Reviewerly/exHarmony huggingface dataset:
Research on the Construction and Application of Power Grid Information Model Based on Information Fusion

In this paper, a hierarchical multi-source information fusion method for power grid fault information diagnosis is proposed. Firstly, this paper carries out feature fusion and decision fusion for wavelet packet transform features and power spectrum features of power grid fault information. At the same time, an unsupervised feature reduction method based on entropy measure is used to reduce the power spectrum features. The system realizes the rapid diagnosis of single fault based on the switching quantity of SCADA system. At the same time, the switching quantity information is integrated with the electrical volume information of fault recorder and WAMS. The simulation results show that this method can get accurate fault diagnosis results by fusing the information of electric volume and switching quantity. This method has a good application prospect in power grid fault diagnosis.
Context-User Dependent Model for Cascade Retweeter Prediction

This paper proposes a retweeter predction model based on attention model and Tranformer encoding–Forwader Prediction Model based on User Preference (FPM-UP). Considering the impact of release time, content, and current external context information in the forwarding process, FPM-UP integrates user attribute embedding and context-user dependency into a temporal and text attention model for the prediction of the next forwarding user. Compared with the existing methods, FPM-UP significantly improves the prediction accuracy.
Research of Chinese relation extraction based on BERT

Facing massive text data on the Internet, it is meaningful to extract valuable information for building knowledge graphs and knowledge engines. Relation extraction is a widespread issue in current research. This paper proposes the BERT-BiLSTM-CRF model for Chinese relation extraction. This paper focuses on ten types of Chinese named entity relations, and the model achieves excellent results in the extraction task of these ten relations. This paper first constructs word vectors using the pre-trained BERT encoding model and then extracts features using a bidirectional long short-term memory network (BiLSTM) followed by semantic encoding processing. Finally, this paper uses a conditional random field (CRF) to predict relation categories and complete Chinese relation extraction. Experiments show that the performance is improved after applying the model.
Research on Risk Warning Technology of Electric Power Backbone Communication Network Based on Knowledge Graph

With the development of the electric power communication network, there is a large amount of services transmitted through the network. The scale of data gets larger, and types and sources are diversified in the communication network. Meanwhile, the reliability of electric power communication system is increasingly important in the production department. The traditional methods governing network data are greatly influenced by subjective factors, big data governance technology of the communication network has become the mainstream direction in researches. The paper introduces the event graph electric in the actual operational work of the electric power backbone communication network, then it could predict and diagnose network faults, which effectively avoid potential faults and risk in the network, and further ensure the safe and reliable operation of the electric power network.
A Blockchain Covert Communication Method Based on Voting Contract

In order to meet the increasing demand for information transmission with high confidentiality, it is important to propose new covert communication methods. The performance of covert communication technology includes security and hiding rate. At the same time, blockchain technology is characterized by decentralization, anonymity and traceability, and is a natural hidden communication carrier. Ethereum is one of the classic and commonly used blockchain currency applications. Therefore, this paper uses the voting contract in Ethereum smart contract to build a hidden channel based on the blockchain, and encodes information through different options in the voting contract. In addition, we analyze the channel from security, scalability and cost.
General Framework

This chapter describes a general framework of Deep Cognitive Networks (DCNs) in the context of an example task of vision language navigation. The framework elaborates major principles from the viewpoint of cognitive psychology, when jointly modeling multiple cognitive mechanisms in terms of attention, memory, reasoning and decision.
ResNect: An Accurate and Efficient Backbone Network for Text Detection Model

As an instance segmentation model, Mask R-CNN can be well applied to text detection tasks, but the accuracy and efficiency of its backbone network, such as ResNet or ResNeXt, are relatively low. To improve the accuracy and computational efficiency, we propose a novel backbone network for Mask R-CNN, called ResNect (Residual Network with channel mixing). ResNect increases model accuracy (reflected by the F1 score on MTWI dataset) by mixing multi-scale features, and improves the efficiency (reflected by the runtime tested on CIFAR-100 dataset) of the backbone network by reducing the module expansion. Through these two methods, the computational requirements are reduced while increasing the accuracy. The experimental results show that, compared with the backbone networks ResNet, ResNeXt and Res2Net, the runtime of ResNect tested on CIFAR-100 is reduced by 15.8%, 34.5% and 29.3%, and the Mask R-CNN with ResNect as the backbone network also has the highest F1 score on MTWI.
A Single Image Deraining Network Based on Global Feature Perception

