Reviewerly/exHarmony huggingface dataset:
MODULE DEVELOPMENT IN HOTS-ORIENTED IPS SUBJECTS BASED ON INQUIRY AT SMP NEGERI 7 PURWOKERTO

This study aims to (1) Determine the feasibility of social studies subject modules; (2) Knowing the responses of teachers and students after using the Inquiry-Based HOTS-Oriented Social Studies Learning Module; and (3) Knowing the effectiveness of using the Inquiry-Based HOTS-Oriented Social Studies Learning Module. This type of research is development research (Research and Development) with the following steps: 1) potential and problems, 2) data collection, 3) product design, 4) design validation, 5) design revision, 6) product trials, 8) usage trials, 9) product revisions and, 10) mass production. The subject of the study was a class VIII student of SMP Negeri 7 Purwokerto. The data collection techniques used are interviews, documentation, and questionnaires. The collected data were analyzed by qualitative and quantitative descriptive analysis. The effectiveness test was carried out with pretest experiments-posttest control group design. The results showed that the expert assessment of the module showed that the module was feasible to use with a percentage of 75% valid, while the effectiveness test with an n-gain value showed an effective category of 0.5502 &gt; 0.07. Thus, the IPS module is worth using.
IMPLEMENTATION OF THE COURSE «SCIENTIFIC AND TECHNICAL TRANSLATION FROM THE PRIMARY FOREIGN LANGUAGE»: THE CURRENT STATE OF THE PROFESSIONAL TRAINING OF TRANSLATORS FOR PERFORMING SCIENTIFIC AND TECHNICAL TRANSLATION

The article analyzes the prerequisites and substantiates of the need for the development of an electronic (multimedia) course "Scientific and technical translation of the first foreign language" on the Moodle educational platform for training students of higher education (first / bachelor's degree) Specialty 035 – Philology, specializations 035.043 Germanic languages and Literature (translation included, first – German at the Yuriy Fedkovich Chernivtsi National University. In order to identify the level of development students' professional competence to perform various types of scientific and technical translation, an anonymous survey of applicants of the educational and professional program "German-Ukrainian translation and translation of the second foreign language" at the Department of Germanic, General and Comparative Linguistics was conducted in the online format using a questionnaire created in Google Forms. The results of the sociological survey of higher education students helped to identify the main problems in the study of scientific and technical translation, to reveal the level of formation students' professional competence for performing its various types: full written, annotative and abstractive translation. The confirmatory pedagogical experiment was conducted in the form of control work in order to check the level of formation students' professional competence of higher education in scientific and technical translation, who studied certain types of it within the related academic disciplines "Basics of field translation", "Translation and editing of professional texts". The results of the conducted control work, the analysis of the results of the applicants' survey, as well as the comparison of the educational and professional programs of the leading higher education institutions of Ukraine in the specialty 035 – Philology helped to confirm the relevance and feasibility of developing a separate electronic course "Scientific and technical translation of the first foreign language" within the framework of our scientific research for students of the first (bachelor) degree of higher education, which can be offered to students as an optional discipline.
The role of internal auditor's in the development of an internal Control system: An applied study at Al-Nahrain University

Internal audit is one of the essential responsibilities in the economic unit on which senior management and various departments and individuals rely as a source of high-quality data and information. As one of the parties responsible for internal control, the research aims to determine the importance of the internal auditor's involvement in establishing the internal control system through its supervisory and monitoring responsibilities. Also, developing more methods to reduce the risks and issues associated with the internal control system's failure. This study aims to demonstrate the role of internal audit in developing the internal control system in the economic unit by clarifying the relationship between internal audit and the internal control system, highlighting how internal audit can activate the internal control system and what the internal auditor's responsibilities are, and working on the periodic evaluation of the internal control system.
Design of Russian Digital Resource Construction Platform

With the continuous promotion of the digitalization of Library and document resources, there are more and more types and scales of outsourced and self built databases in University Libraries and information units at home and abroad. In order to obtain complete digital resources, it needs to spend a lot of time searching in various types of resource systems. Therefore, at present, digital library is a user-oriented service resource system based on digital resources, which can integrate information resources, information services and information activities as a dynamic mechanism. Based on Visual Studio c# and SQL Server development environment, the Russian digital characteristic resource construction platform expounds the process of Russian database construction and Russian document resource construction from the system design and platform implementation according to the development process of software engineering. This platform has the advantages of convenient operation, fast pre search, effective management of Russian literature and strong support for the use of literature.
SemKey: Boosting Secret Key Generation for RIS-assisted Semantic Communication Systems

