bluuebunny/crossref_metadata_2025 huggingface dataset:
The winning strategy for the Nim game-Jewish chess
Authors: Hu Xinrui
Published: July 2023

Nim game is a part of the theory of game, which is important of the theoretical math. Jewish chess, a popular Nim game in China, is interesting people to discover its winning strategy. The basic rule is that each player can put 1-3 buttons each time from a single line or a single row. Whoever puts the last one loses the game. In this study, a good solution has been found, which can improve the winning percentage with random players up to about 90%. The strategy is based on the mirroring strategy, which means mirroring what the opponent does. By doing this, the goal of leaving the last button to the opponent can be achieved. However, in some cases that the basic mirroring strategy does not work, new methodologies to solve the problem have also been come up with. The study divides the cases into three sorts and give solutions to all of the three cases. These solutions are also based on the idea of mirroring the opponent's move. Nevertheless, the perfect strategy to gain 100% winning percentage has not been found yet. Also, the Jewish chess contains various types of patterns. The winning strategy of those patterns are not found, neither.
An application of deep learning on bitcoin price forecasting
Authors: Zhong Rui
Published: July 2023

The long short-term memory (LSTM) network and a cutting-edge method that combines wavelet decomposition and LSTM (W-LSTM) were applied to deep learning in this study's analysis of Bitcoin's price and movement. To be specific, it predicted next days both price and price movement (trend) with historical data. The input of the model is close price itself, basic trading information, and technical indicators calculated solely on basic trading information. Large number of numerical experiments come to the same conclusion that: for price prediction, only close price as input obtains the best performance for regression, and minor improvement achieved after 1-order wavelet decomposition; for price movement, no improvement after changing the number of input features or with the model W-LSTM has been spotted for the same network structure and hyper-parameters, and enlarging time step and batch size will improve accuracy and Matthews correlation coefficient despite of number of input and model used in this paper.
Backstepping-based control for a new semi-passive biped model
Authors: Feng Xiaoying
Published: July 2023

In this work, we propose a new Semi-Passive Biped (SPB) Model and a Center of Gravity Shift (CGS) control method of this model. The passing friction between the swing leg and the ground of passive robots locomotion consumes robots energy, resulting in the model cant walk passively. In order to analyze this problem in a more practical way, we propose a new SPB Model and analyze its energy consumption. Also, we suggest a CGS control method, which can make the proposed new SPB model walk stable. Finally, an illustrative experiment is presented to verify the SPB model and the effectiveness of the CGS control method.
Comparison of edge detection operators in digital image processing
Authors: Xiong Xinyi
Published: July 2023

The Internet has been developed so far, computer has developed rapidly, and the use of computer for image processing has been widely used in all walks of life, the use of MATLAB for image processing has become a hot topic today. Image processing can find the information it wants in many images repeatedly compressed on the network, and restore information to a certain extent. Edge processing step is included in various digital image processing models. So Edge processing is particularly important for image processing. Common edge detection algorithms include Prewitt, Sobel, Canny, and so on. Choosing a more appropriate and effective operator becomes particularly important for processing the image. The three operators are processed by grayscale, binarization, edge detection of different operators, and the final results are compared. Gaussian white noise is added to compare the white noise interference ability of the three operators. Finally, the Canny operator works better.
Reasoning: A capability of AI contributing to human-computer interaction design
Authors: Ren Yuheng
Published: July 2023

Artificial Intelligence (AI) has gone through significant development in the past decade. During this period, the ability of AI for reasoning has improved as well. At the same time, the software and the interactive digital product have made great progress. Therefore, it would be reasonable if the reasoning ability of AI could be applied to Human-Computer Interaction (HCI) design, which could provide more efficient methods for advancing user experience and help designers create products that are more satisfying to users. In this paper, we introduce the current developing status of the reasoning ability of AI. Then, we propose four characteristics of HCI and explore how these features could correspond with the application of AI reasoning. Furthermore, we present the existing applications in this field. Finally, we show our insights on the future of this combination in replacing users roles, interactive data analysis, user experience evaluation, and template production.
Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience
Authors: Jiang Weihang
Published: July 2023

