Reviewerly/exHarmony huggingface dataset:
Monte Carlo simulation for scanning white light interference measuring data of fracture surface

Scanning white light interferometry (SWLI) has become an established method to measure the geometrical form of objects. However, due to its complicated condition on the fracture surface, the noises severely degrade its interference signal quantity, consequently cause large reconstruction error. To improve the reconstruction accuracy, this paper proposes a method which adopts the Monte Carlo algorithm to simulate the interference data of a fracture surface. In our method, a random Gaussian surface is first generated as the fracture surface in the following experiments. Then, a numerical model of white light interference is constructed and thereby interference images based on Gaussian surfaces are simulated by the use of Monte Carlo method. In the experiments, the influence of surface parameters and repeated times of calculation on the results of simulation is observed and the process of longitudinal equi-distance step scanning in interference detection is simulated. The experimental results show that when the repeated times is large enough, the characteristics on the interference image becomes obvious. By observing the longitudinal interference signal, the interference curve shows similar properties to a interferogram signal. Finally, we analyze the theoretical feasibility of a parallel integral filter for the experimentally measured interference data. The results prove that the simulated interference data possesses primary capability in restoring surface information.
Length of non-projective sentences: A pilot study using a Czech UD treebank

Lengths (in words) of projective and non-projective sentences from a Czech UD dependency treebank are compared.It is shown that non-projective sentences are significantly longer (in addition, the same result was obtained in this study also for Arabic, Polish, Russian, and Slovak).The hyperpascal distribution, which was suggested as the model for frequency distribution of sentence length measured in words, fits well the data from both projective and non-projective sentences; however, its parameters attain different values for the two groups.Proportions of non-projective sentences in the treebanks used are presented, together with a discussion on factors which can influence them.
Manajemen Pembelajaran Kelas Bilingual di MI Muhammadiyah Karanganyar

This research aims to describe learning management of bilingual class in MI Muhammadiyah Karanganyar in terms of 1) curriculum preparation, 2) teachers preparation, 3) learning activities implementation, and 4)evaluation of the learning activities implementation.This qualitative research used ethnography research design with the data collecting technique of direct observation, depth interview and document analysis. The validity of the data was tested with triangulation data. Data analysis consisted of collecting data, reducing data, presenting data, and drawing conclusion.The result of the research shows that: 1) the curriculum preparation for bilingual class was conducted by preparing the whole year program, semester program, syllabus, lesson plan, and academic calendar, 2) the preparation for bilingual teachers was initiated by selecting teachers with good ability in English and then followed up with English training program at the beginning of academic year, 3) the implementation of learning in bilingual class consisted of some learning components covering a) the goal of learning was delivered at the begining of the lesson and would help the teacher in determining material, method,strategy, tools, media and learning resources to be used, b)thematic learning material was arranged according to the learning goal and elaborated from basic competence, c) thematic learning method was conducted with active-student method such as practice, demonstration, exercise and questioning-answering, d)thematic learning media used video, pictures, or things around, e)learning evaluation used covering 3 aspects, i.e knowledge, skill and attitude, 4) the evaluation of learning activities implementation were conducted by the principal’s observation and supervision, and then followed up by improvement efforts based on the findings of the principal, e.g. internal training, and peer teaching activities.
Perancangan Geographic Information System Berbasis Android Untuk Potensi Mebel Di Kecamatan Tahunan Kabupaten Jepara

&lt;p&gt;Jepara is a city called carving city because it has a large furniture and carving industry&lt;/p&gt;&lt;p&gt;potential. Tahunan Subdistrict is an area located in Jepara regency with the biggest potential of the furniture industry compared to other districts. Everyone who wants to buy furniture from Tahunan may visit the showroom which is located not far from the main road because of the limited information about the potential of furniture, especially small and medium business category in less strategic locations. Judging from these problems, it is necessary to make an information system about the potential of furniture in Tahunan district based on android that is easy to use and integrated with Geographic Information System (GIS) to make it easier for users to find directions to the location. The system development method used is Rapid Application Development (RAD) with Construct 2 as the maker of the application.&lt;/p&gt;
Incremental Learning Techniques for Part-Based Semantic Segmentation

In this work we use an Incremental Learning approach to try to develop a model for the Part-Based Semantic Segmentation. Our framework uses two networks where the second one is fed with the output of the first. The first network performs the Semantic Segmentation task with 21 classes from the Pascal-VOC, while the second one performs the Part-Based Semantic Segmentation task with the Part-Based Semantic Segmentation with 108 classes/parts on the Pascal-Part dataset.
Assertive Illocutionary Act Adapted in Donald Trump’s Political Speech: A Pragmatic Study

