Reviewerly/exHarmony huggingface dataset:
Validity and reliability of cognitive tests study and development of elementary curriculum using Rasch model

The instrument for measuring knowledge in the subject of Elementary School Curriculum Study and Development has been created to measure students' understanding of teacher candidates in applying concepts, implications and curriculum development at the elementary school level. In order to make reliable and feasible instrument for measuring students knowledge in the subject of Elementary School Curriculum Study and Development, this study aims to produce empirical evidence about the validity and reliability of test instruments using the Rasch model analysis. The study was conducted by testing 20 items on 142 elementary school teacher candidate at one of the State Universities in the City of Mataram, West Nusa Tenggara. The validity and reliability of the instrument were measured by the Rasch analysis model using the Winstep program. The one-dimensional testing of 20 items has a variance measured at 42.7% which exceeds the minimum points of 40.0% desired by the Rasch model. The reliability index of the respondents was 0.65 and the item reliability index was 0.98. All items show a positive value for Point Measure Correlation (PMC) in terms of item polarity which means there is no conflict between the item and the construct being measured. Outfit Mean Square value also shows that all items that almost all items have an MNSQ Outfit value smaller than 1.5 which means the measurement value can be said to be productive except for item 13 (3.77) and item 16 (3.77). Both of these items need to be re-examined because they have problems in measuring their validity. The results of this study have proven that the knowledge measurement test instrument in the Elementary School Curriculum Study and Development course has validity and reliability values that meet and are empirically feasible to be used in measuring Basic School Curriculum Study and Development knowledge for prospective teacher students.
THE EFFECT OF AUDIOVISUAL METHOD ON STUDENTS’ SPEAKING ACHIEVEMENT AT SMPN 2 KENDARI

This study aims to find out whether there is a significant effect of applying audio-visual method on students’ Speaking achievement of second grade students at SMPN 2 Kendari or not. Population of this research is the entire second grade class in academic year 2017/2018 of SMPN 2 Kendari. The total numbers of the population are 280 students where class VIII B as experimental class and VIII A as control class were chosen as the sample of this study used purposive sampling technique. This study applied quasi-experimental design which concerned on two groups; they are control group and experimental group. These two groups received a different treatment. The implementation of audio-visual method was applied on experimental class. The data which analyzed in this study was quantitative data; it gained from the pretest and posttest result of the two classes which analyzed using descriptive statistic and inferential analysis (Independent sample t-test). The result shows that the gain of the mean score of experimental class 1.76 is higher than the gain score of control class 0.57. It shows that teaching speaking by Audiovisual method in the experimental class has a better effect in enhancing students’ speaking achievement. Then the result of hypothesis testing showed that the value of Sig. (2-tailed) is 0.044 which smaller than (&lt;0.05). Hence, it can be concluded that H0 is rejected and H1 is accepted which means that there is significant effect of Audiovisual method on students’ speaking achievement at eight grade of SMP N 2 Kendari. Keywords: Speaking, Audiovisual, Learning method
A Virtual Training Environment: A Framework and an Effectiveness Measurement Method

The virtual training enables training elements i.e. trainers and trainees to obtain the needed skills and experience through simulation of a real training process.Nowadays, there is extreme need for distance education and training due to the spread of the Covid 19 epidemic.For this reason, we proposed framework for virtual training system and made a simple evaluation to prove its efficiency and effectiveness.we conducted an interview with one lecturer and made questionnaire for 11 computer science master students.These ways can measure the return of the training and therefore enhance its quality and address some issues that the traditional training face.
RUKYATUL HILAL: KELAYAKAN TEMPAT OBSERVASI PANTAI BAROMBONG KOTA MAKASSAR

The dating of Qamariyah is the date of Muslims in determining the entry and the feast of days, months, and years. In this dating system, it has a very close association with the setting of the sun which then the crescent (Hilal), then the observation process is done by this change, to know the entry of the beginning of the new month, this process is called Rukyatul Hilal. There are several techniques in the process of Rukyatul Hilal, so this process can be implemented. The success rate of the Rukyatul Hilal process is influenced by internal and external factors. One of the internal factors is related to weather conditions and the external factor of urgency is the location of the observation. Barombong Beach feasibility level as a place of Rukyatul Hilal can be measured based on geographical location and climatic conditions at the site. So, the means used to determine the level of feasibility of Barombong Beach as a place of Rukyatul Hilal can be data collected and also do a direct observation of the field research. With the condition of the western horizon that is free of all things that can obstruct the process of the pass and the weather condition are stable, then Barombong Beach is declared worthy as a place of Rukyatul Hilal. While keeping an eye on the existing systematics so there is no failure in the process of the Rukyatul Hilal.
Rhythm in Post-tonal Music

