Reviewerly/exHarmony huggingface dataset:
The Development of Image Classification Models Based on Computer Vision

Image classification is a fundamental problem in computer vision, which deserves much attention in the past decade. To provide a comprehensive recall for the image classification task based on the deep learning algorithms, this paper first provides a brief history of computer vision and convolutional neural networks. Then, the current state of research and the direction of development of deep learning-based convolutional neural network models for image classification are examined. In addition, the basic model structure, convolution feature extraction, and pooling operations of standard and convolutional neural networks are also introduced. This paper summarizes the development of convolutional neural network models in recent years. In summary, based on the review of the development of image classification algorithms at home and abroad, the current mainstream image classification algorithms and frontier progress are summarized and analyzed, and the existing problems and future development directions of image classification are summarized and prospected. It can be concluded that deep learning-based methods has a great potential in this case.
Research On Image Stylization Methods Based on Deep Network

For the purpose of promoting the research of deep neural networks, image stylization has received extensive attention. Researchers have proposed a huge number of image stylization methods based on deep neural networks. In this paper, image stylization methods are divided into two categories: image stylization method based on reference and based on domain. A series of stylization methods. Several classicals methods are summarized, and the performance of the two categories of stylization methods on the common data sets of image stylization tasks are compared respectively, the developments of image stylization in video, human face and brushwork are proposed, the current progress of image stylization and the future research direction are mentioned. At the end of the article, the full text is summarized and summarized.
How do students assess the impact of integrating digital technologies on the mathematics classroom?

The current research sought to test the effect of using technology on students in various mathematics courses. The study was conducted in the mathematics department of our college of education. We tested how the students evaluate our teaching method and how involved they are in the acquisition of knowledge while using different digital applications with respect to the basic features of the traditional compared to the constructivist classroom. We compared the students’ attitudes towards the role of digital technology integration in learning/teaching mathematics in different courses taught with and without technological tools. The results indicate that the students’ attitude towards teaching mathematics with the use of technology correspond with the constructivist approach to teaching, while their assessment of the classes without the use of technology matches characteristics of traditional classroom. The obtained result aligns well with the notion that integration of technology in mathematics teaching makes it possible to achieve a qualitatively higher level of teaching this subject at school.
Analysis of Network Virtual Property based on the Theory of Property Rights

Although the new Civil Law has put the protection of network virtual property on the legal level, it has not yet further clarified the relevant concepts and the scope of protection, and there are still problems such as difficult evidence, difficult value assessment and imperfect redress mechanism in practice. Based on the property law, this paper puts forward corresponding improvement suggestions to effectively protect the legitimate rights and interests of network users and operators, and to create a fair, just, green and healthy network environment.
The Role of Text Structure in Reading Comprehension

Lack of understanding of the text structure is the major problem in English reading teaching in junior high school and senior high school at present. High school English teaching is not only for the college entrance examination, but also to cultivate students' ability of listening, speaking, reading and writing. Because traditional teaching focuses on the interpretation and grammar of words. Teachers help students understand the text by explaining the text word for word, combing the language points and grammatical phenomena of the text, and practicing the words and phrases repeatedly, traditional teaching focuses on the explanation of words and syntax, there is lack of macro-processing of chapters in reading teaching, which restricts students’ reading comprehension level. Text structure, as an important internal feature of the text, plays a positive role in improving the correctness and efficiency of reading comprehension. Adopting correct reading teaching strategies and imparting text structure knowledge can greatly improve the efficiency of reading teaching in the long run.
Fast retrieval and efficient identification of monument images using features based adaptive clustering and optimized deep belief network

Content-Based Image Retrieval (CBIR) is an extensively used image processing technique for retrieving similar images (general, images, monument images, etc) from large datasets. Some existing approaches' accuracy and retrieval time are ineffective for monument image retrieval. To overcome these challenges, the monument images are identified and retrieved efficiently using the visual feature extraction and adaptive density-based clustering approach (ADCA). Features required for efficient retrieval is obtained and the obtained multiple features are fused into a single feature using the hybrid weighted and average weighted methods. An optimized Deep belief network (ODBN) is designed to label the clusters based on the grouped images and then retrieve the images relevant to query images. From the experimental results, the overall accuracy achieved by the proposed framework are 99.63% (Architectural heritage dataset), 98.88% (synthetic dataset) and 99.1% (Kaggle dataset), respectively. The experimental results proved the efficiency of proposed in retrieving monument images.
Naive Bayesian Spam Filtering

