Reviewerly/exHarmony huggingface dataset:
Research on Trajectory Planning Based on A* Algorithm

Aiming at the problems of robot path planning in complex environment, an improved A* algorithm is proposed to search the shortest path of robots. Firstly, PRM algorithm is utilized to construct probability map. Secondly, the constructed probability map is used to replace the nodes in the conventional A* algorithm. Then, A* algorithm is applied to search the shortest path in the probability map. Finally, a host of experiments verity the effectiveness and feasibility of the proposed method.
New Framework for Highlights Removal Based on CycleGAN

Highlights detection and removal is hot topic in computer vision. It exists in high-gloss leather, glass, plastic, metal parts, and other mirror-reflective objects. Deep convolutional neural network-based highlight removal techniques can learn material rendering parameters supervised by a large number of paired datasets to automatically remove highlights from object surfaces. However, in reality, the datasets are usually unpaired. It is difficult to obtain a large number of paired images. To deal with the unpaired data, dual learning (CycleGAN) can make full use of unlabeled data. Therefore, based on S2D-NET, a new framework is proposed to address the above problem. The main contribution is that: a confidence map with independent averages is proposed to guide the network to quickly search for the initial value, which can solve the problem of slow network convergence due to the lack of a very strict mathematical definition to distinguish specular reflection components from diffuse reflection components. The experiments are compared with the latest three methods. It is found that the SSIM and PSNR values of our proposed algorithm are improved.
On the Complexity of Dark Chinese Chess

Complexity is an important metric to measure the difficulty of the game and is widely used in Al research on board games. This paper provides a complexity analysis for the game of dark Chinese chess (a.k.a. "JieQi"), a variation of Chinese chess. Dark Chinese chess combines some of the most complicated aspects of board and card games, such as long-term strategy, large state space, stochastic, and imperfect information, which make it closer to the real world decision-making problem. Here we evaluate dark Chinese chess from a game theoretic perspective. We design a self-play program to calculate the game tree complexity and average information set size of the game and propose an algorithm to calculate the number of information sets.
Application of Track Fusion Kalman Filtering Algorithm in Mobile Robot Positioning

Kalman filtering is an optimal state estimation algorithm that can realize linear minimum variance estimation for linear systems. It is widely used in navigation, radar, fault diagnosis, survey and other fields. Based on the localization of indoor mobile robot, combined with the Kalman estimation of odometer and laser rangefinder, this paper studies the application of track fusion Kalman filtering algorithm. Simulation examples show the feasibility and effectiveness of the proposed algorithm.
Multi-Cue Depth Estimation from Epipolar Plane Image

Depth estimation is essential in many light field applications. In this paper, we present a depth estimation method from epipolar plane images(EPIs) by combining both linear consistency cue and regional distribution difference cue to improve the occlusion handling capability. For the linear consistency cue, a rotational linear entropy (RLE) metric is proposed to measure the pixel color consistency of an EPI line. For the distribution difference cue, a height-variable SPO (HSPO) that integrates the color consistency constraint as a new weight in SPO, is introduced to measure distribution difference. Integrating the two data costs is shown more robust to complex occlusions and varying planes. Cost volume filtering and global optimization are applied to improve the accurate of the final depth map. The experimental results demonstrate that the proposed method outperforms other state-of-art light field depth estimation methods in both qualitative and quantitative evaluations.
HFGAN-CN: T2I Model via Text-Image Hierarchical Attention Fusion

In this paper, we propose a simple and effective text-to-image generative adversarial network named Hierarchical Fusion via GAN with Capsule Network discriminator (HFGAN-CN) , which uses only one generator and one discriminator to generate high-resolution images directly from text. We introduce a text-image feature fusion module via spatial and channel attention mechanism into the generator, which performs hierarchical fusion on the features of the two modes in the image generation process, so as to guide the generator to generate high-quality images in line with text semantics. We propose a match-aware discriminator to synthesize text-semantic compliant images and employ a capsule network to reconfigure the network structure of the discriminator without introducing an additional network. Experiments on the benchmark dataset show that our method is superior to the existing techniques and can effectively generate high-resolution images conforming to the text description.
Top-Down Meets Bottom-Up for Multi-Person Pose Estimation

