Reviewerly/exHarmony huggingface dataset:
Visual–Inertial Odometry of Structured and Unstructured Lines Based on Vanishing Points in Indoor Environments

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method.
Demystifying Recommendations: Transparency and Explainability in Recommendation Systems

Abstract: Recommendation algorithms are widely used, however many consumers want more clarity on why specific goods are recommended to them. The absence of explainability jeopardizes user trust, satisfaction, and potentially privacy. Improving transparency is difficult and involves the need for flexible interfaces, privacy protection, scalability, and customisation. Explainable recommendations provide substantial advantages such as enhancing relevance assessment, bolstering user interactions, facilitating system monitoring, and fostering accountability. Typical methods include giving summaries of the fundamental approach, emphasizing significant data points, and utilizing hybrid recommendation models. Case studies demonstrate that openness has assisted platforms such as YouTube and Spotify in achieving more than a 10% increase in key metrics like click-through rate. Additional research should broaden the methods for providing explanations, increase real-world implementation in other industries, guarantee human-centered supervision of suggestions, and promote consumer trust by following ethical standards. Accurate recommendations are essential. The future involves developing technologies that provide users with information while honoring their autonomy.
Current Status of Machine Learning and Artificial Intelligence in Cervical Cancer Screening and Diagnosis: A Systematic Review

Background: Cervical cancer poses a substantial global health challenge, predominantly affecting underprivileged countries. The limitations of current screening methods, such as cytology and visual examination, underscore the need for improved techniques. Artificial intelligence (AI) and machine learning (ML), particularly convolutional neural networks, offer promising solutions in this regard. Methodology: Fifteen studies meeting the inclusion criteria were examined. The PRISMA criteria guided the exploration of cervical cancer screening studies employing AI, ML, and deep learning on PubMed/MEDLINE and Google Scholar. The search focused on "artificial intelligence" and "Pap smear." The investigation specifically delves into English-language studies post-2019 that pertain to the machine learning and deep learning classification of cervical cancer using mobile devices. Histology, animal research, and pre-2019 investigations are excluded. Titles and abstracts were carefully reviewed for any discrepancies and subsequently discussed. The process of data extraction involved compiling information from the selected articles. Result: The systematic review investigates the impact of AI and ML on cervical cancer detection, screening, and diagnosis. Our review reveals enhanced accuracy and efficiency in innovative technologies such as CytoBrain and computer-aided diagnostic systems employing Compact VGG and ResNet101. ML techniques like logistic regression, MLP, SVM, KNN, and naive Bayes prove beneficial for managing complex datasets, particularly when combined with class-balancing procedures. The promising aspects include the application of deep learning for automation and AI-assisted digital microscopy. These findings signify a transformative shift in cervical cancer screening, underscoring the potential of ML and AI technologies to elevate diagnostic accuracy and accessibility. Conclusion: Our study demonstrates advancements in both accuracy and responsiveness. Despite recognizing scientific and ethical considerations, the study underscores the potential of AI to enhance cervical cancer care. This systematic review advocates for policymakers and healthcare practitioners to use ongoing research for informed decision-making in this rapidly evolving field.
The Positive Identification of Skeletal Remains from the Church of the Assumption of the Virgin Mary in Přeštice: a Case Study

The reconstruction and subsequent rescue archaeological excavation in the baroque chapel of St Barbara in the Church of the Assumption of the Virgin Mary in Přeštice has resulted in the discovery of one atypical burial in a small bricked vault. The vault contained skeletal remains in a very poor state of preservation. The discovery of the skeletal remains in the chapel was unexpected given the absence of any signs of a grave being present. In this article we describe the attempt to identify the person to whom the skeletal remains belong. Based on anthropological analysis, these remains were interpreted as probably female over 50 years of age. By combining archaeological context, anthropological analysis and historical sources, the skeleton was interpreted as the remains of Marie Anna Lindauer, a resident of Pilsen and the mother of Josef Ondřej Lindauer, the dean in Přeštice and the bishop in Budweis.
EXPLORING THE DETERMINANTS EFFECTING PROMOTION OF RESEARCH CULTURE AT UNIVERSITY LEVEL

The study in hand was undertaken to explore the determinants which are effecting promotion of research culture at university level. Although many factors are effecting but the most relevant determinants affect was checked which are environmental and institutional determinants. The population was comprised of 1477 the faculty members of BZ University, Multan &amp; Islamia University Bahawalpur as well as Gomal University, Dera Ismail Khan and University of Science and technology, Bannu. The selected sample from the population was comprised of 253 faculty members hailing from the above-mentioned higher education institutions out of 1477 was selected for study. The research design was quantitative in which survey questionnaires were distributed for data collection. T-test and ANOVA were used for analysis of data. The results offered significant information for reaching the conclusion, and making the desired decisions. Also, some recommendations have been extracted from results offered to the policy-makers and future researchers about research issues.
What are the challenges in integrating LLMs into organisations' data workflows?

