Reviewerly/exHarmony huggingface dataset:
The Implementation of Classical Puppet Drama Performance to Improve English Speaking Skill for International Class Program Students

This study analysed the implementation of classical puppet drama performance to improve students’ speaking skill in International Class Program of one of universities in Salatiga. The teaching and learning processes in drama class were(1 ) asking the students with the question, (2) designing drama project plan, (3) creating drama preparation schedules, (4) monitoring the students in preparing the drama, (5) assessing the outcome, and (6) evaluating students’ drama performance. This study was qualitative research in the form of case study. To collect the data the researcher used observation, interview, documentation, and questionnaire. The results showed that by using drama performance, students can improve their pronunciation, fluency, accuracy, handle the voice, and body language to improve their self-confidence to speak in English. Moreover, the used of 4Cs skills activities namely critical thinking, communication, collaboration, and creativity skills in the classroom can stimulate students to improve their group work and interaction among students.
IMPEDIMENTS TO THE DEVELOPMENT OF IMMERSIVE VIRTUAL REALITY IN EDUCATION

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 1282-1288ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0424Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
A Novel Watermarking Method using Hadamard Matrix Quantization

One of the most used watermarking algorithms is Singular Value Decomposition (SVD), which has a balanced level of imperceptibility and robustness. However, SVD uses a singular matrix for embedding and two orthogonal matrices for reconstruction, which is inefficient. In this paper, a Hadamard matrix is used to get a singular matrix for the reconstruction process. Moreover, SVD works with a floating-point value, which takes long processing time, while the Hadamard matrix works with an integer range, which is more efficient. Visual measurement showed that SVD and the new method had average NC values of 0.8321 and 0.8293, whereas the average SSIM values resulted in the same value (0.9925). In terms of processing time, the proposed method ran faster than SVD with an embedding and extraction time of 0.6308 and 0.2163 seconds against 0.8419 and 0.2935 seconds. The proposed method successfully reduced the running time while maintaining imperceptibility and robustness.
InterLib: Collaborative Tool for Translators and Interpreters

The spread of information and communication technology (ICT) has revolutionized the way we share information.Collaborative tools have a great importance for teaching-learning activities, so as to assist in the dissemination of knowledge.In the area of education, the Brazilian Sing-Language translator or just Libras translator and interpreter is the main agent enabling the communication between teachers and Deaf students.This specialist needs to overcome different challenges, such as proficiency in the languages they translate, interpretation techniques, history and deaf culture.They also must lead with to specific contents taught to the Deaf student during different disciplines.This paper presents the development of a collaborative tool called InterLib, which has as main objective the qualification of these professionals toward these specific contents through the socialization and dissemination of knowledge.Thus, in regards of the lack of specific technologies for this purpose, a mobile application was developed aiming at facilitating the interactivity and collaboration of the Libras interprets.
Determining abbreviations in Kariyer.net domain

In this paper, studies determining abbreviations and their meanings in job texts are explained. The data used in this study consist of job texts stored in the Kariyer.net database. The applied method consists of two separate steps: first, the words and phrases in all job text documents are vectorised with the Word2Vec model. The phrases and abbreviations that are compatible with each other in the proximity of these word vectors are then checked and matched. In the second step, sentences with abbreviations and their meanings in the dataset are defined by the rules determined by Regex. Then, the appropriate abbreviations are collected and added to the dictionary.&#x0D; &#x0D; Keywords: Word embeddings, text mining, abbreviation detection.&#x0D; &#x0D;
DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning

This paper examines how deep learning (DL) representations, in contrast to traditional engineered features, can support semantic interaction (SI) in visual analytics. SI attempts to model user's cognitive reasoning via their interaction with data items, based on the data features. We hypothesize that DL representations contain meaningful high-level abstractions that can better capture users' high-level cognitive intent. To bridge the gap between cognition and computation in visual analytics, we propose DeepVA (Deep Visual Analytics), which uses high-level deep learning representations for semantic interaction instead of low-level hand-crafted data features. To evaluate DeepVA and compare to SI models with lower-level features, we design and implement a system that extends a traditional SI pipeline with features at three different levels of abstraction. To test the relationship between task abstraction and feature abstraction in SI, we perform visual concept learning tasks at three different task abstraction levels, using semantic interaction with three different feature abstraction levels. DeepVA effectively hastened interactive convergence between cognitive understanding and computational modeling of the data, especially in high abstraction tasks.
Derin Öğrenme Mimarilerinde Akustik ve Fonotaktik Öznitelikleri Kullanan Türkçe Ağız Tanıma