Body posture, as a basic feature of humans, occupies an important position in human-computer interaction and other fields. In order to accurately recognize human body posture features and eliminate misrecognition caused by different human movements. This paper proposes a human body posture detection method that combines top-down and bottom-up two-way. This method divides the input image into two forks, and uses the Mask R-CNN model to extract key point features and human target location features. The key point recognition feature map is divided into two, and using CNN to extract Part Affinity Fields and Part Confidence Maps. The obtained confidence map is added with the human body position feature map. Then use PAFs to segment the key points of different individuals in the result. Finally, the Hungarian Algorithm is used to process the maximum matching of the bipartite graph to obtain the human body posture detection result. Experimental results show that this method can accurately recognize human posture. It has good performance on different data sets and eliminates the estimation accuracy defects of traditional models due to the complex high-dimensional motion of the target.
A Method for Contour Descriptor based on Enhanced Scale Space of the Main Projection Map

Contour information description is an essential and challenging section in image retrieval. Because contour-based image processing algorithms can achieve a high retrieval rate without the influence of illumination and color, a large number of feature description approaches based on closed contour are proposed, but the existing methods are still facing the challenges of accuracy and computational efficiency, and misjudge the image category when the images are taken from different angles. Scale-Space-based descriptor is regarded as a promising contour description method because it has a solid theoretical foundation and at the same time possesses the strength of appealing invariance properties. In this paper, a descriptor based on enhanced scale space and main projections (ESSMP) is proposed for image retrieval. The characteristics of a closed contour are effectively captured by the proposed descriptor. Meanwhile, the proposed descriptor has good properties, such as the invariance of the geometric transformation and the starting point of an object. We evaluated this descriptor on two popular benchmarking datasets, including The Princeton Shape Benchmark and MPEG-7. The experimental results confirm that our method is better than comparable complexity approaches and performs a competitive retrieval rate with respect to more complex and state-of-the-art descriptors. In addition, our method shows brilliant robustness to distortion and occlusion, which is better than other existing similar algorithms.
Design of Einstein Chess algorithm based on CNN and UCT algorithm

For Einstein Chess pruning operation, a UCT algorithm fused with convolutional neural network is proposed by us, which is divided into two main parts. In the first part, to obtain the dynamic valuation function - UCT algorithm, the dynamic valuation function is integrated into the UCT algorithm. This algorithm adds a probability layer to the UCT algorithm based on the "attack value" and "threat value" of the two players, and can adjust the valuation method based on the state of the position. But the valuation of this algorithm is proportional to depth, which increases the time consumption. As a result, the dynamic valuation function-UCT algorithm line data is used as a training data set and trained with a neural network to produce a model with an error of around 2%. In the second part, the training model is introduced into the Einstein Chess line process in two stages: the trained neural network model is initially used for pruning, and then the UCT algorithm is used for decision making. To test the algorithm's viability, the upgraded neural net-UCT algorithm is played against the dynamic valuation function-UCT algorithm, and experimental results show that the neural net-UCT algorithm has a 63. 37% win rate.
An improved Image Inpainting Algorithm Based on Attention Fusion Module

When we try to reproduce existing inpainting algorithms, our performances are often not stable, and the results are not up to expectations. In this paper, we devise an improved image inpainting algorithm mainly constructed by an attention fusion module (AFM) and a refined inpainting module. The AFM evaluates the existing methods' results pixel-wise, similar to humans judging the quality of the inpainting results with naked eyes. The evaluations make a quantitative judgment on the inpainting quality and fused with pre-inpainted images. The refined inpainting module focuses on the areas with poor quality and improves them. This method address the shortcomings of other models like smoothness and texture sharpening. The experimental results on multiple datasets show significant improvements based on existing methods.
Collision detection framework based on Unity3D

With the development and application of Unity3D, more and more individual developers and small teams use it to develop their own products. However, in order to get the products as soon as possible, individual developers and small teams often ignore the problem of collision detection. In order to reduce the cost of collision detection technology for individual developers and small teams in simulation or game development, this paper proposes a universal collision detection framework. The main algorithms of the framework are AABB bounding box, SDF and GJK. In the aspect of logic processing, the framework distinguishes and processes the different collision situations between static objects and dynamic objects. Finally, the experimental results show that the frame rate can be effectively improved in a certain range.
A Bayesian Network structure learning algorithm using experts’ knowledge

