Reviewerly/exHarmony huggingface dataset:
Analysis of the feelings of the population’s opinion in social media: a look at education

This research presents a work in which we identify and systematize how the vertiginous growth of social media allows the monitoring of public opinions, with a special focus on analyzing the feelings of the population's opinionated arguments about Education. We have brought together different methods in order to produce better results for the classification and summarization of various documents considering education as the basis of analysis. The proposed model is based on the steps of i) classification of patterns based on Deep Learning; ii) analysis of contexts and visualization of different associative paths in publications through the Implicative Statistical Analysis; and iii) validation of opinion abstracts. The results presented in this study refer to the database made up of 42,062 publications related to the city. The collective social discourses, resulting from the analysis of the summarize the opinions of 820 posts that presented representative terms for the education axis in the negative polarity, of the total of 975 posts classified by the dataset.
ORCHESTRATION AND PITCH PRECISION IN THE ORCHESTRAL MUSIC OF MARC SABAT

Abstract This article examines the relationship between orchestration and microtonality in the music of Marc Sabat through a score-based analysis of two recent works, Asking Ocean (2016), for string quartet and large ensemble, and The Luminiferous Aether (2018), for large orchestra. Excerpts from these two compositions are discussed to highlight the challenges of composing for orchestral forces in a musical style that demands a high degree of microtonal pitch precision. Through retuning, alteration, and a sensitivity to the construction, techniques and performance practices of orchestral instruments, Sabat has developed a unique manner of orchestrating that is at once timbrally rich and uncompromising in pitch precision. After a brief introduction to the extended just intonation framework that Sabat employs, his concepts of ‘fixed microtonal pitches’ and ‘tuneable intervals’ are discussed and connected to orchestration in his scores. Drawing upon this analysis, connections are made between the microtonal system with orchestration and musical aesthetics broadly.
On the Link Between Refactoring Activity and Class Cohesion Through the Prism of Two Cohesion-Based Metrics

The practice of refactoring has evolved over the past thirty years to become standard developer practice; for almost the same amount of time, proposals for measuring object-oriented cohesion have also been suggested. Yet, we still know very little about their inter-relationship empirically, despite the fact that classes exhibiting low cohesion would be strong candidates for refactoring. In this paper, we use a large set of refactorings to understand the characteristics of two cohesion metrics from a refactoring perspective. Firstly, through the well-known LCOM metric of Chidamber and Kemerer and, secondly, the C3 metric proposed more recently by Marcus et al. Our research question is motivated by the premise that different refactorings will be applied to classes with low cohesion compared with those applied to classes with high cohesion. We used three open-source systems as a basis of our analysis and on data from the lower and upper quartiles of metric data. Results showed that the set of refactoring types across both upper and lower quartiles was broadly the same, although very different in actual numbers. The `rename method' refactoring stood out from the rest, being applied over three times as often to classes with low cohesion than to classes with high cohesion.
Efektivitas Model Pembelajaran Snowball Throwing dan Talking Stick Terhadap Hasil Belajar Matematika Siswa Sekolah Dasar

This research is a quantitative research that aims to determine whether the Snowball Throwing learning model can influence learning outcomes of Matehematics that are better and more effective than the Talking Stick learning model on addition and subtraction of various fractions students in grade V. The research used the eksperimental research method whit pretest post-test control group design. Its population was all of students in Grade V of State Primary School in target area Imam Bonjol of Laweyan Sub-district in Academic Year 2017/2018. Cluster Random Sampling was used to determine its samples. They consisted of the students in Grade V of State Primary School Totosari No.102 and students in Grade V of State Primary School Tunggulsari I No.72.the data of research were collected through test of learning result in Mathematics. Prior to application, the test was experimented at State Primary School Tunggulsari II No.179. they were analyzed by using the statistical t test at the signifikan level of 0.05. the result of the research shows that the aplplication of the Snowball Throwing and Talking Stick learning model can affect the students’ learning result in mathematics. ((thitung = 2,056; t 1,989). The results of this study it can be concluded that students who are taught with the Snowball Throwing model have learning outcomes in addition material and a reduction in various forms of fractions better than student learning outcomes that are taught with the Talking stick model.
Lexicalization Patterns and Lexical Inferencing

