Reviewerly/exHarmony huggingface dataset:
Exploiting an Ontological Model to Study COVID-19 Contagion Chains in Sustainable Smart Cities

The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy to find studies focused on COVID-19 contagion chains. A deep analysis of contagion chains may contribute new findings that can be used to reduce the effects of COVID-19. For example, some interesting chains with specific behaviors could be identified and more in-depth analyses could be performed to investigate the reasons for such behaviors. To represent, validate and analyze the information of contagion chains, we adopted an ontological approach. Ontologies are artificial intelligence techniques that have become widely accepted solutions for the representation of knowledge and corresponding analyses. The semantic representation of information by means of ontologies enables the consistency of the information to be checked, as well as automatic reasoning to infer new knowledge. The ontology was implemented in Ontology Web Language (OWL), which is a formal language based on description logics. This approach could have a special impact on smart cities, which are characterized as using information to enhance the quality of basic services for citizens. In particular, health services could take advantage of this approach to reduce the effects of COVID-19.
Energy‐aware virtual machine consolidation based on evolutionary game theory

Abstract Cloud data center consumes enormous amount of electrical power resulting in high operating cost and carbon dioxide emission. Virtual machine (VM) consolidation strives to reallocate VMs to the minimum number of physical machines dynamically to reduce energy consumption. An approach is proposed to solve consolidation problem of VMs toward optimization of their energy consumption. A novel algorithm is presented, and an energy consumption model is also presented to support the algorithm to find optimal solutions. Experimental results show that energy consumption can be reduced greatly by dynamic VM consolidation. In comparison with other classic algorithms, our proposed algorithm outcomes them and achieves average savings of 42% on energy consumption.
SLAM-based Indoor Navigation in University Buildings

The article discusses the use of SLAM (simultaneous localization and mapping) technology, with the help of which it is possible to build Indoor navigation systems using augmented reality technology, including on mobile platforms. The article also provides an overview of the positive and negative aspects of the SLAM technology its principle of operation for positioning a navigator using augmented reality in a university building within the framework of a student project are reviewed. The already implemented projects on similar topics, but using other technologies are considered their features are described. An example of the implementation of an indoor positioning system in a university using SLAM is given.
A Deep Learning Framework for Image Super-Resolution for Late Gadolinium Enhanced Cardiac MRI

Cardiac magnetic resonance imaging (MRI) provides 3D images with high-resolution in-plane information, however, they are known to have low through-plane resolution due to the trade-off between resolution, image acquisition time and signal-to-noise ratio. This results in anisotropic 3D images which could lead to difficulty in diagnosis, especially in late gadolinium enhanced (LGE) cardiac MRI, which is the reference imaging modality for locating the extent of myocardial fibrosis in various cardiovascular diseases like myocardial infarction and atrial fibrillation. To address this issue, we propose a self-supervised deep learning-based approach to enhance the through-plane resolution of the LGE MRI images. We train a convolutional neural network (CNN) model on randomly extracted patches of short-axis LGE MRI images and this trained CNN model is used to leverage the information learnt from the high-resolution in-plane data to improve the through-plane resolution. We conducted experiments on LGE MRI dataset made available through the 2018 atrial segmentation challenge. Our proposed method achieved a mean peak signal-to-noise-ratio (PSNR) of 36.99 and 35.92 and a mean structural similarity index measure (SSIM) of 0.9 and 0.84 on training the CNN model using low-resolution images downsampled by a scale factor of 2 and 4, respectively.
Interactive Modules for Flight Training: A Review

