Reviewerly/exHarmony huggingface dataset:
University Students’ Perceptions of MALL in EFL Classes

&lt;p&gt;&lt;em&gt;Mobile technologies are rapidly attracting new users, providing increasing capacity, and allowing more sophisticated use&lt;/em&gt;&lt;em&gt; such as accessing the Internet for searching information, emailing and reading e-books. As the mobile technology has been more powerful and inclusive in people’s daily lives, the issue of mobile assisted language learning (MALL) has also been widely studied. Many researchers of MALL consider the emerging mobile technologies to have potential for the effective language learning. &lt;/em&gt;&lt;em&gt;The majority of MALL activities appear to make use of mobile phones, and nowadays, smartphones replace feature phones. &lt;/em&gt;&lt;em&gt;This study focuses on the investigation of&lt;/em&gt;&lt;em&gt; students’ perceptions of the benefits and challenges associated with the use of smartphones for learning.&lt;/em&gt;&lt;/p&gt;
Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree

Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning. The entropy kernel potentially links its deviation and decision tree classification performance. This paper presents a decision tree learning algorithm based on constrained gain and depth induction optimization. Firstly, the calculation and analysis of single- and multi-value event uncertainty distributions of information entropy is followed by an enhanced property of single-value event entropy kernel and multi-value event entropy peaks as well as a reciprocal relationship between peak location and the number of possible events. Secondly, this study proposed an estimated method for information entropy whose entropy kernel is replaced with a peak-shift sine function to establish a decision tree learning (CGDT) algorithm on the basis of constraint gain. Finally, by combining branch convergence and fan-out indices under an inductive depth of a decision tree, we built a constraint gained and depth inductive improved decision tree (CGDIDT) learning algorithm. Results show the benefits of the CGDT and CGDIDT algorithms.
COMPUTER DIAGNOSTICS OF PROSPECTIVE TEACHERS’ READINESS FOR PROFESSIONAL SELF-DEVELOPMENT

The authors developed and tested diagnostic instruments (a computer testing system) aimed at the identification of prospective teachers’ readiness for continuous professional self-development by the following criteria: the awareness of necessity for purposeful continuous professional self-development; the level of acquired professional knowledge, integrative and theoretical training; the formation of abilities and practical skills for professional self-development; practical training; a set of individual and psychological qualities important for continuous professional self-development and future professional activity in general; the focus on self-analysis, self-assessment of professional self-development. The developed survey “Diagnostics of Readiness for Professional Self-development” includes original parts of questionnaires and tests, which were creatively redesigned and adapted according to the research. Among them: “Dispositive Characteristic of Self-development of Personality”, “Diagnostics of Success Motivation”, “Diagnostics of Self-development Needs”, “Motivation of Professional Activity”, Cattell’s 16 PF-questionnaire, and others. For the purpose of implementing the authors’ concept, the technology of prospective teachers’ training for continuous professional self-development is developed. This technology is considered to be a system of contents, principles, methods, forms, substantiated organizational and pedagogical conditions that provides implementation of the authors’ concept and guarantees achievement of the planned results – increasing the level of prospective teachers’ readiness for continuous professional self-development. After the implementation of the technology, the computer diagnostics was applied again. The results of the final diagnostics showed the effectiveness of the training technology of the prospective teacher for continuous professional self-development and the computer method “Diagnostics of Readiness for Professional Self-development”.
2D-to-Stereo Panorama Conversion Using GAN and Concentric Mosaics

We describe a learning-based technique to automatically convert a 2-D panorama to its stereoscopic version. In particular, we train a generative adversarial network using perspective stereo pairs as inputs. Given a 2-D panorama, we partition it into overlapping local perspective views. To satisfy the panoramic stereo condition, we generate a sequence of left and right stereo view pairs and stitch them to produce concentric mosaics. We also describe experiments on synthetic and real datasets as well as comparisons with competing state-of-the-art techniques, which validate our technique.
TF/TA Optimal Flight Trajectory Planning Using a Novel Regenerative Flattener Mapping Method

In this paper, a new methodology has been proposed to enhance the conformal mapping applications in the process of optimum trajectory planning in Terrain Following (TF) and Terrain Avoidance (TA) Flights. The new approach uses the conformal mapping concept as a flattener tool to transform the constrained trajectory-planning problem with flight altitude restrictions due to the presence of obstacles into a regenerated problem with no obstacle and minimal height constraints. In this regard, the Schwarz–Christoffel theorem has been utilized to incorporate the height constraints into the aircraft dynamic equations of motion. The regenerated optimal control problem then is solved employing a numerical method namely the direct Legendre-Gauss-Radau pseudospectral algorithm. A composite performance index of flight time, terrain masking, and aerodynamic control effort is optimized. Furthermore, to obtain realistic trajectories, the aircraft maximum climb and descent rates are imposed as inequality constraints in the solution algorithm. Several case studies for two-dimensional flight scenarios show the applicability of this approach in TF/TA trajectory-planning. Extensive simulations confirm the efficiency of the proposed approach and verify the feasibility of solutions satisfying all of the constraints underlying the problem
Decision Support System for Determining Thesis Supervisor using A Weighted Product (WP) Method