High-performance machine learning algorithms have always been one of the concerns of many researchers. Since its birth, machine learning has been a product of multidisciplinary integration. Especially in the field of neuroscience, models from related fields continue to inspire the development of neural networks and deepen people's understanding of neural networks. The mathematical and quantitative modeling approach to research brought about by machine learning is also feeding into the development of neuroscience. One of the emerging products of this is computational neuroscience. Computational neuroscience has been pushing the boundaries of models of brain function in recent years, and just as early studies of visual hierarchy influenced neural networks, computational neuroscience has great potential to lead to higher performance machine learning algorithms, particularly in the development of deep learning algorithms with strong links to neuroscience. In this paper, it first reviews the help and achievements of machine learning for neuroscience in recent years specially in fMRI image recognition and look at the possibilities for the future development of neural networks due to the recent development of the computational neuroscience in psychiatry of the temporal difference model for dopamine and serotonin.
The future of video-based face detection: Conventional methods VS. deep learning
Authors: Liao Qingyao
Published: July 2023

Recently, the problem of intelligent video analysis in the field of computer vision is receiving considerable attention worldwide. Several studies have proposed some classic methods to implement video-based face detection and face recognition. However, it has confirmed that the performance of video analysis can still improve in order to make it applicable to more scenarios. This article discusses using conventional methods and deep learning to achieve face detection. Two main issues need to be solved to accurately observe the performance of face detection, method selection, and model construction. AdaBoost and multi-task convolutional neural network (MTCNN) are selected to conduct the experiment. The results indicate that it is possible to combine these two methods to realize video-based face detection with higher performance.
Obstacle avoidance control algorithm of self-driving electric vehicle based on DRL
Authors: Wu Yuhan
Published: July 2023

Facing the increasingly severe pressure of environmental pollution, road safety and traffic congestion, intelligent vehicle has become the development focus of modern automobile industry, and it is also the main direction and key competitive field of automobile technology development in the future. This trend puts forward higher requirements for vehicle control system. Under the background of the vigorous development of electric vehicle and automatic driving technology, through the overall planning of obstacle avoidance strategy of automatic driving electric vehicle, studying the correct control method and designing obstacle avoidance system under different working conditions, it has theoretical guiding significance and practical application value for the development of obstacle avoidance control system of automatic driving electric vehicle in the future. This chapter first introduces the basic theoretical knowledge of reinforcement learning, then introduces the DL theory and classical neural network, and finally introduces DQN and DDPG algorithms in DRL, so as to interact RL data. Combining DL with reinforcement learning, the feature representation of complex driving scene is established, which is used as input to train intelligent vehicle obstacle avoidance control strategies that can complete various driving tasks, and transplanted to practical applications, so that they have better performance in automatic driving obstacle avoidance control. To sum up, DRL has great research and application value for obstacle avoidance control of self-driving electric vehicles, which can greatly promote the level of intelligent transportation system.
Review on the application of deep learning algorithms in video game AI agent
Authors: Ge Muqing
Published: July 2023

AI algorithms have been applied in video games more than before. Since the early 2000s, the benchmark of the application of AI agent in video games has been put forward by researchers and engineers. This paper introduces the application of four different deep learning algorithms in video games, namely Component-based Hierarchical State Machine (CBHSM) algorithm, Monte Carlo Search Tree (MCTS) algorithm, Generative Adversarial Networks (GAN) algorithm, and the combination of A* algorithm and Q-learning algorithm. It can be summarized that both CBHSM and MCTS algorithms can modify and optimize the traditional Finite State Machine (FSM) algorithm applied in NPC (Non Player Characters) system. Additionally, A* algorithm can be combined with Q-learning algorithm to solve the problem of the difficulty on huge state space and limited time, and GAN algorithm, a model of unsupervised learning, can generate results by fewer training sets.
Difference in the lifeexpectancy between countries: Mortality, health, lifestyle habits, national conditions, and related socioeconomic status (SES)
Authors: Liu Qianyu
Published: July 2023

Our world has made remarkable progress since the year 2000 in increasing life expectancy. But this progress has been fragile and uneven across countries. The objective of the study was to estimate the factors that affect inequality in life expectancy, particularly between developed and developing countries. There is some evidence that the inequality gap has increased in different countries. It was observed that some variables have a significant correlation with life expectancy, while some of them take on obvious region characteristic. Proposed explanations for correlated differentials in life expectancy will be explored. The paper discusses logistic regression (LR) and the t-test as methods that can be used to determine what the independent variable would be, obtain the significant factors related to life expectancy and investigate the coefficient for all countries in the data set. The scatter diagrams and tables were constructed in the period 2000-2015 by linking life expectancy on some variables that cross 133 countries. Mortality, health, lifestyle habits, national conditions, and related socioeconomic status (SES) can all influence the distribution of life expectancy around the world. The result shows that health outcomes could benefit from a number of government measures, such as reducing adult mortality, productive use of resources, or promoting education. While Percentage spending, Infant deaths, and Alcohol are associated with life expectancy in developing countries more than in developed countries. These findings are intended to contribute to the growing body of literature that is needed to inform policies or strategies for a fairer and healthier world for all.
Identifying and updating local optimization methods in extended Kalman filter SLAM
Authors: Liu Hanmo
Published: July 2023

