Real-world Knowledge Research Articles

Is dental autotransplantation underestimated and underused by Syrian dentists?

Dental autotransplantation (DAT) is a surgical procedure in which a donor's tooth is extracted and transplanted from one site to another in the same person. This treatment modality has received considerable attention worldwide in recent years due to its potential advantages over implants. A survey-based study evaluated dentists’ attitudes towards and practice of DAT in Damascus, Syria from September to December 2020. We asked respondents whether they considered this treatment modality when developing treatment plans and whether they view it viable. Only 73 of the 258 respondents (28.3%) stated that they considered DAT as a treatment option. Additionally, 153 respondents (59.3%) either did not view DAT as a viable treatment option or did not know whether it is viable. DAT was underestimated and underused among Syrian dentists. Given this gap in real-world knowledge and practice, academic dental institutions in Syria should place a greater focus on emerging evidence-based knowledge and protocols regarding this treatment option.

Soloist: BuildingTask Bots at Scale with Transfer Learning and Machine Teaching

AbstractWe present a new method, Soloist,1 that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i)Soloist creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, Soloist significantly outperforms existing methods; and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.

Developing leadership qualities in environmental sustainability through university co-curricular activities

Purpose This study aims to provide insight into the question of whether graduate students who deliver environmental education workshops/residencies to elementary school children will develop environmental sustainability leadership qualities in themselves: a goal set in the University of Calgary’s Institutional Sustainability Strategy. Design/methodology/approach Survey research was undertaken in a case study setting. The researchers collected and analyzed data related to environmental leadership qualities, using the theory of planned behavior and an adaptation of the competing values framework. Graduate students participating in the co-curricular program responded to questions about the effect that the activity had on their knowledge, awareness and leadership characteristics. Findings Graduate students demonstrated considerable leadership potential in environmental sustainability. The survey results showed that their participation in a community educational program impacted their attitudes and awareness favorably in developing stronger competencies for leadership. In addition, they gained real-world knowledge about environmentally sustainable practices and skills to influence pro-environmental behavior changes in the community. Originality/value Through a partnership between a non-profit organization and the university, graduate students in an interdisciplinary sustainable energy development program used their formal education and previous work experience to adapt and deliver engaging and educational environmental content to younger children. This informal co-curricular activity brought together local educational institutions, educational content providers, graduate students, and elementary school children in an effective experiential learning platform to develop leadership characteristics both in the graduate students and elementary school children.

Nursing Students' Perceptions about Effective Pedagogy: Netnographic Analysis.

BackgroundEffective pedagogy that encourages high standards of excellence and commitment to lifelong learning is essential in health professions education to prepare students for real-life challenges such as health disparities and global health issues. Creative learning and innovative teaching strategies empower students with high-quality, practical, real-world knowledge and meaningful skills to reach their potential as future health care providers.ObjectiveThe aim of this study was to explore health profession students’ perceptions of whether their learning experiences were associated with good or bad pedagogy during asynchronous discussion forums. The further objective of the study was to identify how perceptions of the best and worst pedagogical practices reflected the students’ values, beliefs, and understanding about factors that made a pedagogy good during their learning history.MethodsA netnographic qualitative design was employed in this study. The data were collected on February 3, 2020 by exporting archived data from multiple sessions of a graduate-level nursing course offered between the fall 2016 and spring 2020 semesters at a large private university in the southeast region of the United States. Each student was a data unit. As an immersive data operation, field notes were taken by all research members. Data management and analysis were performed with NVivo 12.ResultsA total of 634 posts were generated by 153 students identified in the dataset. Most of these students were female (88.9%). From the 97 categories identified, four themes emerged: (T) teacher presence built through relationship and communication, (E) environment conducive to affective and cognitive learning, (A) assessment and feedback processes that yield a growth mindset, and (M) mobilization of pedagogy through learner- and community-centeredness.ConclusionsThe themes that emerged from our analysis confirm findings from previous studies and provide new insights. Our study highlights the value of technology as a tool for effective pedagogy. A resourceful teacher can use various communication techniques to develop meaningful connections between the learner and teacher. Styles of communication will vary according to the unique expectations and needs of learners with different learning preferences; however, the aim is to fully engage each learner, establish a rapport between and among students, and nurture an environment characterized by freedom of expression in which ideas flow freely. We suggest that future research continue to explore the influence of differing course formats and pedagogical modalities on student learning experiences.

