Reviewerly/exHarmony huggingface dataset:
Pengelompokan Hasil Evaluasi Pembelajaran Metode Hafalan Al Qur’an Tawazun Menggunakan Metode K-Means

The method of memorizing the Qur'an Tawazun is a method that maximizes the use of the right brain and left brain, allowing a person to memorize, understand, and believe. Each category has several assessment points as a benchmark for the ability of students, which are used to overcome the level of failure of students in each category of learning the method of memorizing the Qur'an tawazun. The results of the evaluation of the learning of the tahfidz Islamic boarding school Daarul Huffadz Indonesia in 2020 were felt to be less than optimal, because the learning process was carried out simultaneously. This can be seen from the difference in scores that are quite different in each category of assessment. Based on the previous problem, it is necessary to group the results of the evaluation of learning the Qur'an memorization method. The goal is that every student gets maximum treatment and provides convenience for the institution, as well as teaching staff to carry out learning. The purpose of this study is to determine the optimum number of clusters as well as members of each cluster by measuring cluster performance using the Davies Bouldin Index (DBI) method and implementing the K-Means algorithm. The K-Means algorithm is a non-hierarchical data clustering method that is able to group large amounts of data, relatively quickly, and efficiently. This study uses 401 observational data and 12 attributes. From the calculation results, the optimal number of clusters lies in 2 clusters, with a Davies-Bouldin Index (DBI) value of 1.439. There are 26 members of cluster 1, and 375 members of cluster 2.
Significance

Closely related to the problem of the reliability presented in the previous chapter is the problem of determining if the difference between multiple performance evaluation measurements is statistically significant. If the interest is in the statistical significance of differences between performance evaluation scores of machine learning models, the discussion of the previous chapter showed that there are two major sources of randomness that need to be respected: one is the randomness of the test data sample on which the models are to be compared. The other is the inherent randomness of the machine learning procedure, exemplified by meta-parameter variations.
Penalized Model-Based Clustering with Group-Dependent Shrinkage Estimation

AbstractGaussian mixture models (GMM) are the most-widely employed approach to perform model-based clustering of continuous features. Grievously, with the increasing availability of high-dimensional datasets, their direct applicability is put at stake: GMMs suffer from the curse of dimensionality issue, as the number of parameters grows quadratically with the number of variables. To this extent, a methodological link between Gaussian mixtures and Gaussian graphical models has recently been established in order to provide a framework for performing penalized model-based clustering in presence of large precision matrices. Notwithstanding, current methodologies do not account for the fact that groups may be under or over-connected, thus implicitly assuming similar levels of sparsity across clusters. We overcome this limitation by defining data-driven and component specific penalty factors, automatically accounting for different degrees of connections within groups. A real data experiment on handwritten digits recognition showcases the validity of our proposal.
Talk too much? The Impact of Cybersecurity Disclosures on Investment Decisions

High-profile cybersecurity breaches have raised concerns regarding how organizations disclose security management information to the public. The American Institute of Certified Public Accountants (AICPA) developed a cybersecurity risk management (CSRM) reporting framework to better help organizations convey their cybersecurity programs to the public. In this article, we attempt to provide evidence of how cybersecurity disclosures, as developed by AICPA, affect investment decisions. Our findings suggest that nonprofessional investors are less likely to invest in breached firms with the disclosure of CSRM reports alone. Disclosing the risk management report with an independent assurance report does not result in the mitigation of the negative impact of security breach news. We discuss the corresponding implications.
Looking Back, Looking Ahead

In this final chapter of the book, let us take stock of the ground we have covered and look ahead to what is next for automated essay evaluation. Going back to the beginning and to the 1966 Ellis Page’s eloquent argument in support of the endeavor of automated essay scoring, let us first consider how far the field has come, against the backdrop of the expectations and the hurdles laid out in that paper. The discussion in this chapter draws heavily on the theme paper on automated essay scoring presented by the authors at the 2020 meeting of the Association for Computational Linguistics (Beigman Klebanov & Madnani, 2020).
Establishment of Malawi Institute of Procurement and Supply Library and Learning Resources Center: Challenges and Opportunities

