Reviewerly/exHarmony huggingface dataset:
Leadership for Education in a Digital Age

The article deals with some pedagogical aspects of the digitalization of education. The idea of a digital teacher as the leader of digital educational activity is in the focus of the theoretical analysis presented in the article. It is noted that knowledge and information, together with sustainability initiatives, organizational and technological innovations, start-ups, next-practice platforms, smart solutions, systems, technologies and instruments, etc., have become the present-day key drivers of innovative educational platforms, online courses, digital textbooks, etc. influence on the transformation of the educational environment. Education is becoming more personalized. The content of teaching and learning is being changed radically according to its new values, meanings, orientations, as well as educational and pedagogical discourses. It is stressed in the article that teacher’s digital skills and competencies should be identified as the key professional pedagogical skills and competencies for his or her life-long learning, professional development and professional growth due to the challenges of the 21 st -century knowledge, information and innovations society and education. As it is stated in the article, to fulfill a system of professional-pedagogical and educational functions more effectively and to guarantee the optimal implementation of information and communication technologies. It is quite necessary for teachers to have adequate skills and competencies that characterize his or her digital professional culture of teaching.
A Systematic Review Web Content Mining Tools and its Applications

In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data.Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society.Because of such availability of huge information, utilization of the required information becomes more challenging.This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM.The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction.The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail.WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users.Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine.It guides the search engine to provide better productive results for every search based on their importance.The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.
UNDERSTANDING CYBERCRIMES AS PRACTICE IN CAMEROON

The impact of cyber-crimes is one, which the world’s economy cannot claim a blind eye to. This is evident as daily; institutions around the world and Cameroon in particular are suffering from this criminal practice. Nonetheless, stamping it out looks almost impossible and it leaves us asking if there are any steps taking to realize it complete eradication. Hence, this paper seeks to look at the practice of cyber-crimes, which permits us to look at the different forms of cyber-crimes as amongst many other aspects in Cameroon. It does so by reviewing existing literature relating to cyber-crimes. The ensuing results reveal the various different forms of cyber-crimes in Cameroon and what motivates most perpetrators of the crime.
Some results on certain properties of intuitionistic fuzzy sets

An operator is a special symbol for performing a specific function. Several operators like modal operators, topological operators, level operators, etc. have been defined over intuitionistic fuzzy sets. At the same time, so many operations were introduced and studied. The key objective of this paper is to study those operations over intuitionistic fuzzy sets and to investigate their properties. Some new results are obtained and proved.
Computer Security at the Forefront of Emerging Topics in Computing

This installment of Computer’s series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Emerging Topics in Computing.
Intervenções psicológicas frente à COVID-19: uma revisão integrativa da literatura

Scientific productions focused on psychological interventions performed in relation to COVID-19 were analyzed.The descriptors ("Psychological Interventions" AND "COVID-19"), ("Public Health" AND "Psychology" AND "COVID-19") were searched in Scielo, Science Direct, Lilacs, Pubmed and Capes's Portal databases.) and ("Community Health" AND "COVID-19" AND "Psychology").Twelve articles that met the inclusion criteria were selected, five empirical studies, two brief communications, three literature reviews and two experience reports, all from 2020, one Brazilian and the other international.It was evidenced the prevalence of remote interventions, among them psychoactive and therapeutic groups in the perspective of cognitive-behavioral therapy.Interventions focused on stressors, adaptation difficulties, as well as sleep regulation, emotional regulation, guilt, fear of being discriminated against and helplessness stood out.We suggest the development of research aimed at the elaboration of intervention protocols, respecting the phases of prevention, mitigation, prevention, response and reconstruction, as directed by Psychology in Risk and Disaster Management.
Recovering Spatial Structure in Spatio-Temporal Visual Statistical Learning

