Reviewerly/exHarmony huggingface dataset:
Polygon Shape Formation for Multi-Mobile Robots in a Local Knowledge Environment

The formation is an important task in multi-mobile robots coordination in a defined environment. In a local knowledge environment the multi-mobile robot formations are realized using small robots with minor hardware requirements. The localization, path planning and obstacle avoidance processes are required to perform formation. In this paper a static strategy for polygon shape formation is implemented using a several number of mobile robots. This strategy has a better efficiency, since it use the cluster matching algorithm instead of the triangulation algorithm in completing the formation. Also, the visibility binary tree algorithm and the reciprocal orientation algorithm are used in this paper. This strategy has better performance in the multi-robot formation, since it use the cluster matching algorithm instead of the triangulation algorithm.
Collaborative SPARQL Query Processing for Decentralized Semantic Data

Decentralization allows users to regain freedom and control over their digital life. As a global shared data space, the Linked Data already supports decentralization. Data providers are free to publish their data on their web domains and users can execute decentralized SPARQL queries over multiple data sources. However, decentralization makes query processing challenging, raising well-known problems of source discovery, answer completeness and performance. Existing approaches for decentralized SPARQL query processing raise issues related to autonomy and answer completeness. In this paper, we propose Qasino, an original approach for querying decentralized RDF data that targets both answer completeness, and source autonomy. Qasino is based on a decentralized random service that allows for discovering all relevant data sources. To speed up query processing, sources executing similar queries cooperate by sharing their intermediate results. Our experimental results demonstrate that collaborative query processing can significantly speedup query processing in a decentralized setup.
Assessment of Ant Colony Optimization Algorithm for DAG Task Scheduling in Cloud Computing

The quality of task scheduling plays vital role towards the trust to use the services of cloud computing.Large numbers of tasks are submitted to the cloud environment in each moment and these tasks are executed on the virtualized resources that can be provisioned dynamically by the cloud.Optimal allocation of resource to a set of tasks follows a workflow schedule and it is an important step to improve the overall performance of the cloud.Ant Colony Optimization (ACO) is a meta heuristic approach that imitates the foraging behaviour of real ants.It is a probabilistic technique that can be used to solve the combinatorial optimization problems like workflow scheduling.In this research work, a novel ACO algorithm and its variant Improved ACO (IACO) performs workflow scheduling through multi objective optimization process is put forwarded.The IACO incorporates with a new heuristic information value based on the processing cost and execution time to achieve the desired objectives.These meta heuristics are simulated on the benchmark scientific workflows Montage, CyberShake and Ligo Inspiral.The simulation results of IACO were compared with ACO and Genetic Algorithm (GA), IACO reports an optimal schedule which results into the reduction of makespan and total execution cost.
Dynamic Linkages between Mobile Money and Banks' Performance in Nigeria: An Autoregressive Distributed Lag (ARDL) Approach

This study investigated the relationship between mobile money and Nigerian banks with the purpose of ascertaining if forward and backward causal linkages exist and the direction of causation between the two variables.This inquiry was motivated by the inherent challenges of a low level of awareness, enrolment and utilization of mobile money services in Nigeria despite the efforts of the government to replicate East Africa's success story in Nigeria.This study employed econometric techniques such as the modern autoregressive distributed lag (ARDL) model and Wald causality to respectively assess the relationship between the explained and explanatory variables and the direction of causality between mobile money and various banks' performance metrics using quarterly time series data between 2014 and 2018.The empirical results revealed that there is a causal but insignificant relationship between mobile money and banks' performance indicators, such as profit before tax and total assets.On the premises of the empirical findings, the study concludes that mobile money has forward and backward linkages with banks' performance in Nigeria.This implies that Nigeria's mobile money market as presently constituted does provide attractive incentives for banks to leverage on in their quest to improve performance.
PROBLEM-BASED GROUP DISCUSSION: AN EFFECTIVE ELT TECHNIQUE TO IMPROVE VOCATIONAL HIGH SCHOOL STUDENTS’ TRANSACTIONAL SPEAKING SKILLS

