the method.
Design and implementation of a deep learning-based decision system for Xuezhan Mahjong

Aiming at the problems of large state space, many game actions and complex game information of Xuezhan Mahjong, a method of semantic segmentation and feature extraction of Xuezhan Mahjong situation is proposed, and a Xuezhan Mahjong decision system based on DenseNet network and human empirical knowledge is designed and implemented according to the extracted game situation. The experimental results show that the model produces good training results in the original data and has high accuracy on the test set. The proposed Discard model outperforms CNN, ResNet, and XGBoost Discard models, and the decisions made by the system are human-like in playing with real players. It provides a research basis for the subsequent Xuezhan Mahjong Discard model that can surpass human.
Intelligent Control for a Non-holonomic Constrained Mobile Robot with Proximal Policy Optimization

The dominant control algorithm using in the non-holonomic constrained mobile robot usually depends on accurate modeling and complex calculations. This paper proposes a new deep reinforcement learning-based method to solve the control problem of the non-holonomic constrained mobile robot. An end-to-end control of the mobile robot is realized by proximal policy optimization learning algorithm. The control policy based on reinforcement learning can more efficiently calculate the actions according to the current state of the robot. This rapid autonomous response is expected to open up a path for the real-time controllable robot. Moreover, this paper establishes an obstacle environment based on the kinematics model of the non-holonomic constrained mobile robot to verify the effectiveness of the algorithm. Finally, this simulation results illustrate the control method with favorable robustness and generalization for the non-holonomic constrained mobile robot control.
Pre-trained Bert for Natural Language Guided Reinforcement Learning in Atari Game

Inspired by the dream of using natural language to guide an Agent, we propose a language understanding model that links natural language commands with particular target states, so that the agent can better understand the tasks expressed in natural language. The agent performs exploration in the environment according to human intentions through the decision-making method of reinforcement learning, which will improve the performance and efficiency of exploration. By using the most advanced text-based pre-trained language model Bert, the language understanding model is robust to human commands.
Transformer-Enhanced Visual-Semantic Representation for Text-Image Retrieval

Text-image retrieval aims to find semantically relevant results from another modality when given an image or a sentence as a query. The key challenge is that the data representations of different modalities are inconsistent, so it is difficult to directly measure the similarity. The existing attention-based methods generally only consider the pairwise association between text and image, and ignore the semantic association between the different regions in an image which causes the learned image features or text features are insufficient at the abstract semantic level. In this paper, we propose a Transformer-Enhanced visual-semantic Representation Model (TERM) for text-image retrieval by designing Transformer-Enhanced (TE) module which play an important role in mining the context relationships between the local regions in images or between the words in sentences, therefore can provide more fine-grained clues for image and text matching. In addition, a graph structure is introduced in our method to effectively model the high-level semantics, which can accurately measure the similarity between the image and text. Experiments on Flickr30K and MSCOCO datasets demonstrate the effectiveness of our model, which achieves the state-of-the-art result for text-image retrieval. The Recall@1 on Flickr30K by our model improves image to text retrieval by 3.8% and text to image retrieval by 4.6%.
Straight line detection algorithm of truck point cloud based on statistical line chart

Line feature extraction has always been an important part of point cloud processing. In the task of cement automatic loading, the line feature of truck side point cloud plays an important role in model reconstruction and dimension calculation. This paper introduces a line detection algorithm for truck side edge based on statistical line graph. Firstly, we correct the horizontal posture of the truck based on the geometric characteristics of the truck. The straight lines of front board, rear gate and side gates of the truck after correction are approximately parallel to a coordinate axis. Then, using the edge line detection algorithm of statistical broken line graph proposed in this paper, we draw the coordinate value-density distribution diagram, so as to determine the edge position of the truck side. and finally, calculate the edge straight line of the truck side point cloud. The algorithm proposed in this paper can effectively eliminate the interference of foreign bodies on the truck side and accurately extract the truck side point cloud. The simulation and real experiments verify the robustness and accuracy of the algorithm.
Switching Filtering Algorithm with Image Preprocessing and Gesture Tracking

Denoising has always been a critical technology in the image processing community. Gaussian noise and impulse noise are the most common image noises. A single filtering algorithm cannot suppress these two noise conditions. Besides, existing hybrid flitering methods have high time complexity and are difficult to meet the real-time requirements. For this, we propose a filtering algorithm based on conditional switching, which establishes an adaptive median filtering subsystem and a fast guided filtering subsystem. It can automatically determine the main noise and select a suitable filtering subsystem for image denoising. To prove the effectiveness of our method, the proposed method is apply to a Kinect-based gesture tracking system. Experimental results show that the switching filtering algorithm can fulfill both accuracy and real-time performance requirements.
IMM Based Sequential Fault-tolerant Fusion Estimation with Heavy-tailed Noises

