Reviewerly/exHarmony huggingface dataset:
Researches Advanced in Image Generation based on Deep Learning

Image generation has always been a study hotspot in machine learning, which aims to build models to learn specific semantic distributions from massive image data to generate realistic simulated images. Thanks to the deep learning technology’s quick development, generative models are constantly being developed and huge success has been achieved in image generation tasks. According to difference between generative models, the existing image generation methods based on deep learning can mainly be separated into three models: image generation based on Variational Autoencoder (VAE), image generation based on Generative Adversarial Network (GAN) and image generation combined the VAE and GAN. Focusing on the three frameworks, in this paper, the development process and related principles of each type of generation model are described respectively. After that, the different generation results of different generation models for the agreed training set are compared intuitively, the advantages and problems of various models are proposed, and reasonable improvement measures are proposed for some problems. Finally, the development prospects of various models are prospected.
Research and Analysis of VR in the Field of Education

In recent years, virtual reality has become a hot spot for applications in several fields. As a relatively new technology, every breakthrough it makes in a particular field is closely watched. The viability of this novel technology, has even begun to be applied in education. This paper mainly analyzes the teaching problems faced by some professions (medical education, science experimentation, and art education) and the application and help of virtual reality for these professions. First, this paper examines real-world uses of virtual reality in medical education, science experimentation, and art instruction. The benefits and drawbacks of these cases are presented and explored. Besides, this research also covers the professionalism of virtual reality in education, which not only boosts students' learning motivation but also improves classroom teaching quality. Finaly, this article examines VR's future uses in a variety of sectors, as well as summarizing and addressing some of the most crucial issues of its usage in education.
Investigations on the Performance of Pre-established CNN Model in Music Emotion Detection

Music is a medium for emotional artistic expression. Different people have different understandings of music. Music emotion recognition (MER) has thus become a novel branch in computer music. The goal of this essay is to investigate in the performance of established CNN architectures, such as AlexNet and VGG16, to recognize emotions contained in a song. CAL500 dataset is used as it covers a variety of genres. The dataset is transformed to spectrograms, which can be understood by computers through image recognition. The result of this investigation turned out to be that previous architectures would lead to overfitting within the training of a few batches. Possible explanations for this are that the parameters used in the model are too large for a simple regression task. This research provides some understanding of how CNN works as a network initially designed for image classification. Understanding emotions using spectrograms might require less complex CNN models or new models that are specialized in such tasks.
A Series of Models based on Long Short Time Memory for Temperature Prediction

Given the increasingly serious climate problems that have been appearing around the world in recent years, as part of the climate research, finding ways of smart, accurate machine learning to carry out the temperature prediction is of great significance both for human activities and the earth's ecosystem. In this study, 3 kinds of the Long Short Time Memory (LSTM) models, which differs on the way how the data is input and output and the network structure, respectively Directly-Multiple-Output (DMO), Single-Step-Scrolling (SSS), and Convolutional Neural Networks Plus Long Short Time Memory (CNN+LSTM) are built and trained by Pytorch on the Jena Climate dataset to compare their predicted performance. The training loss of these 3 models are 0.0053, 0.1568 and 0. 0079; testing loss are 0.0048, 0.1764 and 0.0096; Mean Absolute Percentage Error (MAPE) are 3.42%,6.58% and 3.30%. The result turns out that CNN+LSTM is the best model in comprehensive consideration with the least convergence time, low loss and MAPE, followed by the DMO model with the least loss and low MAPE but longer convergence time. SSS performs worst with increasing high loss. In general, CNN+LSTM is suitable for temperature prediction while DMO is also good when short convergence time is not needed. SSS is not recommended for temperature prediction.
Improved Methods based on Too Many Cooks

In the paper “too many cooks”, the authors developed a method called Bayesian Delegation to enable human-like coordination by inferring the sub-tasks of others quickly. However, some limitations which could be solved to improve the whole programme still exist in the partial work of the code. First, the implement of sub-task in terms of efficient actions or which agent(s) should work on it is not specified. Second, the sub-tasks may be finished in many different orders since the ordering of sub-tasks is partial and so agents probably have blank time. Third, with the increasing running times the action of different state could be recorded to improve the efficiency of the programme. So, this paper will focus on these three issues and try to solve them. In this work, some solutions to these challenges using Deep Q-Learning (DQN) and Bayesian inference are proposed. In the DQN experiment, value approximation has good performance in the simple multi-agents’ environment, the limitation that the implement of sub-task in terms of efficient actions or which agent(s) should work on it is specified in our model. Besides, in the Bayesian inference experiment, the code could give accurate action prediction with enough database, and also avoid some time consuming because the blank time between subtasks is saved.
Difference between ECDSA with ED25519 and Their Future

