Reviewerly/exHarmony huggingface dataset:
Online practicum during pandemic: “they’re in the classroom but i’m online”

ABSTRACTABSTRACTThe study aims to reveal preschool teacher candidates' online practicum experiences during the pandemic – the teacher candidates connected to preschool classes online while the children were face-to-face with their class teachers. The participants of this phenomenological study were 18 preschool teacher candidates studying at a public university in Turkey. Data were collected via individual interviews, micro-teaching observations, and portfolios. Thematic content analysis was used for data analysis. The results showed that teacher candidates had to deal with technical problems. Still, they gained experience teaching online with young children and improved their digital skills and technological pedagogical content knowledge. They did not have the opportunity to develop teaching self-efficacy for face-to-face education. Teacher candidates especially expressed their concerns about classroom management and bonding with children. Disclosure statementNo potential conflict of interest was reported by the author(s).
Kant’s <i>Die falsche Spitzfindigkeit</i> and Proof-theoretic Semantics

According to Schroeder-Heister 2018, proof-theoretic semantics is ‘an alternative to truth-condition semantics. It is based on the fundamental assumption that the central notion in terms of which meanings are assigned to certain expressions of our language, in particular to logical constants, is that of proof rather than truth. In this sense proof-theoretic semantics is semantics in terms of proof. Proof-theoretic semantics also means the semantics of proofs, i.e. the semantics of entities which describe how we arrive at certain assertions given certain assumptions.' This text advocates that proof-theoretic semantics, as described above, was an approach to semantical issues in logic that appeared in mainstream philosophical literature at least a couple of centuries before the tradition of general proof theory (cf. Prawitz 1971) came into being. This is done by means of an interpretive analysis of Kant’s 1762 essay Die falsche Spitzfindigkeit der vier syllogistischen Figuren, in which two theses are argued: first, that the issue of justifying the logical validity of inferences is approached by Kant in this text, in a strong sense, in terms of proofs; and second, that the purpose of his effort is to establish a point concerning the semantical equivalence between certain distinct valid inferences.
Coherent state representation of squeezing operator and its applications

In a previous paper Zhan et al. (2022) proposed the coherent state representation for linear operator, which shows a beautiful theoretical value. Inspired by the work, we further extend it to the nonlinear case, including single- and two-mode squeezing process. Based on squeezing operators’ transformation relation, we introduce their coherent state representations and derive their normal ordering forms by using the technique of integration within product of operator. As applications, we examine some operators’s normal ordering forms and show the entanglement property of prepared quantum states via the Schmidt decomposition. In particular, we further consider two cascaded two-mode squeezing operators, in which a generalized SU(1,1) interferometer is involved. In addition, the application of two-mode squeezing operator followed by photon-subtraction to generate non-Gaussian entangled states in quantum teleportation is discussed. Our results will be useful in preparation of quantum states and quantum metrology.
Determination of nursing students' attitudes toward and readiness for mobile learning: A cross-sectional study

Mobile learning is a learning method that enables nursing students to learn anytime and anywhere.This study was designed to determine nursing students' attitudes toward and readiness for mobile learning.A descriptive, cross-sectional study design was used.The study was conducted at a nursing faculty in Turkey.The sample size was determined using the stratified randomization method by considering the participants' years at school, and 290 nursing students were included in this study.Data were collected using the Sociodemographic Characteristics Questionnaire, Mobile Learning Attitude Scale (MLAS), and Mobile Learning Readiness Scale (MLRS) using the survey method. Statistical Package for the Social Sciences, version 25.0, was used for all data analyses.The mean scores of the participating nursing students obtained from the MLAS and MLRS were 76.48 ± 9.28 and 79.64 ± 16.62, respectively. Compared to first- and second-year students, fourth-year students, those who had mobile learning experience, and those who wanted to use a mobile learning system in the future obtained higher MLAS and MLRS scores. A positive, weak correlation was found between variables, age and level of computer use, and the MLAS and MLRS scores.It was determined that the attitudes of nursing students toward mobile learning were positive and they were ready for mobile learning. The results of this study can be used in designing and implementing mobile learning systems.
An analysis of ConformalLayers' robustness to corruptions in natural images

