Reviewerly/exHarmony huggingface dataset:
An Ontology to Integrate Multiple Knowledge Domains of Training-Dietary-Competition in Weightlifting: A Nutritional Approach

This study is a part of weightlifting “Training-Dietary-Competition” (TDC) cycle ontology. The main objective of TDC-cycle is to build a knowledge framework for Olympic weightlifting, bringing together related fields such as training methodology, weightlifting biomechanics, and nutrition while modelling the synergy among them. In so doing, terminology, semantics, and used concepts are unified among athletes, coaches, nutritionists, and researchers to partially obviate the problem of unclear results and paucity of information. The uniqueness of this ontology is its ability to solve the knowledge sharing problem in which the knowledge owned by these experts in each field are not captures, classified or integrated into an information system for decision-making. The whole weightlifting TDC-cycle is semantically modelled by conceiving, designing, and integrating domain and task ontologies with the latter devising reasoning capability toward an automated and tailored weightlifting TDC-cycle. However, this study will focus mainly on the nutrition domain. The intended application of this part of ontology is to provide a useful decision-making platform for a sport nutritionist who gathers and integrate relevant scientific information, equation, and tools necessary when providing nutritional services. The system is constructed by using Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), and Semantic Query-Enhanced Web Rule Language (SQWRL). The use of weightlifting TDC-cycle ontology can be helpful for nutritionists to create a well-planned nutrition program for athletes (especially, in the process of nutrition monitoring to identify energy imbalance in athletes) by reducing time consumption and calculation errors.
Question Answering System over Linked Data: A Detailed Survey

As the amount of information in the world is growing very quickly, in the case of the semantic web this increasing amount of information is becoming more difficult to find and manage the exact answers to our various questions. To overcome these difficulties some systems have been developed that make it work for us. But there exists many challenges in developing these systems that require a lot of improvement. In this tutorial, we give a basic understanding of Semantic web, RDF triple, SPARQL query language. Here we will discuss the main obstacles for the QA system in processing the questions and a detailed survey of the existing systems. We also provide some advantages and disadvantages of existing QA systems. We also discuss the evaluation campaigns of the existing models based on their precision, recall and F-1 scores on the QALD dataset.&#x0D;
Mispronunciations in Graduate Students’ Presentation Projects

&lt;p class="AbstractText"&gt;&lt;span lang="EN-GB"&gt;This paper explored the mispronunciations produced by Indonesian graduate students in their presentation projects. The data were collected from presentation projects of 11 graduate students of the English Education Master’s Program of Sanata Dharma University. The researchers used a document analysis as a technique to analyze the data. Results showed that 89 mispronunciations existed in the presentation projects. The mispronunciations were categorized into three types of errors, namely: 26 mispronunciations belonged to the first type, the problems in the pronunciation of non-native sound; 36 mispronunciations belonged to the second type, the carry-over of pronunciation regularities from the mother tongue (L1); and 27 mispronunciations belonged to the third type, the overgeneralizations of target language (L2) regularities. It is expected that the findings can become a stepping stone for graduate students to improve their English pronunciation.&lt;/span&gt;&lt;/p&gt;
CONTENT-BASED IMAGE RETRIEVAL FOR MOBILE DEVICES USING MULTI-STAGE AUTOENCODERS

Content-based image retrieval (CBIR) composes of retrieving the most similar images from an online or offline database concerning a query image. In this article, we proposed a CBIR technique which uses a multi-level stacked Autoencoders for feature selection and dimensionality reduction. The present work more focus towards an image retrieval approach on a mobile device such as mobile phones and tablets. The approach uses feature descriptors derived from a stacked autoencoder where both the abstract feature extraction and the dimensionality reduction are handled simultaneously in the autoencoder stages. A query image space is created first before the actual retrieval process by combining the query image as well as similar images from the local image database (images in device gallery) to maintain the image saliency in the visual contents. The features corresponding to the query image space elements are searched against the characteristics of images in a global dataset (target images from cloud space, servers, etc.) in a weighted manner, and the target images are ranked based on the similarity scores. The top ranked images from the global dataset is then selected based on the thresholding of similarity score. The proposed CBIR scheme is experimented over standard public datasets, and performance is compared against state-of-the-art image retrieval approaches. The results show significant improvements in the overall precision, recall and time cost and justifies the image search over mobile devices with less computation capability.
Implementation of Extreme Programming in "SIATAP" Sistem Informasi Tanya Apoteker Based on Android and Website

