Reviewerly/exHarmony huggingface dataset:
Understanding Internet Fraud: Denial of Risk Theory Perspective

Internet fraud has become a global problem attracting the attention of researchers, practitioners and policy makers. Existing empirical theoretical studies on internet crimes have mostly used neutralization and deterrence theories. Despite the insights from these theories, we are still observing an increase in the number of internet crimes. We argue that Denial of Risk theory may provide new insights on internet crimes such as internet fraud. We examined how each of the three dimensions of Denial of Risk theory (scapegoating, self-confidence and comparing of risk) serve as antecedent of the intention to commit internet fraud. Using responses from 350 individuals from internet fraud hotspots, we showed that scapegoating, self-confidence and comparing of risk are positively related to intention to commit internet fraud. The study offers theoretical and practical contributions to research in the spectrum of internet fraud and the theoretical application of denial of risk in cybercrime research.
An Automatic Conversion Tool for Caffe Neural Network Configuration oriented to OpenCL-based FPGA Platforms

Recently, the research of image classification and recognition in deep learning area has reached an unprecedented break with the highest accuracy rating that seemed most nearly human. However, the pervasive application of deep learning is critically limited by the issues of efficiency and power consumption. Fortunately, FPGA (Field Programmable Gate Array) provides an effective solution for the on-line inference of various deep learning applications. Nonetheless, the general program designed for CPU and GPU application cannot be directly applied in the FPGA heterogeneous computing platform. In this paper, we focus on the migration of neural network configurations from CPU/GPU-based computing platforms to FPGA-based computing platforms under the framework of Caffe. Our contribution presents an automatic conversion tool to apply the neural network configuration in Caffe model to FPGA-based heterogeneous acceleration platforms. This method takes full advantage of the dictionary and template functions of Python program language. Firstly, we should construct the mustache template for head files according to the neural network model. After finishing the template construction, Python program reads and analyzes the neural network configuration in the prototxt file supported by Caffe framework. Next, it fills in all the corresponding positions of the template with the analytic parameters for neural network configuration. Furthermore, it generates a C++head file that configures the neural network for OpenCL program. This tool helps to achieve the migration of Caffe model from CPU/GPU platform to OpenCL-based FPGA heterogeneous acceleration platforms. Finally, we illustrate that our method promotes the implement of FPGA applications in artificial intelligence field with a high efficiency.
Authentication of Keys Distributed by the Diffie-Hellman Method for Mobile Devices based on Authentication Codes and Magnetometric Data

The wide distribution of mobile wireless devices (smartphones, tablets, etc.) warrants cryptographic protection of information transmitted by these devices, which requires supplying these devices with keys and providing their authentication. Recently, research on key authentication methods within scenarios of pairing mobile devices, has been increasingly relevant. In these conditions, mobile devices are located close to each other, up to the physical contact, and an additional key sharing authenticated channel protected from interception is established for purposes of key authentication.&#x0D; The analysis of additional channel versions: visual, acoustic, vibration, tactile, and magnetometric shows advantages of the latter one in terms of speed and reliability of authentication, as well as easer se of this channel compared to other methods. A magnetometer channel forms if there are magnetometers in mobile devices that measure the Earth’s magnetic field. Random fluctuations of the magnetic field at the measurement point allow to generate in a pair of mobile devices random sequences that coincide with a high probability and which can be used to authenticate session keys.&#x0D; The “Magparing” protocol of mobile devices keys authentication, which are distributed by Diffie-Hellman method is studied. his protocol is based on the magnetometer data measuring. We demonstrate that the protocol is succeptible to “man-in-the-middle” attack. This fact is confirmed also by software AVISPA simulation.&#x0D; A new key authentication method based on the use of authenticating code (A-code) is proposed. Authenticators for Diffie-Hellman values are generated based on A-code blocks and random sequences obtained by the reading of magnetometer’s values. An approach for A-code parameters optimization, that conforms to the requirements for authentication key length and both to the probabilities of false key removal and false key deception, is investigated. We present an example of code parameters for key authentication with the length of 256 bits, providing the probability of a false key agreement of not greater than 10-9 and a probability of false rejection of a true key equal to –10–6, which shows viability of the proposed authentication method.
Performance Analysis and Optimization Techniques for Oracle Relational Databases

