Reviewerly/exHarmony huggingface dataset:
Search for Inter-Pausal Units: application to Cheese! corpus

The development of corpora inevitably involves the need for segmentation. For most of the corpora, the first segmentation to operate consist in determining silences vs Inter-Pausal Units-i.e. sounding segments. This paper presents the Search for IPUs feature included in SPPAS-the automatic annotation and analysis of speech software tool distributed under the terms of public licenses. Particularly, this paper is focusing on the use and the evaluation of this feature on Cheese! corpus, a corpus of read then conversational speech between two participants. The paper reports the number of manual actions that are required for a user to check the automatic annotation: add new IPUs, ignore un-relevant ones, move boundaries, etc. Such evaluation validates the proposed method.
Smart Visa System with Improved Security Features

With the growing number of fake and fraudulent companies that issue visas to UAE, people abroad seek a reliable system application to apply for UAE visa. Wasting money on non-creditable resources pushed people to complain about the non-trusted companies that deliver visas.This paper aims to mitigate the risk of being subject of fraudulent or suspicious ways of getting the visa. This research is about building a visa application system, which allows its users to apply for a UAE visa through a mobile application insecure manner. The Mobile App provides a list of trusted companies in UAE, which through them users can apply for UAE visa. It aids its users to apply for a new visa without compromising their confidentiality. The App is built with high-security features and to be able to integrate with other platforms through many API layers.
Improved Filtering of Noisy Images by Combining Average Filter with Bacterial Foraging Optimization Technique

Biologically inspired algorithms have attracted a large number of researchers and found to have application in many areas of computer science including image processing. This paper presents the application of a biologically inspired algorithm, bacterial foraging optimization algorithm (BFOA), to optimize the filtering of images for denoising and its performance comparison with existing noise reduction techniques like average filter. Usually, images suffer from noises which will corrupt image quality and appearance. Hence, denoising of images plays a great role in the image processing and the frequently used filters are average filter, median filter, etc., to remove noises. This research paper explores the suitability of applying BFOA on the filtered image produced by average filter to result a further denoised image. In this proposed method of bacterial foraging-based optimization, peak signal-to-noise ratio (PSNR) is used as fitness function to denoise the noisy images. The implemented code is tested for noisy images (Gaussian noise and salt–pepper noise) filtered with average filter, and results show the optimization capability of BFOA-based method and that it improves the denoised images produced by average filter.
Software Development Methodology Selection with Human Resource Management Approach and a New System Design on Database: Blockchain Application

The aim of the study is primarily to investigate the applicability of Blockchain technologies in the design of the ongoing recruitment process in the companies. This can bring a more professional perspective especially to the international companies by providing document transaction in fast and secure way. Many of the problems in Human Resources departments includes recruitment problems. Today’s recruitment process built on accessing information about candidates and eliminating those who does not meet job requirements based on their resumes. Therefore, Human Resource specialists often use career websites and it is significant to have an impressive number of resume in their resume pools. In the end, there is a resume pool that has been built with a very large unqualified and even false information about candidates. In addition, there are difficulties in getting the employees' equivalence certificates or transcripts about their education and proving their experience and certificates gained abroad. This causes delays in gathering and verifying the information and documents of job applicants. Although some mistakes are difficult to remedy, it is of great importance to be against fraud and to improve the hiring process. This is possible thanks to Blockchain Technologies and strategic process flow approach. Since Blockchain’s main asset is solving the problem of trust between individuals, Blockchain based model is recommended with a new recruitment approach. BHR Platform represents a Platform which can be used for keeping records and transaction of documents in secure way. This platform recommends to create a decentralized Peer to Peer(P2P) network which stores reliable, trusted, transparent information. This will assure an effective recruitment process while decreasing operational costs.
The Effects of Pre-service Early Childhood Teachers’ Teaching Competencies and Problem-solving ability on Their Job Performance Efficacy