No Free Lunch in imbalanced learning

The No Free Lunch (NFL) theorems have sparked intense debate since their publication, from theoretical and practical perspectives. However, to this date, no discussion is provided concerning its impact in the established field of imbalanced domain learning (IDL), known for its challenges regarding learning and evaluation processes. Most importantly, understanding the effect of commonly used solutions in such a field would prove very useful for future research. In this paper, we study the impact of data pre-processing methods, also known as resampling strategies, under the framework of the NFL theorems. Focusing on binary classification tasks, we claim that in IDL settings, when given a learning algorithm and a uniformly distributed set of target functions, the core conclusions of the NFL theorems are extensible to resampling strategies. As such, given no a priori knowledge or assumptions concerning data domains, any two resampling strategies are identical concerning their impact in the performance of predictive models. We provide a theoretical analysis and discussion on the intersection between IDL and the NFL theorems to support such a claim. Also, we collect empirical evidence via a thorough experimental study, including 98 data sets from multiple real-world knowledge domains.

Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs

Inductive link prediction---where entities during training and inference stages can be different---has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing approaches do not take into account semantic correlations between relations, which are commonly seen in real-world knowledge graphs. To address this challenge, we propose a novel inductive reasoning approach, namely TACT, which can effectively exploit Topology-Aware CorrelaTions between relations in an entity-independent manner. TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs. Specifically, we categorize all relation pairs into several topological patterns, and then propose a Relational Correlation Network (RCN) to learn the importance of the different patterns for inductive link prediction. Experiments demonstrate that TACT can effectively model semantic correlations between relations, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the inductive link prediction task.

Relational Learning to Capture the Dynamics and Sparsity of Knowledge Graphs

The rapid growth of large scale event datasets with timestamps has given rise to the dynamically evolving multi-relational knowledge graphs. Temporal reasoning over such data brings on many challenges and is still not well understood. Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This observation has given rise to the recent interest in low-shot learning methods that are able to generalize from only a few examples. The existing approaches, however, are tailored to static knowledge graphs and not easily generalized to temporal settings, where data scarcity poses even bigger problems, due to the occurrence of new, previously unseen relations. The goal of my doctoral research is to introduce new approaches for learning meaningful representation that capture the dynamics of temporal knowledge graphs while tackling various existing challenges such as data scarcity.

FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion.

Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.

A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain.

With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.

Student Actions for Social Justice-Oriented Mathematical Tasks

Five approaches led to multiple valid solutions, considered students’ real-world knowledge, and increased students’ interest.

Protocol for the Acute Myocardial Infarction Study in Northeastern China (AMINoC): a real-world prospective cohort study.

IntroductionAcute myocardial infarction (AMI) has become one of the major causes of mortality and morbidity in China. However, little is known about the characteristics, medical care and outcomes of patients with AMI in Northeastern China. The Acute Myocardial Infarction Study in Northeastern China (AMINoC) is aimed at obtaining timely real-world knowledge in terms of characteristics, clinical care and outcomes of patients with AMI and at providing care-quality improvement efforts in Northeastern China.Methods and analysisThe AMINoC is a real-world, prospective, multicentre cohort study. The study selected 20 hospitals using stratified cluster sampling from different levels of hospitals among nine districts throughout Jilin Province. Hospitalised patients with a primary diagnosis of AMI in each site are consecutively enrolled for 1 year. Demographic characteristics, clinical data, treatments, outcomes and cost are collected by local investigators. Patient follow-up after discharge is planned for up to 2 years.Ethics and disseminationThe protocol has been approved by the ethics committee at the Second Hospital of Jilin University. The findings of this study will be published in peer-reviewed journals and medical conferences.Trial registration NCT04451967.

DATAtourist

Collaborative and content-based recommender systems are widely employed in several activity domains helping users in finding relevant products and services (i.e., items). However, with the increasing features of items, the users are getting more demanding in their requirements, and these recommender systems are becoming not able to be efficient for this purpose. Built on knowledge bases about users and items, constraint-based recommender systems (CBRSs) come to meet the complex user requirements. Nevertheless, this kind of recommender systems witnesses a rarity in research and remains underutilised, essentially due to difficulties in knowledge acquisition and/or in their software engineering. This paper details a generic software architecture for the CBRSs development. Accordingly, a prototype mobile application called DATAtourist has been realized using DATAtourisme ontology as a recent real-world knowledge source in tourism. The DATAtourist evaluation under varied usage scenarios has demonstrated its usability and reliability to recommend personalized touristic points of interest.

