Reviewerly/exHarmony huggingface dataset:
TYPOLOGY OF CONTEXTS AND CONTEXTUAL APPROACH PRINCIPLES IN MULTIDISCIPLINARY SCIENTIFIC RESEARCH

The purpose of the article is the creation of a typology of contexts based on the comparative analysis of the concept of contexts and classification of context types in various areas of theoretical and applied knowledge that reflect the specificity of the contextual approach trends in interdisciplinary research. The specificity of the subject/interdisciplinary areas of contexts extraction should be taken into account to interpret the context knowledge. For this objective, contexts systematization of text and non-textual modality has been done. The represented typology of context knowledge, automatically extracted from distributed information environment, (depending on research objectives) may be supplemented by the types of contexts, not being extracted automatically, but which perform important functions in the development of the subject area. In substantiating the typology of contexts and contextual knowledge, the synthetic method and method of content-analysis, the principles of systemic approach and ontological approach are used. The principles of classification and functions of contexts in Computer Science, various subject areas of scientific knowledge, and in the field of communication are represented. A typology of contexts, which includes different levels of contextual knowledge interpretation on the base of multi-level structural analysis, is formed. The devised typology of contexts including different levels of contextual knowledge interpretation is tested in the pilot study analysis of the terminological base development of interdisciplinary direction ‘Digital economy: the State e-Governance and Smart Technologies’. Contextual knowledge clusters of this subject area identified with the term-concepts, allow you distinguishing thematic trends to form the conceptual model of this interdisciplinary direction, and determine the tendencies of its development. Further work involves creating a thesaurus and subject domain ontology of ‘Digital Economy’. The results of the research can be used to analyze the development of terminological databases of promising interdisciplinary research directions that has important theoretical significance for Cognitive Linguistics, Semantics, Humanitarian Informatics. The principles of the contextual approach can be used in teaching practice to achieve self-actualization by students and in the field of social communications.
Helping Future Citizens Navigate an Automated, Datafied World

If there is one thing everyone has an opinion on, it is education. Everyone has been to school and many people go on to have opinions on the experiences of their children. I am therefore conscious that I am neither a school principal nor a teacher, but rather stand at a unique point in the broader education system. In particular, I teach tertiary students enrolled in a law degree and conduct research at the intersection of law and technology. One subject I teach is practical – how to design expert systems and run a small technical project for a not for profit to improve access to justice. This requires students to think about technical solutions to assist in resolving a practical challenge in access to justice (navigating complex information, under-resourced centres, understanding community needs). The other subject is more theoretical – what legal issues arise as technology changes, how does law deal with disruptive innovation, and how can we retain core protections in the face of developments such as automated data-driven decision making. I am therefore accustomed to encouraging students to think outside the disciplinary box to which they are otherwise assigned. Because of my role, I do have some insights into the challenges of teaching in an interdisciplinary context. I am also researching legal and ethical challenges that society is facing relating to datadriven inferencing and decision-making. Many of the new tools being developed are described as artificial intelligence, and I will discuss the relationship among these ideas below. Developing technologies including those associated with artificial intelligence, automation and machine learning, are changing the world that young people need to learn to navigate. When tools are used by those who don’t understand them, there can be individual and community harms. There are thus important questions being asked about how the education system needs to take account of these new technological developments. This essay articulates a vision or idea about how education could best equip students to navigate an increasingly automated and datafied world. It also develops a broader argument about crossdisciplinary learning that can be applied to other important modern challenges that cannot be solved within a single discipline.
Fine-tuning of Fully Convolutional Networks for Vehicle Detection in Satellite Images: Data Augmentation and Hard Examples Mining

Earth observation satellites, both from private companies and governmental agencies, allow us to take a deep look at the Earth from above. Images are acquired with shorter revisit times and spatial ...
Security Analysis and Improvement of Elliptic Curve Digital Signature Scheme

In digital signature, the digital signature based on elliptic curve cryptosystem has higher security. Elliptic curve cryptography (ECC) is the transfer of discrete logarithmic cryptography to elliptic curve. Compared with other cryptosystems, elliptic curve cryptography has the advantages of short key, small storage space, narrow bandwidth and high security. This paper focuses on the security of elliptic curve digital signature scheme. By analyzing the signature scheme of Zhang Qingsheng et al., we find that this scheme has the risk of replacing messages to forge the signature. In view of this risk, this paper proposes an improved scheme based on elliptic curve discrete logarithm problem. Furthermore, in this paper, we analyze the security of our improved scheme against the random number attack, the unknown plaintext and ciphertext attack and the replacement messages forged signature attack.
Agrarian Enterprises Mathematical Ontological Model Development

