Reviewerly/exHarmony huggingface dataset:
Improving Anomaly Detection in Smart Grid Big Data with Knowledge Graphs

A knowledge graph is an intelligent database that integrates artificial intelligence technology and traditional database. It is a knowledge base that represents concepts, entities and their relationships in the objective world in the form of a graph. It can well reflect the relationship between entities. In the current era of big data, although the accuracy of abnormal value detection of power data is much higher than that of traditional methods, big data is lack of explicability, so it is difficult to trace the origin of abnormal values. Abnormal values caused by errors must also be checked manually on site, which wastes human and material resources. As a knowledge embodiment of "big data + artificial intelligence", it has natural advantages in the integrity and interpretability of behavior description. Therefore, this paper studies and discusses the relevant theories and technologies of knowledge graph construction and anomaly detection algorithm. Aiming at the power database table processing process that will cause errors, this paper constructs the knowledge graph and uses the Neo4j diagram database for storage, so as to visualize the data processing process, clarify the data processing relationship, and facilitate the tracking and traceability of the data. In addition, it also carries out experiments and analysis of different anomaly detection algorithms on the sample power consumption data.
Analysis of The Change of Softmax Value in The Training Process of Neural Network

Classification is an essential field in deep learning. Generally, the category corresponding to the maximum value of softmax is mainly used as the prediction result and the softmax value as the prediction probability. However, whether softmax can indeed serve as a prediction probability needs further confirmation. This paper first focuses on the classification of paintings through Convolutional Neural Network. To deal with the imbalanced dataset problem, only those with more than 200 paintings are selected. Besides, class weight is also taken into consideration. Next, data augmentation is applied to enlarge the dataset and add more relevant data. For the modeling and training part, transfer learning is employed to avoid training from scratch on a new dataset and reduce the cost of later training. Techniques such as ‘EarlyStopping’ and ‘ReduceLROnPlateau’ are also used to avoid overfitting. The final prediction accuracy can achieve 99 percent on the training and 87 percent on the validation sets. Furthermore, the paper studies the change of softmax distribution during the training process and the relationship between the average maximum value of softmax and the classification performance of classes. The experiments show that the maximum value of softmax will gradually shift to the corresponding correct label during the training process. Still, there is no correlation between the classification performance and the average maximum value of softmax. Therefore, softmax cannot be used as a probability value for classification.
3D reconstruction based on monocular image sequences

Aiming at the complex operation of active 3D reconstruction technology, which is easily affected by external environment and equipment, this paper adopts a series of monocular image sequences taken by cell phone cameras from different angles to realize 3D scene reconstruction. Firstly, Structure-From-Motion (SFM) algorithm is used for sparse point cloud reconstruction, and the similarities and differences between incremental and global SFM algorithms for sparse reconstruction are compared; secondly, Multi-View-Stereo (MVS) is used for dense point cloud reconstruction, and finally, the surface and texture information of the object is recovered. The surface and texture information of the objects are recovered to achieve the reconstruction of 3D scenes. The experimental results show that the method can achieve better 3D reconstruction of monocular image sequences.
A residual gated convolution summary generation model based on contrast learning

Contrastive learning achieves better results in several tasks in natural language processing. However, there are fewer studies on Chinese text summary generation tasks. In this paper, a new summary generation model is proposed. This model uses the reference summary of random word order as a negative sample. Let the model learn to predict the distribution of representations more reasonably. Using RoBERTa pre-trained language model to encode the text, the summary text is generated using the gated convolutional unit with the residual mechanism. The experimental results of the LCSTS dataset show that the model performs in this paper better than the baseline model.
Interpreting Deep Networks

Most deep neural network models are black boxes and lack interpretability. As the interpretability is critical for deep models applied to high-risk tasks, many visual approaches are proposed to reveal what the model learns in recent years. However, the quality and completeness of important regions, which are exported by the most existing visualization methods, are still unsatisfactory. Thus, a method, interpreting deep networks by ensemble learning and visualizing the perturbation, is presented in this paper. The method firstly perturbs the original image using the mask to obtain the perturbed image. Secondly, it imports the perturbed image into the model to obtain the loss of the update mask. Finally, an Adaboost-like algorithm, which combines the extremal perturba- tions and the meaningful perturbation, is introduced to improve the interpretation. Experimental results confirm that the method can improve the quality and completeness of the important regions, and provide a more reasonable explanation for the prediction results.
Imbalanced Nodes Classification for Graph Neural Networks Based on Valuable Sample Mining