he essence of simultaneous location and mapping (SLAM) is an estimation problem. It requires mobile robots to use the sensor information to estimate the structure of the external environment in real-time and their position in the background. Smith and Cheeseman first applied the EKF estimation method to slam in the last century. The SLAM algorithm based on an extended Kalman filter (EKF-SLAM for short) is one of the research hotspots of slam technology. The EKF is improved based on the Kalman filter to make the Kalman filter suitable for nonlinear dynamic systems to solve the estimation problem of the nonlinear system model. Therefore, the Kalman filter can be applied to slam technology. The slam model based on filtering mainly includes three equations: Firstly, is to use the equation of motion, which will increase the uncertainty of robot positioning. Then the equation for initializing the guidepost according to the observation initializes the new state quantity according to the observation information. Lastly is the projection equation of the landmark state to comment, update and correct the condition according to the observation information to reduce the uncertainty. In the implementation, there exists a problem regarding storing the obstacle location. The current standard practice is to abandon all the existing data when the location number reaches the threshold. This will cause calculation time waste and instant high error [1]. In this paper, several improvement methods try to fully utilize the received message by different algorithms built on the ROS system. These include abandoning local data logic, updating data instead of deleting data, and retaining user data when updating new data. These advanced methods designed for EKF-SLAM will provide a more precise outcome with a low error range.
A study on the classification of Chinese folk instruments based on deep learning
Authors: Zhou Chuhan
Published: July 2023

Due to the Internet's tremendous expansion in recent years, digital music has seen explosive growth. It is becoming increasingly important to provide fast and accurate music retrieval for users, and music information retrieval has gradually become a research hotspot. Among them, the classification of musical instruments is one of the hot research directions. As an important part of the world's musical instruments, the Chinese folk musical instrument tradition has great research value, but there is less research in this direction. Traditional music classification methods can be roughly divided into two steps: manual feature extraction and traditional machine learning. But traditional music classification has several shortcomings, as follows. Firstly, manual extraction of music features has a high error rate and it is very difficult to ensure the accuracy of the features, and the labor cost is high; secondly, traditional machine learning still has problems with multiple classifications and training on large scale data. Therefore, in this paper, based on previous research, the original data features are extracted from the Mel Frequency Cepstrum Coefficient (MFCC), which is a feature that can represent musical timbre, and deep learning is proposed to be used to classify traditional Chinese folk instruments. This paper finds that the results of identifying and classifying Chinese folk musical instruments using different classical classification methods are compared when the training set and test set data are the same. According to the experimental results, the use of KNN has the best performance among these classification methods.
Impact of different transaction features on credit card fraud detection by neural networks
Authors: Wei Ertong
Published: July 2023

A perfect credit card fraud detection model is necessary because of the economic loss caused by credit card fraud. Since many models have already gained great performance, this research focuses on the influences of different credit card transaction features on the accuracy of the fraud detection model constructed by a neural network, which is favorable for economists to study credit card fraud better. The data used in this research from Kaggle contains a million credit card transaction records with seven features for each. To analyze the importance of different details, different parameters are used in the input layer of the neural network model to compare the performance. Furthermore, the result is that the feature ratio to the median purchase price is the most significant one. The second important feature is the distance from home, and then online order is followed. Compared with the accuracy when inputting all the seven features(99.81%), the model performs well with only the above three features in the input layer(95.43%).
Discussion on image recognition under different conditions of Resnet and Densenet
Authors: Li Guangrui
Published: July 2023

As two mainstream and excellent neural networks, ResNet and DenseNet have been the main research directions of many scholars and papers. This paper mainly focuses on these two neural networks through a theoretical analysis, summary discussions, and practical experiments. The author explains their previous advantages and disadvantages, and, as a potentially obsolete technology, whether ResNet will be better or more efficient than DenseNet in some cases. The final experimental results show that although ReseNet is a somewhat outdated neural network model, it still outperforms DenseNet in some aspects. In addition, this paper provides some further optimization schemes for reference through the analysis of the two neural network models. At the same time, the basic structure and algorithm principle of the two neural networks are further explained in the analysis to make it clearer for readers.
Research on the reliability of bloom filters to defend a distributed denial-of-service
Authors: Liu Jiaran
Published: July 2023

