Reviewerly/exHarmony huggingface dataset:
Visual Analysis of Business Chinese Research Based on CiteSpace

This article uses the China National Knowledge Infrastructure (CNKI) database as the data source of literature, takes all the domestic literature about the Business Chinese from 2016 to 2020 in the database as the research object. And then, with the support of big data technology and Java program, the scientific document analysis system CiteSpace is used to transform the relevant data into visual charts for analysis. It sorted out influential institutions, authors, keywords, and research hotspots and directions in the field of Business Chinese research in China in the past five years. The results show that the number of relevant research papers in the past five years has been developed steadily, but the core author group in this field has not yet been formed. Research hotspots include "Business Chinese teaching", "Business Chinese textbooks", and "Business Chinese research in the context of the Belt and Road Initiative", etc. At the same time, through the research and analysis of various graphs, it is predicted that the future development trend of the Business Chinese research field is to combine Business Chinese class with modern teaching techniques and develop new class forms such as Microlecture and Flipped Classroom.
Overview of Bayesian Inference

We overview the basis of Bayesian inference and MCMC, which are the fundamental steps before appropriately using Stan. The mathematical implementation of MCMC will not be mentioned in this book.
Usages of MCMC Samples from Posterior and Predictive Distributions

We have discussed various statistical models and their implementation so far. However, the ways we used the MCMC samples from the posterior distributions and predictive distributions were only kept on very basic levels, such as computing the intervals and visualizations. In this chapter, we will introduce more advanced usages of the MCMC sample. They would be helpful in practice because it is very common to encounter the situation where we need to extract more information from the MCMC sample.
THE EFFECT OF DIFFERENT TYPES OF COMMENTS (PRAISE, SUGGESTION, AND CRITICISM) ON STUDENTS’ WRITING ACHIEVEMENT

Teacher’s comments as a respond to the students’ draft has an important effect on students writing motivation and achievement during the teaching and learning process in writing. These comments or the teacher reply for the students’ draft are usually waited by the students as it is assumed as a two- way communication during completing the final draft. However, many students may have different responds in accommodating these comments. This study investigated whether praise, suggestion, and criticism as teacher’ comments have significant effects on the students writing paragraph achievement. Through a quasi-experimental design using quantitative methods for students in the third semester of IAIN Tulungagung's English Department, the research shows that there are significant differences in writing paragraph scores between students taught before and after using different types of comments. In addition, among these three strategies, the suggestion gave more improvement than the other two types of comments.
IMPLEMENTASI TEKNOLOGI PEMBELAJARAN KAHOOT GEMBIRA SOLUSI PEMBELAJARAN JARAK JAUH

The prolonged pandemic period requires students to accept the fact that they cannot attend school for face-to-face learning as usual. The implementation of distance learning (PJJ), although on the one hand it is considered to have advantages, because it is able to provide solutions to the world of education during this pandemic, it has many weaknesses. The more time students participate in Distance Learning, the more it is felt that the feeling of boredom is increasingly felt by students. Learning also feels less effective. Facing this situation, SDN 01 Kulu Pekalongan Regency, is trying to find a new breakthrough as a solution in Distance Learning which is named "KAHOOT GEMBIRA". This assessment program is presented in the form of technology-based games which are very popular with children. The data collected shows that the enthusiasm of students to engage in learning, which was initially only 91 children or 69% involved, increased to 114 children or 86%, then decreased to 110 children or 83%. The original highest point was 9844, then decreased slightly to 9841, and finally increased back to 9843. The level of absorption of education units, which was originally 48%, increased to 52% and again increased to 56%. This innovative program, with game nuances but still containing elements of this learning, is proven to be able to overcome various problems faced in Distance Learning at SDN 01 Kulu, Pekalongan Regency.
On the key-exchange protocol using real quadratic fields

To prevent an exhaustive key-search attack of the key-exchange protocol using real quadratic fields, we need to ensure that the number l of reduced principal ideals in K is sufficiently large. In this paper we present an example of a family which are not valid for this protocol.
DEVELOPING AN EFFECTIVE MACHINE LEARNING ALGORITHM SYSTEM IN THE EARLY DETECTION AND DIAGNOSIS OF ALZHEIMER’S DISEASE

