Reviewerly/exHarmony huggingface dataset:
Referendum Poll System: A Blockchain-Based Solution for Direct Democracy

The project, “Referendum Poll System,” aims to enforce referendums and enable people to express their opinions directly, re-engaging them with politics and democracy. The current representative democracy fails to provide direct participation of people in the decision-making process. Online voting/polling systems can encourage active participation and prove to be cost-efficient and accessible to both state and voters. However, traditional voting systems pose challenges such as time-consuming processes and physical presence requirements. Vote tampering also poses credibility issues, and the risk of getting hacked or submitting to an authority with administrator access to the system exists. The blockchain technology used in the Referendum Poll System distributes authoritative power and provides data confidentiality, integrity, and voter anonymity. The voters can read and pass judgement on every clause in a policy and approve or reject the policy as a whole or just the clauses. The “policy flaw detection feature” helps identify problematic clauses in policies, and real-time poll results add transparency. Overall, the Referendum Poll System provides an opportunity for direct and secure participation of the public in the decision- making process, and the blockchain technology used in the system addresses several challenges posed by traditional voting systems.
Belief Base: A Minimal Logic of Fine-Grained Information Dynamics

This paper proposes a minimal logic of fine-grained information dynamics via belief bases. The framework is shown to be able to accommodate explicit belief, implicit belief, awareness of and awareness that, where awareness of agents is not treated as a tacit premise. A sound and complete axiomatization of static logic is established, upon which a series of dynamic operations are defined. It is argued that these dynamics adapt different scenarios. Our logic is minimal because to each agent we only attach two databases, from which a variety of epistemic attitudes are generated.
AdvST: Generating Unrestricted Adversarial Images via Style Transfer

Recent years have witnessed extensive applications of Deep Neural Networks (DNNs) in various vision tasks. However, DNNs are vulnerable to adversarial images crafted by introducing perturbations into inputs to induce incorrect predictions. Unlike <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{p}$</tex-math></inline-formula> -norm restricted adversarial attacks, many unrestricted attacks have been proposed by modifying attributes of the image (e.g., edge, color), while the critical components of the image are preserved. However, most existing unrestricted attacks easily introduce unnatural distortions, colors, stains and schemes, in the generated adversarial images. This paper proposes a novel unrestricted attack (named AdvST) to create stylized, natural-looking, and high-transferability adversarial images. The basic idea of AdvST is to embed adversarial perturbations when transferring the style from the reference image onto the original image (i.e., rendering the original image's semantic contents into the reference image's style). To further improve the image quality of generated adversarial images, we refine two kinds of reference images (i.e., photographs and artworks) based on different attractive styles and design two attacks accordingly. For photorealistic attack, we incorporate semantic information obtained from segmentation maps to improve the photo realism of adversarial images. For artistic attack, we propose integrating edge information extracted by the Laplace operator to preserve the structural integrity of the original image. Extensive experimental results validate the superior performance of AdvST in terms of adversarial image quality and black-box transferability compared to benchmark methods.
2022 Revised Curriculum and Secondary School Physics Education

A curriculum is broadly defined as ‘the totality of student experiences that occur in the educational process’, which starts from the basic questions of why, what, when, and how to teach students, and can be said to be a ‘practical act of selecting, organizing, executing, evaluating, and improving learning experiences to be provided to learners.’ A curriculum must begin with concerns about what kind of learning experiences students need in the near future society in which they will live. Therefore, the curriculum is not fixed, but can change according to the orientation of school education and social changes required by the times. In case of Korea, the government has revised the national curriculum several times in accordance with social changes and demands. Most recently, the Ministry of Education announced ‘2022 Revised Curriculum’, aimed at ‘cultivating competencies’ to prepare for the future society. In this paper, the main contents and characteristics of the 2022 revised science curriculum are introduced, focusing on physics in middle school and high school.
2 Integration of blockchain techniques in augmented and virtual reality

Unquestionably, the public is interested in modern, cutting-edge technology like virtual reality (VR), augmented reality (AR), and blockchain. Many foreign investors are paying attention too. Although VR, AR, and blockchain - originally intended for entertainment - at first appeared to have nothing in common, over the past few years, a variety of use cases have shown how these concepts may be combined successfully. The current options are being taken into account. This chapter examines hybrid VR/AR/blockchain technologies that have helped businesses and academic researchers in a wide range of application fields address issues affecting conventional services and goods.
Improved decoy-state quantum key distribution with uncharacterized heralded single-photon sources

