Reviewerly/exHarmony huggingface dataset:
Empirical Analysis of Factors Affecting RMB Exchange Rate and Policy Implications Under Epidemic Conditions

This paper first summarizes the current situation of RMB exchange rate fluctuations from 2020 to 2022.On this basis, the causes of RMB exchange rate fluctuations are analyzed from five aspects: the epidemic, the international political environment, the Russia-Ukraine conflict, the interest rate hike of the Federal Reserve and the supply and demand of the foreign exchange market.Find out the factors and trends affecting the fluctuation of RMB exchange rate.Finally, aiming at China's response to the fluctuation of RMB exchange rate, the paper puts forward to further promote the two-way symmetric opening of the capital account and improve the formation mechanism of the RMB exchange rate.To accelerate the upgrading of industrial structure and improve the competitiveness of the countermeasures and suggestions to promote the further development of RMB internationalization.
Prediction of US Stocks Based on ARIMA Model

Time series analysis method is an important part of statistics.It has practical applications in various fields from economics to engineering.Time series analysis includes analyzing time series data in order to extract meaningful features of data and predict future values.Box-Jenkins method belongs to regression analysis method and is the basic method of time series analysis and prediction.This paper describes the modeling method and implementation process of ARIMA.A time series is a series of data points, usually measured at uniform time intervals.Autoregressive integral moving average (ARIMA) model is a kind of linear model that can represent stationary and non-stationary time series.ARIMA model depends on autocorrelation mode to a large extent.This paper will discuss the application in stock price forecasting, especially the time sampling at different time intervals, to determine whether there are some optimal design frameworks and whether the stock autocorrelation patterns in the same industry are similar.
Research on the Development of the Digital Economy based on Blockchain Technology

As the digital economy becomes a key trend in the economic transformation and upgrading under the new normal of the world economy, a decentralized system, and distributed database technology, blockchain links relevant data in single blocks to form a central system in a cycled chain pattern by encrypted signature verification. The fusion of blockchain technology and big data technology can not only conquer the bottleneck of big data technology but also provide new methods and ideas for the development of the digital economy, upgrade the developmental mode of the digital economy, and promote the rapid growth of the digital economy. Through the popularization of blockchain technology, the traditional internet based on information is transformed into a value-based internet. The status of the traditional economy is mapped into the virtual world, further integrating reality and virtuality.
Cellular Automata-Based Sentiment Analysis

Analyzing the opinion of human beings such that there is continued development as per individual’s needs is one of the primary objectives of sentiment analysis. To understand the perception of customer’s reviews and their opinions, the data retrieved are organized in a structured manner. There is always room for people’s reviews and feedback in all fields of society. So, if we are able to distinguish whether those opinions are positive, negative, or neutral then the services of various resources can improve significantly. This paper focuses on the use of cellular automata to determine the sentiments from the reviews as positive or negative. Our approach produces 92.44% of accuracy for the sentiment tweets dataset by using various parameters which are extracted from the cycle length values generated by elementary cellular automata without using any kind of pre-trained language models.
Exploring and Practicing Smart Teaching Mode by Integrating Innovation and Empowering Data for College English

In view of the new requirements of education and teaching reform under the background of information technology, this paper, based on the characteristics of talent cultivation of Anhui University of Science and Technology, carries out practical exploration and makes intelligent innovation keep pace with the times as well as fosters students' virtue through education. This paper aims to build a studentcentered, innovative and intelligent teaching model of integrated innovation and data empowerment under the guidance of the value of Three All-around Education .Based on the textbook of New Horizon College English, advantages of the school's information technology and application of the LearningPlatform have been given full play.Practice has proved that we should promote the construction of college English ideological and political digital wisdom curriculum, so as to form a good ecological teaching chain, and achieve remarkable talent training results.
Cloud computing: A study of mechanism and cloud cryptography

