Reviewerly/exHarmony huggingface dataset:
Design and Analysis of Substitution Boxes in GOST Lightweight Security Algorithm

Recently, lightweight symmetric cryptography became one of the most vital topics for researchers in cryptology. It is introduced as a smart solution to protect the data communication in constraint resources environments. S-boxes play a very vital role in the security of the modern block ciphers. They form the only non-linear element of a block cipher. ‎Therefore, S-boxes have to be chosen carefully to make the ‎cipher rigid against all kinds of attacks. Specially, the compact S-boxes that have been selected to be used in limited resources devices. ‎So, it is essential to understand some of the design criteria that the S-box must satisfy. In lightweight designs, 4-bit S-boxes are preferred to save the area (Gate Equivalents). In this paper, we in our primary analysis, we consider two versions of the GOST algorithm S-Boxes to be tested and evaluated. The Central Bank of the Russian ‎Federation Version and the Most Recent Version are ‎introduced. ‎GOST is one of the most famous modern symmetric ‎block ciphers.
Recruiting Fault Tolerance Techniques for Microprocessor Security

The growing threat of various attacks on modern microprocessors and systems calls for major design overhauls ranging from plugging micro-architectural side channels such as due to speculative execution to implementing cryptographic accelerators for side-channel and fault attack resistance. In this paper, we suggest to focus on the similarities and the differences between fault tolerance techniques and countermeasures against attacks on security sensitive systems. Modern digital circuits and systems use a diverse set of techniques to ensure operational correctness in the presence of faults. From a security perspective, the goal is to ensure a set of stated security properties hold in the presence of 'security faults' (extending the notion of conventional faults to include injected faults as well as vulnerabilities such as passive side-channels). A point of note here is that under some security faults, the operational correctness may not be compromised. This paper advocates the re-purposing of some of the known fault tolerance techniques, and show how those can be useful for enhancing security in the presence of active side-channel attacks. As a simple illustration of these ideas, we present an experimental case study in fortifying a cryptographic sub-component of a RISC-V based secure system-on-chip, against a formidable fault attack called SIFA.
Multilayer Architecture for Content-Based Image Retrieval Systems

In this paper, we present a novel architecture for content-based image retrieval systems. Effective storing, browsing and searching collections of images is one of the most critical challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as database and service-oriented frameworks. With the vast growth of the Internet multimedia content, web services for CBIR systems are highly desirable. The proposed solution is based on a multi-layer architecture, which allows replacing any component without recompilation of other components. The approach is elastic and highly scalable. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and k-means clustering as the image indexer. We also adopted an external solution, i.e. the CEDD descriptor into our system. The presented architecture is intended for content-based retrieval experiments as well as for real-world CBIR desktop and web applications.
An Approach for Study of Traffic Congestion Problem Using Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps-the Case of Indian Traffic

The aim of this paper is to find the reasons for traffic congestion problem and its solution using Neutrosophic Cognitive Maps (NCMs) and Fuzzy Cognitive Maps (FCMs). Fuzzy theory only measures the grade of membership but fuzzy theory has failed to characteristic the perception when the relations between concepts in problems are indeterminate. Addition of concepts of indeterminate situation with fuzzy logic forms the neutrosophic logic. Since, some of the reasons for traffic congestions are indeterminate we use Neutrosophic Cognitive Maps to find a solution. The discussion is based on Indian road scenario.
Monitoring Function Design of Radio Monitoring Management System Based on C/S Architecture

Radio engineering has become one of the main channels for human beings to obtain and disseminate information. Radio resources are easily contaminated. Harmful radio sources can disrupt the order of the air. Radio monitoring and management system with integrated management capability can effectively manage radio information. This paper used C++ language, combined with SQLite database, developed the task monitoring function module of radio monitoring management system. It solves the shortcomings of existing radio monitoring and management system, such as the monitoring function is not perfect, data-sharing capabilities are weak, software compatibility is low, software upgrades lack flexibility. After many researches, this system realizes real-time monitoring, full-time monitoring and timing monitoring functions.
A Progressive Network Based on Residual Multi-scale Aggregation for Image Super-Resolution

