Reviewerly/exHarmony huggingface dataset:
Online Trust Determinants, Consumer Perception, and Purchase Intent in Saudi E-Commerce: Exploring Determinants and Evidence

Authors:
raw_author_name: Sager Alharthi
raw_author_name: Sufyan Habib

Abstract: This study aimed to explore the growing use of the internet as a commercial sales channel, emphasising the pivotal role of trust in online consumer relationships. This research focused on Saudi e-commerce platforms (e.g. Amazon.sa, Noon, Namshi, and Shein), due to their remarkable growth over previous years, by investigating the effect of consumer trust on online purchase intentions. The study surveyed 449 Saudi online shoppers, using a structural equation model to analyse the factors that determine online trust (perceived information quality, company competency, security, and online consumer reviews) and their influence on consumer online trust and perceived risk. The results revealed significant relationships between consumer online trust and perceived information quality, company competency, and security, challenging expectations regarding the link between online consumer reviews and consumer online trust. Surprisingly, online consumer reviews primarily influenced perceived risk rather than consumer online trust, impacting purchase intention to a limited extent. The research underscores the substantial influence of consumer online trust on perceived risk and purchase intention, offering micro perspectives on online consumer behaviour. By identifying key influences, including information quality and security, this study provides theoretical and managerial insights for e-commerce platforms, guiding the development of responsible online channels. KEYWORDS company competency, consumer reviews, e-commerce antecedents, information quality, perceived security, Saudi consumers
Invisible Publications: A Study of Academic Productivity in the Web of Science Database

Authors:
raw_author_name: Eliandro Schvirck
raw_affiliation_string: Federal University of Technology - Paraná (UTFPR)
raw_author_name: Luiz Alberto Pilatti
raw_affiliation_string: Federal University of Technology - Paraná (UTFPR)

Abstract: The present study aims to outline the profile of uncited or rarely cited articles indexed in the Web of Science database between 2017 and 2021. We used stratified random sampling of 14,425 journals to extract our study sample of 373 journals. The dependent variable was defined as the number of citations, with independent variables being the JCR index, maturity, quartile, language of publication, and whether the research was funded. Descriptive statistics were used to characterize the sample according to field of research and variable. A negative binomial regression method was used to estimate which variables influence the number of citations received by articles in the sample. We found that of the 256,524 articles that make up the research corpus, 39,469 (15.39%) received no citations and 91,963 articles (35.85%) received up to four citations. The variables that positively influenced estimates of the number of citations were maturity (0.481), JCR index (0.159), and funding (0.020), while quartile (-0.170) and language (-1.032) had a negative influence on number of citations. We conclude that the pressures of academic productivism create a vicious cycle in which studies are referenced based on their ranking in the struggle for privileged editorial space, with articles in higher quartile journals being more prone to citation.
Risk First Mindset in “Security First vs Compliance First” Debate

Authors:
raw_author_name: Pranith Shetty

Abstract: The idea of Security first vs compliance first approach has been plaguing the Cybersecurity industry and others, for a while now, Subject matter experts on both sides of the aisle have been debating and trying to make their point heard across the other side, explaining why is it that a certain approach is better than the other!. All of these points are valid and backed with facts and use cases.
Logical analysis of data and knowledge without using an axiomatic approach

Authors:
raw_author_name: Boris A. Kulik

Abstract: Logical analysis of data and knowledge is required not only in artificial intelligence systems, but also in mathematical proofs. This work shows the difficulties of logical analysis caused by the indisputable application of the formal axiomatic approach adopted at the turn of the XIX and XX centuries as the only theoretical basis of modern logic and the foundations of mathematics. In this approach, the main role is played by the axioms and methods of syntactic analysis, which underlie predicate calculus. Instead of set theory, we propose to use more simple algebra of sets as a theoretical basis of such a logic, for which the substantiation of the basic laws does not require axioms. A new approach to logical analysis based on the laws of set algebra is considered. Some advantages of this approach are shown, and a list of unresolved problems is provided.
Ontology-oriented geoanalytics for determining concentration of road accidents on areas of the road network.

