Reviewerly/exHarmony huggingface dataset:
Ses Tanıma için Derin Öğrenme Mimarileri Üzerine Derleme

Deep learning is a branch of machine learning that uses several algorithms which tries to model datasets by using deep architectures with many processing layers. With the popularity and successful applications of deep learning architectures, they are being used in speech recognition, as well. Researchers utilized these architectures for speech recognition and its applications, such as speech emotion recognition, voice activity detection, and speaker recognition and verification to better model speech inputs with outputs and to reduce error rates of speech recognition systems. Many studies are performed in the literature that use deep learning architectures for speech recognition systems. The literature studies show that using deep learning architectures for speech recognition and its applications provide benefits for many speech recognition areas and have ability to reduce error rates and provide better performance. In this study, first of all, we explained speech recognition problem and the steps of speech recognition. Then, we analyzed the studies related to deep learning based speech recognition. In particular, deep learning architectures of Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks and hybrid approaches that use these architectures are evaluated and the literature studies related to these architectures for speech recognition and the application areas of speech recognition are investigated. As a result, we observed that RNNs are the most utilized and powerful deep learning architecture among all of the deep learning architectures in terms of error rates and speech recognition performance. CNNs are other successful deep learning architectures and have closer results with RNN in terms of error rates and speech recognition performance. Also, we observed that new deep architectures that use either hybrid of DNNs, CNNs, and RNNs or other deep learning architectures are getting attention and have increasing performance and could reduce error rates in speech recognition.
Best Innovation Practices of Pedagogical Education Enhancement in the Kaliningrad Region

The objective of the article is to describe the practices of pedagogical education enhancement in the Kaliningrad region, Russia. The research method of the designated issue is the method of results analysis and modeling of development prospects, making it possible to transfer the positive experience of the Kaliningrad region to other regions. The article represents the strategies that the Institute of Education of the Immanuel Kant Baltic Federal University (Kaliningrad, Russia) applies to solve the main problems in the field of education: the lack of practical training of graduates of educational programs, the lack of an effective system of professional orientation, the discrepancy between teacher training and labour market needs, the graduates’ problem of joining the profession (low percentage of employment, lack of independent evaluation of the qualification obtained).
Comparing Multiple MATLAB/Simulink Models Using Static Connectivity Matrix Analysis.

Model-based languages such as MATLAB/Simulink are crucial for the development of embedded software systems. To adapt to changing requirements, engineers commonly copy and modify existing systems to create new variants. Commonly referred to as clone-and-own, this reuse strategy is easy to apply and beneficial in the short term, but it entails severe maintenance and consistency issues in the long term, leading to a huge amount of redundant and similar assets. Moreover, a later transition towards structured reuse such as with software product lines inevitably requires the comparison of all existing variants prior to the actual migration. However, current work mostly revolves around the comparison of only two systems and despite approaches proposed that can cope with more, such are not applicable to embedded software systems such as MATLAB/Simulink. In this paper, we bridge this gap and propose Static Connectivity Matrix Analysis (SCMA), a novel comparison procedure that allows for the evaluation of multiple MATLAB/Simulink model variants at once. In particular, we transform models into a matrix form which is used to compare all models and to identify all similar structures between them, even with model parts being completely relocated during clone-and-own. We allow engineers to tailor results and to focus on any arbitrary variant subset, enabling individual reasoning prior to migration. We provide a feasibility study from the automotive domain, showing our matrix representation to be suitable and our technique to be fast and precise.
Pedagogical Training of Masters in Ecology in Institutions of Higher Education

The article substantiates the expediency of pedagogical preparation of masters in ecology for forming in them the readiness for teaching the disciplines of professional direction. The scientific sources review on the problem ofconcept innovative rethinking of modern ecological education is carried out, students’ specific teaching principles and environmentally oriented methods are highlighted. The main result of professional education of ecologists should be the formation of an ecocentric type of consciousness, which will allow them to understand the development of nature and humanity as a process of co-evolution, mutually beneficial unity. The results of researches on the problem of pedagogical preparation of masters with highlighting of its contradictions are generalized. The pedagogical conditions of effective pedagogical preparation of masters-ecologists are substantiated, the content of the special course, developed for deepening the pedagogical component of their professional training, is analyzed. The organization, carrying out and results of the ascertaining and formative stages of the experiment on forming readiness for pedagogical activity of masters in ecology are described. The results obtained testify to the effectiveness of the pedagogical experiment.
Designing Declarative Language Tutorials: A Guided and Individualized Approach.