In this millennial era, English speaking skills are very necessary for young learners, especially for Vocational High Schools students. However, few teachers still have difficulties in choosing the right technique to teach the speaking skills. Consequently, students feel that speaking is the hardest skill in English. This paper is aimed at discussing how to improve the students’ transactional speaking skills (group discussion) using problem-based group discussion technique at Vocational High School 5 Gowa. A quantitative research design was used to carry out this study with support from the data collection instrument i.e. speaking video test (group discussion). The design of this study conducted using a quasi-experimental with nonequivalent control group design. Twenty (20) students in two classes i.e. ten students for the experimental class and ten students for the controlled class of the intermediate level were purposively selected to participate in this study. The results of this study showed that the problem-based group discussion technique is able to improve students’ transactional speaking skills. It was proved by the result of the statistical analysis of the level significance P = 0.05 with the degree of freedom (df) = 9 indicated that the t-test values of the students’ transactional speaking skills (3.498) was higher than the t-table value (2.262). The outcome of this research indicates that this technique is effectively able to improve the students’ transactional speaking skills in exchanging information, taking a part in formal discussions and even learning to work in real-life situations
Assisting Users to Create Stronger Passwords Using ContextBased MicroTraining

In this paper, we describe and evaluate how the learning framework ContextBased MicroTraining (CBMT) can be used to assist users to create strong passwords. Rather than a technical enforcing measure, CBMT is a framework that provides information security training to users when they are in a situation where the training is directly relevant. The study is carried out in two steps. First, a survey is used to measure how well users understand password guidelines that are presented in different ways. The second part measures how using CBMT to present password guidelines affect the strength of the passwords created. This experiment was carried out by implementing CBMT at the account registration page of a local internet service provider and observing the results on user-created passwords. The results of the study show that users presented with passwords creation guidelines using a CBMT learning module do understand the password creation guidelines to a higher degree than other users. Further, the experiment shows that users presented with password guidelines in the form of a CBMT learning module do create passwords that are longer and more secure than other users. The assessment of password security was performed using the zxcvbn tool, developed by Dropbox, that measures password entropy.
Recovery of an Analytical Signal Distorted by First-Order Aliasing

This paper relates to one of the areas of digital signal processing in a wide frequency band based on the use of undersampling.In this case, the sampling rate is less than the Nyquist-Shannon sampling theorem requires.In this direction, compressed sampling methods are intensively used, based on the fact that the sampling theorem formulates sufficient requirements for the sampling frequency while under certain conditions they are not necessary.Very often these methods use random sampling.Our article uses deterministic undersampling.Undersampling results in aliasing that distorts the signals.Known signal restoration methods are based on multi-channel processing.At the same time, the channels carry out preliminary analog signal processing with subsequent undersampling with a singlefrequency frequency in all channels.The proposed approach is based on the rejection of preliminary analog processing in the channels, but the frequency of the undersampling in different channels is different.Then the integration of the processing results in the channels for signal restoration is carried out.Keywordssubnyquist sampling, first-order aliasing, restoration of a signal distorted by aliasing, analytical signal, two-channel two-rate sampling.
The Impact of Data Normalization on Predicting Student Performance: A Case Study from Hashemite University

The main goal of mining and analyzing educational data is to identify interesting patterns that might influence learning quality and student performance.Supervised machine learning can be utilized to predict student performance.More specifically, classification algorithms are used to construct a student performance prediction model.One of the main challenges to generate the desired model is adopting appropriate data preprocessing techniques.The central idea of the work presented in this paper is to generate a student performance prediction model and investigate the impact of various data normalization techniques on its effectiveness.The influence of various data normalization techniques is assessed on three novel academic datasets collected from the Hashemite University in Jordan.The obtained results provided empirical evidence that the adopted data normalization technique has a significant effect on classification model effectiveness.The SVM classifier coupled with the Z-score normalization technique produced the most effective student performance prediction model.
Upper Stomach Disorder Detection System using Backpropagation Artificial Neural Network

The study aims to make simple detection of upper stomach disorders of the iris image based on the chart to iridology, making an upper stomach disorder detection system using backpropagation artificial neural network method and determining the accuracy of the system.Backpropagation artificial neural networks is a type of neural network that trains the network to get a balance between the network's ability to recognize patterns used during training as well as networking capabilities to provide a correct response to similar input patterns but not identical with patterns during training.Iridology is the science of analyzing the subtle structures of the iris.This research was conducted from the shooting stage of the eye image using the camera, data obtained as 40 iris images.Detection of disorders using chart to iridology from the iris imagery data of 20 pairs of eyes consisting of left and right.This results of 10 pairs of eye image showed that upper stomach disorder and 10 pairs of eye image showed no upper stomach disorder.
Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Artificial Intelligence

