Reviewerly/exHarmony huggingface dataset:
D032 N07 C0MpU73: Exploring (Post)Human Bodies and Worlds with/in Droidial(ity) and Narrative Contexts

This article focuses on droidial bodies in children’s literature to explore how speculative literacies foster necessary spaces for thinking about (non)human and more-than-human connectivity. Specifically, we share what was produced when we applied a framework underpinned by posthumanist concepts to three children’s books centering robots. Using Jackson and Mazzei’s thinking with theory to plug into these books, this article raises (re)new(ed) questions about the intersections of literacy, humanism, and droids. It proposes that pairingposthumanist concepts with droidial texts can be generative in thinking about, critiquing, and predicting changes with the (ever-developing) relationship(s) between humans and machines.
Statistical and regression analysis of impact of faults on object oriented software

These days the Object-Oriented (OO) paradigm is used extensively in the development of software systems. The OO metrics can be employed to access the quality of these OO systems. Many metrics and metric suits have been developed by the scientists to access the quality of software. The properties of object-oriented designs can be measured using object-oriented metrics. With the help of metrics, estimations of project milestones can be attained more accurately. It is possible to develop software projects with nominal faults. A study shows that an estimated 42% on account of corrective maintenance can be saved by using object-oriented metrics. This paper assesses the capability of Object Oriented metrics to identify fault-proneness in Object Oriented software systems using statistical and regression analysis. Three projects from the NASA data set have been used to determine the applicability of object-oriented CK metrics. The CK metrics are used to predict the bugs in the class. The fault-proneness using OO metrics has been calculated. The information collected using this method will increase the quality and reliability of the OO software systems.
Peer Learning and Edutainment Technologies in the Development of Professional Competencies of Media Students

The article describes and analyzes the educational and training project of the Department of Mass Communications of the Peoples Friendship University of Russia Day of Specialties: Journalism, Television, Advertising and Public Relations. Also presented is the experience of organizing and conducting the festival of educational films and training MediaEnvironment at the branch of the Lomonosov Moscow State University in Sevastopol. These special educational and cognitive events are considered as elements of internal and external communications of universities, developing the professional competences of participants and demonstrating the possibilities of using the concept of peer learning and edutainment technologies for practical emotional-motivated learning of students of media specialties. The authors describe the methodology of event preparation in relation to educational standards, linking the organizational stages with the formation of specific student competencies. The authors conclude not only about the effectiveness of such forms of educational communication, but also about the prospects of their use in the conditions of online education. Representation model of special events is universal and can be used not only in universities with such directions like journalism, television, advertising and public relations.
Predicting the Adoption of Password Managers: A Tale of Two Samples

Using weak passwords and re-using passwords can make one vulnerable to cybersecurity breaches.Cybersecurity experts recommend the adoption of password managers (PMs), as they generate and store strong passwords for all accounts.Prior research has shown that few people adopt PMs.Our research examined PM adoption in a sample of 221 undergraduates from psychology courses and a sample of 278 MTurk workers.We hypothesized that PM adoption could be predicted using a small set of user characteristics (i.e., gender, age, Big Five personality traits, number of devices used, frequency of using social media, and cybersecurity knowledge).The results showed that compared to students, MTurkers used more devices, used social media less often, had higher levels of cybersecurity knowledge, and were more likely to know what PMs are (93% vs. 33% ).Of those who knew about PMs, only 40% of MTurkers and 47% of students reported using one.Those higher in cybersecurity knowledge were more likely to use PMs.Logistic multiple regression was used to predict which participants used PMs.The results showed that the results differed for MTurkers and students.For students, the model correctly classified 84.2% of participants; two predictors were significant.Those with higher levels of cybersecurity knowledge were more likely to use PMs, and those who used social media more often and were more likely to use PMs.For MTurkers, the model was not significant.The results may be useful to those who develop and implement campaigns to promote password managers and other recommended cybersecurity practices.
The Impact of Customer Satisfaction on Loyalty in Jordanian Banks: The Mediating Role of Corporate Reputation