Rainy weather can degrade the quality of image captured outdoors, which in turn reduces the effectiveness of the subsequent computer vision algorithm. Therefore, as a way to improve the performance of subsequent visual tasks, single image deraining has become an important research topic. In this paper, a single image deraining method based on global feature perception is proposed, which can effectively locate the rain streak and estimate corresponding background content via Swin Transformer mechanism. Different from convolutional neural network and traditional Transformer, the proposed network can extract the global features of an image while reducing the amount of computation. Combing advantages of Swin Transformer and convolutional network, a three-stage deraining network is proposed. First, the coarse features of an image are extracted through the convolutional layer. Then, using the feature refining module based on Swin Transformer, global features are extracted and fused. Finally, through the image reconstruction module, the clean image is generated by using residual learning. The experimental results show the superiority of the proposed network.
A Recommendation Algorithm Incorporating Moth-Flame Optimization Algorithm and Fuzzy Clustering

Aiming at the issues of data sparsity, restrictions on discovering nearest neighbors, and accuracy of similarity calculation of conventional collaborative filtering techniques, a hybrid recommendation algorithm based on MFO-optimized fuzzy clustering and combined similarity is developed. The computation of similarity for the differences in feature attributes of various users is done using a multi-attribute similarity calculation model that incorporates user feature attributes. Based on the different fuzzy clustering affiliation levels, the nearby neighbors are filtered, the mothballing method (MFO) is included, the initialized clustering center is identified, and rating prediction of the clustered results is carried out. The experimental results on the MovieLens dataset demonstrate that the algorithm successfully addresses the issue of data sparsity, dramatically lowers the recommendation error and provides a more thorough examination of the recommendation outcomes than the conventional approaches.
Image Defogging Based on Joint Contrast Enhancement and Multi-scale Fusion

Although the single image dehazing techniques now in use work well in certain circumstances, real-world scenarios' complex and ever-changing nature makes it impossible for them to function well. This research offers a contrast enhancement and exposure fusion-based efficient paradigm for picture dehazing. Three basic preprocessing techniques are utilized to create intermediate pictures for contrast enhancement. The first processing method is gamma correction (GC), employed to improve an image's local quality. The second technique is the newly developed color-preserving histogram equalization, which combines each channel to enhance the overall contrast. The third kind of processing is called ACE, which uses adaptability to deal with a complex and shifting environment. This paper fuses the input from GC, CPAHE, and ACE during the fusion stage using the adaptive kernel size fusion technique based on rapid structural patch decomposition. According to the experimental findings, it outperforms other cutting-edge visual and quantitative analysis techniques. The average FADE scores for the daylight and night-time scenes have increased by at least 0.09 and 0.08, respectively. This paper also describes how this strategy may be used in various contexts.
Research on Multi-task Semantic Segmentation Based on Attention and Feature Fusion Method

Recently, single-task learning on semantic segmentation tasks has achieved good results. When multiple tasks are handled simultaneously, single-task learning requires an independent network structure for each task and no intersection between tasks. This paper proposes a feature fusion-attention mechanism multi-task learning method, which can simultaneously handle multiple related tasks (semantic segmentation, surface normal estimation task, etc.). Our model includes a feature extraction module to extract semantic information at different scales, a feature fusion module to refine the extracted features, and an attentional mechanism for processing information from fusion modules to learn information about specific tasks. The network architecture proposed in this paper trains in an end-to-end manner and simultaneously improves the performance of multiple tasks. Experiments are carried out on two well-known semantic segmentation datasets, and the accuracy of the proposed model is verified.
Lightweight skeletal key point extraction algorithm based on TransPose network

TransPose achieves excellent results in human pose estimation tasks, but it has disadvantages such as a lot of network parameters, high computational complexity of the model, and slow convergence speed. The TransPose model is improved to address the above problems. First, we use the Ghost module to change the ordinary convolutional structure in the convolutional backbone network HRNet to decrease the quantity of model parameters and calculation complexity; second, we use the multi-head mechanism of attention to increase the encoder learnable information and improve the model accuracy; third, we introduce the normalization structure of the front layer to accelerate the model convergence. It is proved by experimental results that the amount of computation of the improved TransPose model on COCO dataset is reduced from 21.8G to 15G, the number of parameters is reduced from 17.5M to 4.85M, and the model converges faster while maintaining a high accuracy rate.
Privacy protection scheme based on certificateless in VSN s environment

