Reviewerly/exHarmony huggingface dataset:
A simple interpretation of undirected edges in essential graphs is wrong

Artificial intelligence for causal discovery frequently uses Markov equivalence classes of directed acyclic graphs, graphically represented as essential graphs , as a way of representing uncertainty in causal directionality. There has been confusion regarding how to interpret undirected edges in essential graphs, however. In particular, experts and non-experts both have difficulty quantifying the likelihood of uncertain causal arrows being pointed in one direction or another. A simple interpretation of undirected edges treats them as having equal odds of being oriented in either direction, but I show in this paper that any agent interpreting undirected edges in this simple way can be Dutch booked. In other words, I can construct a set of bets that appears rational for the users of the simple interpretation to accept, but for which in all possible outcomes they lose money. I put forward another interpretation, prove this interpretation leads to a bet-taking strategy that is sufficient to avoid all Dutch books of this kind, and conjecture that this strategy is also necessary for avoiding such Dutch books. Finally, I demonstrate that undirected edges that are more likely to be oriented in one direction than the other are common in graphs with 4 nodes and 3 edges.
Proposing a New Image Watermarking Method Using Shearlet Transform and Whale Optimization Algorithm

Digital images watermarking is a method for data hiding that ensures the security of multimedia data. In these ways, a watermark can be a digital image or data stored within digital content. The Shearlet transform, a multi-resolution and multi-directional conversion, can be used for watermarking in digital images. Due to its superior features, this conversion can increase the efficiency of applications such as watermarking of images. In this paper, Shearlet and SVD transforms are used with the Whale optimization algorithm to obtain the most appropriate scaling factor in the watermark extraction step after applying different types of image processing attacks. Shearlet Transform has more transparency than traditional converts. The SVD transform also increases the robustness of watermarking operations. The results of different experiments show that the new method presented in this paper, in terms of robustness and imperceptibility compared to the methods tested, performs better than most image processing attacks.
Who are Millennials? And what they want from libraries, bookstores, and librarians

Understanding a generation’s “peer personality” provides librarians a unique opportunity to develop services and programs tailored to the needs and unique attributes of a group of individuals. Master of Library Science students enrolled in a graduate research class at East Carolina University in Greenville, North Carolina, surveyed 245 18-to 24-year old members of the Millennial generation at various locations throughout the state to ascertain their use and perception of libraries and bookstores. Most important to Millennials are helpful library staff, good collections, and convenient hours. Least important are bargain items for sale, coffee, and the library or bookstore as a place to be with others.
Optimal Feature Subset Selection Based on Combining Document Frequency and Term Frequency for Text Classification

Feature selection plays a vital role to reduce the high dimension of the feature space in the text document classification problem. The dimension reduction of feature space reduces the computation cost and improves the text classification system accuracy. Hence, the identification of a proper subset of the significant features of the text corpus is needed to classify the data in less computational time with higher accuracy. In this proposed research, a novel feature selection method which combines the document frequency and the term frequency (FS-DFTF) is used to measure the significance of a term. The optimal feature subset which is selected by our proposed work is evaluated using Naive Bayes and Support Vector Machine classifier with various popular benchmark text corpus datasets. The experimental outcome confirms that the proposed method has a better classification accuracy when compared with other feature selection techniques.
The Effect of Using Smartphone Based Learning Media to Improve Students’ Critical Thinking Skills During Covid-19 Pandemic

This research aims to determine the effect of smartphone-based learning media in the students' critical thinking skills.This research was a quasi-experimental with only Posttest Control Group Design.The sample of this reasearch were students of class XI MIPA 1 and XI MIPA 2 at SMAN 1 Ngaglik in the academic year 2019/2020 who were taken by simple random sampling.Data collection instrument was developed based on indicators critical thinking skills namely understanding, application, analysis, evaluation, and generalize.The analysis of this research used descriptive analysis and independent t test.The results of t test analysis showed critical thinking skills of experimental class were significantly higher than the control class.It means that the use of smartphone-based learning media has an effect in improving the critical thinking skills of students.
Penerapan Multimedia Interaktif Untuk Meningkatkan Motivasi Belajar Dan Hasil Belajar Siswa Pada Pembelajaran IPA

