bluuebunny/crossref_metadata_2025 huggingface dataset:
Family-School Communication: How Conflict between Family and School Expectation Influences Children’s Development
Authors: Zou Zhiwen
Published: April 2023

Effective family-school communication has a positive impact on children's education and learning, as well as the educational process in schools, according to research done in the area of family-school communication and the opinions of parents and teachers. Especially for families in kindergartens and primary schools, schools need parents to participate more in children's education and development, and family-school communication is an important factor that can promote parental involvement and help children develop in different fields. An effective home-school communication mode is conducive to the mutual expression of family and school expectations. Through communication, the expectations of both groups can modify and reach the same level, which will encourage the healthy growth of the relationship between family and school and increase cooperation between the two. This paper introduces the literature on family-school communication and makes a systematic review. At the same time, from the perspective of the school, some specific strategies for family-school communication are proposed.
A Critical Analysis of the Role of Herodotus's Histories in Representing the Conflicts between the Persian Empire and the Greek States
Authors: Wang Zirui
Published: April 2023

Herodotus' Histories is the first truly historical work in Western literature. It describes how and why wars took place from 680 BC to 479 BC. As a historical record, Herodotus used documentary techniques to record the human geography, national customs and political ideas of the society at that time. This information served as the background and causes of the Greek-Persian War so that later generations could better understand the history of that period. This paper takes the first volume as an example to interpret Herodotus' description of the Lydian Empire, the Persian Empire and the core figures of the empire as well as the theological thoughts mixed in the explanation of the war, and to enrich the reasons for the occurrence and results of the war through the description of the national political system and other background information. Therefore, Herodotus' Histories not only records the war but also shows the influence brought by the war, such as the exchange and development of culture. By recording and describing the Greek-Persian War, Herodotus revealed the history of the ancient Greek period and expressed his sense of responsibility and mission as a historian.
Research on the Teaching Concept of Undergraduate Education Based on Vocational Education
Authors: Qiu Bei
Published: April 2023

Undergraduate vocational education is an important part of China's modern vocational education system, and the implementation of undergraduate vocational education pilot is the key to deepen the reform of vocational education in China. The system problem is a fundamental and overall problem of university teaching management. Strengthening the research on undergraduate teaching management system and its reform has become the practical need of undergraduate teaching management reform in China. Teaching management plays an extremely important role in ensuring the efficiency of running a school and improving the quality of teaching. Undergraduate course syllabus should reflect the modern vocational education concept, that is, the course syllabus related to vocational qualification examination in undergraduate colleges should be based on the vocational qualification examination syllabus, and the assessment knowledge points should be integrated into several training projects to carry out project-based teaching. Project-based teaching can solve the problem of "building blocks" teaching scattered knowledge points, which is conducive to the cultivation of application-oriented talents in undergraduate colleges. Therefore, it is necessary to take positive measures in such links as transformation positioning, optimization of discipline and professional structure, innovation of talent training mode, improvement of quality monitoring and evaluation, and strengthening of the construction of application-oriented teaching staff, to implement dislocation competition and characteristic development, and to improve the quality of talent training in undergraduate vocational education and the core competitiveness of the school.
Research on the Integrated Education Mode of High-tech Integration in Automobile Inspection and Maintenance Specialty
Authors: Wang Yuting , Zhang Yingjie
Published: April 2023

With the arrival of the new century, China's automobile industry has achieved rapid growth and reached the standard of a world automobile power. Therefore, China's requirements for the quality of employees in the automobile industry are getting higher and higher. Many higher vocational colleges have set up automobile inspection and maintenance majors and trained a large number of graduates with high comprehensive ability. In order to adapt to the change of automobile maintenance industry and improve the quality of training skilled talents, the integrated education reform of automobile maintenance specialty in technical colleges has been carried out. However, there are still some shortcomings in the implementation of the integrated education reform, which fails to fully show its advantages in the training of skilled talents. This article mainly studies the application of the integrated education mode in the automobile repair major of secondary vocational and technical schools. Firstly, starting with the basic concepts of the integrated education mode, combining with the characteristics of its implementation in the education process, it focuses on the necessity of using the integrated education mode. The increasing demand for high-quality technical talents in modern enterprises has seriously exposed the drawbacks of the traditional "instillation" education method, which focuses on storytelling. This requires teachers to attach importance to innovation and reform outdated education methods. Among them, the application of integrated education mode in the education of automotive testing and maintenance majors plays an important role in improving education quality.
Prediction of Telecom Customer Churn Based on MIPCA-XGBoost Method
Authors: Zhuo Chen
Published: April 2023

