Reviewerly/exHarmony huggingface dataset:
OPERATOR System: Control, Monitoring, and Diagnostics of Software and Hardware in Instrumentation and Control System

The paper describes the system of control, monitoring, and diagnostics (SHA) included in the OPERATOR integration platform for instrumentation and control systems. The article presents the architecture of the SHA and considers different types and ways of diagnostics, monitoring, and control implemented within the system. The work pays special attention to the diagnostics integrity and configuration of an instrumentation and control system.
Research on The Improved Method of D-S Evidence Theory Based on The Fusion of Support and Confidence Entropy

In view of the fact that Dempster-Shafer (D-S) evidence theory is unable to fuse data of multiple different kinds of sensors, an improved D-S evidence theory method based on the fusion of support and confidence entropy is proposed. Firstly, the identification framework of evidence theory is improved; secondly, Spearman correlation coefficient is introduced to represent the correlation between evidences; thirdly, a new confidence entropy is defined to describe the inconsistent uncertainty and non-specific uncertainty between evidences; then, the evidence set is modified by the combination of correlation and confidence entropy; finally, Dempster combination rule is used for information fusion. The simulation results confirm that the improved method of D-S evidence theory is feasible and more effective than the traditional algorithm.
Situation Assessment based on Dissimilarity Calculation and Evidence Combination

Evidence theory is one of the main methods to realize situational awareness. In this paper, the importance of evidence is measured and modified by dissimilarity calculation, and the measured value of dissimilarity calculation is taken as the basis of dynamic combination of evidence. A situation assessment method based on dissimilarity calculation and dynamic combination of evidence is proposed. The method includes four stages: rule metrics, evidence revision, rule fusion and situation decision. Experiments show that this method can avoid the paradoxical problem in situation fusion using evidence theory, and is superior to existing typical methods in terms of efficiency and accuracy of situation assessment.
An Improved Faster R-CNN Method for Car Front Detection

With the further development of deep learning, object detection is widely used in vehicle detection in road environment and plays an indispensable role in the construction of intelligent transportation system. Aiming at the problems of low accuracy of current mainstream object detection algorithms and difficulty in detecting small objects, an improved Faster R-CNN algorithm is proposed. Firstly, in the feature extraction stage, the SE attention module is introduced to focus on the details in the image and improve the detection accuracy of the network model for small objects. Secondly, the Spatial Pyramid Pooling (SPP) structure is used to replace the pooling layer to enhance the robustness of the network and learn feature information of different sizes to further improve the detection accuracy of the model. Finally, experiments are conducted on the CAR-DATA dataset made by ourselves to train appropriate weights for testing. The experimental results show that compared with Faster R-CNN, the AP and APs of the proposed network model in this paper are improved by 1.29% and 1.74% respectively, and the car position can be accurately detected and located.
Improved algorithm for pedestrian detection of lane line based on YOLOv5s model

Aiming at the problem that YOLOv5 has a high missed detection rate in the task of pedestrian detection in complex scenes, the ECA-YOLOv5 pedestrian detection algorithm is proposed. Aiming at the problems of high missed detection rate and complex scene in the complex crowded pedestrian detection task, the original BottleneckCSP module is replaced with the C3TR module with TransformerBlock, which has better performance in the detection of high-density occluded objects in the case of pedestrian detection. Aiming at the problem of excessive parameters such as redundant expansion of model training calculation, a lightweight general upsampling operator CARAFE is introduced. Compared with other upsampling methods, it can not only achieve better performance in multi-scene tasks, but also greatly reduce The parameter calculation amount is reduced, the detection speed is improved while the detection accuracy is preserved. The self-designed efficientCoordAtt high-efficiency attention mechanism module is used to enhance the receptive field and the model's ability to accurately locate the target, improve the detection accuracy in various complex scenes, and strengthen the model's ability to capture local information. The experimental results show that the improved ECA-YOLOv5s algorithm can effectively improve the mAP of pedestrian detection while maintaining the high real-time performance of the original algorithm.
Data cleaning method of resource management knowledge graph

Knowledge graph is an important part of artificial intelligence. In terms of resource management, knowledge graph can label and aggregate resources, so as to complete intelligent matching and rapid retrieval of resources. However, when we constructed knowledge graph of resource management, there were some phenomena, such as redundancy of entities, isolated entities and so on. So in the face of these problems, we propose a data cleaning method.Its implementation has four stages: In stage 1, we use NLP technology to detect and classify problematic data. In stage 2, for the single problematic entities, we make pattern statistics and design a relationship supplement method based on relationship reasoning and a new entities alignment method. In stage 3, we recursively supplement and align knowledge fragment sets formed by interconnection of multiple problematic entities. In stage 4, for these isolate knowledge fragment sets, we propose an alignment and fusion method based on multiple entity relationships. Finally, we conducted experiments on our knowledge graph to prove the accuracy and effectiveness of our method.
Research and Application of Gestures in Interactive Works

