Relevant bibliographies by topics / Arabic language – Computer network resources

Academic literature on the topic 'Arabic language – Computer network resources'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Arabic language – Computer network resources"

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Mahmoud, Adnen, and Mounir Zrigui. "Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic." International Arab Journal of Information Technology 18, no. 1 (December 31, 2020): 1–7. http://dx.doi.org/10.34028/iajit/18/1/1.

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Paraphrase detection allows determining how original and suspect documents convey the same meaning. It has attracted attention from researchers in many Natural Language Processing (NLP) tasks such as plagiarism detection, question answering, information retrieval, etc., Traditional methods (e.g., Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Latent Semantic Analysis (LSA)) cannot capture efficiently hidden semantic relations when sentences may not contain any common words or the co-occurrence of words is rarely present. Therefore, we proposed a deep learning model based on Global Word embedding (GloVe) and Recurrent Convolutional Neural Network (RCNN). It was efficient for capturing more contextual dependencies between words vectors with precise semantic meanings. Seeing the lack of resources in Arabic language publicly available, we developed a paraphrased corpus automatically. It preserved syntactic and semantic structures of Arabic sentences using word2vec model and Part-Of-Speech (POS) annotation. Overall experiments shown that our proposed model outperformed the state-of-the-art methods in terms of precision and recall
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Alali, Muath, Nurfadhlina Mohd Sharef, Masrah Azrifah Azmi Murad, Hazlina Hamdan, and Nor Azura Husin. "Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification." Electronics 11, no. 8 (April 9, 2022): 1193. http://dx.doi.org/10.3390/electronics11081193.

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Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem.
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Chouikhi, Hasna, Mohammed Alsuhaibani, and Fethi Jarray. "BERT-Based Joint Model for Aspect Term Extraction and Aspect Polarity Detection in Arabic Text." Electronics 12, no. 3 (January 19, 2023): 515. http://dx.doi.org/10.3390/electronics12030515.

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Aspect-based sentiment analysis (ABSA) is a method used to identify the aspects discussed in a given text and determine the sentiment expressed towards each aspect. This can help provide a more fine-grained understanding of the opinions expressed in the text. The majority of Arabic ABSA techniques in use today significantly rely on repeated pre-processing and feature-engineering operations, as well as the use of outside resources (e.g., lexicons). In essence, there is a significant research gap in NLP with regard to the use of transfer learning (TL) techniques and language models for aspect term extraction (ATE) and aspect polarity detection (APD) in Arabic text. While TL has proven to be an effective approach for a variety of NLP tasks in other languages, its use in the context of Arabic has been relatively under-explored. This paper aims to address this gap by presenting a TL-based approach for ATE and APD in Arabic, leveraging the knowledge and capabilities of previously trained language models. The Arabic base (Arabic version) of the BERT model serves as the foundation for the suggested models. Different BERT implementations are also contrasted. A reference ABSA dataset was used for the experiments (HAAD dataset). The experimental results demonstrate that our models surpass the baseline model and previously proposed approaches.
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FATTAH, MOHAMED ABDEL, FUJI REN, and SHINGO KUROIWA. "SENTENCE ALIGNMENT USING FEED FORWARD NEURAL NETWORK." International Journal of Neural Systems 16, no. 06 (December 2006): 423–34. http://dx.doi.org/10.1142/s0129065706000822.

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Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on feed forward neural network classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuate score, and cognate score values. A set of manually prepared training data has been assigned to train the feed forward neural network. Another set of data was used for testing. Using this new approach, we could achieve an error reduction of 60% over length based approach when applied on English–Arabic parallel documents. Moreover this new approach is valid for any language pair and it is quite flexible approach since the feature parameter vector may contain more/less or different features than that we used in our system such as lexical match feature.
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Gul, Shabana, Rafi Ullah Khan, Mohib Ullah, Roman Aftab, Abdul Waheed, and Tsu-Yang Wu. "Tanz-Indicator: A Novel Framework for Detection of Perso-Arabic-Scripted Urdu Sarcastic Opinions." Wireless Communications and Mobile Computing 2022 (July 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/9151890.

