Reviewerly/exHarmony huggingface dataset:
POWER-SUPPLaY: Leaking Data from Air-Gapped Systems by Turning the Power-Supplies Into Speakers

It is known that attackers can exfiltrate data from air-gapped computers through their speakers via sonic and ultrasonic waves. To eliminate the threat of such acoustic covert channels in sensitive systems, audio hardware can be disabled and the use of loudspeakers can be strictly forbidden. Such audio-less systems are considered to be \textit{audio-gapped}, and hence immune to acoustic covert channels. In this paper, we introduce a technique that enable attackers leak data acoustically from air-gapped and audio-gapped systems. Our developed malware can exploit the computer power supply unit (PSU) to play sounds and use it as an out-of-band, secondary speaker with limited capabilities. The malicious code manipulates the internal \textit{switching frequency} of the power supply and hence controls the sound waveforms generated from its capacitors and transformers. Our technique enables producing audio tones in a frequency band of 0-24khz and playing audio streams (e.g., WAV) from a computer power supply without the need for audio hardware or speakers. Binary data (files, keylogging, encryption keys, etc.) can be modulated over the acoustic signals and sent to a nearby receiver (e.g., smartphone). We show that our technique works with various types of systems: PC workstations and servers, as well as embedded systems and IoT devices that have no audio hardware at all. We provide technical background and discuss implementation details such as signal generation and data modulation. We show that the POWER-SUPPLaY code can operate from an ordinary user-mode process and doesn't need any hardware access or special privileges. Our evaluation shows that using POWER-SUPPLaY, sensitive data can be exfiltrated from air-gapped and audio-gapped systems from a distance of five meters away at a maximal bit rates of 50 bit/sec.
Teachers’ Leadership in the Structure of the University Image: A Psychological Study

Our paper defines psychodiagnostic tools of research to carry out factor analysis and find out the individual-psychological features of academic staff leadership by the structural components of University image.Psychodiagnostic techniques aimed at the study of transformational leadership, social and psychological attitudes of the individual in the motivational sphere, communicative and organizational abilities, psychological and individual-typological features of the examined.We defined structural components of University image based on the results of empirical studies and factor analysis.We determined that the internal University image is a complete structural and functional organization of mental activity of academic staff which functions within 5 of its components: F-1 leadership, F-2 motivational, F-3 communicative, F-4 personal, F -5 typological with relevant functions of leadership, motivation, interaction, stabilization and integration.We have formed the author's definition of leadership based on the results of theoretical and empirical research.We consider leadership as its individual-psychological characteristics in the structure of the University academic staff as a holistic polymotivated structural and functional organization of the mental activity of the individual, which determines the effective level of functioning due to the integrated homeostatic regulation of transformational leadership variables, motivational, communicative, personal and typological spheres of personality examined as leading components, which determine the high status of the individual, ability to influence the behavior and activity of the environment to achieve the goal and to develop a positive University image.
LibGuides: Graphic Novels: Home

This guide shows the value of Graphic Novels not only as a source of entertainment for kids, but as a tool for increasing learning skills.
Genetic Programming for Vegetated Channel

The problems in river hydraulics is too complicated to develop exact, generalized empirical or regression equations.Regression analysis has been used extensively in the past to address these problems but owing to its inadequacies pertaining to a first-hand functional form determination and clustering effect of influential points and groups of points that could even have been erroneous, regression has failed to deliver satisfactory results.With the advent of huge computational resources, it has been possible to develop statistical models based purely on analysis of historical data.Alongside, the development of the new paradigm of evolutionary algorithms, based on the modus operandi of a plethora of natural phenomena, has enabled the modern modeler to develop very precise models which are able to explain the variability in data to a suitably satisfactory level and then predict the state variables corresponding to the combination of circumstances that are entirely new to the model developed.Genetic programming is one such paradigm which is free of many limitations of regression, empirical and dimensional analyses and gives the flexibility of the choice of parameters of the algorithm in a robust manner that allow the model for best description of variability as well as reasonable predictability.Present work uses genetic programming for flow prediction in vegetated channel.The developed model shows very high predictability of flow resistance.
Supersingular Isogeny Key Encapsulation (SIKE) Round 2 on ARM Cortex-M4.

