Reviewerly/exHarmony huggingface dataset:
The Proposed Framework for Cloud Computing System Security and Privacy Services

Public cloud computing has emerged as a popular and efficient solution for organizations to store, process, and access their data and applications. However, with the increasing adoption of public cloud services, concerns regarding security have become a critical factor in decision-making processes. This study aims to investigate the security aspects of public cloud computing and identify potential vulnerabilities and threats that organizations may face. The aim of this study is to examine the challenges associated with cloud computing and its security concerns. Cloud computing is a technology that utilizes the internet to share data and resources. It is characterized by five key features: On-Demand self-service, Resource Pooling, Broad network access, Rapid elasticity, and Measured Service. However, despite these benefits, cloud computing has several open issues, including Security, Availability, Scalability, and Interoperability. Traditional security techniques are insufficient in fully securing cloud computing. Security and privacy issues often arise during cloud deployment and delivery models. As a result, researchers are interested in learning more about cloud computing security and its methods. Industries are creating architectural models with a high level of protection for their data center to solve current security challenges. This article examines the security of cloud deployment strategies and analyzes cloud security and privacy challenges. The cloud computing service models are also discussed, along with any security issues. The study emphasizes the significance of Audit as a key factor in offering a security solution that covers all relevant security characteristics as determined by the literature. The study's conclusions present a fresh challenge to cloud service providers and suggest a new security paradigm that explains customer security needs. Overall, this study offers a parameter-by-parameter assessment of available security solutions and makes recommendations for enhancements to address cloud security issues.
Research on Talent Cultivation and Industry-Education Integration Path Construction of College Education under the Perspective of Informatization

Abstract This paper extracts the basic elements of the integration of industry and education under the perspective of symbiosis theory and builds a symbiosis model in the communication and cooperation between symbiosis units according to the symbiosis units in the system of integration of industry and education. Influential factors were screened from different levels, and the DEMATEL method was used to determine the importance degree of system factors in combination with the explanatory structural model so as to construct the structural framework of influential factors. The weights of the influencing factors were finally confirmed through analysis, and empirical research was conducted on three colleges and universities, S1, S2, and S3. The overall performance level of S1 college and university integration is excellent, and the comprehensive judgment value of the college and university integration performance of this college and university is 85.342. The overall performance level of the two colleges and universities, S2 and S3, is good, and the comprehensive judgment value of the college and university integration performance of the two colleges and universities is 77.933 and 81.930, respectively. The evaluation study on the integration of industry and education can provide better recommendations for the integration path.
Approximating Decision Trees with Priority Hypotheses

This paper addresses the problem of creating decision trees for identifying hypotheses, also known as entities, in a setting where the cost of an action is dependent on the true hypothesis. Specifically, we consider the scenario where n hypotheses are divided into m groups based on their priority levels. Taking an action on a higher priority hypothesis incurs a higher cost. This is relevant to many real-world applications where cost-sensitive decisions need to be made. For example, in a medical diagnosis task, the goal is to take a series of actions (such as medical tests) to identify a cause. Each action in this process requires conducting a test on the patient and observing the outcome, which can take anywhere from a few minutes to several weeks depending on the test. In this case, the cost (the result of waiting for the outcome) is higher if the true hypothesis is more time-sensitive. For example, if the true hypothesis is toxic chemical exposure (as opposed to a chronic disease such as diabetes), a delay of a few minutes could significantly increase the patient’s risk of mortality. We propose a group greedy algorithm to solve this problem. We demonstrate that under worst-case scenarios, our algorithm has an approximation ratio of $$O(m\log n)$$ . Importantly, when $$m=1$$ , meaning there is only one group of hypotheses, our result is consistent with the logarithmic approximation bound for the traditional optimal decision tree problem.
Solving the smallest ball problem based on convex programming theories

The mathematical method known to be Quadratic Programming is a branch of Convex Programming or Convex Optimization, which is then a peculiar case of Mathematical Optimization given a series of restrictions including a convex function to minimize. The Smallest Ball Problem is a problem where we seek to find the smallest enclosing ball of given points on a plane. Convex Optimization provides a solution to the Smallest Ball Problem. There are several ways to characterize a convex programming problem and its solutions, the most important of which is called the KKT conditions. By relating the Smallest Ball Problem to solving a Convex Programming Problem and using the Python packages, the radius, as well as the central point of the Smallest Ball, will be found. In addition, the underlying algorithm for solving Convex Programming Problems is studied. It can be concluded that Convex Programming, or more specifically, Quadratic Programming, gives a feasible solution to the Smallest Ball Problem.
The application of convolutional neural networks in face detection

