Reviewerly/exHarmony huggingface dataset:
StarTrek: Combinatorial variable selection with false discovery rate control

Authors:
raw_author_name: Lu Zhang
raw_affiliation_string: Department of Statistics, Harvard University
raw_author_name: Junwei Lu
raw_affiliation_string: Department of Biostatistics, Harvard University

Abstract: 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

Authors:
raw_author_name: Ilsang Ohn
raw_affiliation_string: Department of Statistics, Inha University
raw_author_name: Lizhen Lin
raw_affiliation_string: Department of Mathematics, University of Maryland

Abstract: 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

Authors:
raw_author_name: Liu Shukun
raw_affiliation_string: City University Malaysia

Abstract: 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

Authors:
raw_author_name: Jon-Chao Hong
raw_affiliation_string: National Taiwan Normal University, Taiwan
raw_author_name: Kai-Hsin Tai
raw_affiliation_string: National Taiwan Normal University, Taiwan Soochow University, Taiwan

Abstract: 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

Authors:
raw_author_name: Pei-yu Chen
raw_affiliation_string: National Taipei University of Education, Taiwan
raw_author_name: Yuan-Chen Liu
raw_affiliation_string: National Taipei University of Education, Taiwan

Abstract: 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
Characterization of causal ancestral graphs for time series with latent confounders

Authors:
raw_author_name: Andreas Gerhardus
raw_affiliation_string: Institute of Data Science, German Aerospace Center

Abstract: 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

Authors:
raw_author_name: Y Gorbounov
raw_affiliation_string: Laboratory of artificial intelligence, robotics and embedded systems, New Bulgarian University and University of Mining and Geology "St. Ivan Rislky", Sofia, Bulgaria, IEEE Senior member
raw_author_name: H Chen
raw_affiliation_string: China University of Mining and Technology, 221000 Xuzhou, P.R.China, IEEE Senior member

Abstract: 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.
Neural network approximation in the Lipkin-Meshkov-Glick model

Authors:
raw_author_name: Margarita Savova
raw_affiliation_string: New Bulgarian University, 21 Montevideo str. 1618 Sofia, Bulgaria

Abstract: Abstract In this paper we approximate the wave function of the Lipkin-Meshkov-Glick (LMG) model by a simple feed-forward neural network. By using the NetKet toolkit we obtain the ground state energies of LMG systems with different interaction strengths and find out that they are very close to the exact values.
Artificial intelligence in international business

Authors:
raw_author_name: Vanessa Ratten
raw_affiliation_string: La Trobe Business School La Trobe University Melbourne Australia

Abstract: Abstract Artificial intelligence is poised to transform international business through the adoption of new digital technology practices as part of the fourth industrial revolution. The how, when, and why of artificial intelligence implementation in international business is still somewhat unknown but will likely significantly influence performance outcomes, market interpretation, risk prediction, consumer interactions, and technology‐based metrics. With greater emphasis on artificial intelligence‐based tools, the future practice of international business will require digital literacy. This means business managers need to improve the way they analyze artificial intelligence in terms of appreciating its usefulness while maintaining privacy and mitigation strategies. In this editorial, we discuss the current developments regarding artificial intelligence in international business with the goal of identifying future research areas of importance.
Code-Switching and Slang: An Analysis of Language Dynamics in the Everyday Lives of Generation Z

Authors:
raw_author_name: Ardik Ardianto
raw_affiliation_string: Universitas Terbuka

Abstract: This study explores the dynamics of code-switching used in Generation Z’s (Gen-Z) communication practices, revealing it as a dynamic and adaptive strategy employed in diverse social contexts. Gen-Z exhibits context-specific types of code-switching, utilizing informal and casual forms, such as slang and non-formal language, in peer interactions. Within familial settings, a seamless transition between formal and informal language aligns with established family norms, while professional environments witness the use of formal and professional language. The strategic use of code-switching and slang by Gen-Z serves as a nuanced communication approach, catering to specific social scenarios. In informal peer contexts, the incorporation of slang fosters a sense of camaraderie, while code-switching acts as a tool for expressing emotions and personal identity, reflecting individuals’ comfort in conveying specific feelings or ideas in a chosen language. This research contributes to a comprehensive understanding of Gen-Z’s language practices, showcasing their adeptness in navigating diverse linguistic landscapes, expressing identity, and adapting to social expectations. Such insights are pivotal for fostering effective intergenerational and cross-cultural communication, bridging understanding between different linguistic and cultural groups.
Design, Fabrication, and Experimental Analysis of a PV Panel for a Smart Sunflower System

Authors:
raw_author_name: Ali H. Numan
raw_affiliation_string: Electromechanical Engineering Department, University of Technology, Baghdad, Iraq.

Abstract: The Smartflower, an innovative compact energy-generation system inspired by sunflowers, stands out in energy innovation. Unlike traditional photovoltaic (PV) panels, it integrates foldable solar cells within a foundational structure for solar tracking aligned with the sun's path. The present paper focuses on designing, fabricating, and analyzing a proposed Smartflower-PV panel solar system. The study aims to comprehensively evaluate the performance of the proposed PV panel under different atmospheric conditions. The significant impact of insolation and temperature on the panel's efficiency was revealed by comparing empirical results from the PV sunflower panel with analytical calculations using MATLAB (m. file code). Enhanced solar radiation improved the system’s performance and efficiency, resulting in higher power output. Analytical insights showed a direct correlation between a 104% increase in solar radiation and parallel increases of 115% in peak power production and 100% in output current. Conversely, higher temperatures reduced power output, with a 400% temperature rise causing an 11.11% power reduction. Empirical observations align with analytical analyses under equivalent conditions, validating the model’s accuracy. This study serves as a catalyst and guide for completing and advancing the Smartflower system's manufacturing, including control, tracking, and the entire energy-generation framework.
Flood Modelling of Premulung River, Bengawan Solo