Distributed denial of service (DDoS) attacks are one of the threats to network security, and reasonable means of defense are sought. Bloom filter is a commonly used detection method, the research have listed different attacks in the paper and made it possible to compare it with other detection methods by analyzing how Bloom filter works. The research shows that the Bloom filter works by comparing the element to be queried with an already stored vector. If the query result is zero then the element does not exist, if it is one then it means that in most cases the element exists, but false positives are not excluded causing the element to be judged as existing. In this paper, the comparison with other methods also needs focus, the time complexity of inserting, searching, and deleting data is an important feature to consider when Bloom filters outperform other methods. The research also shows that the Bloom filter is to some extent better than other detection methods in terms of insertion, search, and deletion time. At the same time, the research showed that the use of Bloom filters in response to DDoS attacks is also very impressive, for the detection of link flooding attacks and the detection and processing speed of packets is commendable, can take up less memory to achieve better results.
Accuracy improvement of edge image recognition AI in dark light environment by image enhancement
Authors: Wu Jiyue
Published: July 2023

In order to solve the problem of poor image recognition accuracy of edge devices in the dark environment, the author simulate the change of brightness and contrast in the dark environment by using matlab's internal function on the image through the point operation method in matlab, and add Salt & pepper and Gaussian noise to simulate the noise generated by the image in the dark environment. The modified images are imported into the image recognition system trained by migration learning to compare the changes in recognition accuracy and to investigate the main factors affecting the image recognition accuracy in low light conditions. Meanwhile, the main factors affecting the image recognition accuracy are improved by median filtering and Wiener filtering to find the image enhancement method that is most beneficial to improve the image recognition accuracy of edge devices in the dark light environment. The experimental results show that the main factor affecting the image recognition accuracy in the dark environment is the Salt & pepper noise, and the median filtering can remove the Salt & pepper noise well and improve the recognition accuracy of a single image up to 70%.
Speech recognition for companion robots
Authors: Zheng Zhuoyan
Published: July 2023

This paper is based on the goal of solving a global problem the aging problem. Part of the recognition process is completed by using the Hidden Markov Model and the N-gram model as two main models. This paper clarifies the mechanism of speech recognition and develops a project to achieve simple speech recognition using an interdisciplinary research approach. The results show that the basic recognition of speech content can be achieved, especially when it comes to recognizing male voices. The results of this study suggest that further research and development is needed to identify female voices or higher-pitched voices.
Exploration of continual learning based on the comparison and analysis of LWF and EWC
Authors: Wang Zihang
Published: July 2023

Nowadays, continual learning is seen as one of the important fields of strong artificial intelligence. People focus on it and propose a lot of methods in order to alleviate catastrophic forgetting problem which is serious and essential and cannot be avoided. Among all the methods, EWC and LWF are two typical methods and they have some similarities and differences. This paper adopts comparison method to research the effect of LWF and EWC applied on different datasets and also compares some similar methods like R-EWC and LFL. Meanwhile, some analysis and suggestions about these methods are also given. From the results, both methods improve memory ability. But LWF performs better in testing old knowledge. In contrast, EWC has a stronger ability to learn new knowledge. Both EWC and LWF have some limitations. There are some problems which cause errors for the principle of EWC while for LWF, the confirmation of hyper-parameters has great influence on the experimental results and the method itself has some errors.
Comparison of deep learning algorithms using non-local methods for lung nodule classification
Authors: Wang Peiyu
Published: July 2023

Lung cancer has been identified as a serious and fatal disease due to its high morbidity and mortality. It is of vital importance for lung cancer patients to obtain early detection of the disease so that the later treatments may bring good effects. Lung computed tomography (CT), as a normal method to diagnose the disease, can be used to recognize typical lung cancer, but it is possible to confuse cancer with some other diseases, such as innocent tumour and phthisis. Therefore, an accurate diagnostic tool is required to help clinical disease recognition. Machine learning (ML), especially deep learning (DL) is an ideal technique to classify CT images thanks to the great capability of image processing and feature recognition. However, the task for ML faces several challenges that have made a negative difference in the accuracy of algorithms. The main problem is that lung nodules can have heterogeneous sizes and shapes varying in a wide range, thus both local and global features of data should be considered to enhance the classification results. In this work, the author investigated advanced works in the territory of lung cancer identification using ML and the comparison results of newly proposed models and proper analysis are provided. In addition, possible future improvements are discussed.
A survey of algorithm and hardware architecture for bilateral filter and application
Authors: Lin WeiQian
Published: July 2023

