Reviewerly/exHarmony huggingface dataset:
DAMPAK USER EDUCATION TERHADAP PEMANFAATAN SUMBER DAYA PERPUSTAKAAN UIN IMAM BONJOL PADANG

This research is motivated by the function of the university library as a very vital element in meeting the information needs of the academic community in carrying out the tridharma of higher education and none of these roles can be realized without an effective User Education program because this program aims to create awareness about how to make effective use of library services and resources. The purpose of this study was to describe the effectiveness of User Education activities on library use by students of the Faculty of Adab and Humanities UIN Imam Bonjol Padang. The research method is descriptive quantitative with a questionnaire as a primary data collection tool. The population of this research is all students of the Faculty of Adab and Humanities UIN Imam Bonjol Padang 2021-2022. Research questions were answer and analysis was performed using the mean scores. Research findings reveal that students of the Faculty of Adab and Humanities have various levels of understanding of the concept of User Education. The study also found that the User Education program positively influenced student use of the library and helped meet their academic information needs. The results of the study also found several problems that hindered User Activities effectively such as excess participants, lack of infrastructure, less innovative human resources, shortage of teaching materials, limited time allocated for activities, less conducive supporte and inadequate space. This study concludes that with a central place in student education, and to make user education programs indispensable, adequate resources should be provided to ensure that the ideal goals of the program are realized.
DETECTION OF ANOMALOUS EVENTS BASED ON DEEP LEARNING-BILSTM

Video anomaly detection in smart cities is a critical errand in computer vision that plays an imperative role in intelligent surveillance and public security but is challenging due to its differing, complex, and rare event in real-time surveillance situations. Different deep learning models utilize a critical amount of training data without generalization capabilities and with long time complexity. In this work, and to overcome these problems, an algorithm for reducing the size of the extracted features have been suggested, and this was done by combining every 15 video frames to generate the new features vectors which will be fed into our classifier model, the values of new features vectors represent the summation of the values of original features vectors got from Resnet50. Finally, the new feature vectors are fed into our classifier model to detect the abnormality. We conducted comprehensive tests on a variety of anomaly detection benchmark datasets to verify the proposed framework's functionality in complex surveillance scenarios. The Numerical results were carried out on the UCF-Crime dataset, with the proposed approach achieving Area Under Curve (AUC) scores of 93.61% on the database's test set.
Fock-Space Schrieffer–Wolff Transformation: Classically-Assisted Rank-Reduced Quantum Phase Estimation Algorithm

We present an extension of many-body downfolding methods to reduce the resources required in the quantum phase estimation (QPE) algorithm. In this paper, we focus on the Schrieffer–Wolff (SW) transformation of the electronic Hamiltonians for molecular systems that provides significant simplifications of quantum circuits for simulations of quantum dynamics. We demonstrate that by employing Fock-space variants of the SW transformation (or rank-reducing similarity transformations (RRST)) one can significantly increase the locality of the qubit-mapped similarity-transformed Hamiltonians. The practical utilization of the SW-RRST formalism is associated with a series of approximations discussed in the manuscript. In particular, amplitudes that define RRST can be evaluated using conventional computers and then encoded on quantum computers. The SW-RRST QPE quantum algorithms can also be viewed as an extension of the standard state-specific coupled-cluster downfolding methods to provide a robust alternative to the traditional QPE algorithms to identify the ground and excited states for systems with various numbers of electrons using the same Fock-space representations of the downfolded Hamiltonian. The RRST formalism serves as a design principle for developing new classes of approximate schemes that reduce the complexity of quantum circuits.
Study the Negative Effects related to Inappropriate Storage Conditions on Archaeological Ceramic, Indicate Some Practical Solutions

