Reviewerly/exHarmony huggingface dataset:
Αρχιτεκτονικές και FPGA υλοποιήσεις για κρυπτογραφικούς αλγορίθμους χαμηλής πολυπλοκότητας

This diploma thesis deals with the design and implementation of Lightweight Cryptographic Algorithms. The chosen algorithms are SIMON and RoadRunneR. The reasons of their choice are their wide range of application, their different structures and their low decryption-overhead. These algorithms were deployed using the architectures Reuse, Unrolling and Pipeline with their combinations. The repetitive nature of the algorithms is the reason we chose to implement these exact architectures. Their combination was implemented so as to study comprehensibly their characteristics. The preferred unrolling factors are 1, 2, 3 and 6 with 2 stage pipelining. RoadRunneRs total rounds for completion is the reason of this choice.We chose to implement these architectures in three editions, regarding the algorithms. We talk about the Encryption-Only edition, the Decryption-Only edition and the Combined edition. We used VHDL and the free to use software Modelsim from Mentor Graphics to describe in hardware these algorithms. For validation we developed C and Python programs.This procedure was followed by experimental measuring using Vivado tool from Xilinx. The measured quantities are Maximum Frequency, Area, Throughput, Throughput/Area and Energy per Bit. Our conclusion regarding the algorithm performances came from the study of these quantities when we implemented our models with the pre-mentioned architectures. For the experiments we chose the Kintex-7 xc7k70tfbg484 FPGA device and the Spartan-7 xc7s50fgga484 FPGA device. Finally, apart from the algorithms performance comparison, we compared the performances of those two devices, in terms of the pre-mentioned quantities as well. From the study of the prementioned experimental measurements we come across important conclusions regarding the overall performance of the algorithms, the benefits of Pipelining and the impact of the unrolling factor on the algorithms performance.
Does Competence of Central Bank Governors Influence Financial Stability?

This study investigates whether the competence of central bank governors affect the stability of the financial system they are responsible for. Using publicly available information about central bank governors from 2000 to 2016 together with data on financial stability and the macro-economy, the findings reveal that central bank governors’ competence promotes financial stability, depending on how competence is measured. Specifically, the findings reveal that the financial system is more stable when the central bank governor is older and male. The financial system is also stable during the tenure of a central bank governor that has a combination of cognitive ability, social capital and technical competence in economics. The gender analyses reveal that the financial system is also stable during the tenure of a female central bank governor that has high social capital or high cognitive abilities while the financial system is relatively less stable during the tenure of a male central bank governor that has high social capital or high cognitive abilities. Comparing developed countries to developing and transition countries, the findings reveal that the financial system of developed countries is more stable during the tenure of a central bank governor that has high cognitive ability, social capital and technical competence in economics while the financial system of developing and transition countries is less stable during the tenure of a central bank governor that has high cognitive ability, social capital and technical competence in economics. Also, there is evidence that the financial system of developing and transition countries is more stable during the tenure of a central bank governor that has knowledge in disciplines other than economics. The findings are consistent with the view that certain characteristics of central bankers shape their beliefs, preferences and choice of policy, which in turn, are consequential for policy outcomes during their tenure.
Hardness of Samples Is All You Need: Protecting Deep Learning Models Using Hardness of Samples.

Several recent studies have shown that Deep Neural Network (DNN)-based classifiers are vulnerable against model extraction attacks. In model extraction attacks, an adversary exploits the target classifier to create a surrogate classifier imitating the target classifier with respect to some criteria. In this paper, we investigate the hardness degree of samples and demonstrate that the hardness degree histogram of model extraction attacks samples is distinguishable from the hardness degree histogram of normal samples. Normal samples come from the target classifier's training data distribution. As the training process of DNN-based classifiers is done in several epochs, we can consider this process as a sequence of subclassifiers so that each subclassifier is created at the end of an epoch. We use the sequence of subclassifiers to calculate the hardness degree of samples. We investigate the relation between hardness degree of samples and the trust in the classifier outputs. We propose Hardness-Oriented Detection Approach (HODA) to detect the sample sequences of model extraction attacks. The results demonstrate that HODA can detect the sample sequences of model extraction attacks with a high success rate by only watching 100 attack samples. We also investigate the hardness degree of adversarial examples and indicate that the hardness degree histogram of adversarial examples is distinct from the hardness degree histogram of normal samples.
The Mother of All Leakages: How to Simulate Noisy Leakages via Bounded Leakage (Almost) for Free