Abstract Databases provide an efficient way to store, retrieve and analyze data. Oracle relational database is one of the most popular database management systems that is widely used in a different variety of industries and businesses. Therefore, it is important to guarantee that the database access and data manipulation is optimized for reducing database system response time. This paper intends to analyze the performance and the main optimization techniques (Forall, Returning, and Bulk Collect) that can be adopted for Oracle Relational Databases. The results have shown that the adoption of Forall and Bulk Collect approaches bring significant benefits in terms of execution time. Furthermore, the growth rate of the average execution time is lower for Bulk Collect than Forall. However, adoption of Returning approach doesn’t bring significant statistical benefits.
The Investigation of the Relationship between Online Games and Acquisition of Turkish Vocabulary

This study examines the relationship between online games and Turkish vocabulary acquisition.It adopted the sequential explanatory design of the mixed methods research.The quantitative data were collected through cluster sampling technique from 225 students studying in two secondary schools.The qualitative data were collected through the criterion sampling technique of the purposeful sampling methods from 20 participants.While vocabulary knowledge assessment form was used to collect quantitative data, a semi-structured interview form was used for the qualitative data.According to quantitative findings, whilst there was a significant difference between the scores in vocabulary knowledge test and the duration of the game, experience in playing the game, and time spent in watching broadcasts, there was not a significant difference between vocabulary scores and following League of Legends on media or social media.The experience of the participants in playing games, the weekly hours of playing games, and weekly hours spent watching broadcasts significantly predicts the scores in the vocabulary test.Whereas the qualitative findings revealed the relation of watching broadcasts and playing League of Legends to learn words of foreign origin with its reasons, there was not a significant relationship between following social media and acquisition expressions of foreign origin.
The effects of political and financial risks on foreign direct investments to the MENAT countries

Political risk and financial risk have important effects on many macroeconomic variables in the economy. Among those macroeconomic variables, foreign direct investment is the crucial one. Foreign investors refrain from investing in the countries with high political and financial instability. However, through knowledge and capital accumulation, foreign direct investment (FDI) is expected to lead to higher growth rate in the host country by promoting the incorporation of new production factors and foreign technologies into the production function of the host country. Therefore, in this study the political and financial determinants of FDI inflows to the Middle East and North Africa and Turkey (MENAT countries) are evaluated using annual dataset from the period 1984 to 2014. Using panel data estimations methods, the results reveal that variables of investment profile, religious tensions, and current account balance are the potential determinants of FDI inflows. The analysis also indicates that a positive investment profile, profits repatriation and payment delays, lower religious tensions and lower risk points of current account are associated with higher volumes of FDI flows into the MENAT countries.
Construction of a Diversified Integrated Platform Based on Home Care Services

Facing the increasingly serious problem of an aging population and pension mode, the government put forward the main forms of family endowment in the process of practice how to work more effectively and efficiently, this paper proposed a multivariate comprehensive platform, uniting old man, society, family and government and service agencies, etc., to make the parties meet the needs of the old man to occupy the endowment service in a timely manner.
Computer technology in the didactic games in science lessons in junior high

Problem and goal. In terms of informatization of school education, the use of information technology in teaching students any discipline, obviously, should be correct, justified. The article discusses the methodological aspects of the use of information technology in didactic games in computer science lessons in the lower grades. Methodology. The Federal state educational standard of primary general education is studied, the scientific and methodical literature in the field of informatics and a technique of training of informatics is analysed, the analysis of educational and methodical providing applicable at lessons of informatics is made, own experience of teaching in younger classes is generalized. Results. It is revealed that the use of various information technologies in teaching younger students improves the quality of their education. Reasonably appropriate use of information technology in the didactic games on the lessons of informatics in primary school. Conclusion. Introduction of didactic games allows to achieve efficiency of training, at the expense of giving to process of assimilation of knowledge of emotionally positive coloring. The use of information technology in didactic games in computer science lessons becomes a pronounced positive character to increase motivation, cognitive abilities, and, consequently, the quality of education of younger students. The inclusion of the developed system of exercises and tasks in the content of computer science training allows younger students to develop attention, thinking, imagination, to increase the volume of the acquired educational material.
Alternate Structural-Textural Video Inpainting for Spot Defects Correction in Movies

We propose a new video inpainting model for movies restoration application. Our model combines structural reconstruction with a diffusion-based method and textural reconstruction with a patch-based method. Both proposed energies (one for each method) are alternatively minimized in order to preserve the overall structure while adding textural refinement. While the structural reconstruction is obtained jointly with optical flow computation with several proximal approaches, the textural reconstruction is processed by a variational non-local approach. Preliminary results on different Middlebury frames show quality improvement in the reconstruction.
Data quilting: Art and science of analyzing disparate data

