Reviewerly/exHarmony huggingface dataset:
Robustness Analysis of Traffic Sign Recognization based on ResNet

Autonomous driving has always been an important research topic and application task of artificial intelligence, which has attracted the attention of a large number of researchers. As an important component of the environmental perception module in autonomous driving tasks, traffic sign recognition can help drivers understand road information in a timely manner and avoid potentially dangerous driving operations. To this end, accurate recognition of traffic signs is crucial from both a strategic and a practical point of view. The early recognition technology of traffic signs is mainly based on the detection of color and shape, whose recognition accuracy is limited due to the fading and deformation of traffic signs. Numerous researchers have successfully used these deep learning-based object identification algorithms for traffic sign detection and recognition thanks to the development of the Faster R-CNN and YOLO series algorithms. However, we argue that few efforts focus on the recognition performance of the model in different scenarios, which is especially important in the process of autonomous driving. Based on this observation, this paper first constructs the ResNet model. Part of the image recognition accuracy predicted by the model reached 99%. Afterward, the robustness of the model is explored by simulating complex scenes by changing illumination and noise, and it is proved that the model has good generalization ability and practical application ability.
Depth Analysis of Microsoft's Acquisition of Activision Blizzard: Thinking Based on the Development of the Metauniverse

In this pandemic era, more and more people pay attention to games. At this time, a news that shocked the game circle came out. It was Microsoft that announced that it would complete the acquisition of Activision Blizzard in the 2023 fiscal year. As a host game player, we decided to use SWOT Analysis and PEST Analysis to deeply analyze the impact of this acquisition from eight aspects, especially on the development of the Metauniverse. In the process of research, we found that this may be an opportunity for Microsoft to complete the third growth curve. If all goes well, we will see the birth of the game company with the third largest global revenue in 2023. For the acquisition, it is widely believed in the industry that Microsoft and Activision Blizzard will achieve a win-win situation. Therefore, we suggest that you should pay more attention to this acquisition and make reasonable investment in your spare time.
Fashion Outfit Recommendation and Transformation Using Generative Adversarial Network Methods

Over the last few decades, generative adversarial network (GAN) methods have been increasingly applied and improved in fashion-related scenes, especially during the pandemic. This article provides an overview of several mainstream GAN models for fashion recommendation and fashion transformation, including compatibility matrix-regularized GAN, Outfit GAN, transformer-based GAN, conditional analogy GAN, disentangled cycle-consistency try-on network, and flow-navigated warping GAN. Within each GAN method, the improvements made to perform more related and deliciated are presented with detailed loss functions, the specific evaluation matrixes, and the examinations of generators or discriminators. Moreover, possible directions of improvements for future research and problems with existing models are involved. As for the common restrictions of included methods, including the problem of incomprehensive and non-diverse datasets, the standard evaluation of modish aesthetic value, and the number of fashion items involved in one outfit, are analyzed within fashion outfit recommendation and fashion transformation. Therefore, such restrictions provide possible directions for future analysis in fashion-related networks.
Synthesizing Human Characteristics based on String Instruments

As music technology continues to evolve, new software enables us to digitally produce audio. Although plenty of progresses have been made in music genres (e.g., EDM or hip hop), many string instruments have yet to be thoroughly synthesized. Instruments, such as the cello, require incorporating human characteristics to evoke emotion. On this basis, Nyquist and Audacity are utilized in this paper to analyze and create the synthesized pitch of the cello. Some may argue that computers are incapable of human characteristics in music, but the results of this study clarify the feasibilities at some extent. Based on both visualizing and listening, the output creates a similar sound hardly indistinguishable from a human. It presents the possibility of expanding onto other string instruments including the violin, viola, etc. Instead of the necessity for an entire orchestra, anyone can have their own symphony at their fingertips. From movies, video games, or any form of entertainment, the advancements in this field of research could revolutionize the music industry. These results shed light on guiding further exploration of synthesizing human characteristics in context of string instruments.
Research of Convolutional Neural Network on Image Classification

With the progress of artificial intelligence, technology based on deep learning is becoming more and more mature, and the application of deep convolutional neural network for image classification has become a popular topic for researchers. The number of the structure of deep convolutional neural network for image classification is keep increasing, and its performance is consistently improving, gradually replace that of traditional methods. According to the process of model development and model optimization, this paper divides the convolutional neural network into two models: classical deep convolutional neural network model and attention mechanism deep convolutional neural network model. The construction methods and characteristics of various kinds of deep convolutional neural network models are comprehensively reviewed, and the performance of various classification models is compared and analyzed. Finally, the problems of deep convolutional neural network model are presented.
Breast Cancer 6th Stage Prediction Based on Machine Learning Models

