Reviewerly/exHarmony huggingface dataset:
Fusion Prediction of Aero-engine Performance Parameters based on Cubature Kalman Filter

Accurate performance parameter prediction plays an important role in improving the operation performance, service safety and the accuracy of aircraft health management and maintenance strategies. Aiming at the problem of engine performance parameter prediction, a fusion prediction method based on Cubature Kalman Filter (CKF) is proposed in this paper. Firstly, the mechanism model of aero-engine based on T-MATS is established, and then the Cubature Kalman Filter (CKF) is used to fuse the state data of the aero-engine model with the observed data of sensors. The model was simulated by using foreign engine test data set, and the results show that compared with the Unscented Kalman filter (UKF) fusion, the error of the data after CKF fusion is smaller and the robustness is better.
Deep Retinex image enhancement algorithm under weak Light Conditions

An improved deep Retinex enhancement algorithm is proposed to solve the problems of large color deviation of reflection component and low detail of illumination component in low light condition. The Convolutional Block Attention Module (CBAM) is embedded in the enhanced network to extract the spatial and channel information of the image to improve the color distortion. Bilinear interpolation method was used to highlight details with the weight of adjacent spatial information. Finally, the enhanced image was obtained by merging R and L components pixel by pixel. The experimental results show that the subjective visual effect of the algorithm is more natural, and the objective evaluation indexes are greatly improved.
Path Planning Algorithm of Mobile Robot Based on Improved Q-learning Algorithm

To solve the problems of slow convergence speed and poor stability of reinforcement learning method used on mobile robot for path planning tasks, an improved reinforcement leaning algorithm is proposed. In this paper, the algorithm allow the agent to move with 8 optional self-adapting directions. Also, we apply route expansion method to initialize the final route. Finally, we set dynamic exploration factor to accelerate the convergence of the final route. Simulation experiments undertaken on grid maps created by canvas indicates that the improved reinforcement learning can largely increase the speed of the convergence for the final path comparing to the classic Q-learning algorithm and A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, which has highly application value in the future.
Mean-gradient based two-dimensional histogram image segmentation algorithm in polar coordinate system

Traditional two-dimensional histogram image segmentation algorithms are susceptible to noise interference, poor segmentation accuracy and huge computational effort to find the best threshold. To solve these problems, this paper proposes a two-dimensional histogram image segmentation algorithm based on mean-gradient in-formation in polar coordinate system. Based on the traditional two-dimensional histogram, the image gradient information is introduced, and the mean and gradient values of the neighbouring pixels of the image are selected to form a two-dimensional histogram to increase the noise immunity and improve the segmentation accuracy. In addition, this paper maps the pixel points from the right-angle coordinate system to the polar coordinate system, and through the processing of noise and boundary points, the image pixel points are concentrated around 90°, so that the threshold segmentation can be performed only by the polar diameter information in the polar coordinate system, which greatly shortens the running time. Experimental results show that the algorithm improves segmentation accuracy and running speed in the case of more severe noise.
Two-Path Motion Excitation for Action Recognition

Capturing discriminative motion information is the key to effective Spatio-temporal modeling for video motion recognition. 3D CNN can capture multi-frame motion information, but the computational effort is too large. Traditional 2D CNN is computationally small but powerless to capture motion information. Therefore, to solve this dilemma, we design a module that can be embedded in a 2D CNN. This module consists of two parallel short-term motion excitation (SME) paths and a long-term motion excitation (LME) path. The short-term motion excitation path is to compute feature variations between adjacent frames and use it to excite motion-sensitive channels in the image. The long-term motion excitation path calculates the feature changes among multiple frames, selects representative frame sequences, and aggregates motion information on time series with 3D convolution kernel. Compared with previous methods, this module focuses on solving the problem of poor motion information capture in long time series, while taking into account motion information capture in short time series. Experimental results show that the method has achieved good performance on benchmark data sets.
Research on Multi-pose Face Recognition based on Block Occlusion

Aiming at the low accuracy of face recognition under occlusion conditions with different poses, the paper proposes a block occlusion multi-pose face recognition method based on feature point extraction. First, the face is divided into blocks according to the occluded area. Different weights are given to different face blocks by adaptive weighting method. The occluded face recognition is completed by using convolution neural network, which can effectively reduce the impact of pose changes and occlusion on face recognition. The research shows that this method has higher recognition rate and better robustness for multi-pose occluded faces.
Lighter and Faster Face Mask Detection Method Based on YOLOv5

