Reviewerly/exHarmony huggingface dataset:
A study of inverse reinforcement learning and its implementation

When dealing with complex tasks, such as robots imitating human actions and autonomous vehicles driving in urban environments, it can be difficult to determine the reward function of the Markov decision-making process. In contrast to reinforcement learning, Inverse Reinforcement Learning (IRL) can infer the reward function through the finite state space and the linear combination of reward features, given the optimal strategy or expert trajectory. At present, IRL has many challenges, such as ambiguity, large computation and generalization. As part of this paper, we discuss existing research related to these issues, describe the existing traditional IRL methods, implement the model, and then propose future direction for further research.
Expression synthesis based on progressive growing of generative adversarial network and StyleGAN2

An expression synthesis method based on Progressive Generative Adversarial Network (PGGAN) and StyleGAN2 is proposed to create customized datasets in order to address the problems with traditional expression dataset creation methods, such as their high cost, length of time, and difficulty in avoiding the influence of subjective annotation factors. In order to separate identity features from expression features in expression images, this paper first uses PGGAN to generate images of various resolution layers. It then builds a feedback network for this image and obtains the feature latent codes corresponding to the generated images of each resolution layer. The target face image is then produced with specific expressions based on the image generation direction dictated by the fusion result, using the StyleGAN2 network model to fuse the latent codes of identity information in the target face image with the latent codes of expression information in the original expression image. Finally, the Fréchet onset distance (FID) and structural similarity (SSIM) of the synthesized image and the original image are compared. The mean values of FID and SSIM compared with the original image in this paper are 34.61 and 0.90, respectively. It is difficult for human visual perception to distinguish the real from the fake, proving the authenticity of the synthesized image and the efficacy of the synthesis method.
A coverless image steganography method based on invertible neural network

Recently, coverless image steganography (CIS) has become a hot topic in the field of image steganography. However, currently most CIS methods do not build an invertible two-way mapping between the secret information and image, so that the steganography capacity of CIS is limited. Moreover, the generated stego-images have a visible distinction between real-images, and it is difficult to deceive the deep-learning-based steganalysis, which makes CIS unsafe. To address the above issues, we propose a coverless image steganography method based on invertible neural networks, we call it as CIS-INN. Our method uses an invertible flow-based model, Glow model, and we combine it with adversarial generative network (GAN) to improve generated images quality. In encryption phase, secret information is encoded into latent variables by proposed Gray-code-based coding method, then the Glow model takes these latent variables as a prior and generates stego-images. To against steganalysis, we make CIS-INN more secure by introducing adversarial examples when generating stego-images. In decryption phase, secret information can be recovered through the reverse process of the glow model. Experiments demonstrate the CIS-INN achieves significant improvement of the steganography capacity (4 BPP) and maintains reliable security when confronted with multiple steganalysis methods.
Research and application of cone valve seal detection algorithm based on yolov3

In order to solve the problems of traditional manual cone valve seal detection, such as time-consuming and laborious, low detection accuracy and low efficiency, etc., we propose a method based on YOLOv3 framework for cone valve seal detection. In this paper, we propose a cone valve seal inspection method based on YOLOv3 framework. Under the self-built simulated test environment, we use a high-definition camera to collect image data samples of high-resolution bubbles and make our own bubble dataset, on which we realize the accurate detection of cone valve seal test under the inspection scenario. The experimental results show that the detection accuracy of yolov3 model can reach 95.5%, and the model can detect 15 pictures per second. Moreover, the method can provide reliable supervision information for whether the sealing test is qualified in the process of cone valve detection, which can not only effectively avoid the time-consuming and laborious manual detection, but also improve the timeliness of safety management.
MSTDKD: a framework of using multiple self-supervised methods for semi-supervised learning