Cloud Computing is the on-demand delivery of computing services, databases, storage, applications, networking, and many more through the internet.Cloud Computing technology refers to pay-as-you-go pricing.The cloud delivers a hosting environment that is immediate, flexible, scalable, secure, and available which saves enterprise money, time, and resources.As we are going to use the internet services, security is the major factor to be taken into consideration.Cloud cryptography is a way through which we can secure our data on the internet.Cryptography adds several layers of protection to avoid breaching, hacking, or getting affected by malware.So in this survey, we are going to study the mechanism of cloud computing and how the activities on the cloud can be secured over the internet.In comparison to private clouds, which can scale effortlessly and on-demand, the public cloud has a massive infrastructure.IaaS is a method of delivering cloud-computing infrastructures such as servers, storage, networks, and operating systems to customers.Customers can get on-demand access to materials here.Cloud cryptography is a type of encryption that employs a number of algorithms to turn plain text into cipher text.Because symmetric cryptography is substantially faster than asymmetric cryptography, it is ideally suited for bulk data encryption.Using a single key, this technique enables data authentication and permission.The strength of an asymmetric key in assuring the security of information supplied during transmission is significantly greater.
An ensemble classification approach for prediction of banknote authentication

Banknotes are financial norms used by any nation to finish cash related activities and are every country asset which every country needs it to be genuine.A couple of heretics present fake notes which look to some degree like exceptional note to make incongruities of the money in the cash related market.It is problematic for individuals to tell authentic and fake banknotes isolated especially because they have a lot of similar features.In this examination, we played out a broad relative investigation of troupe procedures, for example, boosting, packing and stacking for Banknote Authentication.During the last many years, in the space of AI and information mining, the advancement of outfit strategies has acquired a critical consideration from mainstream researchers.AI troupe strategies join different learning calculations to acquire preferable prescient execution over could be gotten from any of the constituent learning calculations alone.Outfit techniques utilize different models to improve execution.Outfit strategies have been utilized in different exploration fields like computational insight, measurements and AI.The consequences of the investigation show that troupe strategies, like packing and boosting, are powerful in further developing the forecast exactness of frail classifiers, and display palatable execution in distinguishing hazard of Banknote Authentication.A greatest increment of 7% exactness for feeble classifiers was accomplished with the assistance of troupe arrangement.
STIMULUS RESPONSE APPROACH USED BY THE TEACHERS IN TEACHING ENGLISH AT SMA NEGERI 5 ENREKANG

This research aimed to find out the stimulus response used by the teachers and how do teachers provide stimulus to students in English learning at SMA Negeri 5 Enrekang.The method used in this research was descriptive qualitative. This research used a semi-structured interview as an instrument to collect the data. This research used ten questions as a data collection instrument. The subjects in this research were teachers of SMA Negeri 5 Enrekang. The research subjects consisted of three teachers.The result of this research show that teachers apply all types of stimulus in the teaching and learning process such as the application the law of readiness, the law of exercise, and the law of effect in English learning. Teachers apply the law of readiness to ensure the readiness of students in receiving material for example by checking student attendance. As for the application the law of exercises was used by teachers to ensure students’ understanding of the material that has been given, for example, by providing questions to be done individually, and the application the law of effect itself was used by teachers to reward students for what they achieved during the learning process, for example giving praise and good grades. The stimulus used was very beneficial to increase their learning motivation, especially in English Learning process.
Developmen Of Digital Scrapbook Learning Media On Human System Excretion For Grade 8th Junior High School

Learning media are tools used to help convey learning material in the teaching and learning process so that it makes it easier to achieve learning objectives. To increase students 'interest in reading learning media, learning media is needed that can attract students' attention, one of which is scrapbook. Scrapbook is a book made by attaching pictures or words and inscriptions on the book. Along with advances in technology, scrapbooks can also be made digitally and can be accessed using cellphones and computers. This type of research is development research using a 4-D model consisting of define, design, develop and disseminate. but the disseminate was not done. In the field of definition, it consists of beginning-end analysis, student analysis, task analysis, concept analysis and learning objective analysis. At the design stage, a digital scrapbook learning media is designed. At the development stage, a validator test was carried out, namely a science education lecturer at FMIPA UNP and a practical test by grade 8th teachers and students of the Darul Falah Lubuk Tarok Islamic Boarding School. human excretion system material can be used as a learning medium, with the high validity category of the validator. The level of practicality of digital scrapbook learning media with a high category by the teacher and a very high category for students
PIRAP: AASM: An Advanced Attribute-Based Security Model for Enhancing Trustworthy Data Sharing in Cloud Computing

