Reviewerly/exHarmony huggingface dataset:
Literacy Problems in the New Digital Reality (by the Example of Schoolchildren)

The article centers on the problems of reading in the new digital reality associated with the appearance of texts of a new digital format and the changed reading practices. This issue becomes topical, as digital reading starts to prevail over traditional which affects individual readers and the educational system. We conducted a pilot study of the reading skills of fourth-grade students (106 people) and analysed their reading practices. Testing according to the method of L. Yasyukova showed that most pupils (70%) have incomplete reading skills. Our respondents spend more time on digital reading (searching for information and communicating with friends on the Internet) as compared to reading paper books. In classes where pupils spend more time on the Internet, a larger percentage of pupils showed incomplete reading skills. Based on the data obtained, we formulated a hypothesis that requires further verification. The time spent by pupils on the Internet and the amount of digital reading may affect the level of understanding of texts on paper.
A QoS-Oriented Novel Optimization Schemes for Web Service Composition for Improved Healthcare

Due to massive increase in Web service in order to provide users an improved service it is necessary to develop a dynamic approach based on multi constraint Quality-of-Service (QoS) driven web service composition model to recommend suitable services to intended users is a big challenge. QoS plays a prominent role in selecting a Web Service. QoS is mainly determined by several non-functional parameters like availability, reliability, robustness, integrity, accessibility, interoperability, accuracy and security. It is evidenced based on experimentation that the proposed Improved Rider Optimization scheme achieves near optimal solution where group of riders racing towards a target location and attains adaptability and scalability by making a more confident web service selection with QoS prediction.
Impact of School from Home in Teaching and Learning English for Young Learners

The objectives of this study were to examine the impact of school from home in teaching and learning English for young learners. This study employed a case study approach, which is an embedded single-case design. A set of questionnaires was distributed to twelve young learners in the fourth grade of an elementary school in Singaraja and one English teacher. The young learners in this study were selected with purposive sampling, where participants were selected based on their parents' and teachers’ consent. Furthermore, an interview was also conducted with one English teacher from an elementary school in Singaraja to obtain the supplementary data. The findings indicate that the impact of school from home in teaching and learning English had some positive and negative impacts. Therefore, this study ends with suggestions for teachers and parents during school from home.
"It's Just the Job": Investigating the Influence of Culture in India's Commercial Content Moderation Industry

As user-generated content (UGC) on social media becomes near-ubiquitous, the need to review potentially insidious text, images, and video before and after they surface has resulted in the emergence of a new industry in global digital labour: commercial content moderation (CCM). A psychologically taxing job, a handful of scholars and journalists have studied the emotional burden for content moderators in the West, many of whom are predominantly based in Silicon Valley. However, much of this work is increasingly outsourced to the Global South, namely in India and the Philippines. Despite the fact that these overseas moderators filter UGC from Western countries, where the cultural norms on topics like hate speech and sexuality differ from those in Eastern countries like India, the academic research to date has neglected to study foreign content moderators and the ways in which their culture may impact their decision-making processes in reviewing UGC. This thesis takes a qualitative approach using semi-structured interviews to study content moderators in India with the objective of understanding not only the country’s booming and rapidly-evolving content moderation industry, but more essentially the subjectivities of Indian moderators and how their culture might influence their work. In addition to presenting novel findings in the general field of CCM, this research ultimately demonstrates that many of the assumptions about Indian content moderators – primarily that they are unable to separate their own cultural upbringings from the US-centric policy guidelines they are meant to uphold – are erroneous and in need of revisiting.
A Comprehensive Video Dataset for Multi-Modal Recognition Systems

This paper presents a comprehensive, highly defined and fully labelled video dataset. This dataset consists of videos related to 67 different subjects. The videos contain similar text and the text contains digits from 1 to 20 recited by 67 different subjects using the same experimental setup. This dataset can be used as a unique resource for researchers and analysts for training deep neural networks to build highly efficient and accurate recognition models in various domains of computer vision such as face recognition model, expression recognition model, speech recognition model, text recognition, etc. In this paper, we also train models related to face recognition and speech recognition on our dataset and also compare the results with the publically available datasets to show the effectiveness of our dataset. The experimental results show that our comprehensive dataset is more accurate than other dataset on which the models are tested.
Randomly under Sampled Boosted Tree for Predicting Sepsis from Intensive Care Unit Databases

