Reviewerly/exHarmony huggingface dataset:
Developing Model for library Ontology Using Protégé Tool: Process, Reasoning and Visualisation

Library information system on web in based on HTML which can display information simply, mostly restricted to manual keyword searches which may results in irrelevant information retrieval.Semantic web overcome this issue, but cannot do without ontology.In this paper education sector is considered and library ontology is presented to pivot the role of library information system that will ensure the use and re-used knowledge and shared common understanding of library information system, which can be communicated between people and across application systems.The methodology is based on the tools used in developing the ontology, several tools are used to create ontology.This paper will discuss some terms of library through library ontology.And protégé tool with its extensible plug-in will be use to create the library ontology.Focusing on the facilities and services of a library, various aspects like super class and sub class hierarchy, creating a sub class, instances for class illustration, query retrieval process, visualization and graph views have been demonstrated.
B4HEALTH - AN ARCHITECTURE MODEL FOR PERSONAL HEALTH RECORDS WITH HL7 FHIR AND HYPERLEDGER FABRIC

Health Institutions use the EHR (Electronic Health Record) for many years to register the patients' information electronically.On the other hand, this data is still in many different systems of the health institutions, which the patient interacted over life.During a medical appointment, one does not have a current view of the patient's health history.One of the goals of the PHR (Personal Health Record) concept is to obtain a unified view of the records scattered in various health organizations.Many experts now believe that blockchain technology allows storing a patient's pertinent medical information where needed, allowing both the patient and health care professionals to have access to up-to-date information.However, among the challenges to achieve the goal of an interoperable PHR model is the choice of a blockchain platform that supports the storage of this data in a secure, transparent, and decentralized manner.This article proposes an architecture model for health records based on blockchain.The main outcome is storing part of an electronic medical record in the proposed solution.It aims to process and store medical data, which comes from different health institutions safely inside the blockchain network.Then the EHR data is available to authorized stakeholders in a unique viewpoint through a decentralized blockchain application.After experimenting with two different blockchain frameworks, we decided to implement the prototype in a permissioned blockchain.We created and deployed a business network composed of two different organizations, which stored and shared EHR data among the network participants.Performance tests while retrieving EHR data from the blockchain showed an average response time in milliseconds of 201.08 with 500 users and 15,402.9with 1000 users.Our proposed solution was able to address the main challenge faced by patients that have EHR data scattered in many distinct locations.The prototype supplies a unique view of this data where the participating organizations accessed the EHR data.However, a more robust hardware setup is necessary to test the blockchain solution in future works.Additionally, our intent is to focus on internal aspects of HIS, especially on how to convert the current EHR data from any health data standard to HL7 FHIR, as this would allow many different health institutions, which are using different data standards to take part in the network.
Natural Gestures to Interact with 3D Virtual Objects using Deep Learning Framework

This paper presents a system for freehand interaction with 3D objects using gestures. A secondary contribution of this work is to present a system to recognize a set of suitable gestures which are used for manipulation of the 3D objects interactively, with the help of a deep learning framework using the 3D raw images captured via Leap motion interface. For this study, we have used our own dataset, a collection of images acquired using Leap motion controller comprises with six naturally occurring gestures. A deep learning framework built with CNN has been trained on this dataset and the validation accuracy has been found to be as high as 99%. This model is then used to predict the user's gesture to interact with the 3D objects with bare hands. The application has been tried and assessed by ten subjects. The subjects had no prior experience on how to interact with Leap motion sensor, making it an interesting study to explore the possibility of its usage by mass.
New Image Watermarking Algorithm Based on DWT and Pixel Movement Function PMF

In this paper, we propose a new algorithm of image watermarking based on Discrete Wavelet Transform (DWT) including a function for pixels movement. The proposed algorithm uses DWT of two levels in order to compact a higher energy in component LL1, and Contrast Sensitivity Function (CSF) to improve the invisibility and robustness, the new Function of Pixel Movement (PMF) is applied to increase the security properties. Pixel Movement Function (PMF) is a function of N iteration inside each block, this function required a changeable key K calculated in each iteration N for the position of each block. Numerical experiments are performed to demonstrate that the proposed method can improve watermarking quality in terms of imperceptibility of watermark, capacity of insertion and robustness against different attacks such as Joint Photographic Experts Group (JPEG) compression, noise addition and geometrical attacks.
Pose Analysis of Humanoid Robot Imitation Process Based on Improved MLP Network

