Reviewerly/exHarmony huggingface dataset:
A Study on the Industry Practitioners’ Perceptions for the Activation of AI in the Domestic Construction Sector

최근 국내 건설산업은 스마트건설 기조에 따라 AI(Artificial Intelligence, 이하 AI) 기반 스마트 기술 도입을 시도하고 있다. 전 산업에서의 AI 활용 생태계 조성을 위해 정부는 인공지능 국가전략 등 정책을 추진해 왔으나 업계수요와 실태의 반영이 부족하다는 한계점이 제기되었으며, 특히 건설산업에 특화된 AI 정책과 제도가 부재한 상황이다. 본 연구는 국내 건설산업의 AI 활성화 전략 수립을 위한 기초연구로, 건설 업무단계별 AI 동향을 파악하고 건설분야 AI 도입과 수요에 대한 실무자 인식 수준을 진단하기 위해 건설업계 실무자 107명을 대상으로 설문조사를 실시하였다. 실무자 인식 분석 결과, 국내 건설업계는 AI 도입에 긍정적이며 2030년 이후인 시점에 AI가 활성화될 것으로 전망하고 있다. 현 AI 기술 수준에서 AI는 단순 보조적인 업무 지원의 수단으로 인식되며, 실무에 필요한 AI 적용 업무단계는 기획 · 설계단계, 유지관리단계, 시공단계 순으로 나타났다. 또한, AI 도입 · 활용 시 대표적인 장애요인은 건설에 특화된 양질의 학습용 데이터와 솔루션 부족, 전문 인력 및 인식 부족, AI 결과물에 대한 신뢰성 우려로 나타났다. 이러한 장애요인을 공공차원에서 정책적으로 해결하기 위한 방안으로 AI 기반 업무수행 법적 근거, AI 인프라, AI 신뢰성 및 품질검증과 실증 환경을 제시하였다. 본 연구 결과는 기관에서 AI 도입 및 활용전략 방향 설정 시 기초자료로 활용될 수 있을 것으로 기대된다. The domestic construction industry is attempting to introduce smart technologies based on artificial intelligence (AI) in line with smart construction trends. However, current national AI policies and strategies have limitations in addressing industry demands and conditions. Moreover, there is a lack of specialized AI policies and systems for the construction sector. To address these issues, 107 construction industry practitioners were surveyed to understand AI trends in each construction stage and assess their perceptions of its adoption and demand. The survey revealed a positive outlook for AI implementation in the construction industry, with expectations of implementation after 2030. In its current state, AI technology is seen as a basic and auxiliary means of supporting tasks, and the practical stages of AI implementation were identified as planning and design, maintenance, and construction. Obstacles to AI adoption included a scarcity of high-quality construction-specific training data, limited solutions, a shortage of skilled personnel, low awareness, and concerns about reliability. Proposed policy areas involve establishing legal frameworks for AI-based tasks, developing AI infrastructure, and ensuring AI reliability and quality verification in test-bed environment. The findings are expected to provide foundational data for institutions planning to introduce and utilize AI in the construction industry.
Research on Countermeasures Against the Cyber Weapon System of the Chinese Hacking Group Xiao Qi-ying: SQL Injection and OSINT-Based Known Vulnerability Attacks