Image classification is a basic task in the field of computer vision, and general image classification task training requires a large amount of labeled data to achieve good generalization performance. However, in practical applications, the cost of obtaining labeled data is expensive. In contrast, unlabeled images are easy to obtain, so semi-supervised image classification is more meaningful for research. This paper pro- poses a framework for semi-supervised classification utilizing multiple self-supervised methods. Our approach is divided into three steps, firstly, pre-train multiple models on unlabeled data using different self-supervised methods. Then use the labeled data to fine-tune these models except the model pre-training by Contrastive learning to obtaining multiple self-supervised teacher models. Finally, the multi-teacher knowledge distillation framework is used to transfer the knowledge of multiple self-supervised teacher models to the model pre-training by contrastive learning to help it achieve further performance. We conducted experiments on cifar10 and miniimagenet60. Our method achieves further results than using only a single self-supervised method, and also achieves superior performance compared to other semi-supervised methods.
Chinese community topic classification method based on graph model

Community topic classification is the basis of hot topic discovery. Existing graph models ignore the importance of key information to the text when performing text classification and increase the influence of irrelevant data. To address these problems, we propose a community topic classification model DGAT that incorporates key information as well as information about the topic itself. An integrated algorithm is proposed to extract keywords to avoid the problem of inaccurate keyword extraction. Then a composite complex network model containing both topic and keyword nodes is built. Finally, the graph attention mechanism is used to update node features and incorporate semantic-level attention to learn the effect of different graph structures on the current node classification. An example validation on the Qingdao community topic dataset proves the effectiveness of the method and outperforms the baseline models.
3D object detection method based on voxel index R-CNN

In terms of 3D target detection, a voxel-based detection method is proposed: voxel index R-CNN, aiming at the balance between the accuracy of detection results and detection efficiency. In order to improve the timeliness of target detection, this paper proposes a voxel index query method, which uses the index difference constraint to reduce the computational loss of the query process for quantified spatial voxels. On this basis, a voxel feature extraction module suitable for it is designed. apply index query to optimize the ROI pooling layer to speed up voxel feature extraction. The experimental results on the 3D dataset of KITTI show that the results are 90.63%, 81.74%, and 77.23% on three different detection difficulty levels of cars, and the frame rate of detection processing is 28.5FPS. Compared with other methods, this method of a faster detection speed can be achieved while maintaining a higher accuracy rate.
Research on binocular ranging method based on feature point extraction and matching

At present, artificial intelligence has become a hot topic, and the development of its related fields is also developing rapidly, among which visual ranging and image processing are particularly important for the development of artificial intelligence. Now there are many ranging methods are not fast enough, and measurement accuracy is not high, leading to the resulting estimates there are large deviation distance and the actual distance, such as unmanned vehicle, unmanned aircraft in operation process, cannot be accurately driving distance and obstacle avoidance, and existing deviations in robot grab, which cause personnel life safety is threatened, economic property damage. In order to solve the above related problems, this paper uses SIFT algorithm and ORB algorithm to extract feature points, and then through BFmatcher, FlannBasedMatcher, KnnMatch to match, finally get the corresponding distance, from the algorithm and accuracy of the two aspects of relevant research. It is concluded from the experimental measurement that FlannBasedMatcher takes into account both speed and accuracy after SIFT algorithm extraction, while ORB algorithm is faster than SIFT algorithm.
High-resolution human pose estimation based on location awareness

In crowded and complex scenes, it is easy to cause problems such as poor human pose estimation and low-key point positioning accuracy. In this paper, a high-resolution human pose estimation algorithm based on position awareness was proposed. The algorithm introduced the coordination attention (CA) in the feature extraction module, which realized the accurate acquisition of the spatial position information of key points, finally improved the human pose. Estimate the detection accuracy of the algorithm. The AP value of the improved algorithm was 76.5%, which was 2.1% higher than the original algorithm, the AP50 was increased by about 3.1%, and the AP75 was increased by about 2.8%. The experimental results showed that the proposed algorithm could effectively improve the detection performance in crowded and complex backgrounds and had higher detection accuracy.
3D human pose estimation with temporal and position enhancement