Employee’s Attractiveness in A Change Phenomenon

Every day, changes come to meet human’s daily life. This is basically related to the technological domination that has been changing individuals’ behavior. Considering the human resources being the core capital of the company, the latter is automatically and directly affected by this change phenomenon; especially in attracting talented employees which is the first step of the recruitment process. In front of this situation, the company is facing the challenge of proving its agility and providing solutions, especially that the job market is nowadays marked by the increasing talent war and the extensive complexity. The company needs to go with the flow and to surf on the wave of the change, in order to keep its place in the market or even to create a competitive edge. In this article we will be studying the characteristics that define the 45-65’s and the Y generations behavior. Through this study we will find out the new elements that attract the new generation (generation Y), to finally illustrate how can the employer (company) bend the new situation to his advantage and develop a new attractiveness strategy using the Y generation tools. The study is first based on a deep review and analysis of literature. Then, a qualitative ethnographic case study bringing out contextual real-world knowledge about the behaviors, social structures and shared beliefs of a generation Y belonging to the call center industry.

Facebook-supported tasks for exploring critical and creative thinking in a physics teaching course

Research shows that traditional teacher-and-content-centered education doesn’t give students good preparation in critical and creative thinking. This article presents a qualitative study of student performances in two original learning tasks, one related to critical thinking and the other related to creative thinking. The study was carried out in an obligatory physics teaching course for undergraduate students. The first learning task focused on critical thinking, in which students were asked to evaluate various defects in an artificially contextualized electrostatic exercise. Students’ performances, collected via Google Classroom, show that they were able to detect and justify its contextual defects using real-world knowledge. A big challenge to students was to provide quantitative arguments against noticed huge electric charge allegedly created in described electrostatic cling. The second learning task focused on creative thinking, in which students engaged in a multi-step learning sequence to elaborate one explanation and two predictions related to enigmatic behavior of a tomato. A secret and closed Facebook group was administered to present the subtasks in the sequence and receive students’ answers in real time. The results show that students performed better in the subtasks that called for a near knowledge transfer than in other ones calling for a far knowledge transfer. In their reflective comments about the sequence, students recognized the importance of “thinking out of the box” for deeper learning of physics. Based on the results, suggestions on the design of critical and creative thinking related tasks are discussed for future implementation.

Emosensory Competence, as an Undisclosed Construct of Communicative Competence, Predicts L2 Speaking Ability

Given the undeniable significance of communication in language education, different models have been proposed to render communicative competence (CC) and its constructs. However, there seems to be an undisclosed construct which considers individuals’ real-world knowledge and sensory experiences essential in initiating and maintaining communication. To have an update on the existing models, this study introduces the emosensory account to the underlying competence of CC. Specifically, the study pursued to, first, develop and substantiate the emosensory competence (ESC) questionnaire and, then, to investigate the extent to which ESC predicts L2 speaking ability. To this end, 254 EFL learners filled out the ESC questionnaire in phase 1. In phase 2, the degree of ESC of 184 participants was analyzed in relation to their IELTS speaking scores. Results revealed that the ESC questionnaire enjoys psychometric properties and that, unlike socioemotional and sensory domains, cognitive topics are negative predictors of L2 speaking ability.

83678 Bridging Gaps to Equalize Community-Academic Partnership: A Comparison of Capacities With Research Needs Across CTSA Program Hubs

ABSTRACT IMPACT: Our research identifies key opportunities for increased cross-CTSA collaboration, as a means to improve community-research cooperation and better CBPR practices. OBJECTIVES/GOALS: Currently, team science training prioritizes developing the collaborative competencies of interdisciplinary scientists to work with each other and, more recently, with communities. Community-facing team science resources are scarce but present among some CTSAs, suggesting that capacity gaps might be remedied through cross-hub collaboration. METHODS/STUDY POPULATION: We reviewed online information provided by the 62 current CTSAs to identify: (1) which hubs engage in community research, and (2) what resources the hubs utilize to orient, train, and support community stakeholders as research partners. We then examined the capacities of the collectively available CTSA resources to address needed knowledge, skills, and attitudes that community-engaged researchers have identified as essential for community-based stakeholders to partner equally in research. Finally, we explored practical challenges in team-based dynamics (e.g., interpersonal difficulties, expertise gaps, resource management) that may facilitate or hinder communities’ research endeavors, and suggest resources that CTSAs might implement to facilitate team science dynamics. RESULTS/ANTICIPATED RESULTS: Hubs (n=59) have community engagement programs, 12 of which provide community-based participatory research toolkits. Toolkits vary from basic checklists to fully developed modules. Some hubs also offer consultation services and partner match-making. Learning objectives include: outcome definition, logic models, and goal-setting. Learning resources remain underdeveloped to help communities appreciate the benefits of research engagement and convince academic partners of the value of real-world knowledge and community improvement relative to scientific advancement. Also lacking is easily accessible support to understand the research process, build verifiable trust, maintain bidirectional knowledge and assets, and implement consistent, best practice methodological and reporting protocols. DISCUSSION/SIGNIFICANCE OF FINDINGS: Gaps between current hub offerings and community needs suggest prioritizing creation of resources whose learning objectives highlight the benefits of research engagement for community partners; foster mutual values affirmation between partners; and offer tools that build warranted community-researcher rapport.

