Reviewerly/exHarmony huggingface dataset:
Application of atomization management scheme based on network security technology with SOAR

Nowadays,more and more enterprises have begun to treat it as the core part of the security infrastructure and apply automation to help solve the problem of “security, cost and efficiency” difficult to balance in enterprise security operations. But the traditional network security is protected based on a concept of stacking security devices, many types of security devices have their security capabilities overlapped. This paper discusses how the atomic device control strategy can be used to standardize the management of network security devices, guide the planning of device deployment and implement automatic security emergency response on various SOAR platforms. For a certain enterprise, its internal network security devices are limited in types and the overall workload is acceptable.
Research on encryption algorithms in terahertz communication systems

Aiming at the problem that the terahertz wave beam is narrow and directionality is strong, which makes it easy for the enemy to eavesdrop on the data information, and thus the information cannot be transmitted safely, a pseudo-random sequence generation method based on n-LFSR and AV-NL chaotic mapping is proposed. Different algorithms are selected according to the number of LFSRs. At the same time, the absolute value nonlinear chaotic map is generalized. Finally, the generated two pseudo-random sequences are XOR combined to form the final pseudo-random sequence for source encryption. The simulation results show that this method takes into account the advantages of simple design of linear feedback shift register and high chaotic randomness. The generated pseudo-random sequence has both high linear complexity and the advantages of chaotic non-linearity. The high randomness used in source encryption can improve the security of the system.
An improved method of weak signal erasometry based on Duffing oscillator

This paper is aimed at improving the previous experiments of duffing oscillator detecting weak signals. The influence of phase transition direction, signal phase, frequency and amplitude on Duffing system is analyzed and a new method for detecting weak signal by Duffing Oscillator is proposed. This method detects weak signals by the way of reverse detection, and calculates the threshold through Poincare section, so that the detection is more accurate.
Research on image features extraction based on machine learning algorithms

Image features are essential components for physical detection, classification of objectives and downstream tasks. Specifically, the image features can be utilized to automatically detect the characteristics of images and realize physical information mapping into information domains. However, existing image features are concentrated on the decrease the contrast, which can reduce the influence of lights. Another extraction process converts images into digital images and utilize digital information techniques to obtain the features. In this paper, we utilize the machine learning model to extract the features of images with enough training iterations. Initially, we utilize the CIFAR-10 data set, which contains the 10 categories of physical objectives and simulate as the training set. Indeed, the establish machine learning model is utilize to train through inputting the 80% of total data set. After training process, the output of machine learning mode can obtain the features of any physical images. Finally, we compare our proposed model with existing image features extraction methods and utilize 20% data to evaluate our model. From our extensive experimental results, we can conclude that our established model can effectively achieve the image features extraction with higher extraction accuracy and acceptable computation time through comparing with traditional mathematical analysis methods.
Improved traffic sign detection algorithm of YOLOv3

A traffic sign detection algorithm based on improved YOLOv3 in natural environment is constructed to address the problems of the existing traffic sign detection model with a large number of small-sized traffic sign, low accuracy rate and slow recognition speed for traffic sign with small object. Firstly, model compression is performed on the network structure, using the network pruning method to reduce the model size and improve the operation speed; Secondly, one scale detection structure is added on the basis of YOLOv3 network structure, and four-scale detection structure are used to fully enable the network to obtain more feature information of small traffic sign and effectively improve the detection rate of small object traffic sign; Finally, the K-means++algorithm is used to re-cluster and calculate the size of the bounding box of the object in the TT100K data set to generate a smaller size prior bounding box, which improves the recognition accuracy of the model for small target traffic sign. The experimental results on TT100K traffic sign data set show that the proposed method has better accuracy and recall than the original YOLOv3 model.
Stripe pooling and densely connected DeepLabv3 plus efficient semantic segmentation

Currently, DeepLab is unable to utilize multiscale feature information at multiple levels, and there are often problems such as blurred segmentation boundaries, unclear detail extraction, and incorrect segmentation.This article optimizes the DeepLabv3 plus model The backbone network has been converted to a lightweight MobileNetV2 network. In Atrous Spatial Pyramid Pooling (ASPP), stripe pooling has been used to replace global average pooling, and the original hole ratio combination of 6, 12, and 18 has been changed to 3, 7, 9, and 17. A branch with R=3 has been added, as well as the use of dense connections. The improved ASPP has the advantage of higher acceptability. The experiment shows that the average intersection ratio of the improved DeepLabv3 plus model on the dataset is 69.71%, and the average pixel accuracy is 79.45%. Compared with the original network model, the improved average intersection ratio is increased by 3.2%. Using the above improved methods has improved the performance of DeepLabv3 plus, enabling more detailed information to be obtained, and improving the resolution of the model.
An improved target tracking learning detection algorithm

