Reviewerly/exHarmony huggingface dataset:
An Empirical Study of the Relationships between the Flexible Work Systems (FWS), Organizational Commitment (OC), Work Life Balance (WLB) and Job Satisfaction (JS) for the Teaching Staff in the United Arab Emirates (UAE)

The research study examines the relationships between the flexible work systems (FWS), organizational commitment (OC), work life balance (WLB) and job satisfaction (JS) for the teaching staff in the United Arab Emirates (UAE). The respondents are chosen from the accredited colleges in the United Arab Emirates (UAE) by convenience sampling method from the cities of Sharjah, Dubai, Abudhabhi and Ajman. They differed in terms of gender, qualifications, tenure of work, department and also the nature of work. A suitable conceptual framework was developed and the proposed model illustrates that flexible work system (FWS) is thought to have an influence on both organizational commitment (OC) and work life balance (WLB). It has been proposed that WLB significantly mediates the relationship between flexible work system and job satisfaction. It is also anticipated that WLB significantly mediates the relationship between organizational commitment (OC) and job satisfaction (JS). A sample size of 224 was chosen and a structured questionnaire was used for collecting the data from the respondents in UAE. The questionnaire consists of 34 statements and they are rated on a likert scale of 1 to 5. For this study, statistical tools like confirmatory factor analysis (CFA) and structural modeling equation (SME) methods have been used through SPSS (version-25) and AMOS (version-25) for assessing the nature of relationships that existed between the studied variables (34 statements in the questionnaire). Prior to testing through structural equation modeling (SEM), the most fundamental criteria tests such as normality and confirmatory factor analysis (CFA) were conducted. As normality was established, the factor loadings were extracted by the procedure of CFA. The results from the data analysis indicated that all the hypotheses are supported and it facilitated for the acceptance of the proposed model. Thus, flexible work system (FWS) had direct effect on organizational commitment (OC) and it also had direct effect on work life balance (WLB). Similarly, organizational commitment (OC) is positively related to work-life balance (WLB). Consequently, the work life balance (WLB) displayed positive relationship towards job satisfaction (JS). The results of mediation analysis revealed the indirect effect of flexible work system (FWS) and organizational commitment (OC) towards job satisfaction (JS) through work-life balance (WLB). The study demonstrates that all the proposed hypotheses are accepted and this is evident from standardized path coefficients.
Self-supervised Visual Reinforcement Learning with Object-centric Representations

Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoencoders to encode a scene into a low-dimensional vector that can be used as a goal for an agent to discover new skills. Nevertheless, in compositional/multi-object environments it is difficult to disentangle all the factors of variation into such a fixed-length representation of the whole scene. We propose to use object-centric representations as a modular and structured observation space, which is learned with a compositional generative world model. We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills. These skills can be further combined to address compositional tasks like the manipulation of several different objects.
Full Text Coverage Ratios: A Simple Method of Article-Level Collections Analysis

Traditional methods of collections evaluation and assessment that focus on the journal container are increasingly in tension with both user experience and academic discovery systems design, both of which prioritize the individual item or article. This paper explores the potential applications and limitations of a new metric, the full text coverage ratio (FTCR), for revealing new insights about academic serials collections. Given a discovery service search that represents a particular subject area, the FTCR is the ratio between the number of items returned by a search scoped to full text and its corresponding “expanded” or un-scoped search. While the idea itself offers the potential for valuable and actionable insights, it may be that further analysis and refinement is required before it can be more widely adopted. This paper will describe these ratios in more detail, explain how they can be calculated, discuss some premises or prerequisites they require, present some of their limitations, briefly describe an application already pursued at one particular institution, and suggest additional applications and potential future directions for research in this area.
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, The Netherlands

