Reviewerly/exHarmony huggingface dataset:
An Empirical Study on Measurement and Influencing Factors of High Quality Development Level of Listed Companies in Digital Music Industry

Developing a green economy is the focus of China's industrial restructuring, and the digital music industry contributes to the transformation and upgrading of the green economy.This paper focuses on the core problem of the high quality development efficiency of digital music industry and constructs the super-efficiency DEA model of non-parametric estimation and Malmquist index model of dynamic comparison respectively.The paper empirically analyzes the high quality development efficiency of digital music industry by using 2695 observation data of listed companies from 2011 to 2021.The results show that: (1) The high quality development efficiency of digital music industry is relatively high, but there are great differences within the industry.The overall efficiency interval is 0.363,2.251.(2) The high-quality growth effect of the digital music industry is significant, increasing year by year.Malmquist index is 1.2218, maintaining an average annual positive growth of 22.18%.(3) It is necessary for digital music industry to increase investment in technological innovation and promote high-quality and sustainable growth of the industry.57.14% of enterprises are inefficient in technological progress.(4) Both internal and external factors have significant influence on the high quality development level of digital music.Both Logit model and OLS regression model pass the robustness test.
Designing Sneakers as A Medium to Support Teaching and Learning Speaking

The goal of this study was to create the board game Sneakers as a teaching and learning tool for speaking and to determine whether or not eleventh-grade students would find it useful and appropriate for that purpose. Card games and board games make up the medium. The teacher and students at the school utilized a book, and the materials were obtained from the internet. The three parts of the process—Analyze, Design, and Develop—were derived from Branch's (2009) ADDIE Model. Students require dynamic, visually appealing media during the analysis phase to participate in the teaching and learning process. The media's structure, as well as all of the elements that were increasingly its primary focus, were all covered throughout the design process. The development process was carried out to create the fundamental components of the media, such as the game's rules. Sneakers are frequently employed as a medium to promote or facilitate the teaching and learning of speaking for the eleventh-grade students at SMA Muhammadiyah 2 Pontianak, according to the evaluation's findings
Portable and Efficient Implementation of CRYSTALS-Kyber Based on WebAssembly

With the rapid development of quantum computers capable of realizing Shor's algorithm, existing public key-based algorithms face a significant security risk.Crystals-Kyber has been selected as the only key encapsulation mechanism (KEM) algorithm in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) competition.In this study, we present a portable and efficient implementation of a Crystals-Kyber post-quantum KEM based on WebAssembly (Wasm), a recently released portable execution framework for high-performance web applications.Until now, most Kyber implementations have been developed with native programming languages such as C and Assembly.Although there are a few previous Kyber implementations based on JavaScript for portability, their performance is significantly lower than that of implementations based on native programming languages.Therefore, it is necessary to develop a portable and efficient Kyber implementation to secure web applications in the quantum computing era.Our Kyber software is based on JavaScript and Wasm to provide portability and efficiency while ensuring quantum security.Namely, the overall software is written in JavaScript, and the performance core parts (secure hash algorithm-3-based operations and polynomial multiplication) are written in Wasm.Furthermore, we parallelize the number theoretic transform (NTT)-based polynomial multiplication using single instruction multiple data (SIMD) functionality, which is available in Wasm.The three steps in the NTT-based polynomial multiplication have been parallelized with Wasm SIMD intrinsic functions.Our software outperforms the latest reference implementation of Kyber developed in JavaScript by ×4.02 (resp.×4.32 and ×4.1), ×3.42 (resp.×3.52 and ×3.44), and ×3.41 (resp.×3.44 and ×3.38) in terms of key generation, encapsulation, and decapsulation on Google Chrome (resp.Firefox, and Microsoft Edge).As far as we know, this is the first software implementation of Kyber with Wasm technology in the web environment.
Zero Trust and the future of cybersecurity in healthcare delivery organizations