At present, the face mask detection model based on deep learning is more complex and difficult to deploy on edge devices with limited computing resources. In this paper, a lighter and faster face mask recognition method based on YOLOv5 is proposed. ShuffleNet and GhostNet modules are introduced respectively in the backbone and neck sections to reduce parameters. An attention mechanism is added after each feature fusion part to make it pay more attention to the important information on the feature map. Experimental results shows that the proposed algorithm achieves a higher 1.6% mAP compared to YOLOv5 while the model size is reduced by 89.4%, which is easier to deploy.
Classification of Dunhuang Mural Image Based on Small-sample and Semi-supervised Learning

Dunhuang mural images classification belongs to the research task in the field of image recognition. In this paper, the semi-supervised model is established with multidimensional features extracted by transfer learning. A small number of labeled samples were used to obtain a large number of unlabeled data, combined with Active Learning and iterative strategy for multiple rounds of label transfer of selected samples. After several rounds of iterations, we can get a more powerful classification learner. Experiments on the self-built Dunhuang mural dataset have shown that the results can approach or even exceed some supervised learning methods when the number of known label samples is less than 4%. Our research implements the cultural resource image classification algorithm based on small samples, which is conductive to improving the accuracy when label samples are scarce.
Multi-agent Reinforcement Learning with Multi-head Attention

Multi-agent reinforcement learning(MARL) methods have become an important approach to solving the decision making problems of agents. As the environment’s complexity increases, the attention model can effectively solve the problem of information redundancy. However, the introduction of attention models in reinforcement learning may also lead to over-focusing and neglecting other potentially useful information. Moreover, the presence of attention would slow the convergence in the early stages of training. To address the above problem, we propose a divided attention reinforcement learning approach: (i) the involvement of an attention regularization term to make agents more divergent in their focus on different directions; (ii) the use of a layer normalization network structure and the use of a Pre-Layer Normalization(Pre-LN) network structure for the attention optimization in the initialization phase of training. It allows the agents to have a more stable and smooth gradient descent in the early stages of learning. Our approach has been tested in several multi-agent environment tasks. Compared to other related multi-agent methods, our method obtains higher final rewards and training efficiency.
Challenges in Establishing Causal Validity of Studies on Influencing Factors in Education

One of the popular approaches to educational research is to investigate multiple influencing factors on an outcome of interest. Researchers ask, “Why do students drop out of school?” or “Why did the math score decrease?,” and try to find potential factors that lead to such consequences. The findings are then used to develop an appropriate policy to intervene to facilitate a desirable outcome or to inhibit an undesirable one. This approach belongs to a type of question referred to as “causes of effects,” which is contrasted to “effects of causes.” Interestingly, many causal inference researchers believe that the former is much more difficult to answer than the latter. However, the theoretical basis for such a belief has not been well discussed in the Korean educational research field despite the popularity of the approach. Using causal graphs, this article explains why the causal interpretation of the findings from many studies on influencing factors cannot be established with certainty. In fact, multiple regression coefficients from a single equation could have different interpretations, such as one as a direct effect and the other as a confounded noncausal association. It is emphasized that with the lack of knowledge of true data-generating process, it is almost impossible to correctly interpret statistical findings in terms of causal validity. The article concludes with a discussion of the importance of causal inference for educational research.
Discovering technology and science innovation opportunity based on sentence generation algorithm

Science and technology are crucial elements in discovering innovation opportunities. They have their own practical and theoretical unique meaning in innovation factors. Scientific or technological information can be collected by patents or papers, and various approaches for innovation opportunities discovery are being proposed using text mining. Since a previous methodology using patents and papers discovered opportunities in science or technology itself, they cannot discover opportunities reflecting the science and technology relationship. In addition, since discovered innovation opportunities are formed within the keyword or phrase level, they cannot provide innovation direction or purpose. Therefore, this study suggests a new approach to discovering science and technology innovation opportunities that reflects the science–technology relationship and their concrete directions/purposes using the sentence generation algorithm. An algorithm-generated sentence can contain contextual flow and connection between keywords. In contrast, the generated sentences from the sentence model can reflect science and technology from mass data in a readable sentence. Key innovation factors from science and technology are extracted from generated sentences and then innovation opportunities with specific directions and purposes are suggested.
An Image Semantic Segmentation Method with Improved Network Structure

