Reviewerly/exHarmony huggingface dataset:
Research of Image De-noising Based on Deep Learning

Image denoising is a technology to restore the image by changing the information before and after the image sequence. The restored image will become very clear, and it is used a lot now. Due to the imperfect equipment, when the image is transmitted, it will be affected by noise, thus losing the original details. In order to solve this problem and make the noise image clear and obvious, this paper proposes an image denoising method based on deep learning, and constructs the image denoising network structure based on convolutional neural network. At the same time, the directional propagation algorithm is optimized to speed up the training speed of the model and effectively improve the denoising effect. By comparing the experimental data of the classical algorithm experiment, it is concluded that this study has achieved a better denoising effect. Finally, the key issues of image denoising research are summarized, and the development trend of future research in this field is discussed.
Research on Dim Target Detection Model Based on Eye Movement Data

Dim targets detection under complex backgrounds is a classic problem in the field of automatic target recognition (ATR). The human visual attention mechanism can effectively avoid background clutter interference, which provides a new idea for solving this problem. Traditional visual attention models are limited in practice due to task definition, computational accuracy and other reasons. This work focuses on dim object detection and high-precision visual attention modeling by using deep neural network. Firstly, the head-eye movement measurement technology is used to conduct visual attention experiments and build a complete human visual dataset. And then, a visual attention calculation model with complete visual processing capabilities is conducted based on the generative adversarial network (GAN) framework. Finally, based on the attention model, a new algorithm for small object detection driven by visual attention is proposed. This detection method can achieve a detection probability of 0.78 and a false alarm rate of 0.19×10-4 on the test dataset, which proves its effectiveness. Therefore, the research has reference significance for promoting the development of ATR-related technologies.
Construction of Knowledge Graph for Special Machining

Special machining refers to the general term of processing technology which does not belong to the category of traditional machining technology. The data sources of special machining knowledge are various and rich, including structured, semi-structured and unstructured data sources. According to the characteristics of multi-source heterogeneous knowledge of special machining, this paper uses the powerful semantic machining ability and data organization ability of knowledge graph to solve the problem that the knowledge of special processing is difficult to be mined, so as to create the knowledge graph of special machining and realize the effective fusion and alignment of multi-source heterogeneous data. Therefore, this article uses technologies such as knowledge acquisition, knowledge integration, and network crawlers, and adopts a special processing knowledge diagram from the top - down graph construction model, which realizes the visualization of the Neo4j -based map, making special processing knowledge more intuitive and complete.
Learning to Predict Tendency of CVE Vulnerability

Predicting the trend of software vulnerabilities enables proactive warnings, aiding in mitigating security incident losses. Capturing the long-term dependencies among data is crucial for achieving highly accurate predictions. Leveraging the remarkable data relationship-capturing capabilities of learning-based methods demonstrated in various domains, we implemented several learning-based approaches to forecast the outbreak trend of software vulnerabilities. The results show that Informer, Autoformer, and DLinear achieved accuracy rates exceeding 90%. Informer, with its residual connection module, obtained significant prediction results with fewer data points. Autoformer, benefiting from its data decomposition structure, performed better in datasets with periodic patterns. DLinear, on the other hand, exhibited superior performance in relatively stable datasets. However, LSTM performed poorly across multiple datasets due to the cumulative error effect.
Research on Chinese Event Extraction Method with No-trigger

Event extraction is a key task in the field of natural language processing, which aims to structure text in preparation for tasks such as subsequent knowledge graph construction. Most of the existing event extraction methods are difficult to handle well the case of containing multiple events within the same document and to distinguish efficiently for negative obfuscation samples. In this paper, we propose a model called RCEENT (A RoBERTa-wwm-based Chinese Event Extraction method with No-Trigger). RCEENT based on RoBERTa-wwm encoder and PTPCG (Pseudo-Trigger-aware Pruned Complete Graph) decoder to address the above problems. The RCEENT model can improve the accuracy of the event detection module without adding too much complexity by using multiple binary classifiers, and improve the results of the event extraction task by using graph pruning and PTPCG model for decoding. RCEENT achieves a 1.4% F1 score improvement in the event detection task and a 3.9% F1 score improvement in the event extraction task on Duee-fin compared to the latest baseline method.
Automated Channel Pruning Strategy Based on Artificial Bee Colony Algorithm