Lexical inferencing, which is an effective vocabulary development strategy, has been investigated by many researchers in the field of reading to date; however, it has received far too little attention in listening. In this context, this paper explores the role of L1 lexicalization patterns in lexical inferencing in listening. To this end, a listening experiment was carried out with 15 short texts containing 15 target words to both native (N=8) and four groups of non-native (N=32) speakers of English. We included 15 Motion verb target words since Chinese, Korean, and Japanese are known to have different lexicalization patterns for Motion events. The results show that the two groups of Chinese-speaking participants achieved higher inferencing scores than the Korean- and Japanese-speaking participants because English and Chinese have similarities in lexicalization patterns of Motion events. In addition, Korean and Japanese listeners achieved significantly different inferencing scores, although these two languages are categorized into the same typological group. Hence, it is proposed that L1 lexicalization patterns play an essential role in guessing the meaning of target words in listening. We also present some results on the knowledge sources used in the lexical inferencing of the target words.
Quantitative Analytic Framework of Relations among Unstructured Data

Various relations exist in the recent big data and are important in understanding enormous information. We have proposed an analytic framework of relations among unstructured data focusing on miss-classified items in clustering algorithms. The framework has succeeded in finding out somewhat relations among substances. The analytical framework, however, could only perform the subjective analysis but quantitative analysis of those relations.In this paper, we, therefore, have developed the quantitative analytic framework of those relations. We also carried out experiments using news articles and quantitative analysis of the relations among nations following the previous work.
Artificial Intelligence

In recent years, artificial intelligence (AI) has gained attention from policymakers, universities, researchers, companies and businesses, media, and the wide public. The growing importance and relevance of artificial intelligence (AI) to humanity is undisputed: AI assistants and recommendations, for instance, are increasingly embedded in our daily lives. The chapter starts with a critical review on AI definitions since terms such as “artificial intelligence,” “machine learning,” and “data science” are often used interchangeably, yet they are not the same. The first section begins with AI capabilities and AI research clusters. Basic categorisation of AI is presented as well. The increasing societal relevance of AI and its rising inburst in our daily lives though sometimes controversial are discussed in second section. The chapter ends with conclusions and recommendations aimed at future development of AI in a responsible manner.
Modeling and simulation of autonomous quadrotor systems and their onboard sensors

Simulators are valuable tools to evaluate autonomous systems and methods of their control in a cost-effective and safe fashion. There are several implementations of simulators for autonomous systems in the literature. However, they provide limited capabilities in terms of the simulation of specific autonomous methods, for example control, flight, or sensor dynamics. In this paper, we present our design of a simulator for an environment that handles multiple quadrotors with their own set of sensors. We simulate three main sensors to get spatial and environmental information: an Inertial Measurement System that contains a three-axis gyroscope and an accelerometer; a pressure sensor; and ultrasonic sensors. To provide a realistic environment, we implement the thrust force, hub force, total torque, rolling moments, and equations of motion. We provide several attitude controllers, altitude controllers, obstacle detection methods, positioning methods, and flocking methods. We then present a comparison of their performance and behavior. Until the writing of this paper, there were no other simulators that provide this number of various autonomous methods. Moreover, the object-oriented design of our simulator makes it easy to support the addition of any other controller or method. Our simulator provides a comprehensive testbed for current and future methods for autonomous quadrotors.
Mitigation of the effect of Standard Networks Attacks in SSL Encrypted Traffic by Encrypted Traffic Analysis

With increased use of encryption, cyber threat landscape is changing. For general public this transition shifts to more private and safer internet experiences, but at the same time it is a serious concern for security personnel now. For them it hinders control over the traffic moving on their network and poses difficulty in traffic analysis and management. Security personals are interested in knowing how the network is being accessed, whether or not that traffic contains some malware and is safe enough and compliant with your organization’s policies. This project is not about decrypting the encrypted content of the packet’s payload as it will highly degrade network performance plus some advanced encryption algorithms like AES are assumed to be perfect. So the aim of this project is to analyze encrypted traffic and find out some interesting patterns without the need for bulk decryption. The analysis will be based on flow based features and metadata. Encrypted Traffic Analytics maintains the integrity of the encrypted flow and doesn’t affect the privacy of users.
The formulas for sum of products of sequences associated with the metallic means

In this paper, the regularities of convolution of sequences c of Fibonacci numbers {Fn} generated by metallic means and the sum of products of two statistically independent sequences {Fi} and Jn=j∙sin(0.5π(n-j)) are investigated. It is shown that the known closed forms of sums for convolution and product are similar. Attention to the study of the convolution of two sequences of discrete data is associated with the use of this method for statistical signal processing. This problem involves calculating finite sums as discrete analogs of definite integrals. Such a problem is considered solved if the formula for the sum is expressed in a closed form as a function of its members and their number.
Requirements for learning automated information system