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Automatic sarcasm detection in textual data is a crucial task in sentiment analysis. This problem is complex because sarcastic comments usually carry the opposite meaning and are context-driven. The issue of sarcasm detection in comments written in Perso-Arabic-scripted Urdu text is even more challenging due to limited online linguistic resources. In this research, we proposed Tanz-Indicator, a lexicon-based framework to detect sarcasm in the user comments posted in Perso-Arabic Urdu language. We use a lexicon of over 3000 sarcastic tweets and 100 sarcastic features for experimentation. We also train two machine learning models with the same data to compare the performance of the lexicon-based model and machine learning-based model. The results show that the lexicon-based model correctly identified 48.5% sarcastic and 23.5% nonsarcastic tweets with the recall of 69.6% and 87.9% precision. The recall rate of Naïve Bayes and SVM-based machine learning models was 20.1% and 24.4%, respectively, with an overall accuracy of 65.2% and 60.1%, respectively.
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Saleh, Hager, Sherif Mostafa, Lubna Abdelkareim Gabralla, Ahmad O. Aseeri, and Shaker El-Sappagh. "Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models." Applied Sciences 12, no. 18 (September 7, 2022): 8967. http://dx.doi.org/10.3390/app12188967.

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Sentiment analysis (SA) is a machine learning application that drives people’s opinions from text using natural language processing (NLP) techniques. Implementing Arabic SA is challenging for many reasons, including equivocation, numerous dialects, lack of resources, morphological diversity, lack of contextual information, and hiding of sentiment terms in the implicit text. Deep learning models such as convolutional neural networks (CNN) and long short-term memory (LSTM) have significantly improved in the Arabic SA domain. Hybrid models based on CNN combined with long short-term memory (LSTM) or gated recurrent unit (GRU) have further improved the performance of single DL models. In addition, the ensemble of deep learning models, especially stacking ensembles, is expected to increase the robustness and accuracy of the previous DL models. In this paper, we proposed a stacking ensemble model that combined the prediction power of CNN and hybrid deep learning models to predict Arabic sentiment accurately. The stacking ensemble algorithm has two main phases. Three DL models were optimized in the first phase, including deep CNN, hybrid CNN-LSTM, and hybrid CNN-GRU. In the second phase, these three separate pre-trained models’ outputs were integrated with a support vector machine (SVM) meta-learner. To extract features for DL models, the continuous bag of words (CBOW) and the skip-gram models with 300 dimensions of the word embedding were used. Arabic health services datasets (Main-AHS and Sub-AHS) and the Arabic sentiment tweets dataset were used to train and test the models (ASTD). A number of well-known deep learning models, including DeepCNN, hybrid CNN-LSTM, hybrid CNN-GRU, and conventional ML algorithms, have been used to compare the performance of the proposed ensemble model. We discovered that the proposed deep stacking model achieved the best performance compared to the previous models. Based on the CBOW word embedding, the proposed model achieved the highest accuracy of 92.12%, 95.81%, and 81.4% for Main-AHS, Sub-AHS, and ASTD datasets, respectively.
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Butnaru, Andrei-Mădălin. "Machine learning applied in natural language processing." ACM SIGIR Forum 54, no. 1 (June 2020): 1–3. http://dx.doi.org/10.1145/3451964.3451979.