In original images/videos, disturbance and noise are inevitable. Therefore, image and video filtering technology has been proposed and developed rapidly. In recent years, bilateral filtering has been widely utilized as an efficient filtering method that can effectively preserve edge information while reducing noise. Researchers have extensively studied the bilateral filtering algorithm and its hardware structures, generating several excellent optimization methods. This paper will comprehensively analyze typical bilateral filtering algorithms and hardware structure optimization design. Some directions for future bilateral filtering optimization will be provided, including algorithms and hardware structures, with analysis and discussion.
A comparison of canny edge detection algorithm and edge detection algorithm based on fuzzy logic
Authors: Wang Yujie
Published: July 2023

Edge detection techniques in digital image processing are a very valuable area of research. In recent years, there are two more popular edge detection algorithms, one is Canny edge detection algorithm,and the other is edge detection algorithm based on Fuzzy Logic. Both algorithms have been used in various fields. The main work of this paper is to do a brief introduction of two algorithms and compare the results from them.
An improved hibbard interpolation algorithm based on edge gradient feature detection using Sobel operator
Authors: Chen Shuaiming , Yin Shuhui , Zhao Yizhan
Published: July 2023

Color interpolation algorithms have important applications in color image processing, and many interpolation algorithms have been proposed one after another. The bilinear interpolation algorithm, Cok interpolation algorithm and gradient edge interpolation are three classical interpolation algorithms, however, they may cause problems such as zipper effect and Mohr's stripe effect. In this paper, this paper optimize the gradient edge algorithm, calculate the gradient by using Sobel operator and fit the direction of the edge in a weighted way, and propose a gradient edge weighted interpolation algorithm based on Sobel operator to improve the performance of the interpolation algorithm. Through the validation of color image recovery, the results show that the subjective visual effect and objective evaluation indexes such as structural similarity index, peak signal-to-noise ratio and minimum mean square error of the improved algorithm proposed in this paper are better than the traditional algorithm, which is of great significance to the improvement of the interpolation algorithm.
To describe the content of image: The view from image captioning
Authors: Hou Xiaohan
Published: July 2023

The aim of developing the technology of "image captioning," which integrates natural language and computer processing, is to automatically give descriptions for photographs by the machine itself. The work can be separated into two parts, which depends on correctly comprehending both language and images from a semantic and syntactic perspective. In light of the growing body of information on the subject, it is getting harder to stay abreast of the most recent advancements in the area of image captioning. Nevertheless, the review papers that are now available don't go into enough detail about those findings. The approaches, benchmarks, datasets, and assessment metrics currently in use for picture captioning are reviewed in this work. The majority of the field's ongoing study is concentrated on robust learning-based techniques, where deep reinforcement, adversarial learning, and attention processes all seem to be at the heart of this research area. Image captioning entails a brand-new field in research on computer vision. Generating a comprehensive natural language description for the source images is the fundamental issue of image captioning. This essay explores and evaluates earlier work on image captioning. Image captioning's application and task situations are introduced. The merits and disadvantages of each approach are explored after the analysis of the image captioning algorithms based on encoder-decoder and template structure. The assessment and baseline dataset for picture captioning are therefore shown. Ultimately, prospects for image captioning's progress are presented.
Review of computer vision in sports
Authors: Shao Xinyu
Published: July 2023

All industries employ machine learning extensively, and one of the most promising fields is computer vision. Computer vision is a simulation of the human visual system that uses cameras and computers to take the role of the human eye to find the target, follow it, and gather data from it so that a decision may be made on whether to take further action or provide recommendations. The various uses of computer vision in sports are covered in this paper. Currently, computer vision is mostly utilized for broadcast enhancement, tracking and detection of players and balls. Although the games graphics has been substantially improved by this technology, there are still several flaws. For instance, some areas are not suited to employ this technology. Another is the issue of players being blocked in multiplayer sports. For broadcasters, computer vision has significant commercial value. For athletes, this technique can improve their performance.
A never ending warfare: Fighting cheaters in online video game
Authors: Zhou Alexander
Published: July 2023