Composers coming of age in the early twentieth century inherited from the Western European art tradition at least two ways to articulate temporality, both dependent on the highly organized language of tonality and a vocabulary of proportionally related durations (whole notes, half notes, quarter notes, etc.). This musical time evokes an ordered, continuous flow – a sense of moving forward toward a goal, one where complexity arises from possible diversions in an ongoing flow and a pattern of metric accent. The temporal identities are determined by their placement within a phrase and within a hierarchical, fundamentally periodic series of beats, measures, and hypermeasures; for example, a chord may occur on a beat within a measure and (often) a hypermeasure, and also at the beginning, middle, or end of a phrase.
EXPERIENCE OF INTEGRATION OF HISTORICAL AND PEDAGOGICAL KNOWLEDGE IN MAINTENANCE OF PROFESSIONAL PREPARATION OF MODERN TEACHER

On the modern stage of development of pedagogical theory and practice ahistorical and pedagogical component is in maintenance of professional preparation of future teachers, as a rule, present person. Indeed from recent pores there is a tendency to the exception of educational object “History of pedagogics” as a separate educational component from the curricula of preparation of bread-winners of higher education. But historical and pedagogical material is entered to the course of general pedagogics and, usually, used for legitimizationof pedagogical position of teacher or formal review of that, how an idea developed about that or other pedagogical higher or process. Thus an address to the historical and pedagogical context often carries informing character especially, that, unfortunately, does not assist to development of the pedagogical thinking and becoming of pedagogical world view of future teachers. Thus there is a fully appropriate problem of integration of historical and pedagogical knowledge in maintenance of professional preparation of future teachers, question of creation of space for a question of creation of space is for a pedagogical dialogue in relation to logic of historical development of theory and practice of education, motive forces and determinants of historical and pedagogical process, his intercommunication with the economic, political, cultural evolution of society, mechanisms of becoming and development of important pedagogical traditions. The article is sanctified to the problem of integration of історико-педагогічногоhistorical and pedagogical knowledge in maintenance of professional preparation of future teachers; labours of home and foreign scientists, practical teachers-workers are analysed in relation to cognitive, heuristic and prognostic potential of historical and pedagogical knowledge, his role and place in the structure of modern pedagogical education; essence and features of quest technology of studies are reflected; possibilities of her introduction are outlined in the educational process of preparation of bread-winners of higher education; the example of historical and pedagogical quest and algorithm of work with him of teacher and students are offered during the study of pedagogical disciplines
LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network

Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the design of classifiers, such as the local, global, or a combination of them. However, very few studies have focused on hierarchical feature extraction and explore the association between the hierarchical labels and the text. In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels. Besides, hierarchical information is shared across levels while preserving the hierarchical label-based information. Separate local and global document embeddings are obtained and used to facilitate the respective local and global classifications. In our experiments, LA-HCN outperforms other state-of-the-art neural network-based HMTC algorithms on four public HMTC datasets. The ablation study also demonstrates the effectiveness of the proposed label-based attention module as well as the novel local and global embeddings and classifications. By visualizing the learned attention (words), we find that LA-HCN is able to extract meaningful information corresponding to the different labels which provides explainability that may be helpful for the human analyst.
Computational stylometry: predicting the authorship of investment arbitration awards

This chapter presents an overview of the legal stylometry literature, and its utility in providing insight into the authorship of otherwise anonymous legal texts. After first exploring the use of machine learning stylometry models, and their relevance to analysing the texts produced by investor-state arbitration proceedings, the chapter then demonstrates how the stylistic features of these texts can be used to infer authorship. The resulting analysis suggests both that there is significant variation in styles between academic writing and the applied legal writing that arises in arbitration proceedings, and that there is significant variety within arbitral award writing styles, even when the texts have the same authorship.
Exploration of Course Ideology and Politics Merging into Education of Organic Synthesis

In order to achieve organic integration of ideological and political education and organic synthesis curriculum effectively, this article discusses how to design course system of organic synthesis.It is included how to play a dual educational function of organic synthesis curriculum, how to deepen innovative thinking and dialectical thinking in the course teaching, how to enhance the awareness of harmonious symbiosis between the development of green chemistry and the environment, and how to discover more examples connecting ideological and political education and organic synthesis education.An ideological and political course system for professional courses is constructed after exploration and training.The practice will assure to realize the purpose of cultivating talents by organic integration of ideological and political education and organic synthesis education.
Performance Analysis of Speck Cipher Using Different Adder Architectures