A broad term used to describe diseases and conditions that cause deterioration in memory, language, and other mental capacities sufficiently extreme to communicate with day-to-day existence is "dementia". Alzheimer's disease is the most well-known type of Dementia influencing the mind's parts. As per range, this disorder influences 6.2 million Americans and 5 million individuals in India matured 65 and more seasoned. In 2019, the latest year for which information is accessible, official passing declarations revealed 121,499 deaths from Promotion, Alzheimer's, the "6th driving reason for death in the nation". In this paper, we propose AI calculations like Decision trees (DT), SVM, Linear regression, and Naive Bayes determines Promotion at the beginning phase. The Alzheimer's Sickness Neuroimaging Drive (ADNI) and the Open Access Series of Imaging Examinations give informational collections used to identify the disease in its beginning phase. The datasets comprise longitudinal X-ray information (age, orientation, small-scale mental status, CDR). By taking into; account many variables in every strategy, for example, accuracy, F1 Score, Review, and explicitness are determined. The outcomes acquired 93.7% of the greatest precision for the DT Calculation.
ILSA: an automated language complexity analysis tool for French

Estimating language complexity is an important aspect of educational measurement and assessment that can be used, for instance, to control for unwanted variance due to language, or to provide students with texts that are conducive to learning. Automatic language processing techniques can be used to extract various linguistic features that reflect the complexity of vocabulary and sentence structure. In this paper, we present a new tool called ILSA (Integrated Lexico-Syntactic Analyzer), which we developed for research and educational applications. We summarize how the tool works and present the types of attributes it can extract. We then apply ILSA to 600 texts used in Quebec elementary and secondary schools and analyze the correlations between the attributes and the school grade associated with the text. The results show the potential of ILSA for modeling the complexity of French texts.
Characterizing Performance Limits in Payment Channel Networks

With their instant transaction confirmation and high scalability, payment channel networks (PCNs), running off-chain and in parallel with blockchain systems, have recently attracted a substantial amount of research attention. It has been shown that there exists a significant gap between the theoretically optimal performance and the performance achievable given the stringent privacy requirements in practice. However, it remains unclear what the fundamental performance limits and key factors involved are, which turns out to be a challenging problem due to the unique characteristics in PCNs. In this paper, we, for the first time, develop a mathematical model capturing the PCN performance, and examine the impact from a number of factors including channel capacity and transactions. We are articularly interested in obtaining the gap between the theoretically optimal performance and the performance achievable in practice, which characterizes the design space in PCNs for scheduling transactions. Specifically, we derive how different transactions and channel capacities affect the PCN performance and the performance gap. Our analytical characterization of PCNs offers an in-depth understanding on their fundamental trade-off, and provides important insights on the design of PCNs.
OPTIMASI PENEMPATAN DAN KAPASITAS KAPASITOR UNTUK MEMINIMALKAN SUSUT DAYA PADA PENYULANG TABANAN DENGAN MENGGUNAKAN METODE ANT COLONY OPTIMIZATION (ACO)

Electricity is one of the needs of society that must be met, because electricity is very instrumental in helping everyday life, such as lighting, and for the electronic needs. In distributing power to consumers there is a power loss in the distribution system channel. To reduce power loss, it is necessary to install capacitors. This study aims to reduce power losses in Tabanan feeders using the Ant Colony Optimization (ACO) method with 10 attempts using MATLAB. The total power loss before the capacitor is installed is 0.421 MW (6,4%), after the analysis is obtained the placement of the capacitor located on bus 10 with a capacity of 1000 KVAR, can reduce the power loss to 0.145 MW (2,2%)
Robust Estimation in Reproducing Kernel Hilbert Space

Our work shows that estimating the mean in a feature space induced by certain kinds of kernels is the same as doing a robust mean estimation using an M-estimator in the original problem space. In particular, we show that calculating the average on a feature space induced by a Gaussian kernel is equivalent to perform robust mean estimation with the Welsch M-estimator. Besides, a new framework is proposed that was used to build four new robust kernels: Tukey’s, Andrews’, Huber’s and Cauchy’s robust kernels. The new robust kernels, combined with kernel matrix factorization clustering algorithm, were compared to state-of-the-art algorithms in clustering tasks. The result shows that some of the new robust kernels perform in a par with state-of-the-art algorithms.
Retracted: Audit Data Analysis and Application Based on Correlation Analysis Algorithm

[This retracts the article DOI: 10.1155/2021/2059432.].
PP–GSM: Privacy-preserving graphical security model for security assessment as a service