Food sustainability is a one of the most-important problems of humans for today and tomorrow. Information and Communication Technologies integrating in Agriculture can solve some problems in this direction. This way based on such newest technologies as IoT, Cloud Computing, AI and others. But this technologies not guarantying any positive result without according business logic models and Agro knowledge gathering and sharing.An aim of this paper to justify a choice of tools and methods for design a data and knowledge models for agricultural enterprise control and management systems implementation. This solution must to solve main targets formulated by the Common Agricultural Policy of EU.An attention focused on ontology technology of knowledge presenting and collecting. In the work is proposed a simple example of farm enterprise ontology, which can be used like a base for developing of corresponding control system. Also proposed a way for advance abilities and usability of this ontology model.
Electronic resources librarianship

With ten years’ experience in technical services as a cataloging paraprofessional, I decided to begin an MLS program in 2004. I could see then that electronic resources management was where I wante...
An Optimal Utilization of Cloud Resources using Adaptive Back Propagation Neural Network and Multi-Level Priority Queue Scheduling

With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, on behalf of the comparison of three algorithms like Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR). BR gives a better result with 60 hidden layers Neurons to other algorithms. BR gives 2.05 MSE and 95.8 regressions in Validation, LM gives 2.91 MSE and 94.06 regressions with this and SCG gives 3.92 MSE and 91.85 regressions.
A case study research on Scrum Framework

Agile methods have been widely implemented in the industry while teaching these methods is a substantial part of Computer Science and Engineering program curricula. Evidencedriven data of agile pra ...
Deep Reinforcement Learning and Games [Guest Editorial]

The articles in this special section address the topic of deep reinforcement learning (DRL) and reports on recent major advancements.
Dampak Penggunaan Media Sosial Terhadap Hasil Belajar Geografi Siswa

This study aims to: determine the intensity of social media usage on General High School students in Palopo City and find out the effect of social media usage on student geography learning outcomes. The variables in this study are the use of social media consisting of 4 indicators, namely 1) owned social media accounts, 2) intensity of accessing social media, 3) the use of social media, and 4) the impact of using social media. This type of research is quantitative descriptive. The population in this study were 2298 students and 10% of the samples were taken, 230 students. Sampling in this study using Simple random sampling technique. With. The results showed that 230 students taken in this study accessed social media in the highest frequency a day at&gt; 5 hours a day, which was 61 students or 26.52% and from 6 schools studied only 3 schools where social media had an effect on learning outcomes the geography of students with a large influence of 10-15% with a weak degree of relationship, while the factors that cause social media to influence student learning outcomes come from internal and external factors of the students themselves. while the other 3 schools, where social media does not affect learning outcomes because they tend to use social media for the benefit of entertainment, friendship, online games, and others. Because the influence of the use of social media is weak and is only found in 3 schools, in this study Social Media has no effect on student geography learning outcomes.
Segmentation of brain biomedical images to compute the volume of gliomas

The paper presents an algorithm of segmentation of brain imaging examination results − computed tomography (CT) and positron emission tomography (PET) − to identify, compute, and visualize the brain tumors. Computer software was developed in the MATLAB environment using active contour methods: Edge and Chan-Vese. A review of the current treatment plan for patients with diagnosed glioblastoma multiforme was included to emphasize the significance of the solved problem. The work presents executed steps in the algorithm with a detailed description of thresholding process. Visual results of tumors were performed and presented.
A PRELIMINARY STUDY TO UNDERSTAND THE SMES’ READINESS ON IOT IN MALAYSIA

IR4.0 is the fourth industrial revolution that has started to take effects on the current domain of production systems. It was caused by the advancement of digitalization systems and integration with IoT, and smart objects. Due to changing nature of technology, the new source of capabilities will emerge, while the existing one will become irrelevant for sustaining competitive advantage. As a result, firms worldwide including SMEs have left without many options, but to prepare and get ready with the change. As a part of major pillars of technological advancement that make happens the IR4.0, this study is focusing on the issues on IoT readiness among SMEs in Malaysia. The data was collected from the SMEs and descriptively analyzed with the SPSS v.20 statistical package. The findings have suggested the respondents are quite optimism with the benefits of IoT, but lacking of innovativeness to pioneer the introduction. In addition, although the respondents do not feel discomfort with IoT, they do seem undecided either to trust it or not. It was also found that top managements feel more optimism with IoT, but at the same time also feel more insecure with it. With these findings, it was suggested that the SMEs need more information and training to increase their knowledge for successful adoption of IoT in Malaysia. A few suggestions to improve the respondents’ awareness on IoT and the readiness level are also highlighted.
DSPF: A Digital Signal Processing Based Framework for Information Retrieval

