Reviewerly/exHarmony huggingface dataset:
A Study on the Development of Convergence Design Curriculum in the Visual Communication Design Department for Educational Innovation in the Age of the 4th Industrial Revolution: Focusing on the 3-Year junior college curriculum

With the arrival of the Fourth Industrial Revolution, the combination of digital media technology, art, humanities and sociology, etc. with design has become a global trend in the field of design and art. In addition, in design-related education at universities as well, various contents are produced and education applying new methodologies is being conducted. This move of trend was made because it was recognized that the existing education framework had limitations in fostering design experts required in the era of the 4th Industrial Revolution. The technology of the 4th industrial revolution is erasing the boundaries between industries, and at the same time eliminating the boundaries between each cultural area and department. In accordance with this situation, the convergence of design education with various fields has also become necessary. Therefore, in this study, by paying attention to the design innovation required in this rapidly changing era, and suggesting academic possibilities in the field of visual design and educational plans for the 3-year visual design department in junior colleges, research was conducted to set a reasonable direction for visual design education suitable for various media and rapidly changing technologies. The significance of this study is in that visual design education according to social needs is to be conducted to provide a unique academic area for junior colleges based on this research and that it suggested the direction according to the perception that the preceding of the related curriculum should be established.
A Study of Art Museums Convergence Education Guidelines Model for Social Improvement and Talents Development of Mild Autism Spectrum Disability

In order to establish an effective museum convergence education guide and model, this study first sought ways to redefine the local and social role of art museums for the people with autism developmental disabilities. Second, the effectiveness of art enjoyment, mental recovery, and self-exploration were analyzed, and through the participation of family guardians in art museum programs. Third, various attempts and new role implications were derived to expand the social convergence platform of art museum exhibition-linked education. Fourth, an integrated model such as communication, healing, empathy, sharing, and support was established in the guidelines for convergence education at the Museum of Art for the Disabled in the Autism Spectrum. This study verified the effectiveness by analyzing the operation of two curriculums, Dreamcatcher Craft Class and Ribbon Embroidery Family Frame Collaboration Class, in line with the theme of ICOM Museum Week. As a result of the study, this study presented physical and social solutions to increase accessibility to art museums. It also suggested the need for a dual strength that can lead to art museum education. In particular, it emphasized the need for art museum, organizations for the disabled, and public institutions to cooperate with governance. In addition, by studying the necessity of active learning in art museums, the needs to expand guidelines for integrated art museum convergence education was suggested.
METHODS FOR DEVELOPING COMMUNICATION SKILLS BY MEANS OF MUSICAL THEATRE

The article discusses the possibilities of using theatrical methods in music lessons. Based on a review of the repertoire of children's musical theatres in Russia, an interview with S. Malyukova, artistic director of the "Kamerton" Theatre, as well as an analysis of the research literature devoted to the problem of drama pedagogy, a hypothesis of its significant potential in music lessons is put forward. Among the possible forms of drama pedagogics are theatrical performances of children operas and musical plays, dramatization of chamber-vocal and instrumental works, methods of plastic intonation. The description of each method is supported by examples from pedagogical practice; recommendations for teachers and ways for further cooperation between teacher and students are outlined. Examples of creative work reveal the potential for the development of children's communicative skills in the process of comprehending music.
Application of attention mechanism in image search

Under the background of high precision requirements of image search technology, new image search technologies emerge in an endless stream. Image search is one of the important research directions in deep learning. Among them, chromosome image search is a key step in medical imaging and modern genetic methods. In this paper, the principle of image search technology is analyzed, and a VGG16 neural network model using attention mechanism CBAM is presented to search for chromosome images. Finally, the optimization model is experimented and analyzed. The conclusion is that the addition of attention mechanism enhances the ability of the model to extract chromosome image features and efficiently completes the image search task. This method provides a new idea for the development of image search technology in the future.
Continuous sign language recognition based on 3DCNN and BLSTM

