Reviewerly/exHarmony huggingface dataset:
Temperament estimation of toddlers from child–robot interaction with explainable artificial intelligence

Personality estimation is a vital ability for communicating with others. It can help robots that interact with humans to model various human behaviors with a few parameters. Numerous studies have proposed models for estimating human personality from human–robot interaction. However, a limited number of methods have focused on the personalities of toddlers, which are dominated by innate temperaments. In this study, we propose a regression model that estimates the toddler temperament from images acquired by a teleoperated childcare robot named ChiCaRo. We gather a dataset from actual interactions between toddlers and ChiCaRo, and extract features from the data to train the regression model. Moreover, an explainable Artificial Intelligence model known as Shapley additive explanations (SHAP) is employed to understand the estimation tendency of the trained model and to compare the tendency with the temperament definition. The proposed model achieved a mean squared error of 0.024 for the average of all temperament factors. The analysis of SHAP confirmed that the model could reasonably learn the tendency compared to the definition in most temperament factors and suggested the possibility of data bias under a specific temperament factor.
The effectiveness of using edublogs as an instructional and motivating tool in the context of higher education

Abstract The aim of this research is to study the impact of blogs on improving self-reported motivation and learning in a higher education context. A quasi-experimental study was carried out in a social science core unit of the Primary Education degree at the University of Murcia, Spain. The study involved two groups of student teachers, with a total of 101 participants, who created their own blogs as a framework for exchanging their views and sharing their ideas on a variety of contemporary issues related to social science teaching. Data were collected through pre- and post-tests to measure whether the expected changes had occurred as a result of using this digital information platform. The results indicate that the use of blogs helped students to develop their digital literacy, to enhance their social and civic skills, and increase their self-perceived motivation. It is therefore worth updating the current communication processes with regard to online learning with a view to promoting digital access to information and fostering a meaningful learning network through these web resources.
An Approach to Adjust Effort Estimation of Function Point Analysis

This study presents a modified approach to adjust a software development effort estimation. The AdamOptimizer-based regression model is adopted to adjust and enhance the accuracy of effort estimation. This approach is derived into three phases. The first step deals with the logarithmized formula of effort estimation computed by Function Point Analysis and Productivity Delivery Rate. The Adam-Optimizer-based regression model is examined in the second phase, and the ISBSG repository 2020 release R1 is considered as a historical dataset in this paper. Moreover, the K-Fold cross-validation technique is adopted to tunning the training model. In the following phase, all results are evaluated by statistical significance and the goodness of fit measure. Finally, a proposed approach is compared with others: Capers Jones, and the Mean Effort.
Scimago Institutions Rankings for the Assessment of National Education System and Particular University

The article analyzes the results of the Scimago Institutions Rankings for the entire period of its existence and presents the detailed description of the rating methodology and necessary condition for the inclusion in the ranking list, i.e. the publication of more than 100 works by university stuff in the Scopus database. There has been an almost twofold increase in the number of universities in the ten-year ranking lists. The article illustrates the dynamics for a number of countries and analyzes the reasons for their shifts. The competition between China and the United States is demonstrated. It is noted how the adoption of political decisions, such as the “May decrees” in the Russian Federation or projects “211”, “985” in China increase the number of universities in the country participating in the ratings. Such countries as Vietnam, Iran, Indonesia succeeded in multiplying their positions in the university rankings. The article estimates the rate of growth in the number of publications for university in order to maintain its position in the scientometric rankings. The research conducts the analysis of the time required by the ranking agencies to implement and reflect the changes initiated by state competitiveness improvement programs.
Indexed Dataset from YouTube for a Content-Based Video Search Engine

