Reviewerly/exHarmony huggingface dataset:
The problem of the spiritual thing

In this response, I briefly consider a Maori philosophy of reaction and disconnection, relating to certain Pakeha New Zealanders' vehement, sometimes mocking, rejection of Maori spiritual entities (such as the taniwha). While Maori are often publicly reported on as being outraged at this rejection, what is not so widely cited is the concern that many Maori have at the rangirua (fundamentally fragmented) state that these individuals occupy. I suggest that rangirua engages with notions of control, to the extent that the mocking individual is so highly concerned with disavowing the spiritual that implications arise for what is, from a Maori perspective, their wellbeing. Kingsbury argues for a particular kind of reality for the taniwha but not from Maori foundational propositions of existence. Her argument draws from the rules that are credible for analytic philosophy but not so much for a Maori perspective. Nevertheless, within those limitations, she argues well for a notion of taniwha that could make taniwha palatable for those Pakeha individuals.
Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework

Abstract The basic Markov-switching model has been extended in various ways ever since the seminal work of Hamilton (1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57: 357–84). However, the estimation of Markov-switching models in the literature has relied upon parametric assumptions on the distribution of the error term. In this paper, we present a Bayesian approach for estimating Markov-switching models with unknown and potentially non-normal error distributions. We approximate the unknown distribution of the error term by the Dirichlet process mixture of normals, in which the number of mixtures is treated as a parameter to estimate. In doing so, we pay special attention to the identification of the model. We then apply the proposed model and MCMC procedure to the growth of the postwar U.S. industrial production index. Our model can effectively control for irregular components that are not related to business conditions. This leads to sharp and accurate inferences on recession probabilities.
FORMING ECOLOGICAL CULTURE OF STUDENTS THROUGH INNOVATIVE TECHNOLOGIES

Since the possibilities of innovative education in the formation of the ecological culture of students are not used to the full extent at their sufficient level in the educational process, the contradictions between the need to form the ecological culture of students and the insufficient elaboration of theory and practice are clearly visible. Therefore, one of the ways to form the ecological culture of students on the basis of nature protection is the preparation of a methodology, the expansion and deepening of general theoretical research in pedagogy devoted to this problem. The article analyzes the concept of ecological culture from a pedagogical point of view, clarifies the features of the issue under consideration, identifies ecological culture of students,as well as the features of the organization of the process of forming the ecological culture of students with the help of innovative teaching technologies in biology lessons. The volumes, performance indicators and levels of formation of the ecological culture of students with the help of innovative teaching technologies in biology lessons when creating an integrated system for the formation of ecological culture of students using innovative teaching technologies were determined and its effectiveness was proved.
Code Mixing in Online English Classes: A Qualitative Analysis of Teacher-Student Communication at Junior High School Level

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study investigates the utilization of code mixing in online English classes at the junior high school level, focusing on the type and frequency of code mixing employed by both the English teacher and students. Data were collected through video recording and observation at SMPN 3 Candi in Sidoarjo, employing a qualitative research design. Muysken's theory (2000) was used to analyze the variation of code mixing in the English class, which included alternation, insertion, and congruent lexicalization. The most prevalent type of code mixing observed was alternation, used as an alternative teaching strategy to enhance student comprehension. The study involved a non-participant observation approach, with data categorized by dates and dialogues classified based on code mixing types. Results indicated that alternation was the dominant code mixing type used by the teacher and students, facilitating improved understanding of English materials. The research concludes that code mixing serves as a valuable pedagogical tool in the learning process, acting as a bridge between students and teachers. The implications suggest further exploration of code mixing in educational institutions and the development of teaching techniques based on the findings to enhance language learning experiences.&#x0D; Highlight: &#x0D; &#x0D; &#x0D; The study examines code mixing patterns in online English classes at the junior high school level, focusing on the usage by teachers and students.&#x0D; &#x0D; &#x0D; The most frequently used code mixing type is alternation, which serves as an effective alternative teaching strategy for facilitating student comprehension.&#x0D; &#x0D; &#x0D; Code mixing functions as a bridge between teachers and students, enhancing the learning process and aiding in understanding English materials. Further research in educational institutions can explore its benefits and potential for developing effective teaching techniques.&#x0D; &#x0D; &#x0D; Keyword: Code Mixing, Online English Class, Junior High School, Alternation, Educational Institution.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Impact of the School Literacy Movement on Reading Skills of Grade I Students