Node classification is an important task in graph neural networks, but most existing studies assume that samples from different classes are balanced. However, the class imbalance problem is widespread and can seriously affect the model's performance. Reducing the adverse effects of imbalanced datasets on model training is crucial to improve the model's performance. Therefore, a new loss function FD-Loss is reconstructed based on the traditional algorithm-level approach to the imbalance problem. Firstly, we propose sample mismeasurement distance to filter edge-hard samples and simple samples based on the distribution. Then, the weight coefficients are defined based on the mismeasurement distance and used in the loss function weighting term, so that the loss function focuses only on valuable samples. Experiments on several benchmarks demonstrate that our loss function can effectively solve the sample node imbalance problem and improve the classification accuracy by 4% compared to existing methods in the node classification task.
A Video Synopsis Algorithm Based on Target Detection for Ghosting Optimization

In recent years, with the development of deep learning and computer vision, pedestrian surveillance video synopsis technology has also become the focus of research in the field of intelligent surveillance analysis. The video synopsis technology compresses the video content and strives to quickly extract the content of interest in the video. In the field of public safety, it has played an irreplaceable role in maintaining social order. Based on the exploration and analysis of existing video synopsis technology algorithms, this paper proposes a video synopsis method based on deep learning target detection to achieve ghosting optimization. The detection and tracking algorithm is used to extract the foreground moving targets in the video, and a background modeling algorithm is designed to establish the background. Considering that the moving speed of the target will cause the targets to chase each other, the target whose moving speed difference between the targets is within a certain threshold is placed in the same or similar time period of the video, it can minimize overlap of goals. Finally the target can be placed merge into the background image. Experiments result shows that this method can effectively condense surveillance video. Compared with the existing method, it has a better compression ratio. Meanwhile, it can completely extract the key information and effectively reduce the overlap between targets.
Credit Card Fraud Detection Based on Combination of Sparse Autoencoder and Support Vector Machine

Credit card fraud has become a serious issue for banks and businesses as more and more transactions take place online. A number of machine learning methods have been developed to learn patterns in credit card frauds. These methods usually depend on sophisticated feature engineering because gaining representative features can improve their performances. However, manual feature engineering is impossible due to the ever-increasing amount of data and complicated relationships between transactions. In this work, a Sparse Autoencoder-Support Vector Machine (SAE-SVM) model is proposed to solve the issue of feature engineering. The SAE is an unsupervised machine learning method that learns representative features from the raw data. The SVM model later uses these features to predict whether the transaction is fraudulent. This SAE-SVM method achieves 0.80 F2 score on the Credit Card Fraud Detection dataset on Kaggle, compared to only 0.71 F2 score by the SVM method alone. In addition, the SAE-SVM model outperforms other autoencoder-based models regarding the F2 value. It is shown that the SAE can extract useful and low-dimensional features without much loss of information. The deployment of the SAE may relax supervised machine learning from complicated feature engineering. Furthermore, the SAE model is robust toward the concept drift issue, making it preferable over manual feature engineering.
Detection of railway signal light based on improved YOLOR

Transportation is not only an importantly strategic hub of a country, but also an indispensable part of social life. With the development of science and technology, smart transportation has gradually been changed from a concept only to a reality. In consequence, automatic driving technology has become a hot research topic that attracts a number of attentions. Autonomous driving covers fully automated driving and assisted driving. And automatic driving technology based on deep learning is becoming more maturing and advanced. One of the necessary conditions for the development of automatic driving is to recognize traffic signals firstly. And the application of automatic driving technology in highway traffic has begun to show great achievements. However, research in the railway field is still relatively weak, and railway transportation plays a pivotal role in both national strategy and people's livelihood. Based on this gap and need, this paper proposes a lightweight to improve YOLOR object detection algorithm for railway traffic signal recognition method. The method uses YOLOv4-tiny as the baseline model, selectively adds implicit knowledge used by YOLOR, and improves the loss function to enhance the model's ability to prevent overfitting and solve long tail problem adaptively. In addition, the number and size of anchor frames in the self-made data set are optimized. Compared with the same lightweight YOLOv4-tiny model, AP has increased by 2.44, and the model file size only occupies 31.4M.
Research on the architecture of community policing platform based on blockchain technology

