Reviewerly/exHarmony huggingface dataset:
Enveloping analysis of data from educational platforms by area of knowledge

In the presence of the pandemic caused by covid-19, digital platforms were implemented to continue with the education processes from isolation. In the case of higher education that imparts various areas of knowledge, different results were identified in the use of the same digital platforms. In response to this, it was necessary to analyze how the technological characteristics implemented at a higher level in Mexico have influenced the participation, attendance and evaluation of students by area of knowledge. Therefore, a data envelopment analysis was performed to measure the efficiency of the inputs and outputs described by the literature; the limitations of the research was circumscribed by the amount of data to be analyzed. As a result, it was identified that the greater the number of features added to the digital platforms, the better levels of participation, attendance and evaluation of students by academic area. Likewise, when considering future lines of research, it is considered ideal to elaborate on the various methods of digital evaluation, such as the digital portfolio and feedback.
Predictors in the satisfaction of managing virtual resources: the role of the learner

With the spread of SARS-CoV-2, all universities were forced to implement the virtual modality in their curricula so as not to paralyze the educational system. Likewise, the Universidad Nacional Mayor de San Marcos, located in the city of Lima, Peru, continued with non-face-toface classes through virtual resources to help its students in the learning process. Therefore, this research aimed to determine the factors of the student role that influenced the satisfaction of managing virtual resources. The study procedure included the collection of 184 surveys through Google Forms and the analysis of validity, structural model, reliability and hypothesis tests using the non-parametric Spearman’s Rho test. Some of the limitations of the analysis were due to generational differences and socioeconomic and geographic variables of the participants. This paper found, according to the order of the highest score of the correlation coefficient, that the factors student-content interaction, gamified participation, motivation, self-efficacy in internet and computing, and perceived usefulness predicted the satisfaction of managing virtual resources in students. It was concluded that the five independent variables were significantly correlated in the dependent variable.
Evaluation of self-regulated learning in university students. Analysis from online education

A self-regulated student is guided by task-oriented strategies, seeks to achieve his personal goals, and monitors his or her behavior in terms of his goals. This paper presents the results of a quantitative study whose purpose was to describe the components of self-regulated learning used by students in their online learning processes and to determine if there are significant differences in the way that these components are used. In this investigation, a questionnaire was applied to 306 students of online courses, and the data obtained were processed using descriptive statistics and ANOVA of one factor of repeated measures. The results showed that most of the factors and strategies of self-regulates learning are used in an acceptable way; in addition, significant differenceswere found of the motivational component and in the strategies of the behavioral and contextual component. The findings of the study provide instructional designers with information on the aspects that can be emphasizes to motivate students, which would contribute to the development of different types of skills and methods to self-regulate their learning, training that will allow them to advance more successfully through their education, regardless of the modality in which they work.
Virtual Gamification and Problems of Students’ Social Interaction

The article concerns the systemic problem of students’ social interactions in the context of digitalization of education and usage of virtual gamification technologies in pedagogical process. The authors conducted an interdisciplinary analysis of influence of the phenomena and conditions of virtual gamification on the “vertical” and “horizontal” processes of students’ social interactions, which determine constructive or destructive vectors of their socialization and professionalization in a digital society. The authors concluded that professional use of gamification pedagogical technologies can significantly reduce the social risks of digitalization of education and increase the level of safety of communicative and educational environment.
Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review

Context: In recent years, the recognition of human activities has become an area of constant exploration in different fields. This article presents a literature review focused on the different types of human activities and information acquisition devices for the recognition of activities. It also delves into elderly fall detection via computer vision using feature extraction methods and artificial intelligence techniques. Methodology: This manuscript was elaborated following the criteria of the document review and analysis methodology (RAD), dividing the research process into the heuristics and hermeneutics of the information sources. Finally, 102 research works were referenced, which made it possible to provide information on current state of the recognition of human activities. Results: The analysis of the proposed techniques for the recognition of human activities shows the importance of efficient fall detection. Although it is true that, at present, positive results are obtained with the techniques described in this article, their study environments are controlled, which does not contribute to the real advancement of research. Conclusions: It would be of great impact to present the results of studies in environments similar to reality, which is why it is essential to focus research on the development of databases with real falls of adults or in uncontrolled environments.
Many-qubit protection-operation dilemma from the perspective of many-body localization