Integrating Large Language Models (LLMs) such as GPT into organizations’ data workflows is a complex process with various challenges. These obstacles include but are not limited to technical, operational, ethical, and legal dimensions, each presenting hurdles that organisations must navigate to harness the full potential of LLMs effectively.
Online Trust Determinants, Consumer Perception, and Purchase Intent in Saudi E-Commerce: Exploring Determinants and Evidence

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

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

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

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.
Questions of Managing the Trajectory of E-Learning Using Neural Network Technologies

This article discusses the application of neural networks in managing the educational trajectory in electronic educational systems. A model of learning management in an electronic information and educational environment is built, and an algorithm for managing adaptive learning is described. A test example of a neural network is constructed, showing the capabilities and problems of applying neural networks to the tasks of constructing trajectories of students’ adaptive personalized training. The following stages of constructing a neural network model are implemented: determining the structure of a neural network and its software implementation using the PyTorch deep machine learning library; preparing a test training sample for training the neural network; importing the initial data for training the model using the modules of the Pandas library; training the model using torch.optim Adam, Adamax, and Rprop optimizers; visualizing the obtained data in the form of graphs based on the matplotlib library; exporting the obtained numerical results and analysing the data. The paper also states the problems of preparing a training data set for teaching a neural network. The paper concludes that using neural networks in the field of building personalized adaptive educational trajectories has great prospects and will be one of the urgent tasks in the coming years.
Ontology-oriented geoanalytics for determining concentration of road accidents on areas of the road network.

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.
Property, Institutions, and Social Stratification in Africa By Franklin Obeng-Odoom. New York: Cambridge University Press. 2020. 364 pp., $27.70 Paperback.

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A Forward Approach for Sufficient Dimension Reduction in Binary Classification for Large-scale Data

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

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.
Analysis of The Construction Management Application to Project Success Using Importance Performance Analysis on Superflat Concrete Floor Work

In pursuit of Indonesia's equitable and sustainable development, the surge in industrial city development, notably in Karawang, has led to the construction of various facilities such as factories, warehouses, and workshops. Efficient planning methods, encompassing cost, time, and quality considerations, are imperative for these projects. A critical aspect is the construction of concrete floors, vital for supporting diverse loads like heavy vehicles, static loads from goods, storage racks, and large machines. The ongoing development of the Superflat Concrete Floor Method, with its productivity and precision advantages, underscores the need for effective construction management. This study aims to evaluate the substantial impact of construction management on project success and to map application indicators on the IPA diagram – Importance Performance Analysis. Employing both quantitative and qualitative methods, the research distributed questionnaires with Likert scales to 30 respondents, including contractors, owners, and design planners. Smartpls 3.0 software analysis reveals that Knowledge Management significantly and positively influences the success of the superflat concrete floor project at the Karawang Industrial Factory site. The IPA mapping places Knowledge Management and Total Quality Management in Quadrant I (Keep Up the Good Work), emphasizing their pivotal roles in ensuring project success. This study provides essential insights for stakeholders involved in industrial development, guiding them toward more effective construction management strategies for optimized outcomes.
Redefining university infrastructure for the 21st century: An interplay between physical assets and digital evolution

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

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.
A Study on Computing Sample Quantiles of Discrete Probability Distributions

In this paper, we studied the methods of computing sample quantiles for the case of discrete probability distributions. There are two main methods which are introduced in statistics textbooks. We compared these two methods by simulation from practical and educational necessity. We considered some cases of Poisson, binomial, geometric, negative binomial, and discrete uniform distributions by setting up the parameters, and for each distribution we performed 10,000 times of simulation of drawing random samples of size 20 and 50. At each time of simulation, we computed the difference between the population quantile and the sample quantile obtained by each method. We compared the two methods by using two criteria: one is the mean square error over 10,000 times of simulation, and the other is the frequency of obtaining closer sample quantile to the population quantile than the competing method. We also obtained the estimated probabilities of exact estimation of population quantiles for the two methods, and performed the tests of homogeneity on the probability distribution of under-estimation, exact estimation, and over-estimation of the population quantiles.