This paper explores the performance of speck cipher based on different adder structures and is implemented incorporating two architectures namely folded (round based) and unfolded. Folded architecture reduces huge areas at the cost of an increment in delay which can be negligible when the area is primary metric. VLSI implementations show that Kogge–Stone tree has delay improvement in the range of (6.31–29.28)% at the cost of an increment in the area by (22.03–30.44)% when compared with the Sklansky tree, whereas Han-Carlson tree when compared with Sklansky tree, achieves an improvement in the delay for about (2.24–22.16)% at the cost of an increment in the area less than 5%.
LibGuides: Trumpet: ALLIES

This is a library guide for students and professors of the trumpet. This page will tell you how to request a book from another library.
Kernel learning approaches for summarising and combining posterior similarity matrices

When using Markov chain Monte Carlo (MCMC) algorithms to perform inference for Bayesian clustering models, such as mixture models, the output is typically a sample of clusterings (partitions) drawn from the posterior distribution. In practice, a key challenge is how to summarise this output. Here we build upon the notion of the posterior similarity matrix (PSM) in order to suggest new approaches for summarising the output of MCMC algorithms for Bayesian clustering models. A key contribution of our work is the observation that PSMs are positive semi-definite, and hence can be used to define probabilistically-motivated kernel matrices that capture the clustering structure present in the data. This observation enables us to employ a range of kernel methods to obtain summary clusterings, and otherwise exploit the information summarised by PSMs. For example, if we have multiple PSMs, each corresponding to a different dataset on a common set of statistical units, we may use standard methods for combining kernels in order to perform integrative clustering. We may moreover embed PSMs within predictive kernel models in order to perform outcome-guided data integration. We demonstrate the performances of the proposed methods through a range of simulation studies as well as two real data applications. R code is available at https://github.com/acabassi/combine-psms.
A Prize and a Prediction

While it is difficult to identify the 1990s as an extreme decade, it was clearly one of rapid technological change. Basic technologies from the 1970s and 1980s—processor architectures, memory designs, and network systems— expanded across commercial, educational, and social markets, driven by manufacturing processes that reduced the cost and increased the capacities of computing devices. If there is a radical nature to the 1990s, it can be found in the new modes of software engineering. The proponents of these modes suggested the then-current methods of software were insufficient to create software systems that fulfilled customer requirements, were free from errors, were delivered on time, and were completed within budget. With names such as Extreme Programming (or XP),3 Scrum,9 or Agile,1 they suggested that software development needed a disciplined athleticism, more focus, more energy, and more teamwork.
Implementation of Data Mining Grouping Product Order Distribution Center In PT. Indomarco Prismatama Medan Branch

Data mining is the technique of extracting previously unknown information in a set of data in the database. Data mining has been applied in various fields which require extracting information. One of them in groupings of data. Grouping is used to divide a set of data into several sections that are useful to more easily identify a class of data. Distribution companies can use groupings of one to determine the intensity of the volume of goods ordered. The study analyzes the application of data mining algorithms k-meansclustering to elicit information from the data ordering goods contained in centerPT distribution. Indomarco Prismatama Medan branch. That is by using a number of items and the total amount of the quantity of each item ordered.
Computer-based Adaptive Test Development Using Fuzzy Item Response Theory to Estimate Student Ability

The field of computing has developed so rapidly.Various theories of computational evolution to support human needs are continually being pursued; one of them is the field of education, especially in terms of teaching, testing, and evaluation of exam results.This study aims to develop computerized adaptive tests (CAT) to measure the student's abilities.Students will be measured for their cognitive abilities in Mathematics and Science subjects.It starts with developing a question bank that has been tested with 720 students to classify items based on its characteristic, i.e., easy, medium, and challenging.This research uses the item response theory approach with the model 2 logic parameters (2PL), namely item difficulty and item difference power.The selection of test items for each participant will depend on the response of the previous answer.Fuzzy algorithm is used in analyzing test items through four stages, namely fuzzification, implications, inference, and defuzzification.Meanwhile, to measure the ability of test-takers, the maximum likelihood estimation method, MLE, is used.Based on the testing of 73 students, it was found that each student received a different test item, both in the number of questions and the level of difficulty of the questions, according to student's abilities.The results of the CAT program's measurement of the test taker's ability estimation were stated to be more effective compared to conventional methods, as indicated by the average test length of 15 items compared to traditional tests, which had a length of 50 items.Therefore, the CAT program with the fuzzy item response theory can be used as support to measure students' abilities.
The Studion’s Library collection in Vasyl Stefanyk National Scientific Library of Ukraine in Lviv: fragments of history, study experience and preservation issues