In order to solve the problem that the nonlinear information of data in the field of telecom customer churn prediction is not fully used, or even ignored, which leads to inaccurate prediction, this paper introduces the mutual information feature selection method (MIPCA) to filter the features and reduce the dimensions of customer data, and proposes an XGBoost method based on the mutual information feature selection method(MIPCA-XGBoost), which improves the accuracy of the prediction results. By using the data set of telecom industry customers published on Kaggle website, compares the prediction result of this method with that of machine learning algorithms commonly used in this field, and proves the accuracy, recall and F_Score of MIPCA-XGBoost method is higher than other algorithms.
Research on Wildlife Trade Based on SARIMA and Multiple Regression
Authors: Liao Zhihong , Song Jinyuan , Yang Jingyu
Published: April 2023

At the start, this paper firstly converges the data set given in the topic to count out the data from 1990-2021. After that, uses the count function of MATLAB on the pd library to statistically analyze the family data, so as to get the greatest number of wild animal groups and species transactions, and concludes that: among the species varieties, the category of Cercopithecidae has the most number, followed by Felidae, Cebidae. among the specific genera of animals, Macaca had the highest number, followed by Papio and Potos. Next, this paper analyzes the statistical options so as to obtain the most important trade purposes, and concludes that: the analysis of the global wildlife trade records from 2003 to 2021 shows that the purposes of wildlife trade are the most used for wildlife trade, zoo use, and circus performances. After that, this paper uses the number of trade imports and exports over the years as the basic data to measure the state of the trade market, and later analyzes it by constructing the growth rate of trade transactions as an indicator to assess the state of the wildlife trade market. The concludes that: with the change of time, the economy was relatively stable in the pre-wild trade market, and there was a market turmoil in 2012, and then it stabilized. Market turbulence was observed in both 2018-2022, with a depressed status quo in the wild trade market. Then, this paper will use multiple regression analysis to analyze the number of import and export transactions of wildlife trade each year and the degree of epidemic, so that the correlation between the two can be judged by the regression coefficient, and the conclusion is: the severity of the epidemic will indeed have a certain impact on the wild trade market, and this relationship shows a typical negative correlation, that is, the higher the severity of the epidemic, the lower the import and export The lower the severity of the epidemic, the higher the import and export trade in the wild trade market. Last, the annual wildlife trade import and export quantities are used as the basic data, and the total global GDP is collected as an indicator of the global economy, so that a multiple regression model is constructed to analyze the relationship between the global wildlife trade import and export quantities and the total global GDP, and concludes that: based on this analysis, this paper concludes that the global wildlife trade ban needs to be adhered to at least for a long period of time, and that the wildlife trade does affect the global economic situation.
Unsupervise contrastive learning person re-identification Method based on Transfomer
Authors: Chen Duiyu , Chen Jing , He Chengxu
Published: April 2023

Unsupervised learning person re-identification is a meaningful and challenging person retrieving problem resulting from its application on secure surveillance and missing of label. To handle the lack of person identities, CNN-based method has been proposed to use self-contrastive learning based on a memory dictionary. However, CNN's partial bias is not well addressed in the field of ReID. To overcome these limitations, we propose a transformer-based framework called (TransCC). Specifically, we take the ViT transformer encoder trained on ImageNet as the feature extraction network, which makes up for some disadvantage of the CNN model. In addition, to overcome the issue of cluster inconsistency in instance-based memory method, unique cluster vectors are used to represent clusters in Characteristic space stored in the memory dictionary. By updating the cluster centriods and calculating contrastive loss, the model can be optimized and learn person-identifying traits. The experiment results outperform that of most methods, which demonstrate the effectiveness of our approach on USL ReID.
A Survey of Face Recognition Methods Based on Mask Region Restoration Algorithm
Authors: Wei Jingyu
Published: April 2023

The complex and diverse background conditions, the variability of human face and the artificial deliberate transformation have all become the problems that traditional face recognition technology can't solve, and they can't really reflect the imperceptible advantages of face recognition. After wearing masks, the most important mouth and nose areas of human face are covered by these masks with different shapes and colors, and some facial features are hidden, and the key feature points that can be extracted are greatly reduced. In this paper, the existing mask region repair algorithms at home and abroad are classified and summarized, mainly including mask occlusion face recognition based on robust occlusion, mask occlusion face recognition based on sparse representation classification and mask occlusion face recognition based on neural network. It is pointed out that partial occlusion is one of the main difficulties. The main methods and shortcomings of face recognition based on mask region repair algorithm are systematically analyzed and summarized, and the main problems and possible research ways in the future are analyzed.
Multimodal Emotion Recognition and Fluctuation: A Study on Sentiment Analysis of Online Public Opinion
Authors: Sun Lin
Published: April 2023