What is an optimal parameter landscape and geometric layout for a quantum processor so that its qubits are sufficiently protected for idling and simultaneously responsive enough for fast entangling gates? Quantum engineers pondering the dilemma might want to take a look on tools developed for many-body localization.
Einstein-Podolsky-Rosen steering in symmetrical Gaussian states

We have explored quantum Einstein-Podolsky-Rosen steering in symmetric two-mode Gaussian states using Gaussian and non-Gaussian measurements. For Gaussian measurements, we show that steering between the output modes of a symmetric beamsplitter is possible regardless of purity when a threshold input-state quadrature variance compression is achieved. Using the non-Gaussian operators introduced in [1] we show that non-Gaussian measurements can outperform Gaussian measurements for symmetrical states. We also analyze the possibility of asymmetric measurements setups made possible by non-Gaussian measurements and provide examples where such asymmetry is optimal for revealing steering. [1] Ji, SW., Lee, J., Park, J. et al. Quantum steering of Gaussian states via non-Gaussian measurements. Sci Rep 6, 29729 (2016). https://doi.org/10.1038/srep29729
Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation

Extreme Learning Machine (ELM) is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation (DA) in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation (DELM-CDMA) for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation (CDMA) into the hidden layer of ELM to reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis (LDA) is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation (DKELM-CDMA) as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches.
Palynology, Paleoenvironment and Stratigraphy Relationship of Tungan Buzu Hill with Adjacent Valley Gwandu Formation, Sokoto Basin, Northwestern Nigeria

Palynological study of Tungu Buzum Hill was investigated with a view towards determining the age of the outcrop, compare its miospore content with the adjacent Tungu Buzu Valley of carbonaceous shale, their stratigraphic relationship and paleoenvironment of deposition of the litho-sequence. Field study involved litho-description and logging achieved by noting the rock type, mineralogical composition, bed thickness, texture, sorting, roundness, structure, fossil content and diagenetic effect. Palynological slide preparation involved decarbonisation, digestion, sieving, maceral separation using zinc bromide, mounting on slide and petrographic analysis. The litho-sequence is composed sequentially upward of claystone, siltstone, indurated silty claystone, mudstone and ferruginized ironstone characterized by loadcast, fracture, lithification and bioturbation. Palynologically, the litho-section exhibits few marker forms such as Belskipollis elegans, Peregrinipollis nigericus and Retibrevitricolporites obodoensis depictive of early Miocene age. The paleoenvironment was deduced based on the occurrence of peridinacean forms such as Andalusiella sp., Senegalinium sp., Lejeuncysta diversiforma and Paleocystodinium golzowense indicative of marginal marine, while deeper marginal marine forms of gonyaulacacean phytoplanktons present are Micrhystridium sp., Canningia capillata and Dinogymnium sp. There are similarities in palynomorph assemblages of this study and Tunga Buzu Valley section in terms of age and similarity to Gwandu Formation though varied in paleoenvironment in the valley litho-section, marked by continental to marginal marine settings. Field relationship suggests that the Tunga Buzu Hill is stratigraphically overlying the Tunga Buzu Valley, separated by a fault. The Tunga Buzu Hill section represents the footwall while the Tunga Buzu Valley is situated within the hanging wall of a normal fault structure.
Stunting Sistem Cerdas Mendiagnosa Stunting pada Anak Menggunakan Mesin Inferensi