Abstract Encoding system plays a significant role in quantum key distribution (QKD). However, the security and performance of QKD systems can be compromised by encoding misalignment due to the inevitable defects in realistic devices. To alleviate the influence of misalignments, a method exploiting statistics from mismatched basis is proposed to enable uncharacterized sources to generate secure keys in QKD. In this work, we propose a scheme on four-intensity decoy-state quantum key distribution with uncharacterized heralded single-photon sources. It only requires the source states are prepared in a two-dimensional Hilbert space, and can thus reduce the complexity of practical realizations. Moreover, we carry out corresponding numerical simulations and demonstrate that our present four-intensity decoy-state scheme can achieve a much higher key rate compared than a three-intensity decoy-state method, and meantime it can obtain a longer transmission distance compared than the one using weak coherent sources.
Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network

Air pollution condition is getting worse with the advancement of society development, environmental pollution has gradually intensified, and smog frequently occurs in more and more cities. On foggy day, the saturation and contrast of an image could be low, and colors tend to drift and distortion. As a result, seeking a simple and effective image de-fogging technique is important for the subsequent research. In this study, three existing classical de-fogging algorithms are reproduced: histogram equalization, dark channel prior method, and convolutional neural network. The three de-fogging algorithms were compared respectively under the conditions of thin fog, thick fog, high brightness, and low brightness, so as to analyze their advantages and disadvantages. It is concluded that there is no obvious difference among the three algorithms in the de-fogging effect under the conditions of thick fog and high brightness, but relatively speaking, the de-fogging image generated by the dark channel prior is more real. When the fog is thin, the dark channel prior and convolutional neural network work better. Under the condition of low brightness, the histogram equalization has a better de-fogging effect.
A series of machine learning methods for user rating prediction

Users feedback online for services or products reflects their satisfaction. For example, higher rating implies better user satisfaction and experience. It is crucial to provide users satisfying services or products to ensure user experience and thus improve the long-term value of the platform. Manually reviewing all candidate services and products to select potentially user-preferred ones it time-costuming due to large amount of candidates. Therefore, this paper studies a series of machine learning based algorithms to automatically predict the user feedback for an online shopping website, i.e., user ratings in this scenario. To be specific, three machine learning models are investigated, including logistic regression, multi-layer perceptron, and convolution neural network. Starting with the pre-processing of raw data crawled from Amazon, an international online shopping website such three models are first trained over the training set and then evaluated on the testing set. The experiments demonstrate the convolutional neural networks give the highest accuracy for the unseen data.
Application of virtual reality technology in helping patients overcome treatment anxiety

Patients undergoing surgery face several medical anxieties such as White Coat Syndrome, Iatrophobia, and Trypanophobia, which cause great inconvenience to both the medical staff and the patients. In recent years, with the advancement of science, virtual reality technology has been developed widely and plays a significant role in dealing with anxiety issues. This paper, with the theme of VR technology helping patients overcome treatment anxiety, summarizes and discusses patients' anxiety and fear during treatment, how VR technology helps patients overcome anxiety and fear, and some challenges and prospects, analyzing the effects of VR technology in relieving patient anxiety. This paper finds that virtual reality technology is currently in the development stage in the field of medical applications, and it plays a significant role in the field of psychotherapy. It can serve as a very effective tool to help patients get better treatment.
Breast cancer prediction and feature importance estimation leveraging logistic regression model

In 2022, breast cancer become the largest cancer among the world. It is necessary to help people know how to prevent breast cancer. Machine learning use algorithms and statistic models to help the computer find some rules based on mass data. It can rapidly analyse, predict, and process the data in a timely enough manner. By significantly reducing errors caused by data duplication and other inaccuracies, it can simplify time-intensive documentation in data entry. In medicine, it can facilitate accurate medical prediction and diagnosis by quickly identifying high-risk patients, recommending a range of drugs, and predicting readmission rates. This paper will use Logistic Regression to build a model to predict the whether a patient will develop cancer and find the dominant factors from the characteristic values. The research found that Bare Nuclei, Clump Thickness, and Single Epithelial Cell Size are the key factors that will create a large impact to the result of the predication.
An overview of big data mining and data privacy protection technologies