Numerous researches on content-based video indexing and retrieval besides video search engines are tied to a large-scaled video dataset. Unfortunately, reduction in open-sourced datasets resulted in complications for novel approaches exploration. Although, video datasets that index video files located on public video streaming services have other purposes, such as annotation, learning, classification, and other computer vision areas, with little interest in indexing public video links for purpose of searching and retrieval. This paper introduces a novel large-scaled dataset based on YouTube video links to evaluate the proposed content-based video search engine, gathered 1088 videos, that represent more than 65 hours of video, 11,000 video shots, and 66,000 unmarked and marked keyframes, 80 different object names used for marking. Moreover, a state-of-the-art features vector, and combinational-based matching, beneficial to the accuracy, speed, and precision of the video retrieval process. Any video record in the dataset is represented by three features: temporal combination vector, object combination vector with shot annotations, and 6 keyframes, sideways with other metadata. Video classification for the dataset was also imposed to expand the efficiency of retrieval of video-based queries. A two-phased approach has been used based on object and event classification, storing video records in aggregations related to feature vectors extracted. While object aggregation stores video records with the maximal occurrence of extracted object/concept from all shots, event aggregation classify based on groups according to the number of shots per video. This study indexed 58 out of 80 different object/concept categories, each has 9 shot number groups.
Comparative Analysis of Convolutional Neural Network Architectures for Real Time COVID-19 Facial Mask Detection

Abstract The late 2019 outbreak of Coronavirus Disease (COVID-19) had an indelible imprint on the humanity. The world is recovering from the outbreak but there is danger of a second wave of the outbreak. To get rid of the outbreak it is necessary to prevent the viral transmission and it is need of the hour to maintain social distancing and wear masks in public areas. The governments are providing strict guidelines to wear masks in public places. It is not manually feasible to check if people are wearing masks or not. In this paper, process of detecting face masks in public places is automated using Convolutional Neural Networks by performing comparative analysis on Sequential bi-layered CNN, VGG-16 CNN and MobileNetV2 CNN architectures. Among these three architectures MobileNetV2 outperformed with a performance accuracy of 99.2%. The efficient Deep Learning architecture of detecting face masks can be achieved with the help of IoT (Internet of Things) devices and cameras, of those who are not following guidelines in public places. Such a system is very useful in post outbreak period and can be installed in public places such as Railway Stations, Airports, Parks, Schools, colleges, offices etc. to track and ensure wearing of masks by people. The contribution of this paper is not to reel-off the finding from the original paper on Face Mask detection with various architectures rather to provide results on the efficiency of using the MobileNetV2 architecture in comparison with Sequential CNN and VGG-16 architectures for crowd analysis mask detection.
Financial System and SMEs Access to Finance: A Market-Oriented Approach

Abstract The study uses a market-oriented approach to investigate the relationship between the financial system and SMEs access to finance in Nigeria between 1995 and 2019. Both indicators from the capital and money markets are used as independent variables while some macroeconomic variables such as inflation rate, interest rate and exchange rate are also used. The study made use of Auto-Regressive Distributed Lag to explore the long and short-run relationship and the result shows that the capital market has a more significant impact on SMEs access to finance than the money market. Variables such as inflation rate, exchange rate and interest rate all have a significant influence on access to finance by the SMEs. it is recommended that the money market as an important aspect of the financial system in Nigeria should be made to devote more credit to the SMEs sector as it has shown from this study that the bulk of the credit going into the private sector from the money market might not go into the SMEs sector. The inflation rate should also be controlled as well as reducing the lending rate and guide against unreasonable currency devaluation to promote access to finance by the SMEs in Nigeria.
RV-TEE: secure cryptographic protocol execution based on runtime verification

Abstract Analytical security of cryptographic protocols does not immediately translate to operational security due to incorrect implementation and attacks targeting the execution environment. Code verification and hardware-based trusted execution solutions exist, however these leave it up to the implementer to assemble the complete solution, imposing a complete re-think of the hardware platforms and software development process. We rather aim for a comprehensive solution for secure cryptographic protocol execution, which takes the form of a trusted execution environment based on runtime verification and stock hardware security modules. RV-TEE can be deployed on existing platforms and protocol implementations. Runtime verification lends itself well at several conceptual levels of the execution environment, ranging from high level protocol properties, to lower level checks such as taint inference. The proposed architectural setup involving two runtime verification modules is instantiated through a case study using a popular web browser. We successfully monitor high and low level properties with promising results with respect to practicality.
Human Activity Recognition for Multi-label Classification in Smart Homes Using Ensemble Methods

