Reviewerly/exHarmony huggingface dataset:
Behaviours and Characteristics of Digital Natives Throughout the Teaching-Learning Process: A Systematic Review of Scientific Literature from 2016 to 2021

Information and communication technologies have generated transformations in the behaviour of digital native university students, which affect the teaching-learning process in the scenario of the Covid-19 pandemic. The objective of this study was to systematize the scientific evidence on university teaching strategies related to the behaviour of digital natives and the characteristics of their learning. The method employed was a systematic review of scientific articles from the EbscoHost, Scopus, Proquest, Eric and Scielo databases through the Boolean operators AND and OR. Initially, 51 638 records were obtained, of which only 26 met the inclusion criteria (years of publication, language, subject matter, free availability). The results show in the articles that most digital natives actively use social networks on a daily basis, while others conduct research or produce digital content with varying degrees of difficulty, some adapt quickly to new innovations related to technology, and many easily multitask and perform self-regulated learning, while demanding a more flexible teaching where learning is fast and practical. However, the attention of digital natives is dissipated by other interests such as social networks (generating cyber laziness), a situation that can lead to antisocial behaviour and self-exclusion from physical reality (phubbing). In conclusion, digital natives require greater attention from their university teachers, who must innovate in the teaching-learning process using the virtual environment, raising the interest of digital natives, to generate an autonomous learning where they can strengthen their skills and competencies for the achievement of their professional development.&#x0D; &#x0D; Received: 11 December 2021 / Accepted: 23 March 2022 / Published: 5 May 2022
Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet, social networks and the internet of things, the social Internet of things has increasingly become a significant research issue. In the social internet of things application scenario, one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources. Although a variety of recommendation algorithms have been employed in this field, they ignore the massive text resources in the social internet of things, which can effectively improve the effect of recommendation. In this paper, a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed. The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the “thing-thing” relationship information in the internet of things to improve the effect of recommendation. Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.
SCE-Net: Self- and Cross-Enhancement Network for Single-View Height Estimation and Semantic Segmentation

Single-view height estimation and semantic segmentation have received increasing attention in recent years and play an important role in the photogrammetry and remote sensing communities. The height information and semantic information of images are correlated, and some recent works have shown that multi-task learning methods can achieve complementation of task-related features and improve the prediction results of the multiple tasks. Although much progress has been made in recent works, how to effectively extract and fuse height features and semantic features is still an open issue. In this paper, a self- and cross-enhancement network (SCE-Net) is proposed to jointly perform height estimation and semantic segmentation on single aerial images. A feature separation–fusion module is constructed to effectively separate and fuse height features and semantic features based on an attention mechanism for feature representation enhancement across tasks. In addition, a height-guided feature distance loss and a semantic-guided feature distance loss are designed based on deep metric learning to achieve task-aware feature representation enhancement. Extensive experiments are conducted on the Vaihingen dataset and the Potsdam dataset to verify the effectiveness of the proposed method. The experimental results demonstrate that the proposed SCE-Net could outperform the state-of-the-art methods and achieve better performance in both height estimation and semantic segmentation.
Selection of Main Effects

Model specification is the most difficult part of prediction modeling. Especially in smaller data sets, it is virtually impossible to obtain a reliable answer to the question: which predictors are important and which are not? In this chapter, we focus on the problems that are associated with model reduction techniques such as stepwise selectionStepwise selection, including overfitting and the quality of predictions from a model. Specific issues include instability of selection, biased estimation of coefficients and exaggeration of p-values. Alternative approaches are discussed, such as limiting the number of candidate predictors, e.g. based on a meta-analysis of available literature, and some modern selection methods, such as the LASSO and elastic net.
Students’ Digital Media Literacy: Effects on Social Character