This paper studies the negative effects of storage conditions on archaeological ceramic artifacts since they may spend much of their lives in storage areas and units (in the excavation or Museums).The ceramic artifacts are subject to deterioration from chemical interactions to some extent between the main matrix of it and the surrounding deterioration factors in these areas and units or sometimes happen from the materials used in treatment steps as old or new restoration.Thus, archaeological ceramic artifacts can be unstable even over short periods.Maintaining the proper environment, like control of light, temperature, relative humidity, ventilating, air pollutants, and pests inside the storage areas and units, is required.So diligent and continuous observation of these conditions is required.In addition, these areas must be adequate to accommodate the particular characteristics and quantity of objects.The paper studies some affected ceramic samples through different investigative and analytical methods to recognize the harmful deterioration effects, such as Digital Optical Microscope to investigate the destructed ceramic artifacts, The Scanning Electron Microscope equipped with the Energy Dispersive system (SEM-EDX) detailed ceramic surface composition and its initial structure.X-Ray Diffraction (XRD) recognizes the mineral composition and crystalline phases in ceramic; in addition, Differential Thermal Analysis (DTA) determines the almost firing temperature of ceramic artifacts.This study evaluates the different deterioration aspects of archaeological ceramic artifacts; it gives some practical solutions to avoid a high percentage of deterioration problems before it happens by providing an appropriate storage environment, materials, and units.
Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression

The development of data storage hardware is very rapidly over time. In line with the development of storage hardware, the amount of digital data shared on the internet is increasing every day. That way no matter how big the size of the storage device we have, of course, it will only be a matter of time until that storage space is exhausted. Therefore, in terms of maximizing storage space, a technique called compression emerged. This study focuses on a comparative analysis of 2 Lossless compression technique algorithms, namely the Huffman algorithm and Lempel Ziv Welch (LZW). A number of test files with different file types are applied to both algorithms that are compared. The performance of the algorithm is determined based on the comparison of space saving and compression time. The test results showed that the Lempel Ziv Welch (LZW) algorithm was superior to Huffman’s algorithm in .txt file type compression and .csv, the average space savings produced were 63.85% and 77.56%. The degree of compression speed that each algorithm produces is directly proportional to the file size.
Improving out of vocabulary words recognition accuracy for an end-to-end Russian speech recognition system

Automatic Speech Recognition (ASR) systems are experiencing an active introduction into our daily lives, simplifying the way we interact with electronic devices.The advent of end-to-end approaches has only accelerated this process.However, the constant evolution and a high degree of inflection of the Russian language lead to the problem of recognizing new words outside the vocabulary (Out Of Vocabulary, OOV) because they did not take part in the training process of the ASR system.In such a case, the ASR model tends to predict the most similar word from the training data which leads to a recognition error.This is especially true for ASR models that use decoding based on a Weighted Finite State Transducer (WFST), since they are obviously limited by the list of vocabulary words that may appear as a result of recognition.In this paper, this problem is investigated on the basis of an open data set of the Russian language (common voice) and an integrated ASR system using the WFST decoder.A method for retraining an integral ASR system based on the discriminative loss function MMI (maximum mutual information) and a method for decoding the integral model using a TG graph are proposed.Discriminative learning allows smoothing the probability distribution of acoustic class prediction, thus adding more variability in the recognition results.Decoding using the TG graph, in turn, is not limited to recognizing only vocabulary words and allows the use of a language model trained on a large amount of external text data.An eight-hour subset from the common voice base is used as a test set.The total number of OOV words in this test sample is 18.1 %.The results show that the use of the proposed methods allows to reduce the word recognition error (Word Error Rate, WER) by 3 % in absolute value relative to the standard method of decoding integral models (beam search), while maintaining the ability to recognize OOV words at a comparable level.The use of the proposed methods should improve the overall quality of recognition of ASR systems and make such systems more resistant to the recognition of new words that were not involved in the learning process.
Data-Driven Revision of Conditional Norms in Multi-Agent Systems

In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms that guarantee the achievement of the objectives in every operating context. Also, these objectives may change over time, thereby making previously defined norms ineffective. In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at prohibiting agents from exhibiting certain patterns of behaviors. We propose DDNR (Data-Driven Norm Revision), a data-driven approach to norm revision that synthesises revised norms with respect to a data set of traces describing the behavior of the agents in the system. We evaluate DDNR using a state-of-the-art, off-the-shelf urban traffic simulator. The results show that DDNR synthesises revised norms that are significantly more accurate than the original norms in distinguishing adequate and inadequate behaviors for the achievement of the system-level objectives.
Financial development and economic growth in Botswana: New evidence from disaggregated data

