Reviewerly/exHarmony huggingface dataset:
A Study on the Application of e-Teaching Portfolio for Systematic Teaching in University

본 연구에서는 e-티칭 포트폴리오에 대한 교수자들의 전반적인 인식 분석을 토대로 e-티칭 포트폴리오의 확산 방안에 대해 탐색하고자 하였다. 이를 위해 K대학에서 사용 중인 e-티칭 포트폴리오 사용 경험이 있는 교수자 10명을 대상으로 e-티칭 포트폴리오 시스템의 화면구성, 상호작용, 내용구성, 기술지원, 교수평가 영역에 대한 인식을 분석하였다. 또한 대학 내에서 e-티칭 포트폴리오의 활성화를 위해 e-티칭 포트폴리오의 기술적 측면과 지원 측면에서의 개선 방안을 도출하였다. 연구 결과는 다음과 같다. 첫째, e-티칭 포트폴리오는 지속적인 교수(teaching) 자극을 통해 수업 실행을 위한 구체적인 노력과 과정을 되돌아 볼 수 있는 체계를 지원하는데 효과적인 도구로 활용되고 있음이 확인되었다. 둘째, e-티칭 포트폴리오를 활성화하기 위해서는 티칭 포트폴리오 개발(제작)을 위한 워크숍 등의 프로그램을 별도로 지원할 필요가 있다. 셋째, e-티칭 포트폴리오 작성 가이드를 제공하여 교수자들이 쉽게 작성하고 활용할 수 있도록 도울 필요가 있다. 넷째, 티칭 스타일 진단 결과에 따라 추가적인 도움을 요구할 수 있도록 e-티칭 포트폴리오 시스템 내에서 상호작용을 높일 필요성이 있다. 마지막으로, e-티칭 포트폴리오를 교수평가 측면에서 강의 평가 결과와 연동하고 지속적 개선 및 환류를 위해 CQI 보고서 양식을 제공하여 수업 개선에 대한 성찰을 할 수 있도록 지원할 필요가 있다.This study explored diffusion methods based on an analysis of professors’ perceptions of e-teaching portfolio. The research analyzed the perceptions of screen composition, interactions, content composition, technical support, and pedagogical evaluation of the system. In addition, in order to activate the e-teaching portfolio, the university derived improvement measures in technical and support aspects of the e-teaching portfolio. The research results are as follows. First, it was confirmed that the e-teaching portfolio is used as an effective tool to support the system to reflect the specific efforts and processes for class execution through continuous teaching stimulation. Second, to activate the e-teaching portfolio, it is necessary to separately support programs such as workshops for teaching portfolio development. Third, an e-teaching portfolio preparation guide needs to be provided to help faculty write easily and use portfolios. Fourth, there is a need to increase interactions in the e-teaching portfolio system, so additional help may be required according to the results of a teaching style diagnosis. Finally, the e-teaching portfolio should be linked to the results of the lectures in terms of teaching evaluation. In addition, it is necessary to provide a CQI report form for continuous improvement and feedback to support reflection on class improvement.
Exposing Private User Behaviors of Collaborative Filtering via Model Inversion Techniques

Abstract Privacy risks of collaborative filtering (CF) have been widely studied. The current state-of-theart inference attack on user behaviors (e.g., ratings/purchases on sensitive items) for CF is by Calandrino et al. (S&amp;P, 2011). They showed that if an adversary obtained a moderate amount of user’s public behavior before some time T , she can infer user’s private behavior after time T . However, the existence of an attack that infers user’s private behavior before T remains open. In this paper, we propose the first inference attack that reveals past private user behaviors. Our attack departs from previous techniques and is based on model inversion (MI). In particular, we propose the first MI attack on factorization-based CF systems by leveraging data poisoning by Li et al. (NIPS, 2016) in a novel way. We inject malicious users into the CF system so that adversarialy chosen “decoy” items are linked with user’s private behaviors. We also show how to weaken the assumption made by Li et al. on the information available to the adversary from the whole rating matrix to only the item profile and how to create malicious ratings effectively. We validate the effectiveness of our inference algorithm using two real-world datasets.
Efficient reversible quantum design of sig-magnitude to two's complement converters