This research is intended to discuss the illocutionary acts used in a speech mainly assertive. The writer attempts to analyze the categories of assertive illocutionary act occurs in the utterances of Donald Trump’s political speech in last America’s general election. This is a case study that employed a qualitative method with a descriptive approach. The writer uses Ancont software to classify the data. The function of this software is to observe the frequent words occur in the data. Then, it shows that the total number of words that occurred in Donald Trump’s speech is 3046 words. Through those numbers, there are 901 types of words. However, the data used is only the open classes instead of the closed class words or grammatical category. After reducing the data, the five frequent words which are categorized as an open class appeared are ‘job’, ‘is’, ‘new’, ‘American’, and ‘are’. In this case, the data are analyzed based on the frequents words and the writer also reduces the data which are not categorized as assertive illocutionary acts. From the data analysis, it reveals that the categories of assertive illocutionary acts mostly appeared in Donald Trump’s political speech are a statement of fact and assertion. Then, the dominant category used in the speech is a statement of fact containing a convincing. It can be concluded that most of the utterances used by Donald Trump are, besides attempting to represent himself, to convince the citizens supported by some facts about the issue that he believes to be the case in order to establish a positive perspective toward his arguments.
Correction to: Social security: social relationship strength and connectedness influence how marmots respond to alarm calls

Unfortunately, there was an error in the R script which led to the incorrect labeling of two social network measures.
Fairness-Preserving Empirical Risk Minimization

Author(s): Yang, Guanqun | Advisor(s): Roychowdhury, Vwani P | Abstract: The concerns regarding ramifications of societal bias targeted at a particular identity group (for example, gender or race) residing in algorithmic decision-making systems have been ever-growing in the past decade. It is a common practice of machine learning models' participation in these systems through empirical risk minimization (ERM) principle, which is often the cause of unfairness by trading off underrepresented groups for overall performance. Despite the importance of preserving fairness in such systems, there is hardly consensus in defining unified fairness metrics, designing widely-applicable bias-mitigation algorithms, and delivering interpretable models abiding by the ERM principle. The situation is made even more grievous when non-structural data, including text, image, and audio, is involved in these systems due to the unavailability of the well-defined identity attribute. Current approaches attempt to tackle algorithmic bias in non-structural settings from data itself and intermediate representation together with the inference component within models. In this thesis, we propose to unify all three bias-mitigation operations into one streamlined machine learning pipeline. At the same time, to provide interpretable results, the explorations will be made while carrying out debiasing procedures, and theoretical justifications will be provided accordingly. By ameliorating different bias-mitigation strategies through synergistic effects and addressing model transparency issues by investigating internal representations, we show that the proposed pipeline could provide interpretable machine learning models that embody fairness across different identity groups in numerous non-structural data settings.
Regression-Based Approach to Test Missing Data Mechanisms

Missing data are an important issue in the social sciences. Knowing about the type of missing data is essential to appropriately treating data with missing values. This thesis focuses on the handling of missing data mechanisms, both simple and mixed, and how to determine whether missing data are completely randomly distributed or not. It develops an alternative regression-based approach to test missing data mechanisms for continuous and categorical data, and compares it to commonly used tests.Different sets of simulation are provided to understand the power and the behaviour of the different existing approaches to test missingness when the sample size, the data type and distribution, the number, type, and generation mechanism of missing data, as well as the level of correlation between variables vary.Finally, a real case is used to show in a complete procedure how missing data should be treated, with a cross-sectional and longitudinal application to the professional path survey.--Les donnees manquantes sont un important probleme en sciences sociales. La connaissance du type des donnees manquantes est essentielle pour une analyse appropriee des valeurs manquantes. Cette these se concentre sur la gestion des mecanismes simples et mixtes des donnees manquantes et la maniere de tester si les donnees manquantes sont distribuees de maniere completement aleatoire ou non. Une approche alternative, basee sur un modele de regression et applicable aux donnees continues et categorielles, est developpee afin de tester les mecanismes des donnees manquantes. Elle est comparee aux tests les plus usuels couramment utilises pour evaluer ces mecanismes.Plusieurs series de simulations sont presentees afin de mettre en evidence la puissance des approches existantes pour tester les mecanismes des donnees manquantes en fonction de la taille d’echantillon, du type et de la distribution des donnees, du nombre, du type et de la generation des donnees manquantes, ainsi que du niveau de correlation entre les variables.Finalement, une procedure complete du traitement des donnees manquantes est pro-posee dans le cadre d’une etude reelle avec des donnees transversales et longitudinales de l’enquete des trajectoires professionnelles.
The impact of ensemble diversity on learning big data in dynamic environments