Face detection is a popular and challenging issue which is widely studied in the past few decades. Its application includes the identity authentication, human machine interaction, security surveillance and social network. To have a better insight of the application of one of the typical deep learning algorithms called Convolutional Neural Network (CNN) in this field, this paper aims to analyze the current literature and progress about the face detection of low image quality and face detection optimization. The literature of Convolutional Neural Network from 2015 was included in this paper. Past research topics of face detection includes the occlusion, scale, small face cluster, speed, precision and multi-task region proposal network. The comparison between various deep learning-based methods in terms of the performance indicated that there is still no high robustness solution to all problems. The future research agendas of face detection based on the Convolutional Neural Network was also summarized.
Prediction of intel CPUs price with regression analysis

CPU stands for the central processing unit. It is a unit that executes the instruction and allows the computer to run programs, making it an essential computer component. When people are looking for a new computer, either buying a built one or building one, CPU takes up a significant portion of the computer budget. The existence of Moores Law indicates that the CPUs price is predictable. This essay constructed the two models, multinomial linear regression and multivariable polynomial regression models, based on parts of Intel CPUs parameter value to predict their recommended sale price. According to the data set used in this research and the built model, the multinomial linear regression model is more accurate in predicting.
Denoising diffusion model as handwritten digit generator

The diffusion model is the process by which variables are introduced and propagated through a population. The equation can be applied to any physical system that exhibits such processes through time. Generally speaking, diffusion occurs when a physical system is connected to another by a small number of interconnected points; the interconnected points' overall connectivity can be represented by a network. The diffusion model is a mathematically defined process used to analyse the movement of particles from a region of high concentration to that of low concentration. Based on the diffusion coefficient and polarization, several studies have established that the equilibrium port-to-port distance can be calculated. The diffusion model is useful for solving the problem of noise in imaging systems, especially when an object has similar properties in all directions. When discussing diffusion, it is essential to refer to the diffusion coefficient. The literatures find denoising diffusion model to involves the process where a pixel value is estimated based on values at surrounding pixels. On the other hand, a forward process is passing through an image and replacing pixels based on their quality estimates. Reconstruction involves reconstructing an image from its components, including the subsamples and low-quality components. This model achieves satisfactory performance on digital number image generation.
An evaluation of reinforcement learning performance in the iterated prisoners dilemma

This paper uses recurrent neural network-based reinforcement learning to play Iterated Prisoners Dilemma against different game theory strategies. Multiple experiments are carried out to compare the performance of the reinforcement learning agents, e.g., RL-agent vs. Tit for Tat, RL-agent vs. Grudge, RL-agent vs. Tit for Tat then Defect, RL-agent vs. Cooperate or Defect. It shows that both DQN and PPO agent would receive the highest reward by playing against a Tit for Tat agent in Iterated Prisoner Dilemma. Furthermore, DQN agent would perform better, by receiving higher mean episode reward compared to PPO agent.
Concurrency control in tree data structures

With the increasing amount of data, there is a higher demand for fast access to data. Therefore, tree data structures are popular because they can quickly access data. In addition, with the increase of the cores of computer microprocessors, the tree data structure that can be concurrently controlled may be one of the most effective data structures today. However, there is still a lack of a summary of the differences in the data structures of various trees. Therefore, this paper collects code structures of different types of trees and then comments on the speed of various tree data structure types concerning parallel processing situations. The study covers the structure and performance of different kinds of trees and summarizes the method of concurrent control of these data structures. This paper can help to clearly understand the relationships and differences between various tree data structures and help them quickly learn the application of concurrency control in trees.
Network architecture exploration for Chinese character recognition with densenet