The differences between each 6th stage of the breast cancer are subtle, and doctors’ judgement alone is not sufficient to determine the 6th stage accurately. 6th stage is the different levels of breast cancer development and it represents the current status of the cancer. Therefore, it is crucial to determine it correctly in order to conduct corresponding treatments. The incorrect categorization of the 6th stage and misuse of treatments can be catastrophic, and there are currently no such models to help doctors predicting the 6th stage. The dataset Seer Breast Cancer Data is used which include features like race, t-stage, n-stage, etc. This paper proposed to use random forest and K Nearest Neighbor (KNN) methods to build models and use features related to the patients and their cancer as training data. The random forest model achieved a predictive result of 99% for precision, recall, and f1 score after data normalization. The only mistake this model made is when differentiating stage IIIA and IIIB. The KNN model achieved an accuracy of 95% after normalization. The result shows that Random Forest model is best suited for predicting the 6th stage. The random forest model with 99% accuracy can effectively help doctors determine the 6th stage when they are having difficulties.
The Study and Application of Facial Recognition Models

Facial recognition technology is a biometric application that uniquely identifies or authenticates a person by comparing and analyzing facial features based on the width of a person's face. Face recognition technology has gained popularity recently thanks to the development and invention of artificial intelligence and other technologies. The traditional methods of face recognition technology development—the method based on geometric features and the method based on 3D models—as well as the deep learning Convolutional Neural Network (CNN) model VGG-Face for face recognition—are analyzed, compared, and described in this paper. It also provides a detailed description of face recognition technology. In this work, accuracy and TAR values are employed for geometric feature matching by comparing the metric results of each method on a dataset for better comparison. Our results show that while both traditional geometric feature modeling methods and 3D modeling methods can achieve good accuracy, however, they do not perform as well as humans in terms of accuracy. The Vgg-Face based on the deep learning model completely exceeds the 3D model method in terms of accuracy and TAR value, and is even more accurate than humans.
AI Application in Computer Games

It is well-established that artificial intelligence has already been used in nearly all kinds of computer games. The purpose of most games is to entertain (though there exists some educational games which aim to teach), and AI takes the role to improve the player’s experience. First, current use of artificial intelligence in computer games is introduced, then, some AI applications which aim to promote both entertainment and interactive environment is mentioned. Then, the future of artificial intelligence to find the possibility to create a kind of AI which can not only interact with players but also improve as number and experience of players grow. Lastly, the paper will analyse the future use of AI in different fields of computer games including, but not quite, the feedback of game bugs and solutions of players’ problems.
Studies Advanced in Salient Object Detection based on Deep Learning

A very important job in the area of computer vision is salient object detection, its goal is to check salient or obvious objects in images or videos. Thanks to rapidly developing networks of convolutive neurons, especially generative adversarial networks, CGAN has become a popular framework for salient object detection, which has promoted breakthroughs in detection precision and velocity of protruding objects. Focusing on the topic of salient object detection, in this paper, we systematically introduce the research progress of salient object detection. Specifically, we first introduce methods for detecting protracted objects based on general object detection algorithms (such as Fast R-CNN, YOLO, and SSD), including their basic ideas, key steps, advantages and disadvantages, etc. We then focus on CGAN-based salient object detection methods, including method classification, typical method introduction, and application examples. Finally, we quantitatively compare the performance of different saliency detection algorithms on common datasets, and discuss the existing research problems and feasible solutions of the topic.
The Infinity Game with Virtual Reality Technology

Virtual reality technology (VR), refers to the use of computers to generate a kind of visual and auditory directly on the participants, and allow them to observe and operate interactive virtual world technology. However, this work wants VR games to have a psychological impact on people. So, this work came up with a game for that purpose. The whole core feature of the game's design is infinity, and players will learn the true value of life: being brave and taking risks, or staying in your comfort zone. There are two main scenarios for the development of 3D workspaces and environments. The first is the aforementioned corridor, which is the main interactive environment where the player is trapped and needs to find a way out. The second is the hospital, where if the player chooses to leave, he will end up in a hospital bed, which means he will leave his family in the hallway and not be able to see them forever. In the end, this work interviewed nearly 30 different players. Most people gave us positive comments about the game, whether it was the music, the scenes, or the objects, it felt intense and exciting. It also causes them to think about life. Everyone has a different choice. This is in line with the expectations. Therefore, it can be concluded that the success of this game has caused a psychological impact on players and triggered their thinking about life.
A Research on Neural Network and An Improved Model For Stock Prediction