Digital care transformation, the proliferation of disruptive technologies and the changing hybrid workforce have forced the evolution of traditional information technology network boundaries of healthcare organizations. The new landscape has rendered legacy existing perimeter defined and based cybersecurity solutions inadequate to meet increasing regulatory and federal demands for highly secure access management. Emerging compliance requirements, coupled with the concerning increase in healthcare data breaches, ransomware attacks, and security incidents targeting the healthcare sector, have transformed our historic notion of trust into an organizational vulnerability. A “Zero Trust” approach to information security is driven by an imperative to “never trust, always verify,” and requires strict, rigorous and continuous identity verification to minimize trust zones and their associated risk of security breach. Healthcare delivery organizations need to appreciate the importance of a Zero Trust strategy in reducing vulnerabilities, strengthening health system information security, and preventing successful security breaches, while also recognizing how identity and access management serves as the foundation of achieving Zero Trust.
Cybersecurity Strategies for SMEs in the Nordic Baltic Region

Cybercrime has become the most widespread kind of economic fraud and is a serious challenge for businesses around the world. The topic of this paper is how SMEs in the Nordic Baltic Region should face this challenge. Possible technical and organisational tasks to be performed by SMEs in order to ensure cybersecurity of their business are analysed. The paper looks at the different types of hackers and their motives. On this background, current cyberthreats and corresponding security measures are presented. It is concluded that awareness, training, and financial incentives are all important elements in defining a cybersecurity strategy for SMEs. The paper is based on research made in the DINNOCAP project funded by EU regional funds.
Impact of Chinese Capital Outflows on Bitcoin vs. Yuan Relationships: A Multi-Period Analysis

We examine the relationships among Bitcoin (BTC), the Chinese Yuan (CNY), and Chinese capital outflows between 2014-2021. We find that BTC returns strongly comove with CNY returns after 2018Q1, while no significant BTC/CNY relationship exists before 2018Q1. Further, the strength of the BTC/CNY relationship increases throughout 2018 to the present date. Yet, this relationship strength cannot be explained by periods of ascending BTC prices, changes in crypto mining location, nor changes in the use of BTC "mining pools". Instead, we find that the strength of the BTC/CNY relationship is strongly and directly related to Chinese capital outflows. We find no similar relationship with a "bogey" currency, the Euro, implying that the capital outflows -to- BTC/CNY relationship is unique to China and its capital outflow environment. In total, our novel results suggest that BTC is used as part of a process to move economically significant amounts of capital from mainland China.
Architecture and key technology of autonomous controllable programmable control system

Abstract： This study aims to address the technical blockade of programmable control systems， which are the core control component of manufacturing equipment， and to break through complex technical systems for the integration of mechanics， electronics， computer science， bus communications， and other disci• plines.In addition， it responds to the "Made in China 2025" and "Industrial Strong Foundation Project" in• dependent and controllable development goals.This study analyzes the development process and technical system of a programmable control system and proposes a composition scheme based on a real-time Ether• net field bus.The working principle and key technologies of the field bus， master-slave hardware， soft• ware integrated development environment， man-machine configuration software， and other components in the composition scheme are explained.Furthermore， various research methods and the development of a 文章编号 1004-924X
Preserving Collusion-Free and Traceability in Car-Sharing System Based on Blockchain

A car-sharing system has been considered the most promising solution to solve the waste of natural resources, traffic congestion, and greenhouse gas emission in the city. However, the conventional car-sharing system relies on a trusted third party, which is challenging to maximize the benefits of the C2C model. This paper aims to design a decentralized car-sharing system based on the blockchain technique. The demander and supplier can use the smart contract to share the vehicle without the centralized system. In addition, even if the vehicle is damaged, the supplier can track the responsibility for the damage by accessing the order information stored in the blockchain. Simultaneously, the proposed method avoids collusion between the malicious user and the centralized server. Experiments have shown the performance analysis between the centralized car-sharing system and the proposed framework, pointing out the advantages of our new framework.
Ransomware Attacks on the Healthcare Industry