We present the first practical software implementation of Supersingular Isogeny Key Encapsulation (SIKE) round 2, targeting NIST's 1, 2, 3, and 5 security levels on 32-bit ARM Cortex-M4 microcontrollers. The proposed library introduces a new speed record of all SIKE Round 2 protocols with reasonable memory consumption on the low-end target platform. We achieved this record by adopting several state-of-the-art engineering techniques as well as highly-optimized hand-crafted assembly implementation of finite field arithmetic. In particular, we carefully redesign the previous optimized implementations of finite field arithmetic on the 32-bit ARM Cortex-M4 platform and propose a set of novel techniques which are explicitly suitable for SIKE primes. The benchmark result on STM32F4 Discovery board equipped with 32-bit ARM Cortex-M4 microcontrollers shows that entire key encapsulation and decapsultation over SIKEp434 take about 184 million clock cycles (i.e., 1.09 seconds @168 MHz). In contrast to the previous optimized implementation of the isogeny-based key exchange on low-end 32-bit ARM Cortex-M4, our performance evaluation shows feasibility of using SIKE mechanism on the low-end platform. In comparison to the most of the post-quantum candidates, SIKE requires an excessive number of arithmetic operations, resulting in significantly slower timings. However, its small key size makes this scheme as a promising candidate on low-end microcontrollers in the quantum era by ensuring the lower energy consumption for key transmission than other schemes.
Use of Discrete Sumudu Transform in cryptography

Cryptography is a method of protecting information and communications through the use of codes so that only those for whom the information is intended can read and process it. Modern cryptography uses sophisticated mathematical equations (algorithms) and secret keys to encrypt and decrypt data. Today, cryptography is used to provide secrecy and integrity to our data, and both authentication and anonymity to our communications. In this paper we have to encrypt and decrypt secret data using discrete Sumudu transformation and congruence modulo operator relation.
Purification Complexity without Purifications

We generalize the Fubini-Study method for pure-state complexity to generic quantum states by taking Bures metric or quantum Fisher information metric on the space of density matrices as the complexity measure. Due to Uhlmann's theorem, we show that the mixed-state complexity exactly equals the purification complexity measured by the Fubini-Study metric for purified states but without explicitly applying any purification. We also find the purification complexity is non-increasing under any trace-preserving quantum operations. We also study the mixed Gaussian states as an example to explicitly illustrate our conclusions for purification complexity.
Designing Games in Ring

The declarative programming of video games has not gained the interest it deserves in the computer science research community. For example, although Quasi-Engine is a Qt5-based framework that intends to be a complete multiplatform toolset to ease 2D game development, providing ready-to-use QML elements representing the basic game entities needed by most games, this project seems to be abandoned. On the commercial software side, the VPlay engine (now Felgo Engine) is a cross-platform development tool, based also on the Qt framework. Felgo developers use QML, the declarative tongue of Qt, along with an additional language required for scripting such as JavaScript or C++ to create mobile apps and games. Regarding general-purpose programming languages, to the best of my knowledge, I am not aware of any initiative out there, in Python or Ruby or any other “competing” language, that provides a standard, free, open source, and declarative game programming engine besides Ring.
Adjusting Image Attributes of Localized Regions with Low-level Dialogue

Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions are often ambiguous and contain high-level abstractions that tend to correspond to complex editing steps to accomplish. Motivated by this inexperience aspect, we aim to smooth the learning curve by teaching the novices to edit images using low-level commanding terminologies. Towards this end, we develop a task-oriented dialogue system to investigate low-level instructions for NLIE. Our system grounds language on the level of edit operations, and suggests options for a user to choose from. Though compelled to express in low-level terms, a user evaluation shows that 25% of users found our system easy-to-use, resonating with our motivation. An analysis shows that users generally adapt to utilizing the proposed low-level language interface. In this study, we identify that object segmentation as the key factor to the user satisfaction. Our work demonstrates the advantages of the low-level, direct language-action mapping approach that can be applied to other problem domains beyond image editing such as audio editing or industrial design.
사이버범죄협약(일명 ‘부다페스트’ 조약) 가입을 위한 선결과제

Cybercrime is a transnational crime that can spy and attack anywhere in the world with a click. These characteristics of cybercrime are now known by everyone, and the features of cybercrime are becoming more subtle, aggressive and intelligent over the years. Due to the uniqueness of the cybercrime, the consistent proliferation in the number of victims of cybercrime become a important social problem in the world. Many countries realized necessity to cooperate and try to control ubiquitous problems of cybercrime. The European Council’s Ministerial Committee came up with the Convention on Cybercrime, also known as the Budapest Convention on Cybercrime to jointly respond to cybercrime, a transnational crime. It is the first international treaty for countries around the world to control the problem of cybercrime. The main purpose of this treaty is to prevent cybercrime and share relevant information, however sometimes it also plays a role in responding to cybercrime and punishing criminals. About 60 countries, including the United States of America, United Kingdom, Germany, France and Japan, have now joined the cybercrime convention, reflecting transitional legislation within their countries and sharing relevant information. Unfortunately, however, Korea has yet to even join the Convention. Despite the consistent proliferation in the number of victims of cybercrime worldwide, Korea, unfortunately, has yet to even join the Convention. Especially to solve cybercrime problem, the expedited preservation of stored data is important because the internet data is usually ephemeral. That is why many developed countries effect a treaty for rapid cooperation. Coping with transnational crimes including drug crimes, evidences have been provided through the mutual legal assistance. Today, many developed countries have prevented and responded to cybercrime by joining and ratifying the cybercrime convention in advance. To this end, cooperation and information sharing between countries are needed, and in order to solve the problem of dual criminality requirements between countries, a speedy preservation and submission order is needed. Especially Based on such critical awareness, we would like to focus on the legislative measures for fast preservation, which are prerequisites for accession to the cybercrime convention.
Experimental Analysis of Convolutional Neural Networks and Capsule Networks for Image Classification