We show that the most common flavors of noisy leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to a small statistical simulation error and a slight loss in the leakage parameter. The latter holds true in particular for one of the most used noisy-leakage models, where the noisiness is measured using the conditional average min-entropy (Naor and Segev, CRYPTO’09 and SICOMP’12). Our reductions between noisy and bounded leakage are achieved in two steps. First, we put forward a new leakage model (dubbed the dense leakage model) and prove that dense leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to small statistical distance. Second, we show that the most common noisy-leakage models fall within the class of dense leakage, with good parameters. Third, we prove lower bounds on the amount of bounded leakage required for simulation with sub-constant error, showing that our reductions are nearly optimal. In particular, our results imply that useful general simulation of noisy leakage based on statistical distance and mutual information is impossible. We also provide a complete picture of the relationships between different noisy-leakage models. Our result finds applications to leakage-resilient cryptography, where we are often able to lift security in the presence of bounded leakage to security in the presence of noisy leakage, both in the information-theoretic and in the computational setting. Remarkably, this lifting procedure makes only black-box use of the underlying schemes. Additionally, we show how to use lower bounds in communication complexity to prove that bounded-collusion protocols (Kumar, Meka, and Sahai, FOCS’19) for certain functions do not only require long transcripts, but also necessarily need to reveal enough information about the inputs.
선교사의 조선어 학습서 FIFTY HELPS FOR THE BEGINNER IN THE USE OF THE KOREAN LANGUAGE에 관한 연구

The purpose of this study was to investigate the changes in the composition of the longest periodical of the missionaries’ Korean language study books, “FIFTY HELPS FOR THE BEGINNER IN THE USE OF THE KOREAN LANGUAGE”. Based on the characteristics of this book, I discussed the historical value of this book in Korean language education.First, I summarized the bibliographic information of various editions of this book, and explained the changes due to revision of the editions, in particular changes in composition and changes in detailed learning items. We also discussed the meaning of the “Help” used in the title of this book, and reviewed the usefulness of this book.As a result, it was found that the learning items presented in this book were not chapters for grammar learning but chunk expressions of Korean corresponding to English aiming to learn idiomatic expressions more quickly for missionary work. (Meijo University)
Learning to Feel Comfortable on Gut Strings: The Bow

Gut strings sound, feel and react very differently to today’s strings. At the heart of this lesson is the need for a constant two-way flow of information between the ear and the hand, for which both must be in a state of total awareness. The strings are in a constant state of flux, shortening and lengthening as the hand moves around the fingerboard, and responding to the needs of the music with its continual shifts of dynamics. The seven exercises in this lesson help the gut string novice establish a living relationship with the strings, testing their resistance via the bow and the fingers. This process has two phases, the Experimental Phase, in which the fingers, through tactile awareness, feel the string and gauge the correct amount of pressure for obtaining a clear, basic sound, and the Evaluation Phase, when the ear evaluates its resultant quality.
Learning to Sample from Censored Markov Random Fields

We study the problem of learning Censored Markov Random Fields (abbreviated CMRFs), which are Markov Random Fields where some of the nodes are censored (i.e. not observed). We assume the CMRF is high temperature but, crucially, make no assumption about its structure. This makes structure learning impossible. Nevertheless we introduce a new definition, which we call learning to sample, that circumvents this obstacle. We give an algorithm that can learn to sample from a distribution within $\epsilon n$ earthmover distance of the target distribution for any $\epsilon > 0$. We obtain stronger results when we additionally assume high girth, as well as computational lower bounds showing that these are essentially optimal.
Investigating formation of collective identity driving the black lives matter movement during COVID-19

Focusing on the recent Black Lives Matter (BLM) movement associated with the death of George Floyd during the COVID-19 pandemic, we seek to identify the shared collective identity of online and offline participants. Specifically, we collect hashtags associated with the movement and use sentiment analysis to investigate the individual emotions that underpin their involvement during the COVID-19 pandemic. The link between online activism and offline protests is modelled in our study. Users' beliefs serve as factors that direct actions (emotions) resulting in significant outcomes (protests) and are moderated by collective identity of the people participating in the protests. We use natural language processing (NLP) to test for the presence of the identified factors in our tweet corpus of 8 weeks of data (from 05/2020 to 10/2020) from twitter that involves discussions around the #BLM. © AMCIS 2021.
ON IMPROVING THE MATHEMATICAL TRAINING OF PEDAGOGICAL STUDENTS BASED ON TEACHING COMPUTER GRAPHICS AND MULTIMEDIA TECHNOLOGY