Emotion is an important way for individuals to express their views on the Internet and an important variable that shapes public opinion. Considering the multimodality of data, such as text, picture and video, and the subtlety of emotional expression, a multimodal sentiment analysis model that addresses content involving difference senses, such as sight, hearing and touch at the same time is very necessary. This study outlines the basic steps, classification strategies and research methods of sentimental analysis and acknowledges the differences between sentimental analyses on text, picture and video. As multimodal sentiment recognition is still in its initial stage, there’s still room for improvement in cross-disciplinary research on multimodal data of text, picture, audio, video in terms of weighted scoring, complex emotion and intensity recognition. It’s concluded that future studies should focus on the intensity of different emotions, multimodal data fusion and how weighted scoring influences an emotion recognition model and explore application possibilities.
Simulation design and analysis of re modulation structure based on optical network
Authors: Shang Yuyan , Zhang Chuanwu
Published: April 2023

With the development of information age, the optical fiber communication network has also been greatly developed, and a large number of optical fiber systems should be shipped. Wavelength division multiplexing technology has become the most important technology in optical communication, which has a strong practical significance. The aim of this paper is to design and analyze the digital optical communication system by using the optical system. Using the most suitable configuration, including the optical transmission system at 1310nm and 1550nm wavelength as input power, using single-mode and multi-mode fiber as transmission, using zeroing code and non-zeroing code for modulation and demodulation system to code information, so as to maximize the spectral efficiency of the whole system. There are three evaluation indexes, including signal output power, signal noise spectrum, and transmission rate of two kinds of optical fiber.
Cross-age face synthesis based on conditional adversarial autoencoder
Authors: Yan Yifan , Yu Weiwei
Published: April 2023

Face aging aims to render face images with desired age attribute. It has tremendous impact to a wide-range of applications, e.g., criminal investigation, entertainment. The rapid development of generative adversarial networks (GANs) has shown impressive results in face aging. Among them, the Conditional Adversarial Autoencoder (CAAE) proposed in 2017 has achieved good results in face aging. However, the generated faces still have the problems that the aging features are not obvious and the identity information are not well maintained. In addition, research have shown that the human aging process is affected by genes. Different races have different external characteristics of aging. However, the current research does not take the racial factor into account, ignores the racial differences in the aging process. It affects the accuracy of transformation. To solve the above problems, this paper proposes a cross-age face synthesis based on conditional adversarial autoencoder: First, a conditional adversarial autoencoder is used as the infrastructure to build a cross-age face synthesis model based on race constraints. Secondly, the discriminator is composed of a discriminant network and a classification network, and a category loss function is designed to generate a real face that matches the target age; Finally, the model uses a identity feature extractor and a discriminator of the multi-scale architecture. Through multi-level discrimination from pixel values to high-level semantic information, the loss of personal identity features is minimized. UTKFace and MegaAge-Asian datasets are used in the experiment. Three comparative experiments are designed for the above improvements. The results show that the racial constraints make the generated images effectively maintain the racial characteristics, such as skin color and texture; The classification function of the discriminator improves the aging effect; The design of the multi-scale discriminator enables the generated face to have more stable local structure and identity information. Through qualitative and quantitative analysis, it is shown that this method has higher aging accuracy and identity retention than the CAAE method.
Research on CSI 300 Stock Index Price Prediction Based On EMD-XGBoost
Authors: Wang Yu , Guo Li , Zhang Yanrui , Ma Xinyue
Published: April 2023

The combination of artificial intelligence techniques and quantitative investment has given birth to various types of price prediction models based on machine learning algorithms. In this study, we verify the applicability of machine learning fused with statistical method models through the EMD-XGBoost model for stock price prediction. In the modeling process, specific solutions are proposed for overfitting problems that arise. The stock prediction model of machine learning fused with statistical learning was constructed from an empirical perspective, and an XGBoost algorithm model based on empirical modal decomposition was proposed. The data set selected for the experiment was the closing price of the CSI 300 index, and the model was judged by four indicators:mean absolute error, mean error, and root mean square error, etc. The method used for the experiment was the EMD-XGBoost network model, which had the following advantages: first, combining the empirical modal decomposition method with the XGBoost model is conducive to mining the time series data for Second, the decomposition of the CSI 300 index data by the empirical modal decomposition method is helpful to improve the accuracy of the XGBoost model for time series data prediction. The experiments show that the EMD-XGBoost model outperforms the single ARIMA or LSTM network model as well as the EMD-LSTM network model in terms of mean absolute error, mean error, and root mean square error.
Optimizing WinAFL for Image Parsing Engine Vulnerability Discovery in PDF Readers
Authors: Zhang Yue , Du Zhibo
Published: April 2023