Sign language recognition can make the communication between deaf mutes and healthy people more convenient and fast. In recent years, with the continuous development of deep learning, the research method of deep learning has also been introduced into the field of sign language recognition. Compared with the recognition of isolated words, the recognition of continuous sign language is more time-dependent. The current research still has shortcomings in recognition accuracy. Therefore, we proposed a continuous sign language recognition method based on 3DCNN and BLSTM. Based on the spatial feature information extracted by 3DCNN and the short-term temporal relationship established, the global temporal modeling of the video information of continuous sign language is carried out by using the bidirectional semantic mining ability of BLSTM. The CTC loss function is constructed to solve the problem of time series label misalignment. At the same time, we add the calculation of auxiliary loss function and auxiliary classifier. Experiments show that the auxiliary loss function and classifier can effectively reduce the error rate of the network. The word error rate of the continuous sign language recognition algorithm proposed in this paper on the large continuous sign language dataset RWTH-PHONEIX-Weather 2014 is as low as 23.5%, which is lower than the previous algorithm.
Research on visual question answering system based on deep neural network model

With the rapid development of deep learning, computer vision, natural semantic processing and other technologies, Visual Question Answering (VQA) has gradually become to be an important research direction in the multimodal field. VQA technology has a wide range of application scenarios, such as multi-modal search, medical consultation, intelligent driving and so on, and has become the research hotspot and focus of scholars. Based on the classical CLEVR dataset, this paper builds different VQA prediction models by using the BilSTM model and different deep learning algorithms, including modnet, VGG and ResNet models. The results show that the hybrid model of ResNet and BiLSTM has the highest prediction accuracy, reaching 0.978. Compared with other algorithms, the accuracy is 1.3%, 2.5% and 2.7% higher than that of MobileMet, VGG19 and VGG16 models, which means that the hybrid model of resnet50 and BiLSTM is more effective in VQA tasks. At the same time, some problems and deficiencies of our model are analyzed and the future improvement direction is given.
Towards building long-range relationship for super-resolution

A new network super-resolution algorithm is proposed in the light of multi-feature fusion to surmount: the problem of manual feature extraction as well as low information throughput in traditional super-resolution algorithms. The algorithm adopts a learning method of the indirect mapping from the hazing image to the clear super-resolution image, which calculates the similarity between the point and other points, normalize the similarity to obtain the weight between each point, and then multiply it by the feature map value of the corresponding point and use the similarity. In the algorithm, utilizing multi-scale feature fusion enables to rebuild details of the image. Different depths of field combine the contour information obtained by shallow convolution with the detail information obtained by deep convolution to enhance the overall super-resolution’s effect. The experimental results and related data show that the channel throughput has been significantly improved, and compared with other algorithms, it has better detail information and contrast, which provides a new idea for dehazing methods.
TransCGan-based human motion generator

The synthesis of human movement is used in a wide range of applications, such as in military, gaming, sports, medical, robotics and many other fields. The current common approach is based on the acquisition of human motion data by motion capture devices. However, employing this method to gather information on human movements is expensive, time-consuming, and subject to space constraints. In order to avoid these problems, our goal is to create a system that can generate a variety of naturalistic movements quickly and inexpensively. We assume that the creation of human motion is a complicated, non-linear process that is amenable to modeling with deep neural networks. First, using optical motion capture equipment, we collect a range of human motion data, which we then pre-process and annotate. After that-we combine Transformer with Conditional GAN (Cgan) to train this human motion generation model with the collected data. Finally, we evaluate this model by qualitative and qualitative means, which can generate multiple human motions from a high-dimensional potential space based on specified labels.
Design and application of rhythmic gymnastics auxiliary training system based on Kinect

Based on Microsoft Kinect equipment, this paper adopts motion capture technology to collect human motion data in the actual training process of rhythmic gymnastics, designs and develops the functional module of motion data analysis combined with ASP.NET development environment, and integrates and packages other auxiliary functional modules to form an auxiliary training system of rhythmic gymnastics. Aiming at all kinds of problems in the teaching practice of rhythmic gymnastics in colleges and universities, the system will put forward a comprehensive application solution, which takes the physical movements, equipment movements and self-selected complete movements of rhythmic gymnastics students in daily training in colleges and universities as the research objects, takes the transformation and analysis of movement information data as the method, and takes the visual interaction at the network end as the display form. It greatly facilitates the practical application of students and teachers, effectively improves the training effect of students, achieves the purpose of auxiliary training, optimizes the teaching process of rhythmic gymnastics education, and further promotes the process of information teaching construction in colleges and universities.
A Seq2Seq based model for generating diverse sentiment dialogues