In this paper, an attempt is made to study the features of teaching future mathematics teachers in computer graphics and multimedia in the context of informatization of education and to identify some important points that affect the effectiveness of improving their mathematical training. Today, despite the abundance of various application software packages, the problem of learning computer animation remains relevant. The use of computer graphics in educational systems not only allows you to increase the speed of information transfer and increase its level of understanding, but also contributes to the development of imaginative thinking. Of great educational and psychological importance is the fact that the color of graphic images affects thoughts and feelings, stimulating the imagination. The competent use of computer graphics technology allows us to provide not only the best development of educational material, but also create all the conditions for a more effective learning process as a whole.
The Impact of Social Media towards Children's Behaviour

Social networking on social media websites is to connect relationship between friends and family through the use of internet. These websites allow you to share photos, video and information, chat with people, organize an event and play online games. However, social media will also be risky at the same time especially the children who still vulnerable. Although the social media have an age restriction, we can observe that adolescent and even children have own their account in social media. The children might think that become famous is one of the ways to be successful. They will try to imitate what people do online and eventually change their behaviour.Social Learning Theory explains that people can learn something through observation. When the model is able to attract their attention and they will able to recall and imitate the behaviour. Towards the end, they will reproduce and continue the behaviour by motivate by the others. By the conduct online survey towards the parents and interview with the children, this paper will able to look into the way of children’s behaviour changing will same like what the Social Learning Theory proposed and how the parents look at the impact of social media towards children.
METHODS OF IMPROVING CREATIVITY IN SECONDARY SCHOOL STUDENTS ON THE EXAMPLE OF TECHNOLOGICAL EDUCATION

Most researchers who touch on the problem of students technical creativity believe that the most important means of developing students' creative technical thinking is their independent solution of a system of gradually becoming more complex problematic problems. The essence of these problems lies in the fact that on the basis of some data in the condition of the problem, presented explicitly or assumed to be known to the student, and the requirements of the problem, the solver must solve the problem, find what is sought, while carrying out, that is, in the course of the solution, one or several procedures of creative activity.A special role in the formation of a creative personality, capable of high-performance work in the future, technically rich production activity, is assigned to the lessons of technology.
LibGuides: Tails and Tales Make Your Own Bookmark: Adventurous Craft Bookmarks

A collection of directions, craft templates, and pictures for making your own creative bookmarks at home.
간호·보건계열 대학생의 비대면 비교과 교육과정 활동이 자기효능감, 전공 만족도 및 대학생활 적응에 미치는 영향

The purpose of this study was to measure the effects of self-efficacy, major satisfaction, and college life adaptation to nursing and health-related college students who participated in the extracurricular activities through untact education. 56 students were assigned to the experimental group (n=28) and control group (n=28). Survey was performed to investigate self-efficacy, major satisfaction, and college life adjustment. Subjects of the experimental group underwent an 50-min extracurricular program eight times, which was progressed in untact education using online system. This program included educational activities related to exercise therapy, phylotherapy, hand reflexology, ear reflexology, and art therapy, which aims to develop supplementary and integrative educational materials for people with cognitive impairment. Process has led by three steps: (1) the understandings of disability and supplementary and integrative concepts, (2) case application and presentation, and (3) conference. However, subjects of the control group did not attend to the program. There were no statistically significant differences in self-efficacy, major satisfaction, and college life adjustment between the experimental and control groups (p<.05). These findings suggest that untact extracurricular educational courses might not lead to positivie influence on self-efficacy, major satisfaction, and college life adjustment. This results from educational limitations, lack of direct experience of the subjects, and insufficient duration of extracurricular activities. Therefore, a face-to-face extracurricular activities should be recommended for college students in nursing and health fields since they are helpful to improve practical experience in education. Also, it is suggested to operate them in consideration of the composition of the volunteers and the duration of their activities.
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks.

A common observation regarding adversarial attacks is that they mostly give rise to false activation at the penultimate layer to fool the classifier. Assuming that these activation values correspond to certain features of the input, the objective becomes choosing the features that are most useful for classification. Hence, we propose a novel approach to identify the important features by employing counter-adversarial attacks, which highlights the consistency at the penultimate layer with respect to perturbations on input samples. First, we empirically show that there exist a subset of features, classification based in which bridge the gap between the clean and robust accuracy. Second, we propose a simple yet efficient mechanism to identify those features by searching the neighborhood of input sample. We then select features by observing the consistency of the activation values at the penultimate layer.
Institutional and national challenges of teacher education in the context of the COVID-19 pandemic

The purpose of the study is to establish the pattern of institutional and national challenges for teacher education in the context of the COVID-19 pandemic, by conducting regression analysis and research of regulations of the world, to identify challenges for teacher education and the ability to provide quality education in the context of distance learning. Research methods: systematization; comparative analysis; generalization; regression analysis. Results. After conducting a regression analysis, it was found that the value of the coefficient of determination shows that the regression model by 60% reflects the direct dependence of the availability of emergency distance education in 30 countries, based on the results of access to personal computers among students and the current cost of education. It has been established that countries such as Denmark, Switzerland, Austria, Norway and Luxembourg have a favorable climate for emergency distance education.
Unsupervised Learning of Discourse Structures using a Tree Autoencoder.

