Reviewerly/exHarmony huggingface dataset:
Prediction of Learning Performance in Online Course Based on Linear Regression Model: IEEE ITAIC(ISSN:2693-2865)

This paper takes the online learning behavior and performance data of online courses as the research object, analyzes the factors that affect the course performance in online learning behavior, and establishes a performance prediction model. First, the influencing factors of course achievement are analyzed by calculating the correlation coefficient between learning behavior features and course achievement; then, a linear regression performance prediction model is constructed by using single or multiple learning behavior features, and the regression coefficients are solved by the least squares method or gradient descent method; Finally, the mean square error and coefficient of determination are used to evaluate the model performance. The experimental results show that the top three learning behavior features that have the greatest impact on course performance are the audio and video learning time, number of chapter study times and the number of task points completed, while the multiple linear regression model established using these three learning behavior characteristics and assignment scores has the highest prediction accuracy. The research results can provide reference for online course teachers and learners, help to promote online course learning early warning and performance prediction, and improve the quality of online course teaching.
Research on Information Transmission Capability Evaluation of Information Processing System based on Fuzzy Mathematics Comprehensive Evaluation Method

The connotation of information transmission capability of information processing system has a certain fuzziness. To accurately evaluate it, we need to grasp the relationship between various elements contained in the information transmission capability of information processing system as a whole. Fuzzy mathematics comprehensive evaluation method introduces the concept of membership function, which can quantitatively express this relationship, and then get accurate conclusions through calculation. Aiming at the constituent elements of information transmission capability of information processing system, this paper scientifically sets up the evaluation index system, studies the evaluation according to the steps and requirements of fuzzy mathematics comprehensive evaluation method, and gives the evaluation conclusion. Through example analysis, the feasibility of the evaluation method is verified, which provides a new idea for the evaluation of information transmission capability of information processing system.
An implementation method of multi process MPU protection based on Cortex-M processor

The Cortex-M processor uses memory protection unit (MPU) for memory access protection to prevent system exceptions caused by data area destruction and stack overflow. The embedded system using multitasking operation, each task program area is not isolated., resulting in the inability to directly set MPU protection in the shared code area and data area. By designing a lightweight real-time operation system(RTOS) with separate kernel and application., each application is allocated its own code area and data area., and setting the MPU protection strategy. From application installation., startup and operation to task switching., a complete set of memory allocation., protection and release mechanism is realized to ensure the stable and reliable operation of the system.
Pollen texture migration deblurring method based on self-coding generator

Image deblurring is a classical problem in the field of computer vision. The purpose of this paper is to restore the multi-layer pollen image into an image with clear texture when the fuzzy kernel is unknown. In this paper, a pollen texture migration deblurring network based on self-coding generator is designed, which uses the maximum similar texture information between adjacent layers of pollen to recover clear pollen layer by layer. A pollen image deblurring method based on texture migration DeblurGAN of countermeasure generation network is proposed. The texture migration network is used as the recovery network, and the residual pair information is extracted by the residual block in the encoder to simplify the training. Secondly, the jump connection layer is used to retain the information to remove the color offset, and the deconvolution layer of the generator is improved into the alternating layer of nearest neighbor interpolation and convolution, which can help to remove the checkerboard error. Finally, a layer of convolution is used to reconstruct the feature image. The algorithm is evaluated on multi-layer pollen data sets. From the evaluation index and subjective effect, it can be seen that the method proposed in this paper has stronger image restoration ability, retains rich texture information, and can effectively improve the image deblurring effect.
Image-to-Class Metric based on Category Traversal for Few-shot Learning

The purpose of few-shot learning is to learn a classification model with good generalization performance. When there is only one sample or several samples, the model can also be quickly generalized to new categories. In this paper, we use the method based on metric learning to complete the few-shot learning image classification task. In this method, we introduce a feature learning module to improve the representation ability of the feature extraction network. Through category traversal, the module extracts the intra-class common features and inter-class unique features of images. Finally, by comparing the similarity between the input image and the local descriptors of each category, the classification task is completed by the image-to-class measure. This metric is achieved by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -NN search in the local descriptors, in this way, we can make full use of all the local features of a category, thus expressing the distribution of this class more richly and effectively.
Human Abnormal Behavior Recognition Based on 3D Residual Convolutional Network