Vasyl Stefanyk National Scientific Library of Ukraine in Lviv (VSNSL of Ukraine in Lviv) is considered as an inheritor and successor for major Ukrainian libraries and institutions that constituted its base consequently to geopolitical upheavals of first half of 20th century. These are books, manuscripts, old prints, periodicals, notes and fine arts collections etc. from the libraries of Shevchenko Scientific Society, People’s Home in Lviv, monasteries, private collections, as well as Studion’s Library collection, whose substantial part now is being dispersed along the shelves of VSNSL of Ukraine in Lviv. The author defined the role of Metropolitan Andrey Sheptytskyi and Klymentii Sheptytskyi in establishing and maintaining this biggest specialized scientific library in Galicia at that time. The paper describes the rise and evolution of the Studion’s Library collection drawing on archival documents and revealing main stages of its formation, outlines objectives that Metropolitan Andrey Sheptytskyi determined for it and highlighted Metropolitan’s activity in church and social fields resulted in formation of national and cultural institutions. The author also defined the outstanding role of Studion’s in the religious life of Ukrainians. The Studion’s Library collection was officially integrated into Lviv Branch of the Library of the Academy of Sciences of USSR on February 12, 1940. So the dispersion of the once rich collection started. Now those books and periodicals are parts of Rare Book Department, Manuscript Department, Ucrainica Department, Exchange and Reserve Department, Department of European book of 19th–20th cc. and others. These collections were repeatedly examined within several bibliological studies conducted by the Library’s researchers. Thus, it is marked that unique collections of Studion’s like other historical libraries at VSNSL of Ukraine in Lviv need writing their history as well as their bibliographical reconstruction what is now composing one of main objectives of Library’s staff Keywords: Vasyl Stefanyk National Scientific Library of Ukraine in Lviv, Metropolitan Andrey Sheptytskyi, Klymentii Sheptytskyi, the Studion’s Library collection, Byzantine Library.
GraphPi: High Performance Graph Pattern Matching through Effective Redundancy Elimination

Graph pattern matching, which aims to discover structural patterns in graphs, is considered one of the most fundamental graph mining problems in many real applications. Despite previous efforts, existing systems face two main challenges. First, inherent symmetry existing in patterns can introduce a large amount of redundant computation. Second, different matching orders for a pattern have significant performance differences and are quite hard to predict. When these factors are mixed, this problem becomes extremely complicated. High efficient pattern matching remains an open problem currently. To address these challenges, we propose GraphPi, a high performance distributed pattern matching system. GraphPi utilizes a new algorithm based on 2-cycles in group theory to generate multiple sets of asymmetric restrictions, where each set can eliminate redundant computation completely. We further design an accurate performance model to determine the optimal matching order and asymmetric restriction set for efficient pattern matching. We evaluate GraphPi on Tianhe-2A supercomputer. Results show that GraphPi outperforms the state-ofthe-art system, by up to 105X for 6 real-world graph datasets on a single node. We also scale GraphPi to 1,024 computing nodes (24,576 cores).
Beneficial Perturbations Network for Defending Adversarial Examples

Deep neural networks can be fooled by adversarial attacks: adding carefully computed small adversarial perturbations to clean inputs can cause misclassification on state-of-the-art machine learning models. The reason is that neural networks fail to accommodate the distribution drift of the input data caused by adversarial perturbations. Here, we present a new solution - Beneficial Perturbation Network (BPN) - to defend against adversarial attacks by fixing the distribution drift. During training, BPN generates and leverages beneficial perturbations (somewhat opposite to well-known adversarial perturbations) by adding new, out-of-network biasing units. Biasing units influence the parameter space of the network, to preempt and neutralize future adversarial perturbations on input data samples. To achieve this, BPN creates reverse adversarial attacks during training, with very little cost, by recycling the training gradients already computed. Reverse attacks are captured by the biasing units, and the biases can in turn effectively defend against future adversarial examples. Reverse attacks are a shortcut, i.e., they affect the network's parameters without requiring instantiation of adversarial examples that could assist training. We provide comprehensive empirical evidence showing that 1) BPN is robust to adversarial examples and is much more running memory and computationally efficient compared to classical adversarial training. 2) BPN can defend against adversarial examples with negligible additional computation and parameter costs compared to training only on clean examples; 3) BPN hurts the accuracy on clean examples much less than classic adversarial training; 4) BPN can improve the generalization of the network 5) BPN trained only with Fast Gradient Sign Attack can generalize to defend PGD attacks.