Reviewerly/exHarmony huggingface dataset:
Research of News Recommendation Algorithms based on Deep Learning

Deep learning is the key development trend of news recommendation technology, which has been thoroughly studied by most researchers. In today's social, news recommendation has become a very essential way for people to acquire news. The fundamental idea and procedure of news modeling are covered in this paper's main body, and one of our research interests is how to leverage convolutional neural networks to create news recommendation technology. We also looked at the news recommendation evaluation index, which looks at things like satisfaction, accuracy, diversity, and innovation. Additionally, this paper analyses numerous traditional algorithms and contrast the benefits and drawbacks of each. We also outlined a number of the challenges that the current study has faced. To contribute to this research, we looked into the expected future evolution of news recommendation technology.
Comparison of Different Depth of Convolutional Neural Network Deep and shallow CNN comparison based on FER-2013

The purpose of this paper is to cross-compare the different nature of the decision process of convolutional neural networks (CNNs) of different depths, as well as the characteristics of the ensemble model. The Facial Expression Recognition (FER) problem is selected as the carrier of this experiment. The dataset chosen is FER-2013, one of the most challenging FER datasets due to its complicated and natural contents. This research first trained four models that have different architectures using FER-2013. One is the shallow convolutional neural network, and the other three are deep pre-trained CNNs, including ResNet50 with weights from ImageNet, VGG16 with weights from ImageNet, and VGG16 with weights from VGGFaceNet. Then, by using a gradient-based Class activation mapping technique as a visualization technique, this experiment successfully displayed the different decision-making processes of the selected models and illustrated how the depth of the neural network influences the feature extraction process. This work further experimented with convolutional neural networks(CNNs) having different depths by varying ensemble combinations. Finally, all three-model combinations were tested, and the experiment results show that models that are more different in architecture would result in a better ensemble performance than those with a similar architecture. Thus, one of the inspirations we could get from this work is that models with dramatically different architectures could earn a more remarkable improvement for future ensemble models.
Multiple Machine Learning Algorithms for Spam Mail Detection

Due to several issues including clogging mailboxes and obscuring critical personal correspondence caused by a large amount of spam mail, spam filtering application based on machine learning models has become more and more essential. This paper gives a comprehensive evaluation of 4 classic machine learning models including the Naïve Bayes classifier, support vector machine, neural network, and the decision tree in the task of spam email evaluation. In order to make fair comparisons between the algorithms, they were all built by learning from the same training dataset and asked to make predictions on the same testing dataset. More specifically, the Naïve Bayes classifier uses the conditional probability of the Naïve Bayes theorem and consumes the words that appear independently in the emails. The decision tree algorithm keeps recursively splitting the dataset to build a predictive theory. Support vector machine is able to make predictions by transforming the training dataset to a linear equation in n-dimensions. And neural networks can recognize the non-linear relationships between the inputs and outputs by constructing the front, hidden, and output layers. It is observed from the result that all machine learning models have successfully classified spam emails with high testing accuracy which are all above 90%. More specifically, the neural network achieves the highest testing accuracy followed by the support vector machine and decision tree. While the Naïve Bayes classifier does not perform well enough as it must assume the appearance of words happens independently.
Multiple Traditional and Deep Learning-based Prediction Models for Quantity of Domestic Mail

As the main role of domestic mail is gradually changing from the field of communication to the field of economy and culture, it is very important to provide relatively accurate volume prediction and feedback through the visualization process. This paper focuses on finding models with a high matching ability to achieve the purpose of prediction, and selects the best option by comparing multiple models. Eight different models ranging from simple to complex will be used in the following steps. More specifically, the predictive power of the models has steadily improved, from linear to curved, from constant to trend, from a single dimension to adding gradient factors to combining seasonal periods. The prediction principle is also gradually complicated. In the beginning, it focuses on the average, then weights the values in different stages to obtain the weighted average, and then makes a more refined prediction by means of single-layer convolution. To achieve the purpose of obtaining better results, it is of great necessity to introduce a self-learning prediction model, so Recurrent Neural Network (RNN) model and Long Short-term Memory (LSTM) model are chosen. In addition to obtaining the input data of this time, the RNN model will also combine the input data and output data of previous times to make the comprehensive prediction. The LSTM model further solves the situation that the RNN model cannot predict in some cases, by setting the limit to determine whether to keep the data better to fit the data with the trend. The data visualization results show that the performance of LSTM model is the most outstanding among all models, which can provide learners with more accurate prediction information.
Improving the Performance of Deep Q-learning in Games Pong and Ms. Pacman

