Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the duality of convention and ritual: A contrastive pragmatic inquiry

Abstract The aim of this paper is to provide a pragmalinguistically inspired framework for analysing the relationship between linguistic forms and conventional and ritual behaviour. To date, no body of pragmalinguistic research has been dedicated to the relationship between conventional and ritual phenomena, which play a fundamental role in language use. Even more importantly, the examination of this phenomenon provides insight into a longstanding issue in pragmatics, namely, how the relationship between form and language use can be pinned down. We will pursue this question on the basis of an English and Chinese corpus-based examination of expressions, which we define as ‘ritual frame indicating expressions’ (RFIEs). As a case study, we examine the Chinese RFIE ‘duibuqi’ and its English counterpart ‘sorry’. The results indicate that while ‘sorry’ is largely used in a conventional way throughout the contexts in our English corpus, ‘duibuqi’ is predominantly anchored in ritual. This, in turn, reveals how these forms are related to convention v. ritual as far as our data is concerned.
Deep Learning: The Impact on Future eLearning

eLearning as technology becomes more affordable in higher education but having a big barrier in the cost of developing its resources. Deep learning using artificial intelligence continues to become more and more popular and having impacts on many areas of eLearning. It offers online learners of the future with intuitive algorithms and automated delivery of eLearning content through modern LMS platforms. This paper aims to survey various applications of deep learning approaches for developing the resources of the eLearning platform, in which predictions, algorithms, and analytics come together to create more personalized future eLearning experiences. In addition, deep learning models for developing the contents of the eLearning platform, deep learning framework that enable deep learn-ing systems into eLearning and its development, benefits & future trends of deep learning in eLearning, the relevant deep learning-based artificial intelligence tools and a platform enabling the developer and learners to quickly reuse resources are clearly summarized. Thus, deep learning has evolved into developing ways to re-purpose existing resources can mitigate the expense of content development of future eLearning.
Data model for additive manufacturing digital thread: state of the art and perspectives

ABSTRACT Additive Manufacturing (AM) is currently seen as an interesting process for the fabrication of complex and high value-added functional products. AM is being increasingly used in manufacturing bringing forward technological advancements particularly in materials and process optimisations. Simultaneously, the lifecycle of manufactured products is increasingly based on an ‘all-digital’ context using cyber-physical systems, the Internet of Things and Industry 4.0 devices. This has led to huge amounts of digital data being generated in manufacturing projects development. Data management is one of the key issues for diffusing and adopting this technology. However, the commonly used digital thread for AM is based on old solutions, such as STL and G-code, which were developed during the 1980s and are not well synchronised with current advanced developments in AM. Moreover, digital thread needs to be contextualised according to the complexities of the sector in which it is being used. Hence, alternative formats should also take into account global and sectorial application. This paper presents the past and current research for a state-of-the-art data model digital thread, as well as perspectives and recommendations for a performing data model thread adapted to the different needs of the AM community.
Privacy-Aware Personalized Entity Representations for Improved User Understanding

Representation learning has transformed the field of machine learning. Advances like ImageNet, word2vec, and BERT demonstrate the power of pre-trained representations to accelerate model training. The effectiveness of these techniques derives from their ability to represent words, sentences, and images in context. Other entity types, such as people and topics, are crucial sources of context in enterprise use-cases, including organization, recommendation, and discovery of vast streams of information. But learning representations for these entities from private data aggregated across user shards carries the risk of privacy breaches. Personalizing representations by conditioning them on a single user’s content eliminates privacy risks while providing a rich source of context that can change the interpretation of words, people, documents, groups, and other entities commonly encountered in workplace data. In this paper, we explore methods that embed user-conditioned representations of people, key phrases, and emails into a shared vector space based on an individual user’s emails. We evaluate these representations on a suite of representative communication inference tasks using both a public email repository and live user data from an enterprise. We demonstrate that our privacy-preserving light-weight unsupervised representations rival supervised approaches. When used to augment supervised approaches, these representations are competitive with deep-learned multi-task models based on pre-trained representations.
Guideline to Develop an Instructional Design Model Using Video Conference in Open Learning

This research is a quantitative phenomenology study with objectives to (1) Synthesize literature related to teaching using video conference (2) Study opinions from experts' experience on the design of teaching using video conference and (3) Use as a guideline for the design of instructional video conference. The sample group in this research is 25 experts holding an academic position with at least 15 years of experience in teaching technology and education communication. The author uses the purposive sampling method. The data collection tool is an in-depth interview. The data is analyzed by the content analysis approach. The research shows that the instructional design model using video conference consists of connected elements where each item is linked together and becomes an integral part of each other, namely (1) Teaching strategy (2) Preparation of instructor (3) Media and technology (4) Case study (5) Group process and (6) Idea sharing activity.
An Evaluation of Online and Automated English Writing Assistants: Collocations and Idioms Checkers