Model compression is an effective method for compressing neural networks, where channel pruning plays an important role. The traditional channel pruning is a method that picks and chooses channels by judging the importance of weights and the experience of manual pruning, which has certain human interference factors and is prone to produce suboptimal network structures. To this end, an automated dynamic channel pruning method based on an artificial bee colony algorithm, called DPruner, is proposed, which focuses on finding the best pruning structure in the network model by a manual bee colony algorithm and then pruning the network model. In order to solve the complexity of network structure combinations, this paper first reduces the range of initialized pruning structure combinations, and finally filters the best pruning structure combinations by updating the initialized pruning structure combinations. The method avoids the interference of human factors, and the effectiveness of the method was verified in the experiment.
Research on the Application of Artificial Intelligence Technology in Network Security

Health Management (PHM) is a comprehensive method and strategy that aims to monitor, diagnose and optimize the health status and performance of systems, equipment or components. It includes health monitoring, fault diagnosis, and remaining service life (URL) forecast. Among them, URL is a more important field in PHM. Because the physical knowledge used by precise modeling machines is complicated, data -based driving methods based on learning data have become a promising alternative method for model prediction methods. This article proposes a new time convolutional neural network (TCN) with a soft threshold and attention mechanism for mechanical prediction. The method used in this paper avoids the process of complicated artificial extraction, and uses bearings directly as the input of the network model. A soft threshold mechanism is added to the TCN network, so that more useful information is retained in the activation function ReLu. The threshold is self-adaptive learning by the sub-network trained by the channel attention mechanism, not the certainty value of the threshold distribution. Therefore, under the influence of the attention mechanism, each feature diagram has its own threshold, which retains some key features. On behalf of verify the generalization of the method proposed, the three benchmark data sets related to rolling bearings were tested, and the performance of the development method was compared with several advanced prediction methods. The results show that the development of all three cases has produced accurate RUL predictions, and has good robustness and generalization capabilities.
Research on Target Recognition and Tracking Technology Based on Laser Point Cloud

In view of the common road surface, multiple objects in a complex environment, because the detection results of multiple objects are not ideal, resulting in errors in the detection results, in order to achieve the accurate tracking of the target object. The basic principle of Camshift algorithm is emphasized, and Kalman filtering algorithm is added in the tracking process to process the obtained target data to form a moving track, predict the position of the target next time, and solve the problem of follow-up tracking. Through the verification under the complex background of the daily road, it is found that the improved algorithm can effectively and accurately extract the moving target to be tracked from the continuous frames of the point cloud image, and has high robustness. The problem of high real-time requirement for target tracking has been effectively solved. The example shows that the prediction effect is good, and it is feasible.
An Improved Algorithm of Detecting Abnormal Data in Geological Disaster System

A large amount of data is collected during geological hazard monitoring, which is extremely valuable for further data mining, hazard monitoring and decision analysis. However, in the process of data collection and transmission may be affected by interference and other factors, resulting in the generation of abnormal data. To extract higher value information from these basic data, it is necessary to improve the quality of source data first, so it is necessary to detect the abnormal data. The monitoring data of the geological disaster are time-series data, which have the characteristics of time-series correlation. The nearest neighbor difference jump anomaly detection algorithm is suitable for monitoring anomalous data of geological disaster system, but there are shortcomings in selecting floating values and correlation perception. To address these problems, the nearest neighbor difference jumping algorithm is improved and a algorithm is proposed to detect anomalous data of geological disasters, i.e., the data series are segmented by using sliding windows, the selection of floating values is improved, the calculation range of difference values is expanded to enhance the correlation of data, and the concept of change speed is incorporated to better perceive the trend of change before and after the data, and finally the anomaly is determined by using the anomaly probability and correlation. After the experimental comparison and analysis, the accuracy and recall rate of the proposed method on the detection of geological disaster data are improved and meet the expected results.
Motion Planning and Optimal Control of AEB System under Different Adhesion Coefficients Road

To reduce the incidence of rear-end accidents of vehicles under different road surfaces, the Automatic Emergency Braking (AEB) system under multiple road surfaces was conducted. First, the motion planning containing the warning signal phase and the emergency braking signal is proposed based on the vehicle condition artificial potential field method. Secondly, the fit and convergence speed of the particle swarm optimization PID (PSO-PID) controller is comparison of conventional PID controller, comparison of the results concluded that the PSO-PID maximum braking deceleration at the fit is high, faster convergence, there is better stability, can be achieved in the vehicle control system to achieve better deceleration effect. Finally, a joint simulation is performed using Carsim/Simulink on a low, medium and high adhesion coefficient road surface to test the validity of the control strategy. The test results indicate that using the control strategy proposed in this paper, the safe distance of parking is between 1.25-2.56 m, the stop time is in the range of 3.54-5.31 s, and the vehicle successfully stops and avoids collisions on three types of road surfaces mentioned above.
MA-Unet3+ Segmentation Network of Remote Sensing Image Based on ECA Block