Security Assessment-as-a-Service (SAaaS) allows users to outsource security assessments of their systems and networks from the cloud, reducing the burden on users whom do not have sufficient resources to carry out security assessments. SAaaS can be implemented using Graphical Security Models (GSMs), such as Attack Graphs and Attack Trees, that are widely used for security assessments. However, this approach exposes users' sensitive data (e.g., network topology, host vulnerabilities) in the cloud, which would not be acceptable in private systems such as government and/or corporation networks. This paper proposes a framework named privacy-preserving GSM (PP–GSM) for SAaaS. PP–GSM is built with (1) homomorphic encryption (HE) for protecting users' sensitive data by performing security assessment computations on the encrypted network models, and (2) graph obfuscation techniques to confuse attackers trying to reveal users' sensitive data. Moreover, we develop new algorithms to speed up HE by reducing the number of multiplications, which are computationally expensive arithmetic operations in HE schemes. Our experimental results using various realistic scenarios show that PP–GSM can be generated on average in 1,078 s for networks with 60 nodes (and the time taken is linearly proportional to the number of nodes). For evaluations, the time taken can be as short as on average 30 s for computing the cumulative attack success probability. Therefore, PP–GSM is a promising solution for the SAaaS to be used in practice.
Densely connected sub-Gaussian linear structural equation model learning via ℓ1- and ℓ2-regularized regressions

This paper develops a new algorithm for learning densely connected sub-Gaussian linear structural equation models (SEMs) in high-dimensional settings, where the number of nodes increases with increasing number of samples. The proposed algorithm consists of two main steps: (i) the component-wise ordering estimation using ℓ2-regularized regression and (ii) the presence of edge estimation using ℓ1-regularized regression. Hence, the proposed algorithm can recover a large degree graph with a small indegree constraint. Also proven is that the sample size n=Ω(p) is sufficient for the proposed algorithm to recover a sub-Gaussian linear SEM provided that d=O(plog⁡p), where p is the number of nodes and d is the maximum indegree. In addition, the computational complexity is polynomial, O(np2max⁡(n,p)). Therefore, the proposed algorithm is statistically consistent and computationally feasible for learning a densely connected sub-Gaussian linear SEM with large maximum degree. Numerical experiments verified that the proposed algorithm is consistent, and performs better than the state-of-the-art high-dimensional linear SEM learning HGSM, LISTEN, and TD algorithms in both sparse and dense graph settings. Also demonstrated through real data is that the proposed algorithm is well-suited to estimating the Seoul public bike usage patterns in 2019.
Orogenic gold in the Blue Mountains, eastern Oregon, USA

At least 75% of gold produced in the State of Oregon came from the Blue Mountains. Most gold within the Blue Mountains was mined from orogenic gold veins and associated placer deposits. However, the geodynamic, regional, and deposit scale controls on orogenic gold mineralization within the Blue Mountains are not understood. Here, the mobilization, transport, deposition, and exposure of golds are linked to protracted tectonism of the western North American Cordillera through a scale-integrated mineral system. Gold production by mine (t) was estimated from public domain documents of production value (USD) and production time period (years). Lode endowment was then analyzed against criteria such as host rocks, vein structure, and location within the regional tectonic framework. Argillite, schist, and slate formed in accretionary wedge and fore-arc basins host the majority (∼60%; ∼29 t) of the total recorded gold production. Unique geochemical, geomechanical, and hydrological properties made these rock types excellent hosts. These properties were predetermined by both depositional setting (deep ocean basin) and metamorphic conditions (lower greenschist facies). Nearly half (50–55 %; ∼25.5 t) of recorded lode gold production was mined from lodes striking 030° ± 15° azimuth. A reconstructed paleo-stress field supports that these were left lateral faults. It is speculated that these were part of a margin-parallel shear zone that partitioned the strike slip component of oblique convergence between subducting oceanic plates and associated magmatic arcs. At both the district and the deposit scales, minor deviations in vein strike form local high-grade sites (i.e., dilatant zones). Such dilatant zones within major crustal shear zones could also explain the spatial, but not fluid-genetic association of granitic plutons with lode gold. The intersection lines between the veins, host rock fabrics, and secondary veins likely control the locations, thicknesses, and pitch of high-grade ore shoots.
Dynamic Incremental Learning for real-time disturbance event classification