With the advent of the era of big data, data mining techniques have significantly improved their ability to extract valuable information from data. However, privacy dangers are growing. Consequently, securing the protection of personal privacy during the mining of massive amounts of data has become a significant challenge. This paper examines the relationship between data mining techniques and privacy protection measures through a review of the pertinent literature. It provides a concise analysis of the benefits and drawbacks of commonly utilized classification algorithms in data mining. In addition, it examines the interplay between data mining techniques and privacy protection and summarizes important privacy protection techniques. In addition, this paper provides a summary of the most important privacy protection methods. These techniques include data anonymization, association rule concealing, data perturbation, etc. By comprehending these privacy protection techniques, appropriate privacy safeguards can be selected to ensure the privacy and security of the data when conducting data mining.
Review on adversarial attack techniques of DNN

Deep learning models have excelled in both academia and practice in recent years and have made many developments in various areas. Research has shown that they are inherently vulnerable to attacks by adversarial samples that make them misleading. By studying adversarial attack samples in the field of deep learning security, not only the potential attacks on the models can be reduced, but also their properties can be used to make further improvements to deep learning models. This paper reviews the existing results of adversarial attack techniques for deep neural networks. Firstly, the definition, classification criteria, and development of adversarial attacks are introduced, then the classical white-box and black-box attack methods at the present stage are compared and analyzed, and finally, a summary and outlook are made.
Application and analysis of residual blocks in galaxy classification

In astronomy, the automated galaxy classification method based on deep learning has significantly reduced the cost of manual annotation. The degradation problem in convolutional neural networks during galaxy classification tasks limits the accuracy improvement of deep models. Therefore, to address the issue of the model being too deep, which leads to a decrease in accuracy, the paper constructs the galaxy classification model using residual block structures. Specifically, this paper uses an improved ResNet as the backbone, stacking different numbers of residual blocks to extract input features. Meanwhile, smaller and deeper fully connected layers, regularization and activation functions, and Dropout layers are used to improve the model performance. For the best-performing ResNet152 model, the paper analyzes the classification report and confusion matrix and visualizes saliency maps and GradCAM heatmaps. Finally, the experimental results show that the introduction of residual blocks can increase the accuracy of the model by over 30%, and models with more residual blocks perform better, although the influence of the number of residual blocks on accuracy improvement is small. The visualization results show that the model can accurately segment the feature focus areas and points of interest in the original image. The model also focuses more on the central points with high planetary density by stacking multi-level residual blocks.
Pengaruh Waktu Kontak dan Kecepatan Pengadukan Terhadap Penyerapan Zat Warna Methylene Blue Menggunakan Biosorben Kulit Matoa (Pometia pinnata)

Methylene blue is an aromatic hydrocarbon compound or thiazine dye with the chemical formula C 16 H 18 N 3 SCl which is most often used in industry, this cationic dye is very dangerous with a threshold value ranging from 5-10 mg/L in water and is difficult to degrade in the environment because it contains benzene compounds. The purpose of this study was to reduce the impact of methylene blue pollution on the waters. The adsorption method was selected in batches by utilizing plantation waste from matoa fruit peel as an adsorbent in the absorption of MB waste, because it is more effective, inexpensive, and environmentally friendly. Matoa skin contains cellulose and lignin, so it is considered potential to be used as an adsorbent. The results showed that the matoa skin adsorbent was able to adsorb methylene blue dye at an optimum contact time of 120 minutes and a stirring speed of 200 rpm, which was measured using a UV-Vis spectrophotometer at a wavelength of 664 nm.
Comparison of Deep Convolutional GAN and Progressive GAN for facial image generation

Generative Adversarial Networks (GANs) have generated realistic and diverse facial images with promising results. This work demonstrates a technique for creating facial pictures using GANs and evaluates the effectiveness of several GAN designs and training approaches. The CelebA dataset is leveraged for training and evaluating the GAN model, and employ a variety of evaluation metrics, such as the Structural Similarity Index (SSIM) to assess the quality and diversity of the generated images. Progressive GAN outperforms Deep Convolutional GAN in terms of image quality and diversity, and conditional GAN training is more effective than standard GAN training for generating facial images with specific attributes. The combination of Progressive GAN and conditional GAN training produces facial images of the utmost quality and diversity. The findings contribute to a broader comprehension of the use of GANs for generating facial images and have ramifications for a variety of applications, from facial recognition to virtual reality.
Introduction and analysis of bitcoin-based blockchain technology principle