To address the problems of poor attention to the richness of dialogue content and emotional responses in human-computer interaction, it is valuable to study how to make the machine responses diverse and emotional. A Multi-Style Emotional Dialogue Generation (MEDG) model is proposed, which uses Sequence to Sequence (Seq2Seq) as the text generation model. Firstly, the implicit function in the Seq2Seq encoder is mapped using sentiment sentence vector mapping and introducing an attention mechanism, which increases the sentiment constraint of the model in the generation process. Secondly, penalty factors and output vectors are introduced in the decoder to make the decoded results have diversity. Finally, experimental tests were conducted on an open network dataset, and the results showed that the MEDG model had better scores.
Commodity knowledge graph-based TransD-KGAT method for recommendation

In the field of item recommendation, traditional collaborative filtering-based approaches face the problems of data sparsity and cold start. As a heterogeneous network with multiple entities and relationships, the knowledge graph has rich semantic association information to help explore reasonable associations between users-items and items-items and assist recommendation systems in alleviating the problems of sparsity and cold start. Graph neural network-based approaches can explicitly encode key collaboration signals for user-item interactions and have been proven to be effective recommendation methods. However, previous work has not explored how the entity superordinate words and synonyms of the knowledge graph affect the metrics. In this paper, we discuss and experiment from two perspectives of knowledge graph construction and graph neural network embedding to explore how to improve the recommendation metrics comprehensively. In this paper, we construct commodity knowledge graphs and propose the model called TransD-KGAT, which improves three indexes compared with the KGAT model in terms of precision, recall, and ndcg by 2.64%, 4.96%, and 3.9%. It is also concluded that adding knowledge graphs of semantically similar and superordinate words improves the recommendation metrics.
Research advanced in human pose estimation based on deep learning

Human pose recognition is a current research hot spot, which aims to detect the information of human joints, orientations and scales from images or videos and predict the specific pose of the human body based on all the key point information. Benefiting from the rapid evolution of deep learning, target detection algorithms based on convolutional neural networks have achieved breakthroughs in both accuracy and efficiency. On the basis of detailed literature research and analysis, this paper provides a comprehensive evaluation of the research progress of human pose estimation. Specifically, we mainly introduce the classification of human action poses and the methods of human action pose recognition, including the design ideas, basic framework and advantages and disadvantages of representative recognition methods. Finally, we summarize the main challenges and give an outlook on the future research development of object detection.
Multi-level fast personalized recommendation system

Personalized recommendation systems have high speed requirements, and most of the previous methods rely on powerful hardware to meet the real-time requirements. We algorithmically propose a fast recommender system. Our proposed method abandons the previous global comparison method, but uses a local comparison method, which greatly reduces the computational complexity. The experimental results also show that the effect of the method is not decreased, but improved.
Face-based gender recognition with small samples generated by DCGAN using CNN

Gender recognition of face images is an important research field in image classification. Convolutional Neural Network (CNN) has a good performance in this field. To obtain successful results, large and high-quality data sets are essential. However, data scarcity has always been a common challenge in deep learning. The Deep Convolutional Generative Adversarial Network (DCGAN) has a powerful application in data enhancement. The face image generated by DCGAN can be used to enhance face data. In this study, the fake face images generated by DCGAN and the real face images of CelebA datasets are used to train CNN classifier for gender classification of human faces. In the experiment, the training set and test set both are divided into three types, including real face images, fake face images, and real-fake mixed face images. The type of test set is the same as that of the training set, and the number of face images in both the training set and test set is small, including 1000 face images. By comparing the training accuracy and test accuracy of datasets under various conditions in CNN classifiers, it can judge whether the fake face images generated by DCGAN have a good data enhancement effect. The experimental results show that the data enhancement ability of DCGAN is also well applied to CNN classifier, and the face images generated by DCGAN can effectively improve the performance of the CNN classifier to a certain extent.
Research advanced in occluded face recognition

The domain of computer vision's most popular study area has always been face recognition which aims to identify different face images and predict the corresponding identity information through feature analysis and modeling. In practical applications, a number of variables, including lighting, posture, and clarity, have an impact on facial recognition accuracy. Among them, the most challenging scene is occlusion face recognition, which will cause feature loss, local coherence and alignment errors to greatly inhibit the accuracy and generalization ability of the face model. Based on detailed literature research and analysis, this paper provides a comprehensive evaluation of the research progress of occluded face recognition. Specifically, based on the introduction of classical face recognition technology, we further discuss the design ideas, basic framework, advantages and disadvantages of representative occlusion face recognition methods from two aspects: robust feature extraction and robust classifier. Finally, we summarize the main challenges and give an outlook on the future research development of object detection.
A reinforcement learning-based network load balancing mechanism