Chinese character recognition can be widely used in many fields. Though Resnet and Densenet are both used in this area already, using these two networks and making a comparison on training performances between them is a ground that has not been explored. In this paper, these two methods are built and compared. Firstly, a dataset of Chinese character including 5,772 images with 28*28 size will be introduced. Next, Resnet and Densenet model pre-trained on the dataset is selected. Then fine-tuning is done to improve the accuracy of networks. After 50 epochs of training, the final result shows that Densenet is more stable compared with Resnet but less efficient with more epoches to perform well.
Chinese news topic prediction using bidirectional encoder representation from transformers

Nowadays, there are many researches on natural language processing (NLP). Through the research of NLP method, many problems in machine learning field have been solved. However, since the study of Chinese NLP has not developed rapidly until recent years, there is still much to be studied on Chinese NLP. As an excellent pre-training model, whether Bidirectional Encoder Representation from Transformers (BERT) performs well on specific Chinese NLP remains to be studied. Therefore, this paper uses BERT for Chinese NLP, and trains BERT model by collecting news title data to achieve Chinese text classification. Finally, the prediction results are studied by statistical methods. The research shows that BERT method performs well on Chinese NLP and can predict different types of news headlines well. Although it performs differently on different kinds of titles, its performance is satisfactory on the whole, and the prediction results are relatively balanced in different categories. Therefore, BERT can be used as a very practical and efficient NLP method. At the same time, it can also be predicted that it will play a great role in Chinese NLP.
Convolutional neural network for classifying cartoon images augmented by DCGAN

Convolutional Neural Network (CNN) tend to have better results on large data sets and poor performance on small data sets, so the data augmentation is crucial for a CNN to get better performance based on the dataset with limited size. In this paper, Deep Convolution Generative Adversarial Network (DCGAN) was used to augment data to make the AlexNet perform better on an image classification task with small data sets. AlexNet was trained on a small anime face training set with only 160 samples to determine whether the anime face was male or female, and then tested its accuracy on a test set with 240 sample. Then, a pre-trained DCGAN was transferred to train on the male and female training sets respectively. And 2 DCGANs were obtained, one could generate male cartoon faces and another could generate female cartoon faces. The images generated by DCGANs were put in train set, which was used to train AlexNet again and the result was recorded. Other data augmentation methods such as cutout, cutmix and Noise Injection were compared as well. Finally, it is found that AlexNet has the best performance when using the DCGAN augmentation method, which can significantly improve the verification accuracy of the model.
Unpaired image neural style transfer based on Non-Local-Attention-Cycle-Consistent adversarial network

Existing image translation methods already enable style transfer on unpaired data. Although these methods have yielded satisfactory results, they still result in changing the background while changing the object. One reason is that when using convolutional neural networks, global information is lost as the number of network layers increases, and the absence of an effective sensory field leads to the failure to generate high-quality results. This paper proposed a Non-Local-Attention-Cycle-Consistent Adversarial Networks for unpaired images style transfer. The no-local-attention can quickly capture long-range dependencies, better extracts global information, ensures effective focus on the foreground while preserving the background, and can be easily embedded into the current network architecture. Experiments are conducted on neural style transfer task with public dataset, this model can obtain the better result than CycleGAN. It allows better attention to structural features rather than just textural features. It can reconstruct some of the content lost by CycleGAN. Recent research has also demonstrated that the optimizer has an impact on the performance of the network. This paper applies the Nadam optimizer and find that this improves training process.
LFSR state sequence image encryption method based on VHDL language

In the modern society of digitalization, integration, intelligence, and networking, while people enjoy the convenience of information technology to their production and lives, information security in the network, as the cornerstone of information communication, becomes more and more important. The research is to establish a new image encryption (IE) method based on LFSR state sequence (SS)s in VHDL language, and the stream cipher of LFSR is studied in detail to induce the idea of LFSR SSs based on VHDL language. The correlation coefficient (CC) of the original image (OI) and encrypted image (EI) pixel points (PP) are analyzed from horizontal direction, vertical direction and diagonal direction, and the results show that the CC of adjacent PP of the OI is large, which approaches 1. However, using the encryption algorithm proposed in this paper, the correlation coefficient of the PP of the EI is -0.0282 in the diagonal direction, and the highest correlation coefficient in the horizontal direction is only 0.0122, which indicates that the adjacent PP of the EI are almost uncorrelated with each other. Therefore, the encryption method can well resist statistical attacks, which illustrates the effectiveness, security, and reliability of this new IE method.
In-game architectural image translation using improved Cycle-Gan

