Reviewerly/exHarmony huggingface dataset:
Review on the application of deep learning algorithms in video game AI agent

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.
Motion estimation algorithm and architecture survey

Motion estimation is a key part of video temporal characteristics analysis including video filter and compression. Increasing requirements for high-quality video image processing have confirmed the necessity of researching motion estimation. Current motion estimation methods, including high-efficient fast motion estimation algorithms and hardware architecture designs, achieved different performances from different perspectives. In this paper, we focus on analyzing typical fast algorithms and hardware architecture designs for motion estimation, comparing the used methods, and pointing out the basic ideas of motion estimation research schemes. In terms of theoretical implications, the research contributed to a more comprehensive reference for fast motion estimation algorithms and their hardware architecture designs.
Based on natural language processing, human-computer dialogue, image recognition, and machine learning analysis whether artificial intelligence will surpass the human brain

With the popularization and development of the concept of artificial intelligence, the application of artificial intelligence has also begun to deepen into people's lives. While bringing convenience to people, it has also made some people worry about whether artificial intelligence will replace humans. Therefore, In order to make people understand the current development status and bottlenecks of artificial intelligence more intuitively, as well as the difference between artificial intelligence and human brain, this article will turn from speech recognition and natural language processing, human-computer dialogue, image recognition, and machine learning ability, that is, machine listening, reading, and thinking four aspects of research and discussion, and finally summarize why artificial intelligence cannot completely surpass humans.
Vehicle fault diagnosis using FTA and FMEA method

In recent years, users requirements for the richness of car functions have gradually increased. The application of a large number of electronic components in the automobile makes the degree of complexity of the automobile higher, so the user has a higher demand for the stability of the automobile system. Especially once a key functional component fails, driving safety will be seriously affected. Because in the design process, we find and analyze the possible causes of failure, so as to avoid the impact of failure in the design as much as possible. We use FMEA (Failure Mode and Effect Analysis) Analysis method and FTA (Fault Tree Analysis) analysis method to analyze the car headlight fault. These two analysis methods can efficiently and accurately analyze faults and determine the most basic events leading to failure. Based on the above analysis results, the proposed solution to improve the system can be given, so that the system can meet the functional safety requirements.
Generate handwritten images based on DCGAN

Nowadays, more and more machines are applied to artificial intelligence, and generate something itself has become more and more available. To augment databases to get a more accurate result from training a model, generating data is necessary. This paper introduces how to generate new handwritten images by training the existing database. The Generative Adversarial Networks model is used as the basic model. Deep Convolutional Generative Adversarial Networks and the MINIST database are applied as the method and training dataset respectively in this research. After 50 epochs by training 256 images each epoch, the new handwritten-number images are generated.
The advance of neural networks generalization performance

A big mystery in deep learning is the promising generalization performance generated by massive neural networks. While over-parameterization increases the tendency of overfitting in other machine learning models, neural networks seem to magically overcome this hurdle and achieve minor test errors in various tasks. Researchers are motivated to resolve this enigma through a variety of aspects and methods, both theoretically and empirically. This paper aims to comprehensively review the explanations for the generalization power of deep networks. Firstly, the review compares various types of generalization bounds under PAC-Bayes analysis and non-PAC-Bayesian settings. Then, works of regularizers, both explicit (e.g., dropout) and implicit (e.g., batch normalization), and algorithms-caused regularizations are reviewed in this work. Some researchers also explore networks generalization ability from other perspectives, and this review talks about works that investigate the relationship between images and generalization performance. Additionally, works of adversarial examples are included in this review, since adversarial attacks have challenged networks power to generalize well and have become an important field in understanding deep learning. By collecting works from different viewpoints, this paper finally discusses some possible directions in the future.
Face recognition with masks in the lasting era of COVID-19