The pharmacist is a professional degree for someone who has taken the oath of pharmacist position. Pharmacists are expected to be able to improve knowledge, skills, behaviour to be able to interact with patients. The form of interaction is giving information on medicines and counselling. In its service, pharmacists identify, prevent, overcome drug-related problems, and this has the possibility of medication errors. This research aims to build communication media between pharmacists and patients "SIATAP". The application will be developed using the extreme programming method. The advantages of this method can increase client satisfaction due to good communication so that it gets direct input from clients. Android application is designed for communication media between pharmacists and patients, the website application design to manage data for the administrator and pharmacist. To ensure the feasibility of the system, testing is done by using alpha and acceptance testing. The results of the alpha testing of all functional systems can run as expected. The results of the Acceptance Testing, using a questionnaire obtained a percentage of 92.57% with the conclusion that the application can be a good response from users so that the application is feasible to be implemented. The application can be downloaded at the URL: http://bit.ly/2ATy6hT.
QUEUING THEORY BASED ON DECENTRALIZED ONLOAD DATA ARRIVAL USING EXPECTED MAX-MIN PROBABILISTIC DECISION FOR REDUCING WORKLOAD

Calculating probabilistic decision using the queuing theory, is a new technology with the aim of making it easier for the user to obtain the pre-requisite services in hardware and software access to a wide range of Internet-based services. That is, an important aspect of modeling the delivery of workflow data services in the process of efficient load balancing in a shared environment of arrival. In order to provide a service, matrix theory has become a very essential and important aspect between data requests and wait and see responses. Load balancing, as there is less response time and higher output separation including equal load. Various mechanisms have been proposed to provide an efficient load balance. To realize the desired level of computation time, we propose a maximum precedence algorithm for scheduling Meta tasks by static illustration to use Enhanced Max-min Algorithm with (EMM) optimized Enhanced decentralized onload data arrival Mode (DO-DAM) with hyper switching methods.by arriving the service distributions are notated as Kendall Notations as (Max-A/S/Min-R/J/D). A queuing model improves the utilization and consumption to manage the resources that uses switch over Probability service time distribution to allot the job to data allotment support on scheduling application on static queue demands. This proposed queuing theory implements to improve the time consumption, utilization, scalability, make span and throughput performance.
Pengujian Black Box pada Aplikasi Berita Online dengan Menggunakan Metode Boundary Value Analysis

Testing is one level that must be passed in order to produce results from a weighted system. If no testing is done on the system, the quality of the system cannot be known so that if an error occurs it can harm the system. By conducting experiments can find the location of defects in a system that can be known from the beginning that can be fixed immediately. Testing there are several types of system experiments, experiments used are black box testing using a limit value analysis method that is effective in trying the functions of a system. The technique determines the limits on the lower and upper values to produce the actual values. Based on the test results using the boundary value analysis technique that still has a disability that is the lack of data verification, then there must be an improvement in the validation function.
MANAJEMEN PEMBELAJARAN SENI BACA ALQURAN DI PONDOK PESANTREN ALQURAN AL-ITQON BOGOR

LEARNING MANAGEMENT READ AL QURAN IN ALQURAN AL-ITQON BOARDING SCHOOL, BOGORThe art of reading Alquran at boarding school is the major program that could be a uniqueness for the institution and the strengths as well. The excellent output of reading Alquran will support the achievement of the school target, therefore the school needs to develop effective learning management, especially in reading Alquran. This research is focused on learning management in the art of reading Alquran at Al-Itqon Islamic Boarding School Bogor. The study was using a descriptive qualitative analytical approach to describe, explain and analyze objectively about the learning management of reading Alquran at Al-Itqon Boarding School Bogor. The data was taken by interviews about the sub-focus of the research, which were: planning, organizing, implementing and evaluating. The results showed that the learning management of the art of reading Alquran at the Al-Itqon Islamic Boarding School in Bogor, begin with planning as the first step, that consist of:1) setting the goal of the program which is preparing student to be a Qori or Qoriah, 2) setting the main program, which are tahsin and recital. The second step is organizing which consists of 1) setting the organizational structure for both male and female students, 2) organizing the program schedule, 3) organizing the facilitation such, learning method and media. The third step is an implementation: 1) learning steps implementation, 2) allocation of learning schedule and duration, 3) media and learning method. The final step is evaluation, which is setting the evaluation objectives including improving reading quality, remedial, repetition by drilling and awarding graduate certificates
Harmonized Software Quality Improvement Models for Sudanese SME Based on CMMI