Authors:
raw_author_name: Alexander A. Smirnov
raw_author_name: Nikolay N. Teslya

Abstract: Geoanalytics is an important tool in analyzing the urban environment. This work focuses on the development of geoanalytics tools by ontology-oriented integration of information from heterogeneous sources to identify potentially accident-prone sections of the road network, which can be used to build routes along minimally dangerous roads. The heterogeneity of a large number of sources is caused by the variety of types of information provided, collected both centrally by road and emergency services, and distributed with the help of volunteers. The novelty of this work is the combination of heterogeneous sources of information in a single analytical system and conducting a geoanalysis of the frequency of accidents on individual road sections, determining the most frequent type and cause of accidents to build a route bypassing sections of the road network with a high concentration of accidents.
A Forward Approach for Sufficient Dimension Reduction in Binary Classification for Large-scale Data

Authors:
raw_author_name: Seunghwan Park

Abstract: Sufficient dimension reduction, aimed at finding a lower-dimensional subspace in explanatory variables that contains response variable information, typically relies on inverse-based methodologies. These methods are easy to implement but often require linear or constant variance conditions. To address these limitations, techniques for forwardly estimating the central subspace have been developed. In particular, methods utilizing the Reproducing Kernel Hilbert Space have gained attention, but their use in analyzing large datasets is limited due to the characteristics of the kernel space. In this paper, we study a novel forward approach for sufficient dimension reduction in binomial classification of large-scale data. We propose a method that employs a divide-and-conquer technique to split data into subsets, then independently perform dimension reduction on each subset before synthesizing them into a final model. It was shown that when the number of partitions of data was appropriately selected, the loss in prediction accuracy was not significant compared to the existing method, while being efficient in terms of storage space and calculation cost. In addition, simulations in various models showed superior prediction accuracy than other inverse-based techniques. The utility of the proposed method was confirmed through the real data analysis.
DEMYSTIFYING LEADERSHIP: A HOLISTIC ANALYSIS OF ITS ROLE, INCLUDING DEMOGRAPHIC FACTORS IN E-COMMERCE IN SMES IN AD DAKHILIYAH GOVERNORATE, OMAN

Authors:
raw_author_name: None Abtihal Ahmed Mohammed Al Hinai
raw_affiliation_string: Business Studies Department, College of Economics and Business Administration, University of Technology and Applied Sciences, Nizwa, Oman.
raw_author_name: None Abdulkadir Shehu Abdulwahab
raw_affiliation_string: Business Studies Department, College of Economics and Business Administration, University of Technology and Applied Sciences, Nizwa, Oman.
raw_author_name: None Maryam Ali Hamed Al-Mahrouqi
raw_affiliation_string: Business Studies Department, College of Economics and Business Administration, University of Technology and Applied Sciences, Nizwa, Oman.
raw_author_name: None Shikha Mohammed Humaid Hamed Al Shukaili
raw_affiliation_string: Business Studies Department, College of Economics and Business Administration, University of Technology and Applied Sciences, Nizwa, Oman.

Abstract: This quantitative study with a sample of 375 participants from small and medium enterprises (SME) in the Ad Dakhiliyah region of Oman examines the impact of transformational and transactional leadership styles on E-commerce adoption (ECA). A multi-group (MGA) analysis was conducted to investigate the influence of demographic factors on ECA through leadership styles. Using statistical analyses via SPSS and SmartPLS 4, the study shows significant gender differences in the influence of leadership styles on ECA. In addition, comparisons were made with two other demographic factors — age and leadership position. However, caution should be exercised when generalising beyond the study's focus on the Ad Dakhiliyah region. Despite a comprehensive investigation, the age factor shows an insignificant difference in the impact of transformational and transactional leadership on ECA. This study makes an important contribution to the understanding of leadership dynamics in the digital age in Omani SMEs, highlighting the gender nuances. The integration of robust research models and statistical tools enhances methodological rigour and provides practitioners and future researchers with nuanced perspectives on leadership and technology adoption in the Ad Dakhiliyah region and in the broader context of Omani SMEs.&#x0D; Keywords: Leadership Style, E-commerce Adoption, Technology Adoption, Gender, SME, Oman, SmartPLS.
Redefining university infrastructure for the 21st century: An interplay between physical assets and digital evolution

Authors:
raw_author_name: Bunmi Isaiah Omodan
raw_affiliation_string: Faculty of Education, Walter Sisulu University, Komani Campus