Dialects are local forms of speech separated by a certain rate from a standard language. Dialect recognition is one of the popular topics studied in speech recognition. In particular, the spoken dialect is asked to be identified first in order to improve the performance of large scale speech recognition systems. The phonetic differences of speech can be determined by examining the acoustic properties at the physical level. Features such as Log mel-spectrograms are used for this purpose. In addition, the phonotactic term corresponds to the arrangement rules of phonemes in a language/dialect. Phoneme sequences and the frequency of this sequence vary from dialect to dialect. Phoneme sequences are obtained by phoneme recognizers. Another topic that has become popular in recent years is deep learning neural networks. Convolutional Neural Networks (CNN), which is a special kind of deep learning neural networks, are often used in image and speech recognition. Long Short-Term Memory Neural Networks (LSTM) is a deep learning neural network model that produces more successful results than n-gram models in language modeling. In this study, the classification of Turkish dialects with CNN and LSTM type neural networks in terms of acoustic and phonotactic features were discussed. Also, LSTM neural networks are used for language modeling in phonotactic approach. In the experimental study, the proposed approaches were tested and interpreted on the Turkish Dialects Dataset that we collected. As a result of the study, it has been observed that the approaches used reaches 85.1% accuracy rate for Turkish dialect recognition.
Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

Standard cubature Kalman filter (CKF) algorithm has some disadvantages in stochastic system control, such as low control accuracy and poor robustness. This paper proposes a stochastic system control method based on adaptive correction CKF algorithm. Firstly, a nonlinear time-varying discrete stochastic system model with stochastic disturbances is constructed. The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. Simulation results show that the state estimation accuracy of the proposed adaptive cubature Kalman filter algorithm is better than that of the standard cubature Kalman filter algorithm, and the proposed adaptive correction CKF algorithm has good control accuracy and robustness in the UAV control test.
Smart city technology meets smart health assistive systems - on the example of the project AYUDO

Active and Assisted Living (AAL) systems have been developed over the last decade to improve the quality of life, independence and well-being of the elderly. In the future, there will be an increased need to embed context sensitive AAL systems in an existing smart environment, equipped with built-in sensors, wearable sensors or smart devices. Especially in the health sector, AAL system architectures show similarities with application architectures in the field of Smart Homes or Smart Cities. In all these domains the use of context-based middleware is essential. Internet of Things (IoT) platforms, which are used particularly in the area of Smart Homes or Smart Cities, deal with a wider range of data than AAL middleware, for which there is no generally accepted solution yet. The aim of this paper is to present a flexible, model-based coupling of context-based middleware, which is widely accepted especially in the field of Smart Cities, with standard interfaces for use in the AAL area. In this paper we use the project AYUDO as an AAL use case and FIWARE, an open IoT middleware platform, as a context-based middleware candidate for this coupling.
Improvement of Pressure Control Skill with Knife Device for Paper-Cutting

In this paper, we propose an interactive system for controlling the pressure while cutting paper with a knife. The purpose is to improve the cutting skill of novices learning the art of paper-cutting. Our system supports skill improvement for novices by measuring and evaluating their cutting pressure in real-time. In this study, we use a knife with a blade attached to a stylus with a pressure sensor, which can measure the pressure, coordinates, and cutting time. We have developed a similar support system using a stylus and a tablet device. This system allows the user to experience the pressure of experts through tracing. Paper-cutting is created by cutting paper with a knife. The practice system in this paper provides practice in an environment more akin to the production of paper cutting. In the first experiment, we observed differences in cutting ability by comparing cutting pressures between novices and experts. As a result, we confirmed that novices cut paper at a higher pressure than experts. We developed a practice system that guides the novices on controlling the pressure by providing information on the cutting pressure values of experts. This system shows the difference in pressure between novices and experts using a synchronous display of color and sound. Using these functions, novices learn to adjust their cutting pressure according to that of experts. Determining the right cutting pressure is a critical skill in the art of paper-cutting, and we aim to improve the same with our system. In the second experiment, we tested the effect of the practice system on the knife device. We compared the changes in cutting pressure with and without our system, the practice methods used in the workshop, and the previously developed stylus-based support system. As a result, we confirmed that practicing with the knife device had a better effect on the novice's skill in controlling cutting pressure than other practice methods.
Bayesian Multiple Quantile Regression for Linear Models Using a Score Likelihood