Abstract In this study, the causal relationship between financial development and economic growth in Botswana is re-examined using disaggregated data from 1980 to 2020 on financial development. The importance of financial development and economic growth in achieving Sustainable Development Goals (SDGs) cannot be overemphasised. The study used the Autoregressive Distributed Lag (ARDL) approach to cointegration and the ECM-based Granger causality test to examine this linkage. Financial development is measured at an aggregate level by the Financial Development Index (FDI) and at a disaggregate level by the Financial Institution Index (FII) and Financial Market Index (FMI) from the International Monetary Fund (IMF) financial development index database. The study failed to find any causality between financial development and economic growth during the study period. The results apply, irrespective of proxy used to measure the level of financial development and the time frame. This finding points to the importance for Botswana to continue with the Vision 2036 and the National Development Plans that focus, among other goals, on economic growth, to realise an increase in gross fixed capital formation and financial development.
The effectiveness of the digital competency training program in improving the digital competence of elementary school teachers

The purpose of this study was to examine the design of a digital competency training program to improve the digital competence of teachers at the elementary school level. This study used a quasi-experimental method with a pretest and posttest design. The participants in this study were elementary school teachers from Yogyakarta. There were 800 teachers involved with the composition of 400 experimental class teachers and 400 control class teachers. This digital literacy program is carried out through several stages, namely the pre-test, intervention, and post-test stages. This digital literacy program lasts for one semester or 6 months with the main objective of improving the digital competence of teachers from three aspects, namely conceptual, procedural, and attitudes towards digital competence of teachers. The results showed that the intervention of this program was able to significantly improve the digital skills of teachers. The increase in digital skills of teachers in the experimental group is marked by increasing digital skills in three aspects, namely conceptual, procedural, and attitudinal digital competencies. The implication of this research is that digital competency training programs can be an alternative for stakeholders in order to improve teacher competencies while improving school quality. Keywords: Digital competence of teachers, digital competence training program, Internet, digital literacy.
Tingkat Literasi Islam Pelaku Penyalahgunaan Narkoba di Kota Bima dan Kabupaten Bima

The development of various technological in the era 4.0 does not only have a positive impact on people's lives. It will cause negative things, one of which is drug abuse. One of the areas where cases of drug abuse are rampant is Bima. Data on drug abuse by the age of 15-25 years even reached 63 percent. So that researchers are interested in choosing a theme related to the level of Islamic literacy of drug abusers. This study is a quantitative study with a sample of 25 people and using Islamic literacy instruments to collect related data. The results of the study then showed that the level of Islamic literacy had an average achievement of 29.75 which was in the low category. Where for each aspect, namely the belief aspect by 16% (very low), the ritual and surrender aspect respectively by 34% (low) and the knowledge aspect by 35% (low).
The LM-Cut Heuristic Family for Optimal Numeric Planning with Simple Conditions

The LM-cut heuristic, both alone and as part of the operator counting framework, represents one of the most successful heuristics for classical planning. In this paper, we generalize LM-cut and its use in operator counting to optimal numeric planning with simple conditions and simple numeric effects, i.e., linear expressions over numeric state variables and actions that increase or decrease such variables by constant quantities. We introduce a variant of hmaxhbd (a previously proposed numeric hmax heuristic) based on the delete-relaxed version of such planning tasks and show that, although inadmissible by itself, our variant yields a numeric version of the classical LM-cut heuristic which is admissible. We classify the three existing families of heuristics for this class of numeric planning tasks and introduce the LM-cut family, proving dominance or incomparability between all pairs of existing max and LM-cut heuristics for numeric planning with simple conditions. Our extensive empirical evaluation shows that the new LM-cut heuristic, both on its own and as part of the operator counting framework, is the state-of-the-art for this class of numeric planning problem.
FREQUENCY ANALYSIS OF TOPONYMS IN TEXTBOOKS ON THE ACADEMIC DISCIPLINE &amp;quot;CLASSICAL LITERATURE&amp;quot;

The article is devoted to the frequency analysis of place names in textbooks on the discipline &amp;quot;Classical Literature&amp;quot;. The analysis of these lexical units was carried out in two stages – first by computer, and then by manual processing of texts. The objective of the research was to identify the lexical features of the text of the subject area, as well as the features of the frequency dictionary of the discipline &amp;quot;Classical Literature&amp;quot;, due to the uniqueness of the course under consideration.
Samsung’s Organisational Culture Regarding Business Competitiveness And Innovation In The Post-Pandemic Era