• An assessment of robustness to corruptions in images in ConformalLayers, under m C E and relative m C E perspectives • An analysis on the inference time achieved by ConformalLayers-based approaches for Neural Networks • An analysis of the energy consumption of ConformalLayers on networks with varying depth, under CO2 emission perspective ConformalLayers are sequential Convolutional Neural Networks (CNNs) that use activation functions defined as geometric operations in the conformal model for Euclidean geometry. Such construction turns the layers of sequential CNNs associative, leading to a significant reduction in the use of computing resources at inference time. After the layer’s association, both the processing time and memory used per batch entry become independent (i.e., constant) of the network’s depth. They depend only on the size of the input and output. The cost of conventional sequential CNNs, on the other hand, presents linear growth. This paper evaluates the robustness of the classification of ConformalLayers-based CNNs against different kinds of corruptions typically found in natural images. Our results show that the mean top-1 error rates of vanilla CNNs are smaller than ConformalLayer-based CNNs on clean images. Still, our approach outperforms other optimization techniques based on network quantization, and the relative difference to vanilla networks tends to reduce in the presence of image corruptions. Furthermore, we show that processing time and CO 2 emission rates are much lower for ConformalLayer-based CNNs with a depth greater than seven and two, respectively.
Efficient image dehazing algorithm using multiple priors constraints

In this study, a robust and efficient image dehazing technique based on the atmospheric scattering model is proposed, which effectively overcomes the limitations of a single prior condition. It is composed of a transmission estimation module and an atmospheric light estimation module. The transmission estimation module integrates multiple dehazing prior strategies and effectively optimises transmission estimation and application range. The atmospheric light estimation module uses the fuzzy C-means clustering algorithm (FCM) to estimate the atmospheric light of different scenes in an image. Unlike in the previous work, the atmospheric light in this module is a nonglobal value, and a pixel-level atmospheric light value matrix is obtained. Numerous experiments show that the proposed dehazing algorithm is superior to state-of-the-art methods. • To improve the performance of single-prior based image dehazing algorithms, we proposed a novel transmittance estimation strategy, which combines the local and non-local prior effectively, in addition, a global transmittance optimization method is also employed in the proposed algorithm to preserve the image information. • To refining the global atmospheric light in the atmospheric scattering model, we proposed a novel atmospheric light estimation method, which employed the fuzzy C-means Clustering algorithm to refine the atmospheric light value into pixel level. • Extensive experiments are conducted on both synthetic and real-world image dataset. In addition, comparisons are performed against variety of advanced algorithms. The proposed algorithm also shows good robustness.
Interaction detection in configurable systems – A formal approach featuring roles

Modern software systems are increasingly complex due to their configurability and adaptivity. For modeling and implementing such systems, the concept of roles is particularly well-suited as it allows capturing context-dependent properties and behavior. Similar to other compositional approaches, notably the feature-oriented development approach, the detection of (unintended) interactions between roles is a challenging task. We consider two new aspects of interactions. Hierarchical interactions may occur between systems and active interplays describe the actual situations in a sequence of events where components interact. To reason about such interactions, we introduce a compositional modeling framework based on concepts and notions of roles, comprising role-based automata (RBAs). Based on this formal foundation, we present a modeling language for succinctly describing RBAs and an implementation for translating this language into the input language of the probabilistic model checker PRISM. This enables a formal analysis of functional and non-functional properties including system dynamics, context changes, and interactions. We carry out three experimental studies as a proof of concept of such analyses: First, a peer-to-peer protocol study illustrates how undesired hierarchical interactions can be discovered automatically. Second, a study on a self-adaptive production cell demonstrates how undesired interactions influence quality-of-service measures such as reliability and throughput. Third, we illustrate how to incorporate feature-oriented system design in our role-oriented framework by means of the elevator community benchmark system.
Fast simulation for Gaussian random fields on compact Riemannian manifolds

We develop a fully discrete approximation scheme to fast simulate centered Gaussian random fields (GRFs) over compact Riemannian manifolds. These GRFs can be either anisotropic or isotropic ones. The scheme is proposed by the truncation of a series expansion depending solely on the decay of the angular power spectrum, which is independent of the chosen space and time discretization. The pth moment convergence for the scheme is proved and the error bound with its associated rate of convergence is determined. Three simulation case studies are conducted by applying the Laplacian operator to find the required surface harmonics for a torus endowed with a Riemannian metric and by applying the ellipsoidal Beltrami and Laplacian operators to find the required surface ellipsoidal harmonics respectively for anisotropic and isotropic GRFs over ellipsoids endowed with two different Riemannian metrics.
The Application of Artificial Intelligence in Real Estate Valuation: A Systematic Review

Several studies and research papers were conducted experimenting with the use of artificial intelligence and its applications in real estate valuation. This study systematically reviewed several articles published between 2020 and 2022 to explore AI models and applications such as machine learning and ANN along with GIS, and satellite imaging. The findings of the review revealed some shortcomings where machine learning models required adjustments (types of data and separate processing) however, with better accuracy and lower errors in other circumstances indicating that the models were sensitive to research objectives, purpose, and data types. To conclude, the field is still at an early stage and requires further research with high potential to dominate the future practice of real estate valuation.
The Impact of Corporate Entrepreneurship on Corporate Performance: The Moderating Role of Digital Readiness in the Banking Sector