The spam filtering system is used to identify which emails in the received emails are completely meaningless to the recipient and perform operations such as interception and deletion. Nowadays, with the rapid development of the Internet, while e-mail provides convenience for people, spam also comes along with it, which brings many troubles to users. According to statistics, 80% of the emails in the world are spam, and e-spam is really annoying. Therefore, how to solve the problem of filtering emails has important practical significance. Spam filtering using Bayesian theory is a statistical technique applied to email filtering. It essentially uses Bayesian classification to discriminate the attributes of emails, including spam and non-spam. Bayesian-based spam filtering is a very effective technique that can modify the model to meet the needs of specific users and give a lower spam detection rate that is acceptable to users. In this experiment, we use Naive Bayes for experiments, and we use Unigram and bigram methods to preprocess the data, respectively. Finally, it is concluded that the data processing accuracy of unigram and bigram is greater than 0.75, and bigram performs better in four different evaluation indicators.
The Development and Implementation for Encryption of Quantum Communication

Contemporarily, the quantum world is a new area in physics which has been widely explored. With the contributions of the scholars, the whole structure of the quantum world might cause great revolution on all the knowledge in various fields including teleportation, computing etc. Among various applications, quantum teleportation has attracted plenty of researchers, where remarkable results have been achieved based on the advanced facilities and proposed concepts. For quantum teleportation. The encryption is the key of the process. On this basis, the main topic of the whole paper will demonstrate the possible encryption concluded from two main communication methods. To be specific, the history and basic notes of quantum physics will be recalled primarily. In addition, the principle of encryption and applications of quantum communication will be discussed. Moreover, the state-of-art applications and implementations for encryption of quantum communication will be clarified. These results shed light on guiding further exploration of the encryption process of quantum communication.
Deep learning for real-time detection of breast cancer presenting pathological nipple discharge by ductoscopy

As a common breast cancer-related complaint, pathological nipple discharge (PND) detected by ductoscopy is often missed diagnosed. Deep learning techniques have enabled great advances in clinical imaging but are rarely applied in breast cancer with PND. This study aimed to design and validate an Intelligent Ductoscopy for Breast Cancer Diagnostic System (IDBCS) for breast cancer diagnosis by analyzing real-time imaging data acquired by ductoscopy.The present multicenter, case-control trial was carried out in 6 hospitals in China. Images for consecutive patients, aged ≥18 years, with no previous ductoscopy, were obtained from the involved hospitals. All individuals with PND confirmed from breast lesions by ductoscopy were eligible. Images from Beijing Chao-Yang Hospital were randomly assigned (8:2) to the training (IDBCS development) and internal validation (performance evaluation of the IDBCS) datasets. Diagnostic performance was further assessed with internal and prospective validation datasets from Beijing Chao-Yang Hospital; further external validation was carried out with datasets from 5 primary care hospitals. Diagnostic accuracies, sensitivities, specificities, and positive and negative predictive values for IDBCS and endoscopists (expert, competent, or trainee) in the detection of malignant lesions were obtained by the Clopper-Pearson method.Totally 11305 ductoscopy images in 1072 patients were utilized for developing and testing the IDBCS. Area under the curves (AUCs) in breast cancer detection were 0·975 (95%CI 0·899-0·998) and 0·954 (95%CI 0·925-0·975) in the internal validation and prospective datasets, respectively, and ranged between 0·922 (95%CI 0·866-0·960) and 0·965 (95%CI 0·892-0·994) in the 5 external validation datasets. The IDBCS had superior diagnostic accuracy compared with expert (0.912 [95%CI 0.839-0.959] vs 0.726 [0.672-0.775]; p<0.001), competent (0.699 [95%CI 0.645-0.750], p<0.001), and trainee (0.703 [95%CI 0.648-0.753], p<0.001) endoscopists.IDBCS outperforms clinical oncologists, achieving high accuracy in diagnosing breast cancer with PND. The novel system could help endoscopists improve their diagnostic efficacy in breast cancer diagnosis.
Demonstration and Implementation of Quantum Computing in Cryptanalysis