Software institutions recently implement more than one software process model in order to achieve a high value in their products and services. The challenges of implementing several quality models are increasing the risk of Multi-Model Environment (MME) failure and more resources are needed to support the MME. This kind of resources is not available in the Small and Medium Enterprises (SME), especially in Sudanese institutions. This study aims to provide a solution by encouraging the Sudanese SMEs to apply software models in their software development processes. We are seeking to make these institutions understand how to integrate product quality models with software process improvement models. To achieve this goal, we used a systematic harmonization technique to harmonize and map ISO/IEC 25010 standard onto Capability Maturity Model Integrity (CMMI) Dev 1.3 level 2 and 3. Also, we reviewed the related works of the previous studies on MMEs to achieve better results. The results show some of the processes and activities of CMMI Dev 1.3 considering software quality attributes on the early stage of the Software Development Lifecycle (SDLC). On the other hand, we presented how to apply the mapped models and suggest a tool for implementation. This kind of approach gives small organizations the ability to apply multi SPI models.
Recognition of Handwritten Digits with the Help of Deep Learning

Handwritten numerical recognition is becoming the most interesting topic in research area today due to great growth in artificial intelligence and its different learnings and computer visual perception algorithms. This project shows the comparison of digit recognition among machine learning algorithms like support vector machine (SVM), K-nearest neighbor (KNN), random forest classifier (RFC) and with deep learning algorithm like multilayer convention neutral network (CNN) using Keras (Keras is a high-level neural networks library written in Python which is simple enough to be used. It works as a protector to low-level and high-level libraries like TensorFlow or Theano) with Theano and Tensorflow (An open source software library which provides high performance numerical computation. Its architecture is flexible in such a way that it is easily deployed across various platforms like (CPUs, GPUs, TPUs), form desktop to clusters of servers to smart handsets devices). Further looking and comparing for the accuracy produced by above-mentioned algorithms, the results appear to be like this: The accuracy of digit recognition is 98.69% in convolutional neural network, 97.90% in support vector learning (SVM), 96.67% using K-nearest neighbor (KNN) and 96.89% using random forest classifier (RFC) which clearly shows that convolutional neural network produces more accurate prediction with better results comparatively.
Reasoning with small data samples for organised crime

Building upon the possibilities of technologies like big data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions and reasoning tools, even with only small data samples. Due to the fact that the MAGNETO tools have to operate on highly sensitive data from criminal investigations, the data samples provided to the tool developers have been small, scarce, and often not correlated. The project team had to overcome these drawbacks. The developed reasoning tools are based on the MAGNETO ontology and knowledge base and enables LEA officers to uncover derived facts that are not expressed in the knowledge base explicitly, as well as discover new knowledge of relations between different objects and items of data. Two reasoning tools have been implemented, a probabilistic reasoning tool based on Markov Logic Networks and a logical reasoning tool. The design of the tools and their interfaces will be presented, as well as the results provided by the tools, when applied to operational use cases.
Towards the Development of Automatic Speech Recognition for Bikol and Kapampangan

In this paper, we developed continuous speech recognition for Bikol and Kapampangan language using the CMU Sphinx Toolkit. The speech corpus that was collected by the researchers has duration of 7 hours from 150 native Kapampangan and Bikolano speakers. The speech corpus contains commonly used travelling phrases. This research explains how the researchers developed the speech recognition for local Philippine languages by using the traditional machine learning approach of Gaussian Mixture Model based on Hidden Markov Model. Experimentation of different tied states (senones) is integrated to this study. Experiment shows the highest obtained accuracy of 96.41% for Bikol Language; 96.49% for Kapampangan both hit at 600 tied states. While on the combined languages the highest accuracy gained is 95.39% at 1400 tied states.
Optimal selection of distortion model parameters for projection lenses ssing phasogrammetric self-calibration

Three-dimensional measurement of coordinates in different optical metrology techniques involves the measurement of image coordinates and/or phase values as observations as well as system parameters. These system parameters are usually determined through a calibration process. Self-calibration of digital fringe projection systems takes advantage of the fringe projection technique in a photogrammetric mathematical model. Many pieces of research have shown the capability of this technique which is called phasogrammetry, to achieve high accuracy and reliability. However, the difference between projection lenses and imaging lenses has not been investigated yet. In this paper, a set of experiments is performed to analyze the behavior of systematic errors in digital projectors as the basic component of this method. The results indicate that the well-known physical model of camera in close range photogrammetry might be used for digital projectors. The best results if the 3D measurement of the test object achieved where the first term of radial distortion K1 and the first in-plane distortion parameter B1 are involved in the self-calibration of the digital fringe projection system.
Microfacies Analysis of the Mishrif and Kifil Formations of Amara Oil field/South of Iraq

