Reviewerly/exHarmony huggingface dataset:
USAGE OF MULTIMEDIA PRESENTATIONS IN PREPARATION OF THE FUTURE PRESCHOOL TEACHERS FOR CHILDREN'S SPEECH DEVELOPMENT

Reformation of the higher education system in Ukraine requires innovative changes in the priorities of educational policy, improvement of its content, information and technological support of the educational process. So the task to determine such methods of interaction between teachers and future teachers of preschool education institutions in order to stimulate their successful professional development arises. One of the tasks of modernizing higher pedagogical education is to develop students' information and communication competence. The problem of professional training of future teachers has been described in researches of the following scientists: V. Benera, O. Bogich, I. Bogdanova, A. Bogush, G. Bielienka, N. Gavrysh, T. Zharovtseva, L. Zdanevych, I. Kniazheva, T. Ponimanska, M. Roganova, T. Tanko, N. Lysenko, N. Malinovska and others. Issues of using multimedia presentations in the training of future specialists were studied by the following scientists: Y. Avsiukevych, V. Bykov, M. Zholdak, I. Zakharova, V. Klochko, A. Kolomiyets, Y. Mashbyts, I. Pidlasyi, O. Spivakovskyi, A. Khutorskyi and others. Multimedia presentation is a didactic tool that allows to transmit information in a visual and schematic form for improvement its perception and increasing its value. The effectiveness of using multimedia presentations is achieved when the following didactic conditions are met: – a harmonious combination of oral presentation of the material during a lecture session and knowledge acquired by students during practical and laboratory classes with the information of a multimedia presentation; – ensuring maximum implementation of multimedia presentation functions, in particular, stimulating thinking, memory and attention of the higher education students; – interactive orientation of multimedia presentation; – the rational ratio of the time allocated to lecturer to familiarize students with the content of topic of the lecture session and the time allocated for a multimedia presentation; – ensuring the perception of information using the maximum number of analyzers; – availability of information for perception and analysis by higher education students; – rational dosing of the volume of information presented using a multimedia presentation. Comparative analysis of the final exam scores of students in the control and experimental groups of the higher education students confirmed our hypothesis that multimedia presentations are the effective means of increasing the level of knowledge of students, forming their information and communication competence Key words: Keywords: older preschool children, multimedia presentation, language development of children, pre-school teacher.
GIPFA: Generating IPA Pronunciation from Audio

Transcribing spoken audio samples into the International Phonetic Alphabet (IPA) has long been reserved for experts. In this study, we examine the use of an Artificial Neural Network (ANN) model to automatically extract the IPA phonemic pronunciation of a word based on its audio pronunciation, hence its name Generating IPA Pronunciation From Audio (GIPFA). Based on the French Wikimedia dictionary, we trained our model which then correctly predicted 75% of the IPA pronunciations tested. Interestingly, by studying inference errors, the model made it possible to highlight possible errors in the dataset as well as to identify the closest phonemes in French.
A Mobile Robot Recognize Blurred Targets via an Improved Histogram Equalization

Abstract Target recognition is a precondition for guiding robots to complete tasks. However, if the target is blurred, recognition will be difficult, which will lead to an inefficiency operation for robots. For the sake of improving the robot operation efficiency, it is essential to improve the image quality of recognized blurred target. Since image enhancement is taken as a powerful ways for improving image quality, it has gained largely attentions from researches. In fact, most of enhance methods are proposed based on histogram equalization, and until present, focus of research is still on improvements of histogram equalization. Hence, this paper proposes an improved histogram equalization for blurred targets in mobile robot recognition. Compared with the traditional histogram equalization methods, our method has less calculation time and better performance of real-time. Furthermore, the proposed approach also has strong robustness in blurred target recognition via a mobile robot.
TOPIC MODELING IN CLINICAL REPORTS - A SURVEY

Text mining is a process of converting unstructured data into meaningful data. It may be loosely characterized as the process of analyzing text to extract information that is useful for particular purposes. Topic modeling is a form of text mining, a way of identifying patterns in a corpus. The topics produced by topic modeling techniques are clusters of similar words that are frequently occur together. Topic modeling is also a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, a document is about a particular topic, one would expect particular words to appear in the document more or less frequently. This paper, presents a survey on topic modeling in clinical documents.
Ways of Overcoming Ontological Instability of the Concept of a Subject in Modern Philosophy

