Reviewerly/exHarmony huggingface dataset:
Simplicity versus Complexity in Machine Learning Models—Focusing on Soil Resilient Modulus Prediction

The resilient modulus (MR) of subgrade soil is an important factor in pavement design, which can be determined through repeated load laboratory testing. However, due to its high cost, time-intensive nature, and intricate process, geotechnical engineers and researchers have consistently expressed interest in creating an MR prediction model using alternative soil test data. With advancements in prediction tools using machine learning (ML) algorithms, during the past decade, much research has been focused on implementing ML algorithms for the development of MR prediction models. Many recent studies have been focused on using what is referred to as "black box" models, such as artificial neural networks (ANN). While these black box models provide high prediction accuracy, they are not explainable. In other words, there is no closed-form solution in which the relationship between the predicted label and the input features is explicitly stated. Moreover, these black box models are computationally complex and require advanced coding knowledge, making them inaccessible to many design professionals. In this paper, for illustration purposes, a dataset of 279 MR test results that have been extensively used for ML-based model development of MR is adopted from the literature. Different regression approaches, from simple regression models to advanced ML algorithms, are applied to this dataset, and the developed models are compared in terms of accuracy, simplicity, and explainability. Additionally, the bias and variance in error are investigated for different algorithms. It is concluded that the improved accuracy of the developed models is not always adequate to justify the effort, time, and complexity of adopting complex advanced algorithms instead of simple regression models. Moreover, it is shown that overly complex algorithms could lead to high variance, causing the algorithm to model random noise in the training data.
Natural Language Processing and Deep Learning Approaches for Multiclass Document Classifier

With the recent growth of the internet, the volume of data has also increased. A large section of the internet is full of documents, which may contain data, big data, formatted and unformatted data, structured data, and unstructured data. The increase in the amount of this unstructured data results in making it difficult to manage data. As it is difficult to classify the increasing volume of data for various purposes manually, automated classification is required. This paper overviews different approaches to Natural Language Processing and Deep Learning for content-based classification.
Measuring the Influence of Artificial Intelligence (AI) on Online Purchase Decisions-In Case of Indian Consumers

The industry's ostensible technological sophistication contributes to the highly dynamic ecommerce environment. When new technology is made available, many of these companies openly adopt it to stay competitive market. Internet shop owners have embraced a variety of technologies, including artificial intelligence. Technology is rapidly evolving. Artificial intelligence significantly facilitates the conversion of interest into purchase intentions. The majority of the information gathered by e-commerce companies is about prospective customers or prospects. AI can be used to interact with warm leads or cold leads who have indicated interest in a brand or product. Furthermore, AI has been demonstrated to be a highly constructive technique of retargeting customers. Artificial intelligence advancements have increased consumer satisfaction even further, making it even more critical in today's climate. This paper will investigate the factors that influence artificial intelligence's practical implacability in order to better understand how it affects consumers' online purchase plans. This paper explores the various variables influencing consumers' purchase intentions for e-retailing using a technology-based model as the foundation. This study has developed a model that shows how business organisations can incorporate artificial intelligence into retailing in order to comprehend consumer requirements and encourage technology adoption. This research has looked more closely at consciousness, subjective norms, and faith as constructs that heighten the tenacity of artificial intelligence.
Formation of scientific-analytical and communication skills among students of humanities at the international university (Generation Z Humanities Students at an International University)

Today, recruiting organizations, when selecting potential candidates for vacant positions, emphasize the importance of university graduates having developed communicative and scientific-analytical competencies, which are the main components of "soft skills" for students of the humanities cycle, more conducive to the formation of professionalism than "hard skills", due to the shift in society's priorities towards democratization and humanization socio-psychological principles of performance of official duties within the framework of professions. The purpose of this study is to identify methods and techniques for the formation of scientific, analytical and communicative skills at the international University among students of humanities. Intercultural communication in the context of the educational process, on the one hand, complicates the development of these competencies, on the other hand, requires improving the principles of teaching in the context of cultural dialogues, which determines the formation of the studied skills, taking into account the national and cultural specifics of the studied language and communication of the university where the foreign student is studying. These aspects are a prerequisite for successful integration into a potential professional environment. Research methodology: theoretical analysis of scientific literature on the issue under consideration, content analysis, descriptive and comparative methods. The novelty of the research is due to modern trends in improving the "soft competencies" of representatives of all professions in harmony with the global scientific space through the prism of effective intercultural communication. The results of the study indicate a mutual correlation of scientific, analytical and communicative skills, the synthesis of the main components of which contributes to the formation of metacognitive characteristics of students. It is advisable to form the studied competencies through role-playing games, memes, situational tasks, communicative professionally oriented trainings, the method of active problem-situational analysis ("case-study")
Leakage detection based on variational mode decomposition and long short-term memory neural network