The objectives of the project of establishing the library and learning resources center at Malawi Institute of Procurement and Supply (MIPS) were to draft recommendations on the kind of infrastructure in terms of hardware, software, and networking required to establish the library systems; Koha and DSpace, to develop user training manual for e-library systems, to develop a user manual for a recommendation for additional training resources for the library, to install e-library systems, link the elibrary systems to Members Management Information System (MMIS), to create catalog records for information resources and to conduct training on the functionality of the e-library system. The study was a descriptive account of the processes of establishing a library and learning resources center in an organization. Monitoring and reporting of the project activities were done which led to the development of reports, library manuals, and deliverables for the project. The scope of the work involved the establishment of the library currently operational. The period for the entire project was 6 months. Some of the major achievements for the project were the installation of Closed Circuit Television (CCTV) system for the resource center, installation of e-library systems, purchase of new textbooks and subscription to electronic journals, training of MIPS management and users on the functionalities of e-library systems, and recruitment of a qualified Librarian to manage the resource center.
A Comprehensive Study of Page-Rank Algorithm

Web-mining played an important role in the internet. Web-mining approaches are applied to search relevant information from WWW. The process of web-mining mainly depends on the page rank. The immense pool of data related to the user’s queries and this data is stored in the various webpages and increases rapidly. Yet, all data is not relevant to the user. Whenever a user writes a query in the search engine, it should be able to give the relevant information to the user. To provide documents according to the relevance page-ranking algorithm have been applied which used the web-mining methods to order the documents according to their content score, relevance, and importance. There are some algorithms that use Web-Structure-Mining in order to search structure of web or some algorithms that use Web-Content-Mining in order to search the content of documents, whereas some algorithm uses the combination of both algorithms. In this paper, the comparative study on different page-rank algorithms is discussed and found.
Web-Based Co-design in Health Care: Considerations for Renewed Participation (Preprint)

<sec> <title>UNSTRUCTURED</title> The COVID-19 pandemic has shifted the work environment to a new reality of remote work and virtual collaboration. This shift has occurred in various work settings with an impact on spaces, approaches, applied techniques, and tools. This has resulted in the broad use of virtual tools in the health care sector to avoid physical encounters and in-person interactions that will likely outlast the COVID-19 pandemic. Developing effective virtual approaches requires the knowledge and skills of using digital technologies collaboratively combined with a deep understanding of the context or contexts in which these approaches may be used. The implementation of virtual health design methods, including web-based co-design, has increased to meet the realities of COVID-19 restrictions and is likely to outlast them. Adapting the use of co-design methodologies to a virtual configuration requires rethinking methods of collaboration and communication, adapting to virtual environments, and creating new methods of engagement and facilitation. With this viewpoint, we reviewed the current work on co-design (in person and web based) to propose techniques for the design, planning, and implementation of web-based co-design. We propose 7 considerations that may enable web-based co-design projects in the health care sector. The key considerations that affect the success of a web-based co-design approach should be considered in the process of planning, developing, and conducting web-based co-design sessions. These include facilitation, collaboration, accessibility and equity, communication, sensemaking, tangible tools and games, and web-based research ethics. We illustrate this work with a case study of co-design for an emergency department discharge tool developed during the pandemic. </sec>
Strength through simulation

Cyber defence costs rise every year as the number and sophistication of attacks grow. But simulating attacks could help to make you stronger.
Data Mining in the University English Teaching Quality Analysis and Research

The College English teaching quality analysis system based on cloud computing framework needs to use data mining algorithm to implement optimization processing, which can not only fully grasp the practical English teaching efficiency, but also find out the factors that affect students&#x2019; learning ability. Therefore, based on the understanding of data mining and the construction of college English teaching quality assessment system, this paper aims at the application status of data mining in college English teaching quality analysis in recent years, and evaluates the application effect by combining with experimental design.
A Generative Adversarial Constraint Encoder-Decoder Model for the Text Summarization

As a new method of training generative models, Generative Adversarial Net(GAN) has problems when it is applied to the summary generator to generate discrete tokens. This paper considers introducing GAN into the Encoder stage and proposes a new framework EDA(Encoder-Decoder with Adversarial training) for text summarization by combining adversarial training into the traditional Encoder-Decoder architecture. We construct a new training loop and an objective function in this framework to optimize the encoder and decoder. EDA uses GAN to learn the alignment between the encoder representation and the target summary representation to improve the encoding quality and generate more accurate summary by using semantic distance and fine-tuning with decoded summary. It simultaneously alleviates problems of discrete data processing and conditional generation. In particularly, we introduce keyword information of the original document into attention mechanism so that more informative representation and summary can be generated. The results on LCSTS dataset show that our method improves the performance and robustness of Encoder-Decoder model applied in text summarization.
The relationship between the big five personality traits and job satisfaction with virtual teaching among teachers of students with learning difficulties