Summary Deep learning is fast emerging as a potential disruptive tool to tackle longstanding research problems across the sciences. Notwithstanding the success of deep learning in many disciplines, the recent trend toward its overuse is raising concerns among many machine learning practitioners. Recently, seismologists demonstrated the efficacy of deep learning algorithms in detecting low-magnitude earthquakes. Here, we revisit the problem of seismic event detection but use a logistic regression model with feature extraction rather than a deep learning model. We select well-discriminating features from a huge database of time-series operations collected from interdisciplinary time-series analysis methods. With a simple learning model, we detect several low-magnitude induced earthquakes from the Groningen gas field that are not present in the catalog. Our results suggest that the added advantage of simpler models is that the selected features add to our understanding of the noise and event classes present in the dataset. Because simpler models are easy to maintain, debug, understand, and train, through this study we underscore that it might be a dangerous pursuit to use deep learning without carefully weighing simpler alternatives.
When does preconditioning help or hurt generalization

While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization has been called into question. This work presents a more nuanced view on how the \textit{implicit bias} of optimizers affects the comparison of generalization properties. We provide an exact asymptotic bias-variance decomposition of the generalization error of preconditioned ridgeless regression in the overparameterized regime, and consider the inverse population Fisher information matrix (used in NGD) as a particular example. We determine the optimal preconditioner P for both the bias and variance, and find that the relative generalization performance of different optimizers depends on label noise and ``shape'' of the signal (true parameters): when the labels are noisy, the model is misspecified, or the signal is misaligned with the features, NGD can achieve lower risk; conversely, GD generalizes better under clean labels, a well-specified model, or aligned signal. Based on this analysis, we discuss several approaches to manage the bias-variance tradeoff, and the potential benefit of interpolating between first- and second-order updates. We then extend our analysis to regression in the reproducing kernel Hilbert space and demonstrate that preconditioning can lead to more efficient decrease in the population risk. Lastly, we empirically compare the generalization error of first- and second-order optimizers in neural network experiments, and observe robust trends matching our theoretical analysis.
How Will Blockchain Technology Impact Auditing and Accounting? Permissionless vs. Permissioned Blockchain

Blockchain offers a drastically new way to record, process, and store financial transactions and information, and has the potential to fundamentally change the landscape of the accounting profession and reshape the business ecosystem. In this article, we introduce two types (i.e. permissionless and permissioned) of blockchain and layout their technological features. We further discuss implication of blockchain to auditing and elaborate opportunities and challenges of two types of blockchain to auditors. We conclude by making specific recommendations for auditors to adapt, adjust, and elevate themselves to the role of strategic partners in blockchain implementation.
Smartphone + Teaching: Delivery of an Undergraduate Surveying Course Using Rain Classroom

Traditional teaching methods using a blackboard, PowerPoint presentations and resources available through the Internet still play a leading role in university education. The popularity of smartphones and the use of We-Media and other information communication technologies have introduced the possibility of new teaching methods. This new technology combined with a constant endeavor at institutions of higher education to find more efficient teaching and learning tools brings us to a new teaching mode named "smartphone+". This represents an integration of traditional teaching tools and smartphones and can be realized using currently-available smartphone-based APPs. A practical application of the smartphone+ teaching mode has been implemented using the Rain Classroom APP in a surveying course for undergraduates majoring in Geomatics at Lanzhou Jiaotong University. The experiment has shown that the Rain Classroom-based smartphone+ teaching mode is an efficient method of conveying subject matter and is a valuable supplement to traditional teaching modes. It has a number of advantages over traditional teaching methods including online quizzes and tests, real time assessments, fast interaction, before-class preview and after-class online assessments.
Geometric Graphs from Data to Aid Classification Tasks with Graph Convolutional Networks

Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here we show that, even if additional relational information is not available in the data set, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world data sets from various scientific domains.
Socio-cultural Aspects of Informatization in the Context of the Effective General (Secondary) Education Formation