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Machine Learning is present in our lives now more than ever. One of the most researched areas in machine learning is focused on creating systems that are able to understand natural language. Natural language processing is a broad domain, having a vast number of applications with a significant impact in society. In our current era, we rely on tools that can ease our lives. We can search through thousands of documents to find something that we need, but this can take a lot of time. Having a system that can understand a simple query and return only relevant documents is more efficient. Although current approaches are well capable of understanding natural language, there is still space for improvement. This thesis studies multiple natural language processing tasks, presenting approaches on applications such as information retrieval, polarity detection, dialect identification [Butnaru and Ionescu, 2018], automatic essay scoring [Cozma et al., 2018], and methods that can help other systems to understand documents better. Part of the described approaches from this thesis are employing kernel methods, especially string kernels. A method based on string kernels that can determine in what dialect a document is written is presented in this thesis. The approach is treating texts at the character level, extracting features in the form of p -grams of characters, and combining several kernels, including presence bits kernel and intersection kernel. Kernel methods are also presented as a solution for defining the complexity of a specific word. By combining multiple low-level features and high-level semantic features, the approach can find if a non-native speaker of a language can see a word as complicated or not. With one focus on string kernels, this thesis proposes two transductive methods that can improve the results obtained by employing string kernels. One approach suggests using the pairwise string kernel similarities between samples from the training and test sets as features. The other method defines a simple self-training algorithm composed of two iterations. As usual, a classifier is trained over the training data, then is it used to predict the labels of the test samples. In the second iteration, the algorithm adds a predefined number of test samples to the training set for another round of training. These two transductive methods work by adapting the learning method to the test set. A novel cross-dialectal corpus is shown in this thesis. The Moldavian versus Romanian Corpus (MOROCO) [Butnaru and Ionescu, 2019a] contains over 30.000 samples collected from the news domain, split across six categories. Several studies can be employed over this corpus such as binary classification between Romanian and Moldavian samples, intra-dialect multi-class categorization by topic, and cross-dialect multi-class classification by topic. Two baseline approaches are presented for this collection of texts. One method is based on a simple string kernel model. The second approach consists of a character-level deep neural network, which includes several Squeeze-and-Excitation Blocks (SE-blocks). As known at this moment, this is the first time when a SE-block is employed in a natural language processing context. This thesis also presents a method for German Dialect Identification composed on a voting scheme that combines a Character-level Convolutional Neural Network, a Long Short-Term Memory Network, and a model based on String Kernels. Word sense disambiguation is still one of the challenges of the NLP domain. In this context, this thesis tackles this challenge and presents a novel disambiguation algorithm, known as ShowtgunWSD [Butnaru and Ionescu, 2019b]. By treating the global disambiguation problem as multiple local disambiguation problems, ShotgunWSD is capable of determining the sense of the words in an unsupervised and deterministic way, using WordNet as a resource. For this method to work, three functions that can compute the similarity between two words senses are defined. The disambiguation algorithm works as follows. The document is split into multiple windows of words of a specific size for each window. After that, a brute-force algorithm that computes every combination of senses for each word within that window is employed. For every window combination, a score is calculated using one of the three similarity functions. The last step merges the windows using a prefix and suffix matching to form more significant and relevant windows. In the end, the formed windows are ranked by the length and score, and the top ones, based on a voting scheme, will determine the sense for each word. Documents can contain a variable number of words, therefore employing them in machine learning may be hard at times. This thesis presents two novel approaches [Ionescu and Butnaru, 2019] that can represent documents using a finite number of features. Both methods are inspired by computer vision, and they work by first transforming the words within documents to a word representation, such as word2vec. Having words represented in this way, a k-means clustering algorithm can be applied over the words. The centroids of the formed clusters are gathered into a vocabulary. Each word from a document is then represented by the closest centroid from the previously formed vocabulary. To this point, both methods share the same steps. One approach is designed to compute the final representation of a document by calculating the frequency of each centroid found inside it. This method is named Bag of Super Word Embeddings (BOSWE) because each centroid can be viewed as a super word. The second approach presented in this thesis, known as Vector of Locally-Aggregated Word Embeddings (VLAWE), computes the document representation by accumulating the differences between each centroid and each word vector associated with the respective centroid. This thesis also describes a new way to score essays automatically by combining a low-level string kernel model with a high-level semantic feature representation, namely the BOSWE representation. The methods described in this thesis exhibit state-of-the-art performance levels over multiple tasks. One fact to support this claim is that the string kernel method employed for Arabic Dialect Identification obtained the first place, two years in a row at the Fourth and Fifth Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial). The same string kernel model obtained the fifth place at the German Dialect Identification Closed Shared Task at VarDial Workshop of EACL 2017. Second of all, the Complex Word Identification model scored a third-place at the CWI Shared Task of the BEA-13 of NAACL 2018. Third of all, it is worth to mention that the ShotgunWSD algorithm surpassed the MCS baseline on several datasets. Lastly, the model that combines string kernel and bag of super word embeddings obtained state-of-the-art performance over the Automated Student Assessment Prize dataset.
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Essam, Nader, Abdullah M. Moussa, Khaled M. Elsayed, Sherif Abdou, Mohsen Rashwan, Shaheen Khatoon, Md Maruf Hasan, Amna Asif, and Majed A. Alshamari. "Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models." Applied Sciences 11, no. 23 (November 30, 2021): 11328. http://dx.doi.org/10.3390/app112311328.

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The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.
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Husain, Fatemah, and Ozlem Uzuner. "A Survey of Offensive Language Detection for the Arabic Language." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (April 2021): 1–44. http://dx.doi.org/10.1145/3421504.