The lithologic and petrographic studies of the Mishrif and Kifil formations in Amara oil field in wells AM11,AM9, AM5 was revealed that the Mishrif formation consists of limestone and dolomitic limestone. While the Kifil formation is consist of the anhydrite and mud-dominated limestone. &#x0D; The skeletal grains of Mishrif formation includes variety of benthos foraminifera, bivalves (Rudist) ,corals , stromatolite, algae, ostracods, gastropods, echinoderms. Non-skeletal grains are rare and authegenic minerals of pyrite and iron oxide are present. The rocks of the formation are affected by diagenesis such as dolomitization ,dissolution and recrystallization. The Kifil formation rocks do not have any skeletal grains and affected by recrystallization only .&#x0D; The microfacies analysis of Mishrif formation reveals that the formation consists of: mudstone, wackestone, packstone, grainstone and boundstone deposed in the fore environments extend from for reef, back reef to: open marine, reef, shoals platform margin, restricted environment. The Kifil formation divided into tow lithological facies these are limestone and evaporite and we recognized mudstone microfacies only deposited in restricted environment and represents the Mishrif reservoir cap rocks .&#x0D; The research illustrated that the upper contact of Mishrif formation is gradational and conformable with Kifl formation with evidence from a gradual changes from limestone to anhydrite. In this study was considered the evaporites and limestone to Kifil formation. The Kifil formation is presence by thickness (7m) in the Amara Oilfield .&#x0D; &#x0D; http://dx.doi.org/10.25130/tjps.24.2019.088
On Polynomial Approximations of Spectral Windows in Vertex-Frequency Representations

Vertex-frequency analysis (VF) can be considered as a generalization of the classical time-frequency analysis. It provides tools and algorithms aiming to characterize the localized signal behavior in the joint vertex-frequency domain. Localized Graph Fourier Transform (LGFT) is an example of such a tool, with a role in the graphs signal processing which is equivalent to the role of the Short-time Fourier transform in traditional signal processing. Bearing in mind the rapidly increasing amounts of data and large dimensions of graphs related to practical applications, the calculation complexity of each tool for the spectral analysis of signals on graphs shall be continuously revisited. As they provide the possibility to calculate VF representations using only local neighborhoods of vertices, without the need for the eigendecomposition, polynomial approximations of spectral windows are commonly used in practice, mostly in the form of the Chebychev approximation. This paper revisits this choice, compares it with two other polynomial approximation approaches, and investigates their influence on the VF-based graph signal analysis and inversion.
Dynamic Bayesian network state prediction based on variable relationship

In order to improve the accuracy of the state prediction model, a dynamic Bayesian network state prediction model based on the relationship of prediction variables is designed. The prediction model of dynamic Bayesian network structure learning algorithm was improved, integrated into the Gibbs sampling algorithm model prediction, joined the predicted relationship between different factors affecting the node, is given based on the variable relationship between the dynamic Bayesian network structure design, using a moment on the different nodes and state influence factors to predict the probability distribution of the moment state nodes. The experimental results show that the model is simple in structure, more accurate than the traditional learning method of Bayesian network structure, and more practical.
A method to improve corner detectors (Harris, Shi-Tomasi & FAST) using adaptive contrast enhancement filter

A method to improve interested-points detectors in an image that suffers from the problem of illumination was conducted in this paper. Three algorithms are adopted based on Harris, Shi-Tomasi, and FAST algorithms to identify the interested-points in images that are required to match, recognize and track objects in the digital images.Detecting the interested-points in images with bad illumination is one of the most challenging tasks in the field of image processing. The illumination is considered as one of the main causes of damage of the natural images during the acquisition and transition. Detecting the interested-points of these images doesn’t give the desired results, which is why handling this problem for those images is very important. The Adaptive Contrast Enhancement Filter approach is applied for solving this problem.
Demosaicing images in low lighting environments

It is commonly believed that having more white pixels in a color filter array (CFA) will help the demosaicing performance for images collected in low lighting conditions. We present a comparative study to evaluate the performance of demosaicing for images collected in realistic low lighting conditions using two CFAs: the standard Bayer pattern (aka CFA 1.0) and the Kodak CFA 2.0 (RGBW pattern with 50% white pixels). Using a data set containing 10 images collected in low lighting conditions, we observe that having more white pixels does help the demosaicing performance. However, some cautions are needed in quantifying the performance.