Abstract In the process of long-term continuous operation, fluid transportation pipelines are prone to leakage accidents. Therefore, this study investigates the detection of small-sized leaks with a leakage aperture of 13 mm in pipes with a diameter of 100 mm. The experimental investigation is conducted under the following operating conditions: volume flow of 25-80 m 3 /h, pressure of 100-200 kPa. The variations in volume flow and pressure signals during leak occurrences are analysed. To mitigate the interference caused by noise, the variational mode decomposition (VMD) method is introduced. The VMD effectively reduces noise interference in the signals. Furthermore, the denoised signals are utilized to establish a long short-term memory neural network (LSTM). The LSTM model achieves a high accuracy rate of 91.67% for the entire dataset.
Selection among site-dependent structurally constrained substitution models of protein evolution by approximate bayesian computation

The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection.We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models.ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.
ON THE TITLE AND LEXICAL FEATURES OF THE WINTER OLYMPICS NEWS REPORT IN CHINA DAILY

With the successful hosting of the 2022 Winter Olympics, people all over the world have turned more attention to Chinese winter sports. China Daily is major media to know the news about it. This article made an analysis of the title and lexical features of the Winter Olympics through the news from China Daily.
Adaptive novelty detection with false discovery rate guarantee

This paper studies the semisupervised novelty detection problem where a set of "typical" measurements is available to the researcher. Motivated by recent advances in multiple testing and conformal inference, we propose AdaDetect, a flexible method that is able to wrap around any probabilistic classification algorithm and control the false discovery rate (FDR) on detected novelties in finite samples without any distributional assumption other than exchangeability. In contrast to classical FDR-controlling procedures that are often committed to a pre-specified p-value function, AdaDetect learns the transformation in a data-adaptive manner to focus the power on the directions that distinguish between inliers and outliers. Inspired by the multiple testing literature, we further propose variants of AdaDetect that are adaptive to the proportion of nulls while maintaining the finite-sample FDR control. The methods are illustrated on synthetic datasets and real-world datasets, including an application in astrophysics.
StarTrek: Combinatorial variable selection with false discovery rate control

Variable selection on the large-scale networks has been extensively studied in the literature. While most of the existing methods are limited to the local functionals especially the graph edges, this paper focuses on selecting the discrete hub structures of the networks. Specifically, we propose an inferential method, called StarTrek filter, to select the hub nodes with degrees larger than a certain thresholding level in the high-dimensional graphical models and control the false discovery rate (FDR). Discovering hub nodes in the networks is challenging: there is no straightforward statistic for testing the degree of a node due to the combinatorial structures; complicated dependence in the multiple testing problem is hard to characterize and control. In methodology, the StarTrek filter overcomes this by constructing p-values based on the maximum test statistics via the Gaussian multiplier bootstrap. In theory, we show that the StarTrek filter can control the FDR by providing accurate bounds on the approximation errors of the quantile estimation and addressing the dependence structures among the maximal statistics. To this end, we establish novel Cramér-type comparison bounds for the high-dimensional Gaussian random vectors. Compared to the Gaussian comparison bound via the Kolmogorov distance established by Chernozhukov, Chetverikov and Kato (Ann. Statist. 42 (2014) 1787–1818), our Cramér-type comparison bounds establish the relative difference between the distribution functions of two high-dimensional Gaussian random vectors, which is essential in the theoretical analysis of FDR control. Moreover, the StarTrek filter can be applied to general statistical models for FDR control of discovering discrete structures such as simultaneously testing the sparsity levels of multiple high-dimensional linear models. We illustrate the validity of the StarTrek filter in a series of numerical experiments and apply it to the genotype-tissue expression dataset to discover central regulator genes.
Adaptive variational Bayes: Optimality, computation and applications