This research will examine the impact of corporate entrepreneurship on the performance moderating role of digital readiness in the banking sector. Studies on digitalisation in the banking sector have either centered on the financial or consumer viewpoint. Strategically, reports examine and address the effect of digitisation on banking practises and their business models. Based on the literature review, the researcher recognised three foremost possible drivers which affect the performance of the banking industry. The present study refers to banking instar, demonstrating that digital culture must be promoted. Digital technology management built to provide the digitalised solution for the banking sector where the underlying factors are understandable and related to the consequences of emerging technologies will allow banks to use technological opportunities to offer better productivity regarding the performance of the banks having a mediating impact on digital readiness is also significant for banks because many banks are not entirely engaged in digital readiness, possibly because creative digital solutions are not sufficient.
Extracting Semantic Relationship Between Fatiha Chapter (Sura) and the Holy Quran

There is a huge shortage of scientific research in the Arabic language, especially in natural language processing and relationships between Arabic documents and other specific documents. This shortage is also reflected in Arabic Books’ introduction, Topics abstraction, and content summary engines. Furthermore, there are some good samples of Arabic words inside the Quran. The Quran is the holy book of Islam, that is divided into chapters (surah) and verses (ayat) of differing lengths and topics. This paper introduces a framework for both specialized researchers in Islamic studies as well as non-specialized researchers to find hidden relationships between one of the most important chapters of the Holy Quran which is Al Fatiha surah and the remaining chapters of the Holy Quran using Hierarchical Technique data modeling as an unsupervised learning technique. a new framework that can access tokens of the Holy Quran in different granule parts such as chapter (sura) part of the chapter (Aya) of the Holy Quran Sura, words, word roots, Aya roots, and Aya meaning in the Arabic language, Moreover, We had developed a lot of statistics related to Fatiha Sura and the holy Quran like (roots distinct for every Sura, Words Redundancy, Roots Redundancy, Matrix report by roots and every sura, Matrix report shows Percentage of roots similarity by every sura and whole Quran distinct roots, etc.). Furthermore, we enhance the search engine results by adding search by roots and Aya meaning for every Sura. And the results for sample queries show accuracy with more than 3% using meaning and roots compared to the text of the Holy Quran only.
Implementation and effectiveness of a COVID-19 case investigation and contact tracing program at a large, urban midwestern university

The University of Illinois Chicago (UIC) COVID-19 Contact Tracing and Epidemiology Program was critical to the university's COVID-19 incident response during the 2020-2021 academic year. We are a team of epidemiologists and student contact tracers who perform COVID-19 contact tracing among campus members. Literature is sparse on models for mobilizing non-clinical students as contact tracers; therefore, we aim to disseminate strategies that are adaptable by other institutions.We described essential aspects of our program including surveillance testing, staffing and training models, interdepartmental partnerships, and workflows. Additionally, we analyzed the epidemiology of COVID-19 at UIC and measures of contact tracing effectiveness.The program was responsible for promptly quarantining 120 cases prior to converting and potentially infecting others, thereby preventing at least 132 downstream exposures and 22 COVID-19 infections from occurring.Features central to program success included routine data translation and dissemination and utilizing students as indigenous campus contact tracers. Major operational challenges included high staff turnover and adjusting to rapidly evolving public health guidance.Institutes of higher education provide fertile ground for effective contact tracing, particularly when comprehensive networks of partners facilitate compliance with institution-specific public health requirements.
Factors Affecting Customers’ Happiness: A Systematic Review in the Service Industries

Various issues influence customer happiness in the service industry. The ultimate goal is to meet consumer preferences and tastes. However, various factors, including quality of service, professionalism, sensitivity, reliability, and consistency to achieve happiness. Nonetheless, for this study, the fundamental ones included loyalty, trust, and satisfaction. While various aspects affect operations in the service industry, the three are fundamental for this study in achieving customer happiness. These factors determine a customer's willingness to spend as a happy and satisfied customer will likely recommend the quality of service. However, due to changes in dynamics in the service industry, these factors could potentially change with emerging trends and pandemics, as in the case of the COVID-19 pandemic. The study reviewed other studies' findings and evaluated what other researchers had conducted and found the hospitality industry presented was the most researched. Ideally, this means that the industry leads in preferences for researchers based on its changing consumer preferences and tastes that shape consumer happiness levels. The study used a hypothetical approach in relating variables from 30 sources that shape the findings. In the long term, the future of customer happiness in the service industry relies on achieved satisfaction levels.
Neither friend, nor device

