Reviewerly/exHarmony huggingface dataset:
VIRTUAL PRODUCTION TECHNOLOGY VS. ENVIRONMENT

Specialized robotic workplaces or systems are not only a complex issue, but at the same time they are financially demanding in the field of production, especially, when the prototypes essential for mechanical engineering or forestry are concerned.Such a robotic workplace calls for an original project of a small series production and the proposed virtual environment meets the requirements for the verification of the technological properties, reliability and construct possibilities of the prototypes.The specialized robotic workplace is an interactively described 3D object and is programmable in accordance with the real environment requirements.Virtual technologies represent a convenient solution for the preparation of a safe ergonomic, economic and environmental workplace.
A New Approach for Integrated Recognition and Correction of Texts from Images

Automatic recognition and error correction of texts from images are critical for many commercial applications such as receipt recognition, which have very high accuracy requirements. In this paper we propose an integrated image based text recognition and correction approach to improve accuracy. There are two levels of text recognition and correction integration in the proposed approach. Firstly, a beam search strategy is designed to generate a set of text candidates, based on the probability distribution of text prediction outcomes from a deep learning recognition model. Then a word-level lexicon check is applied to select only one from the candidate text sentences, which has the highest prediction probability among those with all words present in the lexicon. Jointly the beam search and lexicon check can effectively correct some recognition errors. Secondly, an encoder-decoder language model based corrector is developed to correct potential recognition errors in the selected output texts that fail the lexicon check. Training samples for the corrector are created from the recognition outcomes and can be expanded by associating multiple text candidates with one image label. We conduct experiments on ICDAR'13 and CH10K datasets to evaluate the proposed approach and the impact of these two levels of integration on accuracy. Experiment results show that the proposed approach outperforms the existing one with higher recall and much higher recognition accuracy through effective exploitation of joint recognition and correction design.
Exploration of CNN Features for Online Handwriting Recognition

Recently, convolution neural network (CNN) has demonstrated its powerful ability in learning features particularly from image data. In this work, its capability of feature learning in online handwriting is explored, by constructing various CNN architectures. The developed CNNs can process online handwriting directly unlike the existing works that convert the online handwriting to an image to utilize the architecture. The first convolution layer accepts the sequence of (x; y) coordinates along the trace of the character as an input and outputs a convolved filtered signal. Thereafter, via alternating steps of convolution and Rectified Linear Unit layers, in a hierarchical fashion, we obtain a set of deep features that can be employed for classification. We utilize the proposed CNN features to develop a Support Vector Machine (SVM)-based character recognition system and an implicit-segmentation based large vocabulary word recognition system employing hidden Markov model (HMM) framework. To the best of our knowledge, this is the first work of its kind that applies CNN directly on the (x; y) coordinates of the online handwriting data. Experiments are carried out on two publicly available English online handwritten database: UNIPEN character and UNIPEN ICROW-03 word databases. The obtained results are promising over the reported works employing the point-based features.
Practical Implementation of a Quantum Backtracking Algorithm

In previous work, Montanaro presented a method to obtain quantum speedups for backtracking algorithms, a general meta-algorithm to solve constraint satisfaction problems (CSPs). In this work, we derive a space efficient implementation of this method. Assume that we want to solve a CSP with m constraints on n variables and that the domain in which these variables take their value is of cardinality d. Then, we show that the implementation of Montanaro’s backtracking algorithm can be done by using $$\mathcal {O}(n\log {d})$$ data qubits. We detail an implementation of the predicate associated to the CSP with an additional register of $$\mathcal {O}(\log {m})$$ qubits. We explicit our implementation for graph coloring and SAT problems, and present simulation results. Finally, we discuss the impact of the usage of static and dynamic variable ordering heuristics in the quantum setting.
On the Way to Non-Hierarchical Communication: Methods of Reading and Writing in Teaching Philosophy to University Students

The article is devoted to the problem of organizing non-hierarchical communication in the university classroom. The authors suggest that some teaching methods can bring the gap in communication between teacher and student. The authors describe and analyze their experience with the use of reading and writing methods adopted in the liberal arts education system for teaching the philosophy to non-humanities majors at a Russian university. This case is significant because it reflects the five-year experience of using reading and writing methods in isolation from the entire humanitarian system. This isolation affects the choice of methods and their transformation. The article outlines the main difficulties that instructors face and the limitations of these methods. Difficulties in assessing the effectiveness of the considered methods are revealed. An analysis of student feedback on the seminars shows that these methods help bridge the gap in communication between teacher and student and create a supportive environment conducive to more effective work on philosophical texts.&#x0D; Keywords: liberal arts education, reading and writing, philosophy education, communication gap
A further result on the potential-Ramsey number of G1 and G2