In order to ensure the safety of passengers in the elevator, this paper takes the abnormal behavior of passengers in the elevator car as the research object, and proposes an intelligent identification method for abnormal behavior of the elevator car based on 3D residual convolution network, which can improve the accuracy and speed of recognition with the abnormal behavior in the elevator car. This paper combines three-dimensional convolution and residual network, establishes a 3D residual convolution network model, and conducts in-depth research and design of each parameter to identify three abnormal behaviors of elevator cars, such as falling, robbery and fighting. The effects of different video frame input lengths, different parameter optimization functions and whether pre-training on the network recognition rate are compared and analyzed. The experimental results show that when the input video frame length is 16 frames, the Adam optimization function is used for optimization, and the pre-training method can achieve better recognition results.
An improved fireworks algorithm

To address the shortcomings of the traditional fireworks algorithm (FW A) in the process of finding the optimal solution, an improved fireworks algorithm with randomization is proposed. Firstly, dynamic switching variation is performed according to the position relationship between the firework explosion operators to increase the diversity of operators of FWA. Secondly, the spark particles beyond the boundary are randomization mapping back into the feasible domain due to randomness. Therefore.j it is convenient for more exploration convergence speed. The experimental results on five standard test functions show that the improved FW A has better optimization performance than FW A and sparrow search algorithm (SSA) in terms of search accuracy and convergence speed. The optimal solution is obtained on two test functions.
Implementation of Mask-wearing Detection for Embedded Platform

With the vigorous development of target detection technology, many mask-wearing detection algorithms have come into being, but the detection task still has the problem of being difficult to detect at the embedded platform. In this regard, a mask-wear detection model based on improved YOLOv5 is proposed, and the implementation of the embedded platform of detection was studied. Firstly, the implementation of the mask-wearing embedding terminal was analyzed; then the mainstream embedded platform was compared, and the NVIDIA Jetson Nano was selected for the embedding terminal deployment; finally, the tensorRT acceleration principle was introduced and the acceleration experiment was completed. The results show that the mAP of the post-feed algorithm reaches 99.6% and the FPS is 30, and the demand for mask-wear detection can be met on the embedded platform NVIDIA Jetson Nano, which has reference significance for practical applications.
Pedestrian detection algorithm based on improved SLIC segmentation and SVM

Pedestrian detection is one of the key technologies to achieve target detection in multiple scenes, which can promote the research of pedestrian recognition, localization, tracking and other technical directions. Therefore, in this paper, we propose a pedestrian detection algorithm combining superpixel-based feature extraction and support vector machine (SVM). First, the original image is Gaussian filtered and bilinearly interpolated and scaled to determine the number of superpixel segmentation. Then, a modified simple linear clustering algorithm is applied to complete the superpixel segmentation of the image. Then, the features on the superpixelated image are extracted from the pedestrian detection sample set, mainly the texture features based on LBP and the gradient features based on HOG. Finally, the extracted features are formed into a feature vector to train the LIBSVM classifier for pedestrian detection binary classification. The experimental results show that the proposed method can achieve 95.1299% Accurary, 98.2042% Precision, and 94.8858% Recall for pedestrian detection. The mean value of Accurary is 95.1299%, the mean value of Precision is 98.2042%, and the mean value of Recall is 94.8858%, which have good robustness and applicability for pedestrian detection in various scenarios.
Human 3D Pose Estimation Based on Sequence Graph Convolution: IEEE ITAIC(ISSN:2693-2865)

Thesis mainly studies the two-stage 3D pose estimation method of human body with 2D keypoint sequence as input. The sequence graph structure is established by intra-frame connection of different joint points in the input sequence and inter-frame connection of the same joint point. Then the graph convolutional network and the dilated temporal convolutional network structure are used to obtain the spatial and temporal features of the sequence spatiotemporal graph to estimate the 3D coordinate estimation of the human pose. Make full use of the feature of dilated temporal convolutional network to gradually reduce the number of input sequence frames to reduce the volume of the high-level sequence spatiotemporal graph structure to reduce the training time and improve the network performance.
Multi-scale Features Fusion Network for Single Image Deraining

Single image rain removal is an important research direction in the field of computer vision. In this paper, the Multi-scale Features Fusion Network (MFFN) is presented for rain removal. MFFN is mainly composed of Multi-features Fusion Module (MFM) and Dual Attention Module (DAM). In the MFM, we make the improved dense block and the dilation convolutions to form the feature extraction branches, which is conducive to improve the receptive fields of network. Subsequently, the bottom-up connection method is adopted between branches to help different branches make full use of image information. Then, feature branches are merged to fusion different features. DAM is consisted of purposed SA Block and standard SE Block. The purpose of DAM is to reduce the non-rain features which is extracted by the network. Experiments show that MFFN can obtain the better result of rain removal than several advanced methods.
Object Tracking Performance Evaluation Method Based on Adaptive Threshold and Background Suppression

