1,720,966 research outputs found
Afaneen: The Design and Evaluation of an Interactive Mobile Game to Enhance Arabic Spelling
No full textSpelling is an important skill for children learning to strengthen their knowledge of a language and enhance their reading and writing comprehension. However, many young Arabic learners nowadays lack spelling competency, which affects their overall learning process. Traditional spelling instruction, which is usually based on the rote memorization of words, has its drawbacks, and this might be one of the reasons for the incompetency. In addition, there is a paucity of technology-based aids for facilitating spelling skills tailored for the specific intricacies of the Arabic language. This paper describes the design and development of an interactive mobile spelling game "Afaneen". The application targets Arab students at the elementary and higher levels, and aims to enhance their Arabic spelling ability. In the game, the learner can listen to words and is required to type the correct spelling for these words in order to move to the next level. Immediate feedback is presented to the learners, and they can access the spelling rules at any time to check their understanding. To evaluate the usefulness of the spelling game, three evaluation approaches were used: a case study; think aloud sessions; and interviews. The results demonstrate an overall positive attitude toward the game, and reveal areas for further improvement and development
Weighted Clustering Ensembles
Full text linkClustering is a popular approach to exploratory data analysis and mining. How- ever, clustering faces difficult challenges due to its ill-posed nature. First, it is well known that off-the-shelf clustering methods may discover different patterns in a given set of data, because each clustering algorithm has its own bias resulting from the optimization of different criteria. Second, there is no ground truth against which the clustering result can be validated. High dimensional data also pose a difficult challenge to the clustering process. Various clustering algorithms can handle data with low dimensionality, but as the dimensionality of the data increases, these algorithms tend to break down. In this dissertation, we introduce novel clustering ensemble techniques and novel semi-supervised approaches to address these problems. Clustering ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature: they can provide more robust and stable solutions by making use of the consensus across multiple clustering results, and they can average out the emergent spurious structures which arise due to the various biases of each participating algorithm, and due to the variance induced by different data samples. We introduce and analyze three new consensus functions for ensembles of subspace clusterings. The ultimate goal of our consensus functions is to provide hard partitions of the data, and weight vectors which convey information regarding the subspaces within which the individual clusters exist. We demonstrate the effectiveness of our three techniques by running experiments with several real datasets, including high dimensional text data, and investigate the issue of diversity and accuracy in our ensemble techniques. We also study scenarios in which limited knowledge on the data (in terms of pair- wise constraints) is available from the user. We develop a methodology to embed such constraints into the ensemble components, so that the desired structure emerges via the consensus clustering. We introduce a mechanism which leverages the ensemble framework to bootstrap informative constraints directly from the data and from the various clusterings, without intervention from the user. We demonstrate the effectiveness of our proposed techniques with experiments using real datasets and other state-of-the-art semi-supervised techniques
Weighted clustering ensembles
No full textClustering is a popular approach to exploratory data analysis and mining. However, clustering faces difficult challenges due to its ill-posed nature. First, it is well known that off-the-shelf clustering methods may discover different patterns in a given set of data, because each clustering algorithm has its own bias resulting from the optimization of different criteria. Second, there is no ground truth against which the clustering result can be validated. High dimensional data also pose a difficult challenge to the clustering process. Various clustering algorithms can handle data with low dimensionality, but as the dimensionality of the data increases, these algorithms tend to break down. In this dissertation, we introduce novel clustering ensemble techniques and novel semi-supervised approaches to address these problems. Clustering ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature: they can provide more robust and stable solutions by making use of the consensus across multiple clustering results, and they can average out the emergent spurious structures which arise due to the various biases of each participating algorithm, and due to the variance induced by different data samples. We introduce and analyze three new consensus functions for ensembles of subspace clusterings. The ultimate goal of our consensus functions is to provide hard partitions of the data, and weight vectors which convey information regarding the subspaces within which the individual clusters exist. We demonstrate the effectiveness of our three techniques by running experiments with several real datasets, including high dimensional text data, and investigate the issue of diversity and accuracy in our ensemble techniques. We also study scenarios in which limited knowledge on the data (in terms of pair-wise constraints) is available from the user. We develop a methodology to embed such constraints into the ensemble components, so that the desired structure emerges via the consensus clustering. We introduce a mechanism which leverages the ensemble framework to bootstrap informative constraints directly from the data and from the various clusterings, without intervention from the user. We demonstrate the effectiveness of our proposed techniques with experiments using real datasets and other state-of-the-art semi-supervised techniques
TibbOnto: Knowledge Representation of Prophet Medicine (Tibb Al-Nabawi)
No full textAbstractThe Quran and Hadith are the two fundamental sources of Islamic legislation. Hadith are narrations passed from Prophet companions regarding the words and deeds of Prophet Muhammad (peace be upon him). Hadith books contain topics related to all aspects of Muslim life. In this paper, we build a domain-specific ontology (Tibb Al-Nabawi ontology) to present the Prophet's medicine in a semantic ontological representation. Our source of knowledge is based on an authentic Tibb Al-Nabawi Hadith. We have identified the main classes and the relationship among them. The proposed ontology can be extended in the future to automatically generate treatments for specific diseases according to the Prophet's actions
Predicting Critical Courses Affecting Students Performance: A Case Study
No full textAbstractPredicting student academic performance is one of the important applications of educational data mining. It allows academic institutions to provide appropriate support for students facing difficulties. Classification is a data mining technique that can be used to build prediction models. In this paper, we use the ID3 decision tree induction algorithm to build prediction models for academic performance. Our models are built based on records for female students in the Bachelors program at the Information Technology (IT) department, King Saud University, Riyadh, Saudi Arabia. The results indicate that reliable predictions can be achieved based on the performance of students in second year courses. We also identify key courses that can be used as performance predictors. We believe our findings are useful for decision makers at the IT department
Pre-University Exams Effect on Students GPA: A Case Study in IT Department
No full textAbstractIn the kingdom of Saudi Arabic (KSA) education community, especially in the secondary and tertiary, there are many arguments about the university admission criteria. Specifically the pre-university exams where in KSA they are GAT and AT. Many students have a very high score in the high school, but they did not enter the college they want because of the GAT and AT scores. In this paper, we applied regression techniques on datasets of graduate and undergraduate students to find if the pre-university exams have a real effect on the students’ college GPA. We found that high school GPA effects the college GPA more than pre-university exams, and that the enrolled year has an unexpected effect on the college GPA. We also found that the mean of students’ college GPA is decreasing by time
Open-ended remote sensing visual question answering with transformers
No full textVisual question answering (VQA) has been attracting attention in remote sensing very recently. However, the proposed solutions remain rather limited in the sense that the existing VQA datasets address closed-ended question-answer queries, which may not necessarily reflect real open-ended scenarios. In this paper, we propose a new dataset named VQA-TextRS that was built manually with human annotations and considers various forms of open-ended question-answer pairs. Moreover, we propose an encoder-decoder architecture via transformers on account of their self-attention property that allows relational learning of different positions of the same sequence without the need of typical recurrence operations. Thus, we employed vision and natural language processing (NLP) transformers respectively to draw visual and textual cues from the image and respective question. Afterwards, we applied a transformer decoder, which enables the cross-attention mechanism to fuse the earlier two modalities. The fusion vectors correlate with the process of answer generation to produce the final form of the output. We demonstrate that plausible results can be obtained in open-ended VQA. For instance, the proposed architecture scores an accuracy of 84.01% on questions related to the presence of objects in the query images
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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