This paper studies how Samsung, a Korean multinational enterprise (MNE), adjusted its cultures to the local environment and exploited organisational cultures to benefit the workplace innovation in its own enterprises.A case study research method was employed to search archives and journal articles for the specific period of 18 years (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022) to observe the continuum of MNE innovation and their business competitiveness support activity/program.The results exhibit that, in order to embed in Western markets successfully, Samsung even applies American Six Sigma (6σ) method to its Chaebol organisational culture and requests its foreign employees to study Korean.Furthermore, this organisational culture of Samsung differs from individualistic organisational culture (e.g.UK) of traditional European MNEs.In addition, Samsung concentrates on six innovations: Cost innovation, global management innovation, marketing innovation, organisational culture innovation, product innovation, and technology innovation.Samsung provides coaching for employees to have them be innovative thinkers.As a result, authors conclude that Chaebol organisational culture positively affects to the business competitiveness and the innovation in Samsung.
Sovershenstvovanie pedagogicheskikh priiomov razvitiia logicheskogo myshleniia doshkol'nikov posredstvom konstruirovaniia iz bumagi

A study aimed at improving pedagogical methods for the development of logical thinking in preschoolers through paper construction revealed that: origami paper design is not part of the main educational program of preschool education, but is used either fragmentarily or in additional classes. At the same time, the pedagogical purposeful management of the development of thinking of preschoolers is based on methods and techniques, most often limited to the methods of imitation and repetition. The study reveals a contradiction between the objective need to introduce significant substantive adjustments to the basic educational programs of preschool education and the insufficient readiness of teachers to implement it. The development and implementation of the technology of pedagogical purposeful management of the development of thinking in preschoolers through the construction of paper using the origami technique made it possible to resolve the identified contradiction and increase the effectiveness of the development of thinking among students.
Analysis of Numeracy Literacy Skills Of Students On The Pythagorean Theorem Material in Class VIII UPT SMP Negeri 7 Medan T.A. 2021/2022

This research aims to determine the numeracy literacy ability of students in class VIII SMP Negeri 7 Medan. This type of research is descriptive qualitative research. The research was carried out in the even semester of the 2021/2022 academic year. The samples taken in this study using purposive sampling were students at UPT SMP Negeri 7 Medan, namely class VIII. The instrument used in this study was a numeracy literacy test in the form of 5 essay questions. Data collection techniques using triangulation techniques, followed by data analysis which includes data collection, data reduction, data presentation, and conclusions. Based on the objectives of the research results that have been made by researchers, it can be seen that there is a difference in the students' numeracy literacy skills in solving post-test test questions, which 50% of students' numeracy literacy skills are obtained at a high level, while 30% of students who have moderate level of numeracy literacy, and 20% of students who have low level of numeracy literacy. From numeracy literacy skills in the high-level category, it can be seen that students are able to solve problems that occur in everyday life.
Hyperparameter Tuning and Optimization Applications

Abstract This chapter reflects on advantages and sense of use of Hyperparameter Tuning (HPT) and its disadvantages. In particular it shows how important it is, to keep the human in the loop, even if HPT works perfectly. The chapter presents a collection of HPT studies. First, HPT applications in Machine Learning (ML) and Deep Learning (DL) are described. A special focus lies on automated ML, neural architecture search, and combined approaches. HPT software is presented. Finally, model based approaches, especially applications with Sequential Parameter Optimization Toolbox (SPOT) are discussed.
Case Study II: Tuning of Gradient Boosting (xgboost)

Abstract This case study gives a hands-on description of Hyperparameter Tuning (HPT) methods discussed in this book. The Extreme Gradient Boosting (XGBoost) method and its implementation was chosen, because it is one of the most powerful methods in many Machine Learning (ML) tasks, especially when standard tabular data should be analyzed. This case study follows the same HPT pipeline as the first and third studies: after the data set is provided and pre-processed, the experimental design is set up. Next, the HPT experiments are performed. The R package is used as a “datascope” to analyze the results from the HPT runs from several perspectives: in addition to Classification and Regression Trees (CART), the analysis combines results from the surface, sensitivity, and parallel plots with a classical regression analysis. Severity is used to discuss the practical relevance of the results from an error-statistical point-of-view. The well-proven R package is used as a uniform interface from the methods of the packages and to the ML methods. The corresponding source code is explained in a comprehensible manner.