At the end of 2019, the pandemic began to have a huge impact on people's lives around the world. In order to control and prevent the virus from spreading, world-wide governments have encouraged all residents to wear masks in their daily lives. Since the 1960s, face recognition technology has gradually emerged and developed. In the 21st century, it has been widely used in security, finance, transportation, and even retail, advertising, smart devices, education, health care, entertainment and other fields, playing a pivotal role in everyone's life. In the era of COVID-19, the public wearing face masks has undoubtedly hindered the smooth technology application of face recognition. The technology of recognizing human face in the case of wearing masks should be developed, followed by the adjustment of the original technology and data. The development of technology also brings certain social impact. Our research reviews the adjustment and progress of face recognition technology in the era of COVID-19, summarizes the impact of technological progress on society and people's lives, and provides help for the government to better prevent and control the epidemic.
Survey on abstractive text summarization using pretraining models and their developments

In these years, pre-training models gain a lot of attention in the summary generation area and demonstrate new possibilities for improving the sequence-to-sequence attention framework. This survey conducts a comprehensive overview of BERT-based pre-training models that can be used in abstractive summaries. Firstly, the BERT model is introduced as a typical pre-training model, followed by baseline models inspired by it. Then problems and developments of previous models are discussed including some recent SOTA approaches. Apart from that, some datasets used for models are demonstrated with main features. Besides, the commonly used evaluation methods are introduced. Last but not least, several potential research directions are suggested.
Exploration of continual learning based on the comparison and analysis of LWF and EWC

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.
Application and development prospect of AI speech recognition technology

In our daily life, artificial intelligence technology has penetrated all aspects, for us, language communication is a kind of media, but also an important way for us to convey ideas and give orders, so now, language communication has become an important way for us to interact with artificial intelligence. This paper through social research and the use of voice command to the existing speech recognition technology products test method puts forward the current development problems and solutions, the current situation of artificial intelligence speech recognition technology and the future in the field of communication systems and the intelligent home application made a simple introduction.
A secret image sharing solution for autonomous vehicles

With the improvement of personal privacy awareness and government agencies' awareness of national security information protection, data access and data security of smart cars is a thorny problem. The fundamental reason is that the key to access and store data is kept in the hands of smart car companies. Although many scholars have made great progress in the research of secret image sharing solutions before, such as progressive visual secret sharing, polynomial based secret image sharing, etc., these encryption schemes are single key encryption and cannot solve the technical problems encountered by the current autonomous vehicle. Therefore, based on the research of previous scholars and taking into account the social problems in solving the existing image sharing, this paper proposes two secret image sharing solutions. One is key sharing solution; The other is Images Sharing solution. Through the theoretical analysis of the application scenario, we can conclude that these two solutions can well solve the problems of whether users, smart car companies or third-party institutions infringe on users' privacy, whether it is due to the security hidden dangers brought by smart car technology, and whether they violate the national information security law.
Reddit sentiment analysis for natural language processing

In the Internet age, social media has fully penetrated into people's lives. As one of the well-developed online platforms with a large user base, Reddit allows users to independently publish current news, life experiences, and interesting life stories. However, sometimes it sends a negative tone that affects the brand of a company or individual and destroys profits and it is necessary to prevent Twitter by identifying hate words. The biggest innovation of this post is that we use reddit data to compare various methods simultaneously. As we process more data, trying deep learning will yield good results. Compared to other machine learning classifiers, the transformer classifier achieves the best results.
Researches advanced in image recognization based on deep learning

Image recognition is a fundamental problem in the field of computer vision community, which aims to understand the content of an image to predict the class of the image. Traditional image recognition methods rely heavily on the quality of handcrafted features, and the recognition accuracy and generalization ability cannot meet practical application requirements. Thanks to the speedy progress of deep learning theory and technology, convolutional neural networks can adaptively learn image semantic features through high-dimensional nonlinear changes, which sensibly rises the exactness of image recognition. At present, image recognition has been generally applied to many fields such as commodity circulation, smart retail, pest identification, medical image analysis and so on. In this paper, we introduce the latest research progress in deep learning-based image recognition. Specifically, following the temporal clues of technological development, we first introduce representative image recognition networks, including their design ideas, basic network structures, advantages and disadvantages, etc. Second, we quantitatively compare the performance of different image recognition networks. Finally, we summarize the existing problems in the sphere of image recognition research and discuss its possible future directions.
Common lossless compression algorithms and their error resiliency performance