In this paper, I attempt to investigate whether online writing checkers, including tools looking at grammar, punctuation, spelling and composition, do in fact help potential ESL writers, and if so, to which extent, in what areas, to what effect and at what cost. The small-scale study involves 23 university level ESL students who compose various texts as part of their training. Data was collected using pre and post writing samples, questionnaires and semi-structured interviews. The analysis used a descriptive statistical method, Likert-scale and correlations and qualitative analysis for the three methods respectively. The results are inconclusive as students reported different opinions. It was found however that students who are frequent users of features such as MS Word Thesaurus and Online Corpora are more likely to incorporate online writing checkers and therefore found them more helpful. It was also found that regardless of their effect, students always welcomed tools available that can provide feedback to their compositions. The writing software used, AccurIT, also showed improvements in students’ ability to write correct collocations.
Improving Primary Health Care: A Systematic Review

Primary health care is the key to build a strong healthcare system. Despite the advances already achieved, the development of approaches to improve the services and practices to respond to challenges are required. This review aims to show approaches or strategies to improve primary health care. Searches were conducted in CINAHL, MEDLINE and SCIELO to identify literature from 2011 to 2015. The inclusion criterion was empirical studies addressed to relevant strategies related to primary health care attributes. The major articles were related to support of care, programs or models to manage the practices, and technology tools. Fourteen articles were related to strategies to improve chronic illnesses care. Seventeen articles aimed to improve coordination of care or comprehensiveness. The intervention strategies related to support of care through mobile or email communication in general were effective in the control of cardiovascular risk factors or chronic conditions. Technology tools showed potential for directly engaging patients in their care and improving the feasibility of collecting aggregate data from independent practices. Sharing experiences and effective practices is an important tool to develop primary health care, and this review may be a relevant mechanism to identify challenges and possible solutions to overcome obstacles and achieve better health outcomes. *Corresponding author Carlos Antonio Bruno da Silva, Universidade de Fortaleza, Programa de Saúde Coletiva. Av. Washington Soares, 1321, Bloco S, Sala S-11, Edson Queiroz, Fortaleza (CE), Brazil, CEP: 60811-904; Tel: 55-8534773280; Email:
Robust H∞ controller design for uncertain 2D continuous systems with interval time-varying delays

ABSTRACT The design of delay-dependent robust controllers is solved here for a class of uncertain 2D continuous systems: those with interval time-varying delays and norm-bounded parameter uncertainties. By constructing a novel augmented Lyapunov–Krasovskii functional and then using the Wirtinger inequality, a new delay-dependent stability condition is developed, that uses the known lower and upper bounds of the time-varying delays to develop less conservative solutions that previous results in the literature. This condition is then applied to performance analysis and robust controller design, using linear matrix inequalities (LMIs). Two numerical examples are presented that illustrate the effectiveness of the proposed method.
An Empirical Investigation on Students' Acceptance of (SM) Use for Teaching and Learning

Abstract—The current work aims at developing a model of teaching and learning measurement through using social media, incorporating the literature of social media adoption on resource sharing, collaborating and communicating for educational purpose. The current study hypothesizes that perceived usefulness, perceived ease of use, attitude toward use, and social media use have certain influence on adoption of resource sharing, collaboration and communication for educational use. Therefore, resource sharing, collaboration and communication influence educational use, while educational use influences perceived ease of use, perceived usefulness, social media use and attitude toward the use of social media for teaching and learning. A Technology Acceptance Model (TAM) version was utilized in this research to be the main framework. Both the processes of collecting and analysing the data followed the quantitative approach. The main tool of data collection was a survey that has been distributed among 236 students using stratified random sampling technique. The view of the students and their implication of social media use for teaching and learning were solicited through the survey. Structure Equation Modelling (SEM) was used as the main tool in the process of data analysis. The results of this study were related to two main constructs: teaching and learning as well as educational use. According to the results, it appears that perceived usefulness, perceived ease of use, attitude toward use, and social media are considered powerful determinants of the former while resource sharing, collaboration and communication were significant indicators of the latter. Educational use, perceived usefulness, perceived ease of use, attitude toward use succeeded in explaining 65.5% of social media use for teaching and learning.
The Traveling Analyst Problem: Definition and Preliminary Study

This paper introduces the Traveling Analyst Problem (TAP), an original strongly NP-hard problem where an automated algo-rithm assists an analyst to explore a dataset, by suggesting the most interesting and coherent set of queries that are estimated to be completed under a time constraint. We motivate the problem, study its complexity, propose a simple heuristic under simplifying assumptions for approximating it, and run preliminary tests to observe the behavior of this heuristic.
Is It Possible to Preserve Privacy in the Age of AI?