Human Activity Recognition (HAR) plays a vital role in recognizing human activities in smart homes environment. HAR is a prominent research area of great significance due to its applicability in different fields such as security, elderly care, and healthcare, to name a few. The study focused on recognizing human activities of daily living using the ARAS dataset, and for this reason, Bagging and Adaboost ensemble methods were employed. The study involved five popular classification algorithms, Logistic Regression (LR), Random Forest (RF), k-nearest neighbours (KNN), Naive Bayes (NB), and Support Vector Machine (SVM), as a base of ensemble classifiers. The experimental results show that the Adaboost ensemble method outperformed compared to the Bagging ensemble Method in both House A and B through Random Forest (RF) classifier.
Summing up Smart Transitions

Abstract Some of the most significant high-level properties of currencies are the sums of certain account balances. Properties of such sums can ensure the integrity of currencies and transactions. For example, the sum of balances should not be changed by a transfer operation. Currencies manipulated by code present a verification challenge to mathematically prove their integrity by reasoning about computer programs that operate over them, e.g., in Solidity. The ability to reason about sums is essential: even the simplest ERC-20 token standard of the Ethereum community provides a way to access the total supply of balances. Unfortunately, reasoning about code written against this interface is non-trivial: the number of addresses is unbounded, and establishing global invariants like the preservation of the sum of the balances by operations like transfer requires higher-order reasoning. In particular, automated reasoners do not provide ways to specify summations of arbitrary length. In this paper, we present a generalization of first-order logic which can express the unbounded sum of balances. We prove the decidablity of one of our extensions and the undecidability of a slightly richer one. We introduce first-order encodings to automate reasoning over software transitions with summations. We demonstrate the applicability of our results by using SMT solvers and first-order provers for validating the correctness of common transitions in smart contracts.
Take a moderndive into introductory linear regression with R

We present the moderndive R package of datasets and functions for tidyverse-friendly introductory linear regression (Wickham, Averick, et al., 2019).These tools leverage the well-developed tidyverse and broom packages to facilitate 1) working with regression tables that include confidence intervals, 2) accessing regression outputs on an observation level (e.g.fitted/predicted values and residuals), 3) inspecting scalar summaries of regression fit (e.g.R 2 , R 2 adj , and mean squared error), and 4) visualizing parallel slopes regression models using ggplot2-like syntax (Robinson & Hayes, 2019; Wickham, Chang, et al., 2019).This R package is designed to supplement the book "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" (Ismay & Kim, 2019).Note that the book is also available online at https://moderndive.com and is referred to as "ModernDive" for short. Statement of NeedLinear regression has long been a staple of introductory statistics courses.While the curricula of introductory statistics courses has much evolved of late, the overall importance of regression remains the same (American Statistical Association Undergraduate Guidelines Workgroup, 2016).Furthermore, while the use of the R statistical programming language for statistical analysis is not new, recent developments such as the tidyverse suite of packages have made statistical computation with R accessible to a broader audience (Wickham, Averick, et al., 2019).We go one step further by leveraging the tidyverse and the broom packages to make linear regression accessible to students taking an introductory statistics course (Robinson & Hayes, 2019).Such students are likely to be new to statistical computation with R; we designed moderndive with these students in mind.
Adaptive Proportional Integral Observer Design for Interval Type 2 Takagi–Sugeno Fuzzy Systems

In this paper, the problem of fault estimation in systems described by Takagi–Sugeno fuzzy systems is studied. A proportional integral observer is conceived in order to reconstruct state and faults which can affect the studied system. Proportional integral observer can easily estimate actuator faults which are assimilated to be as unknown inputs. In order to estimate actuator and sensor faults, a mathematical transformation is used to conceive an augmented system, in which the initial sensor fault appears as an unknown input. Considering the augmented state, it is possible to conceive an adaptive observer which is able to estimate the whole state and faults. The noise effect on the state and fault estimation is also minimized in this study, which provides some robustness properties to the proposed observer. The proportional integral observer is conceived for nonlinear systems described by Takagi–Sugeno fuzzy models.
DIGITAL COMPETENCY OF A SCHOOL PRINCIPAL: THE 21ST CENTURY SKILL DEVELOPMENT

O. Gordashnikova , Y. Fedorchuk , A. Kuznetsov Institute of Education Management of the Russian Academy of Education (RUSSIAN FEDERATION)
Multi-Classification of Fetal Health Status Using Extreme Learning Machine

