Reviewerly/exHarmony huggingface dataset:
Cash Receipt Accounting Information System at Military Court I-02 Medan in 2021

Every agency definitely needs an accounting information system that has been well planned and supervised, to control financial receipts. With the information system, the goals of an agency can be achieved. This study aims to analyze the effectiveness of the application of cash receipts accounting information systems in the context of financial planning and control at the military court I-02 Medan. The data used is qualitative data. The method used in this research is descriptive analysis method. The results showed that the cash receipts accounting information system for financial planning and control at the military court I-02 Medan was adequate because it was appropriate and fulfilled the basic elements of an accounting information system, namely human resources, equipment, forms/documents, records, procedures and reports, and has complied with procedures in internal control. Therefore, until now the cash receipts accounting information system at the Military Court I-02 Medan can run effectively.
Motivasi, Biaya, Dan Lama Pendidikan dan Minat Mahasiswa Akuntansi Untuk Melanjutkan Pendidikan Magister Akuntansi

The purpose of the study was to determine the effect of motivation, education costs, and length of education on the interest of accounting students to continue their Masters in Accounting Education during the covid-19 pandemic. The research was conducted at the Faculty of Economics and Business, Udayana University. The total population is 535 students and the number of samples is 85, with non-probability sampling method using purposive sampling technique. Data was collected by means of a questionnaire. The data collected were analyzed using multiple linear regression analysis techniques. The results showed that quality, career, and economic motivation had a positive effect, while the cost of education and length of education had a negative effect on the interest of accounting students to continue their Master of Accounting Education during the COVID-19 pandemic.&#x0D; Keywords: Motivation; Education Cost; Length of Education; Interest in Continuing Master in Accounting.
ISSUES IN TEFL: A COMPREHENSIVE QUESTION AND ANSWER

English is the language used to gather and disseminate information around the world. English is extremely important. As a result, English is taught in Indonesia as early as kindergarten. However, difficulties in teaching English are unavoidable. Internal and external factors contribute to the problem. Internal factors occur within students. External factors such as school, society, and friends emerge from one's surroundings. Because English is not Indonesia's first language, teaching English presents significant challenges. Teachers play an important role in English instruction. It must be balanced with school and government facilities and support. This has the potential to alleviate difficulties in teaching English in Indonesia.
An Improved K-Means Algorithm Based on Min-Max Distance and BWP Metrics

The k-means algorithm is a conventional unsupervised cluster analysis algorithm, which is fast and easy to implement. Still, the number of clusters needs to be defined, and selecting the centre of mass is uncertain. A K-means algorithm based on the combination of maximum-minimum distance and Between-Within-Proportion (BWP) metrics is proposed to overcome these limitations. The results of simulation experiments on three datasets in the UCI database show that the proposed algorithm outperforms both the conventional K-means algorithm and the maximum-minimum distance-based K-means algorithm in terms of accuracy and clustering effect.
Hashing Learning with Hyper-Class Representation

Existing unsupervised hash learning is a kind of attribute-centered calculation. It may not accurately preserve the similarity between data. This leads to low down the performance of hash function learning. In this paper, a hash algorithm is proposed with a hyper-class representation. It is a two-steps approach. The first step finds potential decision features and establish hyper-class. The second step constructs hash learning based on the hyper-class information in the first step, so that the hash codes of the data within the hyper-class are as similar as possible, as well as the hash codes of the data between the hyper-classes are as different as possible. To evaluate the efficiency, a series of experiments are conducted on four public datasets. The experimental results show that the proposed hash algorithm is more efficient than the compared algorithms, in terms of mean average precision (MAP), average precision (AP) and Hamming radius 2 (HAM2)
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples

Adversarial training, which is to enhance robustness against adversarial attacks, has received much attention because it is easy to generate human-imperceptible perturbations of data to deceive a given deep neural network. In this paper, we propose a new adversarial training algorithm that is theoretically well motivated and empirically superior to other existing algorithms. A novel feature of the proposed algorithm is to apply more regularization to data vulnerable to adversarial attacks than other existing regularization algorithms do. Theoretically, we show that our algorithm can be understood as an algorithm of minimizing the regularized empirical risk motivated from a newly derived upper bound of the robust risk. Numerical experiments illustrate that our proposed algorithm improves the generalization (accuracy on examples) and robustness (accuracy on adversarial attacks) simultaneously to achieve the state-of-the-art performance.
Derandomized knockoffs: leveraging e-values for false discovery rate control

Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomness inherent to the method, different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is undesirable in practice. In this paper, we introduce a methodology for derandomizing model-X knockoffs with provable FDR control. The key insight of our proposed method lies in the discovery that the knockoffs procedure is in essence an e-BH procedure. We make use of this connection, and derandomize model-X knockoffs by aggregating the e-values resulting from multiple knockoff realizations. We prove that the derandomized procedure controls the FDR at the desired level, without any additional conditions (in contrast, previously proposed methods for derandomization are not able to guarantee FDR control). The proposed method is evaluated with numerical experiments, where we find that the derandomized procedure achieves comparable power and dramatically decreased selection variability when compared with model-X knockoffs.
AN ANALYSIS OF STUDENTS’ INTEREST IN EXTENSIVE READING TEXT AT THE EIGHTH GRADE STUDENTS OF MTs NURURRODHIYAH JAMBI

The aim of this research is to know what kind of reading text do the students interest in extensive reading and to analyze what are the factors make the students are interested in extensive reading at Madrasah Tsanawiyah Nururrodhiyah Kota Jambi. This research is a descriptive study with a quantitative approach. The design used is a survey research. The data was collected through a questionnaire. There were 105 students of eight graders at MTs Nururrodhiyah Jambi participated in this research. In this research, the researcher found that first, the highest percentage of the extensive reading text is short story, it proves that short story is the sort of reading text the students interest among extensive reading text at the eight grade students at MTs Nururrodhiyah kota Jambi with the highest percentage 64.8%. Second, The reason why most of students interest in extensive reading text because they like when have a reading assignment text, they feel that reading professional articles, essays, technical reports, short stories, and books affect their behavior and attitude, and they even enjoy to read them wherever they stay because they agree that it is easy to find out those kinds of extensive reading text both at library and internet.
Evaluating Automated Driving Planner Robustness against Adversarial Influence

Evaluating the robustness of automated driving planners is a critical and challenging task. Although methodologies to evaluate vehicles are well established, they do not yet account for a reality in which vehicles with autonomous components share the road with adversarial agents. Our approach, based on probabilistic trust models, aims to help researchers assess the robustness of protections for machine learning-enabled planners against adversarial influence. In contrast with established practices that evaluate safety using the same evaluation dataset for all vehicles, we argue that adversarial evaluation fundamentally requires a process that seeks to defeat a specific protection. Hence, we propose that evaluations be based on estimating the difficulty for an adversary to determine conditions that effectively induce unsafe behavior. This type of inference requires precise statements about threats, protections, and aspects of planning decisions to be guarded. We demonstrate our approach by evaluating protections for planners relying on camera-based object detectors.
Lab spotlight: Exploring neural circuits

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Can We Share the Same Perspective? Blockchain Interoperability with Views

&lt;p&gt;Distributed ledger technology (DLT) provides decentralized and tamper-resistant data storage, replicated among mutually untrusting participants. With the advancement of this technology, different privacy-preserving blockchains have been proposed, such as Corda, Hyperledger Fabric, and Digital Asset's Canton. These distributed ledgers only provide \emph{partial consistency}, which implies that participants can view the same ledger differently. &lt;/p&gt; &lt;p&gt;A \emph{view} represents the states of a blockchain available to a particular stakeholder. The combination of views forms an integrated view that represents a consistent global state shared by all participants. &lt;/p&gt; &lt;p&gt;This paper introduces BUNGEE (Blockchain UNifier view GEnErator), the first DLT view generator, to allow capturing DLT snapshots, constructing views, and performing arbitrary operations on those, such as integrating views. Creating and integrating views allows interesting applications, such as stakeholder-centric snapshots for audits, cross-chain analysis, blockchain migration, and data analytics.&lt;/p&gt;
Measuring the Monetary Value of Online Volunteer Work

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor's value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours amount to 3.4 million USD a year based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.
Prediction of Basic Material Prices on Major Holidays Using Multi-Layer Perceptron