Artificial Intelligence (AI) hopes to provide a positive paradigm shift in technology by providing new features and personalized experience to our digital and physical world. In the future, almost all our digital services and physical devices will be enhanced by AI to provide us with better features. However, as training artificially intelligent models require a large amount of data, it poses a threat to user privacy. The increasing prevalence of AI promotes data collection and consequently poses a threat to privacy. To address these concerns, some research efforts have been directed towards developing techniques to train AI systems while preserving privacy and help users preserve their privacy. In this paper, we survey the literature and identify these privacy-preserving approaches that can be employed to preserve privacy. We also suggest some future directions based on our analysis. We find that privacy-preserving research, specifically for AI, is in its early stage and requires more effort to address the current challenges and research gaps.
Metabolic and taxonomic diversification in continental magmatic hydrothermal systems 4 . 1

Hydrothermal systems integrate geological processes from the deep crust to the Earth’s surface yielding an extensive array of spring types with an extraordinary diversity of geochemical compositions. Such geochemical diversity selects for unique metabolic properties expressed through novel enzymes and functional characteristics that are tailored to the specific conditions of their local environment. This dynamic interaction between geochemical variation and biology has played out over evolutionary time to engender tightly coupled and efficient biogeochemical cycles. The timescales by which these evolutionary events took place, however, are typically inaccessible for direct observation. This inaccessibility impedes experimentation aimed at understanding the causative principles of linked biological and geological change unless alternative approaches are used. A successful approach that is commonly used in geological studies involves comparative analysis of spatial variations to test ideas about temporal changes that occur over inaccessible (i.e. geological) timescales. The same approach can be used to examine the links between biology and environment with the aim of reconstructing the sequence of evolutionary events that resulted in the diversity of organisms that inhabit modern day hydrothermal environments and the mechanisms by which this sequence of events occurred. By combining molecular biological and geochemical analyses with robust phylogenetic frameworks using approaches commonly referred to as phylogenetic ecology [1, 2], it is now possible to take advantage of variation within the present – the distribution of biodiversity and metabolic strategies across geochemical gradients – to recognize the extent of diversity and the reasons that it exists. The distribution of organisms and their metabolic functions in modern environments is rooted in the selection for physiological adaptations that allowed variant populations to radiate into new ecological niches. Such radiation events are recorded in extant organismal distribution patterns (e.g. habitat range), aswell as in the genetic record of the organisms as they are distributed along environmental gradients [3, 4]. These patterns are the result of vertical descent, which is a primary means by which organisms inherit their metabolic or physiological potential from their ancestors, although horizontal gene transfer (HGT), gene loss, and gene fusions are also likely to influence extant distribution patterns. Overall, these phenomena manifest as a posi-
Combinatorial Construction of Seamless Parameter Domains

The problem of seamless parametrization of surfaces is of interest in the context of structured quadrilateral mesh generation and spline‐based surface approximation. It has been tackled by a variety of approaches, commonly relying on continuous numerical optimization to ultimately obtain suitable parameter domains. We present a general combinatorial seamless parameter domain construction, free from the potential numerical issues inherent to continuous optimization techniques in practice. The domains are constructed as abstract polygonal complexes which can be embedded in a discrete planar grid space, as unions of unit squares. We ensure that the domain structure matches any prescribed parametrization singularities (cones) and satisfies seamlessness conditions. Surfaces of arbitrary genus are supported. Once a domain suitable for a given surface is constructed, a seamless and locally injective parametrization over this domain can be obtained using existing planar disk mapping techniques, making recourse to Tutte's classical embedding theorem.
Does Technological Pedagogical Content Knowledge Impact on the Use of ICT In Pedagogy?

The development of information and communication technology (ICT) has led to opportunities and challenges in the educational ideas and practices. This study examines the impact of technological pedagogical content knowledge on the use of ICT in pedagogy. This study applied a quantitative method using Structural Equation Model (SEM). The population of the study was teacher in Junior high School in Mojokerto of East Java Indonesia while the sample was about 302 participants gathered by using proportionate stratified random sampling. The findings showed that first, technological pedagogical content knowledge directly did not affect teachers’ self-efficacy. Second, technological pedagogical content knowledge influences the use of ICT in pedagogy. Third, organizational innovative climate directly influences the teachers’ self-efficacy. Indeed, organizational innovative climate affects the use of ICT in pedagogy. Fifth, teachers’ self-efficacy influences the use of ICT in pedagogy. Sixth, technological pedagogical content knowledge mediated by teachers’ self-efficacy does not directly affect the use of ICT in pedagogy. Lastly, organizational innovative climate-mediated by teachers’ self-efficacy affects the use of ICT in pedagogy. This research could be the first step for a similar study in the future.
Level of physical activity is positively correlted with perceived impact on life 12 months after stroke: A cross-sectional study.