Abstract: In the evolving landscape of the 21st century, universities are at the forefront of re-imagining their infrastructural identity. This conceptual paper delves into the transformative shifts witnessed within university infrastructure, focusing on the harmonisation of tangible physical assets and the expanding world of digital evolution. As brick-and-mortar structures remain pivotal, integrating digital platforms rapidly redefines the academic landscape, optimising learning and administrative experiences. The modern learning paradigm, enriched by this symbiotic relationship, offers dynamic, flexible, and comprehensive educational encounters, thereby transcending traditional spatial and temporal constraints. Therefore, this paper accentuates the broader implications of this infrastructural metamorphosis, particularly its significant role in driving economic development. The synergistic effects of physical and digital infrastructures enhance academic excellence and position universities as key players in addressing and navigating global challenges, setting forth a resilient and forward-looking educational blueprint for the future. In conclusion, integrating physical and digital infrastructures within universities heralds a transformative era, shaping a holistic, adaptable, and enriched academic environment poised to meet 21st-century challenges. This study illuminates the symbiotic relationship between tangible university assets and digital innovations, offering insights into their collective impact on modern education and broader economic trajectories.
Research on Exploratory English Teaching Methods in Art Colleges

Authors:
raw_author_name: Zhaohui Yang
raw_author_name: Haixia Zhou

Abstract: English teaching has met troubles in students specialized in arts who can’t understand the teachers’ explanation. Force feeding duck teaching in classroom hasn’t completed the task of teaching. Inquiry English teaching is introduced in this article to improve the status.
Multilingual Named Entity Recognition in archaeology: an approach based on deep learning

Authors:
raw_author_name: Maria Pia di Buono
raw_affiliation_string: Department of Literary, Linguistic and Comparative Studies, University of Naples "L'Orientale" – Napoli, Italy

Abstract: This short paper describes a collection of BERT models for the archaeology domain. We took existing language specific BERT models in English, German, and Dutch, and further pre-trained them with archaeology specific training data. We then took each of these three archaeology specific models and fine-tuned them for Named Entity Recognition.
Involment of additional constraints of subject domain in machine learning problems

Authors:
raw_author_name: Aleksandr A. Zuenko

Abstract: The study deals with the concideration of a range of issues related to the involving additional knowledge about subject domain when solving machine learning problems. The techniques for accounting such knowlwdge based on the modification of classical methods of classification, clustering, associative rule discovery are described. It is concluded that the analysis of bachgroung knowledge is able to increase reliability and accuracy of classical machine learning methods, although their modification, taking into account additional constraints, sometimes turns out to be a rather time-consuming procedure. In addition, different types of machine learning problems require different types of additional constraints to increase the reliability and accuracy of their results. This provides certaint difficulties in solving complex problems that require gradual involvement of various types of additional constraints.
Generation of PAS-Stained images of glomerular tissue units using a generative adversarial network with spectral normalization colorization method

Authors:
raw_author_name: Jincheng Peng
raw_affiliation_string: Department of Information and Computer Science, Akita Prefectural University
raw_author_name: Guoyue Chen
raw_affiliation_string: Department of Information and Computer Science, Akita Prefectural University
raw_author_name: Kazuki Saruta
raw_affiliation_string: Department of Information and Computer Science, Akita Prefectural University
raw_author_name: Yuki Terata
raw_affiliation_string: Department of Information and Computer Science, Akita Prefectural University

Abstract: In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.
Method for named entities linking with concepts of the Wikidata knowledge base

Authors:
raw_author_name: Nikolay N. Teslya

Abstract: The paper presents a method for automatically linking named entities in Russian-language texts with concepts from the Wikidata knowledge base. It is based on the use of named entity search tools with subsequent semantic analysis of the correspondence of the found entity to the concept in the knowledge base. The resulting links can later be used to form a linked corpus of texts in any subject area. The difference between the presented method and existing ones is the analysis of both the named entity itself, its attributes, as well as associated words without the use of machine learning methods. This approach allows to increase the accuracy of searching for a corresponding concept in the knowledge base and eliminates the need to constantly retrain the neural network model to recognize new entities added to the knowledge base.
Ranking assessment of the popularity of publications for online social media monitoring

