Reviewerly/exHarmony huggingface dataset:
Telugu OCR using Dictionary Learning and Multi-Layer Perceptrons

Dictionary Learning (DL) methods have been applied successfully in image processing applications like image de-noising, inpainting, mostly using their representation and reconstruction capabilities. From our experimentation and also from literature, it is seen that the performance of DL approaches on classification tasks is not satisfactory. Especially, the performance is seen to degrade with higher dimensionality and increasing number of classes. We propose a hybrid approach to overcome the classification problem encountered by DL approaches. The novel approach uses the strengths of data abstraction and reconstruction from the DL methods while realising a high classification accuracy through a simple Multi-Layer Perceptron (MLP). In the proposed approach, data abstraction is achieved by the DL method and learned sparse codes are used as inputs for training the MLP. The training is relatively fast as the entire dataset need not be trained. The objective is to minimize the computational requirements for classifying complex datasets like Telugu OCR, without compromising the performance. The method has been tested on University of Hyderabad Telugu Printed Connected Components (UHTelPCC) and Modified National Institute of Standards and Technology database (MNIST) datasets with results comparable to the state-of-the-art methods.
Linking Social Media Posts to News with Siamese Transformers

Many computational social science projects examine online discourse surrounding a specific trending topic. These works often involve the acquisition of large-scale corpora relevant to the event in question to analyze aspects of the response to the event. Keyword searches present a precision-recall trade-off and crowd-sourced annotations, while effective, are costly. This work aims to enable automatic and accurate ad-hoc retrieval of comments discussing a trending topic from a large corpus, using only a handful of seed news articles.
Research on Construction of Cloud Computing Platform for Railway Enterprises

Aiming at the existing problems of railway enterprise information system in cloud computing environment, this paper combined with extended TOE framework and RBV model, established a cloud computing research model for railway enterprise, and extracted the main factors affecting the adoption, and given some assumptions. This paper given the overall framework of cloud computing platform for railway enterprises, and built a complete enterprise-level cloud computing platform through open source products. At the same time, the paper elaborated the security defense system of Cloud-Pipeline-Terminal, and designed the security architecture of cloud computing, and provided multi-level capability support for railway enterprise information system, such as infrastructure, development platform, application and so on.
Object Detection in Monocular Infrared Images Using Classification – Regresion Deep Learning Architectures

The rapid development of deep learning architectures that have a good performance on object detection in visual monocular images has triggered and interest towards the application of these architectures on other image modalities such as stereovision or infrared images.We propose a framework for multi-class object detection in monocular infrared images that integrates and compares different classification-regression deep learning architectures [1] on a novel benchmark infrared dataset developed by FLIR.The work described is evaluated using standard object detection metrics and an average precision of 82% for pedestrians, 86% for cars and 66% for bicycles is achieved while running at 40fps.
PostAR: Design a Responsive Reading System with Multiple Interactions for Campus Augmented Posters

Augmented reality is a technology which extends measureless contents in the virtual space and makes actual objects not limited in the physical properties. As the most popular cultural media among the campus, the poster have its shortcoming of offering restricted information. In this paper, we introduce a responsive poster-reading system which generates diverse contents step by step with body-based and hand-based interactions, and a design guideline which applies for corresponding posters. The proposed method is able to provide users with a user-friendly, immersive and intuitive experience.
The Mediating Effect of Teacher’s Implementation of Children’s Right Respect on the Relationship between Perception of Children’s Right and Teacher-Child Conflict

The purpose of this study was to investigate the mediating effect of teachers’ implementation of children’s right respect on perception of children’s right and teacher-child conflictual relationship. This study involved 360 child-care teachers from 98 daycare centers in Seoul, In-cheon, Gyeong-gi, and Chung-nam provinces. The data were collected through survey questionnaires completed by teachers, and analyzed using descriptive statistics, three-step mediated regression analysis, and a Sobel test. The result of the study indicate that teachers’ implementation of children’s right respect had a complete mediating effect on the relationship between perception of children’s right and teacher-child conflictual relationship.
Sub-Poissonian single-emitter laser: Coherent pump vs incoherent

We theoretically investigate the properties of a single-emitter laser with coherent pump: two-level atom interacting with a single damping cavity mode which is resonantly excited by a coherent field. With the help of numerical simulation of the master equation we show that such pumping mechanism is more preferably than incoherent pump for achieving of sub-Poissonian photon statistics.
Automatic polyp localization by low level superpixel features