We propose the use of a score based working likelihood function for quantile regression which can perform inference for multiple conditional quantiles of an arbitrary number. We show that the proposed likelihood can be used in a Bayesian framework leading to valid frequentist inference, whereas the commonly used asymmetric Laplace working likelihood leads to invalid interval estimations and requires further correction. For computation, we propose a novel adaptive importance sampling algorithm to compute important posterior summaries such as the posterior mean and the covariance matrix. Our proposed approach makes it feasible to perform valid inference for parameters such as the slope differences at different quantile levels, which is either not possible or cumbersome using existing Bayesian approaches. Empirical results demonstrate that the proposed likelihood has good estimation and inferential properties and that the proposed computational algorithm is more efficient than its competitors.
Teaching Practiceson the Course of" Soil Mechanics and Foundation Engineering "Based on Engineering Education Certification

”outcome-based education”, ”student-centered mode” and ”continuous improvement mechanism” are the core concepts of IEET engineering education certification, and this paper carry out the teaching practices of ”Soil mechanics and foundation engineering” accordingly. In order to embody the outcome-based education, the course teaching group establish a clear correspondence between the course objectives and professional core competencies, and then the teaching content can be arranged in a targeted manner. The introduction of flipping teaching makes the course more and more ”student-centered”. Standard and comprehensive evaluation and feedback system is the motivation and guarantee for the course to improve continuously. All these works have a positive effect on the final pass of the certification, and at the same time clarify the ideas and lay the foundation for the follow-up development of the course, and also provide a reference to others.
Constructing and measuring of the critical success factors of college students international mobility: Application of Analytic Hierarchy Process (AHP) method

With the increasing popularity and importance of globalization issues, "global mobility" has become one of the important education policies of Taiwan's higher education.Many universities have actively applied "Shuei-Hai-Fei-Yan" and "Shuei-Hai-Shi-Jhu" programs to follow the trend of globalization and arrange outstanding students to participate in overseas training or internships in order to strengthen the cultivation of university students' global mobility.However, under the impact of the trends of "globalization", "lower birth rate" and "digitalization", it is necessary to explore the critical success factors of global mobility of college students.This study takes a teacher and Student University in southern Taiwan as the research object, and applies Analytic Hierarchy Process (AHP) and questionnaires on 11 experts and 174 students.The results of the study found that: (1) the major aspects of factors for teachers using AHP to recognize the critical success factors of university students' international mobility are "environmental adaptability", "international language skills", "challenges and adventures", and "global vision"; the perspectives of college students are "challenging and adventurous", "global vision", "environmental adaptability", and "international language ability"; (2) AHP experts recognize that among the 13 key elements of the secondary aspect of the key success factors for international mobility of college students, their weights are ranked in the top five in sequence: "fluent international language or oral expression ability", "oversea life adaptability", "clear message conveyed in international languages", "overseas psychology adaptability", "adaptability for overseas work/study abroad"; and the perspective of university students is "multicultural tolerance or acceptance", "try or experience new things", "dare to challenge difficult tasks", "cross-cultural thinking perspective", and "without the fear of risks or failure".
Graph Based Wave Function Collapse Algorithm for Procedural Content Generation in Games

This paper describes graph-based Wave Function Collapse algorithm for procedural content generation. The goal of this system is to enable a game designer to procedurally create key content elements in the game level through simple association rule input. To do this, we propose a graph-based data structure that can be easily integrated with a navigation mesh data structure in a three-dimensional world. With our system, if the user inputs the minimum association rule, it is possible to effectively perform procedural content generation in the three-dimensional world. The experimental results show that the Wave Function Collapse algorithm, which is a texture synthesis algorithm, can be extended to non-grid shape content with high controllability and scalability.
Building Electronic Health Record Databases for Research

