Reviewerly/exHarmony huggingface dataset:
Introduction of Competence-Based Approach into the Teaching Staff Training System as a Consequence of Integration into the Global Educational System

The article is dedicated to the questions of forming pedagogical courses of general educational competence at the students. The situation of the problem is being analyzed by the author, besides the possibilities of its growth and the usability of general educational competence in educational process. Opens up role and place of general educational competence in preparation of school teachers. The aim of the work was analyze of scientific and pedagogic literature on the research problem, definition of general educational competence and their description. The authors consider the general educational competence as an integrative quality of personality of future teacher, which can be as important indicators of qualified characteristics of a graduate of the university (pedagogical branch). There are considered the questions, which are related to necessity on strengthen in learning activities of skills approach. There is marked transformation of professional competence in education, which is characterized in transition from knowledge paradigm to competence with its leading components: motivating and self-assessment personal qualities. There is noted that in the work among the reasons that determined the society’s need in perfecting the higher pedagogical education through competence approach: firstly, global trends in quality improvement, which is connected with the increased role of science in the development of education and society itself. The development of standards for key and subject competencies has created the prerequisites for successful entry into “a single educational space”. The relevance of the introduction of educational and professional standards is related to international experience in ensuring the quality of education, strengthening the external certification of educational organizations of the Kyrgyz Republic. Secondly, observed by the worldwide trend of movement from the concepts of “knowledge”, “qualification” to the concepts of “competence”. It is expressed in the fact that throughout the world there is an increase in cognitive and information resources in teaching. One of the famous models of the competence approach, known today, is the “TURNING” program—educational structures for the implementation of the Bologna Process and developing a common understanding of the content of the qualifications in terms of competence and learning. This circumstance requires the elaboration in the Kyrgyz Republic of a national strategy for adapting and integrating the education system into the world of educational space.
PENERAPAN ALGORITMA K NEAREST NEIGHBOR UNTUK REKOMENDASI MINAT KONSENTRASI DI PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS PGRI YOGYAKARTA

The graduation time of a student is very important because it relates to many parties, in addition to the students concerned, guardian lecturers, and program study programs and related parties in the time of student graduation. one of the factors which is the result of graduation from informatics engineering study program students is that many students are wrong in taking thesis titles that are not in accordance with their interests and concentration. So research needs to be done to solve this problem. One way to solve problems is to make predictions. Selection of interest by using student data using Case Base Reasoning (CBR). In this study applying the K-NN Algorithm in determining recommendations in choosing the concentration. The results obtained from this study are by classification using the K-NN Algorithm obtained concentrations that match their interests. Keywords : Prediction s, Case Base Reasoning, K-NN Algorithm
Demagnetization Modeling Research for Permanent Magnet in PMSLM Using Extreme Learning Machine

This paper investigates the temperature demagnetization modeling method for permanent magnets (PM) in permanent magnet synchronous linear motor (PMSLM). First, the PM characteristics are presented, and finite element analysis (FEA) is conducted to show the magnetic distribution under different temperatures. Second, demagnetization degrees and remanence of the five PMs' experiment sample are actually measured in stove at temperatures varying from room temperature to 300 °C, and to obtain the real data for next-step modeling. Third, machine learning algorithm called extreme learning machine (ELM) is introduced to map the nonlinear relationships between temperature and demagnetization characteristics of PM and build the demagnetization models. Finally, comparison experiments between linear modeling method, polynomial modeling method, and ELM can certify the effectiveness and advancement of this proposed method.
Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization

Nowadays, we have access to unprecedented quantities of data composed of heterogeneous data types (HDT). Heterogeneous data mining (HDM) is a new research area that focuses on the processing of HDT. Usually, input data is transformed into an algebraic model before data processing. However, how to combine the representations of HDT into a single model for a unified processing of big data is an open question. In this article, the authors attempt to find answers to this question by solving a data integration (DI) problem which involves the processing of seven HDT. They propose to solve the DI problem by combining multi-objective optimization and self-organizing maps to find optimal parameters settings for most accurate HDM results. The preliminary results are promising, and a post processing algorithm is proposed which makes the DI operations much simpler and more accurate.
Applying Support Vector Machine, C5.0, and CHAID to the Detection of Financial Statements Frauds