Colorectal cancer (CRC) is a major public health issue by its high incidence and mortality rate. CRC appears as premalignant lesions growing in the endoluminal wall, called polyps. Currently, a regular screening of CRC during a colonoscopy is the standard procedure to localize and treat polyps. However, evidence suggests 20% - 24% of adenomatous polyps may be missed during a routine colonoscopy. A limited adenoma detection (ADRs) is obtained because colon exploration is a very challenging task: it is highly dependent on the expert training and colon preparation. Hence, a second reader is required to support CRC screening. This paper presents a novel automatic computer-aided method to localize polyps in colonoscopy images. The method starts by segmenting an input frame into a set of superpixels, each of them characterized by concatenating color, texture, and shape features computed either locally, i.e., basic local statistics, or regionally, i.e., any measure is modulated by information in neighboring superpixels. Afterward, this representation feeds a classifier which sets a probability and a polyp is a group of superpixels with high assigned probability. Finally, the resultant groups were enclosed by a bounding box which corresponds to the colorectal polyp localization. The proposed approach was trained with 200 polyps (350 images) and tested with 86 polyps (236 images) of different size. Performance of our method was compared with a baseline based on deep CNN obtaining an average of Annotated Area Covered of 0.90 vs 0.89 and a precision of 0.96 vs 0.95 respectively.
Development of Automatic System ICMLS 2.0 for Improving Educational Technology Competences in Industrial Revolution 4.0

The focus of this research was to develop an "ICMLS (Integrated Communication Mobile Laboratory Simulator)" in the form of mobile laboratories innovations for vocational teachers and students of Computer and Network Engineering. This was produced through the development of R & D with support from industrial partners in order to make all teachers and students in Vocational High School, West Java experiences the quality of the product. The prototype developed accommodated all practical activities for class X to XII in one integrated package for server and client functions. This research was conducted for 2 years, and in the first year, 2018, ICMLS version 1.0 was produced and 2.0 will be produced in the second year. This product can improve the competence of Educational Technology in Computer and Network Engineering Study Programs to compete in the 4.0 Industrial Revolution Era which is more characterized by Artificial Intelligence and Big Data. This was evident from the increase in the average competency test of magister students from 85.13 to 85.53 produced from the lowest initial skill average of 77.9 to the highest of 89.4.
Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules

Change-proneness modules are described as the programming parts in the source code which has a high likelihood to modify later on. Change-proneness prediction causes programming analyzers to streamline and focus their testing resources on the modules which have a higher likelihood of modification. Accurate estimation of characteristics such as effort, quality, and risk which are the major concerns for change proneness, is of significant worry in the software life cycle. Regression analysis and the neural network techniques are the commonly used methods for attribute estimation, as per the literature. Chidamber and Kemerer metric (CKJM) suite have been used in this study as an input for model training using neural network with various algorithms to optimal weights and ensemble techniques. These models are validated using five different version of eBay web services. The efficiency of the developed models are computed using three different performance parameters such as AUC, F-Measure, and Accuracy. The information present in the experimental results suggested that the models trained using levenberg marquardt (LM) method achieved better results when compared to the model built using the other classifiers.
Features of designing and operation of photoelectric systems

The current stage of economic development requires the use of modern service functions, which must provide the necessary technological parameters, reliability and energy efficiency of alternative sources. Operation of photovoltaic systems is subject to the peculiarities of the technological process of solar energy conversion. The purpose of this work is to analyze the capabilities of modern services based on the practical experience of the installers. The study is based on computer simulation, network technologies and features of photovoltaic power plants. The experience of operating solar stations confirms the importance of specialized resources and the feasibility of remote monitoring. This will ensure maximum installation performance. The use of modern service functions greatly simplifies the construction of a photovoltaic station and allows you to perform monitoring using network technologies. Design of a power plant with an installed capacity of 33 kW was carried out. The station operates in parallel with the network. The project was implemented using the ABB Stringsizer tool. The system generates electricity of more than 34000 kWh. This provides a profit of UAH 150000 in green tariff. Reliability indicator is a systematic control of equipment. Leading manufacturers of solar inverters have convenient services. This allows remote monitoring without visiting the facility. The Aurora Vision platform provides continuous analysis of a solar power plant. The platform uses modern means of visualizing system parameters. Aurora Vision is monitored using a wireless connection and a wired LAN connection. A secure portal Plant Management Platform has been created for data storage. To eliminate errors, you need a modern hardware-software complex. It allows you to restore the system. Aurora Manager Lite allows you to read errors and install software on any inverter in-house. Further research is recommended to investigate the features of conventional and microinverter technologies to determine the priority option for roof and ground layout
Adaptive frequency saliency model based on convolutional neural networks: a case study for prostate cancer MRI