Bacterial infection can result in sepsis; a toxic immune response by the body.Although the rate of mortality due to sepsis has fallen within the UK, overall rates remain higher than in Europe.Early detection of sepsis has been linked to elevated successful outcomes.This work focuses on the use of a Random Under Sample (RUS) Boosted Tree for classifying sepsis from intensive care unit databases.Full training set (40,336 subjects) achieved sensitivity and specificity of 53.4% and 83.6% respectively (AUC:77.79%) with 5 fold cross-validation.In the unofficial phase of the challenge the model achieved a normalized utility score of 0.267.The model did not achieve a score on the full test set (team name B-Secur).The results show that the model is capable of detecting sepsis in patients.However, there is more work to be done in order to improve performance.Future work will investigate the use of fixed time windows rather than individual hourly measurements to increase prediction performance.
A Performance Study of Membership Inference Attacks on Different Machine Learning Algorithms

Nowadays, machine learning (ML) algorithms cannot succeed without the availability of an enormous amount of training data.The data could contain sensitive information, which needs to be protected.Membership inference attacks attempt to find out whether a target data point is used to train a certain ML model, which results in security and privacy implications.The leakage of membership information can vary from one machine-learning algorithm to another.In this paper, we conduct an empirical study to explore the performance of membership inference attacks against three different machine learning algorithms, namely, K-nearest neighbors, random forest, support vector machine, and logistic regression using three datasets.Our experiments revealed the best machine learning model that can be more immune to privacy attacks.Additionally, we examined the effects of such attacks when varying the dataset size.Based on our observations for the experimental results, we propose a defense mechanism that is less prone to privacy attacks and demonstrate its effectiveness through an empirical evaluation.
A Novel Tweet Recommendation Framework for Twitter

In order to keep them updated users follow various Twitter accounts to get the latest information. As their social network increases it becomes challenging for them to find the relevant content from the massive collection of information. A Twitter user needs to scan a lot of less relevant posts to find the interesting tweets. Important updates may get lost if user is not able to read all the messages. So there is need that the most relevant updates are shown to the user first. Traditionally, the most retweeted tweets are considered popular and are brought forward. In order to improve the attractiveness of the incoming tweets we propose a personalized tweet ranking method based on the trending topics in the user network. A hashtag ranking model is developed to map the tweets into a ranked list of hashtags. The tweets corresponding to those hashtags are then ranked based on the linear weighted model that considers features related to tweet, author of tweet and the user. Finally, conducting a pilot user study we analyze the effectiveness of the proposed framework.
Classifying Internet Traffic using An Efficient Classifier

The new development in the architecture of Internet has increased internet traffic. The introduction of Peer to Peer (P2P) applications are affecting the performance of traditional internet applications. Network optimization is used to monitor and manage the internet traffic and improve the performance of internet applications. The existing optimizations methods are not able to provide better management for networks. Machine learning (ML) is one of the familiar techniques to handle the internet traffic. It is used to identify and reduce the traffic. The lack of relevant datasets have reduced the performance of ML techniques in classification of internet traffic. The aim of the research is to develop a hybrid classifier to classify the internet traffic data and mitigate the traffic. The proposed method is deployed in the classification of traffic traces of University Technology Malaysia. The method has produced an accuracy of 98.3% with less computation time.
Assessment And Improvement Of Public Administration In The Field Of Education

The methodology for assessing the effectiveness of education management includes the following blocks: ensuring affordable high-quality general and additional education; advanced training of management and teaching staff of educational organizations on the development of the education system; support and development of professional skills of teaching staff; development of a support system for gifted children and talented youth; formation of health-saving and safe conditions for organizing the educational process; development of the education quality assessment system.According to the Ministry of Education and Science, there are 940 educational institutions in the Chelyabinsk region.As a result of the analysis, the following problems in the education management system in the Chelyabinsk region and the ways to solve them were identified: 1. Insufficient provision of the material and technical base in educational institutions, especially in rural areas and a number of municipal areas.This conditions the relevance and timeliness of the decision of the federal ministry on the need to direct part of the federal funds for major repairs.2. The slow process of updating the teaching staff to young specialists.In solving this problem, the Ministry of Education and Science of the Chelyabinsk Region was recommended to develop a departmental program "Pedagogical Personnel".When developing, it is recommended to use the experience of other regions of the Russian Federation and that of foreign countries.
MODEL SPLINE KUBIK DENGAN TITIK-TITIK KNOTS DALAM REGRESI NONPARAMETRIK