While research on visual statistical learning (VSL) is divided into two distinct lines investigating the learning of temporal and spatial regularities separately, such a distinction does not hold in real-world environments, where the two types of regularities are perpetually intertwined as spatial patterns unfold over time. We investigated the interplay between spatial and temporal regularities in a new VSL paradigm, in which spatially defined chunks were continuously moving in and out of the observer's view. First, participants passively observed a stream of stimuli in a task-free setup. Scenes composed of novel shape-pairs (oriented horizontally, vertically, or diagonally) were presented through a 3x3 grid aperture without between-pair segmentation cues. Periodically, the whole scene within the aperture moved a grid to a direction so that some shapes moved out and others moved in the aperture, thus showing particular pairs only partially sometimes. Subsequently, participants completed a 2AFC familiarity-task judging between real and foil pairs. In Experiment 1 (n=20), participants showed the same level of correct responses in this new setup as in classical spatial VSL experiments (M=61.11%, SE=3.19, p=0.003, BF=16.31). In Experiment 2a (n=73) and 2b (n=75), we introduced different levels of spatial noise by biasing the ratio between specific movement directions. More horizontal movement led to significantly more partial presentations (i.e. more noise) of horizontal than vertical pairs, and vice versa. Despite strong differences in the spatial conditional probabilities within the different types of pairs due to these manipulations, learning of pairs was not selectively hindered, observers performed equally well with all pair types. Evidently, observers can rely on the high temporal coherence of the evolving scenes to recover and represent the spatial structure regardless of spatial noise, and their learning is not a direct consequence of exposure frequency.
DenseNet-CNN Architectural Model for Detection of Abnormality in Acute Pulmonary Edema

Acute pulmonary edema (EPA) is a condition of emergency respiratory distress that results from the sudden and rapid build-up of fluid into the lungs. Rapid screening of EPA patients is necessary so that radiologists can make the prognosis as early as possible. In addition, reliance on the expert's knowledge of reasoning also hinders the diagnostic process. This research proceeded by developing an architectural model for machine learning systems with a deep learning approach. With the concept of representative learning, the denseNet-CNN algorithm connects each layer to another utilizing a feed-forward. The data used is Image CXR-14 that is specifically labeled pulmonary edema pathology. Each CXR-14 image is 1024 × 1024 in size with a value of 8 bits grayscale. The architectural model development consists of several stages: the preparation stage, data resampling, data training, and data testing. Optimizer parameters used are Adam's optimizer, a learning rate of 0.0001, weight decay = 1e-5, and the loss used is binary cross-entropy. The resulting mean of AUROC analysis showed that the sensitivity value of the 10% dataset was 71.493%, and the specificity value of 10.011% was obtained at the second hold of the k-fold cross-validation method after holdout validation so that the resulting model was valid. The detection system developed from the denseNet-CNN model is to expectedly help radiologists identify abnormalities in CXR images quickly, precisely, and consistently. The development of the denseNet CNN model is in the form of a heatmap visualization by localizing the features one is watching. With localization in the heat map form, pathological abnormalities detection of PEA is easier to do and recognize.
Impact of Socio-Economic Development on Sustainable Business Development Among Small and Medium Scale Businesses in Nigeria

This study is undertaken to investigate the impact of socio-economic development on sustainable business development among small and medium scale business in Nigeria. The significance of socio-economic development in achieving sustainable business development among small and medium scale business, especially in a developing country like Nigeria, cannot be over-emphasized. This study employed a survey research design as data were elicited from the respondents who agreed to fill out the questionnaires. In this study, several socio-economic development indicator variables such as Self-reliance in Development, Policy Delivery Mechanism, and Access to Health Facilities were employed among others. The findings elicited from this study revealed that socio-economic development recorded a positive impact on sustainable business development among small and medium scale business in Nigeria.
Learning Motion Priors for 4D Human Body Capture in 3D Scenes

