Discriminator-based adversarial networks for knowledge graph completion

Knowledge graphs (KGs) inherently lack reasoning ability which limits their effectiveness for tasks such as question-answering and query expansion. KG embedding (KGE) is a predominant approach where proximity between relations and entities in the embedding space is used for reasoning over KGs. Most existing KGE approaches use structural information of triplets and disregard contextual information which could be crucial to learning long-term relations between entities. Moreover, KGE approaches mostly use discriminative models which require both positive and negative samples to learn a decision boundary. KGs, by contrast, contain only positive samples, necessitating that negative samples are generated by replacing the head/tail of predicates with randomly-chosen entities. They are thus usually irrational and easily discriminable from positive samples, which can prevent the learning of sufficiently robust classifiers. To address the shortcomings, we propose to learn contextualized KGE using pretrained adversarial networks. We assume multi-hop relational paths(mh-RPs) as textual sequences for competitively learning discriminator-based KGE against the negative mh-RP generator. We use a pre-trained ELECTRA model and feed it with relational paths. We employ a generator to corrupt randomly-chosen entities with plausible alternatives and a discriminator to predict whether an entity is corrupted or not. We perform experiments on multiple benchmark knowledge graphs and the results show that our proposed KG-ELECTRA model outperforms BERT in link prediction

Teachers\u27 Use of YouTube in the United Arab Emirates: An Exploratory Study

Teachers around the world are using YouTube movies for different purposes. This mixed-methods study was a preliminary investigation of United Arab Emirates teachers\u27 perceptions about YouTube\u27s advantages in the classroom, current practices, and major challenges faced. Forty-five teachers completed an open-ended questionnaire. Results indicated that perceived advantages included supporting the learning process, increasing interest and efficiency, and enriching content. Moreover, findings revealed that the majority of participants were using videos for presentation purposes in teacher-led classrooms. Connectivity, technical issues, appropriateness of content, and administrative support were perceived as major challenges. © 2013 Copyright Taylor and Francis Group, LLC

Tooth brushing, tongue cleaning and snacking behaviour of dental technology and therapist students

Objective: To determine the tooth brushing, tongue cleaning and snacking behaviour of dental technology and therapist students. Methods: A descriptive cross-sectional study of students of Federal School of Dental Therapy and Technology Enugu, Nigeria. Self-administered questionnaire was used to obtain information on demography, frequency, duration and technique of tooth brushing and tongue cleaning as well as information on consumption of snacks. Results: A total of 242 students responded. Dental technology students made up 52.5% of the respondents and dental therapist in training made up 47.5%. Majority (63.2%) of the respondents considered the strength of tooth brush when purchasing a tooth brush and 78.9% use tooth brushes with medium strength. Seven-tenth (71.9%) of the respondents brush their teeth twice daily and 52.1% brush for 3–5 minutes. About one-third (30.2%) brush their teeth in front of a mirror. Chewing stick was used by 51.7% of respondents in addition to the use of tooth brush. Tongue cleaning was done by 94.2% with only 9.5% using a tongue cleaner. Only 20.2% reported regular snacks consumption. Nine-tenth (90.4%) of respondents were previously involved in educating others, apart from their colleagues, on tooth brushing. Conclusion: This survey revealed that most of the dental therapy and technology students had satisfactory tooth-brushing behaviour. The zeal to educate others about proper tooth brushing revealed in this study suggests that the students may be helpful in oral health promotion

Countering malicious URLs in Internet-of-Thing (IoT) using a knowledge-based approach and simulated expert

This study proposes a novel methodology to detect malicious URLs using simulated expert (SE) and knowledge base system (KBS). The proposed study not only efficiently detects known malicious URLs, but also adapt countermeasure against the newly generated malicious URLs. Moreover, this study also explored which lexical features are more contributing in final decision using a factor analysis method and thus helped in avoiding involvement of human expert. Further, we applied the following state-of-the-art ML algorithms, i.e., Naïve Bayes (NB), Decision Tree (DT), Gradient Boosted Trees (GBT), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), and Random rest (RF), and evaluated the performance of these algorithms on a large-scale real data set of data-driven Web application. The experimental results clearly demonstrated the efficiency of NB in the proposed model as NB outperformed when compared to the rest of aforementioned algorithms in term of average minimum execution time (i.e., 3 seconds) and was able to accurately classify the 107586 URLs with 0.2% error rate and 99.8% accuracy rate

Students Readiness for e-Learning: An Assessment on Hacettepe University Department of Information Management

Students are one of the key elements during the implementation of elearning systems within universities. To be able to build solid and effective elearning systems, it is important to know the level of students’ readiness. In this paper, e-learning readiness of the Department of Information Management (DIM) students at Hacettepe University will be investigated. A 39-item elearning readiness questionnaire (along with some descriptive questions, such as gender and grade-level) that was tested in previous studies was used to obtain the data. The results show that, although some improvements are d, DIM students are at the expected level of e-learning readiness, in general