For many classification tasks, data is collected over an extended period of time and the predictive model learns over time, adapting to changes in the underlying distribution of the data if necessary. To optimize generalization performance, margin distribution is considered to be an important factor. A major concern posed by nonstationary learning for any algorithm is the rate of adaptation to new concepts and the volume of the data. Tackling the problem of learning in nonstationary environments associated with drifting concepts with ensembles of classifiers makes the concept of diversity to be of paramount significance in optimizing the rate of adaptation to new concepts for classification tasks. In this paper, we investigate the impact of ensemble diversity on the rate of adaptation to new concepts in nonstationary learning. The rate of adaptation is analyzed by exploiting the correspondence that exists between voting margins and the double fault measure, a popular diversity measure strongly linked to the margin. We utilize the Adaptive Classifier Ensemble Boost algorithm (AceBoost) to generate diverse base classifiers and optimize margin distribution to exploit different amounts of diversity to generate an optimal ensemble capable of handling different kinds of drift. The experimental results confirm that AceBoost outperforms other state of the art algorithms that exploit ensemble diversity to handle concept drift.
Physics at Lower Secondary Schools: Comparison between the Czech Republic, Estonia, Poland and Slovenia

Results of the worldwide PISA study in science performance from past years (2009, 2012 and 2015) show that Czech pupils are placed in the OECD average, whereas some countries with similar cultural and historical background are statistically significantly above it as well as above the results of the Czech Republic. As an example, Estonia, Poland and Slovenia belong to these countries and therefore they were chosen for the presented comparative study. The study focuses on comparison of national curricula of these countries,especially on fundamental aspects important for physics education at lower secondary schools. The study highlights the comparison of teaching content and learning outcomes in physics, interdisciplinary education and cross-curricula subjects, educational methods and assessment and field-specific key competences. One of the most evident differences that this study has detected is in the level of autonomy that the curricular documents give schools in deciding what the learning process will look like. This result as well as other findings will be discussed in the paper.
Geological Survey of Victoria: North Central Victorian Goldfields ground release

Victoria has re-emerged as an international minerals exploration destination, particularly for gold. A recent increase in minerals exploration activity, especially within the Victorian Goldfields r...
Remodeling Archival Metadata Descriptions for Linked Archives

Though archival resources may be valued for their uniqueness, they do not exist in isolation from each other, and stand to benefit from linked data treatments capable of exposing them to a wider network of resources and potential users. To leverage these benefits, existing, item-level metadata depicting physical materials and their digitized surrogates must be remodeled as linked data. A number of solutions exist, but many current models in this domain are complex and may not capture all relevant aspects of larger, heterogeneous collections of media materials. This paper presents the development of the Linked Archives model, a linked data approach to making item-level metadata available for archival collections of media materials, including photographs, sound recordings, and video recordings. Developed and refined through an examination of existing collection and item metadata alongside comparisons to established domain ontologies and vocabularies, this model takes a modular approach to remodeling archival data as linked data. Current efforts focused on a simplified, user discovery focused module intended to improve access to these materials and the incorporation of their metadata into the wider web of data. This project contributes to work exploring the representation of the range of archival and special collections and how these materials may be addressed via linked data models.
STUDENT SATISFACTION IN HIGHER EDUCATION: FACTORS AFFECTING ENGINEERING STUDENTS’ SATISFACTION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 6782-6792ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1618Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Identifying Different Motions Using Statistical Methods

This bachelor thesis aims to explore how well one can classify different types of motions using only data gathered from a mobile phonesgyroscope and accelerometer. The methods includes extracting features from the covariance estimations of each signal and taking timefrequency transforms of the data and classify the transforms with aconvolutional neural network. The results are positive and shows thereare ways to design algorithms that classify the motions correctly.
Performance analysis of free space quantum key distribution with different wavelengths