Stunting is an unresolved nutritional problem in Indonesia. Stunting is a physical growth disorder characterized by a decrease in growth speed and is the impact of nutritional imbalances. Stunting will cause long-term impacts, namely disruption of physical, mental, intellectual, and cognitive development. Children who are stunted until the age of 5 years will be difficult to repair so that it will continue into adulthood and can increase the risk of offspring with low birth weight. Low economic factors cause people to think twice about consulting on stunting with doctors or nutritionists. In addition, the reluctance to come to the Puskesmas is an indicator of the low level of public awareness of children's health. Whereas the Puskesmas is a place that provides information about the problem of stunting. This is one of the causes of the delay in reducing the prevalence of stunting. An expert system is a system that seeks to adopt human abilities or knowledge into computers, so that computers can work in solving a problem like an expert or someone who has knowledge in a particular field. Utilization of expert systems with certainty factor methods and forward chaining inference engines will greatly help facilitate the community in making an early diagnosis of stunting in children, so that people can take initial treatment if their child is diagnosed with stunting. With this facility, of course, the risk of increasing stunting in children can be reduced and even prevention is carried out.
Development of a PC-based sign language translator

While a hearing-impaired individual depends on sign language and gestures, non-hearing-impaired person uses verbal language. Thus, there is need for means of arbitration to forestall situation when a non-hearing-impaired individual who does not understand the sign language wants to communicate with a hearing-impaired person. This paper is concerned with the development of a PC-based sign language translator to facilitate effective communication between hearing-impaired and non-hearing-impaired persons. Database of hand gestures in American sign language (ASL) is created using Python scripts. TensorFlow (TF) is used in the creation of a pipeline configuration model for machine learning of annotated images of gestures in the database with the real time gestures. The implementation is done in Python software environment and it runs on a PC equipped with a web camera to capture real time gestures for comparison and interpretations. The developed sign language translator is able to translate ASL/gestures to written texts along with corresponding audio renderings at an average duration of about one second. In addition, the translator is able to match real time gestures with the equivalent gesture images stored in the database even at 44% similarity.
A learning-based approach to breast cancer screening using mammography images

&lt;p&gt;The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.&lt;/p&gt;
SENIOR HIGH SCHOOL TEACHERS’ CHALLENGES AND COPING STRATEGIES IN TEACHING LITERATURE IN ONLINE ENVIRONMENT

The teaching-learning process in Indonesia was mostly done online due to the Covid-19 pandemic. This sudden shift caused challenges for EFL teachers. Aside from providing linguistic lessons, EFL teachers also struggled in providing literature lessons, as it had distinct ways of teaching from linguistics. However, only a few studies researched the teachers’ challenges in teaching literature online and the ways they solved them. Therefore, this study aims to explore the teachers’ challenges in teaching literature in an online environment and their strategies to deal with them. This study used a narrative inquiry method and the data were gathered with a semi-structured interview from senior high school teachers. the results showed that the teachers faced challenges with their technology uses, including internet, websites, applications, and gadgets; students, including their internet, understanding, and participation; and subject delivery, including material adjustments, students’ interests, and accessible activities. Teachers’ strategies were: having asynchronous classes, posting materials ahead, utilizing WhatsApp, helping students’ technology experience, assisting their online learning, providing accessible learning activities, using familiar literary works, and giving less demanding activities. This study can help the teachers to identify their challenges in teaching literature in an online environment and find the solutions.
Mastering the power of freedom of information for social justice

Empowering people with the right to access information is a key to ensuring social justice. In this chapter, we share our journey of how the Indian Right to Information Act has become a powerful tool for people, especially vulnerable and marginalised communities, in their pursuit to secure delivery of basic rights and services. We outline, through multiple examples, how the transparency law has been used to prevent capture of social welfare benefits and deliver social justice. We also share examples from other countries and research findings that establish the positive cascading effects of transparency on social justice and equity.
A New Feature-Based Method for Similarity Measurement under the Linux Operating System