Authors:
raw_author_name: Arno Adi Kuntoro

Abstract: Premulung River or commonly known as Kali Premulung is one of many branches of Bengawan Solo River in its upstream area. This river pass through one of the most historical cities in Central Java, Surakarta. The overcapacity of this river leads to flood event that has a negative impact on humans. The purpose of this research is to analyze the Premulung River capacity and simulate the flood caused by rainfall design. The hydrological matter was analyzed using Hydrognomon and HEC-HMS while flood modelling was analyzed using HEC-RAS software one- and two-dimension (1D &amp; 2D) simulation. Model calibrations were carried out based on historical flood events (depth, duration, and area of inundation) and local interview due to data limitation. Based on the simulation, the flood modelling shows that the current capacity of Premulung River cannot accommodate its peak discharge for two (Q2) and twenty years (Q20) return period flood. There are two main spots identified flooded due to Q2 flood with depth varies from 40 to 80 cm and duration from 4 hour to 7 hour. For Q20 flood, there are also two same spots identified flooded with depth varies from 1.2 m to 1.8 m and duration from 6 hour to 9 hour. The result of this study can be a reference for flood dike design in the future which still require further detailed investigation.
Discussion of the role of perception in visualization: From Cleveland to Heer, we understand how much and why it is important

Authors:
raw_author_name: Lingjuan Li

Abstract: This paper starts with the literature review from Cleveland to Heer, and deeply discusses the role of perception in the visualization process, and the importance of understanding this role. The study points out that visualization is not only a process of conveying data, but also a process of interacting with the audience's perception. Effective visualization leads viewers to better understand and interpret the data, which also relies on a deep understanding of the perceptual process. By exploring the role of perception in the visualization, this paper highlights the importance of perception in the design process, and provides designers with suggestions on how to better utilize perceptual principles to improve the effectiveness and influence of data visualization.
Detection of Phishing Sites Using Machine Learning Techniques

Authors:
raw_author_name: None Ayan Mahmood
raw_affiliation_string: UG, Computer Science & Engineering, Sharda University, Greater Noida, Uttar Pradesh, India.
raw_author_name: None Vishal Pandey
raw_affiliation_string: UG, Computer Science & Engineering, Sharda University, Greater Noida, Uttar Pradesh, India.
raw_author_name: None Rohit Raj
raw_affiliation_string: UG, Computer Science & Engineering, Sharda University, Greater Noida, Uttar Pradesh, India.
raw_author_name: None Gouri Shankar Mishra
raw_affiliation_string: Associate Professor, Computer Science & Engineering, Sharda University, Greater Noida, Uttar Pradesh, India.

Abstract: Phishing is a very commonly occurring attack in which the attacker attempt to get the private information of the user like card details, their passwords their transaction details etc. using fake copy websites. Attackers uses the websites very similar to the original websites and not possible for common people to identify. Phishing is the biggest loop-hole in the cyber world. Phishing became a successful business for phishers. Other than fake websites phishers uses different methods to do this job using messaging, spoofed links to make money and to counter Phishing various method are proposed some anti-phishing techniques are blacklist, whitelist but due to exponential growth on new innovations and technologies these techniques falls a little below as new websites contains dynamically work in it due to that these techniques cannot outperform todays websites.
A Study on the Effectiveness of the iTEST Paperless Testing System in English Listening Course

Authors:
raw_author_name: Yu Xia
raw_affiliation_string: School of Foreign Languages, Chengdu College of Arts and Sciences, Chengdu, China

Abstract: In the intelligent teaching environment empowered by information technology, the methods and approaches for course examinations have become increasingly diverse, with a greater emphasis on assessing the learning process and linking it to final exams. Computer-assisted testing, as a new examination system, has gained attention and recognition from educators. It has reformed traditional teaching models to a certain extent, liberating teachers from repetitive and labor-intensive teaching activities while improving the overall teaching quality. Taking the application of the iTEST paperless testing system in English Listening course as an example, this study establishes a testing template and evaluates the role of the iTEST system in teaching. The research methods used in this study include quantitative and qualitative research methods, such as interviews and statistical analysis. By collecting feedback from students and teachers who have used the iTEST system, the study aims to understand the advantages and limitations of the iTEST system as perceived by teachers. Through statistical analysis, the study evaluates the examination results of the iTEST system and compares them with those of traditional paper-based tests. The results indicate that the iTEST paperless testing system can effectively enhance the teaching outcomes of English Listening courses, providing students and teachers with a more flexible, efficient, and accurate testing experience.
Mammogram Images Using Noise Removal of Filtering Techniques

Authors:
raw_author_name: None V. Jeevitha
raw_author_name: None Dr. I. Laurence Aroquiaraj

Abstract: Historically, image denoising methods were proposed for reducing salt and pepper noise in mammography images through enhanced filtering techniques. The Mammogram Image Analysis Society (MIAS) benchmark database for DICOM visuals is used to detect benign and malignant breast cancer. Thus, mean and median filtering techniques, as well as spatial filtering methods, are used to implement medical image analysis with the modalities of low and high radiation for medical images. Similar to these experimental and suggested techniques for removing salt and pepper noise, these statistical techniques assess MSE, SNR, and PSNR data for enhanced quality of signal in the image.