This paper proposes a system, by which humanoid robots could imitate human movements to learn human behaviors. Considering the security problem during humanoid robot imitation process, a method of stability identification based on improved Multi-Layer Perception (MLP) is proposed. Firstly, the positions of the human joints are collected by a Kinect. Secondly, the human joints information is transformed into robot angles information by the inverse kinematics equation. Finally, the stability of the robot is identified by the deep neural network, which has the robot angles as its input. Simulations and experimental results demonstrate that compared with the Back Propagation neural network (BPnn), RBF neural network (RBFnn), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), MLP neural network has the best identification accuracy. And the filters we used can overcome the problem of speed pulse, to some extent, and achieved an ideal imitation result.
Errett H. Callahan (1937-2019): Researcher, flintknapper, and artist

Errett Hargrove Callahan Jr. was a standout American pioneer in experimental archaeology, lithic technology, and replicative experimental studies. Errett, son of Errett Callahan, Sr. and Mary Ingraham Callahan, was born on December 17th, 1937 in Lynchburg, Virginia, where he lived until his death on May 29th, 2019. He leaves his son Tim, daughter Melody, and three grandchildren, Chris, Megan, and Ryan.
COMPUTATION FACTORIZATION OF NUMBER AT CHIP MULTITHREADING MODE

Context. Ensuring high-speed calculation by computer systems of the classical task of factorization of integer value on simple factorsrequires the development of effective algorithmic methods using the latest information technologies. Fast computation of factorization ofnumbers to provide high cryptocapability of information data, using multidimensional representation of one-dimensional sequences ofinformation data and other applications is sufficiently in demand in many practical tasks.Objective.The purpose of the work is to improve the method of trial divisions to compute the factorization of integer value with usingparallelization of computations and efficient use of computing resources of computer systems, which ensures faster computation of thevalues of prime factors of the decomposition.Method. It is proposed to use the residuals of each digit of the binary representation of the factorization number in order to check fordivisibility in the method performing of trial divisions into prime numbers.Results. The result of the study is to develop of a program of parallel execution of the factorization of integer value in computer systemswith multi-core processors.Conclusions. In the research, a method of checking for divisibility using the residuals of each digit of the binary representation of thefactorization number was applied, which allows for multi-threaded mode to execute the decomposition of the number into the factors. Thebasic idea of applying the corresponding mathematical apparatus is to use the residuals of the integer exponent of the number two from primenumbers. As a result, the accumulation of residuals is performed, which is checked for equality with the corresponding prime number and itsdegrees. The possibility of a multithreaded software organization for computing the number factorization ensures its parallel execution inmulti-core processors of computer systems
Eliminating unwanted signals in sound by using digital signal processing system

&lt;p&gt;&lt;a name="_Hlk536186602"&gt;&lt;/a&gt;&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Unwanted signals or noise signals in sound files are considered one of the major challenges and issues for a thousand users. It is impossible to reduce or remove these noise signals without identifying their types and ranges. Therefore, to address one of the big problems in the digital or analogue communication, which is noise signals or unwanted signals, an adaptive selection method and noise signal removal algorithm are proposed in this research. The proposed algorithm is done through specifying the types of undesirable signals, frequency, and time range, then utilizing digital signal processing system which includes design several types of digital filters based on the types and numbers of unwanted signals. Four digital filters are used in this research to remove noise signals from the sound file by implementing the proposed algorithm using Matlab Code. Results show that our proposed algorithm was done successfully and the whole noise signals were removed without any negative consequence in the output sound signal. &lt;/span&gt;&lt;span style="font-family: 'Times New Roman', serif; font-size: 9pt;"&gt;Unwanted signals or noise signals in sound files are considered one of the major challenges and issues for a thousand users. It is impossible to reduce or remove these noise signals without identifying their types and ranges. Therefore, to address one of the big problems in the digital or analogue communication, which is noise signals or unwanted signals, an adaptive selection method and noise signal removal algorithm are proposed in this research. The proposed algorithm is done through specifying the types of undesirable signals, frequency, and time range, then utilizing digital signal processing system which includes design several types of digital filters based on the types and numbers of unwanted signals. Four digital filters are used in this research to remove noise signals from the sound file by implementing the proposed algorithm using Matlab Code. Results show that our proposed algorithm was done successfully and the whole noise signals were removed without any negative consequence in the output sound signal.&lt;/span&gt;&lt;/p&gt;
ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering

We present ImageTTR, an extension to the Python implementation of Type Theory with Records (pyTTR) which connects formal record type representation with image classifiers implemented as deep neural networks.The Type Theory with Records framework serves as a knowledge representation system for natural language the representations of which are grounded in perceptual information of neural networks.We demonstrate the benefits of this symbolic and data-driven hybrid approach on the task of visual question answering.
COGNITIVE VISUALIZATION IN A HYBRID INTELLIGENT RISK CONTROLLING SYSTEM

Currently developed hybrid intelligent systems that use a combination of traditional data processing methods and intelligent methods are able to analyze and assess the existing risks in all areas of human activity.This article shows the positive effect of cognitive computer visualization as a means of activating the mechanisms of visual-figurative thinking of the person making a decision in the tasks of controlling the integral indicator of risks.The essence of the anamorphic method is shown, which is the transition from one visual image built on the basis of Euclidean metrics to another visual image based on the quantitative metric of the selected risk.The mathematical formulation of the dynamic meta-anamorphine method is disassembled.The advantages of the constructed meta-anamorphosis in the risk controlling HIS are given.The problem of calculating the integral risk index, which combines the risks of different spheres of activity and visualizations built on this basis, is analyzed.Conclusions are made regarding the construction of a hybrid model based on the hierarchy analysis method and the dynamic meta-anamorphosis method.The directions of further research are determined.
Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross-Language Aligned Word Embeddings

Neural machine translation (NMT) was shown to produce more fluent output than phrase-based statistical (PBMT) and rulebased machine translation (RBMT).However, improved fluency makes it more difficult for post editors to identify and correct adequacy errors, because unlike RBMT and SMT, in NMT adequacy errors are frequently not anticipated by fluency errors.Omissions and additions of content in otherwise flawlessly fluent NMT output are the most prominent types of such adequacy errors, which can only be detected with reference to source texts.This contribution explores the degree of semantic similarity between source texts, NMT output and post edited output.In this way, computational semantic similarity scores (cosine similarity) are related to human quality judgments.The analyses are based on publicly available NMT post editing data annotated for errors in three language pairs (EN-DE, EN-LV, EN-HR) with the Multidimensional Quality Metrics (MQM).Methodologically, this contribution tests whether crosslanguage aligned word embeddings as the sole source of semantic information mirrors human error annotation.
Analysis of User Acceptance Of ERP Systems On After Sales Functions Using Modeltechnology Acceptance Model 2 (TAM2) (Case Study: PT Wijaya Toyota Dago)

Wijaya Toyota Dago is a company engaged in the service, maintenance, repair, and supply of Toyota's official spare parts under the management of PT. Toyota Astra Motor. Currently PT. Wijaya Toyota Dago has used an information system that is TDSM (Toyota Dealer Management System). TDMS is an internal system owned by Toyota to facilitate the company's operational activities and in serving the needs of its customers. TDMS is based on System Application and Product in data processing (SAP), where SAP is one of the ERP (Enterprise Resource Planning) software. However, system users still have not used the system to the fully. And since the implementation of TDMS system has never been done analysis of user acceptance.&#x0D; Based on the problem described, the proposed solution is to conduct user acceptance analysis of the TDMS system using the TAM2 model from Venkatesh and Davis. TAM 2 is a research model used to assess user behavior in receiving and using an information technology. &#x0D; Based on the analysis results is known that theres an effect between perceived usefulness of users and ease of use to the user's interest which means that if the use of the application continues to be improved, it can also increase user interest in receiving TDMS system.
Data Augmentation Based on Distributed Expressions in Text Classification Tasks

We propose a data augmentation method that combines Doc2vec and Label spreading in text classification tasks.The feature of our approach is the use of unlabeled samples, which are easier to obtain than labeled samples.We use them as an aid to the classification model to improve the accuracy of its prediction.We used this method to classify several text data sets including the natural language branch of the AIWolf contest.As a result of the experiments, we confirmed that the prediction accuracy is improved by applying our proposed method.
Result analysis of Kaiman filter for unobservable systems

As a set of filtering algorithms for recursive computing the optimal estimation of system states, Kalman filtering is widely used in many fields such as control systems and signal processing. However, in practical application, it is a problem whether the observability of the system will affect the filtering result of system states, which needs to be considered and paid attention to. In this paper, the incomplete observable system is decomposed into observable and unobservable parts, and the optimal estimation result of the corresponding state vectors is obtained by Kalman filter. The linear state space model with 3-dimensional state vector and 2-dimensional observation vector is selected as the simulation model, many different initial values of the filtering state are set for simulation calculation and comparative analysis. Simulation results show that the unobservable state vector cannot be accurately estimated using Kalman filtering, and the deviation of the initial value between the filtering state and the real state will affect the Kalman filtering result, and the larger the deviation of the initial value, the larger the initial deviation between the filtering result and the real state.
Rekayasa Sistem Informasi Retribusi Sampah Berbasis Web untuk Optimalisasi Kinerja Bidang Pengelolaan Sampah

