Reviewerly/exHarmony huggingface dataset:
Design of Authenticated Radio Frequency Identification based Electronic Voting Machine

To elect leaders, vote is a powerful right for the people of democratic countries. At present Electronic Voting Machines (EVM) are being used to conduct elections. It needs five to six polling officials per booth to conduct voting. In this paper, an authenticated Radio Frequency Identification (RFID) based EVM is proposed which needs only two to three polling officials per booth. In this, each voter will be given a unique RFID tag, which should be carried out to the polling booth while coming to cast their vote. Based on that, the EVM can easily identify the person is authenticated voter or not and also checks whether he/she had already voted or not. To avoid the chances of bringing one's tag by others, face recognition is added as a second step of authenticity. Results showed that the proposed EVM is more reliable, needs less man power and time to conduct elections smoothly.
Analysis of the Online Support Systems for Curriculum Organization and Implementation in Korea

This study examined the following five websites operated by government for public interest: ‘National Curriculum Information Center’, ‘Edunet T-clear’, ‘School Information’, ‘Student Evaluation Support Portal,’ and ‘High School Credit System’. In order to examine the use of the five curriculum online support systems, a total of 3,391 teachers (860 elementary school, 1,223 middle school, and 1,308 high school) who are the target users of the systems were surveyed. The results of the survey were as follows. First, the main means for acquiring curriculum information was through ‘online systems operated by government’, with the highest user rate at 40.8%. Second, curriculum online support systems were utilized the most in the order of ‘School Information’, ‘National Curriculum Information Center’, ‘Edunet T-clear’, ‘Student Evaluation Support Portal’, and ‘High School Credit System’. Third, the reason for using an online curriculum support system was to plan the curriculum, which was the reason ranked in the top three for all the sites. It was found that ‘National Curriculum Information Center’ and ‘High School Credit System’ are mainly being used to understand the national curriculum, when setting the direction for school curriculums. For an in-depth analysis on the use of curriculum online support systems, a monitoring group of 25 educational officers in cities and provinces and 51 elementary/middle/high school teachers (total of 76) was formed. The study suggested what menus, functions, and practices for curriculum organization and implementation need to be supplemented or added to the five websites.
Paraphrase Identification Using Dependency Tree and Word Embeddings

In this paper, we are trying to develop an efficient and simple model for detecting paraphrase sentences in the English language. The dependency tree was chosen as the main structure to represent the relationships between words in a sentence. To represent the word semantics, we are using pre-trained general-purpose word embeddings. Based on these two key components, we designed a few features that can help to identify paraphrases. Conducted experiments proved that the model is efficient and shows relatively close results to state-of-the-art models.
Forecasting Solar Irradiance Using Machine Learning

Renewable energy is becoming a very popular source for power generation nowadays. In the context of Bangladesh, solar energy has become the most prospective renewable resource for which solar irradiance is a very important parameter. Being able to forecast the solar irradiance accurately can facilitate efficient design of any solar power plant. In this study we have used an Artificial Neural Network (ANN) which is essentially a Machine Learning (ML) approach. As it is time series-based forecasting, we have taken past 15 years' (20002015) daily data from the renewable energy community of NASA database. We have chosen a coastal area for this study case like Saintmartin near Teknaf which has a boundless role in Bangladesh. Here, a feed forward back propagation neural network has been used. Eight important parameters have been considered as independent input variables to forecast daily solar irradiance and the parameters are - air temperature, wind speed, precipitation, humidity, surface pressure, insolation clearness index, and earth skin temperature. The proposed model has provided prediction results with good accuracy and minimal error.
Digital technologies in the formation the students’ supra professional competencies

This paper presents the results of a study on the use of digital technologies for the development of university students' supra-professional competencies. The authors present the modern transformations of the didactic tetrahedron and the influence of digital technologies on the organization and results of the educational process at universities. The key problems of the large-scale implementation of ICT in teaching are identified. As a result of analysis of previous studies, the authors give a correlation of the use of ICT for the development and assessment of students' supra-professional competencies. An online survey of 88 university teachers from 10 regions of Russia showed the relevance and importance of using digital technology in shaping the future of a competitive and comprehensively developed specialist. At the same time, a number of difficulties were identified that impeded the use of ICT in the implementation of academic disciplines at the university. The authors conclude that it is necessary to reduce the technological gap between the development of technologies and their implementation in the educational process, including to increase the efficiency of the development of students' supra-professional competencies.
Virtual Machine Consolidation for NUMA Systems: A Hybrid Heuristic Grey Wolf Approach

