Reviewerly/exHarmony huggingface dataset:
An Improved Global Filter Pruning Algorithm Based on Filter Importance Ranking

Aiming at the problem that it is difficult for the global pruning method to sort the importance of filters and then to achieve more parameters pruning of the network, A global filter pruning algorithm which is based on filter importance ranking is proposed in this paper. According to the given global pruning rate, the importance of the filters is sorted, the filters that are sorted later are pruned. Finally, by pruning the network on the VGG16 model, it is verified that the algorithm proposed in this paper can prune more parameters to a greater extent while ensuring that the accuracy of the model is basically unchanged. The Cifar-10 data set is used in the experiment, and a stochastic gradient descent algorithm is used in the network. Compared with the current main algorithms Filter pruning and Network Slimming, the algorithm proposed in this paper has a significant improvement in the compression rate of computation and the compression rate of parameters while ensuring that the accuracy of the model remains basically unchanged.
Design of Graph Neural Network Accelerator Based on Heterogeneous Architecture

Graph Neural Network (GNN) has super strong graph learning and reasoning ability, so it has attracted more and more attention. The GNN combines the characteristics of graph computing and traditional neural networks, and can execute some complex, non-Euclidean space data, forming a mixed calculation mode of irregular and regular. The traditional processor structure design cannot satisfy the simultaneous processing of graph calculation and neural network acceleration. Therefore, this article proposes a dedicated acceleration architecture for GNNs, customizes hardware computing units and on-chip storage methods, optimizes calculations and memory access, and implements them on the Ultra96-V2 board. The experimental results show that the accelerator designed in this article has achieved good acceleration effect.
An Object Detection Algorithm with Multi-scale Context Information Based on YOLOv4

Most current object detection algorithms have the issue of missing objects due to occlusion. As the great difference of scale between occlusion objects and their integrity is affected, how to reduce the missing rate of occlusion objects has become a challenging problem in object detection algorithms. Aiming at the problem, this paper proposes an object detection algorithm based on multi-scale context information on the basis of the YOLOv4 algorithm framework. Firstly, the object features are extracted through the CSPDarkNet53 feature extraction network. Then, the proposed attention-guided multi-scale context information module (AMCM) enhances the ability to perceive object context information and endows features with different influence factors through attention weighting to improve feature discrimination. The experimental results show that the detection accuracy and recall of the proposed algorithm reach 83.2% and 96.4% respectively on PASCAL VOC dataset, and 83.2% and 91% on KITTI dataset, which improves the detection performance of YOLOv4 algorithm.
Application of Graph Neural Network in Lithology Recognition

Lithology identification is an important field of geological research and an essential part of geological investigation. In the current lithology identification work, professionals still need to conduct laboratory analysis on rock specimens obtained in the field or rock slices made from cores obtained by drilling. The entire process has a large workload, a long time cycle, and identification difficulties. In order to optimize the complicated analysis process and obtain accurate lithology characteristics, this paper applies knowledge graph and graph neural network technology to the lithology recognition model for research, and obtains a satisfactory intelligent lithology recognition model. The framework uses a graph structure to carry knowledge and verify the impact of knowledge dissemination on the enhancement of model effectiveness, where the nodes of the graph correspond to a category, and then the importance of the nodes is judged by a scoring function, which is calculated through the classification network in turn. Finally, network learning is evaluated using cross-entropy as a loss function to improve the consistency of rockiness recognition with background knowledge through a process of re-optimization. The same experimental data are used during the experiments, compared to the benchmark model with the best performance. The experimental results show that the model in this paper outperforms the standard neural network in multi-label classification, thus verifying the reasonableness and effectiveness of the model.
Recognition of Illegal Websites Based on Similarity of Sensitive Features of Mixed Elements

In order to identify illegal websites efficiently, a recognition method based on feature similarity of multiple web page mixed elements is proposed. This method constructs the website feature vector based on the web page text content, access path, and website association platform attribute. The method calculates the similarity between the website to be judged and the historical website in the case base. Then, the average similarity of top k cases is selected as the basis for judging the possibility of illegal websites. The experiment shows that the judgment based on similarity can achieve better results in the context of fewer historical cases and better case data quality. Therefore, the method is suitable for scenes where it is difficult to obtain typical illegal website cases.
Transferring Styles between Sarcastic and Unsarcastic Text using SHAP, GPT-2 and PPLM