In this paper, we explore adaptive inference based on variational Bayes. Although several studies have been conducted to analyze the contraction properties of variational posteriors, there is still a lack of a general and computationally tractable variational Bayes method that performs adaptive inference. To fill this gap, we propose a novel adaptive variational Bayes framework, which can operate on a collection of models. The proposed framework first computes a variational posterior over each individual model separately and then combines them with certain weights to produce a variational posterior over the entire model. It turns out that this combined variational posterior is the closest member to the posterior over the entire model in a predefined family of approximating distributions. We show that the adaptive variational Bayes attains optimal contraction rates adaptively under very general conditions. We also provide a methodology to maintain the tractability and adaptive optimality of the adaptive variational Bayes even in the presence of an enormous number of individual models, such as sparse models. We apply the general results to several examples, including deep learning and sparse factor models, and derive new and adaptive inference results. In addition, we characterize an implicit regularization effect of variational Bayes and show that the adaptive variational posterior can utilize this.
A Moderating Influence of Organisational Capabilities on the Relationship between Teacher Competency and Techer Performance within Private Higher Education in Sichuan

In the dynamic landscape of private higher education in Sichuan, China, the relationship between teacher competency and performance stands as a critical factor shaping academic excellence and institutional success. This study investigates the nuanced interplay between teacher competency, organizational capabilities, and teacher performance within the context of private higher education institutions (PHEIs) in Sichuan. Drawing upon resource-based theory and organizational capability frameworks, this research aims to uncover how organizational capabilities moderate the relationship between teacher competency and performance. The study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive data from faculty members and administrators. The quantitative phase involves surveying a sample of teachers from various disciplines across multiple private higher education institutions in Sichuan. Measures of teacher competency, organizational capabilities, and teacher performance will be assessed using validated scales. Statistical techniques such as regression analysis and moderation analysis will be employed to analyze the quantitative data. In the qualitative phase, semi-structured interviews with faculty members and administrators will provide rich insights into the mechanisms through which organizational capabilities influence the relationship between teacher competency and performance. Thematic analysis will be utilized to identify patterns and themes emerging from the qualitative data. This research contributes to both theoretical understanding and practical implications within the realm of private higher education management. By elucidating the moderating role of organizational capabilities, this study sheds light on strategies that PHEIs in Sichuan can employ to optimize teacher performance through enhancing teacher competency within the organizational context. The findings of this research hold significant implications for policymakers, administrators, and educators aiming to foster a conducive environment for effective teaching and learning in private higher education institutions in Sichuan, China, and beyond.&#x0D;
LIVE-STREAMING PERFORMANCE IN INQUIRY-BASED SCIENCE LEARNING WITH ACTION: TEACHERS’ PERSPECTIVES

Online teaching has become an imperative approach in today’s society. However, as an essential approach, using live streaming to teach students in small groups, particularly rural primary school students, has not been extensively studied. To address this gap, an inquiry-based model, predict-do/observe-quiz/discuss-explain-transfer (P-D/O-Q/D-E-T), was adopted for live streaming with action, and its performance was analysed. Reflection by teachers can lead them to a deeper understanding to capture the profound impact of an educational program. In the present study, eight teachers who had experience assisting rural students in this experiment were invited to rate their points of view on immersion, social interactivity, humanness, and value perception. Examining the consistency of teachers’ viewpoints using the hermeneutic method, the results showed that they highly supported viewing these four constructs using live streaming to conduct inquiry-based science learning with action. As expected, using live streaming to deliver teaching with the P-D/O-Q/D-E-T approach can enrich other online science teaching. Keywords: educational program, inquiry-based science learning, live-streaming, teachers’ perspective
THE IMPACT OF THE OK4R MOBILE PLATFORM ON VOCATIONAL STUDENTS’ IMPROVEMENT OF SCIENCE READING COMPREHENSION AND LOGICAL THINKING

In Taiwan, the education system places a strong emphasis on higher education. Students who perform well in the National High School Entrance Examination are given priority admission to public high schools, while those with lower scores tend to enroll in vocational schools. It's worth noting that students with low scores in this examination often struggle with reading and natural literacy, typically scoring below level 2 in PISA assessments. This study examined the impact of implementing the mobile platform OK4R reading strategy on students' comprehension of popular science reading and their logical thinking abilities. The research was conducted with two classes of first-year vocational school students. The findings indicate that when students used the OK4R mobile platform for popular science reading, it had a positive effect on their popular science reading comprehension and their logical thinking skills. Notably, female students outperformed their male counterparts. The study also revealed that several factors, including gender, basic abilities, and other variables, influenced the effectiveness of the OK4R mobile platform for vocational students. Those with lower science scores appeared to benefit the most from this approach. Keywords: OK4R, popular science reading, logical thinking, vocational high school students, reading comprehension
Geology and Geochemical Characterization of Basement Rocks Around Burumburum Area North Central Basement Complex Nigeria