The ability to declare what a program should include rather than how these features should be implemented makes declarative languages very useful in many visual output programs. The wide-ranging uses of these programs, in domains ranging from architecture to web programming to data visualization, encourages us to find an effective method to teach them. Traditional tutorial systems are usually non-interactive and have a gap between the learning and application. This can leave the user frustrated without a way to move forward in the learning process. A general lack of guidance can lead the student down an incorrect path. To prevent these difficulties, we propose a guided tour followed by novel question types that both direct the student’s learning and creates a focused environment to practice individual skills. Lastly, we propose a study to test the hypothesis that this tutorial is quicker to complete and results in a greater understanding of the declarative language.
Connettivi e portata testuale. Analisi corpus-based degli usi di tuttavia nella scrittura contemporanea

This paper deals with the textual scope of connectives in written language, focusing on the case of the Italian concessive connective tuttavia. The aim of the study is twofold. Firstly, it puts forward a definition of textual scope and presents a battery of tests to distinguish a wide scope from a narrow scope. The tests are based on the observation of semantic, pragmatic and formal features of the linguistic units linked by the connective. Secondly, tuttavia is examined through a corpus-based analysis that combines a quantitative and a qualitative approach. The analysis intends to establish whether syntax and/or punctuation exert an influence on the textual scope of tuttavia. Corpus data show that the sole factor that displays a statistically significant influence on textual scope is the structural scope of the connective (on a sentence vs. on smaller constituents), whereas specific distributions of the connective in the sentence or the presence/absence of commas around it do not play a role.
Stock Price Forecast Based on Stochastic Model

The impact of stock price fluctuations on the global economy increases gradually since Lehman shock in 2008. The importance of research on stock price prediction has been increasing year by year. The stock price prediction method using Bayesian network is proposed in this study. The results show that the result of the proposal algorithm is more accurate than the conventional method such as time series analysis method, Neural Network, and so on.
Iterative Block Ciphers from Tweakable Block Ciphers with Long Tweaks

We consider a problem of constructing a secure block cipher from a tweakable block cipher (TBC) with long tweaks. Given a TBC with n-bit blocks and Γn-bit tweaks for Γ ≥ 1, one of the constructions by Minematsu in DCC 2015 shows that a simple iteration of the TBC for 3d rounds yields a block cipher with dn-bit blocks that is secure up to 2dn/2 queries, where d = Γ + 1. In this paper, we show three results.1. Iteration of 3d − 2 rounds is enough for the security up to 2dn/2 queries, i.e., the security remains the same even if we reduce the number of rounds by two.2. When the number of queries is limited to 2n, d+1 rounds are enough, and with d + l rounds for 1 ≤ l ≤ d − 1, the security bound improves as l grows.3. A d-round construction gives a block cipher secure up to 2n/2 queries, i.e., it achieves the classical birthday-bound security. Our results show that a block cipher with beyond-birthday-bound (BBB) security (with respect to n) is obtained as low as d + 1 rounds, and we draw the security spectrum of d + l round version in the range of 1 ≤ l ≤ d−1 and l = 2d−2 for BBB security, and l = 0 for birthday-bound security.
Data Anonymization Using Imbalanced Data for Deep Learning with Uppersampling and Undersampling

Most real data are imbalanced and thus present difficulties as a subject of classification problems.Some research has been conducted on the topic of imbalanced data.However, managing such data in deep learning has not been considered.In this study, we propose privacy-protection data mining using imbalanced data through deep learning.We discuss existing privacy-protection data mining, study its features, and examine a deep learning based anonymizing tool.Experiments using various anonymization tools confirm that deep learning does not reduce data accuracy by making that data anonymous.Under and uppersampling are both used for imbalanced data, but the tendencies of these approaches to reduce data accuracy because of anonymization have not been confirmed. Input DataWe first consider the "anonymization" and "randomization" of representative methods of input-
FINANCIAL ASPECT OF STUDENT MOBILITY PROGRAMMES: FOREIGN EXPERIENCE

Purpose:This research studies academic mobility of students as a means of higher education systems’ integration. The benefit of this study is the review of European countries’ experience in tackling the financial obstacles to academic mobility of students. The main purpose of the study is to reveal the means of financial stimulation of academic mobility that some European countries use. Methodology:The theoretical basis of the study is the conceptual ideas of modern researchers in the study of academic mobility. We used the comparative and systematic approach to study the existing scientific works on the problem of academic mobility and obstacles to its development. We used the analytical method to study the national reports on the subject under study and synthesized its results to make the conclusions and recommendations for the colleagues.Results:The research presents various approaches to the definition of the notion “academic mobility” based on the Bologna agreements. The main part of the study analyses the main obstacles to academic mobility of students in European countries, focusing on the financial obstacles. The research reviews national reports on the realization of the main Bologna agreements and presents the statistic data with the main figures on the problem under study. The results of the study show that some European countries use effective instruments to tackle the financial obstacles to students’ mobility development. As this problem remains one of the relevant ones, the results of the study may be used in the new mobility programs creation and improving the existing ones. The materials of the research may be useful for the teachers of Foreign and Comparative Pedagogics, universities’ foreign departments and for those researchers who study the Bologna issues.
RESEARCH SKILLS IN HIGHER EDUCATION: DEVELOPING DISCOURSE ANALYSIS COMPETENCE IN THE CONTEXT OF MEDIA STUDIES