Despite the great interest that the scientific community has in quantum computing, the scarcity and high cost of resources prevent to advance in this field. Specifically, qubits are very expensive to build, causing the few available quantum computers are tremendously limited in their number of qubits and delaying their progress. This work presents new reversible circuits that optimize the necessary resources for the conversion of a sign binary number into two's complement of N digits. The benefits of our work are two: on the one hand, the proposed two's complement converters are fault tolerant circuits and also are more efficient in terms of resources (essentially, quantum cost, number of qubits, and T-count) than the described in the literature. On the other hand, valuable information about available converters and, what is more, quantum adders, is summarized in tables for interested researchers. The converters have been measured using robust metrics and have been compared with the state-of-the-art circuits. The code to build them in a real quantum computer is given.
The Readiness of Leading and Teaching Staff to Develop Students’ Sociocultural Competence in the Inclusive Learning Environment

The article aims to identify the level of readiness among the leading and teaching staff to develop students’ sociocultural competence in the inclusive learning environment. The survey of 626 respondents, projective technique, essays, and expert evaluation of in-class and out-class activities revealed a low level of readiness among the leading and teaching staff. They have to improve their work to develop students’ sociocultural competence after analysing the inclusive learning environment.
On the differences between human translations

Many studies have confirmed that translated texts exhibit different features than texts originally written in the given language. This work explores texts translated by different translators taking into account expertise and native language. A set of computational analyses was conducted on three language pairs, English-Croatian, German-French and English-Finnish, and the results show that each of the factors has certain influence on the features of the translated texts, especially on sentence length and lexical richness. The results also indicate that for translations used for machine translation evaluation, it is important to specify these factors, especially if comparing machine translation quality with human translation quality is involved.
Dynamic All-Red Signal Control Based on Deep Neural Network Considering Red Light Runner Characteristics

Despite recent advances in technologies for intelligent transportation systems, the safety of intersection traffic is still threatened by traffic signal violation, called the Red Light Runner (RLR). The conventional approach to ensure the intersection safety under the threat of an RLR is to extend the length of the all-red signal when an RLR is detected. Therefore, the selection of all-red signal length is an important factor for intersection safety as well as traffic efficiency. In this paper, for better safety and efficiency of intersection traffic, we propose a framework for dynamic all-red signal control that adjusts the length of all-red signal time according to the driving characteristics of the detected RLR. In this work, we define RLRs into four different classes based on the clustering results using the Dynamic Time Wrapping (DTW) and the Hierarchical Clustering Analysis (HCA). The proposed system uses a Multi-Channel Deep Convolutional Neural Network (MC-DCNN) for online detection of RLR and also classification of RLR class. For dynamic all-red signal control, the proposed system uses a multi-level regression model to estimate the necessary all-red signal extension time more accurately and hence improves the overall intersection traffic safety as well as efficiency.
Reciprocity: Anaphora, scope, and quantification

We present a relational analysis of reciprocity in the setting of Partial Plural Compositional Discourse Representation Theory (abbreviated as PPCDRT), combining ideas from Plural Compositional DRT and Partial Compositional DRT. Our analysis accounts for a wide range of data that are problematic for proposals involving quantification over individuals and for relational analyses relying on cumulative operators on predicates. We also provide an account of apparent “scope” ambiguities and long-distance readings which have been a focus of attention for quantificational analyses, but have not been adequately addressed by previous relational analyses. Our Partial Plural CDRT analysis also enables us to address other issues in the semantics of reciprocity. First, we provide a simple account of reciprocals with quantificational antecedents, whose analysis has been problematic for previous accounts. Second, it has often been noted that the meaning contribution of the reciprocal varies in strength, with some examples requiring the reciprocal relation to hold between every member of the relevant group, while others allow for a weaker relation. We explore an approach to this problem in our Partial Plural CDRT setting. BibTeX info
Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network

Estimation Solar radiation is the most significant part of the optimization of solar power.This may be achieved if the solar radiation is predicted well in advance.Meteorological stations have radiation measuring devices like pyranometer, pyrheliometer, radiometer, solarimeter, etc. however, it may not be available at the location of interest for researchers.Due to this limitation solar radiation estimation models are devised based on location details like Altitude, Latitude, Longitude, and metrological details like Wind Speed, Ambient Temperature, Relative Humidity, Day Temperature, etc.These radiation models provide Global Solar Radiation (GSR) as output.These models are statistically tested based on error calculation like Mean Bias Error, Mean Absolute Error, Root Mean Square Error, etc.This paper is framed to briefly provide the idea behind different solar radiation estimation models with the methodology used.Soft computing-based models are mainly analyzed here.ANN-based Global Solar Irradiance Estimation Model has been developed using geographical parameters like Elevation, Latitude, Longitude, Longitude, and meteorological parameters like Months of a year, Days of a month, Temperature, Atmospheric Pressure, Relative Humidity, and Wind Speed.The data are downloaded from the National Solar Radiation Database (NSRDB) for 2014 (latest available).From this paper, the reader will come to know about various techniques used in solar radiation estimation.The developed ANN-based model has better results for training, testing, validation, and all with Regression value of 0.94304, 0.9488, 0.94766, 0.94556 respectively.The MSE is found to be 0.0089147 at epoch 0. The obtained values of R and MSE indicates the suitability of the developed model.
Blockchain in Education: New Challenges