Quantum computing has long been a hot topic in both fields of physics and computer science. As a matter of fact, most of the earliest quantum algorithms are related to cryptology on account of its potential advantage over conventional computers. In this paper, Bernstein-Vazirani algorithm, a linear cryptanalysis method performing on quantum computers, is selected as a typical algorithm to demonstration and implementation of the quantum computing in cryptanalysis. To be specific, this study analyzes the method itself, and realizes the method with Qiskit so as to compare it with conventional methods. With the comparison, the superiority of quantum computing in certain fields as well as some current disadvantages to be dealt with are clarified. Thus, it is hoped to present the huge potential of quantum computing in its processing ability and its property of superposition beginning with the field of cryptanalysis. Besides, a direction of future improvement of the technology is also proposed. Overall, these results shed light on guiding further exploration of quantum computing.
Factors Affecting the Design of 3D Game Scenes

With the rapid development of video games at present, 3D games as a big branch of games are also developing rapidly. Different 3D games vary greatly from each other. They bring different sensory experiences to players, game play and so on, but these contents set each other off and rely on each other. In every good 3D game, a good scene design is essential. However, the literature on 3D game scene design is scarce. Therefore, this article will analyze the influence of world view, art style, game genre and player experience on scene design, so as to bring some inspiration and guidance to the scene designers. At the end of the article, this paper concludes that there are many factors affecting 3D game scene design, not only the ones discussed in the article. In addition to the considerations mentioned in the article, a good scene designer should bring himself into the player's point of view. It is from the player's point of view that one can know more clearly what a good scene design is.
Machine Learning Based Sentiment Analysis of Message on Twitter

In the age of information explosion, it is a very important and challenging task to extract the required features from a huge amount of information. The emotion expressed by the text information is one of the most important features of the information. However, there is not much research in this field, so it is of great significance and exploratory to the text sentence emotion analysis. In order to compare and explore better feasibility, both the sequential neural network model and the random forest model were built. Through the contrast between the two models, the machine learning of datasets composed of Twitter comments is carried out to analyze the emotion of the text, and emotion extraction is the research topic of this paper. In this paper, emotion analysis was studied in the order of data processing, model building, and result analysis, and an accuracy of about 90% was finally achieved, which is a good result, from which it can be seen that the constructed neural network model plays a good role.
Exploring The Efficiency of Resnet and Densenet in Gender Recognition

With the development of face recognition and image classification. There are many scenarios where gender classification techniques need to be applied. For example, the stores can recommend different products according to the gender of customers. There are several methods of gender classification, one of them is the convolutional neural network (CNN), which shows its promising potential in image classification. With other relevant variables and parameters being the same, this work tries to compare the Resnet with the Densenet to find a model that is suitable for specific gender classification datasets. The classification efficiency of the model is examined from the three directions of running prediction loss. This paper finds that in the case of small-scale data sets, the classification performanceof the Densenet model ourperforms that of the Resnet model, and the number of parameters used by the Densenet is significantly less than that of the Resnet. When a high-precision classification or recognition model is needed, Densenet is preferred.
Grover’s Algorithm – Implementations and Implications

Ever since the realisation of the potential computational power of quantum-natured circuits, multiple quantum algorithms have been proposed, exploiting quantum superposition or quantum entanglement features to simulate quantum systems that classical computers cannot efficiently probe. The most notable among these strategies is Shor’s algorithm, which takes advantage of the quantum Fourier transform and has been shown capable of solving integer factorisation problems within polynomial time. This article’s focus is on one type of quantum algorithm based on amplitude amplification, namely Grover’s algorithm. This algorithm’s working principle is explained, and a discussion of its recent developments and possible direction of future reach is provided. In addition, implementations of Grover’s algorithm have been performed using the IBM Quantum Lab, followed by a reference to its connection to life sciences.
Comparison Of Neural Network and Traditional Classifiers for Twitter Sentiment Analysis

Sentiment analysis has been a popular topic of study in the field of social media analysis, particularly when it comes to analyzing the emotions expressed in online comments. This is particularly relevant when it comes to IMDb Movie reviews, where users often express their opinions on the films they have watched. By using sentiment analysis techniques, researchers can gain insights into the overall sentiment of a movie and how it is perceived by the public. This information can be useful for movie studios and producers, as it can help them gauge the success of their films and make decisions about future productions. In this analysis, the performance of neural network models is compared with that of traditional classification methods when applied to the task of sentiment classification of tweets. A dataset of tweets collected from IMDb movie reviews is used for training. Three different models are trained on this dataset: a sequential neural network with two dense layers activated by ReLU and SoftMax functions, logistic regression, and random forest. The performance of these models is evaluated using a variety of metrics, including confusion matrices, AUC graphs, and accuracy and loss curves. It is found that the neural network model achieves an accuracy of approximately 90%, outperforming the logistic regression and random forest models, which achieve accuracies of approximately 90% and 83%, respectively.
Influence of Skin Color and Data Pre-processing on Gender Classification