In vehicular social networks (VSN s), cloud service providers can provide many convenient services for vehicles to ensure the safety of driving. However, the wireless communication between entities in VSN s is vulnerable to attacks, which can lead to vehicle privacy leakage. To solve this problem, a certificateless searchable encryption scheme with privacy-preserving features that can resist keyword guessing attacks is proposed based on the VSN s application environment. The scheme combines proxy re-encryption technology, which enables vehicle users to obtain accurate request results without disclosing privacy information to cloud service providers, and achieves the privacy of vehicle identity and confidentiality of transmitted data. In addition, the authorization process of the data service provider not only ensures the security of the data but also achieves revocability of user authorization. Based on the computational Diffie-Hellman problem and the discrete logarithm problem, the scheme is proved to be resistant to internal or external keyword guessing attacks under the random oracle model, and the experimental results show that the scheme has better performance in terms of computational and communication efficiency.
Research of Digital Image Processing Technology of Photoelectric Theodolite’s Target Based on MATLAB

Digital image processing is a technique to digitized image data,which is also called computer image processing. Modern technology keeps digital image processing evolving and expands the application scope. Because of the complexity of the sea scene, as well as the the characteristics of the photoelectric theodolite, the background of uneven homogeneity can be occured in the measured images over the time, and various kinds of clouds and other complex backgrounds may appear a because of the changing weather. How to suppress the diverse backgrounds, enhance the contrast and improve the target extraction ability are the key to successfully capturing the tracking target. This paper takes the processing operation of edge detection and pseudo-color processing. Meanwhile, an improved K-means clustering method is proposed; different kinds of clusters were selected for different kinds of images and realized the rapid and effective image segmentation. The clear edge and detail of image can be realized after the abovementioned operations. In this paper, the processing algorithm of the theodolite image has advantages and disadvantages,the corresponding algorithm can be taken according to specific requirements in practical application, which can be technical reference for the image target recognition and feature extraction.
Breast Cancer Detection Using ResNet with Hyperparameter Tuning

There are more than 2 million new cases of breast cancer each year, which is one of the leading causes of death worldwide. Early diagnosis improves cure rates and reduces mortality and misdiagnosis. However, a common way to diagnose breast cancer is based on mammographic images, which may misdiagnose. In this paper, a convolutional neural network with a pre-trained Residual Neural Network (ResNet) is proposed to diagnose breast cancer based on histology images. The proposed model employs ResNet to extract features from the images, then classify them into benign or malignant. Then a hyperparameter tuning process using the hyperband algorithm is applied to find the optimal hyperparameters in the model. To verify the effectiveness of the proposed method, the default model is compared with the models from tuning results using the histopathological images from other datasets. The comparison result is based on the AUC, ROC, validation accuracy, and validation loss. The experimental results demonstrate that the best-modified model has a 99.63 percent of validation accuracy. The analyses illustrate that the modified ResNet model performs better on detect breast cancer based on histopathological images than the default model.
UAV formation optimization model based on ant colony algorithm and particle swarm optimization algorithm

Based on the research of azimuth-only passive positioning of unmanned aerial vehicles (UAVs) flying in formation, this paper establishes a reasonable positioning information determination mode, which can effectively analyze and judge the flight status of UAVs, minimize the positioning time and improve the accuracy. Based on the ant colony algorithm, for the determination of uav in the signal receiving process, to simulate the way of by drone signal more comprehensive to determine the location of the unmanned aerial vehicle (uav), according to the direction of the received information for unmanned aerial vehicle (uav) position adjustment, through data analysis and calculation more reasonable distribution of unmanned aerial vehicle (uav) location scheme, then using the particle swarm optimization (pso) algorithm, To further optimize the positioning problem of different types of formation UAVs, the previous model is still adopted. At the same time, the particle swarm optimization algorithm is used to improve, upgrade and optimize the latest position of UAVs, and the two groups of reasonable and accurate flight modes and schemes under the conical formation are summarized.
FRTB-Fast Blockchain architecture based on block data and data protection system’s file partition tables

At the beginning of its birth, Blockchain technology solved the "Byzantine general" technology to realize the distributed consensus mechanism, data traceability and other functions in the insecure and untrusted network environment. The earliest application of Blockchain consensus mechanism started from the proof-of-work(PoW) mechanism of Bitcoin application in 2008, but PoW has low service throughput and low performance. The subsequent PoS, DPoS, and Practical Byzantine (PBFT) and other new consensus mechanisms are based on the performance improvement of distributed consensus to improve the Blockchain tampering, low performance and other problems. These consensus mechanisms require a lot of computation for distributed consensus. These also bring the possibility of Sybil Attack and Double Spend Attack by malicious nodes, and the node malicious attack events facing the Blockchain are also increasing. Based on this, this paper proposes a fast Blockchain architecture FRTB, which based on block data and data protection storage file partition table(FAT). On the basis of the original Blockchain architecture, the architecture adds block Data Protection system and block data storage file partition table information, which has a direct role in Blockchain’s traceability and tamper-proof. It has the characteristics of physical tamper-proof, traceability, fast distributed consensus and so on.
CUE: Compound Uniform Encoding for Writer Retrieval