Abstract — The purpose of this study is to use interactive multimedia in learning so that teachers can learn abstract information to be more concrete. The method used was an experimental method with a population of class V SDN 2 Keresek and a sample of 21 students. The results showed an increase in learning motivation based on the test results of 0.72 with high interpretation. Furthermore, the increase in student learning outcomes is 0.33 in the medium category. The application of interactive multimedia can increase learning motivation and student learning outcomes in grade V SDN 2 Keresek learning science.Keywords— Learning Outcomes, Learning Motivation, Interactive Multimedia. Abstrak — Tujuan penelitian ini menggunakan multimedia interaktif dalam pembelajaran agar guru dapat mempelajari informasi yang bersifat abstrak menjadi lebih konkret. Metode yang digunakan adalah metode eksperimen dengan populasi kelas V SDN 2 Keresek dan sampel yang diambil sebanyak 21 siswa. Hasil penelitian menunjukan peningkatan peningkatan motivasi belajar berdasarkan hasil uji sebesar 0,72 dengan interpretasi tinggi. Selanjutnya peningkatan hasil belajar siswa sebesar 0,33 dengan kategori sedang. Penerapan multimedia interaktif dapat meningkatkan motivasi belajar dan hasil belajar siswa kelas V SDN 2 Keresek pembelajaran IPA. Kata Kunci : Hasil Belajar, Motivasi Belajar, Multimedia Interaktif.
Image Pixel Contrast Enhancement Using Enhanced Multi Histogram Equalization Method

Image Enhancement methods produce various sorts of problems, for example, unnatural impacts, over-improvement, and these downsides become increasingly unmistakable in improving dull Images. Histogram Equalization (HE) method is a straightforward and generally utilized Image contrast enhancement procedure. The fundamental task of HE is it changes the contrast of the Image. To perform this task, different HE techniques have been proposed. These techniques protect the brightness or contrast on the final Image that doesn't have a characteristic look. To overcome the drawbacks of HE, Enhanced Multi Histogram Equalization (EMHE) technique is proposed, which divide the Image into a few sub images and again these sub images are divided into sub-images, and traditional HE strategy is applied to each sub Image for getting better results. The improvement is brought about by repetitive data present in sub-pixel moves between relating Lightroom (LR) Images of a similar scene. The principal phase of the development incorporates Image enrollment of LR Images utilizing known parameters and geo-referencing methods for manufactured and genuine information individually. The proposed development of M-HE Images has been assessed on the LR Images obtained from satellite Image datasets to exhibit the clarity of the images by enhancing the contrast on the poor lighting images.
Augmented Reality Applications in Science Experiment Practices

The purpose of this paper is to explore the impact of augmented reality (AG) and the flipped learning model on the attitude of 4th-grade students towards the science and technology course and to determine the opinion of students concerning videos presented through AG. Another objective of the study is to determine parents' opinions concerning AG and FL in science and technology courses. A pre-test / post-test quasi-experimental design was used with the control group to obtain quantitative data. Qualitative data were also included in the analysis to support quantitative data obtained from a case study pattern. In the study, the Science and Technology Attitude Scale was used to measure students' attitudes. The Opinion Scale for Experimental Videos was used to evaluate video usage opinions in science and technology courses. Besides, parents' opinions concerning AG applications and the FL model were collected through the researchers' interview form. Independent sample t-test and ANCOVA tests were conducted to interpret the quantitative data of the study. Qualitative data were evaluated with the content analysis method. The results showed that the AR and FL model's application provides students with a positive attitude towards science and technology; it also helped them solve problems. Besides, families have also expressed positive opinions about AR and FL applications.
Implementation of Data Mining Technique for Performance of WFH and WFO Agents Using the K-Means Method Case Study Study of PT. Infomedia Telkom Consumer Profiling Services

Outbound Call Center PT. Infomedia, consumer profiling service PT. Telkom during the pandemic period divided its agents into 80% WFH agents (Work at Home) and 20% agents WFO (Work from Office). For the division of the working mechanism, it is necessary to measure its performance. In the discussion of this paper, we will discuss the measurement with the application of data mining using the K-Means method, so it is hoped that it will provide an overview, how the cluster of each WFH or WFO agent in terms of performance. The results of this discussion indicate that there is a significant difference between the performance of WFH and WFO Agents.
Penerapan media quizizz untuk meningkatkan hasil belajar siswa pelajaran tematik siswa kelas IV SD Bukit Aksara Semarang

Classroom action research has been carried out to improve and improve student learning outcomes through classroom action research using Quizizz media. This research was conducted in class IV SD Bukit Aksara Semarang for the 2020/2021 school year, with 37 students. This study aims to determine whether learning using Quizizz media can improve student learning outcomes and learning activities. The focus of this research is thematic learning outcomes which include cognitive, affective, psychomotor aspects, as well as student learning activities and science process skills, as well as teacher performance. The results of this study indicate that the activity from the initial conditions, cycle 1, and cycle 2 continues to increase. In the initial conditions to cycle 1 and to cycle 2 the percentage of students whose activeness was in the low category continued to decline, namely 70% - 18.33% - 6.67%. Medium category is from 20% - 56.67% - 50%. While the high category is from 10% - 25% - 43.33%. It can be concluded that thematic learning through Quizizz media can improve student learning outcomes and learning activities.
Algorithm to choose the appropriate test for comparison between two groups

