Reviewerly/exHarmony huggingface dataset:
Internet Application and Technology for E-Government Public Services

Regardless of whether we are referring to those run online or within administrative offices, there is a high demand for government services, and such a demand has been attempted to be fulfilled via the implementation of a variety of online approaches that also aim to not compromise on the quality and speed of such service provision.Ultimately aiming to evaluate Architecture of E-Government public services alongside any other new web-based innovations in the context of their usefulness within government sectors, this paper will summarise and weigh up the past and future of online applications (beginning with Web1, Web2, and SemanticWeb3).Furthermore, this document will also provide some insight into any previous government web-based initiatives that have ultimately succeeded in furthering their productivity.
Promoting journals into international databases: a comparative analysis of the leading Russian universities journals and the journals of foreign countries (2013-2018)

The paper analyzes Russian journals, especially the journals of the universities participating in Project 5-100, in comparison with journals in China, Germany and the USA, as well as with world average scientometric indicators. The journals of the universities-participants of Project 5-100 are considered as a reference group, since the universities of this group deliberately implement measures to promote their journals in the international databases Web of Science and Scopus. The results of the study showed that in most cases, Russian journals included in the Scopus database in the same year with analogous journals in the countries under consideration are inferior to them in citation dynamics, which, in particular, may indicate insufficient visibility of Russian science in the international arena. At the same time, the scientometric indicators of the journals of Project 5-100 universities in a significant number of cases exceed the national average. The analysis also showed that journals published in English or predominantly in English show the highest indicators. It is concluded that the translation of Russian journals into English, as well as a targeted policy to include Russian journals in international scientific citation databases, will significantly increase the visibility of Russian science in the international arena.
Detecting the fingerprint of video data hiding tool OpenPuff

This paper proposes an effective fingerprint detection method against the video steganography and watermarking of OpenPuff, one of the leading tools for its capability to securely hide information. With the aim of attacking the video in SWF, MP4 and 3GP containers, we first elaborate on the mechanism of OpenPuff and analyze every potential and suspicious field for different file formats. By comparison of the binary streams between the original and the marked videos, the suspicious fields are located and used for screening video. For suspect video, the padding bytes in those specific fields are extracted as the signature that determines whether the cover object is innocent or guilty. Experimental works have been done to demonstrate the effectiveness of our proposed method where hundreds of videos in pairs are tested.
Deep neural network based m-learning model for predicting mobile learners’ performance

The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners? performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.
Online Retail Marketing Recommendation System Based on Generalized Sequential Pattern Algorithm and FP-Growth Algorithm

Data mining association is a technique to find the relationship between items where the function can help sellers in determining their sales strategy.The algorithm used in this data mining techniques are Generalized Sequential Pattern Algorithm and FP-Growth Algorithm.Generalized Sequential Pattern Algorithm is an algorithm based on sequential patterns in the formation of rules, while FP-Growth Algorithm is a tree-based algorithm in the formation of rules.This research produces a comparison of the computation time of each algorithms in carrying data mining process associated with the data that has been determined.The result of computational time comparisons show that FP-Growth Algorithm is 11.97% faster than Generalized Sequential Pattern Algorithm based on 30 tests.Generalized Sequential Pattern Algorithm produces 2 rules and FP-Growth Algorithm produces 8 rules by testing 500 transaction data and minimum support value is 3.Where the rules obtained is evaluated using the lift ratio techniques to calculate the value of the rule accuracy generated from each algorithms.
Segmenting video frame images using genetic algorithms

Image segmentation plays an important role in computer vision. It is a process that partitions a digital image into several meaningful regions ,by identifying regions of an image that have common properties while separating regions that are dissimilar. The image segmentation problem is posed as an optimization procedure. In this thesis, an optimization approach based on genetic algorithm is introduced for finding optimal image segmentation. The design and implementation of genetic algorithm image segment or (GSAI) system are described. GSAI system employs finds optimal value using genetic operators crossover operator and mutation operator. The different proposed / implementation segmentation methods of the GSAI system were tested using Gray image are taken from one films and with size 352x240 pixels for video frames images of In this is work focused on genetic algorithm coefficients which affect in direct and active way in the work of GA to study and analysis dependable video images which are taken from video clips after partitioning to multiple frames.
Second order optimality on orthogonal Stiefel manifolds