Fuzzing is a kind of automated vulnerability discovering technique using black-box testing ideas. The PDF file format is very complex and can be embedded in many other formats, providing opportunities for malicious code to hide. In this paper, to solve the problem of high blindness in fuzzing for PDF files by the fuzzing tool WinAFL, we propose a targeted fuzzing scheme for the image parsing engine in PDF readers, optimize WinAFL purposefully, and conduct comparison experiments with the original WinAFL. The experiments show that the optimized fuzzing tool can find an average of 69.43% more unique crashes and 43.28% more path discoveries per unit of time for commonly used PDF readers. So, the method can improve the number of path discoveries and unique crash discoveries, proving the effectiveness and practicality of the method and using this method as an inspiration to propose an improved method for other embedded formats in PDF as the next research direction.
Research on multi-Unmanned aerial vehicle joint delivery mission assignment based on multiple Alliance
Authors: Han Yueming , Han Fei , Xian Darong
Published: April 2023

In order to improve the battlefield delivery ability, for the battlefield joint delivery process task is heavy, time, traditional iron, public, water and other vulnerable to terrain, transportation conditions lead to low delivery efficiency, large-scale delivery mission capacity, the paper considers to use drones joint delivery task, according to the problem, summarize inspired rules, design fast construction model algorithm, using dynamic comprehensive sorting method, improve the task allocation and delivery efficiency, provide a reference for enriching the military diversified delivery mode, and improve the air delivery ability.
Text-to-Classic: A Diffusion Method for Classical Art Generation Based on Text
Authors: Li Yi
Published: April 2023

Text-to-Image generation has recently become a hot research topic and diffusion models have achieved remarkable performance in this task. However, most previous researches aim at real scene generation. Few researches focus on classical art paintings. Besides, diffusion models are commonly heavy-weighted with a large number of parameters, which has a high computational cost. In this paper, we aim to solve the classical art paintings synthesis subtask. We propose a lightweight diffusion model Text-to-Classic(T2C) to synthesize classical art paintings according to text descriptions. Experiment results show that our method can achieve good performance with fewer parameters.
Template attack based on uBlock cipher algorithm
Authors: Xin Jiyuan , Du Zhibo
Published: April 2023

The uBlock cipher algorithm is a family of lightweight block cipher algorithms, which was proposed by Wu Wenling, Zhang Lei and others in 2019. Its block length and key length support 128 and 256 bits. It has certain flexibility in different environments to meet the application of algorithms in different software and hardware conditions. At present, the research of side channel attack against uBlock cipher algorithm is mainly differential fault analysis. In order to explore the application of template attack against uBlock cipher algorithm analysis attack, a template attack method against uBlock cipher algorithm is proposed. When constructing the template, the S-box output of uBlock cipher algorithm is selected as the attack point of energy analysis, and the Hamming weight is used as the analysis model to build the template about S-box output.
Research on speech style transfer algorithm combined with image processing perspective
Authors: Chen Yuanqi
Published: April 2023

Speech, as the acoustic expression of language, is one of the most natural and effective means of human information communication. With the rapid development of the Internet and communication technology, the function of robot voice interaction is more and more popular among people. However, the robotic pronunciation cannot meet people's demand for personalized voice interaction. At the same time, style transfer technology, which is widely used in image and video processing, has been relatively mature. By studying the theoretical methods of generalized style transfer technology (including the style transfer of images and video signals), and comparing and analyzing various machine learning algorithms used by the current voice style transfer technology, this paper draws the following conclusions: First, various models generally have the problem of large demand for training data and difficulty in training. Second, an algorithm model shows the alienation effect in different usage scenarios. Finally, based on the above problems, suggestions for the development of voice style transfer are put forward.
Research on Divert and Attitude Control System Technology of Ballistic Missile Midcourse Maneuver Penetration Warhead
Authors: Dai Chenchao , Qiang Hongfu , Wang Xueren
Published: April 2023

With the continuous development of American midcourse interception technology, it is particularly important to promote the midcourse penetration capability construction of ballistic missiles. The use of maneuver penetration technology in the midcourse of ballistic missiles can effectively deal with the midcourse interception of foreign missiles. In order to promote the development of midcourse maneuver penetration technology, this paper takes the Divert and Attitude Control System (DACS) of maneuver penetration warhead as the starting point, refers to the development of DACS of Kinetic Kill Vehicle in the United States, and summarizes the research status of gas valve, the key structure of DACS. On this basis, combined with the structural characteristics of maneuver penetration warhead, the key technologies related to the construction of DACS of penetration warhead are summarized, which lays a foundation for the development of DACS of penetration warhead in the future.
Homomorphic Encryption and its Application to Blockchain
Authors: Hu Sen , Zhang Zhengquan , (Xiaokang) Mo Kevin
Published: April 2023