In recent years, artworks have received extensive attention due to their unique expressions and conveyed ideas, and interactive artworks have also aroused the interest of the public and researchers. At the same time, with the rapid development of computer science and technology, emerging technologies such as gesture recognition have brought new development directions to interactive artworks. This paper focuses on the application of gesture recognition technology in interactive artworks. By designing a deep learning structure to estimate the 3D hand pose from regular RGB images, the gesture recognition accuracy is maintained while reducing the amount of network parameters, and the gesture recognition network is lightened so that it can be applied to interactive artworks.
Improved Algorithm for Road Multi-target Tracking Based on YOLO

Based on the single-frame target detection results of the YOLO algorithm, an improved algorithm for multi-target continuous tracking in road scenes is proposed. Firstly, the characteristics of the target eigenvalues of the YOLO algorithm are analyzed, and the judgment basis for using the difference degree to track the target between frames is clarified. Secondly, the defect of the original Euclidean distance as the difference degree calculation is analyzed, and a new difference degree definition is proposed; the value rule of the target feature vector in the road scene is analyzed, and the conversion function is introduced. Next, considering the prior knowledge in the road scene, the difference degree and the intersection ratio and IOU are used to jointly determine and optimize the algorithm process. Finally, according to the experimental comparison, it is confirmed that in the road scene, the new improved algorithm can effectively improve the correct rate of multi-target recognition and reduce the false detection rate and missed detection rate while basically maintaining low time-consuming operation.
Research on teaching quality evaluation model of online courses in Colleges and Universities

Through the research on the classification of knowledge points of online courses, the current online teaching quality evaluation has some problems, such as unscientific evaluation index weight, imperfect evaluation system and so on, The deep learning model is applied to the research of text and image classification and recognition of online course evaluation indicators. By collecting the latest research results of big data analysis model for teaching quality evaluation of relevant online courses, By mapping big data models to research objectives, Using the analytic hierarchy process to preliminarily determine the weight index. At last, The big data analysis model is proposed to be applied to the online course evaluation system structure and the online course evaluation index system.
Categorization and Criticality Assessment of Facilities of Critical Infrastructure

Assessment of the critical infrastructure (CI) criticality is an essential task in the task complex to ensure the protection of CI. The article shows the significant distinction between criticality assessment and risk assessment. Based on the concept of criticality, the paper presents the method to the categorization of CI and critical information infrastructure (CII) facilities, along with the developed procedure for identifying and categorizing CI and CII facilities. The presented method allows the development of cybersecurity procedures for various critical infrastructure facilities.
Efficient Feature Enhancement for Few-Shot Object Detection

The neural network has exhibited strong performance in various machine learning tasks, but deep neural network model will deteriorate rapidly when the amount of data is insufficient. Few-shot object detection aims to detect unseen objects under little supervision scenarios. Attention RPN uses support images to enhance the query image, enabling RPN to generate support-relevant bounding boxes and to detect novel categories without fine-tuning. In this paper, We propose a more powerful approach to enhance query feature. Firstly, we explored approaches to generate convolutional kernels containing support information and selected the more useful ones. Then, as the Attention RPN does not take into account the correlation between channels, we added the channel attention associated with the support feature to the query feature to further improve the detection of the model. To verify the effectiveness of the method proposed in this paper, we conducted experiments on the benchmarks and showed good results.
A Random Smoothing Defense Method based on Attention Mechanism against Automatic Modulation Classifier Attack

Although deep learning has been extensively studied in communication tasks such as wireless signal modulation recognition, as the research progresses, researchers have found that deep learning-based system models have security concerns that are dominated by adversarial sample attacks, adding carefully designed subtle additional perturbations to the input signal that may alter the output of the model classifier. The development of adversarial samples poses a great threat to the development of deep learning, and there have been many research results on defending against adversarial samples. However, many defense methods enhance the defense effect while causing serious impact on the normal performance of the system, this paper proposes a new defense method for adversarial sample attacks in signal modulation recognition: a random smoothing defense method based on attention mechanism. By adding adversarial samples to the input signal for retraining, the model is made to rely more on reliable robustness in decision.
Research on the causes of false positives in source code detection