In recent years, with the rapid development of deep learning, single image super-resolution based on convolution neural network has achieved extensive research. However, most CNN-based method has difficulty in training and obtaining high quality images for large scale factors. To address these issues, we propose a network, which reconstructs HR images at large factors by progressively performing 2× SR on the input from the previous level. At each level, cascaded residual multi-scale aggregation blocks are used. The U-residual unit in it makes network simplifier and training easier without performance degradation. The multi-scale dilated unit in it provides more comprehensive information for image reconstruction. Before upsampling, the channel attention mechanism is adopted to recalibrate features. We train the network with two-stage training strategy which could accelerate the convergence and achieve better performance. Experiment results show that our proposed method is superior to the state-of-the-art methods on most datasets, especially on Urban100.
Use of ICT and innovative teaching methods for STEM

The article focuses on the opportunities and challenges for the use ofICT and innovative teaching methods in Science, Technology, Engineer-ing and Mathematics (STEM) education with regard to teachers’ Tech-nological Pedagogical Content Knowledge (TPACK). Thereby, teach-ers’ self-efficacy, gender, teaching experience, teachers’ beliefs have beenstudied by the use of the TPACK framework in STEM classrooms. Theneed for additional support of STEM teachers been recognised, e.g. thetraining to develop STEM teachers’ TPACK for the implementation ofICT in their specific subject teaching; the importance of animating ofSTEM teachers’ for their continuous participation in in-service train-ing; and the examination of the role of STEM teachers’ TPACK in thedeveloping of students’ 21st century skills.
Psychological And Pedagogical Support Of The First-Year Students’adaptation Process To A University

The article deals with the issues of psychological and pedagogical support of the adaptation of first-year student teachers to study at a university. In the article, based on the analysis of research and educational practice on this issue, the essence of the concept of “adaptation” is revealed in the context of teaching freshmen at high school as well as semantic accents and features of first-year students’ adaptation in the logic of various scientific fields. The results of the ascertaining experiment made it possible to identify the main problem areas that make it difficult for first-year student teachers to adapt to study at a university. The theoretical comprehension of the research problem and the experimental identification of the level of adaptation of first-year students to university studies show the importance of creating an effective psychological and pedagogical support for the process of adaptation of first-year students to university studies. The article presents a comprehensive program of psychological and pedagogical support of first-year students to study at a university, in which the main areas of activities are highlighted: informational, diagnostic, psychological, social, didactic, organizational-pedagogical; the levels of implementation of the adaptation program at the faculty: the academic council of the faculty, the Deans Office, the curators of the academic groups under the leadership of the deputy dean for educational work; teachers in the framework of the taught disciplines, as well as curators of groups of the 1st course.
Rare Animal Image Recognition Based on Convolutional Neural Networks

In recent years, due to human destruction, the number of endangered species on the earth is increasing at an alarming rate, and it is urgent to protect the rare species. This paper we propose a new method for rare animal image recognition based on the basic model of Convolutional Neural Networks (CNNs), by which to autonomously extract the image features in the training set and construct an image recognition system to identify rare animals. The method avoids the cumbersome process of manual preprocessing for the target image, and can directly input the original image for recognition, which is more feasible than the traditional image recognition algorithm.
Transform Learning Based Function Approximation for Regression and Forecasting

Regression and forecasting can be viewed as learning the functions with the appropriate input and output variables from the data. To capture the complex relationship among the variables, different techniques like, kernelized dictionary learning are being explored in the existing literature. In this paper, the transform learning based function approximation is presented which has computational and performance advantages over dictionary based techniques. Apart from providing the formulation and derivation of the necessary update steps, the performance results obtained with both synthetic and real data are presented in the paper. The initial results obtained with both the basic and kernelized versions demonstrate the usefulness of the proposed technique for regression and forecasting tasks.
Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility

In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities. In this paper, we consider the problem where an autonomous agent needs to act in a manner that clarifies its objectives to cooperative entities while preventing adversarial entities from inferring those objectives. We show that this problem is solvable when cooperative entities and adversarial entities use different types of sensors and/or prior knowledge. We develop two new solution approaches for computing such plans. One approach provides an optimal solution to the problem by using an IP solver to provide maximum obfuscation for adversarial entities while providing maximum legibility for cooperative entities in the environment, whereas the other approach provides a satisficing solution using heuristic-guided forward search to achieve preset levels of obfuscation and legibility for adversarial and cooperative entities respectively. We show the feasibility and utility of our algorithms through extensive empirical evaluation on problems derived from planning benchmarks.
The development of job sheet practice shield metal arc welding assisted augmented reality