Unlike TD-gammon architecture, deep Q-learning algorithm combines deep learning and reinforcement learning, and it has achieved outstanding results in many Atari games. This study primarily focuses on two games, Pong and Ms. Pacman. It explores several approaches to improve the performance of deep Q-network (DQN). Based on the data obtained, while DQN displays a high-level performance in the simple Atari game Pong, it struggles a bit when learning the more complex game Ms. Pacman, leading to diverged loss. This under-performance may be partly explained by the shorter training time than the original paper due to limited computational resource. Given sufficient time of training and exploring, the model is believed to eventually converge once it identifies the optimal combination of hyperparameters.
The Application of Deep Learning for Network Traffic Classification

The classification, detection, and analysis of routine network traffic has been a hot topic for businesses and research institutions due to the proliferation of Internet of Things devices and the explosive development of networks. Traditional methods for categorizing network traffic primarily employ common machine learning algorithms e.g., decision trees and plain Bayes algorithms, but as deep learning technology advances, more and more traffic classifications are being successfully applied. This study examines existing deep learning-based network traffic classification techniques and focuses on the categorization of computer network traffic. Firstly, the research background of the topic is introduced, and then the traffic classification based on deep learning is mainly described, which includes traffic classification based on Stacked Autoencoder, traffic classification based on Convolutional Neural Network and traffic classification based on Recurrent Neural Networks. Following investigation, this paper comes to the conclusion that Long Short-Term Memory and Convolutional Neural Network models are the best deep learning models for traffic classification, with three-dimensional Convolutional Neural Network outperforming the others.
A Time Series Data Regression Model Based on Multi-kernel KPCA Dimension Reduction and Bagging Algorithms

Regression models for high-dimensional data have always been a hot topic in the field of statistical learning. Considering the case that the predictor variable is a high-frequency time series and the response variable is a continuous scalar, this paper proposes a regression method based on a multi-kernel KPCA Dimension reduction method and the Bagging algorithms. The proposed method adaptively solves the problem of kernel function selection and unsupervised ness in KPCA Dimension reduction by splicing the projection data under various kernel functions and using the Bagging algorithms to mine the relationship between projection data and the response variable. In the real data analysis, this paper selects the multiple regression model, the LASSO regression based on model selection, the multiple regression model based on PCA Dimension reduction and single-kernel KPCA Dimension reduction as comparison methods. The results show that the proposed method has better performance than other comparison methods. Since the basic regressor in the Bagging framework is model-free, some prediction models can be used to improve the prediction accuracy for more complex data situations.
Ethereum: Introduction, Expectation, and Implementation

As the Blockchain technology develops, more and more cryptocurrencies were invented after Bitcoin. This paper introduces the technology of Blockchain, including the basic concept of Blockchain and how Blockchain works to allow decentralization of trades; The system of Ethereum and the cryptocurrency Ether (ETH), how it was invented, what was the central mission of its invention, as well as how it differs from Bitcoin and how can it allow more decentralized application to be developed, which in turn illustrates what it means and where its values lie. As all cryptocurrency markets have a huge fall in value in the year 2022, as shown in figure 1, many people are losing faith in cryptocurrency. Many believe that since it is entirely digital and non-government based, it has no actual value, that the entire cryptocurrency market is a bubble. Meanwhile, cryptocurrencies introduce a very revolutionary concept, which is the decentralization of applications, and this decentralization can apply to many things, leading to a great technological structure modification, even for social structures. Because of its anonymous and democratic nature, there is also always going to be demand for cryptocurrencies. Thus, this paper also analyzes the expectations of cryptocurrency, mainly Ether, and the predictions of its future development.
Regarding the Immersion of VR, What do We Need to do to Make it Better?