본 연구는 예비유아교사의 수업역량 및 문제해결능력이 직무수행효능감에 미치는 영향을 살펴보기 위해 예비유아교사 418명을 대상으로 실시한 설문조사 결과를 분석하였다. 연구 결과를 요약하면 첫째, 수업역량과 문제해결능력, 직무수행효능감은 대체적으로 학년이 올라갈수록 높아지는 경향성을 나타냈다. 수업역량에서는 ‘교사의 정서 및 태도’가 가장 높고 ‘지식’은 낮았다. ‘수업 설계’, ‘교수법’, ‘학습분위기 조성’, ‘영유아평가’에서는 학년 간에 유의한 차이가 없었다. 문제해결능력의 경우 ‘문제의 명료화’가 높게 나타난 반면 ‘계획 실행’은 낮았으며, ‘계획 실행’과 ‘수행 평가’에서는 학년 간에 유의한 차이가 나타나지 않았다. 직무수행효능감의 경우 ‘일과운영 효능감’이 높은 반면 ‘보호 효능감’과 ‘학부모관계 형성 효능감’은 낮았으며, ‘일과운영 효능감’과 ‘학부모관계 형성 효능감’, ‘보호 효능감’은 학년 간에 유의한 차이를 보이지 않았다. 둘째, 수업역량과 문제해결능력, 직무수행효능감 사이에 밀접한 상관이 나타났다. 셋째, 예비유아교사가 가지고 있는 수업역량이 직무수행효능감을 설명하는 의미 있는 변인으로 밝혀졌다. 이는 교원양성교육과정의 교육내용과 교수학습방법에 대한 검토를 통해 예비유아교사의 수업역량과 문제해결능력, 직무수행효능감의 향상을 실질적으로 지원해줄 수 있는 구체적인 방안 마련 및 적용의 필요성을 시사하는 결과로 볼 수 있다.This study analyzed the results of a survey of 418 pre-service early childhood teachers to investigate the effects of teaching competencies and problem-solving ability of pre-service early childhood teachers on their job performance efficacy. To summarize the results of the study, first, teaching competencies, problem-solving ability, and job performance efficacy were found to have a tendency to increase with the higher school grade. In teaching competencies, ‘teacher’s emotion and attitude’ was highest and ‘knowledge’ was low. There were no significant differences between school grades in ‘class design’, ‘teaching method’, ‘creation of learning environment’ and ‘early childhood evaluation’. In case of problem-solving ability, ‘problem clarification’ was found to be high, while ‘plan implementation’ was low. And ‘plan implementation’ and ‘performance evaluation’ did not show significant differences between grades. In the case of job performance efficacy, ‘daily task operation efficacy’ was high, while ‘protective efficacy’ and ‘parental relationship building efficacy’ were low. ‘Daily-task operation efficacy’, ‘parental relationship building efficacy’, and ‘protective efficacy’ did not show any significant differences between school grades. Second, it was found that there was a close correlation between teaching competencies, problem-solving ability, and job performance efficacy. Third, the teaching competencies of pre-service teachers were found to be meaningful variables explaining job performance efficacy. Through reflective review on the contents of the teacher training curriculum and teaching and learning methods, this suggests the need to develop and apply concrete measures that can substantially support the improvement of teaching competencies, problem-solving ability, and job performance efficacy of pre-service early childhood teachers.
Bagged Random Forest Approach to Classify Sentiments Based on Technical Words

As the innovation is developing immeasurably, the data is increases daily. This is used by the reviewers to identify the views regarding a particular thing and accordingly they decide their opinion on the basis of that reviews. Knowledge mining techniques has been used for extraction of elements from these datasets. The algorithm generated and tested can be used to find out technical words; hence we can expose them to appropriate class of technical words. Here we are only considering technical words in English language. We compare the results with the proposed approach. We calculate accuracy with random forest approach as well as bagging and find out that bagged random forest approach with Gini index for feature selection, i.e., proposed approach gives best result. With proposed approach, accuracy 80.64% in case of percentage split using 66 and 34 as training and testing percent and 86.81% in case of cross-validation model.
Online and Offline Object Detection in Video For Forensic Analysis