Recently, researchers mainly focus on three categories of models in the field of Information Retrieval (IR), namely vector-space models, probabilistic models, and statistical language models. The existing studies have always developed IR models through refining or combining these traditional models. However, some new frameworks (e.g., digital signal processing (DSP)-based IR framework) have not been well-developed. In our research, we propose a new DSP-based IR Framework (DSPF) introducing the theories from the field of the DSP and present two corresponding DSP-based IR models, denoted as DSPF-BM25 and DSPF-DLM, which incorporate the term weighting schemes from two well-performed probabilistic IR models, the BM25, and the Dirichlet Language Model (DLM). In particular, first, we consider each query term as a spectrum with Gaussian form. Second, instead of transforming the signals from the time domain to frequency domain, we directly represent the query terms in the frequency domain. It is much more controllable and precise to adjust the values of the parameters for getting better performance of our proposed models. To testify the effectiveness of our proposed models, we conduct extensive experiments on seven standard datasets. The results show that in most cases our proposed models outperform the strong baselines in terms of MAP.
A Stochastic First-Order Method for Ordered Empirical Risk Minimization

We propose a new stochastic first-order method for empirical risk minimization problems such as those that arise in machine learning. The traditional approaches, such as (mini-batch) stochastic gradient descent (SGD), utilize an unbiased gradient estimator of the empirical average loss. In contrast, we develop a computationally efficient method to construct a gradient estimator that is purposely biased toward those observations with higher current losses, and that itself is an unbiased gradient estimator of an ordered modification of the empirical average loss. On the theory side, we show that the proposed algorithm is guaranteed to converge at a sublinear rate to a global optimum for convex loss and to a critical point for non-convex loss. Furthermore, we prove a new generalization bound for the proposed algorithm. On the empirical side, we present extensive numerical experiments, in which our proposed method consistently improves the test errors compared with the standard mini-batch SGD in various models including SVM, logistic regression, and (non-convex) deep learning problems.
The Use of Question-Answer Relationship to Improve Students' Reading Comprehension

The objective of this study was to find out the significant difference in reading comprehension between the students who were taught by using Question Answer Relationship and those who were not of the eighth grade students of SMP PGRI Pagaralam. The writer used quasi experimental design, the population of this study was 278 students and the sample was taken through purposive sampling with 2 classes with the total of sample was 70 students. In  analyzing the data, the writer used paired sample t-test and independent sample t-test. From the results of calculation, in paired sample t-test based on the calculation by using SPSS 20 program, the value of t-obtained 5.599 at the significance value p> 0.05 in two tailed testing with df = 34 the critical value of t-table 2.042, while in independent sample t-test, the value of t-obtained was 2.999 at the significance value p> 0.05 in two tailed testing with df = 68, the critical value of t-table was 2.000. The t-obtained both in paired sample t-test and independent sample t-test higher than t-table. It means that there were significant difference between students’ who were taught by using Question – Answer Relationship and those who were not before and after the treatment. It can be said that Question – Answer Relationship strategy could improve students’ reading compeherension and it could be identified from their reading comprehension achievement. Keywords : Question – Answer Relationship, Reading Comprehension.
Idempotent Factorizations

While it is commonly believed RSA requires two primes p and q, that is incorrect. Infinite examples of RSA encryption moduli n=pq exist with p and/or q composite that generate correct RSA keys. This can be explained in the undergraduate cryptography classroom with support from public domain technologies like the python numbthy library and the Gephi graph processor.
Determinants of ResearchGate (RG) Score for the Top100 of Latin American Universities at Webometrics