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive. When multi-class classification was performed, the accuracy was obtained as 98.12%. The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.
The Design and Evolution of OCB

Abstract We describe OCB3, the final version of OCB, a blockcipher mode for authenticated encryption (AE). We prove the construction secure, up to the birthday bound, assuming its underlying blockcipher is secure as a strong-PRP. We study the scheme’s software performance, comparing its speed, on multiple platforms, to a variety of other AE schemes. We reflect on the history and development of the mode.
Theano: A Greek-speaking conversational agent for COVID-19

Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis.CAs can scale to reach larger numbers of end-users than human operators, while they can offer information interactively and engagingly.In this work, we present Theano, a Greek-speaking virtual assistant for COVID-19.Theano presents users with COVID-19 statistics and facts and informs users about the best health practices as well as the latest COVID-19 related guidelines.Additionally, Theano provides support to end-users by helping them self-assess their symptoms and redirecting them to first-line health workers.The relevant, localized information that Theano provides, makes it a valuable tool for combating COVID-19 in Greece.Theano has already conversed with different users in more than 170 different conversations through a web interface as a chatbot and over the phone as a voice bot.
Digital marketing strategies and international patients’ satisfaction: an empirical study in Jordanian health service industry

The study aims at investigating the role of digital marketing strategies in enhancing international patients’ satisfaction in Jordanian healthcare industry. Specifically, the effect of search engine optimization, social media networks, and e-mail marketing as digital marketing strategies on international patients’ satisfaction. The quantitative approach is adopted and 400 questionnaires are distributed using convenient sample to international patients in different health service organizations (HSO). Only 310 questionnaires were collected with response rate 78%. The study concluded that there is a significant effect of digital marketing strategies on international patients’ satisfaction in Jordanian health service industry. Moreover, each digital marketing strategies (search engine optimization, social media networks, and e-mail marketing) have a significant effect on international patients’ satisfaction in Jordanian health service industry. Moreover, social media networks are the most important strategy which are employed to enhance patients’ satisfaction in HSOs. The study recommends that HSOs have to take the advantages of digital marketing strategies in order to enhance patients’ satisfaction. Practical implications and directions for future research are proposed.
Optimization of Modified Quantum Discord in Projector Space

In a pair of correlated quantum systems a measurement in one corresponds to a change in the state of the other. In the process, information is lost. Measurement along which set of projectors would accompany minimum loss in information content is the optimization problem of quantum discord and is an important aspect of a classical to quantum transition because it asks us to look for the most classical states. This optimization problem is known to be NP-complete and is important because discord is defined through it making it a major obstacle on every computation. The standard zero discord condition helps us move to a stronger measure that addresses the correlated observables, in such a context we show that discord minimizes at the diagonal basis of the reduced density matrices and present an analytical expression of the measure.
Cybersecurity in the Age of the Internet of Things: An Assessment of the Users’ Privacy and Data Security

Cybersecurity has emerged as one of the most critical issues in recent times due to the fact that almost everyone in the world are touched by the influence of Internet or Internet of Things in some way or the other. Personal data of the people shared over the World Wide Web is incredibly large thanks to the mandatory fields that they have to fill while availing any services offered by the government, corporate houses or charitable organizations. Cybersecurity threat looms large both from the custodians of the users’ personal data and the hackers who are out there to take advantage of the loopholes in the systems and processes to steal the critical information and ruin the fortunes and peace of mind of the people through their nefarious activities. Based on a critical and in-depth review of 32 researches on the theme, this article provides a comprehensive insight on users’ privacy and data security risks and concomitant mitigation strategies.
Tensor comprehensions in SaC

We propose a new notation for data parallel operators on multi-dimensional arrays named tensor comprehensions. This notation combines the basic principle of array-comprehensions with syntactical shortcuts very close to those found in the so-called Tensor Notations used in Physics and Mathematics. As a result, complex operators with rich semantics can be defined concisely. The key to this conciseness lies in the ability to define shape-polymorphic operations combined with the ability to infer array shapes from the immediate context. The paper provides a definition of the proposed notation, a formal shape inference process, as well as a set of re-write rules that translates tensor comprehensions as a zero-cost syntactic sugar into standard SaC expressions.