The geology and the geochemical characterization of rocks around Burumburum area north central basement complex of Nigeria was studied. Twelve (12) rocks samples were prepared for petrographic studies through a standard procedure while thirty (30) whole rocks samples were analyzed using X-ray Fluorescence (XRF) and Inductively Coupled Plasma Mass Spectrometry(ICP-MS). The result of the field investigation revealed that the study area is underlain by migmatites, gneisses, biotite microgranites, fine grained biotite granites, medium grained biotite granites, porphyritic biotite-hornblende granites, granodiorites, syenites, diorites, dolerites, quartzite, pegmatites and aplites. The geochemical characterizations of the granites, granodiorites, syenites based on Na&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O/K&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O versus SiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; showed acidic compositions while diorites and dolerites are intermediate to basic compositions. The granites, granodiorites, syenites, diorites and dolerites are generally peraluminous (ASI&amp;gt;1.1) to metaluminous (ASI≤1). The granitic rocks, diorites, dolerites and syenites plotted mainly in shoshonite series while the granodiorites occur in High-K calc-alkaline fields. The granitic rocks, syenites, diorite and dolerites based on A/CNK versus SiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; are of I-type while granodiorites are of S-type. The multi-elements discrimination trends for granites, granodiorites, syenites, diorites and dolerites revealed relative enrichment in Large Ion Lithophile Elements (LILE: Ba, La, Rb, and Th and depletion in High Field Strength Elements (HFSE: Nb, P, Ti and Sr) which indicates crustal contamination of magma. The enrichment in the light rare earth element (LREE) relative to heavy rare earth element (HREE) for granodiorites, fine grained biotite granites, medium grained biotite granites (except medium grained biotite granites S12 and S14 with positive Eu), porphyritic biotite-hornblende granites and syenites with negative Eu anomaly suggest moderate to high degree of fractionation. The enrichment in light rare earth element (LREE) for diorites and dolerites relative to moderate to flat HREE with weak negative Eu anomaly indicates low degree of fractionation. The Y+Nb vs Rb, Y vs Nb, Ta + Yb vs Rb and Yb vs Ta tectonic discrimination diagram revealed that granites and syenites plotted mainly within volcanic arc granite, syn-collisional granite and in the within plate granites fields, the granodiorites clearly plotted in the within the plate granites field. The tectonic ternary molecular proportions MgO – FeOt – Al&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;3 &amp;lt;/sub&amp;gt;revealed that dolerites and diorites plotted mainly in the spreading center island field.
Characterization of causal ancestral graphs for time series with latent confounders

In this paper, we introduce a novel class of graphical models for representing time-lag specific causal relationships and independencies of multivariate time series with unobserved confounders. We completely characterize these graphs and show that they constitute proper subsets of the currently employed model classes. As we show, from the novel graphs one can thus draw stronger causal inferences—without additional assumptions. We further introduce a graphical representation of Markov equivalence classes of the novel graphs. This graphical representation contains more causal knowledge than what current state-of-the-art causal discovery algorithms learn.
Achieving High Efficiency: Resource sharing techniques in artificial neural networks for resource-constrained devices

Abstract Artificial neural networks emerged in the early 1940s as a connectionist approach to modeling the behavior of interconnected living nerve cells within the intricate cognitive framework of the brain. Over the years, this idea has undergone unprecedented proliferation of ever-expanding application areas, accompanied by an exponential surge in the mathematical formalism employed. This prodigious growth poses severe demands on computational hardware infrastructure. The current research suggests a novel approach for optimizing artificial neural network architectures using resource-sharing techniques. It is discussed the concept of merging context-switching and time-division multiplexing in building a single-neuron artificial neural network with the aid of an FPGA device. This would benefit the significant reduction of hardware resources and the possibility of creating small yet very efficient artificial neural networks without deteriorating the dynamical system performance.