Customer satisfaction is a critical factor in improving bank performance and increasing revenue. Banks must continue to strengthen their relationships with customers because the banking system is highly responsive to customer choices. Customers are inundated with offers and services, particularly online, creating dissatisfaction with the status quo. The purpose of this research is to examine the mediating effect of corporate reputation on the relationship between customer satisfaction and loyalty in Jordanian commercial banks. A quantitative research design is used to achieve the research objectives. The data are gathered from the customers of Jordanian commercial banks in Amman. 350 valid questionnaires are used in the analysis. The findings of the study reveal that corporate reputation mediates the relationship between customer satisfaction and loyalty among bank customers. Therefore, the study recommends that banks make better use of their corporate reputation in their strategies, for instance in social initiatives and marketing programs, to ensure effective and sustainable engagement, which will improve the bank’s position in the minds of customers.
Building Immersive Library Environment to Access Virtual Reality Content A Proposed Framework Model

Immersive environments refer to a computer-simulated 3-dimensional virtual world. Libraries worldwide have been adopting and implementing new immersive technologies to enhance users’ experience and learning. The Central Library of IIT Kharagpur offers (i) web-based augmented reality (WebAR), (ii) virtual reality (VR), and (iii) data visualisation immersive services. This research investigates the users’ perceptions on factors that influence their satisfaction with WebAR/VR services. The study surveyed 135 random users to note their immersive library experience; out of them, only 100 users responded to our survey. The results show that 90 per cent of the respondents were satisfied with our services. Secondly, the respondents revealed that data privacy, health, and safety were some of the critical factors that influenced their satisfaction. Further, 14 per cent were apprehensive in terms of their health &amp; safety. In fact, these users reported vision problems, disorientation, dizziness, sweating, and nausea. Based on the data collected, we prepared a framework, using The DELOS DL reference model for information delivery, along with educational and research activities. We believe that this study would lend crucial insights to academic libraries that may be planning to adopt and implement AR/VR as part of their immersive environment.
Empirical evidence on the efficiency of backward contact tracing in COVID-19

Abstract Standard contact tracing practice for COVID-19 is to identify persons exposed to an infected person during the contagious period, assumed to start two days before symptom onset or diagnosis. In the first large cohort study on backward contact tracing for COVID-19, we extended the contact tracing window by 5 days, aiming to identify the source of the infection and persons infected by the same source. The risk of infection amongst these additional contacts was similar to contacts exposed during the standard tracing window and significantly higher than symptomatic individuals in a control group, leading to 42% more cases identified through contact tracing. Compared to standard practice, backward traced contacts required fewer tests and shorter quarantine, but if infected they were identified later in their infectious cycle. Our results support implementing backward contact tracing when rigorous suppression of viral transmission is warranted.
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization

Neural abstractive summarization models are prone to generate summaries which are factually inconsistent with their source documents. Previous work has introduced the task of recognizing such factual inconsistency as a downstream application of natural language inference (NLI). However, state-of-the-art NLI models perform poorly in this context due to their inability to generalize to the target task. In this work, we show that NLI models can be effective for this task when the training data is augmented with high-quality task-oriented examples. We introduce Falsesum, a data generation pipeline leveraging a controllable text generation model to perturb human-annotated summaries, introducing varying types of factual inconsistencies. Unlike previously introduced document-level NLI datasets, our generated dataset contains examples that are diverse and inconsistent yet plausible. We show that models trained on a Falsesum-augmented NLI dataset improve the state-of-the-art performance across four benchmarks for detecting factual inconsistency in summarization. The code to obtain the dataset is available online at https://github.com/joshbambrick/Falsesum
Graph Neural Networks: a bibliometrics overview

Abstract Recently, graph neural networks (GNN) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs’ research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics, and social sciences. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must-read papers and future directions. Finally, the application of graph convolutional networks and attention mechanism are now among hot topics of GNN research.
Developing supplementary materials for descriptive text writing using Ispring Quizmaker

This research was conducted to develop supplementary materials to support writing on descriptive text in companion to the English textbook for Seventh Grade. This research is qualitative based, and the method adopted is by development research. The researcher used the ADDIE model as the model of development of this research. However, due to time limitations to accomplish all steps, this research only implemented three phases: analyzing, designing, and developing (ADD). This research used the semi-structured interview as the data collection technique and an interview guideline as the tool of data collection. The data of the analyzing step of this research were analyzed using the thematic analysis technique. The participant of this research was an English teacher of SMP Kemala Bhayangkari Kubu Raya. The product was developed using Ispring Quizmaker. During the process of Designing and Developing, the researcher evaluated the product of this research using the criteria of good materials by Bugler et al. (2017). In conclusion, the development of supplementary material for descriptive text writing (SEASON) as the product of this research has met the criteria of good materials.
Diagnosis of Alzheimer’s Disease with Extreme Learning Machine on Whole-Brain Functional Connectivity