A non-increasing sequence ? = (d1,. . ., dn) of nonnegative integers is a graphic sequence if it is realizable by a simple graph G on n vertices. In this case, G is referred to as a realization of ?. Given a graph H, a graphic sequence ? is potentially H-graphic if ? has a realization containing H as a subgraph. Busch et al. (Graphs Combin., 30(2014)847-859) considered a degree sequence analogue to classical graph Ramsey number as follows: for graphs G1 and G2, the potential-Ramsey number rpot(G1,G2) is the smallest non-negative integer k such that for any k-term graphic sequence ?, either ? is potentially G1-graphic or the complementary sequence ? = (k - 1 - dk,..., k - 1 - d1) is potentially G2-graphic. They also gave a lower bound on rpot(G;Kr+1) for a number of choices of G and determined the exact values for rpot(Kn;Kr+1), rpot(Cn;Kr+1) and rpot(Pn,Kr+1). In this paper, we will extend the complete graph Kr+1 to the complete split graph Sr,s = Kr ? Ks. Clearly, Sr,1 = Kr+1. We first give a lower bound on rpot(G, Sr,s) for a number of choices of G, and then determine the exact values for rpot(Cn, Sr,s) and rpot(Pn, Sr,s).
The Impact of Ghana's HE Governance and Regulatory Framework on Financial Sustainability

Higher education governance, legal and regulatory regimes in Ghana remain an important factor in the ever-complex higher educational landscape and its attendant funding challenges. The increased global demand for higher education reforms further strengthens the importance of higher education governance, legal and regulatory framework role in assuring sustainable funding of higher education. The purpose of this study is to measure the relationship between the governance and regulatory variables influence on financial sustainability. The research work utilises quantitative research methodology with a well validated research instrument to undertake this correlational study. The study outcome revealed a statistically significant relationship between the combined effects of the variables whiles two out of the eight variables were found be significant in predicting best fit equation for financial sustainability. The research recommendations should afford higher education practitioners the opportunity to undertake relevant reforms of the current governance, legal and regulatory practices in the country.
A Comparative Study Between Frayer Model And Concept Mapping Strategy to Enhance Students’ Vocabulary Acquisition

Abstract&#x0D; This study intends to find out what is the initial knowledge of the students before they were treated using Frayer Model and Concept Mapping Strategy, ascertain of the significant difference between these two teaching strategies, and to know the responses of the students after they were taught using Frayer Model and Concept Mapping Strategy are the aims of this research. This is a quantitative study with comparative design to know the students’ vocabulary acquisition test. The research instrument of this study is pre-and-post-test. This study was conducted among eleventh grader students at SMAN 1 Parongpong. The results of this study showed that the initial score for both respondents are quite similar with the score for FM group which is 30.50 and for CMS group which is 33.40. It is also known that there is a significant difference in students’ vocabulary acquisition between students who are taught with Frayer Model and those who are taught with Concept Mapping Strategy showed by the result of the mean differences from both groups are 0.000 &lt; 0.05. The questionnaire’s result also supports that both teaching strategies are eligible to be applied in teaching active and passive voice construction with the score for FM class which is 55% and for the CMS class which is 80%, it can be categorized as “Good”. It implies that the implementation of Frayer Model and Concept Mapping Strategy enhances student’s vocabulary acquisition.&#x0D; &#x0D; Keywords: Language Proficiency, Vocabulary Acquisition, Frayer Model, Concept Mapping Strategy
EVALUATIVE LANGUAGE IN THE DISCOURSE OF CEBONG VS KAMPRET (‘TADPOLE VS MICROBATS’) ON TWITTER

As an interactive social media, Twitter gives significat role in creating social systems. Evaluative language was intensively used on the social media. The Cebong vs Kampret issue coloured on Twitter and polarized people. By using data Tweet and Reply from Twitter during 2019 this researcher investigates evaluative language. This research results that Twitter community were very emotionally force and defense on the Cebong vs Kampret issue, depicted from many evaluative languages classified into subsystem attitude. Subsystem graduation was also intensively used in accordance to that issue. It means that Twitter community emphasized on semantic scale in evaluating things and person.
Access Control Models in NoSQL Databases: An Overview

Recently non-relational databases known as NoSQL have become most popular for handling a huge amount of data. Many organizations move from relational databases towards NoSQL databases due to the growing popularity of cloud computing and big data. NoSQL database is designed to handle unstructured data like documents, e-mails, and social media efficiently. It uses distributed and cooperating devices to store and retrieve data. As a large number of people storing sensitive data in NoSQL databases, security issues become critical concerns. NoSQL has many advantages like scalability and availability, but it suffers from some security issues like weak authorization mechanisms. This paper reviews the different models of NoSQL databases and the security issues concerning these databases. In addition, we present the existing access control models in different NoSQL databases.
THE USE OF CLUSTERING TECHNIQUE TO IMPROVE STUDENTS’ SKILL IN WRITING DESCRIPTIVE TEXT