러시아-우크라이나 전쟁 이후의 국제 정세 변화의 영향으로 사이버 무기체계의 확산이 국가 안보와 국제 사이버 안전에 점점 더 중요한 요소가 되고 있다. 중국은 사이버 강대국으로 분류되며, 중국의 지원을 받는 것으로 의심되는 해킹그룹 샤오치잉(晓骑营)의 사이버 공격은 전 세계적인 도전으로 대두되고 있다. 본 연구에서는 2023년 1월 21일 샤오치잉의 공격개시 선포 이후 약 2달간 전개된 샤오치잉의 사이버 공격을 분석하여, 공격 양상과 대응 방안을 살펴보고 시사점을 제시한다. 샤오치잉은 주로 SQL Injection과 공개정보(Open Source Intelligence, 이하 OSINT)를 활용한 알려진 취약점 공격(이하, Known Vulnerability)을 공격기법으로 활용하였으며, 이를 고려한 종합적인 대응 방안과 사이버 보안 강화를 제안하고, 이를 통해 기업과 국가 차원에서 사이버 위협에 효과적으로 대응할 수 있는 전략 도출에 기여하고자 한다. The spread of cyber weapons systems is becoming an increasingly important factor in national security and international cyber safety because of the changes in the international political climate following the Russia-Ukraine war. China is classified as a cyber superpower, and the cyber-attacks of the hacking group "Xiao Qi-ying" (晓骑营), suspected of receiving support from China, poses a global challenge. This study analyzed the cyber-attacks conducted by Xiao Qi-ying for approximately two months after its declared attack commencement on January 21, 2023, to examine the patterns of attacks and response measures and to present implications. Xiao Qi-ying primarily utilized techniques, such as SQL Injection and Known Vulnerability attacks utilizing Open Source Intelligence (OSINT). This paper proposes a comprehensive response plan considering these, enhancing cyber security, and contributing to the derivation of effective strategies to counter cyber threats at the corporate and national levels.
Empowering Scientific Computing and Data Manipulation With Numerical Python (NumPy)

NumPy is a Python library for performing numerical data structures and specialized computing. It improves the components of N-dimensional arrays and provides operations and tools to interface with these arrays. NumPy implements the N-dimensional array, or ndarray, and provides Python-specific scientific methods for performing realistic array and matrix operations. When compared to array programming in other languages, it will allow us to do a wide range of mathematical operations and data manipulations. NumPy can be used with other Python packages and programming languages such as C and C++. NumPy now supports object-oriented programming as well. For example, a class called ndarray may be an N-dimensional array with multiple ways of performing various data structure operations and characteristics.
Spread spectrum modulation recognition based on phase diagram entropy

Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%.
Improving the quality of object classification in images by ensemble classifiers with stacking

The object of research is the process of classifying objects in images. The quality of classification refers to the ratio of correctly recognized objects to the number of images. One of the options for improving the quality of classification is to increase the depth of neural networks used. The main difficulties along the way are the difficulty of training such neural networks and a large amount of computing that makes it difficult to use them on conventional computers in real time. An alternative way to improve the quality of classification is to increase the width of the neural networks used, by constructing ensemble classifiers with staking. However, they require the use of classifiers at the first stage with different structured processing of input images, characterized by high quality classification and relatively low volume of calculations. The number of known such architectures is limited. Therefore, the problem arises of increasing the number of classifiers at the first stage of the ensemble classifier by modifying known architectures. It is proposed to use blocks of rotation of images at different angles relative to the center of the image. It is shown that as a result of structured image processing by the starting classifier, processing of rotated image leads to redistribution of errors on image set. This effect makes it possible to increase the number of classifiers in the first stage of the ensemble classifier. Numerical experiments have shown that adding two analogs of the MLP-Mixer algorithm to known configurations of ensemble classifiers reduced the error from 1 to 11 % when working with the CIFAR-10 dataset. Similarly, for CCT, the error reduction was between 2.1 and 10 %. In addition, it has been shown that increasing the MLP-Mixer configuration in width gives better results than increasing in depth. A prerequisite for the success of using the proposed approach in practice is the structured image processing by the starting classifier
The Mediation Effect of Mathematics Anxiety in Relationships Between Pre-Service Early Childhood Teachers’ Self-Directedness and Mathematics Teaching Efficacy