Deep learning-based semantic communications (DLSC) significantly improve communication efficiency by only transmitting the meaning of the data rather than a raw message. Such a novel paradigm can brace the high-demand applications with massive data transmission and connectivities, such as automatic driving and internet-of-things. However, DLSC are also highly vulnerable to various attacks, such as eavesdropping, surveillance, and spoofing, due to the openness of wireless channels and the fragility of neural models. To tackle this problem, we present SemKey, a novel physical layer key generation (PKG) scheme that aims to secure the DLSC by exploring the underlying randomness of deep learning-based semantic communication systems. To boost the generation rate of the secret key, we introduce a reconfigurable intelligent surface (RIS) and tune its elements with the randomness of semantic drifts between a transmitter and a receiver. Precisely, we first extract the random features of the semantic communication system to form the randomly varying switch sequence of the RIS-assisted channel and then employ the parallel factor-based channel detection method to perform the channel detection under RIS assistance. Experimental results show that our proposed SemKey significantly improves the secret key generation rate, potentially paving the way for physical layer security for DLSC.
Dynamic Strategies for High Performance Training of Knowledge Graph Embeddings

Knowledge graph embeddings (KGEs) are the low dimensional representations of entities and relations between the entities. They can be used for various downstream tasks such as triple classification, link prediction, knowledge base completion, etc. Training these embeddings for a large dataset takes a huge amount of time. This work proposes strategies to make the training of KGEs faster in a distributed memory parallel environment. The first strategy is to choose between either an all-gather or an all-reduce operation based on the sparsity of the gradient matrix. The second strategy focuses on selecting those gradient vectors which significantly contribute to the reduction in the loss. The third strategy employs gradient quantization to reduce the number of bits to be communicated. The fourth strategy proposes to split the knowledge graph triples based on relations so that inter-node communication for the gradient matrix corresponding to the relation embedding matrix is eliminated. The fifth and last strategy is to select the negative triple which the model finds difficult to classify.
An 80-20 Analysis of Buggy and Non-buggy Refactorings in Open-Source Commits

In this short paper, we explore the Pareto principle, sometimes known as the “80-20” rule as part of the refactoring process. We explore five frequently applied refactorings, namely extract method, extract variable, rename variable, rename method and change variable type from a data set of forty open-source systems and nearly two hundred thousand refactorings. We address two key research questions. Firstly, do 80% of “buggy” refactorings (where a refactoring has induced a bug fix) arise from just 20% of commits and, secondly, does the same rule apply to “non-buggy” refactorings when applied to the same systems? To facilitate our analysis, we used refactoring and bug data from a study by Di Penta et al. Results showed that refactorings inducing bugs were clustered around a more concentrated set of commits than refactorings that did not induce bugs. One refactoring ‘change variable type’ stood out - it almost conformed to an 80-20 rule. The take-away message is, as the saying goes, that too much of a “good” thing [refactoring] could actually be a “bad” thing.
Implementasi Transfer Learning Pada Algoritma Convolutional Neural Network untuk Mengklasifikasikan Image Objek Wisata