The main tool to study a second order optimality problem is the Hessian operator associated to the cost function that defines the optimization problem. By regarding an orthogonal Stiefel manifold as a constraint manifold embedded in an Euclidean space we obtain a concise matrix formula for the Hessian of a cost function defined on such a manifold. We introduce an explicit local frame on an orthogonal Stiefel manifold in order to compute the components of the Hessian matrix of a cost function. We present some important properties of this frame. As applications we rediscover second order conditions of optimality for the Procrustes and the Penrose regression problems (previously found in the literature). For the Brockett problem we find necessary and sufficient conditions for a critical point to be a local minimum. Since many optimization problems are approached using numerical algorithms, we give an explicit description of the Newton algorithm on orthogonal Stiefel manifolds.
Digital strip searches in Australia: A threat to the privilege against self-incrimination

Law enforcement has experienced difficulties retrieving information stored on the mobile devices of suspects. Over the last years, a number of Australian jurisdictions enacted legislation that allows police officers to compel an individual to unlock their device (such as providing a PIN or fingerprint to unlock the device). Non-compliance with these legislative provisions is punishable by imprisonment. The refusal by suspects to comply with an order to unlock their devices has been addressed in a number of Australian courts. In this article, the authors will discuss this legislative activity and the courts’ responses in light of the privilege against self-incrimination.
ENGAGING YOUNG LEARNERS IN LEARNING VOCABULARY: A STUDY ON LEARNERS’ PERCEPTION

This study examines students’ perception on the implementation of mobile games to engage young learners in learning English vocabulary. Thirty young learners in Islamic elementary school MI As-Syahid, Bekasi were the participant of the study. This qualitative study applied observation, survey, and interview. The findings revealed that: 1) young learners perceived that educational mobile games are effective in improving students’ activeness in learning and boost their motivation to learn vocabulary. 2) Young learners expand their vocabulary knowledge through mobile games, and.3) Learners have positive attitudes towards the use of educational mobile games as a supplementary medium of English learning. Additionally, mobile games can also be a potential tool to stimulate the motivation of children who experience failure in learning. This study contributes to the literature regarding vocabulary learning media and the use of mobile game in Indonesia. It highlights young learners’ perception of learning using mobile games.
Cardio-Oncology in Brazil

![Figure][1] ![Figure][1] Occupying nearly one-half of South America’s landmass, Brazil is the largest country of the continent. The country has a diverse range of tropical and subtropical regions, with wetlands, grassy plains (known as savannahs), and low mountainous landscapes.
A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system

In Content-Based Image Retrieval (CBIR) system, one approach of image representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on Principal Component Analysis (PCA) is implemented. Each image in a database is indexed using 174 dimensional feature vector comprising of 54-dimensional Colour Moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional Gabor Wavelet (GW48) and 40-dimensional Wavelet Moments (MW40). The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest Eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing. The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100.
SIGNIFICANCE OF DIGITAL COMPETENCE OF THE SPECIALIST OF AUTO TRANSPORT PROFILE IN PROFESSIONAL ACTIVITIES

Every day modern technology is radically changing various industries at ever faster paces. The modern era of innovation is accompanied by a number of inventions and know-how – from robotization of production to the introduction of artificial intelligence at state levels.An important component of the training of future specialists in vocational education is the formation of digital competency which involves the ability and ability of the logical and systemic use of digital technologies.The automotive industry is currently undergoing structural changes due to the rapid development and implementation of modern digital technologies such as programmed electric vehicles, unmanned vehicles, robotic mechanisms for controlling mechanisms, remote control and contactless operations.The purpose of the study is a theoretical justification and experimental study of the levels of digital competence formation of future specialists in the automotive industry.To achieve the goal of the study, the following methods were used: analysis, synthesis, comparison, generalization, questioning.The article analyzes the features of the formation and development of digital competence of a motor transport specialist in the process of their preparation in vocational education institutions of developed countries.In the study the total number of respondents was 72 participants. The study was conducted on the basis of the results obtained in the process of questioning graduate students of the specialty "Transport" at the Professional and Pedagogical College of the Glukhov National Pedagogical University named after Alexander Dovzhenko. Based on the results of the questionnaire, the level of digital competency of future specialists in the automotive industry was established.The obtained results allow us to identify some problems in the formation and development of digital competence in the preparation of a motor transport specialist in vocational education institutions.
Aristotle’s Syllogistic as a Deductive System