The prediction of the price of basic necessities on major holidays in Indonesia, such as Eid al-Fitr, Christmas, New Year, Chinese New Year, and Eid al-Adha, is something that needs to be observed, because there are often movements in the prices of basic commodities that increase or decrease very drastically. One of the main ingredients experiencing this is eggs, which often experience a significant increase, so it is necessary to make observations in the form of predictions to keep control of fluctuations, especially before and after the big day occurs. In this study, predictions were made on the price of basic commodities on the big day. With the prediction of the cost of goods on the big day, it is hoped that related parties can be assisted in monitoring and stabilizing the movement of basic commodity prices on the market. In this study, a prediction system for the price of basic commodities was produced using the Multi-Layer Perceptron (MLP) method. This MLP method can predict time-series data that experiences a lot of fluctuation. In this prediction, MLP can make predictions on ten prices of basic commodities on major holidays every day. The results of this study were divided into three groups, namely Worst, Average, and Best. The division of these three groups separates which staple ingredients have the closest predictions to their actual values. The Worst group is the group whose prediction results are still quite far from the actual, the Average group which is close to the actual value, and the Best group which has the best results because it is very close to the actual value. With prediction results measured using MSE, the Worst group consisted of cooking oil (MSE 0.00197), beef (0.00186), rice (0.00118), and sugar (0.00100). Then the Average group consisted of eggs (0.00096), red chili (0.00085), chicken (0.00074), garlic (0.00062), and cayenne pepper (0.00056). Finally, the Best group only consisted of Shallots with an MSE of 0.00040.
Nonlinear Effect of Financial Development on Income Inequality: The Case of Ivory Coast

From the mid-1980s until the 1990s, many developing countries, particularly the countries of the West African Economic and Monetary Union, undertook major reforms of their financial systems in order to free them from numerous internal constraints that limited their development, such as interest rate ceilings, high reserve requirements, administrative credit allocations, etc. This financial liberalisation will promote rapid financial development in these countries. Thus, in most of these countries, there is a relative deepening of the financial sector and an improvement in financial policies. The economic and financial literature suggests that a well-functioning financial system can promote the accumulation of physical capital, improve economic conditions and thus promote long-term growth (Demetrides and Andrinova, 2004), and thus reduce inequality between rich and poor (Anand &amp;amp; Segal, 2017). However, studies conducted so far indicate that the effects of financial development on income inequality remain mixed. Thus, the objective of this study is to analyse the effect of financial development on the reduction of income inequality in C&amp;ocirc;te d&amp;#39;Ivoire. To do so, we used annual data from the World Bank (2018) and the IMF (2018) covering the period 1986-2016. From the estimation of the ARDL and NARDL models, the results show that financial development reduces income inequality in the short run. However, in the long run financial development increases income inequality in C&amp;ocirc;te d&amp;#39;Ivoire. The results also show that financial development has an asymmetric effect on income inequality in the short and long run in C&amp;ocirc;te d&amp;#39;Ivoire.
Cyber Security Norms: Trust and Cooperation

As cyber crime becomes ever more sophisticated and a significant asymmetric threat, the need for effective cyber security is of vital importance. One important cyber security response is through cyber norms. At the same time, calls for multi-sector and multi-domain trust and cooperation are widespread. Yet research on the nature of trust and cooperation in cyber security norms appears to be underdeveloped. Key questions remain concerning the emergence and nature of trust and cooperation in norms. In addressing this gap, the article first considers how we can understand trust and cooperation in cyber norms through leveraging well-established theory from management research on trust building. Next, the paper examines the SolarWinds breach, as an example, to evaluate norms, trust and cooperation. The paper then applies principles from prominent trust-building theory to examine the antecedents, processes of outputs involved in building trust and cooperation. The contribution of this work presents a foundational conceptual framework, to allow the dynamics of norms, trust, and cooperation in managing cyber crime incidents to be studied. In doing so, the literature on examining trust and cooperation in norms is extended. Other researchers’ interest is encouraged as is an agenda for further research on norms, trust, and cooperation to support cyber security management. Implications may help the cyber security community as they construct and manage norms, trust, and cooperation.
Efficient separation of quantum from classical correlations for mixed states with a fixed charge

Entanglement is the key resource for quantum technologies and is at the root of exciting many-body phenomena. However, quantifying the entanglement between two parts of a real-world quantum system is challenging when it interacts with its environment, as the latter mixes cross-boundary classical with quantum correlations. Here, we efficiently quantify quantum correlations in such realistic open systems using the operator space entanglement spectrum of a mixed state. If the system possesses a fixed charge, we show that a subset of the spectral values encode coherence between different cross-boundary charge configurations. The sum over these values, which we call "configuration coherence", can be used as a quantifier for cross-boundary coherence. Crucially, we prove that for purity non-increasing maps, e.g., Lindblad-type evolutions with Hermitian jump operators, the configuration coherence is an entanglement measure. Moreover, it can be efficiently computed using a tensor network representation of the state's density matrix. We showcase the configuration coherence for spinless particles moving on a chain in presence of dephasing. Our approach can quantify coherence and entanglement in a broad range of systems and motivates efficient entanglement detection.
The Use of Smartphone in Improving the Students’ Speaking Skill