Recovering high-quality 3D human motion in complex scenes from monocular videos is important for many applications, ranging from AR/VR to robotics. However, capturing realistic human-scene interactions, while dealing with occlusions and partial views, is challenging; current approaches are still far from achieving compelling results. We address this problem by proposing LEMO: LEarning human MOtion priors for 4D human body capture. By leveraging the large-scale motion capture dataset AMASS, we introduce a novel motion smoothness prior, which strongly reduces the jitters exhibited by poses recovered over a sequence. Furthermore, to handle contacts and occlusions occurring frequently in body-scene interactions, we design a contact friction term and a contact-aware motion infiller obtained via per-instance self-supervised training. To prove the effectiveness of the proposed motion priors, we combine them into a novel pipeline for 4D human body capture in 3D scenes. With our pipeline, we demonstrate high-quality 4D human body capture, reconstructing smooth motions and physically plausible body-scene interactions. The code and data are available at this https URL.
Exploiting Learned Policies and Learned Heuristics in Bounded-Suboptimal Search

Machine Learning (ML) has made significant progress to perform different tasks, such as image classification, speech recognition, and natural language processing, mainly driven by deep learning. Also, ML algorithms, through learning policies or heuristics estimates, have demonstrated potential for solving deterministic problems that would usually be solved using search techniques. Nevertheless, in solving a search problem with purely learning techniques, it is not possible to deliver guarantees regarding the quality of the solution. This research explores how a learned policy or heuristic can be integrated with a bounded-suboptimal search algorithm using Focal Search, sorting the FOCAL list using the concept of discrepancies to speed up the search. On the experimental side, we train a simple neural network as a learned policy and the DeepCubeA as a learned heuristic for the 15-puzzle domain. The results show that a learned policy or heuristic can reduce, at least, one order of magnitude, the expansions than WA* with the same bound and deliver better solution quality.
Symptom‐based improvement recommendations

Abstract For this paper, we have identified a list of 32 characteristic symptoms that is often discussed during assessments, generated by two experienced assessors who had undertaken maturity assessments in more than 300 companies. We then use cognitive mapping and the “five whys” technique to look beyond the symptoms and reveal the underlying problems or causes of the problems. Following that, we evaluate our findings through the design and evaluation of a web‐based tool where users can score statements based on a formulation of the symptoms. This enables us to recommend to users the areas where they probably need to improve. We designed the tool in three learning cycles of design evaluation and ended up in a summative evaluation where we compared the outcome of using the website tool with a CMMI maturity assessment. We conclude that a systematic quest for symptoms coupled with scoring statements based on the symptoms can point to improvement areas. However, we conclude that doing so is no substitute for a maturity assessment; the scoring of statements cannot reveal the maturity of the organization, but it can quickly and easily point in a useful direction and provide recommendations for going in that direction.
Transformers in the loop: Polarity in neural models of language

Representation of linguistic phenomena in computational language models is typically assessed against the predictions of existing linguistic theories of these phenomena.Using the notion of polarity as a case study, we show that this is not always the most adequate set-up.We probe polarity via so-called 'negative polarity items' (in particular, English any) in two pretrained Transformer-based models (BERT and GPT-2).We show that -at least for polaritymetrics derived from language models are more consistent with data from psycholinguistic experiments than linguistic theory predictions.Establishing this allows us to more adequately evaluate the performance of language models and also to use language models to discover new insights into natural language grammar beyond existing linguistic theories.This work contributes to establishing closer ties between psycholinguistic experiments and experiments with language models.
Comparative Study of Data Matrix Codes Localization and Recognition Methods

We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical "L" shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image processing techniques such as edge detection, adaptive thresholding, or connected component labeling are used to identify the Finder Pattern. The recognition rate of the compared methods was tested on a set of images with Data Matrix codes, which is published together with the article. The experimental results show that methods based on adaptive thresholding achieved a better recognition rate than methods based on edge detection.
Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network

The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short-Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull-up, 50 m sprint, and 1000 m long-distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.
Using Jupyter Notebooks with GraphQL and the PID Graph

Two weeks ago DataCite announced the pre-release version of a GraphQL API [Fenner (2019)]. GraphQL simplifies complex queries that for example want to retrieve information about the authors, funding and data citations for a dataset with a DataCite DOI. These connections together form the PID Graph [Fenner &amp;amp;
Internet Access as an Independent Human Right: a Palpable Consequence of the Covid-19 Pandemic?