In this paper, Back Propagation Neural Network (BPNN) and Firefly Algorithm-Back Propagation Neural Network (FA-BPNN) are studied for the purpose of stock price prediction. The result shows that, compared with BPNN, the average prediction time of FA-BPNN increases by 3.2239s, the average relative prediction accuracy increases by 1.2739 %, and the corresponding variance decreases by 0.0003. It shows that the prediction accuracy obtained using FA-BPNN is higher. This is because the weighted value of FA-BPNN can effectively find the extreme value of the loss function, thus avoiding local optimum. But the prediction time is slightly longer, because the algorithm needs to sacrifice the tracking time in order to improve the prediction accuracy. Meanwhile, the variance corresponding to the relative prediction accuracy of FA-BPNN is the smallest. This demonstrates that the prediction effect of FA-BPNN is superior, and the prediction results are more stable and robust after each training. The above outcomes demonstrate that the FA-BPNN model has excellent performance in short-term stock forecasting. It has some feasibility and can be of use to stock market investors.
Performance Analysis of Model-Contrastive Algorithm

Federated learning develops multiple clients with a universal framework platform to train deep learning models with excellent performance. During this process, the local data set is not leaked to the outside, making federated learning a powerful method to protect data security while solving the problem of isolated data islands. However, in some usage scenarios, different clients are holding local data sets which are non- Independent Identically Distribution, i.e., non-IID. There have been significant attempts made to overcome this difficulty, and one of these efforts is the model-contrastive federated learning framework, as known as MOON algorithm. The main concept is to make use of the similarities between the global and local models to adjust the local training. The final global model performs well with non-IID data. The paper aims to replicate and analyze this algorithm to provide data reference for subsequent use. The algorithm is tested on the MNIST data set to get its performance on the handwritten digit classification task. This paper processed the data set to make it non-IID, but the result achieved a high accuracy rate with an acceptable computational cost.
Deep Learning for Detecting Quilt Status of Sleeping Children

Sleeping is important for the physical growth of infants and toddlers. However, because of the growth of their bodies, they may kick off the quilt when they fall asleep and catch a cold as a result. Because it is difficult to extract the all-appropriate features that insists the quilt is not covered in the images taken by the home camera manually, which is suitable for the old types of symbolic AI to recognize, there has not been a suitable solution to this problem in the past. However, thanks to the rapid development of artificial intelligence and neural networks, intelligent target detection, which was previously difficult to achieve, has finally been realized and several approaches to intelligent target detection have emerged, leading to many different perspectives and algorithms. In this paper, by analyzing and comparing the advantages and disadvantages of different algorithms in different application scenarios, we found that YOLO (v5 in this paper) is very suitable for quilt status detection and realized the program by applying YOLOv5 neural network and cosine difference algorithm simultaneously.
Music Generator Applying Markov Chain and Lagrange Interpolation

Algorithmic composition is the process that composes music based on algorithms by formulating specific rules that incorporate relevant music arranging theory and assigning these rules to a suitable computer algorithm. One common approach is to use Markov models to generate note sequences. However, the generated notes do not consider the overall chord progression and the rhythmic pattern of the section, which is not effective when dealing with long multi-track music, especially for more structured works, e.g., classical and pop music. Therefore, this paper introduces an improved approach that uses Markov chains for chord generation, and then uses Lagrange Interpolation to generate melodies and accompaniments for multitrack customized instruments using chord progression notes as anchor points. In this study, only 30 common chord progressions and 30 rhythmic progressions were used as the input data to generate a sample music with cat sounds as the main melody, violins as the sub-melody, and pianos as the accompaniment. It eliminates the mentioned problems associated with the adoption of Markov chains for melodic composition. In this case, it allows for the creation of tonal music with an overall structural, multi-track approach, by using chord progressions as a guide to the melody and even the accompaniment, and by controlling the structural characteristics of the music through rhythmic patterns.
Federated Learning based on Homomorphic Encryption and Digital Signatures