Do flight students prefer interactive learning materials over more traditional methods?3.2.Is there a difference in learning using 2D and 3D interactive materials?3.3.Is there a learning or preference difference between scripted audio for the learning materials and a more conversational tone? Review of Relevant Literature Characterizations of Generation Y and Generation Z Flight StudentsResearch question 1 asked a two-part question: "How do these students learn," and "what do the students need to support their learning?"To answer the first part of the question, it is essential to understand the makeup of the flight student population (e.g., students enrolled in a 14 CFR 141 Pilot School) as well as the broader Generation Y (also known as "Gen Y" or "Millennial") and Generation Z ("Gen Z") student population in general.Higher education institutions have a mixture of Gen Y and Gen Z students who share many similarities and distinct differences (Nicholas, 2020; Parker & Igielnik, 2020).Additionally, many higher education institutions have Generation X ("Gen X") students who are pursuing degrees (Miller & Mills, 2019); this generation was born between the mid-1960s and early-1980s (Bialik & Fry, 2019;Dimock, 2019).Gen Y includes individuals who were born between the early-to mid-1980s and early-to mid-1990s (Dimock, 2019.Gen Z includes those born in the mid-to late-1990s and the early-to mid-2000s (Dimock, 2019).There are currently over 31,000 Gen X student pilots (aged 40 to 54), over 91,000 Gen Y student pilots (aged 25 to 39), and over 53,000 college-aged Gen Z student pilots (aged 17 to 24) in the United States (FAA, 2020d).Notably, the reported numbers include student pilots from Pilot Schools and other training facilities.Gen X students represent the older portion of enrolled students who may have delayed enrollment in secondary education, work full-time while attending college part-time, or may be
Quizizz as Indonesian Learning Media During The COVID-19 Pandemic

During the COVID-19 epidemic, this research aimed to improve students' fundamental Indonesian knowledge using Quizizz as a game-based learning tool. Description qualitative research technique. The study sample consisted of 33 students from the first semester of the Indonesian Language and Literature Education Study Program. The research instrument is multiple-choice questions about standard and non-standard words and Indonesian writing, which is included in the quizizz website. The data analysis technique was carried out in four stages; 1) data collection, data reduction; data presentation; and concluding. Based on the analysis results, it was found that the quizizz learning media in learning Indonesian can increase student interest in learning and is very effective in increasing knowledge of writing or spelling Indonesian and standard Indonesian grammar. Quizizz also prepares lecturers and students for global education expectations. Online interactive games can help enhance language education and boost digital literacy among the youth, both of which are essential for the fourth industrial revolution.
Applying Objective Quality Metrics to Video-Codec Comparisons: Choosing the Best Metric for Subjective Quality Estimation

Quality assessment is essential to creating and comparing video compression algorithms. Despite the development of many new quality-assessment methods, well-known and generally accepted codecs comparisons mainly employ classical methods such as PSNR, SSIM, and VMAF. These methods have different variations: temporal pooling techniques, color-component summations and versions. In this paper, we present comparison results for generally accepted video-quality metrics to determine which ones are most relevant to video codecs comparisons. For evaluation we used videos compressed by codecs of different standards at three bitrates, and subjective scores were collected for these videos. Evaluation dataset consists of 789 encoded streams and 320294 subjective scores. VMAF calculated for all Y, U, V color spaced showed the best correlation with subjective quality, and we also showed that the usage of smaller weighting coefficients for U and V components leads to a better correlation with subjective quality.
The use of modern technologies in the life and work of teachers before and during Covid virus

Modern technologies are present in almost every part of human`s life and work. One of the most important spheres of human's life is an education. The penetration of modern technologies in this life`s sphere is huge, especially in last several years. These facts purport that teachers, professors and other educators must introduce and teach their pupils to use these technologies on appropriate way. This role is very important and complex. Teachers must find appropriate way to realize balance between "digital" skills needed for pupils to accept the school material, use benefits of "digital" environment and continue to use and develop the imagination and other activities used by older "no digital" generations. So, teachers must continuously learn and become acquainted with modern technologies and their benefits and use them in life and work. The modern technologies are different, but in elementary schools, secondary schools and houses of students, the most frequently used are mobile phones, laptops, tablets, Internet, social networks etc. The main goal of this paper is to present the results of use of noted modern technologies in life and work of teachers. Researches presented in this paper were realized by author of this paper as longitudinal researches and included teachers in different educational institutions, such as elementary schools, secondary schools in Niš and in pupil's homes.
Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE study protocol