Virtual machines consolidation is known as a powerful means to reduce the number of activated physical machines (PMs), so as to achieve energy-saving for the data centers. Although the consolidation technique is widely studied in non-NUMA systems, we could only trace a few studies targeting NUMA systems. But the virtual machines (VMs) deployment of NUMA systems is quite different from that of non-NUMA systems. More specifically, consolidating VMs in NUMA systems need to decide both target physical machines and NUMA architectures to host the VMs, and more complicated constraints originated from the real usage of NUMA systems that need to be considered. Being motivated by these challenges, we in this paper formally derive the system model according to the real business model of NUMA systems and based on which, we propose a hybrid heuristics swarm intelligence optimization algorithm HHGWA for an efficient solution. To do the evaluation, extensive simulations that integrate real VM and PM information are conducted, the result of which indicates a superior performance of our proposed algorithm.
On the intersection problem for linear sets in the projective line

The aim of this paper is to investigate the intersection problem between two linear sets in the projective line over a finite field. In particular, we analyze the intersection between two clubs with possibly different maximum fields of linearity. We also consider the intersection between a certain linear set of maximum rank and any other linear set of the same rank. The strategy relies on the study of certain algebraic curves whose rational points describe the intersection of the two linear sets. Among other geometric and algebraic tools, function field theory and the Hasse–Weil bound play a crucial role. As an application, we give asymptotic results on semifields of BEL-rank two.
Speaker Embedding Augmentation with Noise Distribution Matching

Data augmentation (DA) is an effective strategy to help building robust systems with good generalization ability. In the embedding based speaker verification, data augmentation could be applied to either the front-end embedding extractor or the back-end PLDA. Unlike the conventional back-end augmentation method which adds noises to the raw audios and then extracts augmented embeddings, in this work, we proposed a noise distribution matching (NDM) based algorithm in the speaker embedding space. The basic idea is to use distributions such as Gaussian to model the difference between the clean and original augmented noisy speaker embeddings. Experiments are carried out on SRE16 dataset, where consistent performance improvement could be obtained by the novel NDM. Furthermore, we found that the proposed NDM could be robustly estimated using only a small amount of training data, which saves time and disk cost compared to the conventional augmentation method.
The MATLAB/SIMULINK Implementation of a Single Zone Temperature Control Strategies

Two control approaches are implemented in this article. In the first approach, a conventional proportional-integral-derivative controller is designed and implemented for performance comparison. In the second approach, a Fuzzy Logic controller is designed and implemented. Both controllers are implemented in MATLAB R2019b. The aim will be to select the most appropriate controller capable of being applied to control the temperature of a variable air volume of a single zone area, which is one of the most common devices in a large number of HVAC control system applications.
New methods for detecting concentric objects with high accuracy

In fitting concentric geometric objects to digitized data, two approaches are commonly used in practice, the geometric approach and the algebraic approach. The former is iterative, and it requires a good initial guess. This paper focuses on the latter that is based on minimizing the algebraic distances when a constraint is imposed on parametric space yielding non-iterative methods. Each method depends on the constraint imposed on the parametric space and can be solved using the generalized eigenvalue problem. In this paper, we review the two existing methods developed. After establishing a general mathematical framework to solve this problem, the statistical properties of the methods have been established. Our analysis allows us to develop three estimators that outperform the existing ones. Moreover, the superiority of our methods and their practical performances are assessed by a series of numerical experiments on both synthetic and real data.
Calibrated simplex-mapping classification

We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular $(n-1)$-dimensional simplex, $n$ being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.
Segmentation of images through curve fitting analysis by modified Vandermonde matrix and modified Gram‐Schmidt method