Aiming at the problem that Tracking accuracy of Tracking-Learning-Detection (TLD) tracking algorithm decreases when targets are under different light and shade conditions and target scales change, an improved TLD tracking algorithm is proposed. In this paper, Speeded Up Robust Features (SURF) feature point matching method was adopted as the tracking module, and the feature point pairs with low confidence were removed by adding the evaluation of feature point pairs. By introducing Contrast Limited Adaptive Histogram Equalization (CLAHE) into the detection module, a random Circle feature classifier is proposed, and the HOG feature matching method is used to replace the normalized correlation matching method in the nearest neighbor classifier. In addition, the detection range is adjusted adaptively, which reduces the computational complexity and effectively improves the adaptability of the algorithm to multi-scale. Experimental results show that the proposed algorithm can effectively overcome the influence of environmental shading conditions, and has strong robustness to scale changes and high tracking accuracy. Compared with the classical TLD algorithm, the improved algorithm performs better.
Face detection research based on a tilt-angle dataset

In the existing public face datasets, the horizontal frontal and left-right rotation poses are the majority, and the models trained by them can not meet the requirements of face detection in the overlooking situation. Aiming at this phenomenon, the Tilt-angle face dataset TFD is cited and further expanded, and the Tilt-angle face dataset TFD-B is manually collected. The RetinaFace algorithm is adopted to carry out multiple face detection experiments. Typical experiment A shows that compared with WiderFace, the average detection precision of TFD+TFD-B as training set is improved by 4.81% when looking down at 15°, 9.87% when looking down at 30°, 10.56% when looking down at 45°,12.63% when looking down at 60°, and 15.62% when looking down at 75°, which indicates that TFD+TFD-B can effectively improve the precision of face detection in the overlooking situation. At the same time, the experiments carried out further show that expanding the training dataset can improve the precision of face detection. TFD+TFD-B can be obtained at https://github.com/huang1204510135/DFD.
Research on the method of quickly identifying and reading pointer meter

Aiming at the problems of poor detection results and low reading accuracy caused by small targets and oblique shooting angles of pointer instruments in complex background environments, a combined method of depth learning, perspective transformation, and Canny edge detection was proposed to perform pointer instrument readings. This recognition method uses an improved YOLO V7 target detection algorithm to detect and extract instruments in complex environments, and then corrects the extracted instruments through perspective transformation. Finally, the Canny edge detection algorithm and Hough transform are used to determine the center and pointer characteristics to obtain pointer readings. Through experimental comparison and verification, this method is more accurate and reliable than traditional methods, with a certain speed. It provides a more accurate and faster method for identifying pointer type instrument readings for subsequent work.
Research on enterprise classification of ERNIE-textCNN fusion focal loss

Enterprise information contains a large amount of valuable content. Analyzing enterprise information and clarifying the structure of the industry chain in which the enterprise is located can provide assistance in optimizing the structure of the industry chain. For this reason, this article proposes an enterprise classification model that integrates focal loss and ERNIE-textCNN to classify enterprises. The attention mechanism and textCNN are used to extract semantic features at different levels to solve the problem of missing features and contextual semantic relationships in enterprise short text data. To address the imbalance in enterprise data, the loss function in the model is modified to focal loss function. Experimental verification shows that in all samples, the classification accuracy of a small sample category can be improved by 10%.Finally, the enterprise is matched to the industrial chain graph.
Insight into wordle's data set based on deep learning

Nowadays, Wordle became almost everyone's current obsession. To study the reason for Wordle’s explosion, look for the secret behind Wordle. It is beneficial to develop a forecasting model to measure the fluctuations and distributions of the results based on time series and words. In the text used the context processing of words in text sequences in natural language processing to analogize that the same rule can be used for the composition and structure of words, so as to establish a percentage prediction model for the number of attempts of players with the character mechanism of letter position and structure in words. The error uncertainty of the model is evaluated by the MAPE error value. Through the analysis of the MAPE value, the error of the model to the predicted value is about 1.92%, so it is confident that the model can complete the prediction task with an error not exceeding 1.92%. Through this model, Predicting the result of the word "EERIE" as (2.16, 10.90 14.06, 24.49, 25.79, 14.41, 3.45).
Integrated navigation and location algorithm based on iterated split CIF