Federated learning aims to train a centralized federated model using decentralized data sources and to ensure the security and privacy of user data during system training process. Federal learning still faces four challenges: high communication costs, system heterogeneity, data heterogeneity, and data security, The primary attacks facing federal learning include confidentiality, integrity, and usability attacks. Attackers mainly target confidential attacks, while malicious attackers target integrity attacks and usability attacks. Homomorphism encryption refers to the encryption algorithm that satisfies the nature of the homomorphism operation, that is, to the ciphertext of the data after the homomorphic encryption. This article first introduces the federated learning and its possible attacks in the process of modeling behavior, and then introduces how to use homomorphic encryption and digital signature algorithm to prevent the attack method, finally through the experimental reality of federated learning encryption and digital signature and analyzed the above operation on the performance of the federated learning system.
Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Latent Semantic Analysis (LSA) is an approach used for expressing and extracting textual meanings using statistical evaluations or modeling applied to vast corpora of text, and its development has been a major motivation for this study to understand the design thinking approach. We introduced LSA and gave some instances of how it might be used to further our knowledge of cognition and to develop practical technology. Since LSA's inception, other alternative statistical models for meaning detection and analysis in text corpora have been created, tested, and refined. This study demonstrates the value that statistical models of semantics provide to the study of cognitive science and the development of cognition. These models are particularly useful because they enable researchers to study a wide range of problems pertaining to knowledge, discourse perception, text cognition, and language using expansive representations of human intelligence.
Research on the Application of VR in Games

From traditional 3D games to VR games, the technology in gaming has changed a lot. Vr brings people a new experience. It is used in various fields such as health care and education, but it is widely known for gaming. In order to explore the reasons why VR is widely used in the field of games, the principle of VR games and what they bring to people is the purpose of this article. This paper mainly discusses the application of virtual reality technology in games. It describes the facts of evolution of VR technology. Use this as fundamental element, several types of VR games were made out which provide different positive effects on players. There’s difference between the traditional game and VR game. VR game is not yet a mature technology but will have a bright future. People can infer that VR application in the game is very suitable and have lots of potentials.
Studies Advanced in Image Style Transfer based on Deep Learning

Image style transfer (IST) is a hot topic in the computer vision community, which refers to learning the distribution of a given style image to convert any image into corresponding image style while the content of the original image is preserved as much as possible. Early style transfer mainly utilizes texture features. Thanks to the great improvement of deep learning technology, researches on IST based on convolutional neural networks (CNN) have achieved breakthroughs in accuracy and speed. Focusing on the topic of deep learning-based IST, we will introduce the latest algorithms in detail, including their basic ideas, key steps, advantages, and disadvantages. Also, we will give an analysis of the performance of representative methods. Furthermore, we discuss the problems to be solved in style transfer and summarize the challenges and development trends in the future.
Researches Advanced in Generative Adversarial Networks and Their Applications for Image-Generating NFT

A generative adversarial network is a deep learning model, an unsupervised learning method. In computer vision, the generative adversarial network is a research direction with rapid development in recent years; Similarly, the rise of cryptocurrency Non-Fungible Tokens (NFT) in recent years has also attracted much attention to the field of art. As an "irreplaceable currency" NFT provides a more novel and convenient way for content creators and artists to create and increases the continuous income of original creators. At the same time, it has also attracted widespread attention to the financial field. Therefore, this paper is determined to combine the generative adversarial network of the production of NFT and discuss and analyze the autonomous computer generation of artworks. Firstly, this paper starts with the model's structure, the design of the objective function, Block chain technology, and Irreplaceable tokens encrypted using blockchain technology. Then, the image generated by the whole generative adversarial network and transformed into NFT works are described in detail. In addition, this paper briefly discusses the development ethics of human art and machine art and the prospects for its development trend.
MDR‐Net: Multiscale dense residual networks for liver image segmentation

Abstract Liver image segmentation is an attractive topic in the diagnosis and surgical planning of liver diseases. Although deep learning methods have significantly advanced liver segmentation, existing frameworks fail to clearly determine liver boundaries, especially in medical images where various organs have similar grey levels. In this paper, the authors design a multi‐scale dense residual network (MDR‐Net) for liver segmentation, which consists of two blocks: a liver segmentation network and an edge‐aware network. In the segmentation network, the authors introduce a multi‐scale residual pooling module combining channel attention (CA) mechanism and depth‐wise separable convolution to accommodate liver scale variation. Furthermore, the authors employ an edge‐aware loss network to refine edge information and enhance feature representation, which is beneficial to guide the network to iterate towards the ground truth. The authors’ method achieves the best visualization results in qualitative evaluation. In addition, the authors’ method achieves 96.189% on 3D‐IRCADb and 96.889% on the CHAOS dataset in quantitative evaluation with respect to the dice index.