Aristotle’s syllogistic is the first ever deductive system. After centuries, Aristotle’s ideas are still interesting for logicians who develop Aristotle’s work and draw inspiration from his results and even more from his methods. In the paper we discuss the essential elements of the Aristotelian system of syllogistic and Łukasiewicz’s reconstruction of it based on the tools of modern formal logic. We pay special attention to the notion of completeness of a deductive system as discussed by both authors. We describe in detail how completeness can be defined and proved with the use of an axiomatic refutation system. Finally, we apply this methodology to different axiomatizations of syllogistic presented by Łukasiewicz, Lemmon and Shepherdson.
Projection of Dual-Rail DPA Countermeasures in Future FinFET and Emerging TFET Technologies

The design of near future cryptocircuits will require greater performance characteristics in order to be implemented in devices with very limited resources for secure applications. Considering the security against differential power side-channel attacks (DPA), explorations of different implementations of dual-precharge logic gates with advanced and emerging technologies, using nanometric FinFET and Tunnel FET transistors, are proposed aiming to maintain or even improve the security levels obtained by current Metal-Oxide Semiconductor Field-Effect Transistor (MOSFET) technologies and reducing the resources needed for the implementations. As case study, dual-precharge logic primitives have been designed and included in the 4-bit substitution box of PRIDE algorithm, measuring the performance and evaluating the security through simulation-based Differential Power Analysis (DPA) attacks for each implementation. Extensive electrical simulations with predictive Predictive Transistor model on scaled 16nm and 22nm MOSFET, 16nm and 20nm FinFET, and 20nm Tunnel Field Effect Transistor (TFET) demonstrate a clear evolution of security and performances with respect to current 90nm MOSFET implementations, providing FinFET as fastest solutions with a delay 3.7 times better than conventional proposals, but TFET being the best candidate for future cryptocircuits in terms of average power consumption (x0.02 times compared with conventional technologies) and security in some orders of magnitude.
STUDENTS' CONCEPTION OF LEARNING AND LEARNING BEHAVIOR FROM MULTIPLE-GOALS PERSPECTIVE

The purpose of this study was to examine conception of learning and learning behavior for each goal orientation type from the multiple-goals perspective and to clarify the characteristics of each type.Questionnaire surveys were conducted with fourth-year undergraduate students, and responses for 340 students were analyzed.We created four types, namely, <Low-goals>, <High-goals>, <High-Performance-Avoidance-goals>, and <High-Learning-goals>, and each type of conception of learning and learning behavior were examined.The results suggested that conception of learning differs by type.<High-goals> tended to have multiple conceptions of learning, <High-Learning-goals> tended to have Active conception of learning, and <Low-goals type> and <High-Performance-Avoidance-goals> tended to have Passive conception of learning.
Fusion Between Shape Prior and Graph Cut for Vehicle Image Segmentation

In vehicle image segmentation, the traditional graph cut algorithm often have errors, when the original image contains shadows or complex background.To overcome the errors, this paper introduces the shape prior to graph cut algorithm.Our algorithm firstly maps the vehicle image to a weighted undirected graph, and obtains the regional energy and boundary energy functions.Then, the shape prior was added to constrain the image segmentation, and create a new energy function.The star convex was selected as the shape prior, and manually marked.Experimental results show that our algorithm segmented complex background vehicle images, shadowed vehicle images, and incomplete vehicle images more effectively than Otsu's method and graph cut algorithm.The details of vehicles were preserved and extracted efficiently by our algorithm.The research results provide new insights into vehicle image segmentation.
ARIMA Supplemented Security Metrics for Quality Assurance and Situational Awareness

Quality assurance and situational awareness are important areas of interest for CSIRTs and security teams. Significant efforts have been made on defining metrics measuring critical parameters for these fields of application. However, methodical approaches are missing or lacking precision to enable a reliable usage of such metrics for quality assurance and situational awareness. In this contribution, we introduce a method that generalizes the application of ARIMA time series analysis on a well-defined set of metrics (ARIMA supplemented metrics) to facilitate and support quality assurance and situational awareness services. This method is based on research on ARIMA models and metrics and builds on CSIRT best practices. We show how data analysts and security practitioners can incorporate this method into existing best practices for CSIRT services pertaining to quality assurance and situational awareness. The applicability of this method is demonstrated by integrating ARIMA supplemented metrics into exemplary processes for quality assurance and situational awareness to support data analysts and security practitioners in CSIRTs and security teams.
Quantitative convergence analysis of kernel based large-margin unified machines