Writer retrieval is crucial in document forensics and historical document analysis. However, due to the difference in syntactic structure between Chinese and other languages, the existing methods may not be directly applied to Chinese writer retrieval. Previous work on Chinese writer retrieval does not overcome the performance degradation problem when the number of samples grows. In this paper, we propose a novel compound uniform encoding algorithm (CUE) for Chinese writer retrieval, which mainly consists of a combined feature extraction module (CFE) and a prototype substitution module (PS). The CFE module combines two complementary features from image filter response and character contour. It counts local symmetries and edge co-occurrence pairs. PS module substitutes the outliers with the class prototypes to alleviate the influence of the outliers. Finally, the weighted Chi-square distance is applied to measure the similarity between writer and text. To verify the superiority of our proposed method, experiments are conducted on four public datasets and our built dataset. The results validate that CUE outperforms the state-of-the-art algorithms on mAP metric.
New incomplete data imputation based on k-nearest neighbor type framework

Data classification methods such as KNN and LMKNN have received extensive attention in machine learning, but these methods can only perform on complete datasets. However, incomplete data is very common in real life. Therefore, based on the classification method, this paper proposes five new methods to impute missing data: LMKNN_Imputation, LMPNN_Imputation, Weighted KNN_Imputation, Weighted LMKNN_Imputation, and Weighted LMPNN_Imputation. For the first two methods, based on the classification method, the k nearest neighbors / k-nearest pseudo neighbors' local mean vectors in the class closest to the missing value are to be found first and then used local mean vectors to impute the missing data. For the last three methods, a larger weight is assigned to the point / local mean vector of the class closest to the missing value. Then, use the weighted average or local mean vector to substitute the missing value. After completing the data, classification is performed in five different datasets which are imputed based on five new methods, mean substitution, and KNN imputation, and test the effectiveness of five new methods through the classification accuracy. Finally, the experimental results show that these five methods have an accurate prediction for missing values, the LMPNN_Imputation and the three weighted methods performed even better.
HCA Operator: A Hybrid Cloud Auto-scaling Tooling for Microservice Workloads

Elastic cloud platform, e.g. Kubernetes, enables dy-namically scale in or out computing resources in accordance with the workloads fluctuation. As the cloud evolves to hybrid, where public and private clouds co-exist as the underline substrate, autoscaling applications within a hybrid cloud is no longer straightforward. The difficulty lies in all aspects, e.g. global load balancing, hybrid-cloud monitoring and alerting, storage sharing and replication, security and privacy, etc. However, it will significantly pay off if hybrid-cloud autoscaling is supported and boundless computing resources can be utilized per request. In this paper, we design Hybrid Cloud Autoscaler Operator (HCA Operator), a customized Kubernetes Controller that leverages the Kubernetes Custom Resource to auto-scale microservice applications across hybrid clouds. HCA Operator load balances across hybrid clouds, monitors metrics, and autoscales to des-tination clusters that exist in other clouds. We discuss the implementation details and perform experiments in a hybrid cloud environment. The experimental results demonstrate that if the workload changes quickly, our Operator can properly auto-scale the microservice applications across hybrid cloud in order to meet the Service Level Agreement (SLA) requirements.
Soundprint Feature Analysis of Main Transformers in a 500kV Substation

This paper presents the transformer soundrint characteristic quantity analysis method, and then selects a 500kV transformer as the research object. Determine the 500kV transformer from day 19 to day 20. In the case of DC magnetic bias, the system measures the same characteristic trend from A, V, and C three phases, and makes an accurate judgment. Therefore, comprehensive multi-point data collection and analysis can make a better judgment of the DC partial magnetism of the power grid. If the soundprint monitoring linkage is realized in different substations of the power grid, it should be able to be very accurate. The voice print characteristic quantity threshold should be set according to the characteristics of the transformer. At present, the threshold value of the transformer adopts the default value, which is not adjusted according to the actual situation of each transformer, resulting in the deviation of the system judgment state. Considering this analysis, the feature quantity can be adjusted in the following ways, such as the harmonic ratio should be raised appropriately, and the complexity threshold should be lowered. This requires constant tracking, appropriate adjustment of the number of features, to achieve accurate fault alarm, and less false alarm..