In the era of digitalization, many companies, agencies, and even homes in the world have installed closed-circuit television (CCTV) in their premises as surveillance and security purposes. The footage videos from CCTV have been used as the sources of the evidence in the criminal investigation. In video forensic analysis, the footage of CCTV with the potential subject or object is extracted out from the CCTV recordings for the analysis. Law enforcement agencies depend on forensic video analysis software in their process of evidence extraction. However, the majority of the current software is expensive and complex, but it also needs time to analyze. Most of the proprietary forensic software vendors do not provide open access to their source code Sometimes, it does not provide the feature that the investigator needs to conduct the investigation based on certain cases. Object detection is the main key to digital forensics of evidence from CCTV videos. The process of digital forensics can be difficult and it may require a high level of video analysis of lengthy contents that acquire from CCTV cameras. Every image and video collected from CCTV is counted as evidence to create visual documentation of the crime scene. The main purpose of this paper is to assist the forensic investigation by developed an output for an object detection system that able to speed up the process of the extraction of evidence. At the same time, it able to detect objects without human involvement or any external control. To assist the video-based forensic analysis, a convolutional neural networks-based algorithm object detection known as YOLO: Real-Time Object Detection is proposed that can detect and identify potential objects from CCTV footages. This proposed solution able to extract the detected image with a timestamp in online video (such as CCTV and webcam) and offline video (such as CCTV footage and recording video). These implements include deep learning techniques, Graphics Processing Unit (GPU) computing, and efficient, integrated architecture development both for real-time and post-processing.
Social skills at the beginning of adolescence: a diagnosis with schoolchildren of the Alejandro Játiva Martínez center

The study of social skills of school-aged children is priority for their formation and development; therefore, the transformation of the school world demands a need for guidance from a scientific point of view, in order to develop those skills. The general objective of this research is to diagnose the current state of social skills in school-aged children from Alejandro Jativa Martinez school children, located in socio ‐ housing 1, La Prosperina northeast sector of Guayaquil, Ecuador in the 2018-2019 school year. It has been studied 25 subjects of the 6th year, with ages between 10 and 11 years, relating social skills with the entrance to adolescence, which stands out as a complex stage of ontogenetic development. For data collection, different methods were applied: theoretical (historical-logical, analysis and synthesis and induction ‐ deduction), empirical (observation and surveys) and statistics (descriptive statistics). The work is considered of great importance since it offers possibilities for contextualization, as part of the implementation of educational strategies aimed at enhancing attention to diversity from the professional role of the educational psychologist.
PENERAPAN INSTRUCTIONAL SYSTEM DESIGN (ISD) PADA PEMETAAN DAN VISUALISASI INFORMASI TEKNIK DASAR PEMBUATAN KERAMIK

&#x0D; &#x0D; &#x0D; Ceramics Handmade in Jakarta currently has become a new hype. With hand- made ceramics people having a direct connection with the makers by how those things made. Techniques, materials, and the process are the reasons why people are more attracted to get involved by making their ceramics by themselves. The hype caused high demand of ceramic basic techniques information, but people barely find ceramic books or other types of media which commissioned and developed by the actual condition of Indonesia’s independent ceramics studio especially Jakarta. This study structured through Instructional System Design (ISD) by two main cores. The first is to formulate a mapping method to classify the information of ceramics techniques at a different level. The intention is to gather specific information for beginners that would apply on instructional media. The second is designing a media such as conventional books and virtual information video through the augmented book. The result construct augmented book with systematically measurable information. It provides people easy access information and offered a new learning experience by using the augmented book. &#x0D; &#x0D; &#x0D;
Passwords, pistols, and power plants: An assessment of physical and digital threats targeting Canada’s energy sector