This article is devoted to the preparation of an information base for the formation of criteria for assessing the process of general (secondary) education informatization. The situation in education can be considered as unstable, lacking a clear understanding of the social, psychological and technological prospects for the Russian and world communities’ development, which excludes the possibility of correct strategic planning and making correct management decisions. The development source of uncertainty is the very transitional nature of the current situation in ICT — equally in the technological, economic, sociocultural and psychological aspects. The research problematizes the well-established idea of the modern relations of education and ICT, emphasize the lack of empirical data and the bias towards a normative approach in the field of managerial decisions, which in the face of uncertainty leads to the adoption of erroneous decisions (a tactics and strategy contradiction). The research has determined the prospects for a monitoring research system designed to obtain the necessary empirical data in the system, taking into account the change in the technological and sociocultural paradigm over the past decade.&#x0D; Keywords: information society, digitalization of education, efficiency in education, digital divide, resistance to innovation
Utilizing Indonesian Universal Language Model Fine-tuning for Text Classification

Inductive transfer learning technique has made a huge impact on the computer vision field. Particularly, computer visionÂ applications including object detection, classification, and segmentation, are rarely trained from scratch; instead, they are fine-tuned from pretrained models, which are products of learning from huge datasets. In contrast to computer vision, state-of-the-art natural language processing models are still generally trained from the ground up. Accordingly, this research attempts to investigate an adoption of the transfer learning technique for natural language processing. Specifically, we utilize a transfer learning technique called Universal Language Model Fine-tuning (ULMFiT) for doing an Indonesian news text classification task. The dataset for constructing the language model is collected from several news providers from January to December 2017 whereas the dataset employed for text classification task comes from news articles provided by the Agency for the Assessment and Application of Technology (BPPT). To examine the impact of ULMFiT, we provide a baseline that is a vanilla neural network with two hidden layers. Although the performance of ULMFiT on validation set is lower than the one of our baseline, we find that the benefits of ULMFiT for the classification task significantly reduce the overfitting, that is the difference between train and validation accuracies from 4% to nearly zero.
Cybersecurity Management for Virtual Private Network (VPN) Applications: A Proposed Framework for the Governance of their Use in the Kingdom of Saudi Arabia

By using the research process, this study addresses the attitudes of the members of Saudi society towards using Virtual Private Network (VPN) applications and the former’s perceptions of the latter’s concept, security, and privacy, in addition to monitoring their risks to cybersecurity. The main objective of the study is to present a proposed framework for the governance of the use of VPN applications in the Kingdom of Saudi Arabia to strengthen cybersecurity management. To achieve the objectives of the study, the researchers used two methods: the social survey method and the content analysis method. The researchers also relied on the questionnaire tool to collect information from the 455 individuals in the study sample. The study yielded a set of findings, the most important of which are as follows: The use of free VPN applications represents 91% of the sample of the study. Also, the study revealed confusion in perceptions of the actual concept of VPN applications, which may be common among users, and showed a diversity of attitudes and motives for using VPN applications, most of which are related to entertainment. Moreover, the study showed that the study sample individuals had some knowledge about the risks of VPN applications to cybersecurity, although most of them did not use cyber protection means. The study recommended the need to organize and manage the use of VPN applications, conduct an evaluation of VPN applications available on Saudi smartphone stores, and prohibit those which contain security vulnerabilities and malware. The study proposed a framework for the governance of the use of VPN applications in the Kingdom of Saudi Arabia, which comprised three dimensions: legal, organizational and awareness-based dimensions.
Automated journalism and freedom of information: ethical and juridical problems related to AI in the press field