In today’s world, cloud computing is widely used in various applications and services, which refers to the utilization of computational resources as per user requirements through the Internet. However, despite the numerous advancements in cloud services and applications, there are various security threats associated with it, mainly due to data outsourcing to third-party controlled data centers. In this context, this paper introduces a new model called Attribute-based Advanced Security Model (AASM) for Reliable Data Sharing in the Cloud. This model combines the Advanced Encryption Technique (AET) with Attribute-Based Signature (ABS) to ensure secure data sharing in the Cloud while efficiently controlling data access. The model enables encrypted access control from the data owner’s side with advanced access privileges, ensuring user privacy with an anonymous authentication model using ABS. By implementing these measures, the model provides security for cloud providers and users while safeguarding against malicious attacks. The effectiveness of the proposed model is evaluated based on factors such as time complexities, security and accountability.
Adult and Continuing Education

Higher Education AbstractsVolume 56, Issue 3 p. 214-215 ADULT AND CONTINUING EDUCATION Adult and Continuing Education First published: 04 October 2021 https://doi.org/10.1111/hea.2_12174Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume56, Issue3July 2021Pages 214-215 RelatedInformation
Hypercomplex: abstract &amp; fast header-only C++ template library for lattice-based cryptosystems in high-dimensional algebras

The following work presents a C++ library that is dedicated to performing arbitrary-precise calculations on hypercomplex numbers from the Cayley-Dickson algebras (Schafer, 2017).Basic arithmetical operations as well as a few miscellaneous functions are implemented.Its most important feature is the support for encryption/decryption procedures for public-key lattice-based cryptosystems in high-dimensional algebras over truncated polynomial rings. Statement of needThis is a highly specialised software package aimed mostly at computational mathematicians and scientists who operate on high-dimensional numbers, need to carry out arbitrary-precise calculations, or whose focus is the study of lattice-based, post-quantum cryptography (PQC).The library is well suited for a wide range of computationally-challenging projects, from investigating general algebraic properties per se to applied research where a hypercomplex framework serves merely as a mean to an end (as in previously mentioned cryptosystems).
Verifiable Multiparty Delegated Quantum Computation

Multiparty delegated quantum computation (MDQC) allows multiple clients with limited quantum capability to jointly complete a quantum computational task with the aid of an untrusted quantum server. But in existing MDQC protocols, the verifiability that clients should verify whether the server executed the protocol correctly and gave correct results was not handled. Therefore, in this paper, we improve a typical MDQC protocol to enable clients to verify the correctness of computation by inserting trap qubits and develop a novel method to enforce clients to send qubits honestly while avoiding the positions of trap qubits being leaked to the server. The security and verifiability of the improved MDQC protocol are also analyzed. In addition, a specific example of the proposed verifiable MDQC protocol is given and simulated on IBM’s quantum platform.
Self-knowledge distillation based on knowledge transfer from soft to hard examples

To fully exploit knowledge from self-knowledge distillation network in which a student model is progressively trained to distill its own knowledge without a pre-trained teacher model, a self-knowledge distillation method based on knowledge transfer from soft to hard examples is proposed. A knowledge transfer module is designed to exploit the dark knowledge of hard examples, which can force the class probability consistency between hard and soft examples. It reduces the confidence of wrong prediction by transferring the class information from soft probability distributions of auxiliary self-teacher network to classifier network (self-student network). Furthermore, a dynamic memory bank for softened probability distribution is introduced, whose updating strategy is also presented. Experiments show the method improves the accuracy by 0.64% on classification datasets in average and by 3.87% on fine-grained visual recognition tasks in average, which makes its performance superior to the state-of-the-arts.
Bitcoin Vs Gold: Which One is the Most Powerful in Boosting the Shariah Equity Index? Global Evidence

Abstract The study explores the most powerful between Bitcoin and Gold in boosting the Shariah Equity Index in Malaysia, the United Arab Emirates, China, Indonesia, The United States of America (USA), Japan, Oman, and Saudi Arabia in the short and long term. The study uses analysis of the first and second stages of the Granger Causality Test and Vector Error Correction Model (VECM), then Impulse Response Function (IRF) and Variance Decomposition (VDC) over the period 2013 to 2021. The finding proves that only Gold can affect the Islamic Equity Index in the short term, then Bitcoin and Gold proved to contribute equally to the Islamic Equity Index in the long term. However, Bitcoin has the potential to provide positively correlated shocks and dominate the value of Islamic equity indices in the long term. The results demonstrate that government intervention is decisive in maintaining the stability of the Shariah Equity Index from future Bitcoin threats. The study’s finding has practical implications for Islamic capital market Investors, Managers, and Authorities.
The cybercrime illusion: Examining the impact of cybercrime misbeliefs on perceptions of cybercrime seriousness