With the development of communication technology and computer technology, many related industries, such as multimedia entertainment, put forward higher requirements for storing and transmitting information data. The research of data compression technology has attracted more and more attention. Therefore, the error resiliency ability of data compression algorithm is particularly important. How to enhance the error resiliency of data compression communication systems has been a hot topic for researchers. This paper mainly introduces the lossless data compression technology and its basic principle and performance index. Two typical lossless compression codes, Huffman and Arithmetic coding are deeply studied, including the principle of coding and the problem of error resiliency. Huffman coding and Arithmetic coding are two very important lossless compression codes widely used. The ability to resist channel error is an important index for data compression in communication. It is of great significance to further improve the channel adaptability of data compression to study the above two kinds of codes and their ability to resist channel error.
New hide-in-dark image encryption algorithm

In order to protect image information, existing encryption methods pay more attention to transforming images into noise-like images with random pixels. It is acknowledged that attackers can easily notice these encrypted images for there is a striking difference between them and unencrypted ones visible to the naked eye. As a result, these noise-like images may suffer a lot of attacks which could increase the probability of information leakage. For this problem, a method based on discrete wavelet transform and previous encryption methods has been proposed which can make the encrypted images finally look like other pictures. However, the results do not have a high quality, for some obvious noise can be found in the details of the final images. In this paper, a new method called HID is introduced in which the information is hidden in a dark area. Both experimental results and security analysis prove that our method can not only transform images full of noise into meaningful images but also produce images with higher quality.
Detecting homophobic and transphobic texts from youtube comments using machine learning models

Today's world has witnessed an exponential rise in disseminating degrading and offensive content via social media. A global increase in violence against minorities, such as gun violence, murders, and forced displacement, has been connected to using harsh and derogatory language online. The policies enacted to prevent abusive or derogatory language risk stifling free speech and are applied differently. These languages can affect the mental state of social media users. Homophobic and transphobic expressions are a insulting people's sexuality or character among harsh and disrespectful remarks. To study social media information and discriminate between homophobic and transphobic comments, it is necessary to construct language-based automatic categorization methods. This dataset consists of Tamil YouTube comments collected as part of the Shared Task on Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages. This study detects and categorises abusive comments using machine learning models such as SVM, Naive Bayes, Logistic regression, and KNN. In comparison to other classifiers, the accuracy of logistic regression is the highest, at 55%.
A systematic review and outlook on machine-learning-based methods for spam-filtering

This paper reviews the stages of development and common approaches to spam text classification. This was followed by an introduction to the basic knowledge of spam filtering. After that, some research results are presented. The last part will be a discussion of the literature review and some suggestions for the future development of spam filtering techniques. This paper found that the future direction of spam filtering should be a combination of machine learning techniques and deep learning techniques and propose to take into consideration diversified features of the emails other than text such as IP addresses and IP reputation values into the machine learning models.
Applications of deep reinforcement learning Alphago

With the progress of the times, the field of artificial intelligence (AI) has become one of the hottest fields in the 21st century. Currently, artificial intelligence is successfully used in the retail, financial, and medical industries. Especially in 2016, Google's DeepMind used deep reinforcement learning to train AlphaGo and defeated Lee Sedol, which propelled the field into the public eye. Most people are aware of artificial intelligence, but few understand it. This article will focus on analyzing the literature "Mastering the game of Go with deep neural networks and tree search" and other related articles to introduce the basics of deep reinforcement learning and AlphaGo. Finally, readers will understand how artificial intelligence can successfully imitate humans and defeat humans in Go.
A survey of facial expression recognition in the wild