A. Kireeva1, V. Ott2 1MGIMO University (RUSSIAN FEDERATION) 2Peoples' Friendship University of Russia (RUSSIAN FEDERATION)
EXPLORING COMPETING VALUE FRAMEWORK ON HOW PROFESSIONALS’ JOB SATISFACTION AFFECTED BY ORGANIZATION CULTURE

This research aims to evaluate the ideal culture that determines the satisfaction of employees.The level of satisfaction at the job should be based on recruiting more professionals because it is vital to attract and retain professionals.Nevertheless, the organizational culture was addressed as a significant factor affecting qualified professionals' job satisfaction.Thirty empirical papers from the period from 2014 to 2019 were chosen for this research.There are many types of research being conducted about job satisfaction, but there is limited research being conducted to explore professionals' job satisfaction.It reviews competing value frameworks studies and theories on organizational culture toward job satisfaction and also the concepts of clan, market, clan, adhocracy, market, hierarchy, and knowledge sharing cultures.The result of this literature study found that the impact of the clan, adhocracy, and knowledge sharing culture significantly influences the professional's job satisfaction.
User Education and File Standards Best Options to Ensure Open Educational Resources are Truly Open

A Review of:&#x0D; Ovadia, S. (2019). Addressing the technical challenges of open educational resources. portal: Libraries and the Academy, 19(1), 79-93. https://doi.org/10.1353/pla.2019.0005&#x0D; Abstract&#x0D; Objective – To describe common technical challenges of open educational resources (OERs) and recommend solutions.&#x0D; Design – Descriptive study.&#x0D; Setting – Online open educational resources in higher education.&#x0D; Subjects – Open educational resources.&#x0D; Methods – Drawing from the literature and his own experiences, the author explains the necessity of accepted standards of “openness” and describes the many ways OERs fail to meet these standards. The author also describes common technical challenges that impede openness, then proposes solutions to address these challenges.&#x0D; Main Results – Technical limitations often prohibit OERs from being truly open. Providers can design their resources to encourage reuse, redistribution, revision, and remixing. Three strategies for addressing technical challenges in OERs are user education, open file standards, and using Git to facilitate distributed version control.&#x0D; Conclusion – Git is a compelling option for distributed version control, but entails its own technical challenges. User education and established open file standards are the best strategies to ensure that OERs are open in both a legal and a technical sense. The article concludes with the author’s opinions about how OER directors may most realistically implement these solutions.
Optimization of Position of Siren Speakers to Raise Awareness of the Ambulance at Intersections

In 2017, we proposed a new siren system of the ambulance for early perception of its existence or approach. The system has four siren speakers, two on the front and one on each body side. When passing through an intersection or changing a lane, the ambulance crews produce a rapid increase in sound pressure by turning on the side speakers. Since the siren blows from four speakers simultaneously, the sound interference occurs, that is, the sound pressure differs according to location. To produce a larger increase of sound pressure in wide area, we optimized the installation location of the side speakers under some legal constraints via the computer simulation. The simulation was conducted for the sound frequency of 770 Hz and 960 Hz with the FDTD method. As a result of the simple line search, the optimal installation location was 0.5 meters back from the front. By comparing the sound pressure level for four-speakers sounding with that for two-speakers sounding, we confirmed that the sound pressure effectively increases around the body side.
Use of Social Media Platforms and some Relevant Variables as Perceived by Students of Jordanian Universities

The study aimed to identify the degree of appreciation of Jordanian university students for of their use of social networking sites.And whichany of these sites are used more by them more, And and to detect the detection of some variables that can be attributed to it.In order to achieve this, the study followed the analytical descriptive method by applying a questionnaire consisting of (23) sections divided into five fields, on a sample of (694) students from the universities of Northern Jordan (Jadra, University of Technology, Yarmouk University, Faculty of Educational Sciences of UNRWA(, The results showed that the most popular social networking sites were Facebook )95.24%(, and that the total degree for areas used by social networking sites was high (3.67).Where the social rank is placed in the first domain and the political sphere in the last rank.There The study results also indicated that there were no statistically significant differences between the average estimates of the sample of the study sample in all areas of Jordanian university students' use of the social networking sites as a whole due to )gender or college variables or the period of use), while there were differences of statistical significance due to the variable of the program.The study recommended the design of orientation programs for university students based on their psychological needs from the use of social networking sites.
DEVELOPING A CONCEPTUAL MODEL OF PERCEIVED LECTURE QUALITY WITH THE LEARNING EXPERIENCE LEVEL IN HIGHER EDUCATION