This paper applies support vector machine (SVM), decision tree C5.0, and CHAID to the detection of financial reporting frauds by establishing an effective detection model. The research data covering 2007-2016 is sourced from the Taiwan Economic Journal (TEJ). The sample consists of 28 companies engaged in financial statement frauds and 84 companies not involved in such frauds (at a ratio of 1 to 3), as listed on the Taiwan Stock Exchange and the Taipei Exchange during the research period. This paper selects key variables with SVM and C5.0 before establishing the model with CHAID and SVM. Both financial and non-financial variables are used to enhance the accuracy of the detection model for financial reporting frauds. The research suggests that the C5.0-SVM model yields the highest accuracy rate of 83.15%, followed by SVM-SVM (81.91%), the C5.0-CHAID model (80.93%), and the SVM-CHAID model (77.16%).
A Design and Implementation of an Intelligent Care System for the Elderly Based on Internet of Things

The problem of aging populations is attracting more attention. Realizing a real-time care system for the elderly via information technology and finally achieving intelligent care can help alleviate age-related social problems. This paper designs and implements a smart pension system based on the Internet of Things. Via a wrist device, our system monitors the wearer's health status in real-time, achieves accurate positioning, and implements functions such as fall alarm, motion recording, and sleep quality monitoring. The localization aspect incorporates compressed sensing technology, which improves the positioning accuracy and solves the shortcomings of the traditional localization algorithm. The experiment shows that the system has complete functions and high localization accuracy.
Visualizing Linguistic Change as Dimension Interactions

Historical change typically is the result of complex interactions between several linguistic factors.Identifying the relevant factors and understanding how they interact across the temporal dimension is the core remit of historical linguistics.With respect to corpus work, this entails a separate annotation, extraction and painstaking pair-wise comparison of the relevant bits of information.This paper presents a significant extension of HistoBankVis, a multilayer visualization system which allows a fast and interactive exploration of complex linguistic data.Linguistic factors can be understood as data dimensions which show complex interrelationships.We model these relationships with the Parallel Sets technique.We demonstrate the powerful potential of this technique by applying the system to understanding the interaction of case, grammatical relations and word order in the history of Icelandic.
Automatic Information Disclosure with Value Chains Based on Blockchain Technology

The information quality of the current securities market is not high. Using blockchain technology and smart contract, this paper presents a design for a blockchain-based value chains system for information disclosure of listed companies. The framework of value chains system includes three dimensions of integration, control and value-added satisfaction. This paper develops a prototype to demonstrate the functionality of blockchain-based value chains system in real-time accounting, continuous monitoring and fraud prevention and analyses the process of information disclosure. In anticipation of the wider applicability of blockchain technology to support enterprise information systems and continuous monitoring systems, this paper presents an innovative design that utilizes the advantages of blockchain technology that is helpful for improving market information quality and efficiency.
CNN Compression-Recovery Framework via Rank Allocation Decomposition With Knowledge Transfer

Low-rank decomposition is an effective way to decrease the model size of convolutional neural networks (CNNs). Nevertheless, selecting the layer-specific rank is a difficult task, because the layers are not equally redundant. The previous methods are mainly by manual, require expertise, or do not consider the different sensitivity of each layer. This paper proposes a rank allocation decomposition (RAD) method to decompose network by allocating rank for each layer automatically. The idea is to transform the combinatorial optimization problem of rank into a constrained optimal search problem, which can be solved by a greedy algorithm. To recover accuracy of the decomposed network, a novel knowledge transfer based approach is introduced, named SchoolNet. It aligns outputs and intermediate responses from the original (teacher) network to its compressed (student) network while transferring dark knowledge from a strong (headmaster) network with high accuracy to the student network. The experimental results from several advanced models, including AlexNet, VGG-16, and ResNet-50, demonstrate that our scheme can reduce parameters significantly while maintaining a high accuracy level. Specifically, for the VGG-16 on Birds-200 dataset, we achieve 48x compression rate with even 0.13% top-1 accuracy improvement, which outperforms the state-of-the-art remarkably.
Cutting the Size of Compressed Path Databases with Wildcards and Redundant Symbols