The point of video games is that players can reap success and excitement in games that they cannot easily experience in the real world. The quality of translating in-game architectural images to photos determines whether the game has many players and good prospects for development. This research in this paper is to implement the function of image-to-image translation using Cycle-GAN. In this work, the dataset is pre-processed to make it more suitable for training the network. Then images are generated by the generator, and the discriminator determines whether the generated ones seem real or not. The confrontation loss and the cycle loss are performed to constrain the learning of the entire system. However, distractions still exist in this system, such as people in the background of the game could be wrongly identified as part of the building, or as a pillar and hence resulting in odd results. To mitigate it, a self-attention mechanism was added to the network to address this phenomenon, allowing the network to focus on the architecture and not disperse attention to some of the game characters. After optimization and testing, the results show that the network can be well-optimized for game-style images to resemble the realistic architecture more closely.
Using LSTM neural network to generate music

Music generation is a cutting edge and useful research filed, which is helpful for artists to compose novel melodies as well as revealing potential patterns of music. Recurrent neural network (RNN) is a member of the neural network family, which is commonly used for processing sequential data. It can deal with sequential changes in data compared to normal neural networks. Long short-term memory (LSTM) aims at improving the conventional RNN. It is designed to alleviate the deficiencies of gradient disappearance and gradient explosion that possibly happened in RNN during training. In simple terms, LSTM is superior at grasping long term information than normal RNN. It can record the information that requires to be recorded for a long time and abandon these unimportant features. Unlike RNN, which have merely one way of stacking long-term information. It's quite useful for tasks that require long range dependence. In this work the effectiveness of the LSTM is validated on the music generation task.
Simulating real-time tweet sentiment analysis by different machine learning methods based on spark

Sentiment analysis is essential since it benefits many fields, such as politics and economics. Because much data is generated every moment, a real-time processing system can efficiently analyze sentiment. This paper uses Spark to simulate real-time tweet sentiment analysis, and compares the performances of three machine learning methods, Logistic Regression, Naive Bayes, and Decision Tree. The idea of the real-time tweet sentiment analysis system is using Spark Streaming to send a batch of tweets every fixed period to a machine learning pipeline to predict the emotions of tweets. In the pipeline, tweets will be tokenized first, then the stop words in tweets will be removed. After that, the author uses TF-IDF to extract features, transferring data from unstructured to structured. The last stage is using the machine learning method to predict the sentiments of tweets. By comparing, Logistic Regression has the best performance, and the second one is Naive Bayes, Decision Tree performs not as well as the other two methods.
Researches advanced in path planning to indoor fire escape and rescue based on SLAM

Helping trapped people understand the external situation and provide navigation to escape the fire is the key to reducing fire casualties. Thanks to the rapid development of artificial intelligence technology, the combination of simultaneous localization and mapping (SLAM) and path planning technology has gradually become a new research hotspot, which can help provide on-site fire information, maps and navigation for trapped people and firefighters. However, improving the accuracy of SLAM techniques under harsh conditions (e.g., thick smoke, high temperature) is still an open topic. Focusing on SLAM noise reduction and path planning, in this paper, we detail the latest research progress of SLAM technology in fire escape assistance. Specifically, we first introduce the current development and application frontiers of SLAM technology, and then analyze and compare the application of SLAM technology in fire scenarios. In addition, the performance changes of the two continuous A* algorithms and the RRT algorithm during global path planning for fire scenarios are compared. Finally, we discuss the development trend of SLAM in future fire escape and rescue.
The virtual face modeling method based on user facial recognition and Unreal Engine 5 MetaHuman Creator

The face model based on the real face is a technique with hugely increased demands in recent years, The methods for the modelling are still in an early stage and only used in research and business fields. As the increased demands, areas other than business and research will require this technique. However, the cost of professional modelling is unaffordable for individual users. Therefore, through theoretical analysis and a literature review, this paper illustrates the possibility of combining face recognition technology with Metahuman Creator for face modeling so as to achieve the goal of reducing the cost of face scanning and popularizing it among individual users.