An algorithm is proposed for segmentation of digital images using the curve fitting method based on modified Vandermonde matrix and modified Gram-Schmidt method. Modified Vandermonde matrix is applied to linearly smoothed histogram data to find the coefficients of a polynomial of a given degree for fitting the data in a least square sense. Modified Gram-Schmidt method is applied to get polynomial coefficients to minimise the least square distance during the calculation. The threshold value is computed by locating the minimum number of pixels from the grey-level value. Threshold value is used with that of the input image to generate an output segmented image. The output segmented image is further improved by applying morphological operation which has made the segmented edge lines and regions free from any extraneous pixels. The final segmented image has been found to have sharp edges and filled in a region within the image contours. The experimental results show that the proposed method has produced superior performance compared to other state-of-art algorithms. Further, it is observed from the results that the proposed algorithm takes less program execution time than others in vogue algorithms.
Astroturfing Technologies on a Point of Law

Introduction: in the social conditions that are changing under the influence of information and communication technologies, it is of particular importance to protect the interests of consumers from illegal actions of unscrupulous market participants. Increasingly, astroturfing technologies are becoming an integral part of marketing and trading strategies, which make it possible to provide distorted data to the general public under the guise of objective information. The purpose of the study is to find an adequate definition of astroturfing on a point of law; to distinguish legitimate marketing practices from illegal acts that distort perceptions of a product or company; to identify the law enforcement practices in foreign countries; to evaluate the existing approaches to solving the problem in order to correct the Russian legislation. The paper uses the methods of description and interpretation; normative-value, system, logical, dialectical, structural-functional approaches. Results: the forms and features of astroturfing are considered in order to develop its definition; the ways to counteract such technologies are studied and compared; there is a need for amendments that could fill the gaps in the legal regulation of astroturfing. Conclusions: the term “astroturfing” is formulated and its possible forms are listed. It is proposed to investigate the possibility of recognizing contractual relationships, whose subject is online publishing of fabricated reviews, cheating likes, bot mailing via chats, etc. as anti-social transactions. It is considered appropriate to tighten responsibility for the use of astroturfing technologies.
Digital development trajectory as a tool for improving the quality of education

The present article proposes an approach to using students’ digital development trajectories to improve the quality of education. Designing a digital development trajectory has a positive effect on students’ motivation and active participation in deciding on its formation. The goal of the study is to create a conception of using methods and approaches of educational data mining and machine learning in analyzing and predicting digital development trajectories to improve the educational process quality. Implementation of learning management systems with the opportunity to dynamically track students’ academic performance allows timely correcting gaps appearing in students’ knowledge and thereby reducing the risk of them falling behind in their study group. Recording the obtained results of completed assignments and interactions with learning management systems also benefits teachers who get an opportunity to receive feedback in an implicit form and account for gaps appearing in the curriculum in a processed and aggregated form. The analysis is conducted based on a database of additional education in a primary school (grades 1-3). The present study examines several scenarios of using a digital development trajectory in education including students’ progress in an academic performance trajectory based on their grades, a trajectory in terms of time spent on solving problems to determine their complexity, and a heatmap for determining problematic areas and weak spots in each teacher’s educational course. Based on educational data from a learning management system, the authors present a set of recommendations for teachers on the adaptation of their educational course to students’ abilities promoting the increase of the overall quality of education.
Channel-noise tracking for sub-shot-noise-limited receivers with neural networks

Non-Gaussian receivers for optical communication with coherent states can achieve measurement sensitivities beyond the limits of conventional detection, given by the quantum-noise limit (QNL). However, the amount of information that can be reliably transmitted substantially degrades if there is noise in the communication channel, unless the receiver is able to efficiently compensate for such noise. Here, we investigate the use of a deep neural network as a computationally efficient estimator of phase and amplitude channel noise to enable a reliable method for noise tracking for non-Gaussian receivers. The neural network uses the data collected by the non-Gaussian receiver to estimate and correct for dynamic channel noise in real time. Using numerical simulations, we find that this noise tracking method allows the non-Gaussian receiver to maintain its benefit over the QNL across a broad range of strengths and bandwidths of phase and intensity noise. The noise tracking method based on neural networks can further include other types of noise to ensure sub-QNL performance in channels with many sources of noise.Received 27 September 2020Accepted 3 February 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013200Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptics & lasersQuantum communicationQuantum opticsAtomic, Molecular & OpticalQuantum Information
Automated Ontology Population and Enrichment of Scientific Publications