Though research examining cybercrime has surged in recent years, studies exploring perceptions concerning these phenomena have been scant. In fact, little is known regarding the extent to which individuals perceive cybercrime as serious, whether exposure to cybercrime terminology elicit similar perceptions of seriousness as behavioural descriptions of cybercrime, and the factors predicting perceptions of cybercrime seriousness. While research examining offline crime has found a significant relationship between individuals’ adoption of misleading stereotypes and perceived crime seriousness, no study to date has explored this association within a cybercrime context. As such, using data collected from 504 Mechanical Turk adult respondents, the current study examined: (1) the extent to which individuals perceived cybercrime (generally) as serious, (2) whether perceptions of cybercrime seriousness were differentially influenced based on whether cybercrime terms (e.g., “hacking”, “cyberstalking”) were provided relative to behavioural definitions, and (3) whether respondents’ adoption of cybercrime misbeliefs significantly impacted perceptions of cybercrime seriousness, net of other factors (i.e., comfort with and use of technology, demographic traits). The findings revealed that while more individuals perceived cybercrime as serious when behavioural definitions were provided, the majority of respondents did not perceive cybercrime (generally) as serious. Further, greater adoption of cybercrime misbeliefs and frequent device use were significant predictors of perceived cybercrime seriousness only when behavioural definitions were given. In addition, older respondents were more likely to view cybercrime as serious, while men were less likely to view cybercrime as serious in both models. Collectively, the findings reveal the need for effective educational and awareness campaigns, which are discussed in detail.
The Effect of Self-Controlling Difficulty Levels in a Virtual Environment

Abstract In the current study we examine whether, in a virtual environment, self-controlling the difficulty level of a game will result in worse performance compared to a fixed difficulty level. Our hypothesis is evaluated using a novel VR game. Forty-five participants were randomly assigned to two between-participants groups: the Experimental group, in which the participant determined the desired level of difficulty in all five game sets by themselves, and the Controlled group, in which the level of difficulty was set to medium for all participants in all game sets. The results demonstrate that the mean difficulty level selected by the Experimental group was lower than medium in the first two sets, and higher than medium in the last three sets. In the last two sets, an earlier termination of the game and lower scores were demonstrated for the Experimental group compared to the Controlled group. The conclusions are that the participants in an unfamiliar virtual environment might not be able to select their difficulty levels to match the task at hand and might not optimize their learning zones.
Historical notes on Russian cryptography

The article is devoted to the main milestones in the development of encryption techniques and mathematical methods of cryptography in Russia from the period of ancient Russia up to the nowadays. We break down the history of Russian cryptography into several periods and highlight the periods of cryptography development, analyze the most notable achievements and summarize the main results and applications of each period. The review of scientific research and standardization of cryptography in modern Russia is given. The progress of related areas is briefly analyzed: steganography and protection against falsification of documents.
Image quality improvement through turbid media by using graphene nanoplates

Abstract Imaging through turbid media leads to a great loss of information decreasing the image quality. This loss of quality is due to the light interaction with the medium, since part of the light will pass through it, but another part will be scattered generating a blurred image pattern. In this work we try to decrease this problem by adding an absorbent, eliminating part of the scattered radiation responsible for the turbidity. With this aim, we use graphene nanoplates as an absorbent and we also compare the experimental results with black carbon powder.
Deep Clustering With a Constraint for Topological Invariance Based on Symmetric InfoNCE

Abstract We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform well for both noncomplex topology and complex topology data sets. To address the problem, we propose a constraint utilizing symmetric InfoNCE, which helps an objective of the deep clustering method in the scenario of training the model so as to be efficient for not only noncomplex topology but also complex topology data sets. Additionally, we provide several theoretical explanations of the reason that the constraint can enhances the performance of deep clustering methods. To confirm the effectiveness of the proposed constraint, we introduce a deep clustering method named MIST, which is a combination of an existing deep clustering method and our constraint. Our numerical experiments via MIST demonstrate that the constraint is effective. In addition, MIST outperforms other state-of-the-art deep clustering methods for most of the commonly used 10 benchmark data sets.