This article introduces a novel adaptive frequency saliency model (AFSM) that selects relevant information by filtering an image with a set of band pass filters optimally placed in the frequency space using an auto- encoder CNN. The obtained images show a higher signal-to-noise ratio and therefore they improve a classifier performance. The proposed method is challenged by a classification task: prostate magnetic resonance imaging (MRI) to be labeled as cancerous or non-cancerous tissue. Evaluation in this case was carried out by training a convolutional neural network (CNN) with a prostate dataset but at the testing phase, the trained model is assessed with non-filtered and filtered images. The classifier tried with filtered images outperformed the results obtained with the non filtered ones (classification accuracy scores of 0.792&plusmn; 0.016 and 0.776&plusmn; 0.036 respectively), demonstrating better overall performance and the importance of using filtering processes.
Implementation of Situational Leadership in Educational Organizations

The writing of this article aims to find out situational leadership in educational organizations.The writing of this article uses a literature study approach by examining theories that relevant to the principal's leadership with a situational leadership model (contingency).Based on the studies conducted it can be seen that:(1) the contingency model is said to be effective depending on the conditions and situations between leadership behavior and the maturity level of its subordinates; (2) leadership through a situational approach can affect the level of success of a leader in streamlining the performance of his subordinates; (3) contingency the model is taken into consideration in situations that help a leader in deciding leadership styles; and (4) a situational approach is applied by a leader because there are various possibilities of different situations and behaviors in educational organizations.
The Development of Demische Software to Detect and Reduce Misconception in Chemical Equilibrium through Conceptual Change Text Strategy

This research is used to obtain Demische (Decreasing misconception in chemical equilibrium) software that is proper through conceptual change text strategy that is developed as learning media to detect and reduce misconception in chemical equilibrium 11th grade.Software feasibility can be known by three aspects that are (1) software validity based on content and construct validity, (2) practicality based on student response questionnaire supported by student observation activities, (3) effectiveness based on shifting understanding of student conceptions from misconceptions to knowing the concepts.This research uses Research and Development (R&D) method based on Borg and Gall, with 12 subject that are the students of 11th grade at Senior High School 1 Pandaan, Pasuruan in Indonesia who have studied chemical equilibrium and have potential to misconception.The result shows that Demische software was used to detect and reduce misconception through conceptual change text strategy in chemical equilibrium which can be known by three aspects that are (1) the average percentage of content validity is 83,06% and construct validity is 85,60%, both of validity are categorized as highly valid; (2) the average percentage of student response is 91,11% and categorized as highly practical; (3) the average percentage of effectiveness based on shifting understanding of student conceptions from misconceptions to knowing the concepts is 84,92% and categorized as highly effective.Based on the result, Demische software is proper to detect and reduce misconception in chemical equilibrium.
Image Generation Framework for Unbalanced License Plate Data Set

Deep learning based methods have achieved promising performance in the image fields, but they are significantly sensitive to the distribution of data. To obtain balanced data, the acquisition of annotation data is very time-consuming and laborious. Generative Adversarial Networks have made a lot of progress in data generation but may degrade greatly in the unbalanced data set. In this paper, we propose an image generation framework to generate photo-realistic, various, and balanced images of text. Specifically, we add the module for training unpaired images (U-module) and target selector to our framework, and the target selector uses text string to select images in the extended real images, which contain real images and the generated images by the U-module, in addition, global generator and local enhancer network are applied to improve the quality of the generated images. We demonstrate our method on the Chinese license plates, and the unbalance of the license plate data set is shown in the provincial category and the special license plates. The Inception Score and the FID Score are used as metrics to validate our method. The experimental results show that the Inception Score of the generated images is close to that of the real images, and our method achieves the lower FID Score than the state-of-the-art. In the SYSU-ITS dataset, the accuracy of license plate recognition has been largely improved, especially for the provinces with no or few real samples.
The Facilitation of Modern Technics for English Pronunciation Class in Foreign Language Learning in China

Abstract English is learned in China as a foreign language, the foreign language setting made it very difficult to learn the standard pronunciation. But the short comings can be greatly overcome by the proper use and facilitation of modern technics. This passage focused on what and how software and mobile phone Apps can be used in English class to improve the performance of English pronunciation. CCS Concept • Social and professional topics➝Adult education
Ecosystem development support for science, education and innovation