The current manuscript describes the method of selection of statistical tests for comparing two groups. In this article, we discuss the factors on which the selection of tests depends and provide an algorithm for the selection of the inferential statistical test.
Two novel finite time convergent recurrent neural networks for tackling complex-valued systems of linear equation

Compared to the linear activation function, a suitable nonlinear activation function can accelerate the convergence speed. Based on this finding, we propose two modified Zhang neural network (ZNN) models using different nonlinear activation functions to tackle the complex-valued systems of linear equation (CVSLE) problems in this paper. To fulfill this goal, we first propose a novel neural network called NRNN-SBP model by introducing the sign-bi-power activation function. Then, we propose another novel neural network called NRNN-IRN model by introducing the tunable activation function. Finally, simulative results demonstrate that the convergence speed of NRNN-SBP and the NRNN-IRN is faster than that of the FTRNN model. On the other hand, these results also reveal that different nonlinear activation function will have a different effect on the convergence rate for different CVSLE problems.
Sketch and Customize: A Counterfactual Story Generator

Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text. Counterfactual story rewriting is a recently proposed task to test the causal reasoning ability for text generation models, which requires a model to predict the corresponding story ending when the condition is modified to a counterfactual one. Previous works have shown that the traditional sequence-to-sequence model cannot well handle this problem, as it often captures some spurious correlations between the original and counterfactual endings, instead of the causal relations between conditions and endings. To address this issue, we propose a sketch-and-customize generation model guided by the causality implicated in the conditions and endings. In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending. In the customize stage, a generation model is used to fill proper words in the skeleton under the guidance of the counterfactual condition. In this way, the obtained counterfactual ending is both relevant to the original ending and consistent with the counterfactual condition. Experimental results show that the proposed model generates much better endings, as compared with the traditional sequence-to-sequence model.
SYSTEM FOR THE FORMATION OF COMMUNICATIVE-COMPENSATOR COMPETENCE OF THE BASIC SCHOOL STUDENTS

The revolutionary development of innovations in pedagogical science, due to the fundamental tendencies of social life, the expansion of the sphere of contacts between peoples and states within the framework of globalization and internationalization, implies significant changes in the field of basic general education as a strategically important area of the state policy of the Russian Federation, providing for the creation of a competent person and focuses on the need for the formation of communicative-compensatory competence of students of the basic school and the creation of a pedagogical system, the introduction of which will ensure the availability of a system of knowledge, skills, personal qualities that allows integrating strategic moves in the process of communicative interaction that are adequate to the communication situation that contributes to the positive effectiveness of intercultural contact. The developed pedagogical system for the formation of the communicative-compensatory competence of students of the primary school is regulated by the social order and educational normative documents reflected in the normative block; is built on a complementary basis of systemic, competency-based and participatory approaches; consists of five interconnected blocks (motivational-targeted, organizational-substantive, operational-activity and reflective-diagnostic); functions taking into account the basic principles (consciousness, science, accessibility, activity, strength, visibility) and specific principles (emergence, overcoming sociocultural interference, software combination, interactive didactization, personal-vector reception). The main goal of the author’s system is to diagnose a high strategically productive level of formation of communicative-compensatory competence, which will provide students with the knowledge and skills to conduct a constructive dialogue, tolerate the views of a communication partner, understand and adequately interpret the communication situation that has arisen directing her to a productive result. The results of the experimental work and a comparative analysis of the dynamics of the results of primary school students, as well as their diagnosis by the methods of mathematical statistics, showed a significant improvement in the level of formation of communicative-compensatory competence, which indicates the effectiveness of the implementation of the developed system.
HISTOGRAM (History In Hologram): Fun Learning Media to Learn Ancient Relics of Indonesia

Archaeological objects are historical relics that have important values for human life. But not every student can see and feel the atmosphere of ancient objects, because they have long been extinct or maintained their existence. The introduction of ancient objects through the media in the form of images contained in books, two-dimensional drawings and other teaching aids, this is considered ineffective because two-dimensional picture can only be seen on one side only, but by using 3D Hologram technology it is expected that in learning it can make historical lessons about ancient objects can be more interesting and enjoyable as well as helping in introducing efforts to conserve ancient objects. The results of this research are expected to be able to provide 3D Hologram technology by utilizing the principles of holographic work that is able to provide convenience for student by their own gadget.
Haze-level prior approach to enhance object visibility under atmospheric degradation