In recent years, with the rise and development of blockchain technology, the application of blockchain technology has been extended to the Internet of Things, financial services, social welfare, intelligent manufacturing, social management, medical treatment, education, and many other fields. In order to explore the application of blockchain technology in community policing work and promote the construction of smart community policing, this paper first analyzes the feasibility of applying blockchain technology to community policing platform. Then proposing a system architecture of community policing platform based on blockchain technology and analyzing the advantages of the platform. The architecture of community policing platform based on blockchain technology can be divided into six levels: infrastructure layer, data layer, network layer, consensus layer, application layer and management layer. The platform can provide various convenient services, strengthen the information exchange and sharing between community police and community residents, and improve the collaborative governance mechanism of the community, to improve the level of community governance.
Multi-scale Fusion based Multi-stage Small Object Detection in Aerial Images ∗

In aerial images, the objects are mostly small. The number of objects is large and the scale is diverse, so it is difficult to extract the features of multiple scale objects at the same time. The location distribution of object in aerial images is usually dense, making it difficult to locate. These factors bring great challenges to aerial image object feature extraction, and then reduce the performance of detection. Therefore, a multi-scale fusion based multi-stage small object detection method (MSMSD) for aerial images is proposed in this paper. MSMSD adopts EfficientNet as feature extraction backbone and add deformable convolution blocks to achieve better detection performance on objects with irregular shapes. NAS-FPN is leveraged to fuse multi-scale features effectively. A cascade detection mechanism is also designed to reduce noisy detection and mis-detection in this task. In experiment section, the proposed MSMSD outperforms five benchmark object detection algorithms on two aerial image datasets. Experimental results demonstrate that MSMSD can handle the small object detection task in aerial images efficiently.
Improved Inception Network for wild mammal Behavior Recognition

The wildlife resources are significantly important parts of the ecosystem, and protecting wildlife resources is vital to the environment on which people live. Therefore, the behavior analysis of wild animals has become an important initiative to protect wild animals. This paper proposes a convolutional neural network architecture based on spatial-temporal information for action recognition of wild mammal. Since pixel-based object segmentation methods cannot eliminate the influence of background, we use the contour-based method Deep Snake to detect the animal contours in images as spatial features. The skeleton-based animal action recognition model is used to extract the joint coordinates during consecutive frames, then the fluctuate of the joint coordinates is used to distinguish the diversity of different behaviors of wild mammal in temporal space, which helps to characterize the difference of joint point movement speed of different behaviors. In addition, we also compute leg joint angle for distinguishing the behaviors running and standing. Finally, the temporal features and spatial features are fused into the convolutional neural network for action recognition of wild mammal. The experiments analyze the effect of the joint point angle, contour features, joint coordinates as well as their fusion features for wild mammal behavior recognition. It is concluded that the fusion features of coordinate fluctuate of joint points during consecutive frames, contour features and knee joint angle can significantly improve the accuracy of wild mammal action recognition. The model can effectively recognize four representational behaviors of animals: running, sitting, walking, and standing. The average accuracy of the proposed scheme for recognizing behavior of wild mammal achieve 95.5%.
Misleading Image Classification with Multi-shearing and Random Padding

Neural networks are vulnerable when input data is applied with human-imperceptible perturbations, which is called adversarial examples. When used in image classification models, adversarial examples mislead neural netwoks to classify images with wrong labels, posing great threat to network security. White-box attack has achieved considerable success rate, for the model structure is already known. But black-box attack remains to be improved, so as to the transferability. We refer to the model augmentation method in network training process, and apply to generating adversarial examples to reduce overfitting. Consulting fundamental methods in adversarial examples, we propose a multi-cropping transformation method to alleviate overfitting and enhance transferability. Firstly, referring to data augmentation, we multi-crop original images in every iteration with random possibilities in adversarial exaples generating process. Secondly, the gradient of model loss function is calculated, and the perturbations are added to original images. Finally, we generate adversarial examples with iterative perturbations. The validation of our method is verified on single models and ensemble models, and the transferability is improved, compared to other fundamental methods.
Design of WEB Communication Security System

With the development of modern network technology, the application of information sharing is extensive, but at the same time, the security problem of information and communication has become prominent and complex. As most nodes used in network communication exist in the form of clusters, it is easy to cause network traffic data transmitted at the same time, and the network congestion and network service are not able to process the information, which leads to network communication can not timely send out. If the security of network communication cannot be guaranteed, the number of users using network communication will decrease, which is not conducive to the development of network communication. In protecting network security, digital signature technology is a better security protection technology. In the era of rapid development of network communication, digital signature technology not only guarantees the security of network communication but also ensures the privacy and integrity of data in network communication. This paper presents the concept and principle of digital signature and the application of digital signature in WEB communication.
Key Technologies of Electronic Perimeter System Based on Deep Learning