The concept, method, algorithm and application of the advanced field of cryptography, homomorphic encryption, as well as its application to the field of blockchain are discussed in this paper. There are various forms of homomorphic encryption and they can be classified into three categories: partially homomorphic encryption, hierarchical homomorphic encryption and fully homomorphic encryption. We explain the concept, method and algorithm for each category with down to earth examples. The milestone example of RSA algorithm is explained which is still widely used in the internet and in blockchain as well. Applications of homomorphic encryption depends on efficiency of implementation which has been in good progress in recent years.
Power Supply Coal Consumption Prediction Model Based on Long and Short-term Memory Neural Network
Authors: Liu Mengjie , Zhen Chenggang
Published: April 2023

As a measure of the overall economic performance of thermal power plants and an important component of the variable cost of power generation, the prediction of coal consumption of power supply is of great significance for the bidding decision of power generation companies. In this paper, we propose an improved long and short-term memory (LSTM) model for calculating the coal consumption of thermal power units, using a large amount of data stored in the plant monitoring information system (SIS). Firstly, the data are pre-processed using the threshold determination method to filter out the data of stable operating conditions. Secondly, the maximum correlation minimum redundancy (mRMR) algorithm is used to determine the optimal set of special features. The results show that the prediction effect of the improved LSTM-based power supply coal consumption calculation model proposed in this paper is better than other models, and the calculation accuracy is higher, which is suitable for power supply coal consumption calculation.
Research on Reinforcement Learning Explainable Strategies Based on Advantage Saliency
Authors: Yan Dandan
Published: April 2023

Deep reinforcement learning is increasingly being used in difficult environments with sparse rewards and high-dimensional inputs, and it performs well, but its decision-making processes are largely unclear and difficult to explain to end users. Saliency map methods explain an agent's behavior by highlighting state features relevant for the agent to take an action. In this paper, we use the perturbation-based saliency map method, propose the use of advantage function to replace the existing method of calculating state saliency, realize the combination of advantage function and perturbation-based saliency map. A saliency map is generated by noting the saliency of the dependent elements of the agent's chosen action in the Atari game environment. Experimental comparisons show that our method generates more accurate explanatory saliency maps.
Design and Realization of Surakarta Chess Game Program
Authors: Zhang Tao , Jiang Yefeng
Published: April 2023

At present, the research on Surakarta game is relatively less than other games. Aiming at this situation, a Surakarta chess game program is designed. This program implements basic algorithms such as board representation, move generation, game evaluation, and game tree search. The evaluation of the chess game takes into account the number of pieces, moving range, attacking range, attacking power of pieces, board value, arc value, etc. The game tree search algorithm uses the Alpha-Beta pruning algorithm. The whole program is fully functional and has a strong game level, which provides reference significance and reference value for related research. The program won the second prize at the national level in the computer game competition for college students in China, and the second prize at the provincial level in the computer game competition for college students in Liaoning Province, which verified the feasibility of the game program algorithm.
Road Traffic Small Target Detection Algorithm Based on Lightweight YOLO V5
Authors: Liu Chanyi , Huang Dan , Wang Tao , Zhu Tao
Published: April 2023

For the current problem of large number of parameters and large computation of the target detection network, the lightness improvement is carried out on the basis of yolov5s, and the dynamic convolution of OD is introduced to compensate for the accuracy degradation caused by the lightness, and the Map0.5 of the improved Yolov5s- EfficientNetV2-OD network reaches 82.8 through comparison experiments, and the FPS is 59 frames per second. The experiment proves that the network maintains the detection accuracy and detection speed of the network for small targets on the basis of light weighting. It is suitable for deployment on vehicle-mounted devices.
Combination Forecasting Model of R&D Intensity in Anhui Province Based on IGOWA Operator
Authors: Xu Weilun , Wang Yumei
Published: April 2023

Taking the R&D intensity data of Anhui Province from 2006 to 2020 as the sample, the grey prediction model, ARIMA model and Holt Winters non seasonal model were selected to fit and predict the R&D intensity of Anhui Province, and then the IGOWA operator variable weight coefficient combination prediction model based on the minimum criterion of the sum of squares of errors was constructed, and the combination prediction model corresponding to four special values of the operator parameters was taken, Establish an error evaluation index system to illustrate the effectiveness of the model, and analyze the sensitivity of the parameters. Through model evaluation, it can be found that the prediction effect of the combined prediction model in the sample period is significantly better than the three single prediction models, greatly improving the prediction accuracy. Finally, the combined forecasting model is used to predict the R&D intensity of Anhui Province from 2021-2025. The prediction results show that in 2021-2025, the R&D intensity of Anhui Province will continue to rise, and the growth rate is increasing year by year. The scientific research and innovation ability will continue to develop steadily.
Research on group distribution leakage analysis method for RSM
Authors: Feng Yiqin , Wang Yi
Published: April 2023