Standard procedures aimed at the early diagnosis of breast cancer (BC) present suboptimal accuracy and imply the execution of invasive and sometimes unnecessary tissue biopsies. The assessment of circulating biomarkers for diagnostic purposes, together with radiomics, is of great potential in BC management.This is a prospective translational study investigating the accuracy of the combined assessment of multiple circulating analytes together with radiomic variables for early BC diagnosis. Up to 750 patients will be recruited at their presentation at the Diagnostic Senology Unit of Ospedale Policlinico San Martino (Genoa, IT) for the execution of a diagnostic biopsy after the detection of a suspect breast lesion (t0). Each recruited patient will be asked to donate peripheral blood and urine before undergoing breast biopsy. Blood and urine samples will also be collected from a cohort of 100 patients with negative mammography. For cases with histological diagnosis of invasive BC, a second sample of blood and urine will be collected after breast surgery. Circulating tumour DNA, cell-free methylated DNA and circulating proteins will be assessed in samples collected at t0 from patients with stage I-IIA BC at surgery together with those collected from patients with histologically confirmed benign lesions of similar size and from healthy controls with negative mammography. These analyses will be combined with radiomic variables extracted with freeware algorithms applied to cases and matched controls for which digital mammography is available. The overall goal of the present study is to develop a horizontal data integration classifier for the early diagnosis of BC.This research protocol has been approved by Regione Liguria Ethics Committee (reference number: 2019/75, study ID: 4452). Patients will be required to provide written informed consent. Results will be published in international peer-reviewed scientific journals.NCT04781062.
Real Vector Space and Related Notions

Summary . In this paper, we discuss the properties that hold in finite dimensional vector spaces and related spaces. In the Mizar language [1], [2], variables are strictly typed, and their type conversion requires a complicated process. Our purpose is to formalize that some properties of finite dimensional vector spaces are preserved in type transformations, and to contain the complexity of type transformations into this paper. Specifically, we show that properties such as algebraic structure, subsets, finite sequences and their sums, linear combination, linear independence, and affine independence are preserved in type conversions among TOP-REAL(n) , REAL-NS(n) , and n-VectSp over F Real . We referred to [4], [9], and [8] in the formalization.
Estimation and Analysis of Solar Radiation on Horizontal and Inclined Surface for Baghdad City

The knowledge of the quantity of total solar radiation on horizontal and inclined surfaces is very important in the calculations of heating and cooling loads in architecture and in the design of certain solar energy applications such as photovoltaic and solar collectors. This paper estimates the total solar radiation on inclined surfaces in Baghdad (Lat. 330 21' N 440 14' long and 34m above MSL). A good model was used to estimate hourly total solar radiation on the inclined surface with different elevations (150, 300, 450, 600, 750, 900) from a horizontal surface. The mean hourly, daily and monthly variation of total solar radiation on horizontal and inclined surfaces were drawn and discussed. The optimum tilt angle over a period of a year was drawn and discussed. Linear regression equations correlating the mean hourly solar radiation on an inclined surface with mean hourly solar radiation on a horizontal surface was deduced with high correlation coefficient (0.98 – 0.99).
MODEL HIBRID GENETIC-XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS PADA SEGMENTASI DAN PERAMALAN PASAR

Extreme Gradient Boosting(XGBoost) is a popular boosting algorithm based on decision trees. XGBoost is the best in the boosting group. XGBoost has excellent convergence. On the other hand, XGBoost is a Hyper parameterized model. Determining the value of each parameter is classified as difficult, resulting in the results obtained being trapped in the local optimum situation. Determining the value of each parameter manually, of course, takes a lot of time. In this study, a Genetic Algorithm (GA) is applied to find the optimal value of the XGBoost hyperparameter on the market segmentation problem. The evaluation of the model is based on the ROC curve. Test result. The ROC test results for several SVM, Logistic Regression, and Genetic-XGBoost models are 0.89; 0.98; 0.99. The results show that the Genetic-XGBoost model can be applied to market segmentation and forecasting.
A Stylometric Investigation of Linguistic Styles Based on a Vietnamese Corpus