Students have to learn the four basic language skill, they are listening, reading, writing, speaking. One of the most important skill that is Writing. It can make students to share the ideas into written form in the text or paragraph with the right order of generic structure. The purpose of this research was to find out the improving of students writing skill of descriptive text using clustering technique at seventh grade students of SMPN 11 Cimahi in the Academic Year of 2018 – 2019. This research used Classroom Action Research. The research was carried out in two cycles and each cycle consisted of four steps, they are Planning, Action, Observation, and Reflection. Most of the students gradually gained good scores at the end of each cycles. The students responses showed that they interested to learn writing.The score of Minimum Mastery Criterion of English lesson was 75. The Students mean score of pretest 45,7. The mean score in postest 1 was 53,7. The mean score of posttest 2 was 79,3. From this result, the researcher concludes that teaching descriptive text using clustering technique can improve their writing ability and the atmosphere of the class was more alive. Keywords: CAR, Writing Skill
The optimal design of a binaural sparse-aperture system

Optical sparse-aperture (OSA) can greatly improve the spatial resolution of optical system. However, its optical designs are complex. This paper proposes a binaural sparse-aperture system with a simple and symmetrical structure, which is composed of three apertures. Apertures are rotated to obtain rich frequency information. The scheme of rotating apertures is analyzed by taking the ratios of sub-apertures and the number of rotations as variables. The image artifacts are suppressed through processing in the frequency domain. Simulation results analyze the effects on OTFs and imaging quality with different rotation numbers and ratios of sub-apertures. Experimental results show that this system can reach almost the same cut-off frequency as what the equivalent multi-aperture system can reach.
Contrast Enhancement of Power Plant Images Obtained from Industrial Borescope Devices

The industrial Video scope (VS) device is designed for the endoscopic inspection of professional equipment, like motors, pumps, turbines, cavities in buildings and vehicle bodies, etc. The output of this device is images and videos, but some output images may suffer from low contrast and poor details. This paper presents three proposed approaches to enhance the quality of the VS images. The first proposed approach is based on Contrast Limited Adaptive Histogram Equalization (CLAHE) with adaptive gamma correction. By choosing the best clip limits in CLAHE and transfer function (adaptive gamma correction), the first proposal achieves optimum contrast enhancement for the VS images. The second proposed approach depends on the Homomorphic with emphasis high pass filter. The homomorphic filtering is utilized on the video scope image in logarithm domain, Applying this method gives more details in the VS images. The third proposed approach depends on Homomorphic with emphasis high pass filter with histogram equalization. Applying this method gives more accurate and best visual quality of the output VS images. Numerical results show the ability of the three proposals in improvement the VS image quality, and they can be recommended for industrial VS systems.
The Integration of MALL to Enhance Students Speaking Skill: An Autonomous Learning Model

Mobile-Assisted Language Learning (MALL) is a new approach for students to learn language through the use of mobile technology. Learning a language should not only happen in a class setting activity but also happen outside the class in which the students take charge of their learning. Hence, it requires students' autonomy in learning the language. Students are now able to carry out their learning without depending on the teachers, which then leads to a sustainable and independent learning process. Quizlet, a web application, is introduced in this paper as an alternative solution to improve students’ speaking skill through an autonomous language learning activity. This paper used an action research method combined with a questionnaire to analyze the implementation of Quizlet in an autonomous class setting at STMIK AKAKOM Yogyakarta. The practical, usability, and sustainability aspects become the focus of this research. The data gathered from the action research shows that an autonomous learning activity still requires teachers to be the facilitator, and the questionnaire shows positive feedback from the students regarding the usability and sustainability of their language learning using the application.
Data Stream Adaptive Partitioning of Sliding Window Based on Gaussian Restricted Boltzmann Machine

In this paper, the adaptive partitioning problem of data stream under sliding window is discussed. Gaussian restricted Boltzmann machine (GRBM) model supporting decimal input is proposed, which can be trained through iteration for data reconstruction subsequently. At the same time, a data stream adaptive block algorithm based on Kullback–Leibler divergence (KL distance) is proposed to compare the probability distribution difference in the sliding window. Then, obtain the predicted value by the distribution of the previous data and determine whether the KL distance is within the confidence interval, so as to realize the adaptive adjustment of the sliding window, and the divided of data stream.
Instagram as Media in Teaching Writing Recount Text for Senior High School Students