Let a nonparametric regression model , where is respons variable, is regression curve that assumed an unrestricted form and contained in Sobolev space . For estimate curve is obtained by minimizing the Penalized Least Square (PLS). In this case given cubic spline polynomial approaching for optimal knots points, by using Generalized Cross Validation (GCV) method, to obtained optimal estimation model for regression curve. This application of cubic spline using bread turnover data from CV DEDE MAKASSAR. Based on analysis obtained four optimal knots on the months 3, 6, 8, and 11 by estimation equation as follows : Keywords : PLS, cubic spline, optimal knots, GCV.
Energy Efficient Software Development Techniques for Cloud based Applications

Worldwide organizations use the benefits offered by Cloud Computing (CC) to store data, software and programs.While running hugely complicated and sophisticated software on cloud requires more energy that causes global warming and affects environment.Most of the time energy consumption is wasted and it is required to explore opportunities to reduce emission of carbon in CC environment to save energy.Many improvements can be done in regard to energy efficiency from the software perspective by considering and paying attention on the energy consumption aspects of software's that run on cloud infrastructure.The aim of the current research is to propose a framework with an additional phase called parameterized development phase to be incorporated along with the traditional Software Development Life cycle (SDLC) where the developers need to consider the suggested techniques during software implementation to utilize low energy for running software on the cloud and contribute in green computing.Experiments have been carried out and the results prove that the suggested techniques and methods has enabled in achieving energy consumption.
Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

Abstract In recommendation algorithms, data sparsity and cold start problems are always inevitable. In order to solve such problems, researchers apply auxiliary information to recommendation algorithms to mine and obtain more potential information through users' historical records and then improve recommendation performance. This paper proposes a model ST_RippleNet, which combines knowledge graph with deep learning. In this model, users' potential interests are mined in the knowledge graph to stimulate the propagation of users' preferences on the set of knowledge entities. In the propagation of preferences, we adopt a triple-based multi-layer attention mechanism, and the distribution of users' preferences for candidate items formed by users' historical click information is used to predict the final click probability. In ST_RippleNet model, music data set is added to the original movie and book data set, and the improved loss function is applied to the model, which is optimized by RMSProp optimizer. Finally, tanh function is added to predict click probability to improve recommendation performance. Compared with the current mainstream recommendation methods, ST_RippleNet recommendation algorithm has very good performance in AUC and ACC, and has substantial improvement in movie, book and music recommendation.
A Contraction Mapping Method in Digital Image Processing

The topological features of the objects or digital image pictures are characterized by digital topology. A digital image picture is a distinctive arrangement of numbers which are non negative. Decomposing an image picture into its constituent components and investigating its several characteristics with fundamental elements is generally coined as digital image processing. In investigating the fundamental constituents of images, the separation of connected segments are established to enquire the adjacency relationship. During this course of tracking, thinning and coding them, it is kept in mind that the specification of connectedness of the pictures remains unaltered. The characteristics of the constituent subsets and relationships may be stipulated when the image is fragmented into its elementary constituents. Some features of the subsets of these constituent parts are based on their respective positions. Thus, the basic idea for image processing is the primary topological characteristics of digital images like adjacency, connectedness, etc. The fixed point theorems associated to certain kinds of contraction mappings can be utilised in the field of engineering science and technology as computational technique to provide an exclusive programme to explore various problems. Parallel and distributed computation, modeling, simulation and digital image processing are few notables among these techniques. In digital image processing digital contraction mapping is defined and then existence of the solution and its uniqueness is obtained using the fixed point theorem concerned, which is the mathematical basis of border following, thinning and contour filling of a digital image picture. In digital image processing the applicability of fixed point theorem and contraction mappings as a computational technique has been grazed well. To further broaden its pertinency in image processing our interest is to delve into some of contraction mappings as a significant mechanism.
Applying Blended Learning through WhatsApp group chat to improve students’ achievement in writing analytical exposition text at SMA Negeri 1 Terbanggi Besar