The study of energy sector security is in flux. A traditional focus on exploring the nexus between terrorism and physical energy infrastructure has given way to a new and specific emphasis on cyber attacks targeting electrical power grids. A noticeable gap in the literature exists in terms of presenting a more comprehensive assessment of the general threat environment. Our paper, and the larger project from which it stems, intends to fill this void and prompt more nuanced and empirically driven research on the topic that informs Canadian security policy. Our findings are informed by interviews conducted with American and Canadian energy sector officials, and a questionnaire carried out with energy sector companies. By examining a broader suite of disruptive threats to the energy sector, we paint a more inclusive picture of the many gateways through which the energy sector could be targeted.
The Effects of University Students Cultural and Art Experiences on Creative Convergence Capabilities and Self-development

본 연구는 문화예술 경험이 대학생들의 창의·융합 및 자기계발에 어떠한 영향 을 주는지 그 의미를 탐구하고자 하였다. 연구는 비교과 수업에 참여한 4년제 대학생을 대상으로 문화예술 경험 활동 후 설문조사를 실시하였다. 연구결과는 다음과 같다. 첫째, 문화예술 경험은 대학생들에게 긍정적인 사고를 하도록 하였다. 문화예술 경험은 대학생들에게 고정된 사고방식에서 탈피하여 다각적인 시각을 갖추고, 폭 넓은 사고를 하는 계기를 마련해 주었다. 둘째, 문화예술 경험은 대학생들 창의·융합 강화에 많은 도움이 되었다. 문화예술 경험이 유연한 사고와 아이디어를 창출하는데 의미 있는 역할을 한다는 것을 알 수 있었다. 셋째, 문화예술 경험을 통한 창의·융합 역량 강화는 자신이 추구하고자 하는 것을 성취하기 위해 요구되는 것에 대한 보다 구체적인 생각을 해봄으로서 결국 자기계발에 영향을 주었다.The purpose of this study was to explore the effects of cultural arts experience on the creativity, convergence, and self-development of university students. A survey was conducted after the cultural arts experience activities of four-year university students who participated in the comparison and class. The results are as follows. First, the cultural arts experience imbibed the students with positive thinking. Cultural and artistic experiences gave them a chance to break free from an entrenched line of thought and have a multifaceted perspective and think broadly. Second, these experiences helped the students strengthen their creativity and convergence. Cultural and artistic experiences played a significant role in generating flexible thinking and ideas. Third, the resulting creativity and convergence capabilities influenced self-development because the students could think more specifically about what is required to achieve their goal.
All aboard the quantum bandwagon

Money is pouring in to developing quantum computers, but no one is quite sure what they are for, reports Leah Crane
Approximation of distribution law of experimental test data to assess reliability of information-measuring and control systems

Abstract Methods of approximation for the distribution law of experimental data find wide application in problems of reliability assessment in tests of both hardware and software of software modules of information-measuring and control systems (SM IMCS). Thus, the main task is to solve the problem of increasing the accuracy of the approximation and simplify experimental data processing. In this paper various modification types of Pearson distribution for data taking both positive and negative values, and for data taking only positive values are considered. The described modifications allow one to solve a range of problems related to the existing distributions, as well as to improve the simplicity of the approximation procedure. Pearson logarithmic distributions are proposed for experimental data that take only positive values.
Classification of Emotional Adjective for the Hospital Indoor Image Based on Deep Learning