The development of digital technology in the era of the industrial revolution 4.0 requires today’s younger generations to demonstrate media literacy. It is often the case that cyber-crime happens among teenagers because they are misinformed. This study measures students’ media literacy using individual competence framework which include technical skills, critical understanding, and communicative abilities. Respondents were 100 university undergraduate students in Indonesia selected randomly. The results showed that 60-80% respondents demonstrated medium level technical skills, 80-90% of them have low critical understanding, and 45% of them displayed medium communicative abilities. In addition, media literacy was found to significantly influence students’ social character as much as 49%. The finding of this study implies the need for literacy education is imminent because low media literacy may lead to low social character.
Academic Management Android Application For Student Performance Analytics: A Comprehensive Evaluation Using Iso 25010:2011

Software quality standards are very significant matters nowadays, especially that this era reigns with software technology and systems to innovate the work process in any institution. Assessments are conducted to measure the quality of services as well as products. This paper focuses on the comparison of the assessment of an existing alumni tracer system and online student portal using ISO/IEC 25010 model, but only seven dimensions was used namely functional suitability, performance efficiency, usability, reliability, security, maintainability and portability. Twenty I.T. experts were invited to test the existing systems and fill in the survey. The results as describe in this paper shows the significant differences of systems as evaluated by the respondents using the ISO/IEC 25010 model. Performance Assessment is very vital to the academic progress of a student. It should be monitored, and evaluated to allow both professors and students create pre-emptive measures to ensure their success rate. This paper focuses on the assessment of the developed system using ISO 25010:2011 Software Quality Management. The following criteria were used Functionality, Usability, Reliability, Portability and Security. 84 respondents comprising of Professors, Department Heads and IT Professionals assessed and evaluated the system through the use of an adapted evaluation tool. The results shown in this paper shows the evaluation of the respondents to the developed system.
Web Scraping: Applications and Scraping Tools

The world of Artificial Intelligence and machine learning has its common roots with data, which is primarily the most important entity on its own.Data has already impacted so many businesses worldwide and can never take a back seat when it comes to this technical world.To get access to data in its best form, web scraping was brought to use.Data provided on the internet is of so much use that the whole world is running after it.Web scraping was brought into practice long back and is still useful to date.This paper aims to make people aware of this technology to help them expand their knowledge.Tools and applications related to web scraping are also mentioned.
Recognizing the Activity Daily Living (ADL) for Subject Independent

Recently, Human Activity Recognition (HAR) has gained meaningful information for a human being. A wearable sensor like an accelerometer, small and simple to perform, has opened the room for scientists to explore an initial understanding of ubiquitous computing. The wearable sensor has begun to receive attention among researchers in some respects to conduct their studies in a wide area of recognition of human activity. Recent ADL discusses not only simple activities but also cater to the broad categories of complex activities. However, when involving enormous numbers of a subject, the accuracy of recognition tends to reduce. Although a different subject performed the same activity, the acceleration signal acquired considerably differs. This is due to the distinct pattern of action for each subject based on various factors such as subject age, gender, emotion and personality. Thus, by enhancing the accuracy of recognition of ADL, this article proposes the framework for addressing the subject independent matter. The signal acquired from an accelerometer sensor will undergo a segmentation process to extract important features. Some of the characteristics may be meaningless in some instances to determine the class. Therefore, proposing a variety of features to select the most relevant features that can lead to accuracy above 90%. Also, this article outlined a brief empirical evaluation of previous related work. Using several machine learning algorithms, this preliminary work will be examined and analyzed.
Model of Recognition Operators Based on the Formation of Representative Objects

Parametric representations of the model of recognition operators based on the selection of representative objects are considered. The main idea of the proposed model is to build a family of proximity functions in the parameter space. In this case, the proximity function is determined within the framework of the radial function. A distinctive feature of the proposed model is the formation of preferred features with respect to selected representative objects when constructing recognition operators. To verify the performance of the proposed model, experimental studies were carried out to solve the model problem.
Descriptive Statistics