Immersion is an objective factor for the virtual reality experience. It is important to VR experience. By analyzing the application of VR entertainment such as games and movies, the meaning of immersion is shown. Then looked at how to improve immersion through VR hardware, virtual design, and more. After a brief introduction to the definition of virtual reality, this paper analyzes the impact and importance of immersion through the application of two critical areas of VR entertainment, movies and games. Then, the paper roughly classifies VR hardware into two kinds of output and input devices and studies how to improve VR's sensory stimulation and sense of substitution through hardware. Finally, in the areas of sense of embodiment, reality experience, empathy, and socialization in VR environment design, the role of immersion in all aspects of VR entertainment and ways to enhance it are described. The article concludes with a summary and outlook on the development of VR.
ML Classification Methods Comparison for Breast Cancer Diagnosis in Clinical Application Field

With the development of computing resources, deep learning method has made great progress in the field of image recognition. And as a result, deep learning-based image detection methods have been able to make great breakthroughs in medical fields such as disease recognition, microbiological testing, bacterial quantification, and tumour detection. With the advancement of in the ML field, the computer-aid diagnosis system has been improving. This paper reviews basic CAD ML system, and the recently improved DL system. In general, both methods offer the ability to assist the medical expert in giving them opinions. Deep learning, on the other hand, shows promising improvement in breast cancer diagnosis, but still with some limitations, such as requirement of high computational power and cost, as well as data scarcity. The accuracy rate of DL can reach up to 97%, which is the best in the field. Thus, both effectively reduce false positive rate of detecting breast cancer, improving the performance for detection and diagnosis.
The design of a new genre game based on virtual reality

Virtual reality game always provides the player the most verisimilitude experience and with the advancement of the VR hardware, it may become the mainstream about how people feel and attach the world online. The paper is aimed to discuss a possible solution finding a better balance between the two classical genre of VR games, the sensory stimulation and the story telling. To this end, we design a game named “Bury Me Here” in which players can find out an emotional bond between the game protagonist and themselves. The game includes 4 sections, the departure from the hometown, the travel on the train, the work in the office and the life in the penthouse. At the end of the game, the protagonist goes back to his country yard and spend the rest of his life there. All the sections are designed to tell a stranger’s life story to the player, making them to experience the life path of someone else, bonding an emotion connection between the player and the protagonist through story telling. Results show that the game not only provides an immersive visual experience but having the emotive sparks echoes in players’ mind.
Advanced Deep Learning Approaches for Hate Speech Detection

At present, the influence of social networks on a global scale is increasing, the number of users is increasing significantly, and massive amounts of information are being created every day. How to automatically and efficiently identify harmful information, especially hate speech, has become an important issue in the governance of the network environment. In order to improve the screening efficiency of information containing hate speech, deep learning is applied in this field. Although the research work around this task has made great progress, there are very few reviews on this task, lack of a comprehensive review of the latest development in recent years and can not provide help for researchers who are interested in this task. Therefore, we give an overview of the deep learning applied in hate speech, introducing new ideas for solving this task in recent years, and propose potential problems in the task and analyze it.
Composing Real Time Adapting Weather Music based on Nyquist

Contemporarily, as computer music is becoming more popular, plenty of people start to make their own music through the computer. In general, there are so many different types of software or language for people to use to create music. In this paper, we will introduce several basic lines that one can use for making music in terms of Nyquist, and the sound one can produce in Nyquist. The main idea of this paper is to create different pieces of main melodies and add sound effects according to the data obtained from a website related to weather conditions of Earth. By completing the work, we will demonstrate the way to create new scores by writing codes, abstract real time data from a separate media and letting it control the codes on Nyquist. The application of this product can help people emotionally and offer a tool and a direct feel of the variation of the planet by using acoustic techniques. However, there are still limitations that restricts the project from having a higher value, and worth more study and development in the future. These results shed light on further developments in creating music that will change and adapt according to a real time data.
The Advance of Cryptocurrency Wallet with Digital Signature

This paper explains the composition of the cryptocurrency wallet by analyzing a wallet’s components, functions, categories, the algorithm of signing, and security features as three major components used in the constitute of the cryptocurrency wallet, private key, public key, and address are introduced from their generation process with specific Elliptic Curve Digital Signature Algorithm secp256k1 and Keccak hash function. A variety of crypto wallets are divided into different categories by their purpose of use, the form of display, and Internet connectivity, and multiple crypto wallets are explained in the second part of this paper. The elliptic curve secp256k1 is selected in Ethereum for both calculating the public key information and performing signing transactions. This paper details secp256k1 and Keystore used behind the signing process and security of cryptocurrency wallet and outlines potential risks due to the development of computing power and transfer of validation system in Ethereum.
Construction Method of Casino based on Ethereum