Abstract The purpose of this research is measuring the feasibility of a job sheet of shielded arc metal welding practice assisted augmented reality. This research used the method of research and development studies (R&amp;D). The research was conducted on 32 students of class X at vocational high school Muhammadiyah 1 Salam. Data collection techniques were conducted using a questionnaire. The questionnaire was validated by an evaluation of the expert. The instrument test included 30 respondents outside the research sample. Data analysis technique performed with quantitative descriptive analysis technique percentage. The result of this research is in form of a job sheet practice welding SMAW using augmented reality.
Bitcoin, corruption and economic freedom

Purpose The purpose of this study is to test whether the price traders are prepared to pay for Bitcoin, over the market price, is related to a country’s level of corruption and lack of economic freedom, during Bitcoin’s most turbulent period, 2017-2018. Design/methodology/approach Bitcoin premiums (the excess over the market price) are calculated for 17 countries from April 2017 to September 2018, using daily weighted Bitcoin prices compared to the market price and testing against the Corruption Perception Index, the Index of Economic Freedom and changes in the Economic Uncertainty Index. Findings On testing Bitcoin premiums across 17 countries, it is found that the price paid for Bitcoin above the market price goes hand-in-hand with the level of corruption and decline in economic freedom. As corruption increases so does the premium paid for Bitcoin, and as the level of economic freedom declines, the premium increases. This relationship holds during very different market conditions: the increasing Bitcoin price from April to September 2017; the rise and fall in Bitcoin prices from October 2017 to March 2018; the declining market price from April to September 2018; and in December 2017 when Bitcoin reached over US$19,000. Originality/value Rather than focussing on daily returns which measure changes from day to day, this research uses all transactions during the day focussing on an intra-day consensus price measured in local currency. With such large price increases, percentage measures can hide the size of the monetary response. By focussing on the monetary impact, the differences between countries become more apparent.
Development Of Students’ Reflexive Readiness For Self-Education In Teaching A Foreign Language

The relevance of the study of this problem is due to the importance of developing students’ reflexive readiness for self-education in the framework of teaching a foreign language in the context of their ability to enrich their knowledge in the direction of their implementation in a certain field. In this regard, the development of students’ reflexive readiness for self-education is becoming one of the most promising methods of effective education. The following aspects are revealed in the article: The main characteristics of self-education in the study of a foreign language; Levels of reflective readiness for learning a foreign language; The manifestation of student reflection in the context of the analysis of one’s own cognitive activity; The possibilities of individual educational programs in the study of a foreign language on the basis of reflective activity; The role of the teacher in the development of students’ reflexive readiness for self-education. Moreover, the article identifies the types of reflection; the methodological grounds of students’ reflexive readiness for self-education in the form of certain components (axiological, systemic, reflective, creative and functional) are justified; the structural components of reflexive activity are revealed; the role and place of reflection as a factor in the self-organization of an autonomous study of a foreign language is shown; presents a set of necessary reflective skills; the functional aspect of reflection is presented in the form of situational reflection, retrospective reflection and perspective reflection.
Confronting the Digital Environment: Assessing the Challenges to Bibliographic Control of Electronic Resources

In the early 2000s, the dissemination of digital information resources was overwhelming library collections. Libraries struggled to make these materials discoverable in OPACs. The PCC stepped up its priorities to develop training, standards, and procedures for addressing digital content. Two events influenced the thinking and the direction of the PCC: The Bicentennial Conference on Bibliographic Control for the New Millennium: Confronting the Challenges of Networked Resources (2000) and the 2004 CONSER Summit on Serials in the Digital Environment. The discussions and the priorities resulting from these events provided guidance to the PCC.
Advertisement Logo Compositing via Adversarial Geometric Consistency Pursuit

Advertisement logo compositing is aiming to embed some specified logos in a suitable position of target images with alike geometric distortion and same appearance characteristic. Unsupervised learning has gained considerable attention recently. To synthetically address the problems about geometric distortion and appearance realism, we propose a novel learnable module, the adversarial geometric consistency pursuit model (AGCP), which explicitly allows seamless image compositing. On one hand, we design an adversarial structure that generates composite images taking geometric correction and appearance harmonization into account. Our proposed adversarial learning approach is able to obtain better harmonization in the region of interest. On the other hand, a novel geometric consistency pursuit loss is designed which encourages the network to learn the warp parameters of target images while preserving the feature of the source object. Our comparative evaluation demonstrates the effectiveness of the proposed method.
The Big Short Redux and FinTech