Path planning on gridmaps is a common problem in AI and a popular topic in application areas such as computer games. Compressed Path Databases (CPDs) represent a state-of-theart approach to the problem, in terms of the speed of computing full optimal paths and also individual optimal moves. Despite significant improvements in recent years, the memory required to store a CPD can still be a bottleneck for large game maps. In this work we present a new compression approach that can reduce the size of CPDs. Our approach uses an extended notion of wildcards and a novel concept called a redundant symbol. We implement our ideas on top of a state-of-the-art CPD system and, in a range of experiments, we demonstrate a substantial reduction in the size of CPDs.
Sonifigrapher. Sonified Light Curve Synthesizer

In an attempt to contribute to the constant feedback existing between science and music, this work describes the design strategies used in the development of the virtual synthesizer prototype called Sonifigrapher. Trying to achieve new ways of creating experimental music through the exploration of exoplanet data sonifications, this software provides an easy-to-use graph-to-sound quadraphonic converter, designed for the sonification of the light curves from NASA’s publicly-available exoplanet archive. Based on some features of the first analog tape recorder samplers, the prototype allows end-users to load a light curve from the archive and create controlled audio spectra making use of additive synthesis sonification. It is expected to be useful in creative, educational and informational contexts as part of an experimental and interdisciplinary development project for sonification tools, oriented to both non-specialized and specialized audiences.
Application of Fuzzy Logic Control Systems in Military Platforms

The future of warfare will be defined by technological innovation rather than pure destructive power. While development is ongoing, fuzzy logic based artificial intelligence systems are capable of operating in complex battlefield environments and producing better results than existing control systems. Fuzzy controllers in military applications such as mobility, cybersecurity, target tracking, interoperability and unmanned combat aerial vehicle control have shown encouraging results when introduced to uncertainties and complexities of the battlefield. These preliminary results are a ground for further development as their application will increase the effectiveness of a fighting force while reducing collateral damage and unnecessary loss of life. Fuzzy controllers are easier to adopt and modify compared to complex mathematical models in use today. This work adds to the body of evidence that fuzzy controllers can be applied to wide ranging military applications effectively.
No Word is an Island—A Transformation Weighting Model for Semantic Composition

Composition models of distributional semantics are used to construct phrase representations from the representations of their words. Composition models are typically situated on two ends of a spectrum. They either have a small number of parameters but compose all phrases in the same way, or they perform word-specific compositions at the cost of a far larger number of parameters. In this paper we propose transformation weighting (TransWeight), a composition model that consistently outperforms existing models on nominal compounds, adjective-noun phrases, and adverb-adjective phrases in English, German, and Dutch. TransWeight drastically reduces the number of parameters needed compared with the best model in the literature by composing similar words in the same way.
ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network

In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks. However, previous MANNs suffer from complex memory addressing mechanism, making them relatively hard to train and causing computational overheads. Moreover, many of them reuse the classical RNN structure such as LSTM for memory processing, causing inefficient exploitations of memory information. In this paper, we introduce a novel MANN, the Auto-addressing and Recurrent Memory Integrating Network (ARMIN) to address these issues. The ARMIN only utilizes hidden state h_t for automatic memory addressing, and uses a novel RNN cell for refined integration of memory information. Empirical results on a variety of experiments demonstrate that the ARMIN is more light-weight and efficient compared to existing memory networks. Moreover, we demonstrate that the ARMIN can achieve much lower computational overhead than vanilla LSTM while keeping similar performances. Codes are available on github.com/zoharli/armin.
Second-Order Networks in PyTorch

Classification of Symmetric Positive Definite (SPD) matrices is gaining momentum in a variety machine learning application fields. In this work we propose a Python library which implements neural networks on SPD matrices, based on the popular deep learning framework Pytorch.
Study on the Influence of the Number of Features on the Performance of Software Defect Prediction Model

The software defect prediction model based on machine learning technology is the key to improve the reliability of software. The influence of the number of features on the performance of different software defect prediction models was proposed in this paper. First, a new data sets was built, which is increasing by the number of features based on the NASA public data sets. Then, the eight predictive models are experimented based on these data sets. Next, the influence of the number of features on the performance of different prediction models was analyzed based on the experimental results. Next, the AUC values obtained from the experiment were used to evaluate the performance of different prediction models, and the coefficient of variation C·V values was used to evaluate the performance stability of different prediction models while the number of features changed. In the end, the experiments show that the performance of the predictive model C4.5 is highly susceptible to changes in the number of features, while the performance of the predictive model SMO is relatively stable.
Polish harpsichord music in the period of 1936–2016