Image classification, object detection and image semantic segmentation are three core research topics in computer vision. Among them, image semantic segmentation is the most challenging task. In actual segmentation, both the accuracy of segmentation and the speed of segmentation should be considered. The key to improve the accuracy is to determine the regions where different categories are located, that is to accurately define the edge contours of different categories. For this reason, considering on the difficulty of forecasting different samples and the balanced distribution of samples, we designed a new loss function. In addition, we pool the pixels in the closed area formed by the same category to simplify the number of pixel output results, and improve the segmentation speed. The test on KITTI road scene database shows that this method can effectively improve the segmentation speed while ensuring the segmentation accuracy.
De-Biasing user conformity bias and item popularity bias in Group recommendation

The recommendation system is usually faced with the problem of popularity bias, and the collected interactive data often presents quite unbalanced or even long-tail distribution.However, most of the existing studies have considered how to eliminate the negative effects brought by the popularity bias, thus ignoring the two sides of the popularity bias.At the item level, people tend to comment on popular items. So is it all positive? Most research on popularity bias has focused on the conformity behavior and has increased the diversity of personal recommendation lists. At the user level, does everyone want to pursue the preferences of the mainstream group? The reported user responses are skewed toward popular items rather than users’ true interest. We believe that not all popularity bias are harmful. In addition to the consistency effect, the uneven distribution of items can be attributed to the diversity of items’ quality. To address this problem, we propose a new de-biasing mechanism, which uses user conformity bias and item popularity bias to improve the accuracy of recommendation.
Research on Development Mode of Electronic Information System Driven by Software

In view of the present situation and challenges of electronic information system equipment development, on the basis of deep analysis of electronic information system equipment and software, the development idea of electronic information system is discussed, a development model of electronic information system based on software driven is proposed, corresponding innovation means of technology and management, at the same time, the concrete implementation ways are given, it provides a reference for developing related equipment and building an increasingly perfect electronic information system.
Detection and estimation of Micro-motion target based on MM-CBMeMBer filter

A micro-motion target detection and parameter estimation method based on MM-CBMeMBer (Multiple Model Cardinality Balanced Multiple target Multi- Bernoulli) filter is proposed to solve the problem of missing target position information and poor real-time performance in micro-Doppler estimation of rotating target based on time- frequency analysis method. Based on the traditional target CV model CBMeMBer filter, the CV motion model of the target micro-motion component is established. The MM- CBMeMBer filter is used to recursively obtain the most suitable model and estimate it. The image processing problem in time-frequency analysis is transformed into the parameter estimation problem in Bayesian theory. Simulation and experimental data show that the micro- motion target detection and estimation method based on MM-CBMeMBer filter is significantly different from the single-mode CBMeMBer filter, which effectively solves the problem that the traditional filtering method completely loses the micro-motion information and only uses the main echo information.
End-to-end BEV Perception Via Homography Matrix

In recent years, the development of automatic driving is trending and drawing extensive attention both from industry and academia. One of the key elements of automatic driving is the accurate perception of the automatic driving environment. This is critical to planning and control. As the diversity and number of sensors become more and more complicated in equipping the self-driving vehicle, representing features from different views in a unified perspective comes to vital importance. The well-known bird’s-eye-view (BEV) is a natural and straight-forward candidate view to serve as a unified representations. In this paper, we propose an end-to-end birds-eye-view perception based on Homography matrix, relying only on the camera and without post-processing. Our network architecture achieved by two stage network: the image-view network and the Bev-View network, information flows to the Bev-View network through Homography matrix. Extensive experiments demonstrate that our method achieves a new performance.
An End to End joint entity recognition and relationship extraction based on multi-domain Chinese corpus