Blockchain technology is a technology that inherently solves trust issues. It has the character-istics of decentralization, distributed storage, tamper resistance, security, and transparency. It ensures reliable communication between nodes that do not trust each other through consensus mechanisms, smart contracts, and other means. Blockchain stores each transaction data on each transaction node to make the data public and transparent, and generates the data into a blockchain.. As a relatively new distributed database system, blockchain has expanded to many other fields since the initial digital currency, but its development is seriously constrained by problems such as large storage overhead and low query efficiency. In order to find a suita-ble optimization method, select the representative blockchain system Bitcoin, analyze its data structure, data storage and data query processing mechanism, discuss the problems existing in the two functions of storage and query, summarize the existing relevant optimization methods, and look forward to the main research problems of the blockchain system represented by Bitcoin in the future.
Design and implementation of secure student score system

The purpose of designing this system is to provide schools with a new way to make score certifications of a student and a safer way to search for a students previous academic scores. When a student is applying for a new school to further his study, the school always uses his previous academic scores to decide whether to enroll him. The current way for a school to mark a student and to search for a new students score record is depend on the student himself, which faces the problem of illegal modification and data leakage. The system mentioned in this paper can add a certification that includes the time information and score information. It can also use the digital signatures of teachers and headmasters to ensure that the scores are given by the corresponding teacher. This system does not include the direct Grade Point Average (GPA), but just includes the raw scores of each subject. Because different schools have different ways to turn the raw scores into a GPA and they also have different GPA upper limitations.
The realization and application of the data analysis platform of netizen behavior based on Hive

With advances in mobile technology and mobile Internet applications, smart mobile devices, such as smartphones and tablets, have become increasingly popular, and the number of Internet users worldwide continues to grow. In the Internet era, the amount of data is growing exponentially and companies must be able to harness the value of the vast amount of data. Data platforms must integrate massive amounts of data collection, storage, computation and analysis to meet these opportunities and challenges. In this study, the log data of Internet users browsing websites are analyzed and the technologies used in the platform are briefly described. Finally, a draft platform for analyzing offline Internet user behavior data is proposed, taking into account the current common needs of different industries, while incorporating some innovations. Three modules are designed and implemented: data collection, data warehouse and data visualization. The user's data is mainly collected by the data collection module. The data warehouse is mainly responsible for cleaning, modeling and analyzing the data. As part of the data visualization module, the result data from the ADS layer is used as a template to create tables in MySQL, export the results to MySQL periodically using the Sqoop tool, and visualize the data using the data visualization tool. With Flume, Kafka and Sqoop, HDFS is used as the data storage framework, Hive is used as the storage tool, and Spark is used as the Hive computation engine to build the platform in a large context to analyze Internet user behavior.
Problem analysis and future vision for GameFi: A transition from GameFi to DeFi gaming

The increasing popularity of GameFi, a combination of gaming and financial models, has led to the emergence of certain problems with current financial models that can only be overcome through a transition towards de-game, or full-on-chain games. This paper provides an overview of the history and financial models of GameFi, identifies pain points on the path of transition from GameFi to de-game, such as capacity and TPS, and presents a concept for a de-game with achievable performance levels. Both single and double currency models have limitations that prevent them from reaching their full potential, demonstrating the need for a transition to de-game, capable of offering unique gaming experiences and financial rewards. Overcoming pain points such as capacity and TPS will be essential in achieving this transition. Proposed solutions include using sharding technology and optimizing smart contract architecture to enhance system capacity and TPS. Ultimately, this paper highlights both the challenges and opportunities presented by GameFi, proposing the transition to de-game to overcome the limitations of current financial models. Addressing critical pain points such as capacity and TPS, our proposed solutions offer feasible approaches to building reliable on-chain gaming systems. The future vision of GameFi in the form of de-game offers a promising outlook for the gaming industry, where players can engage in immersive games with financial incentives.
Sentiment classification and visualization analysis of tourism comments: A canton tower example

As calculated by the Ministry of Culture and Tourism, there were 308 million domestic tourist trips in China during the Spring Festival in 2023, witnessing a year-on-year increase of 23.1%. And the satisfaction of tourists with certain spots can be partly reflected in the comments and scores they made on social media. Therefore, this research was aimed at mining useful information from the comment and scores of Canton Tower. After collecting detailed comment information from the web, this research used the plot module of Python to make data visualization to observe the distribution of users location, comment time, and comment label as well as the word cloud of remarks. Then the research used the data set to train three different sentiment analysis models including Nave Beyas, SnowNLP, and Bert, then compared their accuracy in predicting. This research shows that over half of the comments came from Guangdong Province, most of the tourists were content with Canton Tower, and the number of comments has increased obviously since 2023. In addition, the research found that the model having the highest accuracy of sentiment analysis is the Bert model, about 90%.