OBJECTIVES To examine the relationship between, and impact of, level of physical activity and perceived impact on life at 12 months post-stroke. DESIGN Cross-sectional study. SUBJECTS A total of 73 participants with first-time stroke included in the Stroke Arm Longitudinal study at the University of Gothenburg (SALGOT study), Sweden. METHODS Perceived impact of stroke was assessed with the Stroke Impact Scale and level of physical activity was assessed with the Saltin-Grimby Physical Activity Scale at 12 months post-stroke. Data were presented with descriptive and logistic regression analyses. RESULTS The physically active group perceived their strength, emotion, mobility, participation and overall stroke recovery as significantly less problematic compared with the inactive group. Being physically active contributed to higher scores in the Strength domain (odds ratio, OR 7.98) and in Stroke Recovery (OR 18.55). In the Participation domain being physically active (OR 8.01) and independent (OR 0.162) contributed to higher scores. CONCLUSION A positive correlation was found between level of physical activity at 12 months post-stroke and levels of strength, participation and stroke recovery.
Clinical outcomes after topography-guided LASIK: comparing results based on a new topography analysis algorithm to those based on the manifest refraction.

(250 words max) PURPOSE:: To compare short-term refractive and visual acuity outcomes after topography-guided laser in situ keratomileusis (LASIK) planned with a new topography analysis system to those based on the manifest refraction. SETTING Retrospective data from 4 clinical sites in the USA. DESIGN Double-arm, non-masked, non-randomized retrospective chart review. METHODS This was a retrospective study of postoperative refraction and visual acuity at least 2 months after uncomplicated LASIK using the Contoura Vision algorithm on the WaveLight Topolyzer VARIO laser. One arm comprised eyes treated using the manifest refraction (Manifest), while the other included eyes treated with an ablation profile determined by the Phorcides Analytic Engine (Analytic). RESULTS Clinical results from a matched group of 317 Manifest eyes and 323 Analytic eyes were available for analysis. Residual refractive results, both sphere and cylinder, were similar between groups. However, significantly more eyes had 20/16 or better (-0.1 logMAR) uncorrected distance visual acuity (UDVA) (62.5% Analytic, 41.3% Manifest) and corrected distance visual acuity CDVA (77.1% Analytic, 51.4% Manifest) in the Analytic group. All eyes but 1 had a CDVA of 20/20 or better after surgery. The number of patients with a UDVA better than their preoperative CDVA was significantly higher in the Analytic group (36.5%) relative to the Manifest group (23.0%). No eye in either group lost more than 1 line of CDVA; significantly more eyes in the Analytic group (42.7%) gained 1 or more lines of CDVA relative to the Manifest group (30.3%). CONCLUSIONS Using the Phorcides Analytic Engine for topography-guided surgery planning increased the likelihood of 20/16 UDVA and CDVA relative to using the manifest refraction.
Use of dienogest in endometriosis: a narrative literature review and expert commentary

Abstract Objective: Endometriosis affects up to 10% of women of reproductive age, and the main goal of treatment is to relieve symptoms. Progestins have been the mainstay of endometriosis suppression, of which dienogest has become an important option in many parts of the world. This is an expert literature review, with recommendations on the use of dienogest in the context of various clinical considerations when treating endometriosis. Methods: A search of PubMed was conducted for papers published between 2007 and 2019 on the use of dienogest in endometriosis. Experts reviewed these and included those they considered most relevant in clinical practice, according to their own clinical experience. Results: Evidence regarding the long-term use (>15 months) of dienogest for the management of endometriosis is presented, with experts concluding that the efficacy of dienogest should be assessed primarily on its impact on pain and quality of life. Fertility preservation, the option to avoid or delay surgery, and managing bleeding irregularities that can occur with this treatment are also considered. Counseling women on potential bleeding risks before starting treatment may be helpful, and evidence suggests that few women discontinue treatment for this reason, with the benefits of treatment outweighing any impact of bleeding irregularities. Conclusions: Overall, the evidence demonstrates that dienogest offers an effective and tolerable alternative or adjunct to surgery and provides many advantages over combined hormonal contraceptives for the treatment of endometriosis. It is important that treatment guidelines are followed and care is tailored to the woman’s individual needs and desires.