The study aims to expose vulnerabilities of the subject-object paradigm in modern philosophy. The article concentrates upon ontological limitations of the concept of a subject and changing the status of an object in subject-object relations in the context of changing characteristics of the object existence. Scientific novelty of the work lies in identifying points of ontological stability and limitations of the subject concept, which could be used in its further theoretical development. As a result of the research, the aspects necessary, from the author’s viewpoint, for building a theoretical model of the modern subject, which would be based on ontological symbiosis of subjective and objective characteristics, are determined.
Football Video Feature Extraction and Coaching Significance Based on Improved Huff Coding

Football video feature extraction and the coaching significance based on improved Huff coding model is analyzed in this manuscript. In the application of intelligent video analysis technology, it is easy to be affected by environmental illumination changes, target motion complexity, occlusion, and other factors, resulting in errors in the final target detection and tracking. However, with the advent of the big data era, the continuous innovation and development of science and technology have promoted development of intelligent video analysis technology and reduced the time and efficiency of the target information retrieval. The intelligent video analysis system is a combination of a modular design. The entire system is mainly composed of a video source, a core video intelligent processing engine, and a management terminal. The football video data set is integrated to construct the efficient processing system. The performance is then validated through the numerical simulation.
Strategi Adaptasi Siswa dalam Menghadapi Bencana Banjir Sebagai Upaya Mitigasi Bencana Banjir di SD dan MI Muhammadiyah Kecamatan Bayat

Jurnal Penelitian Geografi
Professionally Oriented Approach as Efficient Means to Raise Technical Students’ Motivation for Foreign Language Study

The paper aims to reveal specificity of the professionally oriented approach as an efficient means to raise technical students’ motivation for foreign language study. The article analyses theoretical aspects of educational motivation, justifies relevance of professionally oriented education, reveals methodological techniques to improve students’ motivation while studying a professional foreign language. Scientific originality of the research lies in the fact that the author provides a theoretical analysis of professionally oriented approaches to foreign language teaching and considers the global simulation technology as an efficient means for realization of the approaches under study. The research findings are as follows: the author describes motivation techniques based on professionally oriented and communicative approaches (elements of creativity, optimization of students’ autonomous work, efficient control), justifies efficiency of the global simulation technology.
Topic Modeling via Full Dependence Mixtures

In this paper we introduce a new approach to topic modelling that scales to large datasets by using a compact representation of the data and by leveraging the GPU architecture. In this approach, topics are learned directly from the co-occurrence data of the corpus. In particular, we introduce a novel mixture model which we term the Full Dependence Mixture (FDM) model. FDMs model second moment under general generative assumptions on the data. While there is previous work on topic modeling using second moments, we develop a direct stochastic optimization procedure for fitting an FDM with a single Kullback Leibler objective. Moment methods in general have the benefit that an iteration no longer needs to scale with the size of the corpus. Our approach allows us to leverage standard optimizers and GPUs for the problem of topic modeling. In particular, we evaluate the approach on two large datasets, NeurIPS papers and a Twitter corpus, with a large number of topics, and show that the approach performs comparably or better than the the standard benchmarks.
Teaching Reform and Practice of Applied Curriculum Design of Comprehensive Circuit System Guided by New Concept of OBE

The purpose of accreditation is to take the teaching quality management system as fundamental guidance and develop curriculum based on new concept of OBE to improve teaching quality and achieve the reform. National standard for teaching quality of electron and Information suited for communication engineering is made as guidance, applied teaching is the goal of accreditation, and the aim of positioning curriculum and arranging content is to correspond targets with resources, practice, improve the teaching quality and achieve accreditation based on OBE.
Factors affecting entrepreneurship development among the food marketers in Ilorin Metropolis, Kwara State, Nigeria

The study assessed the factors affecting entrepreneurship development among Food Marketers in Ilorin Metropolis, Kwara State, Nigeria. One hundred and ten respondents were randomly selected in four markets in Ilorin. The data was analyzed using frequency count, percentage, mean and Pearson Product Moment Correlation. The result reveals that mean age of the respondents was 30.5 years and about 58.2% were female. Record keeping skills (mean score =2.42) was the highest ranked entrepreneurial information needs of the respondents while lack of start-up capital (mean score =2.40) was the most severe factor affecting entrepreneurial activities of food marketers. There is a weak positive significant relationship between the entrepreneurial information needs and factors affecting entrepreneurial development of the respondents (r=0.407, p=0.0001). The study recommends that there should be provision of training on entrepreneurial skills based on the identified areas of needs and there should be provision of credit facilities to the food marketers as these will enhance their productivity.&#x0D; Keywords: Entrepreneurial Development, Factors, Information Needs, Food Marketers, Ilorin Metropolis
Towards encoding perceptually salient early reflections for parametric spatial audio rendering