Single approaches to soft computing have many limitations and disadvantages. Neural network modelling poses a challenge of architecture building, whilst fuzzy sets are characterized by problematic membership functions. The use of hybrid methods is, by contrast, a rather promising strategy. This study aims to develop a new prediction methodology by integrating Neuro-Fuzzy Systems (NFS) with a Neuro-Genetic Approach (NGA). Such a design combines the learning capacity of the neural networks and the ability of fuzzy systems to extract the linguistic knowledge. This proposal is expected to predict the suitability of parameters and models with fewer errors and high accuracy. The performance of this system is improved through the use of genetic algorithm for optimizing the neural network parameters such as the learning rate, network impulse, and the number of membership functions for each input variable. The proposed methodology was tested using electromyographic (EMG) data. The results showed high efficiency (92%) of the proposed hybrid technique.
Dickens’s &lt;i&gt;Pictures from Italy&lt;/i&gt; vs. Murray’s &lt;i&gt;Handbook to Northern Italy&lt;/i&gt;: An Investigation into Adjective Use

The Nordic Journal of English Studies was created in 2002 in order to offer a forum for Nordic scholars in English language and literature and to promote the field of English studies in the Nordic countries. It is affiliated to the Nordic Association of English Studies (NAES). Starting from 2007 the journal is open access and only published electronically.
A New Lightweight Proposed Cryptography Method for IoT

The key challenge to information security is the requirement for using untraditional philosophies and alternative means, as concentrating on new aspects to achieve security.In this article, a new proposed method has been proposed, using the stored biological data on GenBank with its features.This article uses the new aspects for achieving the cryptography depending on this data.The proposed method can be used in many new fields, especially in IoT and smart cities.The limited resources of the smart devices that form the basis of the IoT, particularly in terms of power and storage, showed the need to find a new method to ensure confidentiality of these devices.A set of analysis based on calculations has been used to test the new technique.The results and the analysis indicate that the new technique is secure.
Translation Techniques in Translating Cultural Issues in the Indonesian Religious Tourism Brochure in Semarang City

ABSTRACT Many foreigners visit Indonesia especially Semarang City for doing some business, research, Javanese Cultural studies or just for vacation. As foreign tourists, they only depend on tourism brochures or use Google Map to find the places they want to visit. Often, the English-translated tourism brochures contain untranslatable words or phrases. This research is aimed at finding out the translation techniques used to translate the cultural issues in the Indonesian tourism brochure texts especially the religious tourist destinations in Semarang City. This research is descriptive qualitative type and uses the tourism brochure texts as the sample. The results of this research show that several translation techniques are used in to translate the Indonesian texts into English which include (1) pure borrowing; (2) established equivalence; (3) pure borrowing-established equivalence; (4) deletion; (5) pure borrowing-deletion; (6) generalization; (7) modulation; (8) generalization-pure borrowing; (9) pure borrowing-modulation; (10) modulation-deletion. The dominant techniques are pure borrowing and established equivalence which are associated with the high accuracy in translating the cultural issues in the Indonesian tourism brochure texts especially the religious tourist destinations in Semarang City. Keywords:translation techniques, cultural issues, tourism brochure especially the religious tourist destination, Semarang City
Quality Evaluation of Audio and Video Signals in Videoconferences

Videoconferencing represents a technology of the future, in modern education.A combination of audio and video information serves in understanding the content of lectures or presentations, in the form of videoconferencing.The evaluation of the quality of videoconferencing is difficult, as the image and sound affects the final quality.In general, occasional image disturbance has less impact on the perception of quality in comparison to the disturbances in an audio track.In this research, we simulated a real packet network environment and tested video sequences that present different teaching content.We artificially degraded the quality of video sequences by packet loss and jitter.Our test aimed to compare subjective methods of video quality evaluation with objective methods and to evaluate the impact of audio quality on the overall video sequence quality.This paper describes a novel process of evaluating the quality of audio and video signals.Timeconsuming subjective measurements were supported by models and programs that simplified the preparation, testing, and processing of results.The contribution of this article is to present and evaluate the results of video sequence quality testing with an emphasis on semantics, which has a significant impact on viewers' sensitivity to video sequence quality.
PENGEMBANGAN MODUL SINTAKSIS BERMODEL DISCOVERY LEARNING UNTUK MAHASISWA PENDIDIKAN BAHASA INDONESIA