As the gaming industry gradually expands, more and more people begin taking notice of the industry. Though with blossom, problems emerge. Many people dont want to take the time to master a video game, so some will then resort to cheating. This is terrible news for developers as it ruins their reputation and player base. This paper will lay the groundwork for anyone interested in the industry on what has already been done to fight cheaters, as little research was conducted. This paper, will introduce the modern online connection architecture, categorize the most common cheats, and most importantly, introduce modern-day anti-cheat methods. The anti-cheat methods will be analyzed on their effectiveness on different online connection architecture and the type of cheat it works against. Lastly, the paper will also introduce the idea of kernel-level and its impact on anti-cheat.
Overview of capability requirement analysis methods for operational concept development
Authors: An Jing , Zhang Xue chao , You Chun lan
Published: July 2023

Scientific and reasonable analysis and determination of operational capability requirements can not only optimize and improve the operational concept, but also ensure the transformation and application of the operational concept. The development and construction of the traction force and the improvement of operational capability play a key role in the transformation of operational theory to actual combat capability. It is urgent to study scientific and applicable operational capability requirements analysis methods to support the development process of the operational concept. On the basis of defining the components of operational capability requirements, this paper combs, summarizes, analyzes and compares the main operational capability requirements analysis methods, points out the problems of existing analysis methods in combination with current research, and summarizes and prospects the next research direction.
Detecting sarcastic expressions with deep neural networks
Authors: Huang Zihang
Published: July 2023

Following the ever increasing trend in social media such as Twitter, Facebook, and Instagram, automatic analysis of peoples conversations and languages have become a problem of great significance for businesses and governments in attempt to understand and analyze peoples habits, thoughts, and patterns towards different subjects of interests. Within the field of natural language processing, sarcasm detection has always been a difficult challenge for sentiment analysis. Recent years, there has been great interests shown by researchers towards sarcasm detection. Neural networks achieve huge success and advancements surrounding this topic, but reviews for this task are very limited and theres a lack of comprehensive review of the development of sarcasm detection so far. Thus, this paper aims to summarize and present the various methods directed towards sarcasm detection, the progress it has made, and examination of potential problems and availability for further improvements.
Human emotion recognition with convolutional neural network
Authors: Zhang Yu
Published: July 2023

The interaction between intelligent robots and humans has always been a hot issue, and researchers hope to make human-robot interaction as harmonious as human-human interaction. To achieve this, it is particularly important to enable robots to recognize human facial emotions automatically. However, many intelligent robots can already understand people's emotions through vocal communication. However, some people do not like to express their feelings through words, so it would be more convenient to let machines can automatically analyze people's facial emotions. This paper aims to make the machine recognize people's facial expressions and automatically analyze their emotions to make human-computer interaction more harmonious. The convolutional neural network has shown great influence on image feature extraction in the development of the machine learning field today. Therefore, this paper will adopt the advanced method of CNN to train the model on the FER2013 dataset. The abundant experiments demonstrate that the final trained model has good accuracy in recognizing three emotions: happy, surprise, and neutral.
Convolutional neural network and vision transformer for image classification
Authors: Lu Jiaqi
Published: July 2023

Visual Transformer (ViT) has been a hot topic for research for the past few years after it first emerged in the field. On image recognitions, due to the amount of information ViT could retrieve from the source image, in cases it can rival the traditionally prevailing Convolutional Neural Network (CNN). Then there emerged different models based on ViT, all being built having a specific field or a flaw not addressed by original ViT in mind. In this paper these models are being tested on the same dataset along with a standard CNN to see how they perform compare to each other, and the best performing ViT model was then changed to see how there would be some possible improvements.
English handwriting recognition based on the convolutional neural network
Authors: Le Shengpei
Published: July 2023

Handwriting recognition is widely used in the retrieval, recognition, and management of various information to improve efficiency in various industries. Convolutional neural networks help people get rid of the feature of extracting information feature sets manually and significantly improve recognition efficiency. In this paper, a generalization-enhanced network recognition model is proposed using an improved lightweight convolutional neural network model. An improved method is used to adapt word recognition by the idea of pre-recognition segmentation. In addition, the generalization is enhanced by diversifying and pre-processing the dataset so that the algorithm can obtain noise-resistant performance and detail retention and allow the recognition system to recognize various types of scenes. The results show that the model achieves an accuracy of 93% on the test set. Compared with other classical network models, the model has higher recognition accuracy, faster convergence, and better generalization ability. The system elaborated in this paper can be used for devices with weak computer processing power.
Sentiment analysis based offensive language identification system for code-mixed data
Authors: Shanmugavadivel Kogilavani , Subramanian Malliga , E. Sathishkumar V
Published: July 2023