The increase of cybersecurity threats poses a significant risk to the healthcare industry, specifically, healthcare organizations, pharmaceutical companies, and clinics. The increase in the development of smart medical equipment and mobile devices has made the healthcare industry increasingly vulnerable to ransomware, one of the most dangerous types of adaptable malware, intended to prevent entry to the system of an entity or establishment. Cybersecurity breaches consist of illegally obtaining healthcare data and ransomware events involving healthcare organizations. The healthcare industry has taken advantage of the rise of the digital revolution, resulting in unparalleled volumes of data. The healthcare industry is one of the most vital in the world, and thus, CEOs of healthcare organizations should adhere to proposed best practices to establish a strong cyberhygiene that facilitates the efficient distribution of confidential information in the face of these threats. This systematic review of the literature thought to determine how healthcare organizations in the United States protect themselves from cyber-attack. EBook Collection (EBSCO), Google Scholar, MEDLINE, PsycINFO, PubMed, SocINDEX with Full Text, and ScienceDirect databases were explored to answer this question. Identified themes include employee training, the need for a response plan, and strong data infrastructure.&#x0D;
A new feature selection algorithm for evolutionary analysis of Aramaic and Arabic script variants

This paper deals with applying phylogenetic modelling to the evolution of scripts (writing systems) as taxa. Aramaic and Arabic script variants are studied in the present cladistic analysis. The selection of the most suitable features of taxa for accurate modelling as part of the feature engineering step could improve the result of the cladistic analysis. The main objective is to filter out features of the taxa under study that could potentially cause homoplasy. The effect of feature filtering is investigated using some widely used phylogenetic software products for biological databases. Studies have consistently shown that the phylogenetic tree (cladogram) generated after filtering out the most variable features is more optimal for less homoplasy than the tree obtained without feature filtering. Hence, the proposed algorithm effectively pre-filters the features that may cause homoplasy. Furthermore, the results also demonstrated that different cladistic methods investigated gave similar results for the dataset under study.
RNNCore: Lexicon Aided Recurrent Neural Networkfor Sentiment Analysis

Sentiment Analysis (SA) or Opinion Mining can help in identifying subjective information conveyed by user reviews for various automation tasks such as building better recommendation systems, identifying user trends, monitoring, and customer support.This paper focuses on sentiment score detection.Traditional SA algorithms suffer from low accuracies in identifying true user intents.However, with the advent of Deep Learning many NLP (Natural Language Processing) tasks including Sentiment Analysis have become feasible with accuracies comparable to that of human experts.An additional advantage of Deep Learning in contrast to supervised learning is that in deep learning a manually tuned feature set is not required.Deep Learning algorithm such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN)and various other have successfully been applied to SA. RNN, in particular, is well suited for this task, however, most of the works done over RNNs require large supervised training sets which are usually not available for all domains.This work proposes a new method called RNNCore which can make use of the pre-trained word embeddings from Stanford Core NLP in conjunction with RNN to improve accuracy and reduce computation cost.Comparison between the results of RNNCore, RNN, and OneRmethod on the IMDB review dataset suggests that RNNCore yields 92.60%F1-measure which is a marked improvement of 17.74% as compared with a simple RNN approach for Sentiment Analysis.
Diagnosis and Prognosis of Oral Cancer using classification algorithm with Data Mining Techniques

Data mining is the process of researching data from different view points and condensing it into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression, Segmentation algorithms, association algorithms, sequence analysis algorithms, etc.,. The classification algorithm can be used to bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. Our method of creating new algorithm GA+ID3 easily identifies oral cancer data set from the given data set. The genetic based ID3 classification algorithm diagnosis and prognosis of oral cancer data set is identified by this paper.
A Critical View on “The Concept of Teaching the ‘History of Russia’ Course” in General Education Institutions of Russia