With the continuous development of facial expression technology, especially the development of deep learning, and the establishment of in-the-wild datesets in recent years, field face recognition has become a hot research field in the wild of facial expression recognition. Different from the traditional facial expression recognition(FER), in-the-wild facial facial expression recognition, the problem of recognition accuracy caused by illumination, occlusion or low image resolution. Therefore, in order to solve these problems, new methods have been put forward continuously in recent years. In this paper, we first continue to summarize the widely used datesets, and then summarize the paper methods of facial expression recognition in the field proposed in the past two years.
Studies advanced in face recognition

Face recognition has always been a hot topic in the field of computer vision research. Its purpose is to enable computers to have the ability to recognize people through face images like humans. The early face recognition technology was mainly based on artificial features, mainly including five parts: face image acquisition, face image detection, face image preprocessing, face image feature extraction and matching recognition. Thanks to the rapid development of convolutional neural networks, face recognition technology based on deep learning has gradually become the mainstream method. Focusing on the above two categories of frameworks, in this paper, we introduce representative algorithms for face recognition, including their design ideas, basic processes and key steps. We also discuss existing problems in the field of face recognition and predict future directions for this topic.
Blockchain-based technology in the healthcare industry and trend analysis

To sort out the current research status and trend analysis of blockchain technology in medical development. Logistic regression techniques are applied in this article to sort out the research and application trends of blockchain technology in the medical industry by using data cleaning and feature engineering, and by collecting domestic and international literature for analysis. At present, the application of blockchain technology in the medical industry is still in the early experimental stage. The medical industry should seize the development opportunities and strengthen technical research and R&amp;D innovation so that blockchain technology can really play a role in reshaping the future information technology of the medical industry.
Comparing supervised and unsupervised learning in image denoising

Recent studies on unsupervised learning have attracted people's increasing attention. In particular, Deep learning has developed rapidly in recent years. With the development of media images, people's demand for image noise reduction is increasing, and the requirements are becoming more and more strict. The traditional methods used for image noise reduction are far from meeting people's requirements, and people are eager to find a more efficient image noise reduction technology. In recent years, the technology of using a convolutional neural network for image noise reduction has become more and more skilled. This paper explores the reliability of image noise reduction technology using a convolutional neural network as an autoencoder, and whether good performance is maintained without using clean images. The article aims to compare the performance with supervised learning and unsupervised learning by deep learning in image denoising.
Image super-resolution reconstruction algorithm based on EDSR and improved SRGAN

Nowadays Image super-resolution reconstruction algorithm has become the dominant research aspect of computer vision, which is about generate high resolution composite image using low resolution original image. SRCNN(Super-Resolution Convolutional Neural Network) algorithm is the pioneer to apply deep learning to image super-resolution reconstruction, while SRGAN(Super-Resolution using Generative Adversarial Network) further explores the fusion of deep learning domain and image super-resolution reconstruction on grounds of SRCNN algorithm, and uses generative adversarial network to fit the extra information after image resolution improvement. Aiming at the defects of SRResNet generation network in SRGAN algorithm, this paper tries to improve the generator in its generation adversarial network by referring to the idea of ENSR(Enhanced Deep Residual Networks for Single Image Super-Resolution) algorithm, and obtains obvious effect. The improved algorithm has a significant improvement in performance.
The object tracking and movement prediction based on the Gaussian mixture model

In modern computer vision study field, it has never been stopped for giving computer human ability and expecting for even more powerful functions. Most research is still in theoretical level and is hard to be applied in individual daily life. Hence it is needed to bring the advantage of new technologies in computer vision field to more people in society. This paper introduces an application in computer vision field, namely the use of Gaussian Mixture Model (GMM) for object track and movement prediction. By using GMM, the computer will have the ability to split an object from the background. Although due to sudden changes in lighting and other external factors, the general modeling of the background is not very clean and clear, the Gaussian Mixture model is one of the most successful modeling methods.