English Department of Tadulako University is a department with the highest number of lecturers in Tadulako University. Thesis supervisor determination at the English Department of Tadulako University still use the conventional way so that the determination of the thesis supervisor is done in a long time. In addition, some lecturers have more workload than other lecturers. Decision support system for the determination of the thesis supervisor in the English Department of Tadulako University was built to overcome this problem using the Weighted Product method. The system is a desktop-based application using the Visual Studio programming language and MySQL DBMS for database processing. The application of a decision support system for determining thesis supervisors can help make the selection of supervisors accurately and quickly with a mathematical calculation system.
A Graph Layout Framework Combining t-Distributed Neighbor Retrieval Visualizer and Energy Models

Graph layout investigates the structure of the graph in order to better obtain the information implied in the graph. To solve the shortcomings of dimension reduction layouts on local adjustment and the insufficiency of energy models to maintain the overall structure of the graphs, this paper proposes a new graph layout framework called “tNEM” that layouts graphs by combining t-distributed neighbor retrieval visualizer (t-NeRV) and energy models. In the process of layout, our algorithm considers global and local structures at the same time. The layout results are more conform to aesthetic standards, meanwhile, maintain the structural information of the graph. We evaluate our algorithm on a wide variety of datasets and compare it with many other methods. We produce better visualization results than tsNET and tsNET* methods by reducing the tendency to crowd points together, and can better capture the global structure of the graph.
A Novel EM Implementation for Initial Alignment of SINS Based on Particle Filter and Particle Swarm Optimization

For nonlinear systems in which the measurement noise parameters vary over time, adaptive nonlinear filters can be applied to precisely estimate the states of systems. The expectation maximization (EM) algorithm, which alternately takes an expectation- (E-) step and a maximization- (M-) step, has been proposed to construct a theoretical framework for the adaptive nonlinear filters. Previous adaptive nonlinear filters based on the EM employ analytical algorithms to develop the two steps, but they cannot achieve high filtering accuracy because the strong nonlinearity of systems may invalidate the Gaussian assumption of the state distribution. In this paper, we propose an EM-based adaptive nonlinear filter APF to solve this problem. In the E-step, an improved particle filter PF_new is proposed based on the Gaussian sum approximation (GSA) and the Monte Carlo Markov chain (MCMC) to achieve the state estimation. In the M-step, the particle swarm optimization (PSO) is applied to estimate the measurement noise parameters. The performances of the proposed algorithm are illustrated in the simulations with Lorenz 63 model and in a semiphysical experiment of the initial alignment of the strapdown inertial navigation system (SINS) in large misalignment angles.
A Study on Factors for Introducing Smart Factory to Improve Competitiveness of Small and Medium-Sized Metal Processing Companies

In order to improve manufacturing competitiveness in Korea, the government aims to improve the productivity of manufacturing enterprises through 20,000 smart factories by 2022. However, many SMEs are not actively investing in new facilities in smart factories due to the deteriorating manufacturing industry. The purpose of this study is to analyze smart factory construction factors and the effect of introduction of smart factory on the competitiveness medium-sized metal processing companies. For this purpose, we conducted an empirical analysis of 140 employees of SMEs in Daejeon TechnoValley, Sangseo-dong, Pyeongchon-dong small and medium manufacturing enterprises. As a result, corporate participation, participation of government support projects, and manufacturing capability have a positive impact on the smart factory construction effect. The results of this study can be a policy reference factor for the successful introduction of smart factory in SMEs and it is meaningful that construction of smart factory plays an important role in enhancing corporate competitiveness.
The Representation of The Tri Hita Karana Ecosophy in The Awig-awig (Customary Law) Text of Tenganan Pegringsingan Village: Critical Ecolinguistics Perspective