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The weak levels of reading and writing skills among the Indonesian population have become a pressing education issue in the country. In response, the government introduced the School Literacy Movement program in 2015, aiming to foster a reading culture among students. This study employs a quantitative approach to analyze the effect of the School Literacy Movement on the reading skills of grade I students at SD Muhammadiyah 2 Tulangan. Data collection techniques included tests and questionnaires, with a sample of 24 students. The results indicate that the School Literacy Movement has a significant positive impact on students' reading skills, as reflected in an Adjusted R Square value of 73.8%. Over 50% of students displayed excellent reading abilities, indicating the effectiveness of the literacy initiative. This research highlights the importance of the School Literacy Movement in enhancing reading proficiency among young learners and calls for continued support and implementation of similar programs to improve education outcomes in Indonesia.&#x0D; Highlight:&#x0D; &#x0D; &#x0D; The study addresses the issue of weak reading and writing skills among the Indonesian population, and the government's response through the implementation of the school literacy movement program.&#x0D; &#x0D; &#x0D; Using a quantitative method, the research analyzes the impact of the school literacy movement on the reading skills of grade I students at SD Muhammadiyah 2 Tulangan, with a sample size of 24 students.&#x0D; &#x0D; &#x0D; The findings reveal a significant positive effect of the school literacy movement on students' reading skills, as indicated by an Adjusted R Square value of 73.8%. More than 50% of students displayed excellent reading abilities, underscoring the success of the literacy initiative in fostering diligent reading habits among young learners.&#x0D; &#x0D; &#x0D; Keyword: Weak Literacy, School Literacy Movement, Reading Skills, Grade I Students, Quantitative Method&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
A Comparative Analysis of Students' Understanding in Social Studies: Unraveling the Nuances of Online and Offline Learning

This research examines the disparity in students' comprehension of social studies materials during online and offline learning for Class IV SDN Pagerwojo. Employing a non-experimental quantitative ex post facto method, documentation of value data collection was employed as the instrument. The results indicate that online learning yields a mean understanding score of 70 (medium category) with a standard deviation of 10, while offline learning achieves a mean score of 73 (moderate category) with a standard deviation of 13. The findings suggest a discernible variance in students' understanding between the two modes of learning, albeit not significantly pronounced. The implications of this study contribute to the ongoing discourse on optimizing learning environments to better support students' comprehension in social studies subjects.&#x0D; Highlights:&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Insightful Comparison: This research delves into a comparative analysis of students' understanding of social studies materials in both online and offline learning environments, shedding light on the nuanced differences between the two.&#x0D; Moderate Disparity: The study reveals a moderate variance in students' comprehension levels between online and offline learning, emphasizing the need for further exploration to optimize educational approaches.&#x0D; Implications for Education: The findings have significant implications for educators, highlighting the importance of tailoring teaching strategies to maximize students' understanding of social studies subjects across different learning platforms.&#x0D; &#x0D; Keywords: Students' Understanding, Social Studies, Online Learning, Offline Learning, Comparative Analysis&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Description of Children's Readiness to Enter Elementary School in Class 1 Students of SD Negeri Jabon District

This descriptive quantitative research aimed to investigate the readiness of grade 1 elementary school students to enter primary education in the Jabon sub-district. A sample of 102 students out of a total population of 110 was selected using non-probability accidental sampling. Data analysis, employing the SPSS 17.0 for Windows program, involved descriptive statistics to assess school readiness based on measures such as mode, mean, median, standard deviation, and variance range. The findings revealed that among grade 1 students at SD Jabon Sidoarjo District, 85.3% (87 students) exhibited high readiness, 8.8% (9 students) demonstrated sufficient readiness, and 5.9% (6 students) displayed less readiness to enter elementary school. These results shed light on the preparedness of children for formal education and have implications for educational stakeholders in fostering optimal learning environments and supporting students' smooth transition to elementary school.&#x0D; Highlights:&#x0D; &#x0D; The study examined the readiness of grade 1 students to enter elementary school, providing insights into their preparedness for formal education.&#x0D; A significant majority of grade 1 students (85.3%) demonstrated high readiness, emphasizing the positive foundation for their academic journey.&#x0D; The findings have implications for educational stakeholders, highlighting the importance of creating supportive learning environments and facilitating a smooth transition into elementary school.&#x0D; &#x0D; Keywords: Readiness, Elementary school, Grade 1 students, School transition, Educational implications.
The Survey of Digital Transformation in Education: A Systematic Review