Discourse information, as postulated by popular discourse theories, such as RST and PDTB, has been shown to improve an increasing number of downstream NLP tasks, showing positive effects and synergies of discourse with important real-world applications. While methods for incorporating discourse become more and more sophisticated, the growing need for robust and general discourse structures has not been sufficiently met by current discourse parsers, usually trained on small scale datasets in a strictly limited number of domains. This makes the prediction for arbitrary tasks noisy and unreliable. The overall resulting lack of high-quality, high-quantity discourse trees poses a severe limitation to further progress. In order the alleviate this shortcoming, we propose a new strategy to generate tree structures in a task-agnostic, unsupervised fashion by extending a latent tree induction framework with an auto-encoding objective. The proposed approach can be applied to any tree-structured objective, such as syntactic parsing, discourse parsing and others. However, due to the especially difficult annotation process to generate discourse trees, we initially develop a method to generate larger and more diverse discourse treebanks. In this paper we are inferring general tree structures of natural text in multiple domains, showing promising results on a diverse set of tasks.
The Evolution of Voice Portal and Virtual Assistants

Voice portals and Voice based internet access gives users access to the web through the Human Voice over a mobile or home phone anytime, from anywhere using rapidly evolving recognition technology. The objective of this research paper is to describe the various technology factors that are making voice portal and virtual assistants the next big opportunity on the web, as well as the various approaches service providers and developers of virtual assistants can follow to utilize this exciting technology. Also discussing the practical application and limitations of the same.Traditionally if you have to get some information over the internet you would need a physical device like desktop, mobile phone or other capable device of some sort with an internet connection, manually go to the website or web portal and search for what you need to get result. This approach can sometimes be very inefficient as it is a little time consuming. Basically voice portal can be defined as speech enabled access to Web based information.With rapidly increasing number of smart devices users such as smart phones, smart home assistant etc. voice portals and voice assistants have become the latest trend in broadening the reach of services for all businesses.
On Undecided LP, Clustering and Active Learning

We study colored coverage and clustering problems. Here, we are given a colored point set, where the points are covered by k (unknown) clusters, which are monochromatic (i.e., all the points covered by the same cluster have the same color). The access to the colors of the points (or even the points themselves) is provided indirectly via various oracle queries (such as nearest neighbor, or separation queries). We show that one can correctly deduce the color of all the points (i.e., compute a monochromatic clustering of the points) using a polylogarithmic number of queries, if the number of clusters is a constant.We investigate several variants of this problem, including Undecided Linear Programming and covering of points by k monochromatic balls.
Image Identification and Classification Using CNN

High precision video mark gauge (request) models are ascribed to colossal extension data. Because of the on-going progression on significant learning examines, Convolutional Neural Network (CNN) based procedure have bet customary article affirmation techniques with a gigantic edge. Notwithstanding, it requires extensively more memory and computational expenses appeared differently in relation to the customary techniques. Along these lines, it is hard to complete a CNN set up article affirmation system regarding a mobile phone where memory and computational power are restricted. In this we take a gander at CNN plans which are sensible for adaptable execution, and propose multi-scale sort out in-frameworks (NIN) in which customers can change the trade-off between affirmation time and precision. We realized multi-hung flexible applications on the two iOS and Android using either NEON SIMD bearings or the BLAS library for speedy computation of convolutional layers, and looked at them with respect to affirmation time on phones. At Final, it has been revealed that BLAS suits iOS, while NEON suits Android, and that reducing the picture size by resizing it is suitable for speeding up of CNN-based affirmation. In this paper, we examine an approach to arrange the pictures of cell phone and to identify/recognizes the highlights of the versatile that are prepared and put away in preparing information priory. Picture game plan is one of the zones of PC vision that is growing rapidly. By virtue of significant learning! Reliably, new calculations/models keep beating the previous ones. Honestly, one of the latest top tier programming structures for object revelation was simply released seven days prior by Facebook AI gathering. The item is considered Detectron that combines different exploration adventures for object location and is constrained by the Caffe2 significant learning framework.