Deep Learning neural networks with the help of Convolutional neural networks (CNNs) have succeeded to find solution to a large diversity of problems related to images. The success is credited to their capacity to learn hierarchical features directly from images and then feeding the feature vectors for classification. Subsampling in CNNs loses the spatial relationships between the features which is essential to determine the correct class of objects. CNNs also fail to handle rotation invariance. To address this concern, capsule networks which are group of neurons use two main algorithms namely dynamic routing algorithm and route by agreement algorithm to model the spatial and pose variability. These networks can address the problem of rotational and spatial variance, deal with reduced quantity of labeled data and handle imbalanced classes in the given dataset. In this paper convolutional neural network is compared with capsule network using augmented CIFAR-10 dataset.
Research on Criminal Liability of Cybercrime Related to Attack Cloud Storage Platform

Attacking cloud platform crime is a kind of crime which is extremely harmful in the web3.0 era. It has the characteristics of strong combination ability, miscellaneous legal infringement category, low crime cost and difficult combat. In order to better criminalize this kind of crime, in addition to the traditional theoretical research of academic circles, it is necessary to study the trinity of practical research, data support and theoretical research. This article starts with the criminal model of attacking the cloud platform, sorts out 300 cases of the referee paper network, and the 42nd "Statistical Report on the Development of China's Internet Network" and other related data, and investigates the relevant substantive departments, and strives to clarify the crime of attacking the cloud platform. "Combining with other cybercrime, analyzing the existing loopholes in criminal liability system, providing a reliable basis for scientifically identifying crimes and managing the network society."
Digital simulator of a random process using Markov model

Abstract Sources (generators) of random processes (signals) are widely used in various technology areas as software modules in computer simulations or hardware blocks in experimental works. The equipment can be built on analog or digital bases. Noise physical sources (generators) form random processes of a certain type (Gaussian noise), which properties and characteristics are set very approximately. Digital generators (sensors) of pseudo-random processes have better technical characteristics, but the range of their types is very limited. The random signal simulator should form its values with specified statistical characteristics (probability distribution and its parameters, correlation properties, etc.) and the possibility of changing them. From this point of view, the Markov model of random process (a simple Markov chain) provides good results. It is the base for a structural scheme of the simple digital algorithm to form pseudo-random numbers and the hardware simulator implementation, to research its capabilities and effectiveness.
A Study on the Influence of Metropolitan and Provincial Offices of Education on Academic Achievement of General High Schools

본 연구의 목적은 일반계고의 학업성취도(국가수준학업성취도평가의 국어·영어·수학 교과 보통학력 이상 도달 비율)에 대한 시·도교육청의 영향을 규명하는 것이다. 연구 목적을 달성하기 위해 설정한 연구문제는 첫째, 학업성취도는 교육청 변인에 의해 설명될 수 있는가, 둘째, 단위학교 학업성취도 차이는 교육청의 어떤 변인들로 설명할 수 있고, 그 설명력은 어느 정도인가이다. 단위학교 및 시·도교육청이 학업성취도에 영향을 줄 것이라는 가정 하에 2수준 다층모형으로 설계하였다. 본 연구의 결과, 첫째, 시·도교육청에 의해 설명되는 학업성취도 차이는 12.330%로 확인되었다. 둘째, 단위학교 수준의 변인을 통제한 후, 시·도교육청 변인들에 의한 학업성취도 차이는 통계적으로 유의한 것으로 나타났고, 학업성취도에 영향을 주는 변인은 교원연수 활성화 노력, 교사의 교육전념 여건 조성 노력, 학교 기본운영비 확대 비율, 교육환경개선 사업비 투자실적 비율이었다. 그리고 일반계고 학업성취도에 대한 시·도교육청 수준의 설명력은 60.74%로 나타났다. 본 연구를 통해 시·도교육청 수준의 변인들이 단위학교 학업성취도에 영향을 주는 것으로 확인된 바, 시·도교육청의 권한과 기능 변화에 따른 정책과 재정 변인들을 충분히 고려해야 한다. 앞으로 시·도교육청 수준의 다양한 변인을 활용한 실증연구가 보다 활발히 이루어질 필요가 있다.The purpose of this study is to investigate the influence of Metropolitan and Provincial Offices of Education on academic achievement (ratio of achieving above the average score of the National Assessment of Educational Achievement) at general high schools. The variables affecting the academic achievement are classified into two categories: those of the schools and those of the Metropolitan and Provincial Offices of Education. This study is designed for a multi-level analysis of level two under the hypothesis including variables of the schools and of the Metropolitan and Provincial Offices of Education that can affect academic achievement of students. The results of this study are as follows. First, variances of Metropolitan and Provincial Offices of Education by academic achievement were 12.330%. This means that the difference of academic achievement is statistically significant to Metropolitan and Provincial Offices of Education. Second, after controlling for school-level variables, the differences between Metropolitan and Provincial Offices of Education measured by academic achievement were found to be statistically significant. The academic achievement rates were found to be statistically significant in the variables depending upon factors such as the effort to revitalize teacher training, the efforts to create teachers devotion to education, the increase in the basic operating cost of the school, and the ratio of investment to educational environment improvement. The explanatory power of the level of Metropolitan and Provincial Offices of Education on the academic achievement of the general high schools was found to be 60.74%.
Interpreting Metaphor In Sidney Mohede’s Song Lyrics