This study addresses a gap in the literature regarding the implementation of digital strategies in educational institutions, particularly universities. Despite significant advancements in the development of digital strategies, there remains a lack of commitment and vision for their effective implementation. This study systematically reviewed the literature to evaluate digital transformation in education across three dimensions: campus environment, teaching methods, and learning experiences. Employing the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines, this study identified ten pertinent articles for thematic analysis. These findings highlight the critical role of digital transformation in various aspects such as data collection, management, academic advising, and personalized learning, revealing a trend towards improved educational outcomes through blended learning, video conferencing, AR/VR, and adaptive learning technologies. This research underscores the transformative impact of digital strategies on education, suggesting a paradigm shift in teaching/learning methods, emphasizing the need for educational institutions to embrace these changes proactively.
Remote Sensing Image Super-Resolution via Multi-Scale Texture Transfer Network

As the degradation factors of remote sensing images become increasingly complex, it becomes challenging to infer the high-frequency details of remote sensing images compared to ordinary digital photographs. For super-resolution (SR) tasks, existing deep learning-based single remote sensing image SR methods tend to rely on texture information, leading to various limitations. To fill this gap, we propose a remote sensing image SR algorithm based on a multi-scale texture transfer network (MTTN). The proposed MTTN enhances the texture feature information of reconstructed images by adaptively transferring texture information according to the texture similarity of the reference image. The proposed method adopts a multi-scale texture-matching strategy, which promotes the transmission of multi-scale texture information of remote sensing images and obtains finer-texture information from more relevant semantic modules. Experimental results show that the proposed method outperforms state-of-the-art SR techniques on the Kaggle open-source remote sensing dataset from both quantitative and qualitative perspectives.
A Visually Inspired Computational Model for Recognition of Optic Flow

Foundation models trained on vast quantities of data have demonstrated impressive performance in capturing complex nonlinear relationships and accurately predicting neuronal responses. Due to the fact that deep learning neural networks depend on massive amounts of data samples and high energy consumption, foundation models based on spiking neural networks (SNNs) have the potential to significantly reduce calculation costs by training on neuromorphic hardware. In this paper, a visually inspired computational model composed of an SNN and echo state network (ESN) is proposed for the recognition of optic flow. The visually inspired SNN model serves as a foundation model that is trained using spike-timing-dependent plasticity (STDP) for extracting core features. The ESN model makes readout decisions for recognition tasks using the linear regression method. The results show that STDP can perform similar functions as non-negative matrix decomposition (NMF), i.e., generating sparse and linear superimposed readouts based on basis flow fields. Once the foundation model is fully trained from enough input samples, it can considerably reduce the training samples required for ESN readout learning. Our proposed SNN-based foundation model facilitates efficient and cost-effective task learning and could also be adapted to new stimuli that are not included in the training of the foundation model. Moreover, compared with the NMF algorithm, the foundation model trained using STDP does not need to be retrained during the testing procedure, contributing to a more efficient computational performance.
Linking Topographical Ring Features to Geochemical and Geophysical Anomalies