This article considers “The Concept of Teaching the ‘History of Russia’ Course in Education Institutions of the Russian Federation Implementing Basic General Education Programs” which was drafted by the Russian Historical Society and adopted in 2020 by the Collegium of the Ministry of Education of the Russian Federation. The role played by this document in the current process of education was assessed by reviewing a number of expert opinions about it published by prominent Russian lecturers of history. The results obtained point to significant weaknesses of both the document and the Historical and Cultural Standard included in it. The potential harm and threat that the latter two can cause to the quality of teaching history to Russian schoolchildren were discussed. It was concluded that any negligence in drafting and adopting such documents might have a considerable influence on the teaching and studying processes, as well as on the educational background of the younger generation. Therefore, they must be designed and approved with participation and input from a larger team of experts in the field.
The Digital Footprint Awareness of the Undergraduate Students in a Private Higher Education Institution in Nueva Ecija, Philippines: A Basis for a Plan of Action

This study sought to determine the digital footprint awareness of the undergraduate students in a private higher education institution in Nueva Ecija, Philippines, to provide a basis for a plan of action.A descriptive research approach was employed by the researcher.Using a survey instrument, the researcher gathered data from 190 college students at a private higher education institution.Results revealed that the respondents were composed of 68% males and 32% females.Most of the respondents were from the first and second year levels.In terms of their age brackets, most respondents belong to the 18-19 and 20-21 age brackets.Respondents expressed that they use the internet for an average of 5 to 6 hours every day and that social networking sites are the most frequently accessed sites, with smartphones being the most commonly owned gadget.An assessment of digital awareness revealed that college students are well aware of their digital footprint in terms of online activities, personal information, online transactions, online platforms and environments, privacy, and security.Overall, their awareness of their digital footprint was very evident.As a result of this study, the researcher suggests activities that would contribute to sustaining digital footprint awareness among college students and may also be used as a set of activities for incoming college students to ensure that future students are also well aware of their digital footprint, leading to becoming responsible digital citizens.
Study on the Necessity of Social Studies Issues-Centered Instruction and Community Political Participation

사회과에서는 시민성 함양을 목표로 지역사회와의 연계를 통한 시민참여를 강조하고 있다. 이에 본 연구에서는 사회과의 쟁점중심 토의 · 토론모형 접목한 지역사회 학생 참여 활동 방안을 제시하였다. 지금까지 대부분 학교를 중심으로 실행되어 온 지역사회 참여가 교실을 중심으로 시행되면서 학생들이 현장에서 경험하면서 얻는 살아있는 지식에 접근하는 것에 한계가 있었다. 이에 사회과에서 활용하는 쟁점중심수업을 토대로 지역사회 참여 프로그램에 적용함으로써 학교와 지역사회 참여 프로그램과의 연계 가능성을 살펴보고자 한다. 이를 위해 사회과에서의 지역사회 참여를 둘러싼 초·중등 교육과정 분석을 토대로 사회과에서의 쟁점중심 수업에서 활용하는 수업 모델을 지자체 지역사회 청소년참여 사업 운영 과정에 적용하였다.그 결과, 학교 중심으로 시행되었던 활동이 지역사회 현장으로 확장되면서 학생들은 보다 적극적으로 자신들과 관련된 문제에 대한 실천방안을 마련하였다. 이를 통해 지역사회에서 시행하는 학생과 청소년 관련 정책의 의사결정에 있어 성인 중심 관점에서 벗어나 학생들의 의견이 반영될 수 있는 가능성을 확인해 볼 수 있었다. 결과적으로 학생 · 청소년 대상 정책수요자 중심의 정책 논의가 학교 교실 뿐 아니라 지역사회 전반으로 연장되었을 때 학생들이 공동체 시민으로서의 정체성과 책임감을 더욱 크게 체감하였다. 본 연구에서는 학교 수업과 지역사회 참여 프로그램 간 연계를 통해 동일한 지역 공간을 살아가는 학생과 청소년들이 능동적이고 살아있는 지식 뿐 아니라 기능과 태도까지 함양할 수 있도록 지역사회 참여의 중요성을 강조하고, 실천 방안을 제시하고자 한다. The objective of this study was to emphasize the importance of communication between participants with various positions in the decision-making process of youth policy participation. It raises a problem from the fact that existing youth participation has been conducted at a justifiable level according to a uniform discussion and discussion method. By raising the need for sharing preferences through communication between subjects with various positions in the youth policy participation process, it emphasizes the process of identifying and mutually sharing preferences of stakeholder subjects with various positions that have been somewhat neglected in the existing youth participation process. To this end, perspectives and preferences of various stake-holders surrounding youth policy were analyzed, focusing on the case of operating a policy participation pilot project based on a local youth council. The importance of decision-making was emphasized by sharing positions of multiple stake-holders.
Impact the Effect of Shamanism in Modern Society in South Korea