Journalism and the Press have always been deeply influenced by technological changes, and so they are in the digital world: from the competition of new media and the challenges of the Web 2.0 to the creation of a new way to produce news, i.e. automated journalism. Between the different notions of the use of AI in the Press field (automated journalism, robot journalism, News-Writing Bots, algorithmic journalism) in this paper the wording “automated journalism” is preferred as long as it seems to describe in a better way the practice of this type of journalism and it seems more used by the scholars who have studied this topic. Automated journalism is the use of AI, i.e. software or algorithms, in order to automatically generate news stories without any contribution of human beings, apart from that of programmers who (eventually) have developed the algorithm. This paper aims to analyse the ethical and juridical problems of automated journalism, in particular, looking at the freedom of information and focusing on the issue of liability and responsibility. From a legal point of view, the analysis shall embrace and share the European concept of the freedom of information and media regulation, focusing in particular on the Italian legal system. Indeed in the range of European legal systems, the Italian system has more broadly developed the idea of freedom of information, and it has multiple approaches to the topic, which are partially explored here. The first paragraph of the paper shall explore the field of the media outputs in which automated journalism – as currently developed – could produce innovations and could be implemented. The utilization of the Italian model serves to understand how the pieces of automated journalism could be framed from a legal point of view. The second paragraph shall analyse the legal and ethical problem of automated journalism by looking at the problems of liability and data use. As a consequence, a first section shall be dedicated to the issue of liability and another one to that of data utilization. In the final remarks, some solutions and guidelines shall be proposed looking at the problems highlighted in the paper.
The Influence of using Hangman game on the seventh graders’ vocabulary mastery

Vocabulary is an important component in learning a language. Vocabulary is used to communicate to each other through listening, writing, speaking, and reading. These skills are closely related to vocabulary mastery. However, students have difficulties in understanding the vocabularies from their reading or listening activities. This research belongs to quantitative study using quasi experimental of nonequivalent control group design to find out the influence of using Hangman game on the seventh graders’ vocabulary mastery. The data were collected through a multiple-choice test. The hypothesis is tested using t-test. From the calculation of t-test, it was found that tobserved = 2.7, with df = 30+29-2 = 57 in ttable = 2.0, because tobserved ≥ ttable or 2.7 ≥ 2.0. Therefore, it can be concluded H0 is rejected, while Ha is accepted. In other words, “Hangman game influences the seventh graders’ vocabulary mastery.”
Penerapan Algoritma K-Means Clustering Dalam Persentase Merokok Pada Penduduk Umur Di Atas 15 Tahun Menurut Provinsi

This research aims to determine the number of people who are smoking the age of 15 years by applying Data Mining. The application uses the K-means algorithm where the data entered is the percentage of smoking. Using this method the data obtained can be grouped into 2 klasters. This research uses secondary data that is obtained from intermediate media that is recorded on the site of the Central Statistics website with the URL address https://www.bps.go.id/. The results gained in this study are grouping the number of smoking percentages that are grouped by as many as 2 klasters is klaster 1 (highest klaster) to get results 22 provinces and klaster 2 (lowest klaster) get 12 provinces.
Students’ expectations in EFL classroom: A case study at management department of a private university in Yogyakarta

Students have their own expectations in the teaching and learning process in the classroom. Students’ expectations are also one of the important things that the lecturer should know and do to get a good outcome. The purpose of this study is to identify students’ expectations in the teaching and learning process in English class. This study uses a qualitative method. To collect the data, the researchers used observation and semi-interview methods. Observation was carried out to investigate the students’ and lecturer’s activities in the classroom, while interview was used to examine the students’ expectation related to the use of English in the future, the content of English subject, and their learning style. Seventh students in the first semester of the undergraduate Management Department became the participants of this study. The result of the study shows that all students expect that they would learn all skills of English as well as grammar and pronunciation. They also believe that they would get good jobs if they could master English well.
Deep learning object detection as an assistance system for complex image labeling tasks

Object detection via deep learning has many promising areas of application.However, robustness and accuracy of fully automated systems are often insufficient for practical use.Integrating results from Artificial Intelligence (AI) and human intelligence in collaborative settings might bridge the gap between efficiency and accuracy.This study proves increased efficiency when supporting human intelligence through AI without negative impact on effectiveness in a finegrained car scratch image labeling task.Based on the confirmed benefits of AI with human intelligence in the loop approaches, this contribution discusses potential practical application scenarios and envisions the implementation of assistance systems supported by computer vision.