Parametric spatial audio rendering promises fast and perceptually convincing audio cues that remain playback-system agnostic and enable aesthetic modifications of the acoustic experience within games and virtual reality. We propose a parametric encoder for spatial room impulse responses that is tested with nine simulated rooms spanning a large range of sizes and reverberation times. A key component of the pipeline is a perceptually inspired model for determining a minimal set of salient early reflections to reduce computational complexity. The results of a listening study with 27 subjects suggest that rendering six early reflections is indiscernible from a fully-rendered reference for the tested speech content and frequency-independent room simulations based on the image source method. However, the proposed model requires further improvements with respect to detecting and selecting the most-salient early reflections. Related: Microsoft’s Project Acoustics is a physics-based acoustic simulation and rendering solution based on Triton.
Digitalization of the English language correspondence course for engineering students

Abstract The paper is devoted to the problem of digitalization of a foreign language course for engineering students. Realizing the advantages of the educational process digitalization in engineering universities, MADI Foreign Languages Department launched the project aimed at the development of digital materials for teaching English. The course is addressed to the students of the University correspondence department at the level of baccalaureate. The digital English materials can be used as an integral part of the traditional course providing flexibility of self-study in any place with an Internet connection, self-paced learning and immediate assessment and feedback. The digital materials are intended to help correspondence students to overcome the main difficulties they have while studying English and to improve their learning outcomes. The paper gives the analysis of the definitions of the terms “digitization”, “digitalization”, and “digital transformation”, shows the difference between them, describes the aims and content of the digital materials, their structure, and the main language skills they develop.
Multilayer Perceptron optimization through Simulated Annealing and Fast Simulated Annealing

The Multilayer Perceptron (MLP) is a classic and widely used neural network model in machine learning applications. As the majority of classifiers, MLPs need well-defined parameters to produce optimized results. Generally, machine learning engineers use grid search to optimize the hyper-parameters of the models, which requires to re-train the models. In this work, we show a computational experiment using metaheuristics Simulated Annealing and Fast Simulated Annealing for optimization of MLPs in order to optimize the hyper-parameters. In the reported experiment, the model is used to optimize two parameters: the configuration of the neural network layers and its neuron weights. The experiment compares the best MLPs produced by the SA and FastSA using the accuracy and classifier complexity as comparison measures. The MLPs are optimized in order to produce a classifier for the MNIST database. The experiment showed that FastSA has produced a better MLP, using less computational time and less fitness evaluations.
TOWARDS AN AUTONOMOUS FRAMEWORK FOR HPC OPTIMIZATION: A STUDY OF PERFORMANCE PREDICTION USING HARDWARE COUNTERS AND MACHINE LEARNING

As the high processing computing becomes even more critical for scientific research across various fields, increasing performance without raising the energy consumption levels becomes an essential task in order to warrant the financial viability of exascale systems.This work presents the first step towards understanding how the many computational requirements of benchmark applications relate to the overall runtime through a machine learning model and how that can be used for the development of an autonomous framework capable of scaling applications to have an optimal trade-off between performance and energy consumption.
Mobile expert for Tobacco Disease Identification Using The Fuzzy Inference System Tsukamoto

Madura Island is a producer of aromatics tobacco known as Madura Tobacco. One type of Madura tobacco that has superior quality is tobacco Campalok. This tobacco is only in the village of Bakeong Guluk-Guluk district of Sumenep. Its price for each kilogram can penetrate up to two million rupiahs. But failing to harvest due to illness or pests can decrease the quality and price of tobacco Campalok, while the access to consult the agricultural experts in Sumenep district is far enough so the public difficulty getting information faster on tobacco disease treatment. This is the underlying research on the expert system for the identification of diseases in the Android-based tobacco crop.This Expert System was developed by utilizing Android-based mobile technology using the Fuzzy Inference System Tsukamoto method. Farmers who will use this application only enter the characteristics of tobacco leaves that are exposed to pests then the expert system will provide a way of overcoming the pest disease based on the expertise of agricultural experts in Sumenep district Using the Fuzzy method. The result of this research showed that 8 from 10 of diseases were successfully detected by the application so that the accuracy of this application compared to the human expert system is 80 %.
LibGuides: Engineering: New Resources

A comprehensive guide to all of the Library service's resources which are tailored to support you with your studies. A selection of resources, recently purchased for your subject area.
Toward a standardized methodology for constructing quantum computing use cases