It is always a trendy topic about virtual currency nowadays in the world. These virtual currencies like Bitcoin and Monero are precious and cryptic, which play a more critical role in the economy since no departments or governments control their transactions and deals. How could the transaction system provide users with security and protect their property? What's more, one of the most important characteristics is that all the transactions are untraceable, which means that the governments and the users cannot figure out the identity of another user. This paper discusses some principles of Bitcoin and Monero. For instance, some encryption algorithms like ECDSA and ED25519 are applied to help users to verify the whole transaction, and Ring signature is used in Monero to conceal the user's identity. Meanwhile, this article will also compare some encryption algorithms' security and efficiency. Moreover, there would be a discussion about some advanced communication technologies. For example, there would be a brief introduction to quantum communication and entanglement. Then, it would also have an intense debate about the future of ECDSA and ED25519 if quantum communication and quantum entanglement were invented.
Solve the Problem of Expensive Drugs in Hospitals by Blockchain

Effective treatment of major diseases has always been one of the key research topics for doctors. Some researchers have found that the lack of drugs and the proliferation of counterfeit drugs have a great impact on disease treatment, especially in developing countries. However, there is still a gap in research on drug cost control and hospital official drug pricing. As we all know, blockchain technology has been used in securities trading and other fields to reduce the costs of both parties. Therefore, the research topic of this paper is how to reduce the cost price of drugs purchased by hospitals nationwide or even globally, so that the price of drugs can be reduced to a win-win situation for hospital patients. The structure of this article is as follows: First, it introduces the current status of the application of blockchain technology at home and abroad, so that people can more clearly understand which transactions around them have used this technology. Later, this article will describe the working mechanism of blockchain technology, and finally introduce how to use this technology in drug trading. Through research, it is found that the point-to-point transaction of blockchain technology can greatly reduce the cost and time for hospitals to purchase drugs, reduce the price of expensive drugs in hospitals, enable more patients to get effective and rapid treatment of diseases, and reduce the phenomenon of family decline caused by illness. This shows that the effective reduction of drug purchase costs through blockchain technology is conducive to overcoming the problems of diseases and maintaining the stable income of hospitals.
Playing Flappy Bird with Two Different Value Learning Algorithms

In this paper, reinforcement learning will be applied to the game flappy bird with two methods DQN and Q-learning. Then, we compare the performance through the visualization of data. Furthermore, more results from other games are summarized to analysis the corresponding advantages and disadvantages. Finally, we discuss and compare these two reinforcements learning methods.
Analysis of Interaction Methods in VR Virtual Reality

Virtual Reality is a new revolution in interaction. As a super technology that can trick the human brain, Virtual Reality is widely used in healthcare, education, business, medical, entertainment and industry. In the field of VR, developers are committed to giving users a full and realistic interactive experience, for example by transmitting the senses of touch, smell, sight and hearing to the user's brain and restoring the user's perception of the real world first-hand. This requires not only powerful technology, but also sophisticated interaction design. Many VR giants are currently working on various technologies and systems to enhance the user's interaction experience in the virtual world. This article analyses three of the main interaction methods (motion capture, eye tracking and haptic feedback) and their associated device products and provide a brief comparison and summary of them. Although the interaction methods in VR are not yet unified and the various virtual reality devices on the market have their own flaws, with more and more players getting involved and the rapid development of technology, it is not difficult to judge that in the near future virtual reality interaction devices will explode like a rocket.
Analysis and Study on Chemical Composition of Ancient Glass Products

Based on the artificial neural network, K-Means clustering and decision tree algorithm, this paper constructs a variety of models to study the relevant factors of the surface weathering of glass relics and the changes of various chemical components.In this paper, the statistical results are obtained by processing the data with glass type, texture and color respectively, and chi square test is carried out with weathering type respectively. At the same time, a logical regression classification model is established to analyze the relationship. Secondly, the data are divided into four groups based on the glass type and weathering degree, and the statistical rules are obtained by analyzing the average, median, maximum, minimum and change degree of each group of data. Finally, a multi-stage weathering backtracking model is built based on multihead self-attention to predict the chemical composition content of the detection point before weathering. The accuracy of the test is 99%, and the effect is good.
YOLO Series Target Detection Technology and Application