Source code is an important part of information system. With the rapid development of Internet, there are more and more information systems, and the work of source code detection is also increasing. At present, the work of source code detection usually depends on automatic inspection tools, but automatic tools bring convenience and some false positives. Therefore, this paper studies the causes of false positives in the process of source code detection, and puts forward several methods to reduce the false positives rate of source code detection. The methods improve the efficiency and quality of source code detection effectively.
Research and application of data-driven intelligent traffic law enforcement system

With the rapid development of the transportation industry, starting from the goal of improving industry governance capabilities, it is urgent to realize a new model of digital law enforcement with the help of information management platforms and intelligent technological means. This paper analyzes the urgency of carrying out intelligent law enforcement and its pain points based on the characteristics and difficulties of current transportation law enforcement. The law enforcement knowledge base model formed by the transformation of various data such as law enforcement procedures, research and propose a data-driven intelligent law enforcement system, design the overall framework, core modules, etc., and verify the effect through application in the transportation industry, in order to achieve deep intelligence Law enforcement and intelligent supervision provide important support.
Neural Networks and Genetic Algorithms applied to the Maintenance Process in an ATM Network

The optimization of infrastructure maintenance costs in Automated Teller Machine networks of financial institutions is a huge problem faced through business intelligence as artificial intelligence for decision-making. This paper addresses the issue to optimize costs through the application of systems with artificial intelligence. So, neural networks were applied to predict ATM failures based on the historical information of specified errors, number and amounts of transactions. Then forecasting was used to determinate failures, after, the optimal maintenance route that the technical personnel must travel was determined by means of a genetic algorithm. Finally, it was estimated that the reduction of maintenance costs when applying the proposed predictive maintenance methodology is around 200,000 USD for an ATM network of 500 devices of a financial institution in Ecuador.
WSNs Link Quality Evaluation Method Based on Multi-Hidden Layer Multi-Channel Extreme Learning Machine

In this paper, we analyze the link characteristics of WSNs (wireless sensor network) and the existing link quality evaluation methods. Considering the link quality assessment affected by susceptibility to multi-path fading and low energy of the link, and evaluates the link quality based on hardware parameters. The received signal strength indicator, link quality indicator and signal-to-noise ratio are selected as the comprehensive evaluation parameters of the link quality, the link quality level is divided according to the packet reception rate, and the link quality level is used as the link quality evaluation index. On this basis, a multi-hid den-layer and multi-channel extreme learning machine is used to evaluate the link quality of WSN s, and a link quality evaluation model is constructed.
Efficiency Study of SSAG on RNN Framework

SGD (Stochastic gradient descent) is widely used in deep learning, however SGD cannot get linear convergence and is not effective in large amounts of data. This paper use SSAG to improve the efficiency. SSAG contains two optimization strategies, one is stratified sampling strategy and the other is historical gradient averaging strategy. It has the advantages of fast convergence of variance, flexible application to big data, and easy work in deep network. This paper studies the efficiency of SSAG gradient optimization algorithm based on RNN framework. The proposed RNN framework comprises a feature extraction layer, a stacked RNN layer, and a transcription layer. The experimental results confirm that the accuracy of SSAG is better than the SGD and the Momentum. Both stratified sampling and historical averaging strategies have the effect of improving task accuracy. Experimental results verified that SSAG has better effect in image classification task.
Multi-view Attention with Memory Assistant for Image Captioning

The attention mechanism is becoming more and more important in the field of natural language processing. The current attention models usually use the weighted average of the hidden layer at each time step as the input of the decoder. That makes the full use of image features become a hot issue in the research field. In this work, we designed a multi-view attention mechanism to solve the problem that the model can not obtain a full range of visual signals in the prediction process. The local and global features of the input image are extracted by the pre-trained convolutional neural network, and the extractor generates multiple local vectors. Through this operation, the model can fully use all-around visual features. In addition, we propose a memory assistant to filter historical text information from the content generated by the decoder, reduce data redundancy, and solve the problem of the poor long-term dependency on the model. Experiments show that our model can significantly improve the results of various evaluation metrics on the AI CHALLENGER dataset, and the ablation studies validate the improvements of our proposed modules.
Research on Wavelet Denoising Algorithm Based on Improved Threshold Function

