Reviewerly/exHarmony huggingface dataset:
Future Directions in Technological Support for Language Documentation

To reduce the annotation burden placed on linguistic fieldworkers, freeing up time for deeper linguistic analysis and descriptive work, the language documentation community has been working with machine learning researchers to investigate what assistive role technology can play, with promising early results. This paper describes a number of potential follow-up technical projects that we believe would be worthwhile and straightforward to do. We provide examples of the annotation tasks for computer scientists; descriptions of the technological challenges involved and the estimated level of complexity; and pointers to relevant literature. We hope providing a clear overview of what the needs are and what annotation challenges exist will help facilitate the dialogue and collaboration between computer scientists and fieldwork linguists.
Quantum interferometry via a coherent state mixed with a squeezed number state

We theoretically investigate the phase sensitivity with parity measurement on a Mach–Zehnder interferometer with a coherent state combined with a squeezed number state. Within a constraint on the total mean photon number, we find, via parity measurement, that the mixing of a coherent state and squeezed number state can give better phase sensitivity than mixing a coherent state and squeezed vacuum state when the phase shift deviates from the optimal phase φ =0. In addition, we show that the classical Fisher information for parity measurement saturates the quantum Fisher information when the phase shift approaches to zero. Thus, the quantum Cramér–Rao bound can be reached via the parity measurement in the case of φ =0.
High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs).The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel.Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs.In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data.This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time.The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM).Real datasets gathered from a WSN configured in a local lab are used for testing the techniques.Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements.The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases.The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance.On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT.This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.
The Impact of Social Media on Customers’ Loyalty toward Hotels in Jordan

The present study aimed at exploring the impact of social media on customers&amp;#39; loyalty toward the five stars hotels located in Jordan, it also aimed at exploring the impact of satisfaction as a mediating variable on the relationship between social media and customers&amp;rsquo; loyalty from the perspectives of customers and marketing managers. In more precise, to measure the mediating effect of satisfaction in the causal relationship between social media and customers&amp;rsquo; loyalty toward five stars hotels. Regression analysis and correlational coefficient were used for analysis purposive of the acquired data that were collected through a self-designed questionnaire.&amp;nbsp; A sample of (200) hotel customers and (32) marketing managers was drawn from the population of the study. The major findings of the study were as follow: social media has a statistical significant effect on customer&amp;rsquo;s loyalty, and on satisfaction. Satisfaction as mediating variable has a statistical significant effect on the relationship between social media and customer&amp;rsquo;s loyalty. Furthermore; marketing managers were found to be having positive beliefs that social media has an effect on customers&amp;rsquo; loyalty. But communication and motivation were found to be having no significant effect on customer&amp;rsquo;s loyalty.
Si-Bidan: Sistem Informasi Kesehatan Ibu dan Anak

Midwives are one of the health workers who provide child and maternal health (CMH) services and family planning. At present, most of the recording of midwife services is still managed conventionally by manual book keeping. It is less effective and efficient which causes the workload to increase, the information retrieval process is quite long and the risk of missing important data is likely to occur frequently. On the other hand, maternal patients are required to visit the midwife directly if they want to know the information on the progress of the pregnancy and their child. Based on these facts, a CMH information system was built that was accessible to midwives and parents. The information system developed consists of two integrated applications, namely web-based applications for midwives and mobile applications for parents. The web application facilitates midwives to record transactions, make reports, and deliver information to patients. While the mobile application makes it easier for parents to monitor the development of maternal and child health and other information provided by midwives. The system was developed using the water-fall software development model. The test results using the black-box test method indicate that the CMH system has been able to meet the user's functional requirements.
Pengembangan e-Book Matematika Berbasis Masalah Pada Materi Kubus Dan Balok SMP/MTs Kelas VIII