This research suggests to classify Emotional adjective for the hospital indoor image. The aim of this study was twofold. First it is an attempt to overcome limitation of overfitting data with pre-process and Second it is an approach for prediction quantified the image data with Emotional adjective. The emotion is important because emotion interact between indoor and human. The hospital indoor image also have specialized emotional effect. Emotional adjective is necessary to verify throughout variety of source qualitative and quantitative research. recently it is getting more harder with I.R.B.(Institutional Review Board) than Emotional adjective data had made. This research is based on deep learning method for emotional adjective quantifiaction that can replace thousands of people’s cognition. In the proposed simulation, emotional colors are firstly processed in the frequency domain to indoor images which can be treated as an emotional image. For pre-processing Emotional colors are extracted from hospital image. and search the emotional adjetive to get indoor images to fed in CNN(Convolutional Neural Network). For the hospital indoor image clustered, emotional indoor image are fed in CNN. The output of the CNNs are fused using TF(TensorFlow) API. The input of the fusion is given to a support of Python language for image classification. The proposed system is evaluated using Tensor board - which is the proved data. This research has concluded that it is desirable to use TF for predicting the set of emotional adjective and it helps for emotion analysis efficiently. TF works for the emotional image classifying the hospital indoor images. The hospital image is classified using deep learning, and analysis of emotion as A is 80 percentage modern and B is 20 percent natural in a second for a thousand emotional colors. It is expected to use these results of research have for implications of emotional analysis that represent functions of the indoor images.
Development of Marker less Augmented Reality for Cardiovascular System in Anatomy and Physiology Courses for Physiotherapy Education

This paper focuses on the development of markerless Augmented Reality (AR) using ARCore platform, where interactive three-dimensional (3D) content was designed and developed based on the learning outcome syllabus to enhance the visualization and understanding of the anatomy and physiology for cardiovascular system topic. Currently, learning method is based on 2D images and slides, plastic models and cadavers have to deal with students’ experience issues such as lack of interactive, uneasy feeling with dead body and cadavers storing and donation. Therefore, more advances using technology such as Augmented Reality (AR) in learning method are needed to overcome the current gap and enhance the students’ learning. Thus, this study aims to develop markerless AR specifically focus on the cardiovascular system for undergraduate physiotherapy program at UniKL, RCMP. In this study, we describe a method used to create markerless AR content using 3D data from MRI images and 3D unity as an authoring tool. We present three processes, where the first design consideration based on author’s previous works derived from systematic search strategy were outlined, the second 3D model was developed using a real object and subsequently converted to an AR asset that can be linked to a unique markerless using ARCore platform and the third AR content creation using 3D unity authoring tool. This application provides a better visualization for the anatomical parts to support for an innovative and flexible learning process. We have successfully analyzed the design consideration using a systematic search strategy and developed the markerless AR specifically for cardiovascular system in anatomy and physiology courses. This study has contributed to knowledge in design and development of AR used in physiotherapy education. Therefore, this will be a step forward to an exploration of design-based research for an AR benefit in experienced-learning approach application.
Design of the Interactive Intelligent Machine-Aided Translation Platform Based on Multi-strategies

The interactive intelligent machine-aided translation platform based on multi-strategies can meet the needs of users for the fast translation, and use various intelligent tools to assist the manual translation, so as to effectively improve the efficiency and the quality of the translation. By adopting the leading technology of the human-computer interactive machine translation in the industry, integrating many advanced technologies such as the neural network machine translation, the statistical machine translation, the input method, the semantic understanding, and the data mining and so on, and cooperating with the bilingual parallel data of the bilingual level, we can provide users with the real-time intelligent translation assistance to help them complete the task of the translation better and faster. The purpose of the product is to pay homage to the artificial translation, assist the artificial translation to complete tasks faster and better, and then explore a new way of the AI enabling translation industry.
Graphical User Interface (Gui) for Visualization of Solutions of Initial Value Problems Using Runge-Kutta Methods

In this paper, a successful approach for the visualization of the solutions of initial value problems using different Runge-Kutta methods by developing a new graphical user interface (GUI), has been introduced. The main objective of the paper is to apply the tkinter, numpy[1] and matplotlib[2] modules of the Python programming language in developing a GUI that would enable the user to appreciate the accuracy of each Runge-Kutta method at different step size/increment. The second purpose of this paper is to develop a python module that contains functions that can be imported to .py files when the need to implement Runge-Kutta methods on initial value problems arises. The GUI was used to solve and plot the solutions of an initial value problem. The variations in the graph patterns illustrate the accuracy of the output of each method. Keywords GUI, Runge-Kutta methods