H. Kawamoto1, Y. Watanabe2, T. Matsuda3 1Tokyo Institute of Technology (JAPAN) 2Tokyo University of Science (JAPAN) 3Tokyo Metropolitan University (JAPAN)
CRANIA: Unlocking Data and Value Reuse in Iterative Neural Network Architectures

A common inefficiency in traditional Convolutional Neural Network (CNN) architectures is that they do not adapt to variations in inputs. Not all inputs require the same amount of computation to be correctly classified, and not all of the weights in the network contribute equally to generate the output. Recent work introduces the concept of iterative inference, enabling per-input approximation. Such an iterative CNN architecture clusters weights based on their importance and saves significant power by incrementally fetching weights from off-chip memory until the classification result is accurate enough. Unfortunately, this comes at a cost of increased execution time since some inputs need to go through multiple rounds of inference, negating the savings in energy. We propose Cache Reuse Approximation for Neural Iterative Architectures (CRANIA) to overcome this inefficiency. We recognize that the re-execution and clustering built into these iterative CNN architectures unlock significant temporal data reuse and spatial value reuse, respectively. CRANIA introduces a lightweight cache+compression architecture customized to the iterative clustering algorithm, enabling up to 9 × energy savings and speeding up inference by 5.8 × with only 0.3% area overhead.
Human resources development for a teacher of private science and engineering institute

Human resource development starts with employment and ends with employment. It all starts with hiring. At Kanazawa Institute of Technology, “cultivation of high character”, “deep technical innovation” and “superb academic-industrial cooperation” are set as the founding principles, and more than half of the teachers have experiences in business activities. Therefore, our university tends to employ teachers with both academic and practical skills. Many young teachers are also experienced in private companies, and our institute pays special attention to the training of young teachers, and assigns them to the section for acquiring skills to facilitate active learning. Here, the training of young teachers after hiring is reported.
LibGuides: Student Support for Remote Learning: Spring 2020 - Important Updates

Your guide and resource for everything you need to prepare for and conquer remote learning. Important information and updates regarding Spring 2020 final exams, withdrawal deadlines, and more.
Evolving Visual Artefacts based on Consumption Patterns

In information visualisation, the visual artefacts should have a functional dimension, allowing the analysis of information, and an aesthetic dimension, to seize the users' attention to the information being displayed. However, in the data aesthetics field, the main concern is to produce aesthetically appealing artefacts. With this project, our goal is to try to join these two fields by exploring the aesthetic dimension of a functional visualisation model characterised by a series of parameters that can make the visualisation more functional and/or more aesthetically appealing. In concrete, we propose a framework based on interactive evolutionary computation (IEC) to evolve the parameters of the visualisation model, enabling the user to explore possibilities and to create different aesthetics over the same data. Our case study will be based on a dataset containing the consumption patterns within a Portuguese retail company. Through different validation methods - automatic fitness function, usage scenarios, and a user study - we show that our system is able to create a wide and diverse set of emergent visual artefacts that can be intriguing and aesthetically appealing for the user.
Analisis Kesehatan Bank Menggunakan Local Mean K-Nearest Neighbor dan Multi Local Means K-Harmonic Nearest Neighbor

The classification method continues to develop in order to get more accurate classification results than before. The purpose of the research is comparing the two k-Nearest Neighbor (KNN) methods that have been developed, namely the Local Mean k-Nearest Neighbor (LMKNN) and Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN) by taking a case study of listed bank financial statements and financial statements complete recorded at Bank Indonesia in 2017. LMKNN is a method that aims to improve classification performance and reduce the influence of outliers, and MLM-KHNN is a method that aims to reduce sensitivity to a single value. This study uses seven indicators to measure the soundness of a bank, including the Capital Adequacy Ratio, Non Performing Loans, Loan to Deposit Ratio, Return on Assets, Return on Equity, Net Interest Margin, and Operating Expenses on Operational Income with a classification of bank health status is very good (class 1), good (class 2), quite good (class 3) and poor (class 4). The measure of the accuracy of the classification results used is the Apparent Error Rate (APER). The best classification results of the LMKNN method are in the proportion of 80% training data and 20% test data with k=7 which produces the smallest APER 0,0556 and an accuracy of 94,44%, while the best classification results of the MLM-KHNN method are in the proportion of 80% training data and 20% test data with k=3 which produces the smallest APER 0,1667 and an accuracy of 83,33%. Based on APER calculation shows that the LMKNN method is better than MLM-KHNN in classifying the health status of banks in Indonesia.Keywords: Classification, Local Mean k-Nearest Neighbor (LMKNN), Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN), Measure of accuracy of classification