The purpose of this study was to produce mathematical E-books based on the problem of cube and class VIII SMP / MTs material. This mathematical E-book development uses the Four-D development model. The model consists of 4 stages, namely, Define, Design, Develop, and Disseminate. This E-book was validated by three validators including one media expert, one material expert, and one junior high school math teacher. After the E-book was declared valid, then it is tested to students. The test subjects for the development of E-books were limited trial subjects, namely six students of class VIII at Al Maarif 01 Islamic Junior High School Singosari. The trial was conducted to determine the feasibility of E-books to be used in the learning process. Based on the results of expert and practitioner validation data analysis, overall the developed E-book was declared valid with the results of the average rating of 3.29 and fulfilling the validity criteria set. Whereas in user trials, E-books were declared valid by obtaining an average score of 3.29. Based on the results of the analysis, it can be concluded that the E-book has met the criteria eligible for use in the learning process.
PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting

&lt;p&gt;In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.&lt;/p&gt;
PENINGKATAN HASIL BELAJAR MATEMATIKA MELALUI MODEL PROBLEM BASED LEARNING BERBANTUAN MEDIA PUZZLE SISWA KELAS IV SD

This study aims to improve student mathematics learning outcomes and describe the steps of the Problem Based Learning) model assisted by media puzzles. The subject of this study was in grade IV SD N Kecandran 01 Salatiga as many as 34 students. The method used is Class Action Research in two cycles. In this study, the data collection is used, namely documentation, observation, and written tests. The data analysis technique used is quantitative and qualitative descriptive techniques. The results showed that there was an increase in student learning outcomes seen in the pre-cycle of 34 students there were 15 students or 41% who completed with an average value of 61. Then the results of the first cycle increased to 24 students or 70% with an average value of 68, then in cycle II it increased again to 30 students or 89% with an average value of 80.
A Bibliometric Study about Internet of Things

The objective of this study was to map the scientific production on the Internet of Things (IoT), through a bibliometric analysis.The methodo logical procedures were delineated by a quantitative approach, the research involved data taken from the scientific database of the Web of Science.The keyword "internet of things" was used for the searches in said database, within a period of 09 years, between the period from 2010 to 2018.In the searches 2,446 publications related to the topic were identified, an average of 271.7 articles published per year.The year with the highest number of publications was the year 2018, with 932 publications.
Social and pedagogical aspects of the development of inclusive education

The article substantiates theoretical and practical aspects of implementing inclusive education in the conditions of integration of Ukraine and other former Soviet countries into a global educational space, and also characterizes important socio-pedagogical aspects of the inclusive environment.It emphasizes a demand for forming a new philosophy of education with regard to changing attitudes towards persons with disabilities, especially in post-Soviet countries, as remaining traditional views and beliefs of today concerning teaching students with disabilities and their biased perception in society are still dominant.Gender stereotypes about inclusive education that characterize the mindset of a particular nation are also considered.Socio-pedagogical barriers of inclusion are presented.The article highlights important aspects of establishing an effective pedagogical cooperation between schools and families of students with special educational needs (SEN) and suggests tentative forms of work with parents at inclusive schools.The authors offer examples of the practical use of didactic materials in ESL/EFL teaching at elementary school, in particular inclusive graphic posters and audio-visual aids for forming democratic values and tolerant attitudes towards students with disabilities and SEN.
Sparse Subspace Clustering for Evolving Data Streams

The data streams arising in many applications can be modeled as a union of low-dimensional subspaces known as multi-subspace data streams (MSDSs). Clustering MSDSs according to their underlying low-dimensional subspaces is a challenging problem which has not been resolved satisfactorily by existing data stream clustering (DSC) algorithms. In this paper, we propose a sparse-based DSC algorithm, which we refer to as dynamic sparse subspace clustering (D-SSC). This algorithm recovers the low-dimensional subspaces (structures) of high-dimensional data streams and finds an explicit assignment of points to subspaces in an online manner. Moreover, as an online algorithm, D-SSC is able to cope with the time-varying structure of MSDSs. The effectiveness of D-SSC is evaluated using numerical experiments.
ON SCALE DEFINITION WITHIN CALIBRATION OF MULTI-CAMERA SYSTEMS IN MULTIMEDIA PHOTOGRAMMETRY