English for Professionals students' perceptions of the relevance of internship to their undergraduate courses and career choices

PurposeThis qualitative study examined English for Professionals students' experience of the internship programme and their perceptions of the relevance of the internship programme to their current and future courses and to their future career choices.Design/methodology/approachThis study employed a qualitative inquiry in which qualitative data were collected using journal writing. Using purposeful sampling, 40 English for Professionals students in Universiti Sains Malaysia (USM) were selected. Data were analysed using thematic analysis.FindingsThis study revealed that the internship programme was beneficial to interns because it helped them to gain real-world experience and knowledge about the environment of real workplace. Interns' negative experience can affect their career selection. The majority of the participants revealed that the internship programme is relevant to most of their undergraduate courses. The participants revealed that the internship programme was effective as it helped them to explore their career choices and to select future courses that match their interests.Originality/valueTaking into account students' negative experience and their perceptions of the relevance of internship to their courses and career choices, improvement of undergraduate programmes can be done. Unlike samples in previous studies, the sample in this study is English for Professionals students. The study provides significant findings which are related to interns' perceptions of the relevance of the internship programme to their career choices. Unlike all data collection methods used in previous studies, journal writing was used to collect qualitative data in this study.

Museum Trip to Enrich Environmental Awareness and Education

Real-world knowledge via exposure activities could be positively enriched with the aid of situated learning. In a way to practice this theory and to revolutionize the educational system, this phenomenological-qualitative research was conducted to analyze how natural history museum supports the interest of STEM students towards the environmental issues. Specifically, this study ought to answer the research questions: how did natural history museum improve the interest of STEM students towards environmental issues?, what are the changes in students’ conceptions of learning environmental issues after the museum exposure?, how museums could make environmental issues seem more ‘real’ and urgent?, what opportunities the students received in meeting museum staff?, and how do students compare what they learn and how they learn environmental issues in formal and informal science settings? Data triangulation technique including observation, semi-structured questionnaire and focus group discussion was administered to 20 STEM students enrolled in a private educational institution in the Philippines through purposive-sampling during the year 2019. The results of the study showed patterns according to the students’ individual sensory experiences from the museum visit. The reports of the students were analyzed and emerged in four themes: solving environmental problems by means of education, causes and effects of environmental issues, inquiry-based learning of natural history, and interesting and realistic learning scheme. It was indicated that the natural history museum do support the interest and learning of STEM students towards environmental issues at some aspects.

DisBot: A Portuguese Disaster Support Dynamic Knowledge Chatbot

This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management.

Top-[formula omitted] star queries on knowledge graphs through semantic-aware bounding match scores

Large-scale knowledge graphs containing millions of entities are very common nowadays. Querying knowledge graphs is essential for a wide range of emerging applications, e.g., question answering and semantic search. A star query aims to identify an entity by giving a set of related entities, which is an important query type on knowledge graphs. Answering star queries can be modeled as a graph query problem. Given a query graph Q, the graph query finds subgraphs in a knowledge graph G that match Q. We face two challenges on graph query: (1) existing graph query methods usually find subgraphs that are structurally similar to Q, which cannot measure whether a subgraph match satisfies the semantics of Q (i.e., real query intention), leading to an effectiveness issue, and (2) querying a large-scale knowledge graph is usually time-consuming because of the large search space. In this paper, we propose a Top-k semantic-aware graph query method over knowledge graphs for star queries, which provides semantically similar matches for Q instead of structurally similar matches. The semantic similarity of a match to Q is measured by an online computed bounding match score. By using bounds, we can efficiently prune the unpromising matches with lower semantic similarities without evaluating all matches. Extensive experiments over three real-world knowledge graphs confirm the effectiveness and efficiency of our solution.