The large difference in target size and the deviation and ambiguity of axis-aligned annotation boxes in public object tracking benchmarks seriously affect the credibility of evaluation indexes based on center location error and overlap rate. To improve the adaptability and credibility of the above evaluation indicators, a target tracking performance evaluation method based on adaptive threshold and background suppression is proposed. The annotated target size is used to adaptively determine the center location error threshold to maintain the consistency of the accuracy evaluation criteria; the background-suppression overlap rate is used to reduce the adverse effect of background information in the axis-aligned annotation boxes on the reliability of performance evaluation indicators. Finally, the effectiveness of the proposed evaluation method is verified by sequences from multiple benchmarks, such as OTB100, TC128, UAV123, and LaSOT.
Design and Implementation of Intelligent Tour Guide Application System

University campuses generally cover a large area. School office buildings, secondary college buildings, teaching buildings, libraries, staff and student canteens, supermarkets, baths and so on are distributed in different places on the campus, which brings relative inconvenience and difficulties to those who are unfamiliar with the campus environment, such as, the newly admitted college students, teachers and students from outside the school for training, and foreign office personnel. Based on the Hybrid App development model, with the support of Cordova project structure deployment which is compatible with Android and IOS system platforms, and the design of a micro-service architecture that combines distributed deployment and synchronization management, we create an efficient, fast, practical, intelligent tour guide APP with high-security, high-performance, which is to design and open a unified standard API interfaces for system management on the mobile phone, through three parallel tour guide modes - intelligent positioning voice playback, text push, and QR code- the access and sharing of data and information on the university campus is realized.
Multi-agent distributed deep reinforcement learning algorithm in cloud environment

In recent years, deep reinforcement learning technology has been continuously applied in the field of cloud computing, making it possible to provide effective and optimized decision-making strategies for complex and high-dimensional multi-agent systems. However, in complex multi-agent scenarios, the existing multi-agent depth The reinforcement learning algorithm not only has a slow convergence speed, but also the stability of the algorithm cannot be guaranteed. In this paper, a multi-agent distributed deep deterministic policy gradient algorithm based on value distribution is proposed, which introduces the idea of value distribution into multi-agent scenarios and preserves expectations. Return complete distribution information, so that the agent can obtain more stable and effective learning signals; Introduce multi-step rewards to improve the stability of the algorithm; Introduce a distributed data generation framework to decouple experience data generation and network update, so that it can make full use of computing resources and speed up the convergence of the algorithm. Experiments show that the algorithm proposed in this paper has better stability and convergence speed in multiple continuous/discrete controlled multi-agent scenarios, and the decision-making ability of the agent has also been significantly improved. Enhance.
A Survey of Graph Neural Network Methods for Relation Extraction

Relation extraction, as an important part of knowl-edge graph and natural language processing, aims to extract semantic relations between entities by understanding text, which has attracted great interest of researchers. Recently, with the rise of the advanced technology of graph neural networks, numer-ous methods have emerged that employ graph neural network techniques to address relation extraction tasks. However, to the best of our knowledge, few studies provide a complete picture of how and to what extent graph neural networks has been applied to this problem. In this paper, we provide a thorough review of graph neural network-based relation extraction methods. We first present a brief introduction of graph neural networks. Besides, we describe the relation extraction task, comprehensively review the corresponding datasets, and discuss the reason why graph neural networks are adopted for relation extraction tasks. After that, we focus on relation extraction existing approaches built upon graph neural networks, including specialized architectures designed for the basic task as well as novel graph neural network models designed to tackle the challenging problems. We also discuss relation extraction methods based on graph neural networks in combination with pre-trained language models. Finally, we give out our conclusion and provide several further research directions for this research topic.
A code error correction system for PDF documents using regex and similarity matching

This paper designs a code error correction system for PDF documents to promote the informatization and automation of work. Its core function is to regularize the text extracted from PDF drawings, and then to correct the error of the extracted contents using similarity matching method, thereby improving the accuracy of PDF document code extraction. The system proposed in this paper is based on regular expression (i.e. regex) and similarity matching method. Regex can realize the preliminary filtering of the extracted contents, and remove the character interferences outside the character set of the presetting code database. The improved edit distance algorithm is used to obtain the correct code closest to the extracted content in code database. The experimental results show that the system can be applied to engineering practice. The regex and the improved similarity matching method can effectively correct the extracted contents that are inconsistent with the code database.
Analysis of illegal behavior in power station based on video surveillance