In order to realize the text style transfer in sarcasm, improve the performance of style transfer in the text. We use News Headlines Dataset for Sarcasm Detection as the dataset and GPT-2 as the pre-training model to train a discriminator that can accurately identify sarcasm. Through the combination of shap and sarcasm detection model, the deletion of text attributes is more intuitive and precise. We fine-tuned the GPT-2 model on the sarcasm dataset, and used the trained sarcasm discriminator on the PPLM as the generation part. Compare the generation effect of fine-tuned GPT-2, PPLM, Combination of PPLM and fine-tuned GPT-2. The results show that the generation effect of using PPLM is better. The new method we propose, in the deletion and generation part of the text conversion, realizes the style transfer of the text between sarcastic and unsarcastic.
Renyi Entropy Threshold Selection on Circular Histograms Based on GWO Algorithm

The H component in the HSI color model has periodicity and the probability distribution of the H component is a circular histogram. Thresholding using circular histogram of H component is an effective way for color image segmentation. In this paper, the threshold selection criterion of Renyi entropy on circular histogram is given, and the parameters of Renyi entropy are obtained adaptively using Gray Wolf optimization algorithm. The images of Berkeley image library and remote sensing image NWPU Data Set are selected for testing, which shows that the method in this paper can effectively obtain the Renyi entropy parameters and get good segmentation results.
Research on Mini French Corpus Construction Based on Decision Tree

To meet the needs of French learners for language training, the mini-language corpus construction and its language evaluation are put forward, introducing noise reduction technology and an extraction feature module. Keywords, statistical main word frequency information, the corresponding paragraph, and the construction principle are marked to learn with the technology. The method needs to collect a representative French corpus and regularly update it. By using new software analysis data, the experiment found that the corpus through a computer program has different combinations, which meets the application needs of students. A comprehensive assessment of French vocabulary greatly improves the application efficiency of the corpus. The result confirms the design ideas proposed by the mini French corpus and effectively helps the students master and use French vocabulary.
Novel Interpretation Teaching and its Implementation Paths Under Informatization and Big Data

With the development of virtual reality, artificial intelligence, and big data, various modes of English teaching have been explored to improve teaching efficiency and efficacy. To date, the college English teaching based on such information technologies has gradually shifted to the level of a "smart classroom" with information technologies. However, the integration of the information technologies in English interpretation teaching and their organic reconciliation remain to be further discussed. In this context, we analyzed the current situation of English interpretation teaching and its demands for information technology to articulate the reconciliation paths of informatization and big data. The research results contribute to clarifying the boundary and combination framework between information technologies and English interpretation teaching.
Classification Technology of Network Multimedia Communication Based on Deep Learning Neural Network

With 5G and the Internet of things, the access of massive devices and the emergence of low-power service signals have become inevitable. In order to adapt to different business scenarios, more business signals emerge, and the identification of signal business types has become an interesting research topic. Nowadays, most of the research on signal recognition and classification focuses on the recognition of the modulation mode of wireless signals. However, this has the disadvantages of complex calculation, and cannot identify all signals uniformly. Therefore, we propose a method of automatic extraction, recognition, and classification of the power spectrum waveform of the wireless signal by constructing a convolution neural network model. After more than 500 training, the recognition accuracy of the neural network model reaches higher than 99%.
Efficient Authorized Keyword Search Supporting Boolean Formula Over Encrypted Data in Cloud Storage System

Recently, personal health record system is a promising patient-centered model for exchange and management of information. Although this cloud-based system brings in much convenience for the management and sharing, the assumption that these service providers can be fully trusted in handling patients' sensitive information seems to be undesirable. To ensure patients' fine-grained access control over privacy, data encryption (e.g., Attribute-based Encryption) is considered to be a emerging solution. However, an important functionality keyword search becomes especially challenging with records stored in encrypted form. In order to achieve access control and keyword search simultaneously, the concept of authorized keyword search was introduced to allow only authorized users can search and further access the encrypted data. However, most existing authorized keyword search schemes only support conjunctive keyword search, while other expressive keyword search schemes constructed over composite order groups are computationally inefficient. In this paper, we present the first efficient and expressive authorized keyword search scheme from bilinear pairings over the prime order groups, which enables any monotonic Boolean formulas to represent access strategies and keyword search strategies. Then, we define its formal security, and prove its selective security in the standard model. Finally, performance evaluation shows its effectiveness.
Research on Improved Algorithm of RDMA Network Congestion Control for Data Center