BACKGROUND: Job satisfaction has been described to be an important for individuals as well as for the society in general. However, several factors were introduced to be crucial for job satisfaction; one of the more influenced factors in satisfaction experiences is individual personality. The present study aim is to identify the relationship between the five major traits of personality and job satisfaction with virtual education among teachers of students with learning difficulties.MATERIALS AND METHODS: The research is a descriptive quantitative design. The research population consists of 94 male and female teachers who are involved in primary and intermediate public schools in Saudi Arabia. Data were then exposed to the analysis, namely, descriptive statistics, Pearson correlation, and Mann–Whitney U test to obtain the answers to the research questions at the level of <0.05.RESULTS: Based on the results, the most common personality trait among the study sample is conscientiousness, while neuroticism was the least common among the five traits. It was also found that there are differences between males and females in all dimensions of job satisfaction with virtual education (satisfaction with the nature of work – the relationship with colleagues and bosses – the relationship with parents) and the overall score in favor of females, except for the dimension of the relationship with colleagues and superiors, where males and females are equal.CONCLUSION: The average score of the personality types and job satisfaction is moderate. Job satisfaction has a significant relationship with personality types. In addition, partial significant differences were found among the study variables. Given the importance of identifying teachers' personality in the workplace, reducing psychological and work factors effects in the educational environment among teachers can be helpful to enhance their job satisfaction. The study therefor, enumerated implications and recommendations based on the findings.
Ferroelectric Capacitor Modelling in LTSpice

Ferroelectrics are materials having switchable spontaneous polarization property. By applying alternating electric field, polarization is switched from one state to another which gives rise to the signature property called hysteresis loop. The ferroelectric material sandwiched between the capacitor plates can impart memory to capacitors, which can be used as single bit non-volatile memory. Ferroelectric capacitor is modelled using Schmitt trigger circuit and RC filters connected in series The circuit model is simulated by changing different physical parameters and the hysteresis property of ferroelectric capacitor is analyzed using Sawyer Tower circuit in LTSpice.
A cloud-based management system for a data infrastructure in four winter sport facilities

In this paper we present the overall vision and the high level requirements of the digital infrastructure which is going to be built in three of the sport facilities which will host part of the Winter Olympic Games Milano-Cortina 2026, and another winter sports professional facility for sking. Not only the infrastructure will enable the performance analysis of athletes, but also it will contribute to the sustainability of the investment for the Games and will provide opportunities of development for the territory and new qualified jobs for local people.
Attribute Based Access Control Model for Protecting Programmable Logic Controllers

Industrial Control Systems (ICS) were traditionally designed as stand-alone systems and isolated from Internet Technology (IT) networks. With the advancement in communication technology, the attack surface has increased; vulnerabilities in ICS components such as Programmable Logic Controllers (PLC), and Human Machine Interfaces (HMI) can now be accessed and exploited. Authentication and access control form the first level of defense for protecting ICS from attacks. Unfortunately, vulnerabilities stemming from improper authentication and access control are very common. We focus our attention to investigate these vulnerabilities, specifically those centered around PLCs, and demonstrate how the use of Attribute-Based Access Control (ABAC) helps protect against them and make ICS more resilient to attacks. We design an ABAC model for PLC, show how it can be enforced, analyze the resulting system and demonstrate their resilience against some sample vulnerabilities.
LWRN: Light-Weight Residual Network for Edge Detection

Edge detection is one of the most fundamental fields in computer vision. With the rapid development of the combination of Convolutional Neural Network and Multi-Scale Representation of image, significant progress has been made in this field. However, most of them have a huge size, which makes it hard to apply in reality, and a huge number of parameters may lead to waste of computing resources. In this paper, we focus on qualitative analysis of the role of each part in the network, and propose a modified light-weight architecture based on our result and the study of former works. Our new architecture is composed of residual-blocks, max-pooling layers and batch normalization layers. Compared with the previous models, the new architecture performs better in memory, convergence and computation efficiency with similar model size. Moreover, the new architecture can achieve better accuracy with smaller model size. When evaluating our model on the well-known BSDS500 benchmark, we achieve ODS F-measure of 0.769 with parameters less than 0.3[Formula: see text]M, which shows a better property than the state-of-the-art result 0.766 at this level.
Mobile Learning Media Based on Science, Technology, and Society (STS) to Improve Science Literacy for V Grade SD Students