Abstract With smart speakers diffusing into society, artificial intelligence is moving from the imaginative reservoirs of dystopian storytelling into vernacular living. How do users perceive communication with it? Are Alexa and Siri considered simple devices, sentient assistants, or even artificial friends? Based on nine qualitative interviews with former smart speaker users in Germany, this study analyzes smart speaker use and related personal epistemologies within a media repertoire perspective. By presenting six interrelated action-guiding principles explaining smart speaker use and people’s ambivalent sensemaking, we argue that smart speakers appear neither as friends nor as mere neutral devices to their users. The identified principles explain the peripheral role of smart speakers within media repertoires as handy but suspicious gimmicks. For future smart speaker adoption, whether smart assistants are interpreted as simple-minded, exploitative gimmicks or relevant, reliable, and trustworthy companions will be crucial.
The Impact of Procurement Agility and Procurement Sustainability on Organizational Performance in UAE’S Semi-government Entities: The Mediating Role of Procurement Centralization

Nowadays, the need to leverage government procurement processes through the adoption of sustainable procurement, and procurement agility has particularly gained deep power in developing economies across the globe. The main aim of this study is to identify the general impact of procurement agility and procurement sustainability on organizational performance in UAE’s semi-government entities. In providing insights, the study will seek to establish a connection between the mediating role of procurement centralization with procurement agility, and procurement sustainability in the UAE’s government business entities. The study’s conceptual framework model will involve four critical variables that include, procurement centralization, organizational performance, procurement agility, and procurement sustainability within the UAE’s government business entities. The data collection methodology in context of the following study will involve the use of specially designed questionnaires distributed to 43 government business entities in the UAE. The research study will adopt a quantitative approach, achieved through administration of relevant data collection tools in the research study.
Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Identity and Access Management Field: Challenges and State of the Art

In order to meet the growing needs of organizations and individuals to access services and systems remotely, especially during the period of the covid-19 pandemic, the use of I&AM systems is increasingly widespread and optimized. Henceforth regulated by the GDPR, these systems must meet privacy requirements in order to strengthen citizens’ control over their personal data. Artificial Intelligence has proven its efficiency in offering methods to secure I&AM processes. In this direction, the main contributions of this paper consist in identifying the challenges facing I&AM process that are: identification, authentication, authorization, auditing/monitoring and accountability. Afterward, we study how these extracted challenges have been addressed by conducting a comprehensive survey of ML applications for the enhancement of I&AM’s five processes. Besides, we conduct an analysis of the studied solutions based on Cognitive Project Management for Artificial Intelligence (CPMAI) methodology. Finally, some future research directions are identified.
An Efficient Low Complexity Denoising Filter Architecture for the Removal of Salt and Pepper Noise

Image denoising has applications in sectors such as astronomy, where there are severe resolution constraints, medical imaging, where high-quality pictures are necessary for analysis, and forensic science, where we must sometimes handle extremely poor-quality photos for evidence. To enhance the image a variety of filtering algorithms are used, one of which is the Median Filter, which removes impulse noise while keeping edge information. It replaces the value of the noisy pixel with the median of the intensity values in its vicinity. The proposed architecture is area efficient, low in complexity, and high speed, thus proposed median filter architecture is tested on a real image, and the PSNR and SNR of the median filter are analysed. A decision filter is applied to the median to filter pixels having noise alone and process it hence increasing the speed and PSNR significantly comparing the performance of a 3x3 window filter with a 5x5 window filter proved that the noise reduction by the latter filter is efficient for higher noise figures and at lower noise Figure both the filter performs optimally.
Enhanced Extreme Learning Machine Algorithm with Deterministic Weight Modification for Investment Decision on Indian Stocks

Predicting stock market performance has long been a popular research topic because of its high potential for financial gain. One important task of stock market analysis is being able to reasonably predict future stock price. Recent years have seen rapid advancement in disciplines like machine learning, which has provided fresh perspectives on this task. This paper presents an enhanced extreme learning machine (ELM) for predicting the stock price. The enhanced ELM uses a new weight adjustment scheme such as deterministic weight medication (DWM) for enhances prediction accuracy and provide faster convergence. A new DWM based ELM (DELM) prediction algorithm is to improve the global convergence rate by increasing the gradient values of error surface. The experimental results are conducted on financial datasets thoroughly trained and tested. The experimental findings revealed that the suggested DELM prediction model produces better prediction performance when compared with other well-known prediction algorithms.