This paper has the purpose of establishing the variables that explain the behavior of ResearchGate for the Top100 Latin American universities positioned in Webometrics database for January 2017. For this purpose, a search was carried out to get information about postgraduate courses and professors at the institutional websites and social networks, obtaining documents registered in Google Scholar. For the data analysis, the econometric technique of ordinary least squares was applied, a cross-sectional study for the year 2017 was conducted, and the individuals studied were the first 100 Latin American universities, obtaining a coefficient of determination of 73.82%. The results show that the most significant variables are the number of programs, the number of teacher’s profiles registered in Google Scholar, the number of subscribers to the institutional YouTube channel, and the GDP per capita of the university origin country. Variables such as (i) number of undergraduate programs, (ii) number of scientific journals; (iii) number of documents found under the university domain; (iv) H-index of the 1st profile of researcher at the university; (vi) number of members of the institution; (v) SIR Scimago ranking of Higher Education Institutions; (vi) number of tweets published in the institutional account; (vii) number of followers in the Twitter institutional account; (vii) number of “likes” given to the institutional count, were not significant.
InNervate immersion

We present a collaborative immersive technology effort, InNervate AR and InNervate VR. These applications meet the need to expand on existing anatomy education platforms by implementing a more dynamic and interactive user interface. This user interface allows for exploration of the complex relationship between motor nerve deficits and their effects upon the canine anatomy's ability to produce movement. Preliminary AR user studies provided us with positive feedback in the quality of learning. The studies show that the dynamic touch interactions in AR definitely benefit students' critical reasoning and spatial visualization in learning motor nerve and muscle relationships. However, users seek a more immersive VR-based learning environment, without the distractions that an AR experience may offer. Based on this feedback, a VR version of this learning experience was created. Preliminary responses show that users are satisfied with this VR environment which allows them to manipulate and control the anatomical content with full-body interactions.
DISCERNMENT OF DIGITAL COMPETENCES IN STUDENTS OF A BUSINESS SCHOOL

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 4737-4747ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1178Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Acoustic emission from successive impacts on elastic membranes: The physics of the screaming balloon

When a hex nut or a ridged-edge coin placed inside an inflated rubber balloon is spun vigorously, it emits a surprisingly loud and clear sound as the spinning object impacts the rubber and triggers vibrations of the membrane, a phenomenon known as the screaming balloon. We identify the mechanisms behind the acoustic emission and show that the fundamental frequency of the sound is given solely by the rate of successive impacts of the spinning object onto the membrane as it rolls without slipping. A counterintuitive observation is that the acoustic power emitted by a given ridged-edge object remains independent of the size of the balloon (over a wide range of volume) in which it spins. This experimental finding is explained by the influence of the tension within the membrane on the acoustic intensity. Finally, we propose a scaling law for the frequency dependence of the acoustic intensity and show that the sound level depends greatly on the number of ridges on the edge of the spinning object.
Using Scientific Identification Theory for Operational Research in Education for analyzing Teachers’ Performance

The purpose of this study is to find out endeavors to improve teacher performance through organizational culture, academic supervision, and emotional intelligence by referring to Scientific Identification Theory for Operational Research in Education.Organizational culture, academic supervision, and emotional intelligence variables serve as independent variables, and teacher performance is the dependent variable.The number of research samples of 138 teachers was taken with proportional random sampling, and hypothesis testing was followed with qualitative analysis through Focus Group Discussion.The results of this study concluded that there is a positive relationship reinforced by qualitative research for variables a) organizational culture with teacher performance with a correlation coefficient = 0.494 and the coefficient of determination = 0.244, b) academic supervision with teacher performance with a correlation coefficient = 0. 327 and the coefficient of determination = 0.107, c) emotional intelligence with teacher performance with correlation coefficient = 0. 221 and coefficient of determination = 0.049, and d) organizational culture, academic supervision and emotional intelligence together with teacher performance with a coefficient correlation = 0.591, and the coefficient of determination = 0. 350.The implication with Scientific Identification Theory for Operational Research in Education is discussed further.
Identity-based Deniable Authenticated Encryption for E-voting Systems

Deniable authentication (DA) is a protocol in which a receiver can generate an authenticator that is probabilistically indistinguishable from a sender.DA can be applied in many scenarios that require user privacy protection.To enhance the security of DA, in this paper, we construct a new deniable authenticated encryption (DAE) scheme that realizes deniable authentication and confidentiality in a logical single step.Compared with existing approaches, our approach provides proof of security and is efficient in terms of performance analysis.Our scheme is in an identity-based environment; thus, it avoids the public key certificate-based public key infrastructure (PKI).Moreover, we provide an example that shows that our protocol is applicable for e-voting systems.