Hate speech identification is the process of categorising textual information into hate and non- hate speech with the goal of identifying hate speech's targeted features. The objective of this research work is to take the Dataset from FIRE 2021 shared task code mixed data that includes YouTube comments and Twitter conversations and to detect whether the messages represents the offensive or non-offensive category. To detect the offensive language sentences, various deep learning models like Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network, Gated Recurrent Unit, and hybrid model like Convolutional Neural Network with Bidirectional Long Short-Term Memory methods were utilised in this research work. The performance of all the mentioned models is evaluated using precision, recall, F1-score, and accuracy. Out of all the models, both LSTM and GRU models perform better with the accuracy of 0.81, precision of 0.85 and recall of 0.95.
The comparison of algorithms of audio fingerprinting
Authors: Xia Linjun
Published: July 2023

With the increasing demand for song recognition on music platforms, the Audio fingerprint system is being used more and more frequently. How to improve the speed and accuracy of the Audio fingerprint system is a problem that is worth studying. Finding a better algorithm is crucial if people who use this system want to improve their efficiency of it. This paper, it is compared the accuracy and speed of extracting music information by the Multiple Hashing Algorithm and the Philips Robust Hashing Algorithm. The Multiple Hashing Algorithm can improve the accuracy of information extraction by increasing the sub-fingerprint bands, but with the increase of sub-fingerprint bands, the algorithm run time is much longer. Therefore, the Philips Robust Hashing Algorithm should be used when processing simple audio information to ensure the speed of the system. The Multiple Hashing Algorithm can improve the accuracy of information extraction by increasing the number of sub-fingerprint bands.
Clinical reasoning auxiliary model based on deep learning
Authors: Yang Yixin
Published: July 2023

Medical data is increasing rapidly. The rapid fusion of medical record text data, medical laboratory data and image data in many regional hospitals have brought light to the screening, diagnosis and treatment of diseases. Medical text data records the patient's detailed condition and treatment process, with abundant information. As a branch of artificial intelligence, deep learning is gradually penetrating the medical field. The application of deep learning in the medical field shows great application prospects. Therefore, mining the knowledge contained in medical texts can provide better medical services for the majority of patients, which has good theoretical and practical significance. In this paper, the content of medical texts was analyzed, and the test questions and standard answers of the USMLE Step 2 Clinical Skills Exam were used as data sets to explore the core content of the medical chief texts. After comparing and analyzing different methods, such as BERT and DeBERTa, an intelligent follow-up system based on DeBERTa was finally proposed, hoping to help clinicians read medical data more effectively and weaken the problem of poor diagnosis results caused by personal academic ability differences.
Achieving a high efficiency on stationary gas turbine
Authors: Wu Yufeng
Published: July 2023

Energy is an essential resource every population relies on. In recent years, the importance of thermal energy has stood out due to the performance decrement of both hydraulic and wind power systems, resulting from severe climate changes. Considering thermal power plants like gas turbine systems, thermal efficiency is always the element that engineers aim to improve on. The efficiency of a stationary gas turbine determines how much useful energy can be produced in a given amount of resources, and that is vital to any power plant economically and environmentally. In this paper, through research from various dissertations, journals, and websites, technologies developed for efficiency improvement for gas turbines are summarized and discussed with specific examples and comparisons, focusing on complex cycles, combined cycles, and maintenance procedures. In the end, the paper concluded that there are many factors affecting the thermal efficiency of a stationary gas turbine. Every gas turbine station varies due to its specific applications, locations, the material available, and project budget, and far more new topics are waiting to be explored.
Sentiment analysis of LDA text surrounding public opinion on China's three-child policy
Authors: Guo Yuyong
Published: July 2023

In a bid to understand Chinese social media tendency on the new Three-Child Policy, this paper analyzes netizens sentiment on Chinese microblogging platform Weibo based on the Latent Dirichlet Allocation (LDA) topic modeling. Specifically, comments from netizens crawling on Weibo were pre-processed so that 2624 valid comments from May 30, 2022 to June 15, 2022, were hereby obtained, sentiment analysis was thus completed by using the LDA. Further analysis showed a mostly negative and neutral sentiment towards the policy among netizens, largely because of the rising cost of child rearing and poor employment security for women.
Sentiment prediction based on neural network
Authors: Kuangyu Chen
Published: July 2023