The article touches on Polish harpsichord pieces written between 1936 and 2016, dividing this time into three periods. The first of them is connected with the renaissance of harpsichord music, the second one focuses on the activity of Elżbieta Chojnacka and avant-garde works expanding the harpsichord playing technique, whereas for the third period the article presents compositions written for modern and historical harpsichord featuring the new media, as well as for other early instruments. An addition to the article are two annexes containing a list of academic publications on the discussed topic discussed, and a catalogue of CDs entirely devoted to harpsichord pieces written by contemporary Polish composers, which were released by Polish recording companies.
Coping with “Exceptional” Patients in META-GLARE

Many different computer-assisted management systems for Computer Interpretable Guidelines (CIGs) have been developed. While CIGs propose evidence-based treatments of “typical” patients, exceptions may arise, as well the need to cope with comorbidities. Though the treatment of both phenomena involves a deviation from the “standard” execution of CIGs, until now they have been managed as different problems, and no homogeneous approach to cope with both of them has been devised. In this paper we present the extensions to META-GLARE to overcome such a limitation. To achieve such a goal, we propose a modular architecture supporting the concurrent execution of multiple guidelines, integrated with an ontological knowledge base and with several reasoning mechanisms, including temporal reasoning and goal-based planning.
A Grid-Map-Oriented UAV Flight Path Planning Algorithm Based on ACO Algorithm

With the extensive applications of unmanned aerial vehicle (UAV), typical algorithm for path planning is usually restricted for its low efficiency and easy failure, especially for the complex obstacle environments. Therefore, in this paper, a new UAV path planning algorithm is proposed based on ant colony optimization (ACO) for such complex obstacle environment. In particular, the proposed algorithm optimizes the distribution of pheromones and modifies the transfer probability by considering the regional security factors. As a result, it can increase search speed and avoid local optimum and deadlock. Simulation results verify the feasibility and effectiveness of the proposed method.
Teaching Design-by-Contract for the Modeling and Implementation of Software Systems

Defensive programming is considered as a software design approach that promotes the reliable software development via the considerations of different cases for the software modules. Design-by-Contract (DbC) applies defensive programming systematically in terms of contracts that are a pair of pre-conditions on the module input and post-conditions on the module output. In this paper, a DbC-based teaching methodology is proposed, which aims to teach undergraduate students how to use contracts for the modeling and implementation of software systems. The teaching methodology consists of three consecutive steps. Firstly, the students will learn how to model software architectures in terms of components and their communication links. The component behaviours are specified as contracts, which are attached to the messages that the components exchange. In the second step, the students will learn how to implement the contractual software architectures in Java using Java’s assertion mechanisms in a way that the contractual design decisions are all preserved in the code. Lastly, the students will learn the Java Modeling Language for combining the contractual modeling and Java implementation in a single model to avoid any inconsistencies between the model and implementation and automatically verify the correctness of the implementation against the modeled behaviours.
Editorial Design Based on User Experience Design

This research deals with editorial design based on user experience design. The traditional editorial design has had to adapt to the new digital media composition, where multimedia audiovisual elements unthinkable a few years ago need to be integrated. The participation of the reader, as an external observer who only receives information through texts and images, now has new scenarios in which he can actively participate and decide what will come to his hands. In this study, a work methodology based on UxD User Experience Design is presented, in which will generate the editorial design of an educational book on environmental issues, which includes augmented reality for children from 6 to 8 years of age. The aim of this study is to know if an editorial product with augmented reality and developed from the user experience design can improve meaningful learning in a playful and active way. For its development, a composition model based on the Fibonacci sequence and the golden ratio will be used. Additionally, its graphic composition will be guided by the Massimo Vignelli canon and will be complemented by the reticular model of Beth Tondreau. The augmented reality markers position will also be based on the composition model previously mentioned, which will allow keeping the attention of the reader in the printed document and in the augmented reality animations. The user experience design will be applied with teachers, parents and students from 4 schools in Quito and Ambato. Once the production is completed, the impact on teaching-learning process will be evaluated with a control and a test group, and the methodology with which they will work in the classroom with the educational material developed will be defined. At the end of the study, copies of the book will be delivered to the participating schools of this research for its implementation.