There are a lot of things that humans cannot do, and one of them is being fair, especially when he is running a casino. However, Ethereum can. Constructing a casino that is regulated by Ethereum can give us the first fair casino in the world. With the help of some technics of cryptography, which are public/secret key schemes and one-way functions, the secrecy and privacy in the gambling of each player can be ensured while still making the game fair by the blockchain. During the construction, Ethereum is also found to be fit for the casino’s other aspects such as propaganda and payment. By successfully constructing such a casino, a way of converting the fixity of blockchain into fairness is proposed in the area that needs it and lacks it the most, which is a casino. Following down this road, any area that lacks fairness can exploit Ethereum or block-chain to find a way to achieve fairness.
Comparative Analysis the Super-Resolution Image Generation Performance Based on BigGAN and VQ-VAE-2

Super-resolution image reconstruction has always been a popular research direction in the field of computer vision, which aims to recover high-resolution clear images from low-resolution images. Traditional super-resolution reconstruction algorithms mainly rely on the construction of constraints and the accuracy of registration between images to achieve the reconstruction effect, but their accuracy cannot meet the needs of practical applications with large multiples. Thanks to the rapid development of deep learning field, super-resolution image reconstruction based on deep learning has become the mainstream, and has achieved great success in reconstruction accuracy and speed. According to the different generative models used, the existing super-resolution image reconstruction methods mainly include two categories: GAN-based and VAEs-based. To quantitatively compare the limits of the two approaches' performance, this study selects two representative algorithms, BigGAN and VQ-VAE-2, and introduces the theoretical details and training process of these two methods, respectively. Furthermore, the reconstruction results of BigGAN and VQ-VAE-2 are further compared. Finally, this paper discusses the future development trend of super-resolution picture reconstruction with the current potential problems of BigGAN and VQ-VAE-2.
Genetic Algorithms for Coverage Path Planning using Mobile Robots

Using the grid method to establish the environmental model, the map is divided into several sub-regions according to the environmental characteristics, and the connection between the sub-regions is established. Next, the Genetic Algorithm encodes all sub-regions, and the base point information between and within each region is established to obtain the optimal coverage sequence. Partial coverage is achieved in each sub-region by means of reciprocating motion, which turns the complete coverage problem into a traveling salesman problem (TSP). Finally, the Genetic Algorithm is used to conduct an experimental simulation to obtain the correlation between the fitness and the number of iterations, and obtain the optimal traversal sequence, which proves that the method is effective and feasible.
Research of the Methods on Multi-Agent Path Finding

Multi-Agent Path Finding is an essential real-life application of multi-intelligent systems for which scholars have solved many classical solutions. And with the evolution of theories related to machine learning, intelligent solutions are emerging daily. However, these algorithms have not been well summarized and concluded, and are comparatively difficult to consult. This essay's objective is to offer a thorough analysis of these issues. The paper will start with Path Finding and describe its three categories of classical algorithms. And then it will focus on the centralized planning algorithms based on the classical algorithms, and the distributed execution algorithms based on machine learning, and provide a theoretical explanation and comparison of their effectiveness. Finally, a prospection and outlook on the currently unresolved issues in the Multi-Agent Path Finding research area, including unification standards and directions for improvement, will be presented.
Studies Advanced in Mask Face Recognition based on Deep Learning

In the era of epidemic, wearing a mask when going out has become a more common phenomenon. And how to complete face recognition and guarantee certain accuracy without letting users take off their masks has been a challenge in recently years’ face recognition field. In this paper, the traditional masked face recognition method and the new MTCNN deep learning masked face recognition method are compared and analyzed, and other similar masked experiments are introduced as data references. It is synthetically found that the accuracy of MTCNN masked face recognition using CASIA-FaceV5 as dataset is more than 90%, which is far better than other traditional recognition methods, but its requirement for dataset image quality is high, and the training time is long and costly. We hope that the process analysis and result comparison of different methods in this paper can shed light on the future development of masked face recognition technology.
Image Classification Under Class-Imbalanced Situation

Image classification technology processes and analyzes image data to extract valuable feature information to distinguish different types of images, thereby completing the process of machine cognition and understanding of image data. As the cornerstone of image application field, image classification technology involves a wide range of application fields. The class imbalance distribution is ubiquitous in the application of image classification and is one of the main problems in image classification research. This study summarizes the literature on class-imbalanced image classification methods in recent years, and analyzes the classification methods from both the data level and the algorithm level. In data-level methods, oversampling, under sampling and mixed sampling methods are introduced, and the performance of these literature algorithms is summarized and analyzed. The algorithm-level classification method is introduced and analyzed from the aspects of classifier optimization and ensemble learning. All image classification methods are analyzed in detail in terms of advantages, disadvantages and datasets.