Recently, YOLO is the most popular algorithm in machine learning. The algorithm has developed rapidly, and there are several versions at present. Each version of the framework is different, and they also have their own application areas. And maybe in one area, not only one version can be used. This paper summarizes the process of target detection, the structures of YOLO network. In addition, this work also analyzed the development, advantages and disadvantages of YOLO target detection. Finally, the application of YOLO in automatic driving and UAV detection are discussed. YOLO may develop faster in the future. YOLO model is a variable model, which has unique functions when detecting different things under different circumstances. At the end of the paper, the thesis is summarized, and the related research has certain reference value.
The Advance of Ring Confidential Transactions

Ring Confidential Transactions (RingCT) is a protocol associated with the privacy-focused cryptocurrency Monero and is used to hide the transaction amount from the third party while still providing the confidentiality of the hide transaction. With the Pederson commitment scheme, ring signature, and other cryptographic constructions, RingCT plays a major role in making the transactions of Monero private. As a privacy coin, Monero has the unique property of fungibility in the cryptocurrency market from the protocols implemented. These protocols provided opportunities and challenges for its future. In this paper, the version of the protocol implemented in Monero is first inspected, including the commitment to zero and the range proof. Then, two critical cryptographic constructions used by RingCT 2.0, the accumulator and the signature of knowledge, are introduced. Finally, the influence of RingCT and other privacy features and the current situation of privacy coin is discussed.
DQN–MADDPG Coordinating the Multi-agent Cooperation

Multi-agent coordinating aims for many agents to finish the same or more mutual goals. To achieve this function, there are two main ideas; the first is making multi-agents communicate with each other and acquire the information for other agents. Another is sharing the same environment, dividing the work, and cooperating to achieve the goal. ‘’Gym-cooking’’ is an excellent model to test the algorithms’ performance in network coordination and game theory; this is a sharing environment. Based on sharing information, the agent have two networks(policy and target) and tries to do something more efficiently. This article will increase the complexity of the environment and use different algorithms to process the experiment. Specially, this paper will use the MADDPG model as the primary model to show its performance in a complex environment and contrast other models like DQN. The MADDPG model in this experiment is unlike the traditional MADDPG; the work trains the MADDPG network to deal with emergencies and accidents.
Comparative Analysis of Reinforcement Learning Algorithm based on Tennis Environment

Reinforcement learning and deep reinforcement learning, as a research hotspot in the field of machine learning, have been widely used in our daily life. In this field, game is playing an extremely important role in the developing of reinforcement learning algorithms. Based on the Tennis environment built by Unity ML Agents, this paper used three algorithms, Proximal Policy Optimization (PPO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG) and Soft Actor-Critic (SAC), combined with PyTorch framework, solved the continuous control problem of this environment. Meanwhile, a group of optimal parameters are obtained through multiple trainings, so that Agents can achieve a perfect effect of solving the continuous control problem in Tennis environment. At the end, this paper compared and analyzed the difference among these three algorithms, summarized the application and properties of each algorithm. For different parameters of the algorithm, this paper also maked a comparison and explained the reasons for some special cases as well which can be used for the future work.
The Advance of Ethereum Digital Signature

Ethereum digital signatures have been found to make progress, but there has yet to be a consensus regarding their significance and process. Using digital signatures in Ethereum ensures the security of users' money and the Safety of their transportation. Elliptic Curve Digital Signature Algorithm (ECDSA), private key, and public key play an important role here. This paper analyzes the details of Ethereum's Digital Signature based on existing information. First, it explained the general background information about Ethereum. Send explain the ERC-20. ERC-20 are digital assets that represent value. They could become more compatible with the new standard and allow other features. Then digital signatures are explained in detail. This part included ECDSA, private key, and public key. ECDSA is the most critical algorithm during the production process. Private and public keys are the key features to ensure Safety. The last part is the Keystore file system. Using the Keystore file system, users can secure their private keys for their cryptocurrency wallets. To access their account, the user must have a password generated by them. The unencrypted files, on the other hand, create a vulnerability in the key store, which is exposed by these unencrypted files. Once an attacker obtains the user's unencrypted key, he cannot only take control of the account but also sign transactions on behalf of the user.
Human Pose Estimation based on HRNet and Feature Pyramids