Blockchain technology is one of the greatest innovations of the last decade as it allows new possibilities for the development of projects in fields such as services, industry, arts, economy, and logistics, among others. In the area of education, blockchain technology is still in its early stages. A large part of existing implementations focus on certificate management, leaving aside other scenarios. This document explores some possible applications of blockchain technology to the field of education, analyzing not only the benefits that blockchain could bring but also its risks and challenges. Through the review of the most interesting experiences completed so far, conclusions and predictions about the paths where this technology can take us are offered to the reader.
Robust Image Hashing with Low-Rank Representation and Ring Partition

Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.
Optimized Polynomial Virtual Fields Method for Constitutive Parameters Identification of Orthotropic Bimaterials

Heterogeneous materials are widely applied in many fields. Owing to the spatial variation of its constitutive parameters, the mechanical characterization of heterogeneous materials is very important. The virtual fields method has been used to identify the constitutive parameters of materials. However, there is a limitation: constitutive parameters of one material have to be a priori; then, constitutive parameters of the other one can be identified. Aiming at this limitation, this article presents a method to identify the constitutive parameters of heterogeneous orthotropic bimaterials under the condition that constitutive parameters of both materials are all unknown from a single test. A constitutive parameter identification method of orthotropic bimaterials based on optimized virtual field and digital image correlation is proposed. The feasibility of this method is verified by simulating the deformation fields of a two-layer material under three-point bending load. The results of numerical experiments with FEM simulations show that the weighted relative error of the constitutive parameter is less than 1%. The results suggest that the variation coefficient-to-noise ratio can perform a priori evaluation of a confidence interval on the identified stiffness components. The results of numerical experiments with DIC simulations show that the weighted relative error is 1.44%, which is due to the noise in the strain data calculated by DIC method.
IMPLEMENTATION OF PROVABLYSECURE DIGITAL SIGNATURE SCHEME BASED ON ELLIPTIC CURVE

Digital Signatures are viewed as the foundation of the online exchanges of the computerized world.They guarantee non repudiation for the activities of the sender and furthermore ensure security.Over some undefined time frame the digital signatures have developed and numerous enhancements have been finished by scientists to the standard plans.This paper develops an understanding of variants in accordance to their computational costs and response to attacks.The authors present a scheme which withstands the drawbacks of its pre-decessor the Jhong's digital signature, implements the solution and proves it to be sturdy with regard to computational costs as compared to other methods and also taking care of the attacks.The results depict the proposed scheme to be worthy either at signature generation, signature verification in terms of its computational costs or its ability to stand the attacks.Overall, the proposed scheme stands tall in all aspects.
Modeling the structure of a complex system based on estimation of the measure of interaction of subsystems

The using of determining the measure of interaction between channels when choosing the configuration structure of a control system for complex dynamic objects is considered in the work.The main methods for determining the measure of interaction between subsystems of complex control systems based on the methods RGA (Relative Gain Array), Dynamic RGA, HIIA (Hankel Interaction Index Array), PM (Participation matrix) are presented.When choosing a control configuration, simple configurations are preferable, as they are simple in design, maintenance and more resistant to failures.However, complex configurations provide higher performance control systems.Processes in large dynamic objects are characterized by a high degree of interaction between process variables.For the design of the control structure interaction measures are used, namely, the selection of the control structure and the decision on the configuration of the controller.The choice of control structure is to determine which dynamic connections should be used to design the controller.When a structure is selected, connections can be used to configure the controller.For large systems, it is proposed to pre-group the components of the vectors of input and output signals of the actuators and sensitive elements into sets in which the number of variables decreases significantly in order to select a control structure.A quantitative estimation of the decentralization of the control system based on minimizing the sum of the off-diagonal elements of the PM matrix is given.An example of estimation the measure of interaction between components of strong coupled subsystems and the measure of interaction between components of weak coupled subsystems is given.A quantitative estimation is given of neglecting the interaction of components of weak coupled subsystems.The construction of a weighted graph for visualizing the interaction of the subsystems of a complex system is considered.A method for the formation of the controllability gramian on the vector of output signals that is invariant to state vector transformations is proposed in the paper.An example of the decomposition of the stabilization system of the components of the flying vehicle angular velocity vector is given.The estimation of measures of the mutual influence of processes in the channels of control systems makes it possible to increase the reliability of the systems when accounting for the use of analytical redundancy of information from various devices, which reduces the mass and energy consumption.Methods for assessing measures of the interaction of processes in subsystems of control systems can be used in the design of complex systems, for example, motion control systems, orientation and stabilization systems of vehicles.
Anonymous, Attribute Based, Decentralized, Secure, and Fair e-Donation

