257 research outputs found
Depression, stigma, social and family support and nutritional status in Filipino TB patients: Impact on adherence to anti-TB treatment. A mixed-methods study.
No full textDepression, stigma, social and family support and nutritional status in Filipino TB patients: Impact on adherence to anti-TB treatment. A mixed-methods study.
No full textDepression, stigma, social and family support and nutritional status in Filipino TB patients: Impact on adherence to anti-TB treatment. A mixed-methods study.
No full textNagasaki University（長崎大学）博士(グローバルヘルス)長崎大学学位論文 学位記番号:博(Global Health)甲第2号 学位授与年月日:令和3年9月17日Nagasaki University (長崎大学), 博士(グローバルヘルス)(2021-09-17)doctoral thesi
Burrows-Wheeler Transform and combination of Move-to-Front coding and Run Length Encoding for lossless audio coding
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Intrathecal Drug Delivery Systems Survey: Trends in Utilization in Pain Practice [Corrigendum]
No full textAbd-Sayed A, Fiala K, Weisbein J, et al. J Pain Res. 2022;15:1305–1314.
The authors have advised there is an error in the author list on page 1305. The author name “Alaa Abd-Sayed” should read “Alaa Abd-Elsayed”.
The authors apologize for this error
Représentations littéraires du sacré dans le roman maghrébin de langue française
No full textThis interdisciplinary study explores how Driss Chraïbi’s L’Homme du Livre (1995), Assia Djebar’s Loin de Médine (1991), and Anissa Boumediène’s La fin d’un monde (1991) present accounts of particular historical moments in early Islam. This study explores the role of the imagination as well as freedom of invention when reconstructing historical events. It engages the novels through a study of the interplay between the literary text and the sources and traditions that impact and shape the text narrative. Gaining direct access to the original sources in Arabic serves to analyze how religious and early historical materials are considered in and reflected by the fictional texts. Because the sources tend to differ in both content and approach, this study examines their preoccupations in order to determine the criteria of selection applied by each novelist.Ph.D.Includes bibliographical referencesIncludes vitaby Hanan Elsaye
SMASH at Qur’an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering Scenarios
No full textThe Qur'an QA 2022 shared task aims at assessing the possibility of building systems that can extract answers to religious questions given relevant passages from the Holy Qur'an. This paper describes SMASH's system that was used to participate in this shared task. Our experiments reveal a data leakage issue among the different splits of the dataset. This leakage problem hinders the reliability of using the models' performance on the development dataset as a proxy for the ability of the models to generalize to new unseen samples. After creating better faithful splits from the original dataset, the basic strategy of fine-tuning a language model pretrained on classical Arabic text yielded the best performance on the new evaluation split. The results achieved by the model suggests that the small scale dataset is not enough to fine-tune large transformer-based language models in a way that generalizes well. Conversely, we believe that further attention could be paid to the type of questions that are being used to train the models given the sensitivity of the data
Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic
No full textTwitter and other social media platforms offer users the chance to share their ideas via short posts. While the easy exchange of ideas has value, these microblogs can be leveraged by people who want to share hatred, and such individuals can share negative views about an individual, race, or group with millions of people at the click of a button. There is thus an urgent need to establish a method that can automatically identify hate speech and offensive language. To contribute to this development, during the OSACT4 workshop, a shared task was undertaken to detect offensive language in Arabic. A key challenge was the uniqueness of the language used on social media, prompting the out-of-vocabulary (OOV) problem. In addition, the use of different dialects in Arabic exacerbates this problem. To deal with the issues associated with OOV, we generated a character-level embeddings model, which was trained on a massive data collected carefully. This level of embeddings can work effectively in resolving the problem of OOV words through its ability to learn the vectors of character n-grams or parts of words. The proposed systems were ranked 7th and 8th for Subtasks A and B, respectively
Classifying Arabic Crisis Tweets using Data Selection and Pre-trained Language Models
No full textUser-generated Social Media (SM) content has been explored as a valuable and accessible source of data about crises to enhance situational awareness and support humanitarian response efforts. However, the timely extraction of crisis-related SM messages is challenging as it involves processing large quantities of noisy data in real-time. Supervised machine learning methods have been successfully applied to this task but such approaches require human-labelled data, which are unlikely to be available from novel and emerging crises. Supervised machine learning algorithms trained on labelled data from past events did not usually perform well when classifying a new disaster due to data variations across events. Using the BERT embeddings, we propose and investigate an instance distance-based data selection approach for adaptation to improve classifiers’ performance under a domain shift. The K-nearest neighbours algorithm selects a subset of multi-event training data that is most similar to the target event. Results show that fine-tuning a BERT model on a selected subset of data to classify crisis tweets outperforms a model that has been fine-tuned on all available source data. We demonstrated that our approach generally works better than the self-training adaptation method. Combing the self-training with our proposed classifier does not enhance the performance.</p
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