The awig-awig (Customary Law) of Tenganan Pegringsingan Village has existed since 11th century before the Tri Hita Karana ecosophy was initiated in Bali; however, this awig-awig was able to preserve the environment, in terms of natural environment (palemahan), social environment (pawongan), and spiritual environment (parhyangan). The present study aims at investigating evaluations and assessment of the THK ecosophy employed in the awig-awig text of Tenganan Pegringsingan village. The research aspires to reveal the value of relationship of human with the natural, social, and spiritual environments. The data of the research were taken from the written text of the awig-awig. The collected data were analysed using critical ecolinguistics approach, referring to evaluation theory proposed by Stibbe (2015) and appraisal Theory by Martin and White (2005). The results showed that the awig-awig text reveals positive, ambivalent, and negative evaluations. The positive evaluations are in a line with the THK ecosophy; the ambivalent evaluation contradicts between positive and negative evaluations; whereas, the negative one contradicts to the ecosophy.
Layout-aware information extraction from semi-structured medical images

Textual information embedded in the medical image contains rich structured information about the medical condition of a patient. This paper aims at extracting structured textual information from semi-structured medical images. Given the recognized text spans of an image preprocessed by optical character recognition (OCR), due to the spatial discontinuity of texts spans as well as potential errors brought by OCR, the structured information extraction becomes more challenging. In this paper, we propose a domain-specific language, called ODL, which allows users to describe the value and layout of text data contained in the images. Based on the value and spatial constraints described in ODL, the ODL parser associates values found in the image with the data structure in the ODL description, while conforming to the aforementioned constraints. We conduct experiments on a dataset consisting of real medical images, our ODL parser consistently outperforms existing approaches in terms of extraction accuracy, which shows the better tolerance of incorrectly recognized texts, and positional variances between images. This accuracy can be further improved by learning from a few manual corrections.
EFFECTIVENESS OF U-NET IN DENOISING RGB IMAGES

Digital images often contain "noise" which takes away their clarity and sharpness.Most of the existing denoising algorithms do not offer the best solution because there are difficulties such as removing strong noise while leaving the features and other details of the image intact.Faced with the problem of denoising, we tried solving it with a Convolutional Neural Network architecture called the "U-Net".This paper deals with the training of a U-Net to remove 3 different kinds of noise: Gaussian, Blockiness, and Camera shake.Our results indicate the effectiveness of U-Net in denoising images while leaving their features and other details intact
Multiple description coding technique to improve the robustness of ACELP based coders AMR-WB

In this paper, a concealment method based on multiple-description coding (MDC) is presented, to improve speech quality deterioration caused by packet loss for algebraic code-excited linear prediction (ACELP) based coders. We apply to the ITU-T G.722.2 coder, a packet loss concealment (PLC) technique, which uses packetization schemes based on MDC. This latter is used with two new designed modes, which are modes 5 and 6 (18,25 and 19,85 kbps, respectively). We introduce our new second-order Markov chain model with four states in order to simulate network losses for different loss rates. The performance measures, with objective and subjective tests under various packet loss conditions, show a significant improvement of speech quality for ACELP based coders. The wideband perceptual evaluation of speech quality (WB-PESQ), enhanced modified bark spectral distortion (EMBSD), mean opinion score (MOS) tests and MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) for speech extracted from TIMIT database confirm the efficiency of our proposed approach and show a considerable enhancement in speech quality compared to the embedded algorithm in the standard ITU-T G.722.2.
The Design of Population Data Application Using Unified Modeling Language

Population data collection at the sub-district level still uses a manual system. It is causing less efficient time. In this study the application of population data is generated in the sub-district, using web applications and using the Unified Modeling Language design. With the above considerations, we need a system that can solve population data problems. With this application, it is expected that it will facilitate the processing of population data. This new application can accelerate the process of population registration with the help of human resources who can run it. Advice needed human resources that can run the application properly.
A multi-task approach to face deblurring

Image deblurring is a foundational problem with numerous application, and the face deblurring subject is one of the most interesting branches. We propose a convolutional neural network (CNN)-based architecture that embraces multi-scale deep features. In this paper, we address the deblurring problems with transfer learning via a multi-task embedding network; the proposed method is effective at restoring more implicit and explicit structures from the blur images. In addition, by introducing perceptual features in the deblurring process and adopting a generative adversarial network, we develop a new method to deblur the face images with reservation of more facial features and details. Extensive experiments compared with state-of-the-art deblurring algorithms demonstrate the effectiveness of the proposed approach.
THE EFFECTS OF SELF-REGULATED LEARNING STRATEGIES ON PRESCHOOL CHILDREN’S SELF-EFFICACY AND PERFORMANCE IN EARLY WRITING