Descriptive statistics summarize or quantitatively describe variables from a dataset. In a SQL Server table, a dataset is a set of the rows, or a rowset, that comes from a SQL Server table, view, or tabular expression. A variable is stored in a column of the rowset. In statistics, a variable is frequently called a feature.
Machine Learning Methods for Analysis Fraud Credit Card Transaction

The use of online banking and credit card is increasing day by day. As the usage of credit/debit card or netbanking is increasing, the possibility of many fraud activities is also increasing. There are many incidents are happened in presently where because of lack of knowledge the credit card users are sharing their personal details, card details and one time password to a unknown fake call. And the result will be fraud happened with the account. Fraud is the problem that it is very difficult to trace the fraud person if he made call from a fake identity sim or call made by some internet services. So in this research some supervised methodologies and algorithms are used to detect fraud which gives approximate accurate results. The illegal or fraud activities put very negative impact on the business and customers loose trust on the company. It also affects the revenue and turnover of the company. In this research isolation forest algorithm is applied for classification to detect the fraud activities and the data sets are collected from the professional survey organizations.
Classification Approach for Breast Cancer Detection Using Back Propagation Neural Network

According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%. According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%.
Multiclass Classification for Self-Admitted Technical Debt Based on XGBoost

In software development, due to the demands from users or the limitations of time and resources, developers tend to adopt suboptimal solutions to achieve quick software development. In such a way, the released software usually involves not-quite-right code that is called technical debt, which will significantly decrease the quality of software and increase the maintenance cost. Recently, the concept of self-admitted technical debt (SATD) is proposed and refers to technical debt that is self-admitted by developers in code comments. Existing studies mainly focus on detecting technical debt by classifying code comments into either “SATD” or “non-SATD.” However, different types of SATD has different impacts on software maintenance and needs to be handled by different developers. Therefore, the detected SATD should be further classified so that developers can understand and remove technical debt better. In this article, we propose a new method based on eXtreme Gradient Boosting (XGBoost) to classify SATD into multiple classes. In our approach, we first preprocess the original code comments and adopt the easy data augmentation strategy to overcome the class unbalance problem. Then, chi-square is leveraged to select representative features from the textual feature set. Finally, we apply XGBoost to train a classifier and use the trained classifier to partition each comment into the corresponding class. We experimentally investigate the effectiveness of our approach on a public dataset, including 62 566 code comments from 10 open-source projects. Experimental results show that our approach achieves 56.66% in terms of macroaveraged precision, 59.07% in terms of macroaveraged recall, and 55.77% in terms of macroaveraged F-measure on average, and outperforms the natural language processing based method by 4.98%, 5.32%, and 3.17%, respectively. In addition, the experimental results also demonstrate that the data augmentation strategy is effective in improving the effectiveness of our approach.
Qubit Allocation

The availability of the first prototypes of quantum computers, in 2016, with free access through the cloud, brought much enthusiasm to the research community. Yet, programming said computers is difficult. One core challenge is the so called qubit allocation problem. This problem consists in mapping the virtual qubits that make up a logical quantum program onto the physical qubits that exist in the target quantum architecture. To deal with this challenge, we have proposed one of the first algorithms to solve qubit allocation. This algorithm, together with its ensuing formulations, is today available in the Enfield compilera concrete product of this work. Our first paper in this field, titled Qubit Allocation, has inspired much research, and our latest qubit allocation design, called Bounded Mapping Tree, stands out today as one of the most effective qubit allocators in the world.
A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations

Recommender Systems (RSs) are essential tools of an e-commerce portal in making intelligent decisions for an individual to obtain product recommendations.Neighborhood-based approaches are traditional techniques for collaborative recommendations and are very popular due to their simplicity and efficiency.Neighborhood-based recommender systems use numerous kinds of similarity measures for finding similar users or items.However, the existing similarity measures function only on common ratings between a pair of users (i.e.ignore the uncommon ratings) thus do not utilize all ratings made by a pair of users.Furthermore, the existing similarity measures may either provide inadequate results in many situations that frequently occur in sparse data or involve very complex calculations.Therefore, there is a compelling need to define a similarity measure that can deal with such issues.This research proposes a new similarity measure for defining the similarities between users or items by using the rating data available in the user-item matrix.Firstly, we describe a way for applying the simple matching coefficient (SMC) to the common ratings between users or items.Secondly, the structural information between the rating vectors is exploited using the Jaccard index.Finally, these two factors are leveraged to define the proposed similarity measure for better recommendation accuracy.For evaluating the effectiveness of the proposed method, several experiments have been performed using standardized benchmark datasets (MovieLens-1M, 10M, and 20M).Results obtained demonstrate that the proposed method provides better predictive accuracy (in terms of MAE and RMSE) along with improved classification accuracy (in terms of precision-recall).
Obstacle Detection and Text Recognition for Visually Impaired Person Based on Raspberry Pi

Autonomous travel is a notable test for visually impaired people and furthermore the expanding accessibility of cost proficiency, superior and versatile advanced imaging gadgets has made a gigantic open door for enhancing conventional checking for record picture securing. We propose a camera based visual help system utilizing raspberry pi for content perusing, movement of items and the sentiments of outwardly hindered people face various challenges to play out their everyday errand. They are absolutely or halfway subject to somebody for help. Their issues have made them to lose their would like to live in this contending society. They look for help from others to control them entire day. This paper expects to make the outwardly debilitated individual completely autonomous in all perspectives. The proposed framework depends on a virtual eye, which conveys to the outside encompassing through a camera. The camera goes about as a consistent wellspring of data to the framework. The information is gotten through the camera. They got signals from the information gadgets are dissected utilizing picture handling in LABVIEW and it reacts to the outwardly debilitated individual through discourse preparing units. The handled data about environmental factors will be educated through the speaker (yield unit) by which outwardly weakened individuals can move and make their work effectively all alone. Also the outwardly weakened individual can naturally control a portion of the home apparatuses like fan utilizing remote correspondence framework.
Attitudes towards Digital Educational Technologies among Russian University Students before and during the COVID-19 Pandemic

The COVID-19 pandemic has dramatically accelerated the digitalization of education around the world. There has been a lot of recent research on university students’ attitudes towards digital educational technologies (DET) in different countries, but much fewer studies examine how these attitudes change during the pandemic. The purpose of the present exploratory study is to compare the attitudes towards DET among Russian university students majoring in psychology before the start of the pandemic and at its different stages. A mixed method research design was used. The quantitative part of the study included The University Students’ Attitudes toward DET Questionnaire developed by the authors, and the qualitative part of this study included percentage and thematic analyses of answers to additional multiple choice and open-ended questions. The main findings of the study confirm significant changes in attitudes towards DET at the very beginning of the pandemic, and their relative stabilization later. Additional analysis based on the literature review revealed that the advantages and disadvantages of DET listed by Russian university students and students from other countries, generally coincide. The data obtained will be useful in the development of digital competence among university students.
Joint Learning of Object Graph and Relation Graph for Visual Question Answering

Modeling visual question answering (VQA) through scene graphs can significantly improve the reasoning accuracy and interpretability. However, existing models answer poorly for complex reasoning questions with attributes or relations, which causes false attribute selection or missing relation in Figure 1(a). It is because these models cannot balance all kinds of information in scene graphs, neglecting relation and attribute information. In this paper, we introduce a novel Dual Message-passing enhanced Graph Neural Net-work (DM-GNN), which can obtain a balanced represen-tation by properly encoding multi-scale scene graph infor-mation. Specifically, we (i) transform the scene graph into two graphs with diversified focuses on objects and relations; Then we design a dual structure to encode them, which in-creases the weights from relations (ii) fuse the encoder out-put with attribute features, which increases the weights from attributes; (iii) propose a message-passing mechanism to en-hance the information transfer between objects, relations and attributes. We conduct extensive experiments on datasets in-cluding GQA, VG, motif-VG and achieve new state of the art.