The role of stylometric methods in linguistics has received increased attention across a number of disciplines in recent years, particularly in forensic linguistics.This study assesses the value of correspondence analysis, a stylometric method, in Vietnamese text analysis.Based on a dataset extracted from VVC (VnExpress Viewpoint Corpus), a 1.3-million-token corpus of Vietnamese opinion articles, linguistic features examined are seven parts-of-speech features to seek relational features characterizing authorial styles.Our focus in the analysis is on feature effects, with the aim to shed light on whether linguistic features of writing styles are consistent across various genders and professions.Seven features altogether produce encouraging results to what is acknowledged to be a difficult problem for Vietnamese language.In addition, we find that when using correspondence analysis for seven linguistic features in the dataset based on authors' gender, conjunctions and verbs perform best.Regarding authors' profession, conjunctions and pronouns offer a striking improvement on stylometric investigation.The discriminating ability was particularly impressive, suggesting that, in a collective sense, parts-of-speech features provide a good set of markers.
Improving importance estimation in covariate shift for providing accurate prediction error

In traditional Machine Learning, the algorithms predictions are based on the assumption that the data follows the same distribution in both the training and the test datasets. However, in real world data this condition does not hold and, for instance, the distribution of the covariates changes whereas the conditional distribution of the targets remains unchanged. This situation is called covariate shift problem where standard error estimation may be no longer accurate. In this context, the importance is a measure commonly used to alleviate the influence of covariate shift on error estimations. The main drawback is that it is not easy to compute. The Kullback-Leibler Importance Estimation Procedure (KLIEP) is capable of estimating importance in a promising way. Despite its good performance, it fails to ignore target information, since it only includes the covariates information for computing the importance. In this direction, this paper explores the potential performance improvement if target information is considered in the computation of the importance. Then, a redefinition of the importance arises in order to be generalized in this way. Besides the potential improvement in performance, including target information make possible the application to a real application about plankton classification that motivates this research and characterized by its great dimensionality, since considering targets rather than covariates reduces the computation and the noise in the covariates. The impact of taking target information is also explored when Logistic Regression (LR), Kernel Mean Matching (KMM), Ensemble Kernel Mean Matching (EKMM) and the naive predecessor of KLIEP called Kernel Density Estimation (KDE) methods estimate the importance. The experimental results lead to a more accurate error estimation using target information, especially in case of the more promising method KLIEP.
Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.
Generation of Virtual Reality Environment Based on 3D Scanned Indoor Physical Space

In this paper, we propose a method for generating a virtual environment in which users can avoid surrounding obstacles and also get haptic feedback from physical objects. By performing plane detection and clustering on the 3D point cloud acquired using a mobile device, and optimizing the layout of virtual objects using a cost function, a virtual environment based on the structure of the physical space is generated. In addition, by performing plane detection with normal constraints, surfaces that the user can touch are presented, and the user can touch objects in the physical space through the virtual environment with haptic feedback. We implemented the proposed method and conducted an experiment in an actual indoor physical space. As the result, we confirmed that a virtual environment was properly generated with many touchable surfaces based on the physical space, and that a user can touch a surface in the physical space.
CoBERT: Scientific Collaboration Prediction via Sequential Recommendation

Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.
EDUCAÇÃO NO ENSINO SUPERIOR EM TEMPOS DE PANDEMIA

Amid the COVID-19 pandemic, the Brazilian Federal Universities have been looking for a solution for non-classroom teaching.The Federal University of ABC was one of the first institutions to implement a remote teaching regime.Thus, the purpose of this experience report is to reflect about this implementation process as a Higher Education professor.During the discipline, given in a non-face-to-face manner, three elements emerged as obstacles: low interaction, time, and socio-emotional issues.These barriers had a great influence on the progress of the work, so that aspects that I point out as learning for the teaching proposals that are being developed in pandemic times emerge, namely: the guarantee of inclusion of students, psychological support to the university community , the expansion of the disciplines beyond the conceptual contents and the provision of sufficient time for the elaboration of proposals that guarantee the quality of teaching for all.