Abstract It includes: the authors analyze the requirements applicable to an intelligent learning automated information system to enhance the efficiency of educational process, as well as consider new artificial intelligence used in educational automated information systems. The authors also propose classification of the information technologies used in the educational process to enhance its effectiveness. The set of requirements for learning automated information systems and their implementation example proposed by the authors can be relevant and useful, both for the program designers of automated systems and for teachers using elements of distance learning at a university in the context of education digitalization.
Effects of Lighting Variations in Virtual Learning Environments

The use of virtual reality in education is giving researchers new designing opportunities to create learning environments with characteristics that are difficult or even impossible to implement in the real world. There are plenty of educational, cognitive, architectural and neuroscience theories suggesting that certain environmental aspects can enhance the learning process. Such an example is the variation of lighting conditions, in terms of intensity (high or low luminance) and/or temperature (cold or warm), that have been shown to improve memory, attention and executive functions. In this paper, we investigate the differences between a real and a Virtual Learning Environment (VLE) regarding the effects of the lighting conditions on several cognitive functions while we apply, for the first time, certain lighting theories directly into a University level VLE. The first main outcome of our study is that lighting conditions in VLEs affect students' cognitive functions in a similar way to traditional/physical learning environments, i.e. high luminance enhance memory, attention and executive functions when compared with low luminance. Our results indicate that if we need to test a cognitive or learning hypothesis, we can test it directly into a virtual environment since it gives us similar results/outcomes with those triggered in real one. Moreover, our results demonstrate that exposure to blue (cold) lighting in VLEs increase students' scores, in the given pseudoword test, in comparison with their exposure to red (warm) lighting.
Cognitive models of social integration processes

This article provides an overview of the rationale behind the social integration processes research. It identifies analytical advantages of the cognitive modelling that are based on the conceptual connections existing in the model architecture and convey dynamics of its operation through their causality. This article demonstrates that the cognitive model of the vertical and horizontal social integration ensures consistent social solidarity, however, it does not allow to accelerate the pace of the social integration processes and thus maintains societal anomie in the process of social reproduction. We propose the cognitive model of sustainable development of social reproduction based on empathy and social cohesion accelerators. Integration procedures in this model result from structural organization of the relationship between the primary social reproduction circuit and auxiliary circuits as well as from activating the mechanisms of dynamic interference between the named circuits. Each auxiliary circuit with positive feedback (growth cycle) is fitted with the social cohesion accelerator (growth factor) that ensures targeted amplification of the empathic potential in a given cycle, while all cycles together contribute to a continued long-term effect of the empathic potential amplification required for stable social reproduction in society.
Use of Smart Contracts and Distributed Ledger for Automation

Blockchain as a service has evolved significantly from where it started as an underlying technology for Bitcoin cryptocurrency when introduced in 2008. Realization of the immense opportunities this technology possesses encouraged the development of several other Blockchain solutions such as Ethereum, which focused more on the unique competencies much beyond just the digital currency. In this chapter, the authors provided insights into the unmatchable capabilities of Blockchain to evade cyber-attacks that can facilitate a much-needed push for the scalable operation of autonomous vehicles by providing a safer and trustable ecosystem through smart contracts. The chapter also discusses the integration of Ethereum Blockchain with Confidential Consortium Framework (CFF) to overcome the shortcomings of Blockchain in terms of speed and volume. Towards the end, they talked about some of the modern technologies such as IoT and AI that can be benefitted by Blockchain.
Blended Learning on Mathematical Analysis in Nomotex Digital Learning System

The paper discusses the methodology and technology of teaching the discipline «Mathematical Analysis» using the new Digital Learning System Nomotex (the Nomotex DLS), developed at the Department of «Computational Mathematics and Mathematical Physics» Bauman Moscow State Technical University (BMSTU). A new conceptual model for conducting lectures and practical classes in blended learning is presented. Examples of interactive computer visualization of some mathematical concepts within the discipline «Mathematical analysis» are presented.
Feature Map Retargeting to Classify Biomedical Journal Figures

In this work, we propose a layer to retarget feature maps in Convolutional Neural Networks (CNNs). Our "Retarget" layer densely samples values for each feature map channel at locations inferred by our proposed spatial attention regressor. Our layer increments an existing saliency-based distortion layer by replacing its convolutional components with depthwise convolutions. This reformulation with the tuning of its hyper-parameters makes the Retarget layer applicable at any depth of feed-forward CNNs. Keeping in spirit with Content-Aware Image Resizing retargeting methods, we introduce our layers at the bottlenecks of three pre-trained CNNs. We validate our approach on the ImageCLEF2013, ImageCLEF2015, and ImageCLEF2016 document subfigure classification task. Our redesigned DenseNet121 model with the Retarget layer achieved state-of-the-art results under the visual category when no data augmentations were performed. Performing spatial sampling for each channel of the feature maps at deeper layers exponentially increases computational cost and memory requirements. To address this, we experiment with an approximation of the nearest neighbor interpolation and show consistent improvement over the baseline models and other state-of-the-art attention models. The code is available at https://github.com/VimsLab/CNN-Retarget.
Hidden Entanglement in Gaussian Cluster States