To enhance the will and the skills to express thoughts explicitly and effectively in early writing among preschool children, self-regulated learning (SRL) was suggested for preschool children as one of the effective learning approaches. This quasi-experimental study involved seventy-five preschool children (5-6 years old) from two public preschools in Selangor, Malaysia. This study investigated the effects of SRL strategies on early writing self-efficacy and early writing performance among preschool children. Interview data was engaged and supported the quantitative result to obtain a deep insight of the findings. Two-way Repeated Measure ANCOVA was employed and confirmed the effectiveness of self-regulated learning intervention with an interaction effect between the test and group for early writing self-efficacy being statistically significant (F(1, 72) = 12.665; p = 0.001, 2= 0.150), with Cohen’s d = 0.84; and early writing performance statistically significant (F(1, 72) = 110.801; p &lt; 0.001, 2= 0.606), with Cohen’s d = 2.84. The result also confirmed that self-monitoring and controlling (F (5, 69) = 17.934, p &lt; 0.001), with an adjusted R² = 0.534, was a strong predictor for early writing self-efficacy, and planning and goal setting (F(5, 69) = 12.706, p&lt; 0.001), with an adjusted R² = 0.442, were a strong predictor for early writing performance. Eleven self-regulated learning strategies used emerged from the interviews’ data pertaining to different contexts. According to the participant children, planning and goal setting, self-monitoring, and self-evaluation were the strategies that will assure their writing quality. These responses supported the results produced by the quantitative data. The findings of this research provide a useful insight into early writing and self-regulated learning instructions in the Malaysian preschool context.
ELEVENTH GRADERS’ ACTUAL USE OF ENGLISH LISTENING LEARNING STRATEGIES AT DUONG VAN DUONG HIGH SCHOOL

It is undeniable that listening comprehension is a fundamental skill in the process of acquiring a language and plays a crucial part in communication although it is not an easy task which requires both background knowledge and linguistic understanding. Therefore, language learners are supposed to employ listening learning strategies to facilitate the process of decoding oral input. This study aimed at examining if the eleventh graders at Duong Van Duong high school used English listening learning strategies and then exploring the frequency of the students’ using these strategies. The present study is a mixed-methods research project in which both qualitative and quantitative data were collected through closed-ended questionnaire and semi-structured interview with the participation of 425 respondents and 20 interviewees. The results of the study indicated that a vast number of the participants employed listening learning strategies at different levels of frequency which were classified into three main groups, namely high use (e.g. cognitive and affective strategies), moderate use (e.g. metacognitive and social strategies) and low use (e.g. compensation and memory strategies).
Constructing a Hybrid Automatic Q&amp;A System Integrating Knowledge Graph and Information Retrieval Technologies

Question-answering (QA) system provides a friendly way for human-computer interaction, which has become an important research direction of smart learning. It provides an easy and individual way for the learner to acquire knowledge. This paper focuses on K-12 education and constructs a hybrid automatic question-answering system which integrates Knowledge Based Question Answering (KB-QA) and Information Retrieval-based Question Answering (IR-QA). The system is built based on Chinese textbooks and a Chinese K-12 knowledge graph (edukg.org). Our QA system covers 9 subjects in K-12 education field, including mathematics, Chinese, geography, history, etc. We evaluate our system on more than 9,000 questions, and achieve average accuracy over 70%. The system could provide effective assistance for teachers' teaching and students' learning.
Trends in Control and Decision-Making for Human-Robot Collaboration Systems [Bookshelf]

This book explores recent breakthroughs in the field of human–robot collaborative control and decision making. The 16 chapters in this book discuss key problems in the field from two different perspectives. The first is the system perspective, which discusses the control of 1) one-to-one human–robot systems, 2) one-to-many human–robot systems, and 3) many-to-many human–robot systems. The other is the methodology perspective, which covers the control of human–robot systems and the decision-making problem in human–robot interaction. The book provides a good summary of and timely reference to the most recent advances in the field of human–robot collaborative control and decision making.
Identifying concepts from medical images via transfer learning and image retrieval

Automatically identifying semantic concepts from medical images provides multimodal insights for clinical research. To study the effectiveness of concept detection on large scale medical images, we reconstructed over 230,000 medical image-concepts pairs collected from the ImageCLEFcaption 2018 evaluation task. A transfer learning-based multi-label classification model was used to predict multiple high-frequency concepts for medical images. Semantically relevant concepts of visually similar medical images were identified by the image retrieval-based topic model. The results showed that the transfer learning method achieved F1 score of 0.1298, which was comparable with the state of art methods in the ImageCLEFcaption tasks. The image retrieval-based method contributed to the recall performance but reduced the overall F1 score, since the retrieval results of the search engine introduced irrelevant concepts. Although our proposed method achieved second-best performance in the concept detection subtask of ImageCLEFcaption 2018, there will be plenty of further work to improve the concept detection with better understanding the medical images.