The analysis of human brain fMRI subjects can research neuro-related diseases and explore the related rules of human brain activity. In this paper, we proposed an algorithm framework to analyze the functional connectivity network of the whole brain and to distinguish Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN). In other studies, they use algorithms to select features or extract abstract features, or even manually select features based on prior information. Then, a classifier is constructed to classify the selected features. We designed a concise algorithm framework that uses whole-brain functional connectivity for classification without feature selection. The algorithm framework is a two-hidden-layer neural network based on extreme learning machine (ELM), which overcomes the instability of classical ELM in high-dimensional data scenarios. We use this method to conduct experiments for AD, MCI, and CN data and perform 10-fold cross-validation. We found that it has several advantages: (1) the proposed method has excellent classification accuracy with high speed. The classification accuracy of AD vs. CN is 96.85%, and the accuracy of MCI vs. CN is 95.05%. Their AUC (area under curve) of ROC (receiver operating characteristic curve) reached 0.9891 and 0.9888, respectively. Their sensitivities are 97.1% and 94.7%, and specificities are 96.3% and 95.3%, respectively. (2) Compared with other studies, the proposed method is concise. Construction of a two-hidden-layer neural network is to learn features of the whole brain for the diagnosis of AD and MCI, without the feature screening. It avoids the negative effects of feature screening by algorithm or prior information. (3) The proposed method is suitable for small sample and high-dimensional data. It meets the requirements of medical image analysis. In other studies, its classifiers usually deal with several to dozens of feature dimensions. The proposed method deals with 4005 feature dimensions.
Face Recognition with Machine Learning

In this paper, we proposed a facial recognition system using machine learning, specifically support vector machines (SVM). First, a training set of different persons’ faces has to be collected and used to train a face recognizer. The resulting face model can be utilized to classify people in specific individuals or unknowns. It is called a Biometric Identification based application that uniquely identifies each individual by analysing their facial expression, face. Even though it was initially used as a computer application, it has gained broader uses in mobile platforms and other technology sectors. Facial recognition technology has become very popular and is being used everywhere from shopping centre, airports, venues, and by law enforcement. This technology can also be used to prevent crimes such as shoplifting by identifying ex-cons. Although this technology is gaining widespread use, there are many concerns about privacy and safety. Online examinations have turned out to be the new normal. However, it is not that easy to proctor the students as rigorously as in in-centre examinations. It is essential to find an approach to proctor the online examinations too as rigorously as possible.
US libraries in search for a standard model of reference services

The article is about the evolution of reference service models in US libraries, starting with the development of a theoretical concept in the first half of the nineteenth century and ending with innovative models of today. The author’s object is to consider the main approaches to the organization of reference services, to give the description of the most common models, and to describe examples of their practical implementation in public and university libraries in the USA. The article reveals how reference librarians are trying to rethink, re-equip and modernize reference services with the help of experiments and initiatives for information interaction with users. The reference desk in the article is marked as the main “stumbling block” on the way to an ideal SBO model. A number of researchers believe that with the increase in the volume of available information and technological innovations, the previous organizational model hinders the effective use of library staff. The development of a reference service concept capable of providing an innovative competitive position that meets the future expectations of reference services users continues to be a key problem in the era of digitalization.
De la educación a distancia a la enseñanza remota de emergencia en clases de español en la universidad

Due to the pandemic caused by the coronavirus (COVID-19), it was necessary to adopt emergency remote education (ERE) as a way to minimize the impacts of social isolation on education. Thus, educational activities, which used to be completely face-to-face, started to be carried out using digital technologies. According to this context, in this article, we try to understand what, in fact, has changed after this first year of ERE, 2020 to 2021, and how, before and during the pandemic, experiences and studies about technologies in education interfere in our educational practices in today’s Spanish language classes in higher education. More specifically, the objectives of this work are: i) to identify characteristics of Distance Education (EaD) adapted to the ERE and ii) to present the digital literacies present in different contexts of the ERE. We draw on Kuklinski and Cobo (2020) with regard to higher-level ERE; in Brazil (2016) for the characterization of the EaD modality y in Rojo (2009) and Dudeney, Hockly and Pegrum (2018) to deal with digital literacy. As a result of the analysis, we found that teachers from higher education institutions (IES), after an abrupt and unplanned first moment, managed to appropriate digital educational resources (RED) and create a multimedia narrative for their subjects, especially those that they teach in Language courses - Spanish.
Building Contrastive Summaries of Subjective Text Via Opinion Ranking