RDMA transmission protocol has the characteristics of high bandwidth and low latency. However, through the test and research of several mainstream RDMA network congestion control strategies in different network scenarios, it is found that these strategies have poor performance when dealing with large-scale Incast traffic. This affects the stability of the RDMA network in the data center. Thus, we propose an improved algorithm QQCN based on the congestion queue at the receiver. The QQCN algorithm maintains a congestion queue at each receiver and feedbacks the congestion status of the receiver to the sender. The sender dynamically adjusts the rate according to this feedback signal. Therefore, the QQCN algorithm improve the overall bandwidth utilization and stability of the network.
Multi-dimensional and Accurate Instructional Design of Pedagogy Major Network Courses Based on Random Semantic Understanding Technology

Arbitrary semantic understanding technology is widely researched in the application of intelligent technologies such as educational big data and cloud computing. Many researchers use data mining, machine learning, deep learning, and other technologies to predict grades. In the post-epidemic era, the ability of arbitrary semantic understanding effectively improves the existing framework of online teaching and students' search and learning effects. Thus, we tested and evaluated several major courses in education to provide the content for future online teaching and improve the overall platform level.
Deep Reinforcement Learning-Based Pricing Strategy in Double-Auction Market for Edge Computing Resource Allocation

Due to the rapid increase in Internet of Things (IoT) terminals, the continuous double auction (CDA) has been applied to achieve the reasonable allocation of edge server resources. Existing research focuses on the matching mechanism of the auction and the convergence of the game with implementation of the same strategy, while neglecting exploration of the advantageous pricing strategy for agents when multiple strategies are applied simultaneously. In this paper, we establish a CDA market where IoT terminals act as buyers, edge servers act as sellers, and cloud servers perform the market clearing work. All agents in the market pursue maximization of their own revenues by competing against others. Zero intelligence (ZI), experience-weighted attraction (EWA), deep Q-learning (DQN), and deep deterministic policy gradient (DDPG) are deployed for both buyers and sellers to enable bidding and asking. Historical trading information of the market is used as a substitute for real-time global information, which is inaccessible. The cases of unpredictable requests and fixed requests are discussed separately. Experimental results demonstrate that the game converges to equilibrium with the two environments and the deep reinforcement learning-based strategies outperform the classical algorithms. In particular, the DDPG-based strategy obtains a superior response faster than does the DQN-based strategy.
Fast Continuous Scalar Multiplication Algorithms on Twisted Edwards Elliptic Curve

In this paper, we research the operation rules and scalar multiplication of Twisted Edwards elliptic curve. Based on the properties of point addition and double point operations, we propose the fast continuous double point algorithm, continuous point addition algorithm and continuous double point-point addition algorithm on Twisted Edwards elliptic curve. These algorithms reduce the amount of operations significantly by reducing the inverse operations. For comparison purposes, the top-level operations of the new algorithm use the non-adjacent form(NAF) and double base number system(DBNS) scalar multiplication algorithms. In contrast, it effectively improves the computational efficiency of the Twisted Edwards elliptic curve scalar multiplication algorithm, in which it is more than 50% faster than the underlying operation using Horner's rule, continuous multiplier algorithm, and about 10% to 35% or more faster than the base operation using the 4-NNAF algorithm.
Real-Time Pill Detection and Recognition Framework Based on a Deep Learning Algorithm

One of the most common pharmaceutical dosage forms is oral pills, including tablets and capsules. Oral drugs are fairly stable and simple to deliver compared to other dose forms, such as syrups and injections. However, misidentifying medicines is not uncommon, whether inside healthcare institutions or after the pills have been delivered to patients. The authors propose a new framework for real-time pill detection and recognition using deep learning algorithms. The proposed framework comprises three main models: firstly, pill strip identification using the YOLOv5; secondly, the text detection model locates the text information in the pill strip image; and finally, the text recognition module recognizes the localized text. The recognition module greatly assists the proposed framework by identifying the text information such as pill name, expiring date, price, etc. This work's training and testing images are captured from various perspectives and varying illumination levels. Despite suboptimal image quality, the proposed framework obtained good recognition accuracy.
Research on Solar Term Poetry Evaluation AI System Using Improved Decision Tree Network

The decision tree algorithm is one of the commonly used data classification methods and a relatively mature information mining technology. The logic of its work process is clear, the work efficiency is high, and the data classification has high accuracy. The research data of the evaluation system obtained by the decision tree is easy to understand and implement and has the advantages of high feasibility, high precision, and strong logic. By selecting 30 solar term poems, we used 20 of them as training samples to establish a comprehensive evaluation index model for solar term poems and determine the weights. A solar term poetry evaluation system is also constructed by using an improved decision tree network. The other 10 are used as testing samples. The results show that the horizontal comparison of multiple samples has an accuracy of higher than 80%, and the vertical comparison of a single sample has an accuracy of higher than 88.89%. It is proved that the model can be used as a solar term poetry evaluation system with high accuracy.
Feature selection based on intuitionistic hesitant fuzzy regularized LASSO regression