Due to finite squeezing effects, some Gaussian states seem to approximate CV graph states that don’t follow the Gaussian state entanglement structure. We show that, the application of single mode squeezing can reveal that structure.
Cyclic Weighted Median Method for L<sub>1</sub> Low-Rank with Nonlocal for Single color image denoising

A challenging problem in computer vision is how to restore clean image from the noisy one. Low rank decomposition based on Li norm has become a popular solution. However, Since the matrix factorization is nonconvex and Li norm is non-smooth, most methods cannot be truly realized and only suboptimal results can be obtained. The cyclic weighted median method alleviates this problem to a certain extent by solving a series of scalars minimization convex sub-problems. However, this method currently can only rely on similar image sequences to find low-rank subspaces, which is seriously degraded in single image denoising. In this paper, we introduce non-local self-similar priors, and apply the cyclic weighted median method for single image denoising for the first time. Experiments prove that our method is better than all competition methods.
An Interactive Clustered base Recommender system for effective Social Data Analysis

Abstract Providing or proposing appropriate material based on the essence of expertise is the most important and checking topic in recommender schemes. While collaborative filtering (CF) is one of the most visible and well documented procedures used for recommender schemes, we suggest another clustering-based CF (CBCF) technique using an incentivized / penalised user (IPU) model only with the appraisals provided by users, which is therefore easy to actualize. We aim to build this fundamental clustering technique without prior data and at the same time improve the consistency of the recommendation. Towards being real, CBCF and an IPU model are inspired by enhanced suggestion execution by cautiously misusing different tendencies among consumers, such as consistency, recall and an F1 scoring. In specific, we are speaking of a convincing enhancement topic in which we aim to extend the recall (or F1 score comparably) for a certain accuracy. For this reason, users are divided through a few clusters depending on the real data assessment and coefficient of Pearson correlation. Some time since, we are both encouraging in a common category by the tendency for interest by the customers. The test results indicate a tremendous change in the pattern of the CF tract without clustering the reminder or F1 score for certain accuracy.
Project-Process Model for Forming "School–University" Partnerships That Implements the Current Educational Paradigm of the Russian Federation

The article discusses the possibilities of forming partnerships in the “school – university” educational system, the features of the development of extended cooperation on the example of a model made in the IDEF0 notation, which is widely used in world practice. Possible ways to optimize approaches and planning of the educational process that would meet the latest trends of educational standards are analyzed. The problem of the need to introduce new pedagogical practices, which is caused by a change in the functional approach, is considered. Various methodologies and practices of process and project management that have the advantage of multi-level activity decomposition are analyzed. The graphical language and visualization of which helps to form a detailed representation of the overall picture for the full implementation of the tasks set. The purpose of the presented process model is to establish network partner relations between schools and universities. The step-by-step, waterfall process model proposed in the article clearly demonstrates the amount of resources needed and determines the step-by-step process of project implementation. The considered possibility of establishing partnership relations is aimed at attracting potential University applicants. The awakening of academic interest among potential applicants is formed through the use of various activities on the part of the educational institution and the activity team.
A Novel Logical Query Representation Learning Model on Knowledge Graph for Interpretable Knowledge Reasoning

Towards interpretable knowledge reasoning task, this paper aims at understanding the logical form of the given query, which is formulated as the first-order logical rule. Then this paper proposes a novel representation learning model for Interpretable Knowledge Reasoning (IKR), which consists of two procedures: Firstly, all the elements (including entities, relations and query) are embedded into the unified semantic spaces; Secondly, semantic similarity is utilized for measure the relevance between the given query and candidate entities, and then the answer entity set is generated. Experimental results on multi-type tasks demonstrate that our work is experimentally applicable for the first-logic rules with more complex length.
A New Accountable Data Sharing Scheme

At present, utilizing data sharing to promote the development of data and related industries has become more important. Most data sharing schemes use complex cryptography to ensure data security, but the scheme is inefficient, and almost without considering accountability. In this paper, we propose an efficient accountable data sharing scheme based on a trusted hardware device SGX (software guard extensions).