Recent years have seen a surge in interest in deep learning, and numerous new neural network models have appeared. In this study, we use CNN and LSTM, two popular deep learning models, to predict sentiment. We use the bidirectional model for the LSTM model. We make predictions by integrating the feature vectors of both sides after receiving sentences inputted in the reverse order from both ends. The recurrent neural network layer is replaced with the CNN model, which defines numerous one-dimensional convolution kernels to conduct convolution operations on the input. The two models are assessed and compared based on how well they anticipate and analyze sentence emotion tones. By comparison, we discover that the BiLSTM model's prediction accuracy on the test set is 10% greater than CNN's, despite requiring more training time.
The development of secure multi-party computation
Authors: Hao Wanqing
Published: July 2023

In an increasingly technological world, some significant issues that have developed in the past few years are privacy and data protection. This is generalized in the broad field of cryptography. People often use concepts from cryptography to find ways to access a distributed set of data without exposing or altering it, protecting them from outside parties obtaining access to their data for malicious purposes. This is often achieved through a specific method called Secure Multi-Party Computation. Throughout the past few decades, improvements in Secure MPC have skyrocketed, becoming an essential part of society. Whether in finance, statistics, or just typical data collection and protection, Secure MPC plays an important role. This paper will discuss the history behind Secure MPC as well as some of its modern-day applications. Furthermore, this paper will analyze the advantages and disadvantages of Secure MPC, evaluating its potential for growth in the coming century.
A study of the lightweight algorithm for recognizing masks incorporating attention mechanism and G-GhostNet
Authors: Deng Cong , Li Wenkai , Wang Xirui , Xin Bingkun
Published: July 2023

As COVID-19 and humanity's protracted battle makes wearing masks a norm, intelligent recognition of mask-wearing and wearing criteria is investigated to lessen the labor of epidemic control and detection personnel. To achieve the purpose of not having to manually identify mask use in a busy, open setting, to be able to provide a real-time warning of the whole mask non-wearing phenomenon in mobile devices, and to address the issue of sluggish efficiency and poor precision of traditional target detection and tracking means in complex scenes, this work presents a lightweight mask identification method that combines G-GhostNet with an attention mechanism. Firstly, YoLov5's BackBone network is replaced with a more lightweight G-GhostNet with fewer convolution kernels to reduce the parameters and computation more significantly; secondly, an attention mechanism is introduced to G-GhostNets feature mixing process to increase discovered features weight values.Then, the dichotomous unmasked and masked datasets are prepared to enhance the model accuracy and speed by the loss function. The experiments compare the effects of Squeeze-and-Excitation Networks (SE), Convolutional Block Attention Module(CBAM), Efficient Channel Attention (ECA), and Coordinate Attention (CA), the four attention processes on the enhanced model with the model without the added attention mechanisms. The results show that adding the attention mechanisms all make the model recognize masks better, among which adding CA has the best recognition effect with a 2% improvement in accuracy but 1.3 times increase in Latency time; in contrast, adding SE, ECA balanced speed and accuracy.
Spam mail classification using back propagation neural networks
Authors: Chen Jingfeng
Published: July 2023

Mail classification methods based on machine learning have been introduced to combat spams. However, few researches focus on the most powerful machine learning model that is neural networks. In this paper, the author trains BP neural networks to detect spams. The inputs of the neural networks are only information about words, punctures, signs, numbers and illegal words. Five neural networks which are different in number of neurons and number of layers are experimented on. All networks apply Rectified Linear Unit (ReLU) functions and Momentum learning technology. The results show that the network with four hidden layers enjoys the best classifying accuracy of 97.0%. In networks with two hidden layers, when the number of neurons in each layer is above 300, the accuracy is between 95.5% and 96.0%; and 100 neurons in each layer result in an accuracy of 93.8%. Although the training only captures information of words, punctures and signs, the networks have achieved high accuracy, and the author suggests that making the computer understand sentences as well as other kinds of improvements can lead to even higher performance.
Detecting fake news with deep neural networks
Authors: Huang Qiaoyi
Published: July 2023

The raise of social media and the easy access to obtain and spread news has increased the probability of various news dissemination. However, an undetermined factor in usage scenario is the authenticity of spreading news, which has posed detrimental effects on individuals and society in current scenario. It is imperative to develop proper mechanism for fake news detection. These years, with the sharp development of deep learning, referring to the method of training multi-layer artificial neural networks, some researchers have utilized deep learning in fake news detection and classification. Although many studies have achieved a preeminent level and high accuracy, scarcity of review work regarding fake news detection with deep learning is still an important problem. This work urges to derive a comprehensive review of fake news detection and classification techniques for further study in the aspect, and mechanism in various studies for sake of raising accuracy. This work also figure out some possible challenges with existing limitation based on previous research.