Managing municipal waste efficiently is a challenge in itself. Waste levies as a source of regional revenue need to be managed optimally so that the waste transportation services for the community can run smoothly. Constraints faced by the City Government of Pangkalpinang are how to anticipate the limited number of garbage lift fleets, the increasing population density, and the manual administration system. To improve the quality of waste transportation services, one of them is by upgrading from manual to a web-based system with the aim of making it easier to manage fleet transport schedules, making it easier for people to pay waste fees, and making it easier to report waste levies. The system will be built using the Object Oriented Analysis and Design (OOAD) methodology because with OOAD the system can be developed gradually and continuously. This web-based levy information system is also expected to be able to facilitate public access to cleaning services
Graph-based Automatic Modulation Classifier for M-ary Generalized QAM Signals

This paper presents a novel automatic modulation classification (AMC) method using graph-based constellation analysis, to classify M-ary Generalized Quadrature Amplitude Modulation (GQAM) signals which employ uniform or nonuniform constellations for the first time. In our framework, a unified grid model is first built from the GQAM signal with the maximum constellation size in the modulation candidate set, and exploited to transform the received signal into graph domain. The graph representation of the received signal is established by mapping its recovered symbol points on the I/Q plane into the unified grid model, and then the eigenvalue(s) and eigenvector(s) of its corresponding adjacency matrix are computed. The modulation feature vector is constructed according to the eigenvector(s) corresponding to the maximum eigenvalue(s). The modulation type is eventually identified by searching the minimum angle between training features and test feature. Monte Carlo simulation results demonstrate that the proposed method can effectively classify the GQAM even in low signal-to-noise ratio.
A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

In wireless sensor networks (WSNs), the problem of measurement origin uncertainty for observed data has a significant impact on the precision of multi-target tracking. In this paper, a novel algorithm based on least squares support vector machine (LS-SVM) is proposed to classify measurement points for adjacent targets. Extended Kalman filter (EKF) algorithm is firstly adopted to compute the predicted classification line for each sampling period, which will be used to classify sampling points and calculate observed centers of closely moving targets. Then LS-SVM algorithm is utilized to train the classified points and get the best classification line, which will then be the reference classification line for the next sampling period. Finally, the locations of the targets will be precisely estimated by using observed centers based on EKF. A series of simulations validate the feasibility and accuracy of the new algorithm, while the experimental results verify the efficiency and effectiveness of the proposal.
Exploring Prediction Modeling of Students Alcohol and Drug Addiction Affecting Performance using Data Mining Approach

In our society, there are number of problems arises due to the students consuming alcohol and drugs during its teen age.Retrieving exact and accurate students which consumes alcohol and drugs is the main task.It is a real-world problem in our society.The major challenge for finding alcohol addicted students with respect to given data is to find accurate and efficient method which takes less time to generate results.There is large amount of data available, but getting the right information accessible when needed is very important.The availability of educational data has been growing rapidly, and there is a need to analyze hedge amount of data generated from this educational ecosystem.Educational data mining (EDM) has been emerged as a process of applying data mining tools and techniques to analyze the data at educational institutions.This area of research is gaining popularity due to potential benefits to the educational field.Educational institutions use educational data mining (EDM) to gain deep and through knowledge to enhance its assessment, evaluation, planning, and decision making in its educational programs.EDM helps academic programs to identify and discover hidden patterns in the data.These extracted patterns can be used for finding students who are consuming alcohol and drugs and its affect on their academic performance.In our proposed system we will use some educational institute's student's data and generate prediction weather student is alcohol addicted or not, we will do this by using clustering, classification, and filtering methods of data mining.
Guatemalan Ixil Community Teacher Perspectives of Language Revitalization and Mother Tongue-Based Intercultural Bilingual Education

Recent scholarship indicates several benefits of mother tongue education (MTE) in supporting student learning.
Optimising the Machine Translation Post-editing Workflow

In this article, we describe how machine translation is used for post-editing at TransPerfect and the ways in which we optimise the workflow. This includes MT evaluation, MT engine customisation, leveraging MT suggestions compared to TM matches, and the lessons learnt from implementing MT at a large scale.