This paper introduces the overall architecture of electronic perimeter system based-on the deep learning and its key technologies. The system adopts a microservice architecture, using deep learning algorithm services and Spring Cloud-based backend servers to implement the system architecture. Faster RCNN is used as the object detection algorithm and the processing mechanism of real-time video transmission and alarm information processing in the perimeter is introduced. Through the application of the system, the security of the perimeter is greatly improved, and to a certain extent, it has been applied to a certain extent.
Non-Euclidean Space Exploration for Reinforcement Learning State Embedding

With the rapid increase of computer computing speed in recent years, mechanical learning is widely used in the field of artificial intelligence. Among them, the most popular one belongs to the field of deep learning in mechanical learning. The reason why the field of deep learning is attracting attention is the success and popularity of its application in industry. The success of graph neural networks in industry in recent years has made them a very popular direction nowadays. This paper is dedicated to exploring how to encode game states by means of graph neural networks and proposes a game state encoder combining Convolutional Neural Networks and Graph Neural Networks.
The Pre-ordering Model for Statistical Machine Translation of Enhancing the N-best Syntactic Knowledge

Syntactic heterogeneity between source and target languages has an important impact on the performance of Statistical Machine Translation (SMT). On the basis of phrase-based Chinese-English SMT, a method of source language pre-ordering based on N-best syntactic knowledge enhancement is proposed. First, the source language input sentences are analyzed by N-best Syntax, and the high reliability sub-tree structure is obtained by calculating statistical probability. Two optimization strategies are used to optimize the initial rule set: rule deduction and rule probability threshold control mechanism. Second, the source language phrase translation table is used as a constraint to control the sequence between phrases. Finally, the syntax analysis tree of the source-side sentences is pre-ordered. The experimental results of Chinese-English SMT based on the NIST 2005 and 2008 test data sets show that comparing to the baseline system, the BLEU score of automatic evaluation criterion of the N-best syntactic knowledge-enhanced SMT pre-ordering method increased by 0.68 and 0.83 respectively.
FeaturesRank: An unsupervised keyphrase extraction approach based on features representation for Chinese documents

Keyphrase extraction technology can obtain the main content and semantic expression of academic literature, which plays an essential role in text retrieval, classification and clustering. We propose a new method, FeturesRank, to automatically identify meaningful and authoritative keyphrases from Chinese academic texts. FeturesRank integrates three features of keyphrase: frequency, contextual relevance and grammatical relation to measure the likelihood of sequence of words to be a meaningful phrase and introduces a scoring mechanism that combines the influence of words in the network graph with a new “phraseness” feature to calculate a normalized score for every candidate. The experimental results show that the evaluation indexes of the proposed method on Chinese academic datasets are significantly improved compared with the four popular keyphrase extraction methods, which verifies the effectiveness of the method.
Abnormal data detection method based on ant colony clustering

With the rapid growth of data, low quality data emerges, and data quality problems are increasingly serious. Abnormal data detection becomes an important aspect of data quality validity measurement. We propose an improved ant colony clustering algorithm based on LF algorithm [8] by optimizing ant position, attribute weight and moving step speed and then apply it to the abnormal data detection. The improved algorithm is programmed by Python, and the experiment is carried out on UCI data set. The results show that the improved algorithm can obtain better clustering effect, and make the accuracy of abnormal data detection results higher.
Design of Enterprise Inventory Management Program Based on Java EE Programming

Since the outbreak of the novel coronavirus at the end of 19, the competition among SMEs has become increasingly fierce. Based on their traditional management model and low level of informatization, SMEs have poor ability to cope with the impact of the epidemic and cannot quickly meet the new needs of target customers and new market opportunities. In the context of the impact of the epidemic situation, small and medium-sized enterprises need to optimize the inventory management system by introducing computer technology, use big data analysis technology to adapt to market demand, reduce the problem of enterprise inventory backlog, improve the efficiency of enterprise resource allocation, and maximize the avoidance of resource mismatch. This paper uses Java EE programming technology and the Spring Boot framework to design an enterprise inventory management system, which can comprehensively and directly display the enterprise inventory management situation, achieve data recording, storage, and modification. At the same time, it uses enterprise inventory management data to achieve the portrait analysis of target users, improve the efficiency of enterprise inventory management, optimize the enterprise management system, and quickly meet the new needs of customers.