This study classifies the image of a tourist attraction with 9 labels sky, tree, mountain, water, street, temple, garden, stone and ricefield. The results of multi-label labeling can be used to see the frequency and recommendations of tourist attractions in Central Java, and build a transfer learning model to determine the accuracy value. Classification with multi-label images has its own complexity in the labeling process and few people use it. Testing and evaluating the model uses the equation of accuracy and f-1 score. Several previous researchers also stated that the higher the amount of training data and the number of epochs per step, the higher the accuracy produced. Based on the results of training and evaluation of the four training processes, that 210 data using bs 8, lr 1e-3 and epoch 50 showed an accuracy of 0.8598 with a loss of 0.3245, while 290 data with bs 16, lr 1e-3 and epoch 50 showed an accuracy of 0.8685. with a loss of 0.2903. Then 594 data with bs 32, lr 1e-3 and epoch 50 showed an accuracy of 0.8852 with a loss of 0.2756, and 1000 data with bs 46, lr 1e-3 and epoch 50 showed an accuracy of 0.8833 with a loss of 0.2863. This can answer the statement that the greater the number of datas, the higher the accuracy produced, so that the transfer learning model on the ResNet-50 architecture with multi-label image datas can be applied by showing accuracy results close to the accuracy value on ResNet-50 in the imagenet project. In addition, the contribution of this research is to provide recommendations for potential tourist objects in Central Java, namely tourism objects with the theme of nature, then tourism processed by human hands such as historical places, cultural heritage and family recreation areas.
PENINGKATAN KETERAMPILAN MEMBACA PUISI MELALUI MEDIA AUDIO VISUAL OLEH SISWA KELAS X SMA NEGERI 1 LAWE BULAN TAHUN PELAJARAN 2021/2022

This study aims to determine the improvement of poetry reading skills through audio visual media. By Class X Students of SMA Negeri 1 Lawe Month of the 2021/2022 Academic Year. Based on the results of the research and discussion that have been described in the previous chapter, it can be concluded that there is an increase in reading skills of Ws.Rendra's poems from the sun by using Audio Visual Media by class X students of SMA Negeri 1 Lawe Bulan in the 2021/2022 academic year. It can be proven from the average score of the initial test of students' poetry reading skills is 70.91 with a standard deviation of 3.66 and an increase in the value of the final test results with an average value of 79.32 with a standard deviation of 5.33. Based on the data from the results of the students' initial and final tests and if it is associated with the Indonesian KKM score for SMA of 75, the students' ability to read the poem "Sajak Matahari" can be said to be good after audio-visual media in the form of youtube videos are performed. In addition, at a value of &gt; 12.63 &gt; 1.720, therefore from the results of testing the hypothesis above, it can be concluded that there is an increase in Poetry Reading Skills through Audio Visual Media. By Class X Students of SMA Negeri 1 Lawe Month of the 2021/2022 Academic Year.
Governance, Politics, and Economic Development: Some African Perspectives

The article documents the evolution of governance measures in Africa during post-independence: economic freedom, electoral competitiveness, political rights and civil liberties, executive constraint, and polity2. It examines their implications for economic development, considers political instability (PI) in the form of coups d’état and civil wars on the premise that PI results from poor governance. In addition, the article sheds light on the links between the more recent measures of governance – the World Bank’s Worldwide Governance Indicators (WGI) – and economic development outcomes among African countries. The article concludes by paying special attention to potential governance/institutional instruments that might reflect “good governance”, and highlights the implicit risks faced by African countries in their efforts to sustain the continent’s recent economic gains within the current political economy framework.
An Analysis of Jargon Written on the Correspondence by Villas’ Staff in Gianyar, Bali

Jargon, as part of a sociolinguistic study, is a language variation consists of technical terms or phrases used by a particular profession or group of individuals. The research focuses on the three points of discussion, i.e. forms, meanings, and functions of jargon found in business correspondence written in email by the staffs of Front Office Department and Sales &amp; Marketing Department in Djanuraga villa and Letung villa in Gianyar. Forms of jargon consist of acronym, abbreviation, word, and phrase. The data was collected by observation methods of the business correspondence sent through email and also interview techniques to the employees of the Front Office department and Sales &amp; Marketing department in the villas. Qualitative descriptive method was used to analyze data in this study. The finding in the forms of jargon that acronym is the most frequent one used in the correspondence, with 23 data and the second is abbreviation with 6 data. Regarding to the meaning of the jargon, the jargon is described clearly to make the staffs understand about the jargon more clearly. According to the finding of the functions of jargon in the correspondence used by villas’ staffs, the data analysis reveals that there are two functions, i.e. providing a technical or specialist language for particular purpose and as a sign of professionalism that showing sufficient work experience and to make communication more effective.
A battle for the soul of education

Chapter 10 ends with a reflection on the current challenges at hand regarding mainstream higher education and universities, as well as the authors’ thoughts on some potential solutions, which draw directly from the spirit of Zapatismo. As a summary chapter, it provides readers with an overview of how the Zapatistas have prioritised political education, social reproduction, solidarity, collective action, democratic process and praxis in their resistance, movement and pursuit of health. The chapter also reiterates the point that the book should ultimately be a gateway text that motivates readers to refuse liberal bystanding and contribute to collective action and radical change wherever they are, from the local to the global, that is relevant, inclusive and enduring.
Multi-SAP Adversarial Defense for Deep Neural Networks

Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several L∞ attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several L∞ attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.
Critical evaluation of censorship during the Covid-19 pandemic

Censorship is as old as the history of the press. While the degree of censorship differs from one place to another, no society can be said to be totally independent of control. With the potential of being misused by certain elements and to maintain law and order, certain forms of regulations have been imposed from time to time by authorities. There are situations and circumstances where authorities and the media work consistently to fulfil the objectives.
Elite subgroup guided particle swarm optimisation algorithm with multi-strategy adaptive learning

In order to address the premature convergence of the standard particle swarm optimisation (PSO) algorithm, this paper proposes an elite subgroup guided particle swarm optimisation algorithm with multi-strategy adaptive learning (EGAPSO). In order to enhance the particle's ability to escape from the local extremum point, the social cognitive part of originally learning only from the global optimal particle is changed to the part of adaptively choosing to learn from the global optimal particle and the particle in the elite subgroup in the model. Meanwhile, in order to make the algorithm more universal, a variety of learning strategies such as elite particle opposition-based learning, subspace Gaussian learning and mean centre learning with different search characteristics are adaptively selected in the evolutionary process. Combination of the two improved measures can not only increase the universality of the algorithm, but also enhance the diversity of the population, which effectively helps the algorithm escape from the local optimum and avoid the premature convergence. Simulation results on the typical test function set and test results of comparison with other classical and newly improved PSO algorithms show that the proposed algorithm performs better in optimisation and stability.
Energy-based cost model of containers provisioning on clouds

Cloud computing revolutionised the development and execution of distributed applications by providing on-demand access to virtual resources. Containerisation simplifies management and support of the cloud infrastructure and applications. Clouds typically are consumed in a pay-as-you-go pricing model. However, when applied to containerised environments, such traditional models do not consider resource utilisation values, leading to inaccurate estimates. Moreover, these models do not consider energy consumption, a dominant component of the data centre's total cost of ownership. This paper proposes Energy Price Cloud Containers (EPCC), a cost model based on energy consumption that accounts for containers' effective resource utilisation. We compare EPCC with AWS Fargate to highlight the benefits of using an energy-based pricing model. Thus, by comparing the cost of an application running using AWS Fargate with the estimated cost of that application in nome, it is possible to identify the benefits of using an energy-based pricing model. The weekly costs estimated when running computational resources at nome vary between US$ 2.31 and US$ 10.59. In contrast, when estimating the same amount of resources on AWS Fargate, the costs vary between US$ 2.71 and US$ 29.94. Nome resulted in a cost reduction of up to 35%.
Strong Wolfe condition-based variable stacking length multi-gradient parameter identification algorithm

This paper considers the acceleration of the gradient algorithm for the linear models. Traditional stochastic gradient algorithm requires less computation, but it converges to the true parameter slowly. To accelerate gradient algorithm, a novel gradient algorithm using several gradients is proposed. One important issue of the proposed algorithm is how to determine the stacking length. The stacking length defines the number of gradients used in each recursion. A variable stacking length based on the strong Wolfe condition is presented to enable the algorithm to converge faster. The stacking length obtained by using the SW condition can ensure that the proposed multi-gradient algorithm converges faster. Several experiments are made to validate the proposed algorithm.
Topic Modeling on Arabic Language Dataset: Comparative Study

Topic modeling automatically infers the hidden themes in a collection of documents. There are several developed techniques for topic modeling, which are broadly categorized into Algebraic, Probabilistic and Neural. In this paper, we use an Arabic dataset to experiment and compare six models (LDA, NMF, CTM, ETM, and two Bertopic variants). The comparison used evaluation metrics of topic coherence, diversity, and computational cost. The results show that among all the presented models, the neural BERTopic model with Roberta-based sentence transformer achieved the highest coherence score (0.1147), which is 36% above Bertopic with Arabert (the second best in coherence). At the same time, the topic diversity is 6% lower than the CTM model (the second best in diversity) at the cost of doubling the computation time.