We propose a standardized methodology for developing and evaluating use cases for quantum computers and quantum inspired methods. This methodology consists of a standardized set of questions which should be asked to determine how and indeed if, near term quantum computing can play a role in a given application. Developing such a set of questions is important because it allows different use cases to be evaluated in a fair and objective way, rather than considering each case on an ad hoc basis which could lead to an evaluation which focuses on positives of a use case, while ignoring weaknesses. To demonstrate our methodology we apply it to a concrete use case, ambulance dispatch, and find that there are some ways in which near term quantum computing could be deployed sensibly, but also demonstrate some cases ways in which its use would not be advised. The purpose of this paper is to initiate a dialogue within the community of quantum computing scientists and potential end users on what questions should be asked when developing real world use cases.
Digital Assisted Image Correlation for Metal Sheet Strain Measurement

Current methods of correlation and point matching between stereoscopic images produce large errors or are completely inefficient when the surface has a repetitive, non-isotropic, low contrast pattern. In this article a new method of Digital Assisted Image Correlation (DAIC) is presented to match specific points in order to estimate the deformation of the surface in the metal sheets used in the automotive industry. To achieve this, it is necessary to stamp the surface to be measured with a regular pattern of points, then a digital image processing is done to obtain the labels of the circles of the pattern. After this, a semi-automatic search is made in the labels of both images to correlate all of them and perform the triangulation. DIC is used to corroborate the correspondence between points and verify the accuracy and efficiency of the developed method. This allows the 3D reconstruction of the sheet with a minimum of information and provides more efficiency and a great benefit in computational cost. Deformation is calculated by two methods, which show similarity between the values obtained with a digital microscope. It is assumed that quality of marks stamping, lighting, and the initial conditions, also contribute for trustworthy effects.
A SOCIOLINGUISTICS STUDY: STUDENTS’ MOTIVATION UPON ELT IN THE COASTAL AREA OF BINTAN ISLAND

&lt;p&gt;Students who live in the coastal area in Bintan Island, Kepulauan Riau province, are expected to have greater access to the knowledge and information, regarding they stay in the open boundaries, Malacca straits. This social-region circumstance should enabled them to be familiar with English. However, students in this area do not conform to the expectation. This research utilizes qualitative design, where the determination of research question, subject, and design is based on the researcher. Researcher has taken more than a hundred of students, as the research subject who live and study in the coastal area, in Bintan Island. For data collection, questionnaire is arranged by adapting AMTB, Attitude Motivation Test Battery, by Gardner (2004). According to the data analysis, it describes that students have a sizeable motivation in learning English, whereas their capability is not equitable. This shape caused by two main factors, such as English teacher’s role, real-contextual teaching method, and lack of opportunity&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks

Paramedics often have to make lifesaving decisions within a limited time in an ambulance. They sometimes ask the doctor for additional medical instructions, during which valuable time passes for the patient. This study aims to automatically fuse voice and text data to provide tailored situational awareness information to paramedics. To train and test speech recognition models, we built a bidirectional deep recurrent neural network (long short-term memory (LSTM)). Then we used convolutional neural networks on top of custom-trained word vectors for sentence-level classification tasks. Each sentence is automatically categorized into four classes, including patient status, medical history, treatment plan, and medication reminder. Subsequently, incident reports were automatically generated to extract keywords and assist paramedics and physicians in making decisions. The proposed system found that it could provide timely medication notifications based on unstructured voice and text data, which was not possible in paramedic emergencies at present. In addition, the automatic incident report generation provided by the proposed system improves the routine but error-prone tasks of paramedics and doctors, helping them focus on patient care.
Variance-Reduced Accelerated First-order Methods: Central Limit Theorems and Confidence Statements

In this paper, we study a stochastic strongly convex optimization problem and propose three classes of variable sample-size stochastic first-order methods including the standard stochastic gradient descent method, its accelerated variant, and the stochastic heavy ball method. In the iterates of each scheme, the unavailable exact gradients are approximated by averaging across an increasing batch size of sampled gradients. We prove that when the sample-size increases geometrically, the generated estimates converge in mean to the optimal solution at a geometric rate. Based on this result, we provide a unified framework to show that the rescaled estimation errors converge in distribution to a normal distribution, in which the covariance matrix depends on the Hessian matrix, covariance of the gradient noise, and the steplength. If the sample-size increases at a polynomial rate, we show that the estimation errors decay at the corresponding polynomial rate and establish the corresponding central limit theorems (CLTs). Finally, we provide an avenue to construct confidence regions for the optimal solution based on the established CLTs, and test the theoretic findings on a stochastic parameter estimation problem.