Social media is web-based application that allows users to communicate, collaborate, and share resources with other people. Both being easy to access and easy to use make social media become an effective way to communicate and exchange ideas. Students can use social media to keep up with their friends, to stay up-to-date with news and current events, to fill up spare time, to find entertaining content, and to share opinions. The use of social media becomes famous for the students nowadays. Thus, the popularity of the social media among students can be used as a tool in language teaching. One of the famous social media is Instagram. Instagram is a social network that enables its users to take pictures and videos, and share them publicly or privately on the application, as well as through a variety of other social network. Through Instagram the students can post their photos or videos that can be commented by viewers. By using Instagram, teachers are offered great opportunity to develop classroom activities creatively. This paper will give an overview of the use of Instagram in teaching writing recount text in senior high school students.
Pashtu Numerals Recognition through Convolutional Neural Networks

--In the proposed paper we introduce a new Pashtu numerals dataset having handwritten scanned images. We make the dataset publically available for scientific and research use. Pashtu language is used by more than fifty million people both for oral and written communication, but still no efforts are devoted to the Optical Character Recognition (OCR) system for Pashtu language. We introduce a new method for handwritten numerals recognition of Pashtu language through the deep learning based models. We use convolutional neural networks (CNNs) both for features extraction and classification tasks. We assess the performance of the proposed CNNs based model and obtained recognition accuracy of 93.45%.
Enabling Adaptive Deep Neural Networks for Video Surveillance in Distributed Edge Clouds

In the field of video surveillance, the demands of intelligent video analysis services based on Deep Neural Networks (DNNs) have grown rapidly. Although most existing studies focus on the performance of DNNs pre-deployed at remote clouds, the network delay caused by computation offloading from network cameras to remote clouds is usually long and sometimes unbearable. Edge computing can enable rich services and applications in close proximity to the network cameras. However, owing to the limited computing resources of distributed edge clouds, it is challenging to satisfy low latency and high accuracy requirements for all users, especially when the number of users surges. To address this challenge, we first formulate the intelligent video surveillance task scheduling problem that minimizes the average response time while meeting the performance requirements of tasks and prove that it is NP-hard. Second, we present an adaptive DNN model selection method to identify the most effective DNN model for each task by comparing the feature similarity between the input video segment and pre-stored training videos. Third, we propose a two-stage delay-aware graph searching approach that presents a beneficial trade-off between network delay and computing delay. Experimental results demonstrate the efficiency of our approach.
Effectiveness Of Mathematic Learning Media Based On Mobile Learning In Improving Student Learning Motivation

This study aims to determine the effectiveness of the use of mobile learning based learning media on students' learning motivation in the Mathematics Education Study Program FKIP Maros Muslim University. The type of research used in this research was quantitative research with an experimental approach with the research subject being Mathematics Education Study Program students who are taking basic geometry courses in the even semester 2018/2019. The research design used was one group pretest-posttest design. Data was collected through tests and motivational questionnaires. The initial data analysis is used to check the normality of the data. The final analysis used the one sample t-test and normalized n-gain test. The results showed that the average normalized gain of student mathematics learning motivation was in the medium category and there was an increase in student learning motivation after being taught by applying the use of mobile learning in basic geometry learning
Ultra-low-distortion optical system design based on tolerance sensitivity optimization

This study theoretically analyzed the relationship between the primary aberrations and tolerances to meet the distortion requirements of an ultra-low-distortion optical system. Theoretical analysis shows that the distortion sensitivities of the optical system are related to the primary aberrations of the optical surfaces. Consequently, a method of optimizing the primary aberrations on the surface of the optical system to reduce the distortion sensitivities of sensitive tolerances is proposed. A distortion sensitivity analysis of manufacturing and assembly tolerance was carried out for the designed ultra-low-distortion and telecentric optical system. The distortion sensitivities of the sensitive tolerances were reduced by optimizing the primary aberrations on the surface of the optical system. A Monte Carlo analysis of the optimized ultra-low-distortion optical system showed that the proposed method has a remarkable effect on the distortion sensitivity optimization of the tolerances.
Twister Generator of Random Normal Numbers by Box-Muller Model

Twisting generators of the pseudorandom normal variables can use uniform random sequences as a basis.However, such technique could provide poor quality result in cases where the original sequences have insufficient uniformity or skipping of random values.This work offers a new approach for creating the random normal variables using the Box-Muller model as a basis together with the twisting generator of uniform planes.The simulation results confirm that the random variables obtained have a better approximation to normal Gaussian distribution.Moreover, combining this new approach with the tuning algorithm of basic twisting generation allows for a significantly increased the length of created sequences without using any additional random access memory of the computer.