Technology has already had a significant impact on many aspects of life, including education. As a learning tool, the Smartphone has become indispensable for system implementation. The study's goal is to investigate how smartphones can assist students in improving their public speaking skills. The study employed classroom action research in three cycles. This study included 39 second-grade students from a junior high school in West Java. Interviews, observations, and questionnaires were used to collect qualitative data. Furthermore, quantitative data from speaking tests were obtained. According to the findings of this study, using a smartphone during a course activity can keep students' attention and interest. They also gained the confidence to speak up and actively participate in class. Furthermore, smartphones help students stay engaged while learning. The speaking test results revealed that the students' speaking abilities had improved. The mean score for students in cycle one was 70.4, 77.6 in cycle two, and 82.2 in cycle three. As a result, because smartphones are compatible with student characteristics, they can be used as a learning media to assist students in improving their speaking skills, broadening their knowledge, and developing their other English practice.
PRINCIPLE OF HUMANIZATION AND PSYCHOLOGIZATION OF PEDAGOGICAL MANAGEMENT AS A BASIS PERSONAL-ORIENTED IMPLEMENTATIONS TRAINING DURING THE PROFESSIONAL PROCESS TRAINING OF PEDAGOGICAL STAFF

The article deals with the principle of humanismation and psycholigation f pedagogical management during professional training.
A Survey and Evaluation Model for Generative Software Development (GSD)

This survey proposed an evaluation model to analyze and examine different approaches to generativity. In addition to problem domain concepts, the following concepts were used to define this model: Complexity and complexity management, and Systematics view to achieve unified and integrated insight into disparate evaluation criteria. The research's approach to the said concepts is first introduced. Then, the evaluation model is presented.
Book Review: Leading the eLearning Transformation of Higher Education: Leadership Strategies for the New Generation, 2nd ed. Gary E. Miller and Kathleen S. Ives (Eds.)

Olcott, D. J. (2022). Book Review: Leading the eLearning transformation of higher education: Leadership strategies for the new generation, 2nd ed. Gary E. Miller and Kathleen S. Ives (Eds.). Online Learning, 26(2), 1-7.
GlobalWalk: Learning Global-aware Node Embeddings via Biased Sampling

Popular node embedding methods such as DeepWalk follow the paradigm of performing random walks on the graph, and then requiring each node to be proximate to those appearing along with it. Though proved to be successful in various tasks, this paradigm reduces a graph with topology to a set of sequential sentences, thus omitting global information. To produce global-aware node embeddings, we propose GlobalWalk, a biased random walk strategy that favors nodes with similar semantics. Empirical evidence suggests GlobalWalk can generally enhance global awareness of the generated embeddings.
TEACHING ECOLOGY: TECHNOLOGY OF DEVELOPING LEARNING

Currently, there are three main groups of technologies: personality-oriented technologies; technologies based on the effectiveness of management and organization of the educational process; developmental learning technologies; during the teaching of the disciplines of the natural science cycle. The advantages of the last group in teaching ecology lies in the needs of mankind to solve global environmental problems. To do this, it is necessary, first of all, to activate and intensify the activities of students of environmental specialties, because they are the future inventors of means to combat the large-scale problems of our planet.
Semantic reading as a means of students’ ecological worldview development in the process of learning Geography

The paper deals with ecological worldview development among students capable of self-development and self-education on the basis of universal educational actions in the light of modern requirements of the Federal State Educational Standard of General Education in Geography. The issue of environmental competence as an integral part of the scientific worldview is touched upon. The authors have highlighted worldview ecological knowledge as a substantive basis of the worldview for all courses of school Geography; they have also noted ecological skills. The components of the ecological worldview (cognitive, value-normative, moral-strong-willed and practical) are considered. Attention is paid to the issues of intellectual development as the central link of the scientific worldview, the conditions for ecological worldview development, the selection of the content of educational material, the use of intersubject connections, the establishment of cause-and-effect relationships, the use of modern pedagogical technologies. The authors attention is also focused on the technology of problem-based learning as a basis for student-centered learning technologies - technology for project activities development and case technology. Particular attention is paid to the technology for critical thinking development through reading and writing as the most optimal and rational technology for universal general educational action development - semantic reading. It is one of the elements of reading competence related to the key cognitive competence that forms the basis of the cognitive component of the ecological worldview. The results of experimental work aimed at semantic reading skills development are presented.