The article discusses the creation of the National Repository of Academic Texts (NRAT). The general concept of the formation and certain aspects of the activities of the National Repository are described in accordance with the goal and mission of the NRAT proclaimed in regulatory documents. Its role in the development of scientific, educational and innovative activities, as well as in terms of the establishment of principles of academic integrity on a domestic basis, is determined. The mechanism of the positive impact of the national scientific archive on the field of scientific research is demonstrated due to the openness of information, the possibility of operational scientific communication, a significant expansion of the audience in which the hypotheses and the results are discussed. The key aspects of the impact of the availability of quality information on educational processes throughout a person’s life, from primary education to advanced training and the acquisition of new knowledge and skills, are identified. It is shown how the functioning of the National Repository of Academic Texts can help scientists, educators and entrepreneurs overcome the “valley of the death of innovation” through information integration tools. It is proved that the national repository cannot be only an electronic archive of domestic academic texts with the corresponding registry and electronic copies available for review 24/7. It is not enough to save data. It is necessary to ensure their maximum full involvement in circulation. Therefore, NRAT should become the basis of the domestic digital ecosystem of science, education and innovation, built on the principles of inclusion, a comprehensive process in which wide opportunities are provided to all interested parties on the principles of accessibility, equal opportunities, permanence, stability, development. The ability of NRAT to provide valorization, that is, the productive use of scientific data, is demonstrated. The argument is given that the official portal of the National Repository cannot be limited to one function — to serve as an access point to academic texts. It should also maintain academic integrity, open science, provide information support for the main categories of visitors and users. Based on this, an approximate structure of information and reference materials is proposed, which should be placed on the official web portal of NRAT.
Location Privacy Protection Scheme Based on Location Services

Location-based services (LBS) in the mobile internet applications are very important and provide a great convenience. However, at the same time it brings the threat of privacy leak. For location services, a location privacy protection scheme is proposed, which includes location hiding algorithm and query privacy protection algorithm. Q-Tree storage ensures that anonymous location units are as dispersed as possible. The point of interest (POI) with higher query probability is selected as the query content of anonymous location unit, which protects the user's query privacy. At the same time, private information retrieval technology (PIR) is used to provide users with higher privacy and security protection. Finally, the effectiveness of the scheme is proved by privacy analysis and experimental results.
An Interactive Task Modeling Method for the Development of Augmented Reality Maintenance Guiding System

Nowadays, augmented reality (AR) technology has attracted much attention, while the study for AR development is still far from maturity. Aiming for the industrial maintenance operation with AR, this paper investigates the analyzing and modeling approach for interactive tasks, where the elements and their relationships within the augmented reality maintenance guiding system (ARMGS) are explored. A notation for task model specifications, i.e. concur task tree (CTT), is used to obtain system's formal description from the top-level functional tasks to the bottom-level interactive operation tasks. The proposed method can integrate the users' requirements into the interaction design at the abstract level. Therefore, it provides the designers with a common approach for the interaction design of task analysis. Selecting the replacement and maintenance operations of an engine as an example, the interactive task model is established, and the modeling process is described in detail. The corresponding maintenance application system is developed on a head mounted display (HMD) as instrument to verify the interaction effectiveness.
Multimodal Mobile News

Tablets provide a canvas with unique characteristics, by which news institutions and their readers can, respectively, distribute and access news. This paper reports on a study into the multimodal construal of news on the front page/home page of 12 English-language news apps from Australia, the UK, and the USA. The apps studied appear to cluster into a relatively limited number of design choices according to their use of language, page composition, and visual metaphor on the home page. Images are used for a wide variety of news functions and in a variety of different designs, but are ubiquitous. The question of whether the home pages of these apps are image-centric is addressed. A definition of ‘image-centricity’ is proposed.
Automatic Text Generation: How to Write the Plot of a Novel with NooJ

Automatic Text Generation (ATG) is a Natural Language Processing (NLP) task that aims at writing acceptable and grammatical written text exploiting machine-representation systems, such as for instance knowledge bases, taxonomies and ontologies. In this sense, it is possible to state that an ATG system works like a translator that converts data into a natural-language written representation. The methods to produce the final texts may differ from those used by compilers, due to the inherent expressivity of natural languages. ATG is not a recent discipline, even if commercial ATG technology has only recently become widely available. Today, many software environments cope with ATG, as Text Spinner, DKB Lettere, or textOmatic*Composer, just to mention a few.