The objectives of this research were to find out whether there was any improvement on students’ analytical exposition text writing achievement, and their perceptions after the implementation of blended learning through WhatsApp group chat. This research was a quantitative research which was conducted through a one group pre-test and post-test design. The subjects of this research were thirty five students in SMA Negeri 1 Terbanggi Besar. The data were collected through pre- and post-tests in the forms of analytical exposition text and a questionnaire. The data were analyzed by using Paired Samples T-Test and descriptive statistics. The students’ writing achievement were compared between the pre and the post test. The result showed that there was statistically significant improvement of the students’ writing achievement after they were taught through blended learning by WhatsApp group chat. It could be seen from the computation of Paired Samples T-Test where the T-value (8.628) was higher than t-table (2.032) with the significance level of 0.00. Therefore, their perceptions toward blended learning through WhatsApp group chat in terms of five aspects of writing were also positive. Then this research proved that blended learning is effective to be implemented in developing and improving students’ writing achievement.
The shifting architecture of cognition and brain function in older adulthood

Cognitive aging is often described in the context of loss or decline. Emerging research suggests that the story is more complex, with older adults showing both losses and gains in cognitive ability. With increasing age, declines in controlled, or fluid, cognition occurs in the context of gains in crystalized knowledge of oneself and the world. This inversion in cognitive capacities, from greater reliance on fluid abilities in young, to increasingly crystalized or semanticized cognition in older adulthood, has profound implications for cognitive and real-world functioning in later life. This shift in cognitive architecture parallels changes in the functional network architecture of the brain. Observations of greater functional connectivity between lateral prefrontal brain regions, implicated in cognitive control, and the default network, implicated in memory and semantic processing, led us to propose the Default Executive Coupling Hypothesis of Aging (DECHA). In this review we provide evidence that these changes in the functional architecture of the brain serve as a neural mechanism underlying the shifting cognitive architecture from younger to older adulthood. We incorporate findings spanning cognitive aging and cognitive neuroscience to present an integrative model of cognitive and brain aging, describing its antecedents, determinants, and implications for real-world functioning.
InformationSharing Pipeline

In this article we discuss a proposal for creating an information-sharing pipeline/real-time information channel, where all stakeholders would be able to engage in exchange/verification of information about entities in real time. The entities in question include personal and organizational names as well as subject headings from different controlled vocabularies. Three World Wide Web Consortium–recommended protocols are considered as potential solutions: the Linked Data Notifications protocol, the ActivityPub protocol, and the WebSub protocol. We compare and explore the three protocols for the purpose of identifying the best way to create an information-sharing pipeline that would provide access to the most up-to-date information to all stakeholders.
Formation of research skills of future primary school teachers in the process of teaching mathematics

One of the priority tasks of pedagogical education is wide comprehensive training of students, based on the concept of personally oriented education and involves the formation of a holistic system of universal knowledge, skills, as well as experience of independent activities, the basic competencies that determine the quality of education of a modern university student. The purpose of the article is to highlight the problem of formation of research skills of university students, as well as the role of inquiry-based learning approach in solving this problem. The research was based on scientific works of domestic and foreign scientists who studied the formation and development of research skills of students in the learning process. Methods. The author implements the purpose with the help of theoretical and empirical research methods (analysis and generalization of own pedagogical experience and pedagogical experience of domestic and foreign scientists).&#x0D; The results. The article considers and generalizes the views of scientists on the classification and essence of research skills, analyses the role of the inquiry-based learning approach in the formation of studentsʼ research skills in the process of teaching mathematics. The article suggests the effective method on forming the studentsʼ research skills based on the student-centered approach in the process of teaching mathematics (Inquiry-Based Mathematics Education). The special educational material (pedagogical cases) based on IBME was developed. The benefit of the study is that the material was tested during the teaching of mathematics to first-year students of specialty «Primary Education» of the Pedagogical Institute of Borys Grinchenko Kyiv University (the research, the results of which are presented in the article, was conducted within the project «Partnership for teaching and learning of mathematics at the university» (PLATINUM) of the EU program Erasmus + KA203 – Strategic Partnership for Higher Education, 2018-1-NO01-KA203-038887); the data show the increase of studentsʼ interest in educational and research work, and the level of their motivation to study mathematics.&#x0D; Conclusions. As a result the author makes a conclusion that the teaching mathematics with the use of Inquiry-Based Mathematics Education ensures the growth of interest of students in educational and research activity, formation and development of research skills; increases the efficiency of independent work, general motivation for the study of mathematics.