The onslaught of the COVID-19 pandemic has established a new world order which is heavily reliant upon the internet for public access to health, education, employment, and recreation among other services. Furthermore, the growing dependence on technology has also been crucial in the battle against the pandemic. However, despite the irreplaceable utility of the technology, state practices have remained divergent in this field. While a multitude of nations have recognized internet access as a fundamental right, many nations imposed unjustified restrictions on their citizens during the pandemic and some even failed to provide affordable access to internet facilities, which has proven to be detrimental to the realisation of basic individual rights. As the world makes a transition from a pandemic, the intensifying reliance upon the internet has given rise to a need to recognise access to the internet as an independent human right. In light of the above, the present paper seeks to elucidate how access to the internet has emerged as a necessity in the backdrop of a pandemic and examine how the differing state responses in this context have been violating individual rights. The paper also delves into how internet access is being dealt with under the existing international and domestic regime. Further, the authors attempt to philosophically justify a human right to internet access and politically conceptualise the same, and argue in favour of recognition of internet access as an independent human right under international law.
Distance Learning Of The Russian At The Technical University In The Pandemic

The article is devoted to the issues of distance learning of the Russian language.Various electronic resources are presented: specially created textbooks, manuals, well-known platforms and programs.The disciplines studied at a polytechnic university such as "Russian language and Speech Culture", "Theory and Practice of Intercultural Communication", "Russian as a Foreign Language" are considered in this article.The electronic distance course on the speech culture and the Russian language includes video lectures on the Zoom electronic platform and the placement of educational methodological complex blocks on the "Electronic University" platform.It is noted that an electronic manual, which is a hypertext document, was actively used for conducting lectures.The use of modern information technologies in the distance study of intercultural communication is aimed at developing the professional competence of a master student by mastering the main technological approaches to designing his own virtual educational environment.The course for international postgraduates is developed on the Moodle platform, where the teacher has the opportunity to independently create a course taking into account the individual characteristics of the group and each student.It is reported that distance learning of the Russian as a foreign language for the students of the preparatory faculty in the pandemic was carried out using distance courses, Skype, Zoom, Dingtalk, Google services.Finally, it is concluded that the teacher needs to learn how to correctly use the reflexive potential of the network space in order to create a flexible model of professionally oriented distance learning.
Influence of Participative and Achievement Oriented Leadership Styles on Employee Job Satisfaction in Commercial Banks in Kenya

Globalization has ushered immense change in today's corporate world, leading to emerging dimensions in the study of leadership styles under path-goal theory in use at organizational level and the level of satisfaction of employees.This has led to setting of new parameters that organizations must meet in order to achieve competitiveness (Malik, 2013).There are many important reasons why organizations should be concerned with employee job satisfaction (Senel & Senel, 2015).This forms an important reason why evaluation of employee job satisfaction has today become a key aspect of human resource management.Organizations need, therefore, to ensure high levels of employee job satisfaction to inculcate an atmosphere of enhanced performance levels and creation of quality customer relations.Mohammad, Nazari and Mehrdad (2013) attempted to establish the relationship between the leadership styles in use in organizations and the job satisfaction of employees in Abuja Nigeria.The study correlated application of path-goal theory by organizational leadership and the levels of job satisfaction.A positive relationship was established highlighting the important role played by leadership style in enhancing employee job satisfaction.Also, Akhigbe, Finelady and Felix (2014) conducted a study on the effects of achievement-oriented leadership style on job satisfaction of selected commercial banks in River State in Nigeria.From the results of the latter study, it was confirmed that achievementoriented leadership style positively and significantly enhances employee job satisfaction.This again highlights the fact that leadership style in application in an organization is important in determining job satisfaction.