Side channel attack is a new and simple attack idea, and its proposal has created a significant threat to the security of cryptographic devices. With the continuous development of deep learning techniques, deep learning-based side channel attacks have enabled the attack techniques to reach new heights, but the attack efficiency and training time have become new problems. In order to improve the attack efficiency, researchers have proposed a large number of leakage analysis methods, which can effectively extract the intervals with significant leakage in the energy consumption curve and reduce the training parameters and data volume. In order to deal with side channel attacks, researchers have proposed various resistance strategies, among which masking protection is the simplest and widely used way. RSM rotating S-box masking scheme is a particularly important masking protection scheme, which uses a first-order masking scheme to randomize the leakage energy consumption of sensitive information using random masks, thus making the algorithm resistant to first-order side channel attacks. For this type of masking algorithm, this paper proposes a new leakage analysis algorithm, namely "Group Distribution Difference" (GDD). This algorithm is based on the distribution difference of energy consumption, and the energy consumption frequency in the group is calculated instead of the probability distribution. The KL distance is used to calculate the difference between groups and find out the leakage interval, so as to achieve an efficient attack.
Research on Beidou Navigation Interference Monitoring Technology Based on CFAR
Authors: Zhang Xiaodong , Su Benlei
Published: April 2023

Satellite monitoring stations are often affected by strong clutter and interference when monitoring targets. When ground receivers receive target signals, they also receive noise, clutter, and interference signals. These signals have randomness and signal strength changes from time to time. Therefore, constant false alarm detection technology is used to detect interference signals. CFAR detectors construct different system functions in different clutter environments and maintain a constant false alarm rate by adaptively modifying the detection threshold. A comparison of CFAR algorithms shows that CA_ The CFAR algorithm has excellent monitoring performance in uniform environments_ CFAR is suitable for multi target environments, GO_ CFAR performs well in clutter edge environments, SO_ CFAR performs well under various conditions.
Using Bidirectional Prompt Learning in NLP Few Shot Tasks
Authors: Ding Li , Ye Shiren
Published: April 2023

Few-Shot Learning, a subfield of machine learning, aims to solve the problem of learning new tasks with only a small amount of annotated data. Compared to traditional supervised learning, Few-Shot Learning is more challenging because there are very few training samples available during the training process, which means that the model must learn quickly and generalize to new samples. Prompt learning is a recently emerged training paradigm in natural language processing, which can quickly leverage the language capabilities of large pre-trained language models to achieve fast start-up. Based on the prompt learning paradigm, this paper proposes to use its conjugate tasks to further enhance the model's ability in few-shot learning. The experimental results show that the proposed method can effectively improve the performance of the model on multiple datasets and can be combined quickly with other methods for joint optimization.
Study on The Scattering Characteristics of Electromagnetic Wave with Dielectric Band
Authors: Zhou Shuai , Hu Xiaofeng , Zhang Chao , Xie Xining , Wang Lei , Zhang Yu
Published: April 2023

When electromagnetic signal penetrates medium, its signal intensity will decrease and even change the polarity of electromagnetic wave due to the scattering of electromagnetic wave. Electromagnetic wave signal propagation in rainy days, fog days, dust and another medium electromagnetic signal attenuation phenomenon. Since raindrops, haze particles, dust particles and other media have different degrees of electric quantity, in order to measure the effect of charge itself on electromagnetic wave attenuation, this paper involves the attenuation experiment of electromagnetic wave with dielectric plates. PP board, PVC board and PMMA board are selected to conduct experimental research on the influence of electromagnetic signal attenuation.
Defect detection algorithm for image reconstruction based on the self-attentive mechanism
Authors: Fu Han , Gong Jie
Published: April 2023

In this paper, we propose an image reconstruction method for defect detection, which introduces a self-attentive mechanism into the generative adversarial neural network to make it capable of extracting global features in response to the weak ability of the generative adversarial neural network to establish image remote dependencies. To address the problem that the adversarial loss function is not suitable for network training, a pixel-by-pixel loss function is introduced to constrain the image generated by the generator so that the output does not deviate too much from the true value during training. The experimental results show that the trained GAN network can effectively reconstruct the defective areas to separate the defects from the background.
Bias-based Denoising Causal Recommendation Algorithm
Authors: Yang Xu
Published: April 2023