The existing 3D human pose estimation method in exploring the relationship between root joint and other key points, to some degree reflects the local movement, but they ignore the trajectories of the whole of the human body, and to some extent, it reflects the global movement, even explore the method of the root node and other related points made significant progress, However, it is not robust to global motion, and it is not perfect to small local motion. For the above two kinds of situations, we proposed representation of temporal and position enhancement. Position enhancement is to maintain the consistency of the input and output distribution by position encoding the correlation of 2D coordinates of human pose. Temporal enhancement occurs by making connections between the current posture of the same person over time and other postures, as we focus more on the current posture and changes in motion in the forward and backward posture.
Improved mine pedestrian detection algorithm based on YOLOv4-Tiny

In view of the complex underground structure and harsh environment of coal mines, it is easy to cause problems such as low pedestrian detection accuracy and missed detection. An improved mine pedestrian detection algorithm based on YOLOv4-Tiny was proposed. The algorithm introduced spatial pyramid pooling (SPP) module after 13×13 features, which realized the extraction of global and local features of image information, and finally improved the detection precision of the system. The mAP of the improved algorithm was 91.98%, which was 2.32% higher than the original algorithm, the precision increased by 3.87% and the recall by 0.93%. The experimental results showed that the algorithm improved the detection of the mine pedestrian system effectively and had a better detection effect.
Entity recognition algorithm and transaction characteristics analysis of bitcoin blockchain

At present, blockchain is in a period of rapid development. However, there are many hidden dangers in its development process. This paper completed the analysis and verification after studying various indicators of the Bitcoin network and putting forward hypotheses based on experience. It is found that the correlation between bitcoin network trading volume and the amount of account funds remains stable. Most of the early saving accounts are actually still transaction participants in the current ecosystem. At present, most bitcoin transaction network entity recognition and classification methods are still based on network graph structure features. This paper proposed an entity recognition and classification method based on the relevance features by starting from the characteristics of bitcoin’s own multi-transaction association. Some data are obtained from public websites, and it shows that the method in experiments could improve the performance of entity recognition effectively. In addition, this paper also analyzes the importance of features and the relevance of classification. In order to deal with the above transaction network feature analysis and entity recognition of the actual demand, this paper also designed a set of highly automated, timely interruption and recovery, and abnormal alarm function of the system. It can be migrated to other Bitcoin networks, and it is very convenient and efficient. This paper can improve the performance of entity recognition and make our understanding of Bitcoin deeper. This system has great theoretical value and application value for the research of bitcoin de-anonymization.
Analysis of the current application of blockchain in social media

Since the appearance of bitcoin, blockchain technology has been widely treated as an opportunity for businesses and the technology revolution. Until now, blockchain technology has been applied to several fields such as energy system evolution, Cryptocurrency, and social media. In this paper, the author mainly explored the current application of blockchain technology in social media, the algorithm behind blockchain technology, and the existing problem in blockchain technology. For the application in social media, this paper explores fake news detection, user trust framework, and decentralized online social network. For the algorithm behind blockchain technology, the Raft algorithm and Practical Byzantine Fault Tolerant algorithm are discussed in this paper. Moreover, one of the innovative consensus algorithms will be explained in the subsection. The following topics will be discussed in the existing problems section: Internet of Things issues, delayed confirmation, and information security. Finally, the author concluded that the current application of blockchain in social media is still developing, and more possibilities for blockchain application in social media will be found with further development.
Research on library construction driven by multi-technology integration

The deep mining of data and the application of technology integration are the main characteristics of a smart society. This paper is to explore the application of multi-technology integration in library smart construction in a smart society, and study the library construction route driven by multi-technology integration. It promotes the transformation of technology empowerment to service empowerment, and provides a reference for the sustainable development of libraries and the construction of a national smart society. This paper analyzes the connotation and research status of multi-technology integration in a smart society. Based on this, it studies the important role of multi-technology integration in promoting the development of library service innovation, and analyzes the specific application of key technologies, such as 5G, IOT, big data, artificial intelligence, block chain and other technologies. Finally, it proposes the route of smart library construction driven by multi-technology integration.
An efficient channel pruning algorithm for automatic compression and acceleration of neural network models