Learning Bayesian network from data is a non-deterministic polynomial-time (NP) hard problem. Experts’ knowledge is beneficial to determine the BN structure. In this paper, we propose a novel structure learning algorithm using experts’ knowledge. In the stage of experts’ knowledge fusion, in order to improve fusion results when knowledge from experts is contradictory, a conflict fusion method combining knowledge degree and support relationship between evidences is proposed. In the stage of structure optimization, fused knowledge is integrated with the decomposition structure learning algorithm by modifying the set of candidate neighbor nodes and constructing an improved merging rule to learn the structure. Experiments show that our proposed algorithm can own better performances.
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation

Stereo matching using deep network has made significant progress in recent years. However, state-of-the-art methods are based on expensive 4D cost volume, which limits their use in real-world applications. To address this issue, 3D correlation maps and iterative disparity updates have been proposed. Regarding that in real-world platforms, such as self-driving cars and robots, the Lidar is usually installed. Thus we further introduce the sparse Lidar point into the iterative updates, which alleviates the burden of network updating the disparity from zero states. Furthermore, we propose training the network in a self-supervised way so that it can be trained on any captured data for better generalization ability. Experiments and comparisons show that the presented method is effective and achieves comparable results with related methods.
Insulator location for low-light-level image based on SVM

Aiming at the difficulty of a single method to detect the insulator edges from the colour low light level image with unclear salient features and low signal-to-noise ratio under low illumination. A new method which combines Histogram of directional gradient and other algorithms is designed. Firstly, in order to evenly distribute the light and shade of the image and enhance image texture characteristics, a three channel histogram equalization algorithm is used to extract the insulator outline, at the same time, Hough algorithm is used to correct the tilt of the image. Secondly, the directional gradient features of positive and negative samples are computed respectively, and PCA algorithm is used to reduce the dimension of high-dimensional sample eigenvector. Thirdly, the support vector machine is trained with the feature vectors, and using DCWPSO algorithm optimized parameters. Finally, the slider box is used to find the area, which was predicted to the insulator target. Sklearn’s SVM tool library was used in this research, and C-SVM tool box was selected. The experimental results show that the method is robust and accurate, it is also suitable for insulator detection under various lighting conditions. The support vector machine can reach 92.4% recognition accuracy under the condition of small sample training, compared with HOG-SVM algorithm, it is improved by 6.3%.
Design of echo state network with multi-objective optimization algorithm and l<sub>1</sub> regularization

In this paper, a new approach for optimizing the structure and prediction error of echo state network (ESN) is proposed. ESN is a kind of recurrent neural network with simple training and strong generalization ability. Reservoir is an important structure of ESN, which determine network performance. Thus, multi-objective optimization algorithm is used to optimize network structure and training error simultaneously. Moreover, a local search algorithm based on l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization is used to accelerate convergence. The experiment results of time series prediction and standard classification show that MESN can improve the network prediction performance while sparse network structure.
A New Meta-learning-based Black-box Adversarial Attack: SA-CC

Since deep neural networks are vulnerable to human-imperceptible perturbations with attacking intention, the security research of visual sensors using related detection and recognition algorithms is of value. Then currently black-box adversarial attacks against visual sensors become a popular research direction in this security study for their harsh conditions. However, present black-box attacks have shortcomings in information fully utilization while maintaining a high attack success rate. Our research, based on the newly proposed Simulator Attack, introduces a new attack framework SA-CC by attaching two improving modules for accelerating the whole attack progress. Due to our Cos-Loss and clustering module, the angle information of the output results would be incorporated into the basis of fine-tuning process and simple prior information can be achieved when conducting targeted attack at the beginning. Finally, we perform experiments constantly on the CIFAR-10 dataset. The results obviously exhibit that our attack framework can decrease the total amount of queries considerably and obtain our goal of improving query-efficiency.
Design and implementation of multi-billionpixel imaging system

Aiming at the high cost and complex imaging system faced by current billion-pixel imaging systems, a low-cost and easy-to-use billion-pixel imaging system based on SIFT algorithm is proposed. The system first uses Zhang Zhengyou's camera calibration method to calculate the camera's internal and external parameters and distortion coefficients, and geometrically corrects the image; then uses the SIFT algorithm to match the same feature points of the same group of photos, and further uses image stitching to image the image based on the extracted feature points Carry out stitching, and finally apply the beam adjustment method to smoothly optimize the stitched image and eliminate the re-projection error; the imaging effect of hundreds of millions of pixels is realized.
Knowledge-aware Network with Social Information for Recommendation