Abstract E-cash and cryptocurrency schemes have been a focus of applied cryptography for a long time. However, we acknowledge the continuing need for a cryptographic protocol that provides global scale, decentralized, secure, and fair delivery of donations. Such a protocol would replace central trusted entities (e.g., charity organizations) and guarantee the privacy of the involved parties (i.e., donors and recipients of the donations). In this work, we target this online donation problem and propose a practical solution for it. First, we propose a novel decentralized e-donation framework, along with its operational components and security definitions. Our framework relies on a public ledger that can be realized via a distributed blockchain. Second, we instantiate our e-donation framework with a practical scheme employing privacy-preserving cryptocurrencies and attributebased signatures. Third, we provide implementation results showing that our operations have feasible computation and communication costs. Finally, we prove the security of our e-donation scheme via formal reductions to the security of the underlying primitives.
Pengaruh Kondisi Ekonomi Orang Tua Terhadap Motivasi Belajar Pada Siswa Sekolah Dasar

This study aims to determine the effect of economic conditions of parents on students' motivation in SD Negeri 3 Mangaran, Mangaran District, Situbondo Regency.This research is a quantitative research with ex post facto research.The population of this study were all students of SD Negeri 3 Mangaran, Mangaran Subdistrict, Situbondo Regency and the sample in this study amounted to 22 students.The data collection method uses a questionnaire to measure each variable of parents' economic conditions and learning motivation.Data analysis techniques use regression analysis.The results showed that the level of economic condition of parents was high by 40.9% with respondents 9 students, moderate by 50% with respondents 11 students, and low by 9.1% with respondents 2 students.The influence of parents' economic conditions on learning motivation contributed 59.7% with t = 5.447 and a significance value of 0.000, while a contribution of 40.3% was influenced by other variables outside this study.From the results of the study it can be concluded that there is a positive influence between the Economic Conditions of Parents on the learning motivation of students at SD Negeri 3 Mangaran, Mangaran District, Situbondo Regency.
Digital Value-Adding Chains in Vocational Education: Automatic Keyword Extraction from Learning Videos to Provide Learning Resource Recommendations

The digital transformation of industry environments creates new demands but also opportunities for vocational education and training (VET). On the one hand, the introduction of new digital learning tools involves the risk of creating a digital parallel world. On the other hand, such tools have the potential to provide intelligent and contextualized access to information sources and learning materials. In this work, we explore approaches to provide such intelligent learning resource recommendations based on a specific learning context. Our approach aims at automatically analyzing learning videos in order to extract keywords, which in turn can be used to discover and recommend new learning materials relevant to the video. We have implemented this approach and investigated the user-perceived quality of the results in a real-world VET setting. The results indicate that the extracted keywords are in line with user-generated keywords and summarize the content of videos quite well. Also, the ensuing recommendations are perceived as relevant and useful.
T-EA: A Traceable Evolutionary Algorithm

In this paper, the influence of the initial population into successive generations in Evolutionary Algorithms (EAs) is studied as a problem-independent approach. For this purpose, the Traceable Evolutionary Algorithm (T-EA) is proposed. This algorithm keeps track of the influence of the individuals from the initial population over the generations of the algorithm. The algorithm has been implemented for both bit-string and integer vector representations. In addition, in order to study the general influence of each individual, new impact factor metrics have been proposed. In this way, we aim to provide tools to measure the influence of initial individuals on the final solutions. As a proof of concept, three classical optimization problems (One Max, 0/1 Knapsack and Unbounded Knapsack problems) are used. We provide a framework that allows to explain why some individuals in the initial population work better than others in relation with the corresponding fitness values.