In this paper, the problem of interactive multi-model (IMM) based sequential fusion estimation with heavy-tailed noise in an unreliable transmission environment is considered. The measurement values are affected by random packet loss during transmission. For non-Gaussian noise, the Student’s t-distribution is used to model the heavy-tailed noise that appears in some practical scenarios. An IMM estimation method is effectively combined with the Student’s t-distribution to investigate a multi-model filtering algorithm. On this basis, a corresponding sequential fusion algorithm is proposed. Finally, the effectiveness of the algorithm is verified by a target tracking example simulation.
An Improved Feature Pyramid based Two-Stage Haze Removal Network for Marine Ships

The marine ship image is affected by special weather such as sea fog, which leads to low image contrast and color distortion. In this paper, RDFP-Net which is a modified feature pyramid based two-stage dehazing network is proposed for marine ship images. Considering the loss of details and color distortion of the original image caused by deeper features, a Feature Pyramid Network (FPN) is introduced to retain detailed multi-scale information while a Residual Dense Block (RDB) is combined further to better extract detail feature maps for haze image in the first stage. In addition, the Channel Attention Mechanism (CAM) is employed in the fusion of low-level and high-level feature maps. Thus, more attention is focused on the representative channels by assigning different weight. Finally, more image details are recovered through the second stage. Experimental results demonstrated that RDFP-Net achieves better qualitative visual effects and quantitative indicators with other the state-of-the-art methods.
Distributed State Estimation with Unknown Input for Maneuvering Target

This paper is motivated by the desire to realize the cooperative detective of multiple missiles against a maneuvering target in the presence of unknown input. To this end, a consensus-based distributed algorithm within the fifth-degree cubature Kalman filter is proposed to estimate the state of the maneuvering target. The algorithm is built up according to the following steps: first, we rigorously construct the maneuvering target’s mathematical model by combining the target’s kinematics model and the aerodynamic model. Then, we creatively set up pseudo measurement using the state at the previous time step to improve the observability of the target’s state. Next, to combat high system nonlinearity and high dimensional state estimation problems, a novel algorithm combining the distributed fifth-degree cubature Kalman filter and the unbiased minimum-variance estimation method is proposed. Finally, we use numerical simulations to demonstrate the effectiveness of our proposed algorithm.
Photosynthetic Rate Prediction Model of Greenhouse Crops Based on PLS-ELM

Aiming at the problems of low prediction accuracy caused by the large amount of calculation and artificially selected parameters when the traditional extreme learning machine (ELM) algorithm is used to establish the photosynthetic rate prediction model of greenhouse crops, a prediction model of photosynthetic rate based on extreme learning machine combining with partial least squares method (PLS-ELM) is established in this paper. Firstly, the sample data were obtained by measuring the photosynthetic rate under the combination of multiple environmental factors. Secondly, a photosynthetic rate modeling method based on PLS-ELM is proposed, that is, the number of hidden layer neurons and network weights of extreme learning machine algorithm are optimized by partial least squares (PLS), and the Softmax is selected as the activation function to improve the fitting accuracy of the model established by ELM algorithm. Finally, the experimental results show that the photosynthetic rate prediction model established by this method has high accuracy.
Bilateral-Branch Fusion Network for 3D Object Detection

In this article, we focus on exploring the 3D object detection of LIDAR-RGB fusion, but there are still two challenging problems that need to be solved: 1) The lack of information of distant point cloud samples leads to misalignment of multi-sensor data fusion. 2) When the center points coincide and the aspect ratio remains the same, the loss based on the traditional iou and giou remains unchanged, resulting in a deviation in the back propagation. In this work, we propose a multi-mode data Bilateral-Branch Network (MBN), which includes a Bilateral-Branch Network (BBN) and a new 3d iou loss to solve these two problems. Specifically, we designed two branches of representation learning and classifier learning, called the regular learning branch and the rebalancing branch, respectively. The two branches use the same network structure. These two branches use parameters adjusted according to the number of trained epochs to adaptively adjust the entire model. In addition, our 3D IoU loss calculates the ratio of the length, width and height of the anchor box to avoid simplistic optimization of non-overlapping bounding boxes. A large number of experiments in the KITTI benchmark test prove that our performance is superior to the most advanced methods.
Multi-Target Tracking Method for Collaborative RSUs