Shamanism is an indigenous Korean faith that incorporates various beliefs and practices influenced by native Korean Buddhism and Taoism. In Korean, shamanism is called mu (무) and practitioners are called mudang (무당). The function of the mudang is usually held by women who make contact (liaison) between gods and humans. Shamans perform gut ceremonies or offerings to perform healing, bring good luck and intercede for gods by means of possession. Gut ceremonies are also held to guide the spirits of the deceased to heaven. This research aims to 1) to reveal the role of shamanism in culture in the midst of modern South Korean society, and 2) to analyze the 1970s when shamanism was going through difficult times. This research used qualitative descriptive that described and analyzed the data in the form of information. The results of this research is presentation about the cultural asset of South Korea, namely shamanism, it is should be maintained because it is very influential on Korean tourism as an ancient ritual even though it is considered not in accordance with the modernization period.
Breast Cancer Modeling and Prediction Combining Machine Learning and Artificial Neural Network Approaches

Breast cancer strikes women more frequently than it strikes males, which is a major factor in the rising mortality rate for women. Breast cancer, which predominantly concerns women (roughly 1% of cases include non-females), will impact one in eight women in their lifetime. Breast carcinoma, one of the deadliest malignancies, is the main cause of cancer-related deaths in women. Today, early diagnosis and prognosis are crucial to increase survival rates and finally bring them down. Cancer researchers have a number of difficulties when attempting to differentiate between benign and malignant tumors as well as when attempting to make judgments about mild and metastatic breast cancer. Research has turned its attention to machine learning techniques, which have been shown to be successful in the early identification and prognostication of breast cancer. In proposed article, we utilized eight machine learning techniques to the Breast Cancer Wisconsin Diagnostic dataset: Support Vector Machine, Logistic Regression, XGBoost, CatBoost, Random Forest, Artificial Neural Network, Decision Tree, K-Nearest Neighbours. After getting the data, a performance assessment and comparison of these different classifiers is done. The foremost goal of this study is to detect and utilize machine learning to detect breast cancer and determine which method is more efficient in terms of confusion matrices, accuracy, and precision. The Support Vector Machine and Artificial Neural Network have demonstrated superior performance than all other classifiers, with an accurate result of 98.08%.
minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices

Hate speech classification has become an important problem due to the spread of hate speech on social media platforms. For a given set of Hate Terms lists (HTs-lists) and Hate Speech data (HS-data), it is challenging to understand which hate term contributes the most for hate speech classification. This paper contributes two approaches to quantitatively measure and qualitatively visualise the relationship between co-occurring Hate Terms (HTs). Firstly, we propose an approach for the classification of hate-speech by producing a Severe Hate Terms list (Severe HTs-list) from existing HTs-lists. To achieve our goal, we proposed three metrics (Hatefulness, Relativeness, and Offensiveness) to measure the severity of HTs. These metrics assist to create an Inter-agreement HTs-list, which explains the contribution of an individual hate term toward hate speech classification. Then, we used the Offensiveness metric values of HTs above a proposed threshold minimum Offense (minOffense) to generate a new Severe HTs-list. To evaluate our approach, we used three hate speech datasets and six hate terms lists. Our approach shown an improvement from 0.845 to 0.923 (best) as compared to the baseline. Secondly, we also proposed Stable Hate Rule (SHR) mining to provide ordered co-occurrence of various HTs with minimum Stability (minStab). The SHR mining detects frequently co-occurring HTs to form Stable Hate Rules and Concepts. These rules and concepts are used to visualise the graphs of Transitivities and Lattices formed by HTs.