Traditional recommendation algorithms, such as collaborative filtering, make recommendations by learning the relevant relationships between users and items. However, considering only the relationships without considering the underlying causal mechanisms would be unfair, uninterpretable, and would lead to bias. In this paper, we propose bias-based denoising causal recommendation algorithm (BDCR) . First, the method dynamically transforms the explicit user-item feedback into implicit feedback with an embedded representation. Then, a truncation function based on causal inference is constructed to remove false positive noise. In addition, traditional recommendations and denoised causal recommendations are aggregated to obtain predictive scores. Finally, experimental results on two real datasets show that the BDCR algorithm outperforms the classical algorithm in terms of recall and NDCG metrics.
Chinese Named Entity Recognition based on ERNIE
Authors: Qi Xing
Published: April 2023

The traditional named entity recognition model based on neural network uses static word vector, which can’t represent the ambiguity of the word in the context. The ERNIE-BiLSTM-CRF model is proposed. The ERNIE pre-training model can output different word vectors for different contexts by using multiple layers of Transformer, obtaining dynamic word vectors that contain overall sequence information. Secondly, the word vectors are input into the BiLSTM layer, which can obtain sentence context information through forward and backward LSTM and obtain more sentence features, thereby improving the model's effectiveness. Finally, the sequence is labeled through the CRF layer to obtain the globally optimal labeling information and complete the named entity recognition task. The experimental results show that compared with the traditional model, the F1 score of this model has significantly improved.
Intelligent Switch Based on Passenger Flow Detection of Urban Rail Transit
Authors: Teng Xinxin , Li Zhenyu , Huang Zixuan , Wu Yiheng , Chen Peike , Wang Yilei
Published: April 2023

According to the demand of intelligent station construction in urban rail transit, the intelligent control of station hall, platform and other equipment places is improved, and the electrical equipment in the station is adjusted by intelligent passenger flow detection switch, so as to achieve the power consumption principle of " needed and supplied", which can ensure the use of equipment when the passenger flow in the station increases and realize energy saving and emission reduction when the passenger flow decreases. The application of intelligent switch device based on passenger flow detection in subway stations helps urban rail to grasp the passenger flow in time, reduces energy consumption, and effectively realizes energy saving and emission reduction while improving the efficiency and safety of intelligent management of station operation.
Hierarchical federated learning with mobile edge computing in the Internet of Vehicles
Authors: Zhang Senwei , Li Fei , Zhang Yi
Published: April 2023

Federated Learning is a distributed machine learning framework, which can be used in the Internet of Vehicles to train deep learning models without directly accessing the original data of mobile edge vehicle nodes. ECS can access massive data, but it has the characteristics of high latency and high communication overhead. However, mobile edge computing (MEC) platform can directly and efficiently communicate with mobile edge vehicle nodes. Combining the advantages of the two, a three-layer federated learning system of edge car network edge server cloud server is used. This system is supported by the HierFedProx algorithm and aggregates the model output of the edge car to the edge server to improve the model learning efficiency and reduce the global communication frequency. The experimental results show that the system can reduce the training time and improve the accuracy of the model compared with the federated learning without introducing the edge server.
Analysis and Prediction of Wordle Dataset Based on ARIMA and Pearson's Correlation Coefficient
Authors: Wan Zixin
Published: April 2023

In this paper, a time series analysis was performed to ensure the parameters p, d, and q. Finally, the ARIMA (1,1,0) model was chosen to create a prediction interval for the number of outcomes to be reported in the future. The results of the precision test showed good confidence in the prediction with a goodness-of-fit of 98.2%. the prediction interval for March 1, 2023 was [10103,10523]. In addition, this paper used Pearson's correlation coefficient and chi-square test to verify the correlation between each attribute of the word and the percentage of scores in the hard model. In summary, this paper found that the attributes of words definitely affect the percentage of scores, and these scores are mainly the initials and lexicality of words.
Insulator defect detection algorithm based on improved YOLOv5
Authors: Lian Xin , Wang Dewen
Published: April 2023

Aiming at the problems such as small target scale, complex background, difficult detection, false detection and leakage detection of aerial insulators in transmission lines, this paper proposes an insulator defect detection algorithm based on improved YOLOv5. Firstly, CBAM attention module is added to the backbone network of YOLOv5 to improve the feature extraction capability of insulator pictures. Secondly, in the feature extraction part, PANet structure is replaced by BiFPN structure to make full use of the underlying feature information. Finally, the improved K-means algorithm is used to determine the prior frame and improve the defect detection accuracy of the insulator. Experimental results show that this method can improve the identification accuracy of insulator defect detection in transmission lines.
Tea Leaf Disease Classification using Domain Adaptation Method
Authors: Wu Wei
Published: April 2023