Semantic segmentation is a basal task and is a typical computer vision problem. Although semantic segmentation is developing rapidly, the speed and accuracy of model segmentation still need to be further improved. For solve the issue of scale differences between target objects and loss of spatial information in the segmentation task of remote sensing images, by improving the original U-Net3+ network and introducing the attention mechanism, a new network MA-Unet3+ is constructed. In the coding phase, images of unlike scales are fused, and the full-scale connections are pruned, some skip connections are removed, and attention mechanisms are introduced between each layer. The improved model is contrast with some common network models, and the experiment achieves 78.7% average intersection (mIoU) on the Vaihingen dataset, which is 0.8% better than this optimized network U-Net3+, the average category pixel accuracy (MPA) is 92.4%, which is 1.2% better, and the similarity coefficient (Dice) result is 87.3%, which is 0.8% better. 0.8%, it is observed that MA-Unet3+ is precede other algorithms.
Poisson Noise Image Restoration Based on Bregman Proximal Gradient

In view of the fact that data item functions are not smooth in Poisson noise image restoration, while most existing first-order optimization methods require data item smoothness, a Poisson noise image restoration method based on BPG (Bregman Proximal Gradient) algorithm was proposed. Firstly, we establish a Poisson noise image recovery model using the maximum posterior estimation framework. This model aims to recover a high-quality image from noisy input data corrupted by Poisson noise. The model's data term is determined by the Kullback-Leibler (KL) divergence, which corresponds to the likelihood estimation. The KL divergence measures the difference between the probability distributions of the noisy input data and the recovered image. The regular term uses the Tikhonov regular term, which corresponds to a priori estimation. Secondly, the BPG algorithm is employed to address the model, which gives a wider and more satisfactory smoothness based on Bregman distance to replace the traditional gradient Lipschitz smoothness requirements, and maintains the convergence speed of PG (Proximal Gradient) algorithm. Finally, numerical experimental results, it proposed method outperforms the conventional algorithm by a margin of 0.01--6.94 (db) in terms of recovering Poisson noise images, and it also outperforms several algorithms in visual effects.
Small Target Detection Optimization Algorithm Based on Improved YOLOv5

The accuracy of the traditional model algorithms mainly oriented to detection of small target is generally low, the unstable problem, we have made modifications on the basis of YOLOv5 and proposed a more suitable algorithm for detecting small targets. First of all, increased the effective channel attention mechanism, solves the convolution the characteristics of the loss, as a result of the global context information loss in the process of problem, improves the fuzzy image cases of small target detection accuracy. Secondly, the network structure is reconstructed, the multi-scale network model is established, and the target detection layer is combined to improve the detection performance of remote sensing small target model at different scales under the feature information extraction network. Finally, by integrating the loss function SIOU into the whole model, the improved algorithm has made progress in many aspects. The improved algorithm is compared with the original YOLOv5 model. The data obtained from the experiment mean that on the VOC data sets, the accuracy value of ours is higher than original algorithm 0.7%, 0.8% percentage points higher than YOLOv5s small target detection accuracy.
A Study of Multimodal Identification Methods

The purpose of this paper is to investigate unimodal determination and decision layer fusion methods in multimodal face recognition and to evaluate the effectiveness of decision layer fusion. Experiments are conducted on the "VidTIMIT" and "MOBIO" datasets using convolutional neural networks and MFCC feature extraction techniques. The performance of three feature fusion methods, front-end fusion, intermediate fusion and decision layer fusion, is compared. The experimental results show that the recognition accuracies are 94.9%, 94.7% and 94%, 94.1% for the face and voice models only, respectively. However, the accuracy decreases slightly in the case of front-end fusion. The best accuracy was achieved when the decision layer fusion method was used, reaching 95.7% and 95.6%, respectively. This indicates that training face and voice print data separately and fusing classifier output scores can effectively improve face recognition accuracy. This study explores feature fusion methods in the field of multimodal face recognition and experimentally demonstrates the advantages of decision layer fusion in improving accuracy, pointing to a new direction for future research.
Investigating Transformer-based Approach for Small-sample Object Detection