This study aimed to analyze the need, feasibility, and effectiveness of mobile learning media based on Science, Technology, and Society (STS) in improving scientific literacy in fifth grade elementary school students. This study used research and development procedures that refer to the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The research design used was "Quasi Experimental Nonequivalent Control Group Design" with research subjects at SDN Jombang as the experimental class and SDN Ngombol as the control class, totaling 56 students. The research instruments used were observation sheets, interview sheets, expert validation test sheets, written tests (pretest and posttest), and questionnaire sheets (questionnaire). The results showed that the media-based mobile learning (STS) was needed by students in learning. The feasibility of the media, according to the media expert validator, was declared very valid with an average percentage of validity reaching 95.54%, material expert validator 96.68% with very valid criteria. Meanwhile, the teacher's response regarding the practicality of the media obtained a score of 97.73%, with very practical criteria. Students' responses regarding the practicality of the media obtained a score of 89.74% with very practical criteria. The effectiveness of STS-based mobile learning media according to the calculation of the increase in scientific literacy of students in the experimental class was 42.27%, from an average score of 57.15 to 81.31 in the very good category. Meanwhile, the control class increased by 37.56%, from an average score of 52.80 to 72.63.) in the good category. Keywords: Mobile Learning, STS, Science Literacy.
IRPDP_HT2: A Scalable Data Pre-processing Method in Web Usage Mining using Hadoop-MapReduce

Abstract Data preparation is a vital step in the Web usage mining process since it provides structured data for the subsequent stages. Raw server logs should be turned into user sessions in order to generate structured data for pattern discovery phase. In recent decade, popular websites' server log production has risen to many terabytes to petabytes each day. As a result, server logs have Big Data challenges including storage and processing. Data cleaning, user identification, and session identification are data-intensive and deemed-computation-intensive operations in the data pre-processing sub phases. In recent years, Google's MapReduce parallel programming framework for data-intensive applications has grown in popularity as it provides the facility of scalable, flexible, fault-tolerant, distributed, and data-intensive processing over a cluster of nodes. In this paper, we provide IRPDP_HT2, a parallel data pre-processing algorithm using MapReduce that comprises data cleansing, user identification, and session identification approaches. The relevance and scalability of IRPDP_HT2 are also assessed using real web server logs, and it is discovered that the presented approach is both efficient and scalable.
Detailed Review on Cloud Computing and Cloud Security

In the era of Information, we have analyzed that many people are facing the problem of data handling, data storage, and data security. Another problem we have analyzed is that people want an integrated platform with greater accuracy and better security. To solve this problem, we have come up with a solution of integrated cloud storage and browsing application where the user will be allocated some space on the server where one can store their files in a separated and well-mannered form, which will help them to manage their content in an organized manner. We will also provide a marketplace for the user so they can get some required media files from there, which may give integrity to users. For the backend, we will use the concept of data compression using a backpropagation algorithm (using neural networks) which will help to user store more files in a compressed manner on the server. The data will be encrypted using cryptography algorithms, which will increase the security of data by multiple times.
Metalliferous deposits: Tectogenesis and mineralisation controls

Tectogenetic analysis is an alternative method of investigating the tectonic deformation of host rocks to determine the controls on ore mineralized systems. It has been successfully applied in many projects for a number of commodities. Tectogenesis includes an understanding of ore mineralizing systems by analysing deformation and stress events whose timing and development are unique. The more commonly applied interpretation of tectonic evolution and ore genesis is by itself insufficient to explain the origin of metalliferous deposits.
Fluid inclusion evidence for near-surface formation of exotic Pb-Mn-As-Sb mineralizations in Långban, Sweden

The Långban mines in the Palaeoproterozoic Bergslagen ore province in south central Sweden, are well known for their complex and unique mineralogy, particularly within Pb-Mn-As-Sb-bearing fissure systems. Fluid inclusion data from fissure minerals suggest a formation for these assemblages at, or close to, atmospheric pressure, from aqueous NaCl-CaCl2-MgCl2 solutions of moderately high salinity at temperatures of initially c. 180°C via a sequence of boiling events at temperatures fluctuating between c. 80-120°C. This resulted in boiling-induced rapid formation of alternating fissure species at high pH conditions. Characteristic for the process is extensive precipitation of radial baryte and subsequent calcite and major Pb-Mn-As-minerals. It is proposed that the solutions responsible for the specific fissure mineralization in Långban were introduced in a very shallow post-Svecokarelian brittle tectonic environment, in conjunction with a late tectonic episode. The absence of similar mineralized brittle fissure or fault systems on a regional scale suggests that the metals in the Långban fissure system were derived very locally.