In the field operation, crossing the fence is a common illegal behavior, which needs to be paid attention to. Especially, the live part of the power station site is mixed with the power outage part, and some construction workers cross the fence to enter the live area, which can easily cause safety problems. The power station has a wide range of operations, and the manual monitoring method is inefficient. With the popularization of video monitoring devices in power stations, this paper proposes a detection and identification method for fence crossing violations based on video monitoring. The method extracts video frames as input, uses convolution to extract temporal and spatial features, and classifies and regresses the features fused in time and space, which can effectively identify fence crossing behaviors. Finally, a video processing platform is built to process alarms for illegal operations. Engineering practice shows that the method shown in the article can effectively predict the illegal crossing of the fence in the power station and improve the intelligent monitoring level of the power station.
LOM: A Website Fingerprinting Defense Method based on Local Optimal Mutation

Website Fingerprinting (WF) is a traffic analysis attack. The potential local adversary can infer the user's web activity by extracting implicit pattern information from traffic traces. Leveraging the latest advances in Deep Learning, the adversary has achieved 98% detection accuracy in closed-world tests. This poses a new challenge to the traditional WF defense methods. In this paper, we propose a new defense method, the proposed dissimilarity measure and local optimal mutation strategy greatly reduce the computational cost. Experimental results show that the method achieves the generation of adversarial traffic trace with shorter preparation times and better defensive effects. Moreover, it also has great potential in the face of more powerful attacks that may emerge in the future.
BDT-ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise Based on Bayesian Decision Theory for Identifying Clusters with Multi-Densities

Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm based on density distributions, which is able to recognize object groups with arbitrary sizes and shapes in the constant-density datasets while failing to detect clusters with multi-densities. To overcome its drawbacks, some researchers put forward an adaptive DBSCAN algorithm by updating Eps and Minpts adaptively to find each cluster with diverse densities, but failing to discover potential clusters automatically, its limitation was still obvious. Therefore, this paper proposed a new adaptive DBSCAN algorithm based on extended Bayesian decision theory, namely BDT-ADBSCAN, to handle the problem of recognizing multi-densities and realize its automatic clustering. The experiments results show that the BDT-ADBSCAN algorithm is effective to detect datasets with varying densities automatically with higher accuracy and validity.
Lightweight car front detection algorithm based on improved YOLOv4

Aiming at the needs of intelligent transportation systems, a lightweight car front detection algorithm is proposed to solve the current YOLOv4 algorithm's complex network structure, more parameters, high requirements for device configuration, slow detection speed, and large memory usage. First of all, this paper replaces the conventional convolution of the feature fusion network with deep separable convolution to reduce the amount of parameters and calculations of the algorithm. Then, the feature map obtained by the backbone extraction network is introduced into the new attention mechanism Coordinate Attention (CA) module to gather effective features. Finally, the Spatial Pyramid Pooling (SPP) module of YOLOv4 is replaced with the SPP-A module that enhances the receptive field to further improve the accuracy of the network's target detection. The experimental results show that the improved algorithm has reduced the model size by 44% compared with the original algorithm, and increased the number of Frames Per Second (FPS) by nearly 10%, but the mean average precision (mAP) is still slightly improved, indicating that the algorithm can meet the requirements of car front detection in actual scenes.
Evaluation of Joint Information Environment Service Capability of Space-based Network Information System Based on ANP-Fuzzy Comprehensive Evaluation Method

In the increasingly systematic and networked environment of aerospace equipment, the space-based information service capability is not only affected by the platform and payload capacity of the mission spacecraft, but also increasingly restricted by the network characteristics and network environment for space-based information acquisition. In this context, the joint information environment of the space-based network information system is constructed (JIESNIS). The service capability evaluation index system of JIESNIS is constructed pertinently. Analytic Network Process (ANP) and fuzzy comprehensive evaluation method are combined to evaluate the service capability of JIESNIS. Based on the background of the space-based network information system joint information environment support operation under the customized and active service mode, the simulation model is constructed. The proposed ANP-Fuzzy comprehensive evaluation method is adopted to evaluate the capability JIESNIS, meanwhile the effectiveness of the method is verified.