Road Side Unit (RSU) based target tracking takes a key role in Cooperative Vehicle Infrastructure System. In current solution, each RSU implements target detection and tracking independently and lacks effective utilization of scenario information from other RSUs. To improve the tracking performance of road side sensors, a multi-target tracking method for collaborative RSUs is proposed in this paper. The RSU receives the target trajectory information broadcasted by neighboring RSUs as potential targets firstly. Then the measurements of targets detected by road side sensors are associated with existing target trajectories and potential target trajectories. The associated potential target trajectories are considered as existing target trajectories, and the unassociated measurements are utilized for tracking initiation. Finally, the associated existing target trajectories are updated by tracking filter and broadcasted out. The experimental results show that comparing with conventional solution, the proposed method can improve the tracking accuracy and convergence rate of the road side tracking filter.
Traffic violation image processing method based on improved YOLO-v4

Aiming at the situation that vehicles in traffic violation pictures are forced to violate regulations or drive under the command of traffic police, we propose a method for further information extraction of traffic violation images based on the improved Y0L0-v4 network. According to the Y0L0-v4 detection results, make basic judgments on the intersection situation and vehicle status in the traffic violation pictures, and provide further relevant information for subsequent manual screening to reduce the burden of manual work. In view of the characteristics of the target to be detected in the traffic violation image, the Mosaic data augmentation in Y0L0-v4 is replaced by Mixup data augmentation. Due to the low detection accuracy of small targets such as traffic lights and traffic piles in traffic violation images, a variety of attention mechanisms are compared, and finally, the Coordinate Attention attention mechanism which has a better effect on improving the traffic violation image datasets and the fewer parameters is selected. The Depthwise Over-parameterized convolutional layer(DO-Conv) is used to replace the basic convolutional layer to extract input image information more efficiently and further improve the accuracy of network detection. Finally, We propose a lightweight Y0L0-v4, which greatly reduces network parameters and improves the speed of network detection while meeting the detection requirements.
Template Matching Algorithm Based on New Particle Swarm Optimization

A new image matching algorithm based on particle swarm optimization (PSO) is proposed to solve the problems of falling into local optimum, slow search speed and low matching accuracy in the process of image matching. Firstly, the particle swarm optimization algorithm is optimized with improved nonlinear inertial weight to balance the global and local searching ability of particles. Secondly, the adaptive learning factor is improved to further enhance the local search ability, so that the particle swarm can obtain better search ability at different stages, and avoid the particle velocity stagnation to zero in the later stage of the algorithm and fall into the local optimal. Simulation results show that the proposed algorithm improves the global searching ability and convergence accuracy of particles, and effectively prevents premature convergence. Compared with the PSO image matching algorithm based on standard, the proposed algorithm has the characteristics of fast convergence, good robustness and high matching accuracy.
Safety Barrier Certificates for Heterogeneous Quadrotors with Learned Uncertainties

This paper presents Safety Barrier Certificates which ensure teams of differential flatness-based heterogeneous quadrotors achieve collision-free flight under uncertain disturbances. The controller utilizes Incremental Gaussian Processes(IGPs) to learn uncertain disturbances online, and Safety Barrier Certificates are formed based on Exponential Control Barrier Functions(ECBFs) which achieve a great balance on safety and performance. Given a nominal trajectory, the method uses certificates to ensure safety by modifying it in a minimal invasive way. We verify the effectiveness of the proposed method via both theoretical proof and the simulation results.
Constructing Better Primary Capsules

As a promising variant of neural network, CapsNet works well with MNIST handwritten digit recognition, but inferior to CNN on complex datasets. Instead of proposing novel algorithm for dynamic routing which has been confirmed by previous literatures that the improvement of performance is limited, we focus our attention on how to construct effectively primary capsules. In this paper, for learning better representation we replace convolutional layers with pretraining layers, the extracting latent representation can be utilized to construct high effective primary capsules, which is capable of reaching the state-of-the-art performances on complex image datasets compared with other variants of CapsNet and reducing calculation simultaneously caused by fewer capsules. Our results fully demonstrate that CapsNet with better constructing primary capsules can work well with less memory demands on complex image datasets and present a new direction for realizing true potential of CapsNet.
An image defogging enhancement algorithm combining dark channel and haze-line prior

Affected by dust and haze weather, outdoor images will appear distortion and fuzzy to a certain extent. Aiming at the characteristics of dark channel prior and haze-line prior, an improved fog removal method combining the two methods is proposed, which can effectively restore the fogging image of the sky region. Firstly, the image is divided into sky region and non-sky region by threshold segmentation, and the airlight value is estimated by introducing brightness and gradient information into the image. Secondly, dark channel prior model and haze-line prior model are used to estimate the transmission of foreground region and sky region respectively, then gamma correction and guided filtering are used to optimize the transmission map of the whole image. Finally, combined with the hazy image formation model, the restored image is obtained by inverse operation. Then the brightness compensation and contrast stretch correction are used to restore the brightness and contrast of the image, and the final defogging image is obtained. Experimental results show that this algorithm can effectively solve the image distortion, improve the contrast and sharpness, so as to get a higher quality image.