This study aims to (1) describe the process of syntax module development with the discovery learning model, and (2) describe the quality of a syntax module with the discovery learning models on a syntax material. This development model uses the ADDIE model (analysis, design, development, implementation, evaluation). The results of this study are (1) the process of the syntax module development designed through five stages, namely analysis, design, development, application, and evaluation; (2) the quality of the syntax module tested through the criteria of validity, practicality, and effectiveness. The validity data results are obtained from the expert validator assessment is of category very valid. The effectiveness data of student activities is of category good, the activity of the lecturers is of category very good, and the completeness of student learning is complete. The practicality data obtained from the assessment of the implementation of RPS is of category very good, student responses, and lecturer responses are of category positive.
Coinductive Natural Semantics for Compiler Verification in Coq

(Coinductive) natural semantics is presented as a unifying framework for the verification of total correctness of compilers in Coq (with the feature that a verified compiler can be obtained). In this way, we have a simple, easy, and intuitive framework; to carry out the verification of a compiler, using a proof assistant in which both cases are considered: terminating and non-terminating computations (total correctness).
Development and Validation of Robotic Coding Attitude Scale

This study aimed to develop a reliable and valid instrument to measure secondary school students’ attitude towards robotic coding and the results of the analyses regarding the Robotic Coding Attitude Scale (RCAS) developed for this purpose were reported in the current study. To test the construct validity of the first version of the RCAS consisting of 29 items, Exploratory Factor Analysis (EFA) was performed on the data from 196 seventh-grade secondary school students (who had received robotic coding education already) enrolled in the public schools in a city in the northeastern Turkey. As a result of the EFA, the 22-item five-factor model was extracted. Then, the 22-item five-factor model obtained from EFA was cross-validated using Confirmatory Factor Analysis (CFA) and the results indicated acceptable model fits where χ2/(df = 197) = 1.827, NFI = .825, CFI = .911, RMSEA = .065. Cronbach’s Alpha coefficient which was calculated to show whether the RCAS is a reliable scale was found to be .91. In sum, the results indicated that RCAS can be used as a reliable and valid instrument to measure secondary school students’ attitude towards robotic coding.
Vector Quantized Bayesian Neural Network Inference for Data Streams

Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far less widely used than non-Bayesian NNs in practice since they need iterative NN executions to predict a result for one data, and it gives rise to prohibitive computational cost. This computational burden is a critical problem when processing data streams with low-latency. To address this problem, we propose a novel model VQ-BNN, which approximates BNN inference for data streams. In order to reduce the computational burden, VQ-BNN inference predicts NN only once and compensates the result with previously memorized predictions. To be specific, VQ-BNN inference for data streams is given by temporal exponential smoothing of recent predictions. The computational cost of this model is almost the same as that of non-Bayesian NNs. Experiments including semantic segmentation on real-world data show that this model performs significantly faster than BNNs while estimating predictive results comparable to or superior to the results of BNNs.
University outside the system of professional training as a result of many years of reforms in the Russian higher education

The article considers radical changes in the Russian universities activity in the last 30-35 years. The authors believe that such changes were determined by: a) destruction of the Soviet system of higher education; b) its transfer to the Western models of the education organization; c) unification of different university models; d) transfer to the two-level system (bachelor-master); e) universities right to change education programs and earn money by admitting poorly prepared students. These features of the contemporary education led to a significant increase in the number of universities that do not take into account objective social needs in various types of training. Therefore, the university is no longer a final point in the system of professional training, whose previous customers were either the state or employer. The university has turned into an organization providing educational services to the student and his parents. Such a higher-education system does not fulfill its social functions and is wasteful for it contributes to potential conflicts due to the excess of formal holders of higher-education diplomas, for whom there is no place in the real economy. The authors believe that the Russian higher education can change the trend of development by selecting and differentiating a small group of universities according to the future types of their graduates activities (research, technology, education, etc.), and by reliable forecasting of the employers needs.