Motivated by incongruences between today’s complex data, problems and requirements and available methodological frameworks, we propose data quilting as a means of combining and presenting the analysis of multiple types of data to create a single cohesive deliverable. We introduce data quilting as a new analysis methodology that combines both art and science to address a research problem. Using a three-layer approach and drawing on the comparable and parallel process of quilting, we introduce and describe each layer: backing, batting and top. The backing of the data quilt is the research problem and method, which supports the upper layers. The batting of the data quilt is the data and data analysis, which adds depth and dimension to the data quilt. Finally, the top layer of the data quilt is the presentation, visualization and storytelling, which pieces together the results into a single, cohesive deliverable. For illustrative purposes, we demonstrate a data quilt analysis using a real-world example concerning identity theft.
Exploring the Effectiveness of Glog in Language Learning

This paper reports the purpose of integrating Web 2.0 tool, Glogster in language learning tasks. Web 2.0 tools and digital blooms taxonomy have become prevalent in the English classroom with the ubiquitous use of smartphone, tabs and laptops in the language classroom. Certain details in this paper were presented in the Connecting Online (CO12) session in the year 2012. This paper also describes activities which would help in fostering creativity and autonomy in the teaching-learning process. Glogster is a web 2.0 tool which provides a platform for teachers and learners in creating and using glogs for language learning and other subjects. This tool provides a supportive platform for monitoring project based language learning activities. Having studied the use of the glog for language learning with the first batch of 54 students in 2009, in the following semesters 302 students were introduced to the glogging task. In total, 356 B.Tech students at VIT were introduced to web 2.0 tools for language learning tasks. They were introduced to the use of three Glogster tasks for synthesizing their real life and virtual language learning experiences in different semesters.
Visual Relationships as Functions: Enabling Few-Shot Scene Graph Prediction

Scene graph prediction --- classifying the set of objects and predicates in avisual scene --- requires substantial training data. The long-taileddistribution of relationships can be an obstacle for such approaches, however,as they can only be trained on the small set of predicates that carrysufficient labels. We introduce the first scene graph prediction model thatsupports few-shot learning of predicates, enabling scene graph approaches togeneralize to a set of new predicates. First, we introduce a new model ofpredicates as functions that operate on object features or image locations.Next, we define a scene graph model where these functions are trained asmessage passing protocols within a new graph convolution framework. We trainthe framework with a frequently occurring set of predicates and show that ourapproach outperforms those that use the same amount of supervision by 1.78 atrecall@50 and performs on par with other scene graph models. Next, we extractobject representations generated by the trained predicate functions to trainfew-shot predicate classifiers on rare predicates with as few as 1 labeledexample. When compared to strong baselines like transfer learning from existingstate-of-the-art representations, we show improved 5-shot performance by 4.16recall@1. Finally, we show that our predicate functions generate interpretablevisualizations, enabling the first interpretable scene graph model.
Bitcoin has an electronic-waste problem﻿

The surging popularity of bitcoin comes at a huge environmental cost. According to the latest analysis of its sustainability, the electronic currency uses as much energy as a medium-sized country and could generate thousands of metric tons of electronic waste per year (Joule 2019, DOI: 10.1016/j.joule.2019.02.007). Users “mine” new bitcoins with customized computer chips that solve cryptography problems and record their ownership of this cryptocurrency in a shared database called a blockchain. As more bitcoins enter circulation—there are currently about 17.5 million of them—it gets progressively harder to mine new coins, requiring more computing power and, therefore, more energy. Alex de Vries, a blockchain specialist at the accountancy firm PricewaterhouseCoopers in Amsterdam, has calculated that bitcoin mining in 2018 used 40–62 TW∙h of energy and had a carbon footprint of 19 million–30 million metric tons (t) of CO2 emissions. Bitcoin already accounts for at least 20% of the energy consumption
A Study on the Teachers' Perceptions of Error Correction

The purpose of this study is to examine teachers' perceptions of errors in the process of foreigners learning Korean.We use terms such as learner error and inter language for the learner's language, but there is a difference between the researcher and the teacher.How to view the learner 's language and judge the error phenomenon is a problem to be solved by researchers and teachers.In this paper, we surveyed the teachers' perception about how to understand and handle the error phenomenon that appears to Korean language learners.The data required for this study were collected through a report and analyzed using Nvivo 11.As a result of analysis, I was able to confirm what kind of learner language, error identification, error description, error explanation, when to correct errors, problems after error correction, and what to consider in error correction.Based on the results of this study, it is expected that there will be continuous discussions on teacher education, error phenomenon of learner language, and language development.
UPAYA PENINGKATKAN KEMAMPUAN GURU DALAM PENGGUNAAN TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) MELALUI KEGIATAN WORKSHOP DI TK KECAMATAN SUNGAI LALAK