This article investigates methods to automatically compare entities from opinionated text to help users to obtain important information from a large amount of data, a task known as “contrastive opinion summarization”. The task aims at generating contrastive summaries that highlight differences between entities given opinionated text (written about each entity individually) where opinions have been previously identified. These summaries are made by selecting sentences from the input data. The core of the problem is to find out how to choose these more relevant sentences in an appropriate manner. The proposed method uses a heuristic that makesdecisions according to the opinions found in the input text and to traits that a summary is expected to present. The evaluation is made by measuring three characteristics that contrastive summaries are expected to have: representativity (presence of opinions that are frequent in the input), contrastivity (presence of opinions that highlight differences between entities) and diversity (presence of different opinions to avoid redundancy). The novel method is compared to methods previously published and performs significantly better than them according to the measures used. The main contributions of this work are: a comparative analysis of methods of contrastive opinion summarization, the proposal of a systematic way to evaluate summaries, the development of a new method that performs better than others previously known and the creation of a dataset for the task.
Pengembangan Media Pembelajaran Teka-Teki Silang Sastra Melayu Klasik Berbasis Android

This study aims to develop learning media for Indonesian-Malay literary crosswords classical based on android. The android-based crossword puzzle learning media developed with the help of the Proprofs application will be tested on odd semester students taking the History of Literature course, Indonesian Language Education Study Program, FKIP, Bengkulu University. The development of learning media in this study used the Research and Development (R&amp;D) method, where researchers collected research data using a questionnaire based on the Likert scale. This study has three questionnaires: a material expert questionnaire, a media expert questionnaire, and a user response questionnaire of 34 students. As a result, the total score obtained from the combined validation of material experts, media, and users was 139.5, or an average score of 4.1. These results indicate that the learning media of classical Indonesian-Malay literary crosswords that the researcher developed is appropriate for learning activities in the History of Literature course.
A complete traceability methodology between UML diagrams and source code based on enriched use case textual description

Traceability in software development proves its importance in many domains like change management, customer's requirements satisfaction, model slicing, etc. Existing traceability techniques trace either between requirement and design or between requirement and code.However, none of the existing approaches achieved reliable results when dealing with traceability between requirements, design models and source code.In this paper, we propose an improvement and an extension of our design traceability approach in order to tackle the traceability between design, requirement and code.The finetuning of our methodology stems from considering an expanded textual description.A pre-treatment step is added in order to divide the textual description of system functionalities into different parts, each of which represents a specific goal.In fact, the extension consists in extracting an expanded textual description from a natural language text in order to trace between related elements belonging to requirement, design and code while using an information retrieval technique.The proposed method is based on different scenarios (nominal, alternatives and errors), particularly on concepts related to control structures to establish the traceability between artefacts.Furthermore, we implemented our method in a tool allowing the evaluation of its performance.The evaluation is performed on real existing applications that consist in comparing results found by our approach with results found by experts.Our method achieves an average precision of 0.84 and a recall of 0.91 in traceability between requirement, design and code.Besides its promising performance outcomes, our automated method has the merit of generating a traceability report describing the correspondence between different artefacts.
Theory-inspired parameter control benchmarks for dynamic algorithm configuration

It has long been observed that the performance of evolutionary algorithms and other randomized search heuristics can benefit from a non-static choice of the parameters that steer their optimization behavior. Mechanisms that identify suitable configurations on the fly ("parameter control") or via a dedicated training process ("dynamic algorithm configuration") are thus an important component of modern evolutionary computation frameworks. Several approaches to address the dynamic parameter setting problem exist, but we barely understand which ones to prefer for which applications. As in classical benchmarking, problem collections with a known ground truth can offer very meaningful insights in this context. Unfortunately, settings with well-understood control policies are very rare.