High-dimensional binary classification has been intensively studied in the community of machine learning in the last few decades. Support vector machine (SVM), one of the most popular classifier, depends on only a portion of training samples called support vectors which leads to suboptimal performance in the setting of high dimension and low sample size (HDLSS). Large-margin unified machines (LUMs) are a family of margin-based classifiers proposed to solve the so-called 'data piling' problem which is inherent in SVM under HDLSS settings. In this paper we study the binary classification algorithms associated with LUM loss functions in the framework of reproducing kernel Hilbert spaces. Quantitative convergence analysis has been carried out for these algorithms by means of a novel application of projection operators to overcome the technical difficulty. The rates are explicitly derived under priori conditions on approximation and capacity of the reproducing kernel Hilbert space.
Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation

The Pancreatic beta cell is an important target in diabetes research. For scalable modeling of beta cell ultastructure, we investigate automatic segmentation of whole cell imaging data acquired through soft X-ray tomography. During the course of the study, both complete and partial ultrastrucutre annotations were produced manually for different subsets of the data. To more effectively use existing annotations, we propose a method that enables the application of partially labeled data for full label segmentation. For experimental validation, we apply our method to train a convolutional neural network with a set of 12 fully annotated data and 12 partially annotated data and show promising improvement over standard training that uses fully annotated data alone.
Géochimie des muscovites comme indicateur du fractionnement des pegmatites de la région de Kabarore-Mparamirundi (nord-ouest du Burundi, Afrique centrale) [Geochemical signature of muscovites as pathfinder for fractionation of pegmatites in the Kabarore-Mparamirundi area (northwestern Burundi, Central Africa)]

The Kabarore-Mparamirundi area hosts numerous pegmatites spatiotemporally related to leucogranites dated at 986 ± 10 Ma in Karagwe-Ankole belt. The deposits are intensively exploited for columbite-tantalite and cassiterite. Alkali metals in muscovite (Rb 370–7590 ppm, Cs 8–1470 ppm) are modeled by Rayleigh fractional crystallization from a parental leucogranitic composition (K 4.1 wt%, Rb 321 ppm and Cs 9 ppm). The power law declining behavior of the ratio K/Rb versus Cs indicates the Rayleigh fractional crystallization as the main process of differentiation of the various pegmatite facies. Moreover, the continuous trend from granite to the most evolved, exploited pegmatites demonstrates a co-genetic link among them. The fractionation model shows that unmined and abandoned pegmatites are less fractionated (less than 94% of fractionation) while mined pegmatites are highly fractionated and constitute fractionated products of more than 94% of the initial leucogranite composition. The Rb, Cs, Ta, Sn and Li elements in muscovite can be used as a valuable tool in the exploration of fertile and sterile pegmatites in this area.
Integrating with Blender, Maya and Marmoset

Exporting files from Maya and Blender for use in Substance Painter is pretty straightforward. Substance can import most major file types including .fbx and .obj, which can be created by all 3D modeling applications. For our purposes, we will use the .fbx type for exporting files for use in Substance Painter.
VIRTUAL REALITY IN SECOND LANGUAGE ACQUISITION RESEARCH: A CASE ON AMAZON SUMERIAN

Virtual reality (VR) has gained increasing academic attention in recent years, and a possible reason for that might be its spread-out applications across different sectors of life.From the advent of the WebVR 1.0 API (application program interface), released in 2016, it has become easier for developers, without extensive knowledge of programming and modeling of 3D objects, to build and host applications that can be accessed anywhere by a minimum setup of devices.The development of WebVR, now continued as WebXR, is, therefore, especially relevant for research on education and teaching since experiments in VR had required not only expertise in the computer science domain but were also dependent on state-of-the-art hardware, which could have been limiting aspects to researchers and teachers.This paper presents the result of a project conducted at CITEC (Cluster of Excellence Cognitive Interaction Technology), Bielefeld University, Germany, which intended to teach English for a specific purpose in a VR environment using Amazon Sumerian, a web-based service.Contributions and limitations of this project are also discussed.