For the integrated navigation system GNSS/SINS in the process of data fusion, the traditional filtering algorithm does not consider the correlation between the two system and the poor robustness when measurement outliers occur, this paper proposes an iterated split covariance intersection filter algorithm to fuse the data of the two systems. It combines the Split CIF and the Gauss-Newton iteration and separate state covariance matrix into independent parts and dependent parts, and adjusts the posterior state estimation by calculating the Kalman filter gain iteratively during the measurement update process to reduce the error caused by outliers. The simulation show that the Iterated Split CIF based integrated navigation system has higher accuracy and better robustness. Compared with Split CIF and Kalman filter, the east velocity error is reduced by 30% and 35% respectively, and the latitude error is reduced by 22% and 30% respectively. In addition, the position accuracy still remains at a high level when outliers occur, so the algorithm has good robustness.
Research on the processing algorithm of drilling image

In geological exploration, the texture and context information generated by different rocks on the inner wall of the drilling hole is of great significance to understand the geological condition. Drilling camera technology can obtain digital drilling video and hole wall images. For a long time, the method of manual interpretation is the main method of studying hole wall images. This method has problems such as poor real-time performance and low work efficiency .In this paper, the processing algorithm is designed based on the imaging characteristics of hole wall image and the method of digital image processing, including image preprocessing, image expansion and image stitching. First, the radius of the area is determined according to the image features, and then the concentric circle expansion method is used to obtain the rectangular expansion image. Finally, the SIFT feature point is used to complete the accurate matching with the Ransac algorithm, and use weighted average method to complete image fusion. The entire algorithm can obtain a panoramic expansion image of the hole wall area, laying the foundation for the quantitative analysis of drilling data.
SVFL: A secure and verifiable federated learning scheme

Federated learning can effectively alleviate the data privacy problem of the participants, but the parameters or gradients passed in the model training may still leak the private data of the participants. Worse, aggregation server may return fake aggregation results. Existing solutions either use complex cryptographic primitives such as zero-knowledge proofs, or require interaction among participants, causing them high computation or communication overhead. Therefore, this paper proposes a secure and verifiable federated learning (SVFL) scheme. Specifically, SVFL performs privacy protection by introducing noise that can be offset during the aggregation process, and utilizes linear homomorphic hash to verify the correctness of the aggregation results. Compared with existing schemes, SVFL hardly loses accuracy due to the introduced security mechanism, and has low computation and communication overhead. Experimental results show that the performance of SVFL is almost consistent with the original federated learning without any protection, which makes SVFL applicable to edge devices. The computation and communication overhead of SVFL does not increase with the number of participants, which makes SVFL applicable to high-concurrency scenarios.
Regional attention based multi-scale feature fusion for image retrieval

With the development of deep learning techniques, computer vision techniques have also been significantly improved. Image retrieval is a common technique used to retrieve images of interest from image databases, which can help users find the desired images more quickly. However, traditional image retrieval methods often fail to meet user needs because they often ignore complex scale information, e.g., features may differ at different scales. Therefore, an image retrieval based on a region-attention feature fusion mechanism can overcome this drawback, and it can improve the performance of image retrieval by emphasizing multi-scale features through a region-attention mechanism. In this paper, we propose an image retrieval method based on regional attention based multi-scale feature fusion, which can effectively use multiscale features. The effectiveness of RMFF is demonstrated by conducting experiments on mainstream image retrieval datasets.
Prediction of heat transfer performance of vacuum glass based on extreme gradient boosting algorithm

In this paper, a non-stationary detection method based on the artificial intelligence algorithm XGBoost is proposed for the detection of the U-value of the vacuum glass. By analyzing the heat transfer characteristics of vacuum glass and considering the detection efficiency, the features are selected as hot end temperature, ambient temperature, and characteristic temperature change rate. In this paper, the training effect of a model is measured comprehensively by the scores of MAE, MSE, and R2. Three models, KNN, GBDT, and XGBoost, are used to train the dataset and compare the prediction results. After the comparison, XGBoost has the best prediction effect. Finally, the fitted model is validated by 5*2 nested cross-loop, and the analysis results show that the fitted model has better stability, which greatly enhances the credibility of the model. After a series of experiments, it is known that the small sample of non-stationary method and multiple interference problems can all be solved by XGBoost algorithm with certain stability, which can provide ideas for further industrialized testing.
Zero-Shot Cost Models for Parallel Stream Processing