In the free-space quantum communication, the performance of communication systems has a great degradation due to the atmospheric effects, such as atmospheric absorption, scattering and turbulence. However, quantum signals with different wavelengths is differently affected by atmospheric characteristics. In this paper, in order to investigate the effects of different wavelengths quantum signals on free-space quantum key distribution, an entanglement-based continuous variable quantum key distribution transmission model is established. Considering the influence of various atmospheric effects on quantum signals, the secret key generation rates are calculated though the homodyne detectors. The simulation results show that the long-wavelength signal can enhance the secret key generation rates at the same transmission distance. Therefore, the long-wavelength signal is more suitable for the free-space quantum transmission.
Level characteristic of formation of didactic and practical abilities and skills of future special teachers to work in the conditions of educational inclusive space

The article describes the levels of formation of didactic and practical skills of future special educators to work in an inclusive educational environment. The draft higher education standard of Ukraine is characterized for the first (bachelor's) level, bachelor's degree, in the field of knowledge 01 Education, specialties 016 Special education. It is possible to optimize the process of formation of the professional readiness of special educators for work in an inclusive educational space by including new disciplines in the curriculum, using innovative monitoring and educational technologies, which will increase the interest of special educators in activities in an inclusive educational space, and the formation of skills for independent knowledge accumulation and professional use.We have developed experimental tasks for each of the identified criteria indicators in order to ascertain the level of readiness of future correctional teachers to work in inclusive educational institutions. These are such tasks: tests (on readiness for innovative activity, purposefulness, etc.), analysis of problem situations, storytelling, discussions, reports on the scientific and practical seminar “Trends in the development of inclusive education”, round table “Exchange of work experience”, pedagogical games "Methodology of work in inclusion", "Who is better?", analysis of problem pedagogical situations and so on.The analysis of the research results allowed us to conclude, that it is necessary to develop didactic knowledge and practical skills of correctional teachers to work in an inclusive educational environment. The study allowed to determine the features of the preparation of special educators for work in an inclusive educational space.The study allowed to determine the features of the preparation of future correctional teachers for work in an inclusive educational space. Among them: insufficient motivation to work in inclusive institutions, emotional instability; the lack of systematic didactic and practical training of future correctional teachers for work in inclusive classes, the substantial limitations of the modules of academic disciplines that do not contain the bases of didactic and practical training of future correctional teachers for work in an educational inclusive space, insufficient material and technical support, etc
Verifikasi Telapak Tangan dengan Multimodal Biometrik Fermat’s Spiral

The use of biometrics in various issues of data security, transactions, privacy has been widely used. Regarding security, surely the level of accuracy in one's self-recognition must be accurate. The palmprint is one of biometrics which has various unique characteristics. These characteristics include geometrical features such as length, width, and area of the palmprint, characteristics of main lines such as heart lines, headlines, and lifelines, tangled or weak lines, delta dot characteristics, and minus characteristics. Main lines and tangled lines, which are often referred to as line features, have several advantages over other biometric features, such as they can be obtained from low-resolution images, are difficult to duplicate, and are stable because they have experienced slight changes over a long period of time. This research introduces the method of Fermat's Spirals in the extraction of the features of the palmprint which are represented in the time series curve. Testing uses 450 palm images sourced from 50 people, each person taken 9 palmprint images where 5 images as a test image and 4 images as a reference image. The test is carried out based on the threshold value determined from the simulation results based on matching test images with reference images on the ROC graph. Based on the test results it can be concluded that the system shows high performance at a threshold value of 0.036 to get an FNMR value of 2.80%, FMR of 0.88% with an accuracy of 96.31% where the number of reference images to the test image affects the level of accuracy.&#x0D;
On formation of the system of vocational education in Soviet Russia in the early 1920s (on the materials of Moscow and the Moscow Province)

The article is devoted to the formation of the system of professional education in the 1920s in Moscow and Moscow Province. The paper highlights the process of creating a system of Central and local authorities of special education. The characteristic of different types of educational institutions is given, statistical data are given. Special attention is paid to the rules of admission to educational institutions, were based on the principle of class selection. On the example of interaction between educational institutions and the Komsomol organisation the author shows the relationship of vocational education and the Communist party. Based on the analysis of the documents, the author concludes on the importance of the development of secondary vocational education in Moscow Region for the socio-economic development of the entire state.
TRAINING FUTURE ENGINEERS TO BE SKILLFUL AT FITTING DATA

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 8718-8726ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.2077Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Welcome to the Fifth International Workshop on Crowd-Based Requirements Engineering (CrowdRE’21)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.