In recent years, social networks are increasingly developed, and the social functions of various applications have strengthened the connection between users, which also brings a new data source for recommendation algorithms. This paper proposes a model named Knowledge-aware Network with Social Information(KNSI), which extracts users and items representations from the user’s item-space and social-space, and then effectively propagates it in the Knowledge Graph(KG) to obtain high-level features of the entity. In addition, the model uses an attention module to distinguish the contribution values of different neighbors in the KG and provide users with personalized recommendations. Experimental results on the public dataset show that the proposed model works significant in Recall and NDCG over several other models. Further analysis verify KNSI offers a valid explanation for items not interacted with by the user, alleviating the problem of sparse interactive data.
A Terrain Segmentation Method Based on U-MobileNet for Navigation of Outdoor Robots

Terrain segmentation is widely used in the recognition and navigation of outdoor mobile robots, in which deep learning-based segmentation methods have achieved remarkable performance. However, most of the segmentation networks can hardly meet the real-time requirement for outdoor robots. A novel terrain segmentation approach is proposed for segmentation terrains in the navigation of robots based on U-MobileNet. U-MobileNet, a lightweight segmentation network, is formed by transposing the feature extraction structure of MobileNetV2 to the contracting path of U-Net. The network efficiently reduce the computational cost and the error in segmentation by applying depthwise separable convolution, inverted residual and linear bottleneck. The experimental results show that the proposed lightweight neural network has good real-time performance with high-accuracy.
Experimental Validation of an Adaptive NN Learning Controller on a Robotic Manipulator

In this paper, we present experimental validation of a novel method for controlling a robotic manipulator using an experience-based Neural Network (NN) controller derived from Deterministic Learning (DL) techniques. Studies are performed on simulated and real-world environments to quantify performance of this controller compared (as a baseline case) to traditional methods such as Proportional-Integral-Derivative (PID) control. Both of these controller types (NN and PID) are integrated into a 7 Degrees of Freedom (DOF) manipulator system, having the controller available to use on all 7 joints simultaneously. The NN controller utilizes adaptive learning strategies focusing on the state-feedback case. The learning algorithms train Radial Basis Function Neural Networks (RBFNN) to enhance tracking performance along a non-linear recurrent reference trajectory. With convergence of NN weights within the RBFNN, the unmodeled manipulator dynamics are guaranteed to be identified and stored for use with the experience-based algorithms without online adaptation. This paper details the process of integrating a NN controller with system state feedback and employing learning/experience based algorithms in a manner that host system computations are not affected beyond a pre-determined sampling rate.
Research and application of secure access control method for multi-granularity sensitive resource of industrial IoT based on DC-MABAC hybrid model

The secure access method and dynamic authorization management mechanism of multi-grained sensitive data in industrial IoT environment are the key issues of current research in the field of industrial IoT secure communication. In this article, we analyze the dynamic correlation law between multiple permissions, roles, scenarios and resources, introduce a dynamic change mechanism of multi-granularity resource sensitivity and communication environment in the process of information access. We propose a secure access control method for multi-granularity sensitive resource of industrial IoT, establish a secure access hybrid model DC-MABAC for industrial IoT based on dynamic permission assignment, and determine a multi-granularity sensitive resource access mechanism with a combination of roles and attributes. This method can dynamically adjust the access permissions of users, complete hierarchical dynamic access permissions control, and realize the relative isolation of sensitive information such as fault data, processing data, and health status data. We verily the effectiveness of the security access decision mechanism by applying the DC model to the security access control subsystem in practice. And we further evaluate the security of the access control method proposed in this paper in combination with the security constraints defined in the model comprehensively.The example validation shows that this method can improve the security of sensitive resource data access control.
Improved MLP-Mixer for Cars’ Type Recognition

With the development of intelligent manufacturing, the automotive industry, which is an important part of national economy, has attracted attention widely once again and the cars’ type recognition is a crucial part for the automotive industry. The accuracy of cars’ type recognition will directly affect the painting and other operations. Therefore, the car’s type recognition requires a high accuracy over 99%. Some papers propose deep learning models for the cars’ type recognition. However, many existing deep learning models have problems such as requirements for massive samples, slow convergence and difficulty in achieving the accuracy over 99%, which makes them have a little application to the industry. This paper takes the cars’ type recognition as the background and adds the prior knowledge to the deep learning model, which introduces LBP into MLP-Mixer to improve the accuracy effectively.