With recent telemetric advancements, the real-time availability of power grid measurements has opened challenging opportunities for the design of advanced protection and control schemes. Artificial neural networks (ANN) are promising approaches for detecting and classifying disturbance events from measurement data. Numerous offline ANN-based classification algorithms were proposed in the past, which increased the interest for their real-world deployment. However, these algorithms are inadequate due to their conventional offline training procedures, model updating, and large backend computing requirements. Besides, most ANN-based algorithms require disturbance event samples to be collectively available during training. This availability may be uncommon in practice as disturbance events are rare, non-deterministic, and uncertain. Hence, an online training procedure where the model processes the events on-the-fly is required. However, ANNs may also suffer from catastrophic forgetting where the model may unintentionally unlearn an occurred disturbance under the learning of new event types; this means ANN may not detect very similar disturbances of the same type in the future. In this paper, we propose Dynamic Incremental Learning (IL) method for ANN models, which is updated in real-time when a new disturbance is detected. Our proposed method adopts a Replay-based IL strategy for designing long-term IL, balancing the accuracy with catastrophic forgetting of disturbance events. The method is designed in a way to learn efficiently for incoming disturbance data with minimized training time and the highest classification accuracy eliminating catastrophic forgetting. The results describe the methodology’s performance regarding classification accuracy, training time, and storage memory. The findings demonstrate that the Dynamic IL method is promising for efficient learning and event classification.
Multi-dimensional multi-label classification: Towards encompassing heterogeneous label spaces and multi-label annotations

In traditional classification framework, the semantics of each object is usually characterized by annotating a single class label from one homogeneous label space. Nonetheless, objects with rich semantics naturally arise in real-world applications whose properties need to be characterized in a more sophisticated manner. In this paper, a new classification framework named Multi-Dimensional Multi-Label (MDML) classification is investigated which models objects with rich semantics by encompassing heterogeneous label spaces and multi-label annotations. Specifically, MDML generalizes the traditional classification framework by assuming a number of heterogeneous label spaces to characterize semantics from different dimensions, where each object is further annotated with multiple class labels from each heterogeneous label space. To learn from MDML training examples, a first attempt named CLIM is proposed based on an augmented stacking strategy. Firstly, CLIM induces a base multi-label predictive model w.r.t. each label space by maximizing the likelihood of the observed multiple class labels. Secondly, the thresholding predictions from all base models are used to augment the original feature space to yield stacked multi-label predictive models. The two-level models are refined alternately via empirical threshold tuning. Experiments on four real-world MDML data sets validate the effectiveness of CLIM in learning from training examples with heterogeneous label spaces and multi-label annotations.
Incorporating Technology as a Tool Into English Language Teaching and Learning

Educators and teachers generally use a traditional approach in English language teaching. They use books, flashcards, and whiteboards to organize a lecture. However, at present times, teaching through traditional methods is not considered effective. We live in the advanced technology age, where teaching through a blended method is more practical and effective. For this reason, a teacher should be acquainted with the various technology tools to be used effectively in the classroom. The use of technology plays playing a significant role in English language teaching and learning (ELTL). In this paper, the authors will explore the uses of technology such as the internet, websites, electronic dictionaries, softwares and social networking technologies in English language teaching and learning.
Reading Habits Among University Students Studying Engineering in the Digital Age

In this study, engineering students from universities all around India will have their reading interests and habits examined. Reading habits have changed as a result of technology; the most popular change is the rise of text reading from computer screens. Teachers and students face a variety of difficulties and opportunities as a result of the widespread use of screen reading. The usage of annotations and other forms of text interaction by engineering students when reading academic publications is examined in this work along with their general reading preferences, habits, and attitudes. The opinions of 109 students who participated in an online survey were examined. The report highlights a number of issues with engineering students' preferences and habits, and how those issues affect their capacity for critical thought. The researcher presented recommendations to encourage reading habits among engineering students in the continually technologically driven environment of today after conducting research to acquire a thorough overview of millennials' reading preferences.
Walking Side by Side

The digital humanities have become a new avatar of scientific and educational progress and are part of the ideology of progress, of a linear and cumulative science. Therefore, our work questions the marketing pretension of the new digital learning environments for languages that herald an educational revolution registering itself in the idea of technological progress and addresses digital technologies in the teaching of a foreign language. We will insist in particular on the need not to ignore the term innovation, and we will see that a pedagogy that aims to be truly innovative requires a fundamental reflection, both at the educational level and at the technological levels to meet the needs of the current mobility of international students.
The Revolution in Integrating Virtual Reality in E-Learning

The virtual reality (VR) concept has been in existence for more than half-a-century. This technology's growth has been predominant recently because of the availability of plenty of technological solutions that support VR content. Coronavirus (COVID-19), a virus outbreak, has taken the whole world to an unexpected critical phase where social distancing is an inevitable part of life. Notably, the education sector faces a huge challenge, and VR technology has become the need of the hour during this pandemic as most learning takes place in virtual mode. VR equipment and software applications were highly priced at one point. Still, the present scenario is entirely different, with plenty of hardware and software resources available at affordable pricing. This study aims to identify the benefits, needs, and opportunities open for virtual reality-enabled e-learning and offer a generic methodological framework for efficiently implementing VR in e-learning. Analysis of this study indicates that VR technologies have rebooted their presence in the education.