With the rapid growth of communication signal field and the increasing popularity of electronic goods, the dissemination of information has become more and more vital, and image, as one of the carriers of information acquisition and propagation process, have also become very significant. However various noises will appear in an image in the actual application and propagation process, so the denoising of the image is an indispensable operation. In order to obtain a better noise free image, an improved threshold function is raised to ameliorate the deficiency of hard threshold and soft threshold functions. According to the simulation results, the signal-to-noise ratio (SNR) and the mean square error (MSE) were calculated for comparative analysis. The simulation results show that the improved threshold function has good optimization compared with the traditional threshold function.
A Blockchain Enabled Federal Domain Generalization Based Architecture for Dependable Medical Image Segmentation

Federated Learning allows data to be trained together as a global model without leaving the local area. However, federated learning still suffers from central server failures and lacks a principle of trustworthiness. At the same time, in medical clinical practice, the federal learning model performs very poorly when generalized to healthcare institutions that do not participate in the common training of the model. Therefore, this paper proposes a trustworthy medical image segmentation architecture based on blockchain-enabled federated domain generalization. Based on federated learning, the reliability of the segmentation model can be improved based on enhanced model generalization by introducing a Fourier-based domain generalization mechanism and blockchain technology. We evaluated the proposed approach with the optic cup and optical disc segmentation task for retinal fundus images. The evaluation results show that the architecture outperforms fundamental federated learning in terms of model generalization and accuracy, while also providing reliability.
A Video Information Fidelity Evaluation Model in Intelligent Customer Service System

A balanced-weighted vision-information fidelity method (BWVIF) that simulates the multi-scale sensitivity weighting effect of the human visual system is proposed, and the BWVIF model is evaluated by simulation. Based on the verification of images with three distortion types of JPEG, Gaussian Blur and White Noise in the LIVE image library, using the three evaluation criteria of PLCC, RMSE and MAE, the verification results show that the overall evaluation effect of the BWVIF method is better than that of the proposed method. Visual Information Fidelity (VIF) and Multi-Scale-Structural Similarity (MS-SSIM) methods are widely regarded as effective.
DropMask: A data augmentation method for convolutional networks

Convolutional neural networks can learn a powerful feature space and play a great role on the promotion of autonomous driving. On limited datasets, deep neural networks often overfit. Properly adding noise and regularization during training can alleviate this. DropBlock randomly removes blocks on the feature map, however, such randomness may lead to complete removal of objects due to excessive removal of one or a few blocks, as well as contextual information. To solve this problem, we propose a regularization method (DropMask), which strikes a reasonable balance between deletion and retention in the block to be deleted on the feature map. That will avoid excessive removal of contiguous blocks so that improve the accuracy and robustness of model. After extensive experiments, it has been shown that the DropMask proposed in the paper outperforms DropBlock on neural networks. On CIFAR-I0 classification, ResNet-18 architecture with DropMask achieves 95.34% on accuracy, 1.88% improvement over the baseline. On KITTI 2D detection task, Yolov5s with DropMask improves mAP from 77.6% to 79.2%.
Application and Improvement of Convolution Operation in Image Restoration

As a new concept and method proposed in the field of neural network, deep learning is introduced into machine learning, which promotes the rapid development of artificial intelligence and provides new ideas for image processing. One of the most representative algorithms is convolutional neural network. As the most important part of convolutional neural network, convolution can effectively extract the characteristic information of the input image and play an important role in image restoration. At present, many types of convolution methods have also been developed. In order to study the application of convolution in the image restoration and improvement, this paper introduces in detail the application of the convolution in the image processing and the detailed process of the calculation, and sums up the convolution loss of data information and the large amount of calculation, time-consuming long shortcomings, studied the development for the two problems of dilated convolution and grouping convolution and their improvement. The paper analyzes and summarizes the application trend of convolution in image processing. At the present stage, the operation speed of image processing determines the practicability of the algorithm. Larger calculation scale, richer data and higher intensity of calculation will become the focus of the development of convolution.
Training and Adaptation of Self-Learning Systems in the Context of Conflict Interaction

The usage of artificial self-learning systems in the process of modelling and prediction the development of conflict interaction been studied. Such systems generalize the concept of an intelligent agent who, acting in conditions of incomplete information, improves his behavior during a computational experiment depending on the environment's reaction and using artificial intelligence technologies.
An Approach to Organizing Parallel Distributed Processing of Protected Information Queries in Multi-server Environment

The principles of organization of the Internet service designed to implement parallel processing of protected queries in large-scale distributed information systems with many processing servers are considered. The main goal of the service is to improve the rate of queries processing by using the capabilities of a multi-server environment. The application of this service for combining sequential (“pipelined”) and parallel processing of queries is described.