Authors:
raw_author_name: Maxim G. Shishaev
raw_author_name: Vladimir V. Dikovitsky

Abstract: The paper examines the possibility of using various methods for generating ranking estimates of the popularity of messages in online social media. Using experimental data collected from the VKontakte network as an example, it is shown that using quantitative indicators of likes, reposts and comments as the basis for an ranking assessment of popularity gives an equivalent result. The need to use popularity metrics, normalized by the number of views or the size of the community audience, is noted when analyzing several online communities at the same time.
Project management strategies for implementing energy-efficient cooling solutions in emerging data center markets

Authors:
raw_author_name: None Emmanuel Chigozie Ani

Abstract: Project management strategies play a pivotal role in implementing energy-efficient cooling solutions in emerging data center markets. This review explores key strategies for successful implementation, focusing on the unique challenges and opportunities in these markets. Emerging data center markets are characterized by rapid growth and evolving infrastructure needs. As such, implementing energy-efficient cooling solutions requires a strategic approach that addresses local regulatory requirements, environmental conditions, and technological capabilities. Effective project management in these contexts involves several key strategies. Firstly, thorough planning is essential. This includes conducting feasibility studies, assessing site-specific requirements, and developing detailed project plans. Understanding the local context is crucial, as it can impact the selection and implementation of cooling solutions. Secondly, stakeholder engagement is critical. Engaging with local authorities, communities, and industry stakeholders can help ensure regulatory compliance and garner support for the project. Additionally, involving local experts and partners can provide valuable insights and enhance the project's success. Thirdly, implementing robust monitoring and evaluation mechanisms is essential. Monitoring energy consumption, cooling system performance, and environmental impacts allows for timely adjustments and optimizations, ensuring the long-term sustainability of the cooling solutions. Lastly, knowledge sharing and capacity building are key components of successful project management in emerging data center markets. Providing training and education on energy-efficient practices and technologies can help build local capacity and ensure the continued success of the cooling solutions. In conclusion, project management strategies for implementing energy-efficient cooling solutions in emerging data center markets require a holistic approach that considers local context, engages stakeholders, implements robust monitoring mechanisms, and promotes knowledge sharing. By adopting these strategies, data center operators can effectively address the challenges and opportunities in these markets, contributing to a more sustainable and resilient data center infrastructure.
ARTIFICIAL INTELLIGENCE IN EDUCATION: CASES OF USING CHATGPT 3.5

Authors:
raw_author_name: Dmytro Pokryshen

Abstract: Formulation of the problem. This research explores the possibilities and peculiarities of employing artificial intelligence in general secondary education. A retrospective analysis of human utilization of artificial intelligence is conducted. Emphasis is placed on the relevance and significance of aligning the education system with contemporary demands and implementing new technological solutions. The focus is on the correctness of queries to systems and the necessity of analyzing work outcomes. Materials and methods. Data collection and processing were conducted during professional development courses for education professionals in the Chernihiv region (Ukraine) through pedagogical observation of course participants and their work with AI. Throughout the study, 34 training sessions were conducted, involving 748 teachers teaching various subjects. Among the showcased cases of AI usage, the most demanded queries among teachers are presented. Results. Examples of text analysis using the generative system ChatGPT 3.5 are provided. The obtained results are analyzed. The findings emphasized the necessity of clearly formulating queries and providing comprehensive input data and complete texts for analysis. In such cases, the artificial intelligence system can provide more accurate responses. This study demonstrated that the system handles this task quite successfully when providing the text of a program code and requesting commentary. However, solving mathematical problems poses specific difficulties, and obtaining the correct answer is only sometimes achievable. That reiterates the critical approach toward the system's obtained work outcomes. Conclusions. ChatGPT 3.5 can correctly analyze well-known facts and provide accurate information about them, but if the facts are local, the system cannot cope with the task. Simple math problems and tasks are performed correctly, but more complex ones already cause difficulties. It is essential not only to write prompts correctly but also to present input data. Such tasks as paraphrasing, translation, and changing the text's tone demonstrate modern AI systems' capabilities. ChatGPT 3.5 can become a good teacher's assistant or assistant for preparing various documentation. Requests for help in writing lesson plans, preparation of educational activities, formation of project topics, and technological maps of projects are carried out by the system at a high level.