Tea trees are extremely vulnerable to diseases and insect pests in the growth process, which seriously affects the yield and quality of tea leaves. This requires the identification and treatment of infected tea leaves in time. With the rapid development of artificial intelligence and computer vision, there is a new trend to identify the images by computer. However, there often exists environmental changes such as illumination intensity, sample angles and background in the leaf images collected in natural scenes, which results in the difference of data distribution. This makes the traditional deep learning method unable to solve the problem of cross-domain classification very well, thus seriously affecting the accuracy of classification. This study mainly focuses on the cross-domain task with unaligned data distribution. A domain-adaptive based method was proposed to realize the cross-domain classification of tea leaf diseases. The experimental results verify the effectiveness of the presented method and provide new thoughts for the cross-domain classification problem in agriculture.
Image lossless compression algorithm optimization and FPGA implementation
Authors: Dang Fangbin
Published: April 2023

In this thesis, a minimum redundant prefix coding with higher compression ratio and lower time complexity is proposed for lossless compression of HD images. The compression algorithm is based on Canonical Huffman coding, preprocesses the data source to be compressed according to the image data features, and then compresses the data in batches using the locally uneven features in the data, which improves the compression ratio by 1.678 times compared with the traditional canonical Huffman coding. During the implementation of the algorithm, the counting sorting method with lower time complexity and the code-length table construction method without relying on binary trees are used to reduce the complexity of the algorithm and achieve the purpose of real-time data processing by the system. Finally, the proposed compression algorithm is deployed in FPGA to improve the encoding rate by parallel hardware circuit and pipeline design.
Intelligent robot for glass cleaning at high altitude based on rotating shaft connection and stm32 control
Authors: Cui Jiaqi
Published: April 2023

At present, there are few high-altitude glass cleaning equipment on the market that can cross obstacles, usually requiring manual transfer. Even with intelligent obstacle avoidance system, it is still difficult to climb over the floor, the frame between the glass and so on. The automation of the project is low. To solve the above problems, the glass cleaning robot designed in this paper can autonomic obstacle avoidance transfer through the stm32f407 core control board, and Raspberry PI to jointly complete the obstacle avoidance, navigation, movement and other control tasks. And the use of a new mechanical structure, through the quadcopter wing folding contraction, to complete the process of wiping glass push and pull. During the transition from flight to climbing, horizontal to vertical changes are achieved by folding the fuselage. Through many experiments, the design is reliable and practical.
Multimodal Emotion Analysis Model based on Interactive Attention Mechanism
Authors: Zhou Bainan , Li Xu
Published: April 2023

In traditional multi-modal sentiment analysis, feature fusion is usually achieved by simple splicing, and multi-modal sentiment analysis is only trained as a single task, without considering the contribution of inter-modal information interaction to sentiment analysis and the correlation and constraint relationship between multi-modal and single-modal (text, video and audio) tasks. Therefore, a multi-task model based on interactive attention mechanism is proposed in this paper, which uses inter-modal attention mechanism and single-modal self-attention mechanism to train multi-modal sentiment analysis and single-modal sentiment analysis together, so as to make full use of inter-modal and inter-task information sharing, mutual complement, and reduce noise to improve the overall recognition performance. Experiments show that the proposed model performs well on MOSI and MOSEI common data sets for multimodal sentiment analysis.
Three-dimensional path planning of UAV in complex urban environment
Authors: Chen Fankai , Liu Qinyu , Cong Xiaohan , Dong Xiuhuan , Zhang Yuanyuan
Published: April 2023

Aiming at the three-dimensional path planning problem of UAV in complex urban environment, the improved grid method is used to simulate the flight environment, and the safety of path planning is improved by building a safe flight area and introducing a navigation safety cost function. In order to solve the problems of A* (A_Star) algorithm in path planning, such as large number of nodes, large amount of computation and low planning efficiency, we can reduce the redundant checking process in the path search process by expanding the line of sight strategy, improve the algorithm search efficiency and smooth the planned lines. Analyze the characteristics of each stage of path planning, adaptively adjust the weight factor, design the flight cost function, shorten the length of path planning, and improve the speed of path planning. As a result of the experiment, the improved A* algorithm clearly decreased over the time of the path planning, the number of nodes and the total cost of the flight, and it was clarified that the planned route was smoother and more feasible than the traditional algorithm. The performance of the improved algorithm has been fully verified under the complexity of different cities, which provides a reference for the research of UAV path planning in urban environment.