Face recognition and gender classification is one of the hot spots at present, how to get better results in the recognition process is the key. This research was based on neural networks. While some researchers have explored this issue through neural networks, this research focuses on the impact of data processing other than the model on the final results. First of all, through the manual review and screening of data sets, the training set of face images only retained deep color skin face images, and the test set did not do selection processing. The final results of the model which based on selected data set are not much different from that of the model based on the unselected training set, except for a slight decrease in the recall rate of female face recognition. Secondly, by the method of control variables, the effect of the three kinds of data pre-processing is compared. The results show that both random cropping and normalization can improve some attributes of the model to varying degrees, but only random horizontal flipping has a certain negative effect on the model. In this research, it was found that the smaller proportion of white skin face image training sets hardly affected the model performance. Data pre-processing can effectively improve the model, but the use of random horizontal flips in a data set with a great number of small tilt angle images may lead to the deterioration of the results on the training set.
Impact of Artificial Intelligence towards customer relationship in Indian banking industry

The most significant &amp; ground-breaking innovations in the banking industry is the growing emphasis on the needs of the consumer. Consumers that are technically knowledgeable and regularly interact with cutting-edge innovations want banks to provide smooth experiences. For operations like digital money, e-banking, and real cash transfers, financial institutions have extended their industrial landscape to include retail, IT, and telecom in order to meet these requirements. While these developments have made it possible for consumers to reach the majority of banking services at anytime, anywhere, they have also come at a cost to the financial sector. This study also sheds light on the advantages and disadvantages of adopting AI technology in the Indian banking sector. This study is of descriptive nature which describes the usage of artificial intelligence in banking services and the effect on relationship with customer. Data was collected from total 187 customers of Delhi of public and private sector banks using Questionnaire.
The Application of VR in Virtual Design

As a popular technology, Virtual Reality (VR) is getting into our lives at a rapid speed. Following this trend, there are more and more applications in different areas, especially in areas that are highly related to virtual design where VR can help to produce a sense of reality for people. Thus, this paper researched related articles and lectures to conclude current VR applications. This work mainly explores VR applications in movies, games, and media. This research also investigates the advantage, disadvantages, effects, and future development of the VR application in each field. These factories are the most representative area in this field so they can help to show the whole trend. From the study, it can see that the benefits VR brings overweight its disadvantages. VR will be more popular and useful with more effort from companies and universities, and it will change the way people treat these areas.
Face Recognition with Deep Neural Network in Real-World Scenes

The primary aim of this paper is to recognize faces using deep learning models. This method is employed in many areas such as human-computer interaction, automatic image indexing, and ID verification services. Despite its many uses, face recognition still has several difficult features, such as the head position, age, lighting, and facial emotions. In this paper, our method includes three components which are getting the datasets, training the face model, and recognizing faces. Three steps are included in our process. First, the authors introduce the Haar-like features and the first module-get the database by detecting the face, removing the background, and creating the grayscale images of face, then the authors introduce the second module by using LBPH method (Local Binary Patterns Histogram) to detect differences between the model sample face and the detected face. Last, the authors use face recognition predictor to return the recognition result and confidence. Finally, our method successfully recognizes people and show the confidence value at the same time. The experimental results show great potential of our method in face recognition with high accuracy.
Cascade Attentive Dropout for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) aims to classify and locate objects with only image-level supervision. Many WSOD approaches adopt multiple instance learning as the initial model, which is prone to converge to the most discriminative object regions while ignoring the whole object, and therefore reduce the model detection performance. In this paper, a novel cascade attentive dropout strategy is proposed to alleviate the part domination problem, together with an improved global context module. We purposely discard attentive elements in both channel and space dimensions, and capture the inter-pixel and inter-channel dependencies to induce the model to better understand the global context. Extensive experiments have been conducted on the challenging PASCAL VOC 2007 benchmarks, which achieve 49.8% mAP and 66.0% CorLoc, outperforming state-of-the-arts.