This research aimed at describing the metaphors used by the Mohede in the eight songs he wrote. As a qualitative research, this study rellied the data upon the lyrics of the chosen songs. The analysis applies theory of Lakoff and Johnson which divided metaphors into three types namely structural metaphor, orientational metaphor, and ontological metaphor. The analysis followed the steps of Bogdan and Biklen as follows identification, categorization, explanation, and interpretations of the findings. The result showed that the songs confirmed the existence the types of the metaphors such as structural metaphor, orientational metaphor and ontological metaphor. These findings implies a pivotal contribution of figurative speech particularly metaphor in communicating the meaning of a song. Thus, this study foreshadows a further research for comprehending the relationship of metaphor to song lyrics for both linguistic and literary context. Keywords: mathematical ability; students; Problem-Based Learning.
Histogram Methods for Unsupervised Clustering

A new algorithm is presented to find clusters in a dataset of points in Rn with no prior knowledge of possible clustering. The algorithm detects clusters in a top down fashion by testing modality of density functions generated from the dataset and splitting the set accordingly. Results on synthetic and text datasets demonstrate that the method is comparable to other established unsupervised learning algorithms, which do in fact require the number of clusters ab initio. The method proves to be particularly suitable for certain distributions and offers a valid alternative in situations where most of the well-known algorithms do not produce consistent results.
Semantic Representation of an Algorithm Knowledge Profile in Metacognitive Architecture CARINA

An algorithmic knowledge profile is a profile that has a local state of a cognitive function as a local algorithmic state. The semantic representation of knowledge is necessary to acquire a mechanism that stores all the information that a cognitive agent receives. CARINA is a metacognitive architecture for cognitive agents, in CARINA a semantic representation is necessary to store the information found in an algorithmic knowledge profile and thus have access to it for the location of reasoning failures. This article presents a semantic representation of an algorithmic knowledge profile. For this representation of semantic knowledge, a belief map was designed that represents the AKP in the CARINA metacognitive architecture, then a prototype was developed that simulates the precept function generating the AKP at the same time as its belief system. The results showed that when entering a high number of words, time and weight increased when saving the information and throwing the json.
A study on developing student growth monitoring system utilizing student assessment data

The purpose of this study is to continuously, individually monitor a student’s growth by utilizing student assessment data in response to a recent student assessment paradigm and associated policies, so that student assessment data can be used as useful information to help students grow and develop. For this, the direction of the monitoring system for individual student growth was explored by analyzing the current status of student assessment data in schools and previous domestic and abroad cases of the individual student’s growth monitoring system. The
Spoken digit classification using a spin-wave delay-line active-ring reservoir computing

As a test of general applicability, we use the recently proposed spin-wave delay line active-ring reservoir computer to perform the spoken digit recognition task. On this, classification accuracies of up to 93% are achieved. The tested device prototype employs improved spin wave transducers (antennas). Therefore, in addition, we also let the computer complete the short-term memory (STM) task and the parity check (PC) tasks, because the fading memory and nonlinearity are essential to reservoir computing performance. The resulting STM and PC capacities reach maximum values of 4.77 and 1.47 respectively.
SPot: A tool for identifying operating segments in financial tables

In this paper we present SPot, an automated tool for detecting operating segments and their related performance indicators from earnings reports. Due to their company-specific nature, operating segments cannot be detected using taxonomy-based approaches. Instead, we train a Bidirectional RNN classifier that can distinguish between common metrics such as "revenue" and company-specific metrics that are likely to be operating segments, such as "iPhone" or "cloud services". SPot surfaces the results in an interactive web interface that allows users to trace and adjust performance metrics for each operating segment. This facilitates credit monitoring, enables them to perform competitive benchmarking more effectively, and can be used for trend analysis at company and sector levels.