Chinese sentences are flexible and complex, and there are few structured data in Chinese text, which brings great challenges to Information Extraction. Besides, most of the current methods of NER and RE are concentrated in the medical field. In the later part, we present a new joint entity and relationship extraction method. First of all, we use ALBERT to obtain the embedded vector of each character, input it into the multilayer bidirectional transformer encoder to get representation of the text vector. Then use BilSTM to calculate the hidden state of each character in Chinese sentence, and use Dense to calculate the hidden vector of the sentence. Finally, CRF is used to extract the relationship. At the same time, we conducted the experiment, we calculated the F1 score and recall rate of each relation, and both macro avg and weighted avg scores were improved to a certain extent.
An Automatic Configuration Framework for Cloud-Network Infrastructure

The telecom network has expanded significantly in recent years, incorporating increasingly large-scale, diverse business models, and sophisticated technology. Although there is opportunity for business growth, the number of settings and procedures makes manual configuration impossible. In addition, the current automatic configuration solutions often only support a small selection of items. So, for cloud-network infrastructure, we propose an automated configuration framework. The framework covers task design, parameter arrangement, sequence arrangement, scenario design, scheme design, and interaction with configuration tool interfaces. Through parameter configuration and a universal interface, the framework can integrate new configuration tools. The experimental results show that our automatic configuration framework can reduce the configuration efficiency from hour level to minute level, which proves the effectiveness of the proposed framework.
Research on Depth Estimation Method of Single Aerial Image

Aiming at the problem that the current depth estimation of single image mostly uses the ground public data set, and there is less research on aerial images, this paper uses the collected visible and infrared aerial image data sets to study the depth estimation. We used FCRN and LapDRN to extract the depth estimation results of visible aerial image and infrared aerial image under global and single object respectively, and compared the results extracted from the two types of networks by qualitative and quantitative methods. The results show that the depth estimation accuracy of visible aerial image is higher than that of infrared image, but the increase rate of infrared image accuracy index is higher than the increase rate of image depth range. The image depth estimation results extracted by LapDRN method are superior to FCRN in accuracy, structure consistency and edge clarity, which indicates that LapDRN is more effective in the estimation of aerial image depth information.
Research on Entity recognition of Chinese place Names based on BERT

Aiming at the problems existing in the task of entity recognition of Chinese place names, such as the semantic ambiguity of the original model in the training process and the fuzzy entity boundary of extraction. This paper proposes a BERT - based model for entity recognition of Chinese place names. In this model, the text data of Chinese place names are encoded at the word level through BERT pre-training model, and the semantic information of the words is enriched according to the Chinese context, which effectively solves the problem of unclear entity boundaries. The word vector is sent to the bidirectional long and short term memory network for feature extraction. Conditional random field is used to decode and generate label sequence to extract named entity. Our experimental results show that the model can effectively improve the accuracy of Chinese place name entity recognition, and the F1 value obtained by the original model is 5.5 percentage points higher on average.
Multivariate Time Series Anomaly Detection: a Hybrid Method Based on GRU-SAE and GAIN

Anomaly detection in multivariate time series data has increasingly grown in importance as a result of the Industrial Internet's ongoing development. The abnormal operation condition of equipment, in particular, may result in component damage and pipeline obstruction, leakage, and other defects in the context of pump unit fault detection. Based on this, this research suggests a hybrid anomaly detection method for multidimensional time series data based on GAIN and GRU-SAE. The initial step of the method is filling up the missing data as a result of issues like sensor or communication failure using GAIN. Additionally, in contrast to conventional anomaly detection techniques, the method directly treats the reconstruction error produced by SAE as the anomaly score, removing the subjectivity of manually calculating the threshold value and simplifying the model. The proposed method is then used to the pump dataset for the identification of pump faults, and the experiments reveal that it outperforms other methods in terms of Precision, Recall, F1-score, AUC, and other performance evaluation metrics.
Joint Entity Relation Extraction based on Graph Neural Network

In the task of joint entity relation extraction, the problem of redundant relations caused by multiple types of relation extraction in overlapping entities and the problem of overlapping triples remain many challenging problems. To address the above issues, we propose a joint entity relation extraction model based on attention mechanism and multi-layer graph convolutional network (att-MLGCN) that fuses semantic and dependency information. We combine not only semantic features but also syntactic dependency features to fully extract entity features, predict various relation types between entity pairs. We conduct experiments on two widely used public datasets, NYT and WebNLG. The results show that our model has some improvement over the baseline model and some models.