본 연구의 목적은 예비유아교사의 자기주도성과 수학교수효능감의 관계에서 수학 불안감의 매개효과를 알아보는 데 목적이 있다. 본 연구의 대상은 서울의 4년제 대학 1곳, 전라남도, 충청남도 소재 3년제 대학 각 1곳, 총 3개 대학의 유아교육과에 재학 중인 학생 205명이다. 수집된 자료는 SPSS 25.0 통계프로그램을 사용하여 분석했으며, 문항 신뢰도 분석, 기술통계, 상관분석, 중다회귀분석을 실시하였다. 연구 결과로는 첫째, 예비유아교사의 자기주도성과 수학 교수효능감은 보통보다 높은 것으로 나타났다. 반면, 수학 불안감 점수는 보통보다 낮은 것으로 나타났다. 둘째, 예비유아교사의 자기주도성이 높을수록 수학교수효능감이 높은 반면, 수학에 대한 불안감이 높을수록 자기주도성과 수학 교수효능감이 낮은 것으로 나타났다. 셋째, 수학 불안감은 예비유아교사의 자기주도성과 수학 교수효능감 간의 관계를 부분 매개하는 것으로 나타났다. 즉 자기주도성은 수학 교수효능감에 직접적인 영향을 미칠 뿐만 아니라 수학 불안감에 영향을 미치는 과정을 통해 간접적으로도 수학 교수효능감에 영향을 미치는 것이 확인되었다. 본 연구의 결과는 예비유아교사의 수학 불안감을 낮추고 자기주도성과 수학 교수효능감을 높일 수 있는 수학 교사교육프로그램 개발의 기초자료로 활용될 수 있을 것으로 기대한다. Objective: The purpose of this study is to investigate the mediating effect of mathematics anxiety on the relationship between self-directedness and mathematics teaching efficacy of pre-service early childhood teachers. Methods: The subjects of this study were 205 students enrolled in the Department of Early Childhood Education at three universities: one four-year university in Seoul and one three-year university in Jeollanam-do and Chungcheongnam-do. The collected data were analyzed using the SPSS 25.0 statistical program, and item reliability analysis, descriptive statistics, correlation analysis, and multiple regression analysis were performed.Results: As a result of the study, first, the self-directedness and mathematics teaching efficacy of pre-service early childhood teachers were higher than normal. On the other hand, the mathematics anxiety score was lower than normal. Second, it was found that the higher the self-directedness of the pre-service early childhood teachers, the higher the mathematics teaching efficacy, whereas the higher the anxiety about mathematics, the lower the self-directedness and the mathematics teaching efficacy. Third, it was found that mathematics anxiety partially mediates the relationship between self-directedness and mathematics teaching efficacy of pre-service early childhood teachers. In other words, it was confirmed that self-directedness not only directly affected the mathematics teaching efficacy, but also indirectly increased the mathematics teaching efficacy when the mathematics anxiety was low.Conclusion/Implications: The results of this study are expected to be used as basic data for the development of a mathematics teacher education program that can lower the mathematics anxiety of pre-service early childhood teachers and increase self-directedness and mathematics teaching efficacy.
Study tours in China as a tool for improving quality education

In the last decade, China has continued to pursue an active policy of reforming the education system and improving the quality of human capital, and thus creating an environment conducive to the socio-economic development of the state. The education sector is developing in all directions and is no longer limited with only compulsory nine-year education. The authorities stress the need for quality education and are taking steps to improve the educational process. &amp;quot;High quality&amp;quot; education must be comprehensive, as it is a key way of ensuring the long-term economic growth of the whole country. In such circumstances, the development of the study tour segment as a tool to broaden students&amp;apos; horizons and develop their practical skills, has become increasingly popular and has therefore received the support of the authorities. Study tours are organized by schools according to the characteristics of the region, the age of the students and the content needs of the curricula for specific subjects. The purpose of such out-of-school trips is to enrich the knowledge of primary and secondary school students, to acquaint them with nature and culture, and to gain experience of communicating in society and making independent decisions. Despite the challenges posed by the COVID-19 coronavirus pandemic, the segment as a whole has managed to weather the crisis and enter a new phase of development. Shortly before the launch of the &amp;apos;Double Reduction Policy&amp;apos; aimed at reducing homework and extracurricular activities and improving the quality of education and its accessibility to all segments of the population, study tours became an integral part of the educational programmes of primary and secondary schools in the country, but this area is still in the process of formation and standardization. It is now increasingly noted that study tours now inherit and develop the traditional Chinese educational philosophy of &amp;apos;read a thousand books and travel a thousand miles&amp;apos;, and are becoming an important new content and a new tool for quality education in the country.
Owning a Portfolio of Agile Cloud Products