Outdoor captured scenes are degraded by atmospheric particles and water droplets. Due to scattering andabsorption effects in the atmosphere, degraded images lose contrast and color fidelity. Performances of the computervision algorithms are bound to suffer from low-contrast scene radiance. In many single-image dehazing models, thelarger the deviation in estimation of the key parameters such as transmission map and atmospheric light, the higherthe halo artifacts and loss of fine details in the dehazed image. The available models assume that the scattering lightis independent of wavelength, as the size of the atmospheric particles is larger compared to the wavelength of light.The model presented in this paper emphasizes the appropriate estimation of intensified transmission map from thehazy images by exploiting the scattering coe?icient in order to address the issues of haze concentrations. Experimentsconducted on thick and thin hazy images provide an optimal estimation of the model parameters, which can be applieddirectly in real-time situations. The available models are observed to be inconsistent sometimes in the enhancement ofcontrast, saturation and color information either together or independently. The proposed model addresses these issuesby extracting the haze-relevant features from the hazy images, such as hue disparity, contrast, and darkness, which yieldmore vivid saturation results. Moreover, the proposed model addresses different haze densities in the scene without theuse of refinement filters.
Evaluating Software Quality Attributes using Analytic Hierarchy Process (AHP)

The use of quality software is of importance to stakeholders and its demand is on the increase.This work focuses on meeting software quality from the user and developer's perspective.After a review of some existing software-quality models, twenty-four software quality attributes addressed by ten models such as the McCall's, Boehm's, ISO/IEC, FURPS, Dromey's, Kitchenham's, Ghezzi's, Georgiadou's, Jamwal's and Glibb's models were identified.We further categorized the twenty-four attributes into a group of eleven (11) main attributes and another group of thirteen (13) sub-attributes.Thereafter, questionnaires were administered to twenty experts from fields including Cybersecurity, Programming, Software Development and Software Engineering.Analytic Hierarchy Process (AHP) was applied to perform a multi-criteria decision-making assessment on the responses from the questionnaires to select the suitable software quality attribute for the development of the proposed quality model to meet both users and developer's software quality requirements.The results obtained from the assessment showed Maintainability to be the most important quality attribute followed by Security, Testability, Reliability, Efficiency,
Deep Learning Hybrid with Binary Dragonfly Feature Selection for the Wisconsin Breast Cancer Dataset

Breast cancer is the world's top cancer affecting women.While the danger of the factors varies from a place, lifestyle, and diet.Treatment procedures after discovering a confirmed cancer case can reduce the risk of the disease.Unfortunately, breast cancers that arise in low and middleincome countries are diagnosed at a very late stage in which the chances of survival are impeded and reduced.Early detection is therefore required not only to improve the accuracy of discovering breast cancer but also to increase the chances of making the right decision on a successful treatment plan.There have been several studies tending to build software models utilizing machine learning and soft computing techniques for cancer detection.This research aims to build a model scheme to facilitate the detection of breast cancer and to provide the exact diagnosis.Improving the accuracy of a proposed model has, therefore, been one of the key fields of study.The model is based on deep learning that intends to develop a framework to accurately separate benign and malignant breast tumors.This study optimizes the learning algorithm by applying the Dragonfly algorithm to select the best features and perfect parameter values of the deep learning model.Moreover, it compares deep learning results against that of support vector machine (SVM), random forest (RF), and k nearest neighbor (KNN).Those classifiers are chosen as they are the most reliable algorithms having a solid fingerprint in the field of clinical data classification.Consequently, the hybrid model of deep learning combined with binary dragonfly has accurately classified between benign and malignant breast tumors with fewer features.Besides, deep learning model has achieved better accuracy in classifying Wisconsin Breast Cancer Database using all available features.
About this title - Pannotia to Pangaea: Neoproterozoic and Paleozoic Orogenic Cycles in the Circum-Atlantic Region

Special Publication 503 celebrates the career of R. Damian Nance. It features 27 articles, with more than 110 authors based in 18 different countries. These articles include contributions on the processes responsible for the formation and breakup of supercontinents, the controversies concerning the status of Pannotia as a supercontinent, the generation and destruction of Paleozoic oceans, and the development of the Appalachian-Ouachitan- Caledonide-Variscan orogens. In addition to field work, the approaches to gain that understanding include examining the relationships between stratigraphy and structural geology, precise geochronology, geochemical and isotopic fingerprinting, geodynamic modelling, regional syntheses, palaeogeographic modelling, and good old-fashioned arm-waving! The wide range of topics mirrors the breadth and depth of Damian's contributions, interests and expertise. Like Damian's papers, the contributions range from the predominantly conceptual to detailed field work, but all are targeted at understanding important tectonic processes. Their scope not only varies in scale from global to regional to local, but also in the range of approaches required to gain that understanding.