This research is motivated by the still weak ability of teachers in the use of information and communication technology (ICT). This research was conducted in all kindergarten assisted by researchers in the Lalak River District. Sabjek of this study were all kindergarten teachers in the District of Sungai Lalak which numbered 25 teachers. The purpose of this study is to improve the ability of teachers to use information and communication technology (ICT) through workshop activities. This research consists of two cycles, each of which consists of 4 stages, namely, planning, implementation, observation, and reflection. The results of the study prove that in the first and second cycles, it can be seen that the competence in the use of information and communication technology (ICT) in TK Lalak District Kindergarten teachers is generally in the medium category. In the first aspect of the first cycle, 4 people or 16% kindergarten teachers have a high ability regarding the use of information and communication technology (ICT), while the second cycle increases to 10 people or 40%. In the second category there were 14 people or 56% who had moderate abilities regarding the use of ICTs, while the second cycle had 12 people or 48% who had moderate abilities. In the third aspect of the first cycle of 7 people or 28% have not been able to master the use of ICT, while in the second cycle reduced to 3 people or 12% have not been able to master the use of information and communication technology (ICT). Based on the description that has been described, the researcher draws conclusions from the results of research conducted that the implementation of information and communication technology (ICT) workshops can improve the ability of teachers in the use of information and communication technology (ICT) in TK Lalak River District.
A Bayesian framework for extreme learning machine with application for automated cancer detection

Fairly recently, extreme learning machine (ELM) has been proposed as a single-hidden layer feedforward neural network (SLFN), where the input weights are randomly initiated and never updated, and the output weights are analytically computed. Setting the parameters of the hidden layer randomly may not be always effective if the function that is learned is not simple and the amount of labeled data is not small, even if theoretical studies have shown that ELM maintains the universal approximation capability. To address this issue, we propose a new approach inspired by the Bayesian paradigm as an alternative to the random initiation of the hidden node parameters. The idea behind this model is that we can use the information (prior knowledge) about a certain labeled data through the correlation between attributes and decision classes. The prior knowledge is acquired through the Goodman-Kruskal Gamma rank correlation between attributes and labels, assuming that the input weights should be related to the influence of attributes upon labels. Five publicly available high-dimensional datasets regarding cancer (breast, lung, colon, and ovarian) related to cDNA arrays, DNA microarray, and mass spectroscopy are used for experimentation and model assessment. We compared the performance of this classifier with that of three 'neighboring' algorithms, such as a basic ELM, a SLFN trained by backpropagation (BP) algorithm, and a radial basis function network (RBF). The experimental results undoubtedly indicated that the proposed variant of ELM is very effective and its performance is superior to that of the comparison models.
THE IMPLEMENTATION OF FLASH CARDS TO IMPROVE STUDENTS’ VOCABULARY MASTERY

This research is a classroom action research. The objective of this research is to investigate the process of improving students’ vocabulary mastery by using flash cards. The respondents  in this research was seventh grade students of MTs Nurul Islam. There are 30 students. In collecting data, this research used interviews, observation, and tests.  The researchers assessed four aspects of vocabulary in determining students’ scores.This research employed two cycles in this research and perform in four stage, namely; planning, acting, observing, and reflecting. The results of the research showed that the implementation of flash cards can increase students' vocabulary. It can be seen from the development in the mean score of students:  for aspect meaning at pretest cycle 1 is 63 and in post test in cycle 2 is 78,  for spelling aspects at pretest cycle 1 is 58 and in post test in cycle 2 is 81,  for pronunciation aspects at pretest cycle 1 is 65 and in post test in cycle 2 is 83, and for aspects of word use at pretest cycle 1 is 71 and in post test in cycle 2 is 88.
PROBLEMS AND PROSPECTS OF DEVELOPMENT OF RURAL EDUCATIONAL ORGANIZATIONS BASED ON THE RESULTS OF THE INTERNATIONAL CONVENTION IN THE USHINSKY YAROSLAVL STATE PEDAGOGICAL UNIVERSITY

general information about the international scientific-practical conference «Problems and prospects of development of rural educational organizations», held in March 27-30, 2019 in Yaroslavl on the basis of the Ushinsky Yaroslavl State Pedagogical University is presented.An overview of the issues and problems under discussion as well as the results of a scientific event is given.