Businesses are increasingly moving their operations to the cloud, which has resulted in a growing demand for cloud products that can be delivered to stakeholders in an agile fashion, as well as leaders that can own and manage these products. As this portfolio of products continues to expand, organizations face challenges in managing their cloud products effectively. They often find themselves with a disjointed set of tools that are incompatible with each other, resulting in increased costs, inefficiencies, and slower time to market.
New Horizons in Distributed Cloud Computing

The field of cloud computing has rapidly evolved in recent years with the emergence of new technologies and paradigms that have changed the way organizations manage and use their computing resources. One such model that is gaining popularity is distributed cloud computing, which distributes computing resources, such as servers, storage, and applications, across multiple physical locations. It supports the cloud service provider's infrastructure to be geographically distributed and allows users to access computing resources and services from different locations, thus reducing latency and improving performance. The significance of distributed cloud computing is in its ability to provide businesses with greater flexibility, scalability, and control over their computing resources. By distributing resources across multiple locations, businesses can ensure high availability and reduce the risk of downtime. Additionally, distributed cloud computing can provide greater data security and privacy since data is not centralized in a single location. The current market trends in distributed cloud computing show a rapid growth rate with an increasing number of organizations adopting this model. Key players in the market include major cloud service providers as well as emerging startups that offer specialized services. The market is expected to continue to grow as businesses increasingly seek to understand and take advantage of the benefits of distributed cloud computing.
To Cooperate or Not: Blockchain Adoption in a Platform Supply Chain With Upstream Competition and Environmental Sustainability

The proliferation of copycats poses a severe threat to brand manufacturers in the e-commerce era. Product innovation is a key strategy for brand manufacturers to enhance their market competitiveness and effectively combat copycats, which also may be beneficial or harmful to the environment. Yet, due to a lack of reliable quality information delivery mechanisms, it is challenging for brands to gain consumer trust in product innovation quality. Blockchain traceability technology can boost the transparency of the supply chain and thus consumer trust, which has been deployed by many brand manufacturers. Nonetheless, product innovation and blockchain adoption both costs much. In addition, the high energy consumption of blockchain implementation and the environmental influence of quality innovation are often overlooked in current literature. From both the financial and environmental perspectives, this study proposes a platform supply chain including a manufacturer, copycat, and online platform and explores three scenarios, i.e., without blockchain, the manufacturer deploys blockchain independently, the online platform and the manufacturer deploy blockchain cooperatively. Our results show that the manufacturer is more likely to adopt blockchain when the consumer distrust level is high or the quality innovation is not very beneficial to the environment. Furthermore, we find that blockchain implementation could not only achieve a win-win-win outcome among the manufacturer, copycat, and online platform but also benefit the environment. Moreover, we find that the manufacturer may not always be eager to cooperate with the online platform to implement blockchain technology and when cooperation is required, the manufacturer may be less likely to deploy blockchain and lack the motivation to innovate.
Administering and Auditing the Cloud

Cloud computing has revolutionized the way organizations manage their IT infrastructure. However, with the increased reliance on cloud services, there is a growing concern over the security of data stored in the cloud. Administering and auditing the cloud is an essential process for ensuring the security of data and maintaining regulatory compliance. The problem lies in the fact that cloud environments can be complex and traditional security tools and processes may not be enough to secure them.
Application of a multi-channel attention mechanism in text classification of new media

Sentiment analysis of new media text has become a research hotspot in recent years. In order to more effectively analyse the emotional polarity of new media text, this paper proposes a text classification algorithm based on a multi-channel attention mechanism. First, channels based on bidirectional gating recurrent unit (BiGRU) neural network are used to extract semantic features, while channels based on fully connected neural network are used to extract emotional features. In order to extract the key information better, the attention mechanism is introduced into the two channels respectively, and the bidirectional encoder representation technology based on converter is used to provide the word vectors. Then, the real emotional semantics are embedded into the model through the dynamic adjustment of the word vector by the context. Finally, the semantic features and emotional features of the double channels are fused to obtain the final semantic expression. In the experiment part, NLPCC2014 dataset and microblog data captured by crawler are used for comparative experiment. The experimental results show that the proposed multi-channel attention mechanism method can enhance the ability of emotion semantic capture, and improve the performance of emotion classification.
Evolution and trends of intellectual property crime research between 1991 and 2020