Abstract Scientific publications are the most important resources available to the research communities. Researchers want their work to be widely recognized and available and also need powerful search engines to identify other publications and researchers working in the same area. Therefore, a good representation and organization of scientific products is crucial for an accurate retrieval of information. This paper describes an approach for automated population and semantic enrichment of an ontology model that represents scientific publications. Specifically, the type of enrichment used in this approach consists of implementing semantic similarity measurements between publications. Several experiments were performed to identify the best similarity measurement, using a statistical approach and the precision of the measurements.
Multi-source domain adaptation-based low-rank representation and correlation alignment

Domain adaptation is one of the machine learning approaches, which is very powerful and applicable especially when there is no labeled data on the target domain or there are unequal distributions and different feature spaces between the source and target domains. This paper proposes an unsupervised domain adaptation model, which addresses this problem by utilizing two main folds: first, domain shift between source and target is diminished by matching the second-order statistics of distributions, and then the aligned source data along with target data are transferred into a shared subspace where more reduction in distributions discrepancy is occurred by linear combinations of related source samples to each target sample with utilizing low-rank and sparse conditions. So the classification ability of the source domain is transferred into the target domain. The experimental results mention that the proposed approach outperforms competitors.
Characterization of Syrian refugees with work permit applications in Turkey: A data mining based methodology

With the technological advancements in data collection systems, data-driven approaches become a necessity for understanding and managing the socioeconomic systems. Motivated by this, we focus on the formal employment of Syrian refugees in Turkey, and propose a data mining based methodology in order to understand their profiles. In this context, Syrian refugees with work permit applications are examined between years 2010 and 2018. The dataset includes demographic properties of the applicants and characteristics of their workplaces. The proposed methodology aims to extract the hidden, interesting and useful characteristics of the Syrian refugees having formal employment potential. The proposed approach integrates several data mining tasks, i.e. clustering, classification, and association rule mining, and it has four phases. In the first phase, data pre-processing and visualization operations are performed. In the second phase, the profiles of the Syrian refugee workers are determined using clustering. Self-organizing map and hierarchical clustering are implemented for this purpose. In the third phase, decision tree is used to specify the distinguishing characteristics of the clusters. In the fourth phase, the association rules are generated to reveal the interesting and frequent properties of each cluster. The results reveal the profiles of Syrian refugees with work permit applications. The findings obtained from this study can be a basis for developing policies and strategies that facilitate the labor market integration of the immigrants. The proposed methodology can be used to analyze time-dependent patterns and other immigration data for different countries as well.
Crypto-Core Design using Camellia Cipher

Abstract Camellia cipher is another symmetric block cipher which allows the encryption and decryption process to share the same key. The cipher permits a 128-bits input data with three different key size: 128, 192 and 256 bits. This paper presents two hardware design approach of Camellia cipher, which are FPGA and custom-based design approach. These approaches utilized design softwares of Altera Quartus II with device family of Cyclone II and Synopsys Design Compiler. The performance of Camellia crypto-core design is then been evaluated based on the implementation platform in terms of speed, area and power. With an equal base of 50MHz of clock frequency, custom-based design is found more efficient than FPGA-based design due to the execution time achieved with 8.46ns, which is faster than the latter that consumed double the time with 16.075ns. The custom-based design achieved 15.13 Gbps of throughput. Besides, the power consumption of custom-based design is 1.3519 mW which is lower than the FPGA-based design. In a nutshell, the design has successfully done as it achieved expected encryption and decryption outcomes with acceptable performance.
Proposal of a Particulate Matter Measurement Device and an Augmented Reality Visualization App as an Educational Tool

Air pollution contains Particulate Matter (PM), invisible to the naked eye due to its less than 10 micrometers size. These dimensions allow PM to reach deep into the lungs and heart, causing multiple health and environmental issues. Despite the ubiquitousness and danger of these pollutants, to date, there is no educational tool for understanding PM concentrations through visualization and measurement of air quality in real-time. In this context, we developed an innovative educational tool that leverages Internet of Things (IoT) and Augmented Reality (AR) technologies to create an interactive real-time PM levels information layer over the surrounding environment. In this article, we present our first IoT/AR prototype, a proposal on how to use it in a learning environment as an educational tool, and preliminary results of validation tests with high school students. We propose that our system has the potential to promote awareness and understanding of air pollution and its effects on human health.