The Convolutional Neural Network (CNN) enables deep neural networks to be deployed to resource-constrained mobile devices via model compression and acceleration. At present, channel pruning methods select channels based on channel importance or designed regularization, which are suboptimal pruning and cannot be automated. In this paper, a channel pruning algorithm is proposed to get the optimal pruned structure via automatic searching. By setting the super-parameter constraint set, the combination number of pruning structures is reduced. The number of channels for each layer of the CNN is determined using the sparrow search algorithm, and the optimal pruned structure of the model is found. The results of extensive experiments show that the proposed method can improve the model's parameter compression ratio and reduce the number of FLOPS within the acceptable range of model accuracy loss.
Microservice deployment problem in the edge-side of intelligent factories

Microservices is an emerging software development architecture. The orchestration and optimization of microservices can effectively reduce the operating latency and improve the service quality. This paper investigates the problem of microservice deployments in the edge side of the intelligent factory. The microservices make up applications with different topologies. The goal is to minimize the weighted summation of the runtime of applications. The problem is formulated as a mixed-integer programming model and solved by the CPLEX solver. The experimental results show that the microservice deployment scheme presented in this paper is superior to the greedy deployment strategy.
Shilling attack detection based on improved clustering algorithm

Recommendation system is becoming increasingly important in various fields of life. To guarantee the accuracy of recommendation, the detection of shilling attacks must be considered. However, the performance of the existing detection techniques for shilling attacks is relatively low, especially for unknown types of attacks, the existing detection techniques are not universal. In this paper, we propose an improved clustering algorithm-based shilling attacks detection method. This method uses information entropy to select a feature and uses the selected feature to calculate the similarity between two users in the clustering algorithm. Experiments show that the algorithm has good detection performance in the detection of shilling attacks.
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

In this article, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm.
Wavelets and Wavelet Transforms

One collecting manner of experimental data by engineers and scientists is as sequences of values at regularly spaced intervals in time. These sequences are named time-series. The fundamental problem with the data in the form of time-series is how to process them for extracting meaningful and correct information, i.e., the possible signals embedded in them. If a time-series is stationary, one can think that it can have harmonic components that can be detected by applying theFourier Transform Fourier analysis, i.e., $${\text{Fourier Transform}}$$ ( $${\text{FT}}$$ ). Although, it is evident that many time-series are not stationary and their mean properties are variables over time. The waves of infinite support that form the harmonic components are not adequate in the latter case in which one needs waves $${\text{localized}}$$ not only in frequency but in time too. They named wavelets and permit a time-scale decomposition of a signal. Considerable progress in understanding the waveletWavelets processing of non-stationary signals was attained. Although, for getting the dynamics that provides a non-stationary signal, it is crucial that in the corresponding time-series a correct separation of the fluctuations from the average behavior, or trend, is performed. Therefore, people should invent new statistical techniques for detrending the data that have to be combined with the wavelet analysis. In this chapter, we deal with waveletsWavelets and wavelet transforms.
Never the Twain Shall Meet? [Microwave Surfing]

In the prologue to his new book <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , Canadian scientist Jacob Van Dyk reports the following incident from his own life:
Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm

Despite satisfactory clustering performance, current subspace-based multi-view clustering methods still suffer from the following limitations. 1) They usually concentrate on the data features in linear subspaces and ignore the features in nonlinear subspaces. 2) They treat all singular values equally without considering their different contribution degrees, leading to suboptimal problems. Based on the above considerations, we propose tensorial multi-linear multi-view clustering via the weighted Schatten-p norm, named TM2vC. TM2vC integrates high-order relationships learning and latent structure learning of multi-view data into one framework. We apply third-order tensors stacked by low-dimensional representations to capture high-order relationships among multivariate data and the weighted Schatten-p norm to distinguish different singular values. Additionally, we employ hypergraph constraints to conserve high-order local geometric structures in the high-dimensional subspace. Comprehensive experiments on diverse datasets verify the effectiveness and superiority of the proposed TM2vC on six indicators.