The appropriability of innovation benefits is essential to promote business development. Formal mechanisms protect this appropriability, such as patents or intellectual property (IP) rights. But not all countries respect IP rights and this phenomenon has increased in a complex way. There has been an academic interest in studying the trends regarding IP criminal activities. This study aims to identify the authors, universities and regions that publish the most in IP crime research worldwide. Using the Web of Science database, we analysed 589 articles published between 1991 and 2020 on the topic. The results indicate that approximately 75% of scientific production on IP Crime was written in the last decade, which is of great concern nowadays. The most influential authors come from the USA. This economy has stood out making progress in the establishment of formal mechanisms for the appropriation of innovation as a way to encourage more firms and innovators to develop new productive activities. Different developed economies, in Europe and Asia, also showed that academics study how markets advance through the protection of IP rights, but their studies have different focuses.
Chinese machine reading comprehension based on deep learning neural network

Most of the existing machine reading comprehension (MRC) models mainly learn the attention mechanism of interactive alignment between questions and texts, and there are still great difficulties in the characterisation of ultra-long texts. The complex grammar, diverse expressions, and flexible structure of the Chinese corpus make the MRC task even more difficult. In this paper, a transformer-based neural network model extremely brisk and clever net (XBCNet) was proposed for the MRC problem of Chinese text. XBCNet expands the receptive field by introducing a stack of dilated convolution neural networks, and it is also closely coupled with the word vector algorithm. This process aims to reduce the training and inference time of the model, while alleviating the drawback of long questions that are difficult to answer accurately. The experimental results demonstrate that XBCNet improves the performance of MRC under general texts and obtains the best inference effect in limited computing resources.
Unveiling the Digital Tapestry: Exploring the Intricacies of Language Use and Social Interactions on Facebook Among College Students

This qualitative inquiry delves into the enigmatic world of language use and social interactions on Facebook, illuminating the multifaceted nuances that shape the virtual experiences of college students. Through an immersive examination of 30 participants from St. Michael's College in Iligan City, this study unravels compelling revelations that challenge conventional notions of online communication. Drawing upon in-depth interviews and thematic analysis, three captivating dimensions emerge. Firstly, Facebook emerges as a digital theater, where individuals artfully curate their identities, crafting virtual resumes aimed at captivating and impressing their peers. Secondly, the profound symbiosis between participants and their Facebook friends reveals the platform's transformative power as a sanctuary for emotional solace. It becomes a space where heartfelt comments and uplifting posts provide a buoyant lifeline during moments of vulnerability and despair. Lastly, the linguistic landscape of Facebook unfolds as a dynamic arena, wherein participants adroitly navigate between informal repertoires of shared inside jokes and casual banter with close friends, and the adoption of formal language conventions when engaging with professors and academic communities. These thought-provoking findings illuminate the intricate interplay between language, identity, and social interactions on Facebook, offering invaluable insights into the complex tapestry of digital communication. They contribute significantly to the fields of sociolinguistics, digital communication, and social psychology, provoking further contemplation on the evolving nature of online self-presentation, the transformative potential of virtual support networks, and the adaptive linguistic practices that shape the contours of online social environments.
MOTIVATIC WEB: a collaborative work tool for higher education professors

The objective of this research is to present the development process of the educational tool MOTIVATIC WEB, which seeks to offer professors, regardless of their geographical location, the possibility of working collaboratively, leading to the transformation of classical educational paradigms, by improving competencies and skills of educators through informal training processes such as interaction, collaboration, collective decision-making, and exchange of experiences. For this, a methodology composed of four phases is executed: (i) an identification of characteristics in collaborative work tools offered on the web, (ii) a validation of them following a user-centred design process with a group of professors at national and international level linked as experts, (iii) a conceptual modelling for the determination of functional and non-functional specifications of the platform, and finally, (iv) the development of the software in its first version.