This paper addresses the challenge of predicting the level of parallelism in distributed stream processing (DSP) systems, which are essential to deal with different high workload requirements of various industries such as e-commerce, online gaming, etc., where DSP systems are extensively used. Existing DSP systems rely on either manual tuning of parallelism degree or workload-driven learned models for tuning parallelism, which is either not efficient or can lead to costly operator migrations and downtime when there are workload drifts. Thus, we argue for a learned model that can autonomously decide on the right parallelism degree while generalizing across workloads and meeting the current demands of DSP applications. We propose a novel approach that leverages zero-shot cost models to predict parallelism degree while generalizing across unseen streaming workloads out-of-the-box. To reduce training effort, we propose a rule-based strategy that selects parallelism degree and meaningful transferable features related to query workload and hardware that influences the parallelism decisions. We demonstrate the effectiveness of our strategy by evaluating it with different amount of training queries and show that it achieves lower costs for parallel continuous query processing.
Communication signal modulation recognition based on cyclic spectrum features and bagged decision tree

With the increasing diversification of signal modulation types, the importance of signal modulation recognition is increasing, which is an important part between signal detection and demodulation. It has great applied value in jamming identification, electronic countermeasures, intelligent modem and other fields. Aiming at the improvement of recognition accuracy for some modulation types, a communication signal modulation recognition method based on cyclic spectrum features and bagged decision tree is proposed. The method extracts the cyclic spectrum features of signals and inputs them into the bagged decision tree for model training. Simulation results show that the accuracy of the proposed method reaches 93.8%, which is 39.4% higher than that of the traditional recognition method with high-order cumulants and 22.2% higher than that of the method using the original signal directly.
Football player identification based on YOLOv5 backbone and SPD-Conv

With the rapid development of computer technology and Internet technology, the information age has come. The combination of computer technology and sports is one of the most popular research fields. This paper mainly completes the construction of football player number dataset and identification of football players through the jersey number. Firstly, the dataset is constructed based on player detection and number region detection. Then in the player identification task, we uses part of the backbone network of YOLOv5 model as the feature extraction module of the player identification network. Moreover, SPD-Conv module is added to improve the network recognition performance under the condition of small size target and low resolution. A series of experiments were also done to verify the performance of our proposed model. Finally, the recognition accuracy of our proposed model reached 92.75%.
Research on automatic test system based on image matching algorithm

The traditional test of the system using graphical interface determines the test results by manual. With the higher accuracy requirements of the test software system in current industrial domain, the traditional manual testing has the problems of high costs, low accuracy and efficiency. Then an automatic test system based on image matching algorithm is proposed to simulate manual automatic test result. Firstly, the architecture of automatic test system is researched, which reduces the test cost. Then, an automatically selected method by template matching algorithm and ORB algorithm for the complexity of the target images, which improves the matching rate and accuracy. Finally, an image similarity calculation algorithm based on perceptual hash algorithm is built, which improves the test accuracy and efficiency twice. The experimental results show that the accuracy achieved to 97%, which meets the accuracy requirements for the industrial field.
Prediction of insulation performance of vacuum glass based on cascade forest model

In this paper, a new method is proposed for the intelligent prediction of the thermal insulation performance of vacuum glass, i.e., the use of cascade forest algorithm to detect the heat transfer coefficient (U-value) of vacuum glass. By constructing different intelligent algorithm models, random forest, extreme random forest and cascade forest algorithms are used. By evaluating the proposed method using mean absolute error (MAE), mean square error (MSE) and R-squared value, the cascade forest was evaluated with values of 0.0401, 0.0035 and 0.9896, respectively, and the predicted value curve was very close to the true value curve, so it was concluded that the cascade forest algorithm was superior to the random forest and extreme random forest algorithms in predicting the heat transfer coefficient of vacuum glass. In order to avoid the risk of overfitting, k-fold cross-validation was also added to each random forest in the cascade forest during the training process, and the accuracy of the cross-validated data was improved by 1% as shown by the data. It is known from the experimental results that the algorithm with cascade forest gives a new idea for the work of fast detection of heat transfer characteristics of vacuum glass based on small samples.
Research on emotion recognition of eye movement in realistic environment

Eye tracking technology can show how people focus their attention and emotionally react to their surroundings. In this study, wearable eye tracker was used to conduct eye movement experiments in realistic environment. For signal processing of the data, a finite impulse response (FIR) filter was chosen, and an eye movement data set was created. First, 26 features were chosen by a machine learning algorithm for emotion recognition, and the average rate of recognition on GDBT was 71.1%. 22 noteworthy correlation features were chosen after Spearman and emotion state were used for correlation analysis. GDBT has a recognition rate of 74.61%, while XGBoost has a recognition rate of 75.63%. The experimental results prove the validity of our data set and provide data support for the next research.