Object detection algorithm has gained broad attention, being recognized as an essential tasks within the realm of computer vision. Visual Transformer is an image analysis framework that uses a technique called the attention mechanism to process images. Unlike conventional convolutional neural networks, vision Transformer adaptively captures features at different scales in an image. In this design, the importance of context information with respect to the current location is calculated by vectorization representation and weighted at the decoding stage. With the introduction of this self-attention mechanism, the vision Transformer can realize end-to-end image processing while making maximum utilization of the global information within the image. However, it lacks the inductive bias inherent in convolution, which results in more data being required for training, usually pre-training on a large dataset, the JFT-300M, or at least ImageNet. Therefore, this paper studies the dependency of Transformer model on large-scale datasets. Firstly, in the neural net diagram, Fine-grained convolutional attention module CBAM method is introduced into the CNN architecture and trained together with CNN to Boost the model's performance. Simultaneously, an efficient Pre-training by reconstruction mechanism is adopted to reduce the dependence of Transformer based target detection methods on large amounts of labeled data. The upgraded algorithm has made progress in both the COCO dataset and the PASCAL VOC dataset.
Research and Implementation of Image Defogging Based on Deep Learning

Image degradation due to fog and haze is a common problem in the field of life and image research. This article will be designed and implemented of image haze removal algorithm. The traditional methods have some problems such as low brightness, artifact, halo and distortion, but the existing methods based on deep learning have better effect. In this paper, we will propose a de-fogging network model, which is based on adversarial network. The generator in adversarial network is a convolutional neural network with residual network, which reduces the model training parameters and improves the parameter utilization. The discriminator uses the full convolutional network as the discriminator, which enhances the spatial sensitivity of the model to the image. In order to improve efficiency, the discriminator is simplified on the original structure. The loss function of the model is improved so that the model can get better training effect.
Forecasting Harga Komoditi Pokok Jambu Mete di Sumba Barat Daya dengan Menggunakan Metode Supervised Machine

Cashew (Anacardium Occidentale L) is one of the plantation crop commodities which has economic significance and quite potential because its production can be used as raw material for the food industry. According to the Big Indonesian Dictionary (KBBI), commodities are raw materials for agricultural products, merchandise, primary goods and local crafts that can be used as export commodities, such as wheat, curry, coffee, etc. Southwest Sumba Regency is one of the places where cashew is produced to increase farmers' income, but the potential of the cashew crop must be balanced with the right price. Many researchers carry out forecasting using various methods, but in this research, the technique used is supervised learning, namely linear regression, which has a relatively high level of accuracy. This research uses the Linear Regression method to produce accurate price forecasts and to know the influence of future rises and falls in cashew prices in the Southwest Sumba district. The forecasting results in this research were Rp. 14,272/Kg with an RMSE value of 0.794. Keywords: Cashew, Commodities, Machine Learning, Forecasting
Automatic Resume Screening with Content Matching

Candidate screening is the most costly step of the recruitment process. In order to eliminate a large number of candidates applying for a position, resumes are filtered based on features such as graduation department, technical skills, and experience. Within the scope of algorithmic recruitment, there are many studies on the automation of the recruitment process using semantic and data-based approaches. In this study, a natural language processing-based screening algorithm that automatically calculates candidates' suitability for a job posting using different categories is proposed. The proposed method generates a metric for each candidate that indicates their eligibility for the job post they applied for and ranks the candidates by using structured data. BERT based named entity recognition and stochastic gradient based domain classifier were used for the analysis of resume and job posting data. Semantic and fuzzy approaches were used together for the calculation of the similarity metric. The proposed algorithm was evaluated using manually annotated resume and job posting datasets that are filtered to include only software development categories.
Determining Error Proneness in Software Developed with Agile Methodologies

Nowadays, with technological developments, computer software has become an indispensable part of human life. As the importance of software increases, error-free operation, reduced costs and increased customer satisfaction have become important. In parallel with these, becoming agile in software development processes, measuring the quality of software and detecting errors in advance have become critical areas of work in the software world. In this study, a new model has been developed to predetermine the error proneness of object-oriented software developed with agile software development methodologies using static code analysis measurements. Collecting customer feedback early and frequently, which is one of the basic principles of agile working, has been implemented within the scope of the model and turned into a major decision-making element. The aim is to anticipate the problems that may arise with the classes. In addition, it is aimed to both reduce software development costs and contribute to increasing customer satisfaction. It is planned to enrich the model developed in the later stages of the study and turn it into a model based on external quality criteria as well as internal quality metrics of the software. The aim here is to make the changes in classes and modules traceable in the agile software life cycle and to present systemic suggestions.