Excellent feature selection methods can reduce the data dimensionality and improve the efficiency of machine learning tasks. Logistic regression model is one of the models that are widely used for feature selection. In this paper, we propose a regularized LASSO logistic regression model by intuitionistic hesitant fuzzy correlation coefficients, which introduces fuzzy information into the logistic regression model to further enhance its feature selection capability. We design experiments to verify the effectiveness of the feature selection method proposed in this paper. The experimental results show that the method can perform feature selection effectively, and the selected variables can complete the classification task accurately.
Tagging schemes can do more in Named Entity Recognition: Take Chinese as an example

Tagging schemes are essential for sequence labeling tasks like chunking, named entity recognition (NER), etc. However, they are often specified beforehand and receive much less attention in NER now. We conduct research on existing commonly used tagging schemes and form a unified framework to describe existing tagging schemes. Taking Chinese NER (CNER) as an example, we find that tagging schemes can do more in NER. Motivated by the observation that existing tagging schemes tag non-entity tokens with the same label ‘O’, we propose a BIO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ES tagging scheme to tag non-entity tokens surrounding entities with ‘O-’ and ‘O+’ labels, thus utilizing the boundary information of entities. We proposed a tagging scheme with stacked labels to recognize nested entities. Experimental results demonstrate that the proposed BIO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ES tagging scheme can improve the performance without modifying the model. And the stacked labels can help recognize nested entities. The results show that we can carry out more targeted research on the sequence labeling task transferred from NER by adjusting the tagging scheme reasonably.
Research on Head Object Detection Algorithm in Classroom Scene

Teaching quality has always been the focus in the field of education. Computer vision is used to analyze the students' part of the classroom video, detect the students' heads, and provide data support for classroom evaluation such as attendance and classroom attractiveness. In this paper, a one-stage head detection network is built with FPN and PAN structure. This paper designed an improved method of NMS, HIoU NMS. It is designed for classroom teaching scenes to optimize the problem of missing detection caused by occlusion in classroom scenes, and accurately detect the heads of middle school students in the classroom.
Research on Encryption Technology of CAA Software Based on RSA Algorithm and Hardware Information Extraction

CAA software is developed to extend the scope of applications of CATIA, whereas the CAA provides no API to implement the encryption of CAA software, which is to the disadvantage of protecting the security of CAA software and the intellectual property right of developers. This paper proposed an encryption technology of CAA software based on the asymmetric encryption algorithm: RSA, and hardware information extraction, to implement offline and multi-point encryption, with the advantages of rapid encoding and decoding speed, simple authorization, and high security.
DP2Net: Defense Against Adversarial Examples by Detecting Perturbed Pixels

Deep neural network technology is becoming more and more powerful, and its technical performance is obvious to all. However, the emergence of adversarial samples has raised concerns about the security of deep neural networks. In this paper, we first propose a beam search-based approach to define, search, and classify the perturbed pixels in the perturbed samples, and then propose a deep neural network model based on semantic segmentation network, which can do pixel-level detection distribution perturbation classification and classify the perturbed samples with normal samples based on the perturbation compared to previous work. The experiments are based on MNIST and CIFAR-10 datasets, and the three image classification network models are attacked by two perturbation attack algorithms to generate several perturbation samples, which are mixed with normal samples as the training and test sets of this perturbation detection network. The experimental results show that using this network as a predecessor model can effectively detect the scrambled pixels and make sample classification, thus playing a defensive role and effectively improving the model’s robustness and security.
A Low-light Image Enhancement Algorithm Based on Optimized Multi-illumination Fusion

Low-light images blur information and make it difficult to extract details. We propose a Low-light image enhancement algorithm based on optimized multi-illumination fusion. It can improve the over-enhancement of the high dynamic range of the image and the under-enhancement of some dark areas in the existing image enhancement algorithms. The initial illumination is extracted based on the Retinex algorithm, and the smooth illumination components are obtained by the optimization method. An improved CRF model is introduced to obtain the illumination component that suppresses overexposure based on the morphological closing operation and guided filtering on the initial illumination. We design a brightening function as the third illumination component to enhance the global brightness to display image details. The fused lighting components are formed through the weight matrix. The resulting image is obtained by combining the fused illumination component with the reflection component. The enhanced image restores image details to the greatest extent and suppresses overexposure. Good performance was achieved on both subjective and objective assessments.