A popular area of research in the realm of computer vision is human pose estimation, which is the process of recovering human joint points from the given images or videos. Convolutional neural networks, which have great feature representation capabilities, have become a fundamental component of human pose estimation algorithms as a result of the deep learning field's quick development. To enhance feature quality and raise the precision of human pose estimate, in this paper, we propose a new model based on HRNet neural network and feature pyramid to more accurately capture each main part of the human body. The model uses HRNet as the backbone network, taking advantage of its ability to combine low resolution with high resolution, and afterwards adds characteristic pyramids to the HRNetnetwork in order to enhance the capability of detecting small objects by utilizing its ability to solve multi-scale problems. The model can estimate a human's pose accurately, according to experimental results, and on the MPII Human Pose dataset, the addition of the feature pyramid enhances the model's detection performance by 0.05. in order to achieve smoothing after adding feature mapping with different resolutions, the detection performance improves by 0.17 after further improvement by adding convolution. For the elbow, it improves by almost 0.5. The model's ability to increase the precision of human pose estimate is supported by all of the outcomes.
Effect Analysis based on GAN and CGAN Comparison

Image generation has traditionally been. a popular research direction in the vision community of computer, which aims to learn the distribution of a given data set, to generate realistic images obeying this distribution. Owing to the rapid growth of networks of convolutional neurons, image generation which is based on deep learning has made breakthroughs in both accuracy and speed. For different scenarios, however, the results of the same generation algorithm may differ dramatically. In order to probe the application limitations of various algorithms, in this paper, taking the Minst data set as the research object, we choose two representative generation algorithms, GAN and CGAN, to construct models and analyze their generation effects. We initially present the basic models of GAN and CGAN, which include their structures and differences, respectively. By comparing the GAN and CGAN models, we can see that the randomness of generated images is greatly decreased with the extra controls. Finally, the paper analyzes the effect of CGAN handwritten image generation, and different numbers have different effects under the same epoch.
Perspective and the Use of Eye Tracking in Human-Computer Interaction

With the increasing demand for information technology, the traditional human-computer interaction (HCI) mode using keyboard and mouse cannot meet the need, which has inspired the research of multi-channel interface based on new interaction technologies such as eye tracking. The direction of human eye contains the information of current task status, so eye gaze is one of the convenient input channels for HCI. To make eye tracking technology more comprehensive and widely used, it is constantly improved. With the development of eye tracking technology, it has been scientifically explored in HCI to record eye movements. The main purpose of this paper is to summarize the update of eye tracking technology currently used for HCI, summarize the development direction and shortcomings of the current technology, and help new researchers to study various methods and algorithms of eye tracking. This paper will briefly introduce the eye tracking technology, summarizes the current application of eye tracking technology in HCI, and discusses the future direction.
Education using instagram on knowledge, attitudes and eating habits as prevention of anemia in adolescent girls in Makassa

26.8% and at the age 0f 15-24 years is 30.2%. Government initiatives to prevent anemia by dispensing iron supplements without teaching have left adolescent girls with little knowledge of anemia, necessitating substantial informative efforts through social media. Instagram is less popular than Whatsapp as a tool for educating adolescent girls about anemia. The purpose of this study is to compare the effects of WhatsApp and Instagram as instructional media on knowledge, attitudes, and eating patterns among adolescent girls in order to prevent anemia. This study employed a control group and a pre-test post-test quasi-experimental design. In this study, participants (n = 60) were split into two groups: the intervention group (n = 30) used Instagram at Junior High School 30 Makassar, while the control group (n = 30) used WhatsApp at Junor High School 14 Makassar. A general questionnaire and a food frequency questionnaire were used to collect the data, which was then analyzed using the repeated ANOVA test to compare each group's results before and after the intervention and the unpaired t-test to compare the Instagram group to the WhatsApp group. The findings indicated that the Instagram group's knowledge (61.50+82.67 vs 82.67+11.651), attitude (81.93+8.682 vs 89.23+6.683), and eating behaviors (467.10+197.269 vs 486.45+212.911) had different values before and after the intervention. In the WhatsApp group, these differences were 54.17+10.093 vs. 60.83+13.714 for knowledge, 73.20+10.179 vs. 83.40+13.255 for attitude, and 553.10+204.985 vs. 518.28+161.929 for eating habits. With a p-value of 0.05, there were differences between the WhatsApp and Instagram groups in terms of knowledge (0.000), attitude (0.000), and eating habits (0.002). Therefore, this study's conclusion is that educating adolescents using Instagram as a medium is more effective in improving their knowledge, attitudes, and eating habits.