Abstract. In multimedia photogrammetry, multi-camera systems often provide scale by a calibrated relative orientation. Camera calibration via bundle adjustment is a well-established standard procedure in single-medium photogrammetry. When using standard software and applying the collinearity equations in multimedia photogrammetry, the refractive interfaces are modelled in an implicit form. This contribution analyses different calibration strategies for bundle-invariant interfaces. To evaluate the effects of implicitly modelling the refractive effects within a bundle adjustment, synthetic datasets are simulated. Contrary to many publications, systematic effects of the exterior orientations can be verified with simulated data. The behaviour of interior, exterior and relative orientation parameters is analysed using error-free synthetic datasets. The relative orientation of a stereo camera shows systematic effects, when the angle of convergence varies and when the synthetic interface is set up at different distances to the camera. It becomes clear, that in most cases the implicit modelling is not suitable for multimedia photogrammetry. An explicit modelling of the refractive interfaces is implemented into a bundle adjustment. This strict model is analysed and compared with the implicit form regarding systematic effects in orientation parameters as well as errors in object space. In a real experiment, the discrepancies between the implicit form using standard software and the explicit modelling using our own implementation are quantified. It is highly advisable to model the interfaces strictly, since the implicit modelling might lead to relevant errors in object space.
Mining of high average-utility patterns with item-level thresholds

In this paper, we introduce a level-wise algorithm named High Average-Utility Itemset Mining with Multiple Minimum Average-Utility threshold (HAUIM-MMAU), which relies on a novel transaction-maximum utility downward closure (TMUDC) property and a concept of least minimum average-utility (LMAU) to mine high average-utility itemsets (HAUIs). Two efficient strategies, named IEUCP and PBCS, are designed to further reduce the search space, and thus speed up the performance of HAUI mining. Several experiments carried out on both synthetic and real-life databases show that the proposed algorithm can efficiently discover the complete set of HAUIs while considering multiple minimum average-utility thresholds.
MathGraph: A Knowledge Graph for Automatically Solving Mathematical Exercises

Knowledge graphs are widely applied in many applications. Automatically solving mathematical exercises is also an interesting task which can be enhanced by knowledge reasoning. In this paper, we design MathGraph, a knowledge graph aiming to solve high school mathematical exercises. Since it requires fine-grained mathematical derivation and calculation of different mathematical objects, the design of MathGraph has major differences from existing knowledge graphs. MathGraph supports massive kinds of mathematical objects, operations, and constraints which may be involved in exercises. Furthermore, we propose an algorithm to align a semantically parsed exercise to MathGraph and figure out the answer automatically. Extensive experiments on real-world datasets verify the effectiveness of MathGraph.
Information and Communication Technology in Mathematics Education – Integration Readiness in Tanzania Higher Education Institutions

The use of ICT tools in mathematics instructions has been proved to have a positive impact on students' success. However, little is known about the ICT readiness of both teachers and students to incorporate these tools in mathematics teaching-learning process. This study investigates the readiness of teachers and students to integrate ICT in mathematics teaching and learning at a higher education institution in Tanzania. Specifically the study assesses the device, skills and psychological readiness of teachers and students to integrate ICT tools in mathematics instructions. The study employs both qualitative and quantitative methods whereby data are collected through interviews and questionnaires respectively. Seven teachers and 129 bachelor degree students pursuing business studies, form the study population. Descriptive statistics are used to analyze the quantitative data while qualitative data are analyzed through content analysis. Results show that teachers and students have device, skills and psychological readiness to integrate ICT in teaching and learning of mathematics. This is significant as it provides information that will be useful in planning the use of ICT tools to benefit teaching and learning. This study contributes to transfer and diffusion of technology discourses.
A Proposed Program for the Activities of the School Media Literacy in the Development of Some Dimensions of Learning for Students in the Third-Grade Primary Considering the Vision of Egypt 2030.