Circular features in forests seen from air have been studied for several decades at different locations around the world. Forest rings, as they are called in Canada’s boreal forests, express several geochemical (pH, carbonate content) and geophysical (surface potential) anomalies on their 20–30 m wide ring edges. Although it has been proposed that microbial processes may cause these anomalies, the exact mechanisms of ring formation are still unknown. We focused on the Thorn North forest ring in Ontario, Canada to correlate the surface potential anomaly to soil gas concentrations. Field measurements showed that the surface potential drop at the ring edge center is framed by peaks in CO2 production, which is linked to O2 depletion and methane generation. Carbon isotope signatures were found to drop to lighter values (down to −20‰), suggesting increased respiration. Higher concentrations of uronic acids bound to extracellular polymeric substances were found, indicating that the surface potential anomaly is linked to respiration. 16S rRNA gene sequencing of shallow soil did not indicate a dominant microbial group on the edges; instead, principal component analysis showed that the microbial composition was controlled by the substrate (clayey vs. sandy soil), therefore future studies should focus on deeper ground layers.
Automated mapping of regolith units with support vector machine and artificial neural network using data from Landsat-8 OLI, ALOS PALSAR DEM, and Sentinel-1A radar images: the case of the Sissingué Gold Project, Northern Côte d’Ivoire

The Sissingué Gold Project is in Northern Côte d'Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks.
Exploring the Influence of the Information and Communication Technology Dimensions on the Sustainability of Competitiveness in Small and Medium-sized Enterprises in the Hail Region

The main purpose of this research paper is to investigate the role of information and communication technology (ICT) in providing small and medium-sized enterprises (SMEs) with a competitive advantage. To do this, we propose five dimensions to detect ICT (human resources, equipment and devices, databases, software, and networks) and four dimensions (service quality, market control, creativity and development, and operational efficiency) to decide competitive advantage. To achieve this paper’s purposes, a questionnaire was developed to collect data from respondents to a sample of 128 subjects by using the Statistical Package for Social Sciences (SPSS) for the survey data analysis. Several methods have been used in the statistical analysis of the data, including descriptive statistics tests, a normal distribution test (one-sample Kolmogorov–Smirnov test), simple linear regression coefficient (simple regression), variance analysis (one-way ANOVA), and a T-test for independent samples. This study reports a series of findings, underlined by the significant role of ICT with its various dimensions and the competitive advantage of SMEs. This study concludes with several suggestions including that SMEs must increase their investments in ICT components and take more care of creativity and development for the preemption to create new services.
Dynamic Secure Key Distribution Based on Dispersion Equalization and Cellular Automata for Optical Transmission

This paper proposes a dynamic secure key distribution scheme based on dispersion equalization and cellular automata (CAs). The scheme effectively eliminates the key inconsistency problem caused by imperfect channel reciprocity, and dynamic key sequences can be conveniently generated with large key space in long-haul optical transmission. In the process of communication, the legitimate parties obtain the secure core parameter from the frequency domain equalizer algorithm, and a final key sequence is generated through CA iterations on the basis of the core parameter. The randomness and reciprocity characteristics of the channel ensure the security and uniqueness of the core parameter and final key sequence. With 10G Baud 16 quadrature amplitude modulation over 400 km standard single-mode fiber transmission, the proposed scheme is verified with a free key error rate and an unlimited key generation rate. The security robustness of this scheme was theoretically analyzed and verified by sweeping the eavesdropper’s tapping position and improving CA operation processing. The proposed key distribution scheme is compatible with the existing transmission system for different signal modulation formats.
Teaching schoolchildren in programming within the subject "Informatics": problems and possible solutions

In modern conditions, the constantly expanding use of information technologies, the urgent need for high-level IT specialists, which determine both the development of the country's industrial and intellectual potential, as well as its security, and sometimes the very existence of Russia as an independent state, update the problems of teaching programming at school, including the introduction of an independent subject "Programming" into the school curriculum. The study shows that the framework of the school subject "Informatics" is becoming too narrow for the formation of basic training and professional orientation of schoolchildren in the field of algorithmization and programming; there is an urgent need for schoolchildren to study programming as a separate discipline. But the question arises of choosing a programming language to study in a school course. A comparison of the programming languages that are most common in teaching school informatics today, in particular Python and Pascal, allows us to determine their advantages and disadvantages when studying at school as the first programming language and to conclude that preference should be given to the Pascal language, since Python does not allow students to sufficiently develop confident competencies in the field of programming and knowledge of the fundamental elements of programming.