This chapter presents information about the development and use of Electronic Health Record (EHR) Databases. There are petabytes of untapped research data hoarded within hospitals worldwide. There is enormous potential in the secondary analysis of this clinical data, leveraging data already collected in everyday medical practice, we could gain insight into the clinical decision-making process and it’s impact on patient outcomes. In this chapter we outline a high-level overview of some of the important considerations when building clinical research databases.
A pareto front-based metric to identify major bitcoin networks influencers

Bitcoin is a novel digital currency that relies on cryptography instead of a central authority to verify transactions. Without a central authority, Bitcoin requires a complete list of all transactions to be made public so that they can be verified by all users. The major network influencers in a Bitcoin network are defined as users that accumulate a disproportionate amount of wealth compared to others. However, there are some defined metrics to identify major network influencers, considering multiple criteria can improve the detection task. In this paper, a multi-criteria metric is applied to identify the major network influencers based on the history of their activities recorded on the blockchain. The proposed metric is based on the Pareto front on multiple criteria, the maximum increase in wealth with the least amount of activity using non-dominated sorting inspired from multi-objective optimization. The provided descriptive statistics on extracted data demonstrates the efficiency of the proposed metric on identification of major influencers.
SafetyLens: Visual Data Analysis of Functional Safety of Vehicles

Modern automobiles have evolved from just being mechanical machines to having full-fledged electronics systems that enhance vehicle dynamics and driver experience. However, these complex hardware and software systems, if not properly designed, can experience failures that can compromise the safety of the vehicle, its occupants, and the surrounding environment. For example, a system to activate the brakes to avoid a collision saves lives when it functions properly, but could lead to tragic outcomes if the brakes were applied in a way that's inconsistent with the design. Broadly speaking, the analysis performed to minimize such risks falls into a systems engineering domain called Functional Safety. In this paper, we present SafetyLens, a visual data analysis tool to assist engineers and analysts in analyzing automotive Functional Safety datasets. SafetyLens combines techniques including network exploration and visual comparison to help analysts perform domain-specific tasks. This paper presents the design study with domain experts that resulted in the design guidelines, the tool, and user feedback.
Survey on Recommendation of E-commerce products to existing and new user by Analyzing Social data along with e-commerce data

At this moment, the line between Online Media and E-exchange is diminishing.Various online business destinations support the arrangement of social login where customers can sign on the locales using their relational association characters, for instance, their Face book or Twitter accounts.Customers can moreover post their as of late purchased things on smaller scale web journals with associations with the online business thing webpage pages.In any case, customers won't hit a thing in case they can't see it, or they are not roused by the thing the suggesting system expects a fundamental activity in discovering interest things from close unending stock and demonstrating them to likely customers.Be that as it may, two issues are destroying the suggesting structure.One is "the way to manage new customers", and the other is "the methods by which to amaze customers."They can share their purchase nuances through online systems administration media using the association of that purchased thing from that electronic business page.Focusing on the thing proposition to the customers on e-trade destinations by methods for using the information or capacity got from the customers' social records.In cold -start conditions, this will enable us to review the prerequisites of the customer.Cold-Start is a state when the client signs in to the online business site considering the way that and don't have any data about the past occasions, of buys, shopping structures, etc as it isn't yet made or open.They can use the customer's social record information from the customer's posts, friends, share, etc for their bit of leeway.They will apply information mining estimations to get to the little extension that makes the client has made and empty the steady catchphrases and in this way, this information from the tinier degree online journals changes into the clarification behind thing recommendation in Chile beginning conditions.The two segments are uninhibited revealed in the planning sort out and moved into the last proposal for new customers.Moreover, reasonably detail the two issues into a bound together objective and present iterative development estimation.The brisk improvement of the proposed framework is to diminish the multifaceted nature and broad speculative appraisals there are given to check the impediment of our strategy.
Quantum unsupervised and supervised learning on superconducting processors

Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the data, the computation time for training and using these statistical models grows quickly. Here, we propose and implement on the IBMQ a quantum analogue to K-means clustering, and compare it to a previously developed quantum support vector machine. We find the algorithm's accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that it becomes less computationally expensive to implement for large datasets as compared to its classical counterpart.