With the exponential growth of cloud computing demands, load balancing gradually becomes the infrastructure of high concurrency applications. The increased demand makes load balancing the cornerstone of large-scale systems’ stability and efficiency. Due to the diversity of modern client-server architecture, irregular fluctuations in cluster system resource allocation gradually become significant. The existing load balancing algorithms are rule-based, leading to a gap between operational and practical scenarios. The phenomenon causes noticeable tribulations in cluster resource utilization optimization. This research proposes a reinforcement learning-based load balancing model optimization approach. This paper modelled the load balancing problem as a Markov Decision Process and implemented the conjecture with the Q-learning algorithm. This paper performs a load balancer that can efficiently utilize the resources in the cluster system by observing the association between the packet body and node resource utilization rate in the cluster system. The experiment demonstrated that the author’s mechanism substantially improves the average cluster system resource utilization efficiency and reduces the Round-Trip Time performance in real network environments
Influence of images generated by CGAN on the performance of image classification based on CNN

Conditional Generative adversarial net (CGAN) can generate data according to the given constraints. Convolutional neural network (CNN) is a neural network that can be used to process images providing learnable parameters for efficient classification. However, there are few researches on whether it is more beneficial for CNN to classify from the images generated by GANs as the training set in the past studies. To solve the issue, this paper compares the training effect of CNN classification of Handwritten-number images using real images, images generated entirely by CGAN that are results through CGAN’s trained from real handwritten-number images, and images mixed with them as training data sets. The dataset used is a famous dataset called MNIST which consists of 10 categories. Based on it, the corresponding experiments are carried out and the final experimental result indicated that the images generated by CGAN can further improve the performance of the CNN.
Autonomous driving based on deep neural network

Deep learning, the critical part of machine learning, has become influential in different fields, including natural language processing, computational biology, and computer vision. In the last decade, there has been a massive surge in computer vision research since its related application is so promising. Many have proposed various methods to fulfill the automation of driving based on deep learning, but, up until now, there is still a gap between the virtual and reality. This paper focuses on its application in autonomous driving. A new framework is proposed to fill that gap using a deep neural network. Specifically, instead of using the raw images captured by cameras to make decisions, semantic segmentation is applied first to get intermediate products that can better connect virtual and reality. Considering the road landscape needs to be mainly treated, the pre-trained model PSPNet is used to process the original image data. Then this data is provided as input to a deep CNN model for feature extraction and prediction. Compared to the traditional method, a semantic segmentation process is added to help extract useful information within an image and is expected to bring some positive effects.
Medical data sharing scheme based on hybrid encryption with revocable attribute

With the popularization of electronic medical record, internet medical care, AI medical image and other applications, the wave of medical digitization is coming. The electronization of medical data has brought convenience to the sharing of medical data, but it also makes the protection of personal sensitive data in medical data face unprecedented challenges. How to effectively protect personal sensitive information while sharing medical data. In this paper, a hybrid encryption algorithm based on SM4 encryption algorithm and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with revocable attributes is proposed. The algorithm first uses the SM4 encryption algorithm to encrypt the medical data, and then the SM4 encryption key is used as the encrypted data for encryption by CP-ABE. At the same time, aiming at the requirement of CP-ABE algorithm to revoke attributes, this paper proposes to embed a revocation table in the ciphertext to realize the direct revocation and restore of specific attributes of specific users. Through theoretical analysis and experimental verification, the revocable attribute hybrid encryption algorithm proposed has great advantages for the security and efficiency of personal sensitive information in cloud medical data.
The study of object transformation based on CycleGAN

At present, generative adaptive networks (GAN), has become a popular module in deep learning. GAN is very effective in image generation and image style migration. At present, the research on the migration of image style is focused on images such as oil painting and landscape painting, and lacks the research on the conversion between object images. This paper extends the style transfer technology to object recognition, uses CycleGAN method to learn the mapping relationship between zebra and horse, and realizes the transformation between zebra and horse. Viewing the generation effect of different learning methods by changing the learning times and learning rate policy. This work realizes the conversion between zebra and horse, and shows the generated pictures under different training times and different learning situations. Under the same training times, the conversion effect from horse to zebra will be better. After a certain number of trainings, the training effect will gradually decline. The conversion effect of the same type will be improved with the increase of training times. Different learning rate policies will bring different generation effects.