This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper, the performance of the new feature-based algorithm is compared against the performance of seven ontology-based algorithms adapted to Arabic. The results of the evaluation and comparison experiments show that the new proposed algorithm outperforms the adapted word similarity algorithms on the Arabic word benchmark dataset. The proposed algorithm will be included in the AWN-similarity which is free open-source software for Arabic.
Blockchain basic: Definitions, key concepts and characteristics

This introductory chapter gives an overview and description of key issues and concepts related to blockchain. They include crypto-currency, permissionless and permissioned chains, public and private blockchain networks, centralized and decentralized ledgers, distributed ledger technology, smart contracts, blockchain as-a-service and initial coin offerings (ICOs). It discusses the origin, evolution, and trajectory of blockchain. It also touches on the diverse value proposition of blockchain across various industries. Also, the roadmap of the book will be discussed.
The Metalogic of Ground: Pure and Iterative Systems

Abstract I develop a graph-theoretic model theory for pure and iterative grounding logics.
Students' perception of digital tools used with online teaching methodologies in a pandemic context: a case study in Northern Chile

In 2020, in the context of the COVID-19 pandemic, it is of interest to understand how students perceived the access, use, and utility of digital tools in online teaching. This study provides an insight into the needs of students in adjusting to online teaching.This study presents the methodological design of the course “Industrial Hygiene I,” part of a technology-based engineering bachelor’s degree from Universidad Católica del Norte (Chile). Once the theoretical framework was established, three surveys were designed throughout the course to measure students’ perceptions of the access, use, and utility of digital tools used in online teaching during the pandemic. Thirty-five students participated in this quantitative study.Following a data analysis, it was observed that 46% of students use their cell phones to access virtual classes and more than 50% are dissatisfied with the internet connection; therefore, it was necessary to adapt the learning methodology. After that, more than 50% of the students said that they preferred a flipped classroom over traditional methodology. In addition, the quality of the course was not affected by using the internet (60%), and the methodology generates advantages in terms of learning (72%).These findings have several implications in both the design of different subjects in the engineering degree and the practice of different learning methods. Future hybrid teaching designs require taking up the challenges of both improving technology-supported teaching and assessing competence in the same context.
Personalized sentence generation using generative adversarial networks with author-specific word usage

The author-specific word usage is a vital feature to let readers perceive the writing style of the author. In this work, a personalized sentence generation method based on generative adversarial networks (GANs) is proposed to cope with this issue. The frequently used function word and content word are incorporated not only as the input features but also as the sentence structure constraint for the GAN training. For the sentence generation with the related topics decided by the user, the Named Entity Recognition (NER) information of the input words is also used in the network training. We compared the proposed method with the GAN-based sentence generation methods, and the experimental results showed that the generated sentences using our method are more similar to the original sentences of the same author based on the objective evaluation such as BLEU and SimHash score.
Living Without Beth and Craig: Definitions and Interpolants in Description and Modal Logics with Nominals and Role Inclusions

The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating implicit definability is valid. Thus, the CIP and PBDP reduce potentially hard existence problems to entailment in the underlying logic. Description (and modal) logics with nominals and/or role inclusions do not enjoy the CIP nor the PBDP, but interpolants and explicit definitions have many applications, in particular in concept learning, ontology engineering, and ontology-based data management. In this article we show that, even without Beth and Craig, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as ALCO, ALCH and ALCHOI and corresponding hybrid modal logics. However, living without Beth and Craig makes this problem harder than entailment: the existence problems become 2ExpTime-complete in the presence of an ontology or the universal modality, and coNExpTime-complete otherwise. We also analyze explicit definition existence if all symbols (except the one that is defined) are admitted in the definition. In this case the complexity depends on whether one considers individual or concept names. Finally, we consider the problem of computing interpolants and explicit definitions if they exist and turn the complexity upper bound proof into an algorithm computing them, at least for description logics with role inclusions.