Similar to the Internet, which created a revolution in the ability to communicate quickly and cheaply, FinTech companies are using blockchain and digital wallets to create a revolution in the transfer of value across the world. The literature and blog space to date have indicated that digital wallets could overtake the need for traditional banking. The purpose of this article is to provide further testing on whether FinTech banking companies could overtake the need for traditional banking by evaluating whether the number of deposits at various US commercial banks and the growth of digital wallets have impacted US commercial bank market values over the past two to three years. Results indicate a strong positive relationship between the number of deposits and US commercial bank market values and a strong negative relationship between digital wallet proxy measures and US commercial bank market values from 2017 to 2019. The trends indicate a stable amount of deposit in the US banking system to a slight decline versus a large growth in the digital wallet sphere. Results indicate that digital wallets could create a large decrease in the market value of commercial banks in the future. Such a result would indicate the need to consider carefully how FinTech companies should be included in financial sector indices. <b>TOPICS:</b>Currency, performance measurement <b>Key Findings</b> • The growth in digital wallets in the past several years has been rapid. In comparison, the growth in bank deposit accounts has been rather stagnant. • The question is whether digital wallets are a complement to banks or a competitor. Regression results indicate that the number of deposit accounts has a strong positive relationship with bank market value and that digital wallet proxies have a negative relationship. This means the FinTech companies are taking away market value and may be viewed as a competitor. • Assuming that digital wallet growth takes away deposits from banks, results indicate that market values of bank may be on the decline. If such a case exists, it would be wise for exchange-traded funds and index companies to incorporate FinTech companies in their funds sooner rather than later. This will help offset the possible withdrawal from pure traditional banking funds.
Multi-view high dynamic range reconstruction via gain estimation

Multi-view high dynamic range reconstruction is a challenging problem, especially if the multi-view low dynamic range images are obtained from cameras arranged sparsely with limited shared view of vision among them. In this paper, we address the above challenge in addition to the back-lighting problem. We first enclose the geometry characteristic of the scene to rectify the outlier feature points. Consequently, an exposure gain is calculated according to those rectified features. After that, we extend the dynamic range for the multi-view low dynamic range images based on the estimated gain, then, generate a final high dynamic range image per view. Experimental results demonstrate superior performance for the proposed method over state-of-the-art methods in both objective and subject comparisons. These results suggest that our method is suitable to improve the visual quality of multi-view low dynamic range images captured in low back-lighting conditions via commercial cameras sparsely located among each other.
A Brief Review of Recent Progress in Fashion Landmark Detection

During the recent years, fashion landmark detection has attracted great attention of many researchers. The main task of Fashion landmark detection is to recognize the components of fashion in images, therefore it is essential for other fashion applications such as fashion classification, retrieval, design and recommendation. By utilizing various techniques in deep learning, the recently proposed landmark detection methods for fashion achieve notable performance, this motivates us to review the fashion landmark detection works. In this paper, we aim at roughly categorizing these recently proposed methods, discussing how these methods can improve detection performance. The fashion benchmark datasets used for landmark detection are reviewed in this paper as well. To our best knowledge, there is no a single review in the current literature that has discussed and reviewed fashion landmark detection.
Practice of Application-Oriented Undergraduate Object Oriented Programming Course Reform

In order to meet the needs of application-oriented undergraduate teaching,improve students’practical hands-on ability and shorten the gap between school training and enterprise needs,the paper analyzes the existing problems in the teaching of object-oriented programming in Yango University,proposes a series of solutions,such as curriculum group construction,curriculum content and teaching method construction,teacher team construction,student individualized teaching,textbook reform and assessment method reform. Through comparison with the actual teaching results,it is found that the experimental group has greater advantages in practical hands-on programming and the teaching effect is better. The practical results show that these solutions can provide a solution for the application-oriented undergraduate college in the object-oriented programming and related curriculum reform.
Why Is Reading Important

The aim to write this article is to figure out the reasons why we should read, and giving all the advantages of itself. Moreover, social benefits of reading have been discussed by the author. To conclude with, research has been analyzed to get better development of the reading, importance, and benefits of it have been investigated.
Analysis of Research Trends of Korean for Academic Purposes : Focusing on Korean as General Education

The purpose of this paper is to suggest the right direction in the study of Korean as general education language after examining trends in studies on Korean as general education language. To this end, a total of 348 academic journals published from 2000 to 2019 were extracted, they were analyzed as by years, learner characteristics, language functions, language elements, and topics