Children and youth are living today in a multi-device, multi-platform, multi-channel world, and school children are more affected by different means of media.The current study aims to investigate the effectiveness of the media literacy activities in developing the learning dimensions of the third-grade students in the light of the vision of Egypt 2030?After the application of the study tools on an experimental sample of ( 40) children in the third-grade primary, the results showed that the use of the activities of the school media literacy to the dimensions of learning in the light of Egypt's vision led to the improvement of the following learning skills "procedural skills and professional skills, Skills of co-existence and cooperation with others.In addition of improving the following sub-skills (Introduce new and unique ideasproblems solving Respect for opinion and other opinion -Define clear goals -Respect diversity -Selfexpression -Verbal communication -Negotiation and good inquiry -Distinguish between similarities and differences).
Enhancing In-Service Primary Teachers' Technological, Pedagogical and Content Knowledge on Mobile Mathematics Learning

This article reports on the main insights from a study aimed at equipping a group of in-service teachers with the knowledge, skills, and practical experience required to effectively integrate tablet devices within the mathematics curriculum. A professional development program focused on mobile mathematics learning and based on the Technological Pedagogical and Content Knowledge (TPACK) framework was designed and implemented in Cyprus. Six primary school teachers participated in the program. Participants experimented with different ways in which coding apps, and other types of constructivist mobile applications could help students internalize key mathematical concepts across the primary curriculum. They also worked together to develop and/or deliver instructional interventions integrating the use of mobile devices. Findings indicate a positive impact on in-service teachers' perceptions regarding mobile-enhanced mathematics, and on their competence in productively utilizing mobile apps as an instructional tool.
Secured Transmission of Clinical Signals Using Hyperchaotic DNA Confusion and Diffusion Transform

Secured transmission of electrophysiological signals is one of the crucial requirements in telemedicine, telemonitoring, cardiovascular disease diagnosis (CVD) and telecardiology applications. The chaotic systems have good potential in secured transmission of ECG/EEG signals due to their inherent characteristics relevant to cryptography. This article introduces a new cryptosystem for clinical signals such as electrocardiograms (ECG) and electroencephalograms (EEG) based on hyperchaotic DNA confusion and diffusion transform (HC-DNA-CDT). The algorithm uses a hyperchaotic system with cubic nonlinearity and deoxyribonucleic acid (DNA) encoding rules. The performance of the cryptosystem is evaluated for different clinical signals using different encryption/decryption quality metrics. Simulation and comparison results show that the cryptosystem yield good encryption results and is able to resist various cryptographic attacks. The proposed algorithm can also be used in picture archiving and communication systems (PACS) to provide an efficient sharing of medical image over the networks.
Image pre-processing: enhance the performance of medical image classification using various data augmentation technique

The medical image classification system is an important subject in the field of biotechnology.Here, the network is trained with a large amount of computation to obtain high accuracy rate [1].Chest X-rays images (CXRs) are broadly used in identifying the abnormalities in the chest area.Automatic detecting techniques are used in most of the diagnosing process, to improve the accuracy rate of abnormality detection.The main objective of this work is to prove the range of error, loss and accuracy by using the convolution neural networks (CNNs) model for detecting tuberculosis in the chest images.Tremendous progress has been made in deep learning models for classifying medical images.
Indifferentiability for Public Key Cryptosystems

We initiate the study of indifferentiability for public key encryption and other public key primitives. Our main results are definitions and constructions of public key cryptosystems that are indifferentiable from ideal cryptosystems, in the random oracle model. Cryptosystems include: Our schemes are based on relatively standard public key assumptions. By being indifferentiable from an ideal object, our schemes automatically satisfy a wide range of security properties, including any property representable as a single-stage game, and can be composed to operate in higher-level protocols.