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The use of offensive language in user-generated content is a serious problem that needs to be addressed with the latest technology. The field of Natural Language Processing (NLP) can support the automatic detection of offensive language. In this survey, we review previous NLP studies that cover Arabic offensive language detection. This survey investigates the state-of-the-art in offensive language detection for the Arabic language, providing a structured overview of previous approaches, including core techniques, tools, resources, methods, and main features used. This work also discusses the limitations and gaps of the previous studies. Findings from this survey emphasize the importance of investing further effort in detecting Arabic offensive language, including the development of benchmark resources and the invention of novel preprocessing and feature extraction techniques.
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Al-Moslmi, Tareq, Mohammed Albared, Adel Al-Shabi, Nazlia Omar, and Salwani Abdullah. "Arabic senti-lexicon: Constructing publicly available language resources for Arabic sentiment analysis." Journal of Information Science 44, no. 3 (February 1, 2017): 345–62. http://dx.doi.org/10.1177/0165551516683908.

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Sentiment analysis is held to be one of the highly dynamic recent research fields in Natural Language Processing, facilitated by the quickly growing volume of Web opinion data. Most of the approaches in this field are focused on English due to the lack of sentiment resources in other languages such as the Arabic language and its large variety of dialects. In most sentiment analysis applications, good sentiment resources play a critical role. Based on that, in this article, several publicly available sentiment analysis resources for Arabic are introduced. This article introduces the Arabic senti-lexicon, a list of 3880 positive and negative synsets annotated with their part of speech, polarity scores, dialects synsets and inflected forms. This article also presents a Multi-domain Arabic Sentiment Corpus (MASC) with a size of 8860 positive and negative reviews from different domains. In this article, an in-depth study has been conducted on five types of feature sets for exploiting effective features and investigating their effect on performance of Arabic sentiment analysis. The aim is to assess the quality of the developed language resources and to integrate different feature sets and classification algorithms to synthesise a more accurate sentiment analysis method. The Arabic senti-lexicon is used for generating feature vectors. Five well-known machine learning algorithms: naïve Bayes, k-nearest neighbours, support vector machines (SVMs), logistic linear regression and neural network are employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on standard Arabic data sets were conducted, discussion is presented and conclusions are drawn. The experimental results show that the Arabic senti-lexicon is a very useful resource for Arabic sentiment analysis. Moreover, results show that classifiers which are trained on feature vectors derived from the corpus using the Arabic sentiment lexicon are more accurate than classifiers trained using the raw corpus.
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Dissertations / Theses on the topic "Arabic language – Computer network resources"

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Al-Agla, Ali. "Introducing computer supported co-operative learning to the curriculum of Islamic studies and Arabic language in Arabic Language Institute for non-Arabic speakers : teachers' perceptions, students' responses and administrators' views." Thesis, University of Hull, 2001. http://hydra.hull.ac.uk/resources/hull:5402.

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The Saudi education system is facing a climate of change and interest in exploiting new technology and educational approaches to improve teaching and learning. In this climate, the present study explores the feasibility of introducing computer assisted cooperative learning at the Language Institute of Umm Al-Qura University, in terms of teacher attitudes to computers and their experience with/attitudes towards co-operative learning; administrative support for such innovation; and students' responses to a cooperative learning environment. A four-part Likert-type questionnaire was administered to 148 teachers of Arabic and Islamic Studies from four universities, to investigate their positive and negative attitudes to computers, feelings about computers' usefulness, and intimidation about using computers. At Umm Al-Qura University, views on co-operative learning were obtained from 35 teachers of Arabic and Islamic Studies, by means of interviews. Interviews were carried out with five senior administrators at the university, regarding development in curricula and teaching methods, including training and staff development needs and funding issues. Twenty-two students of elementary Arabic from the University's Language Institute took part in computer assisted cooperative learning sessions, using software developed by the researcher and were observed and interviewed. Teachers, irrespective of personal and professional characteristics, generally had positive attitudes to computers, while administrators claimed that efforts to provide computer access and training to teaching staff were underway. Teachers also had generally positive attitudes to cooperative learning, and some sceptics were won over by the sight of their students participating actively and with enjoyment in the sessions conducted by the researcher. There was however evidence of reluctance by teachers to give students responsibility for their learning. Teachers and administrators blamed each other for what they saw as stagnation in educational approaches, but both groups favoured change. The researcher concludes that the introduction of computer-assisted cooperative learning supported by appropriate training could benefit both students and teachers and offers recommendations for its implementation.
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Andrews, Wesley Vern. "Overcoming the failure phenomena: Recouping high school credit through an online language arts course." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3140.

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The purpose of this project was to develop an online educational program that offered the courses in which students are deficient. This program was called The Virtual Senior (VS). In the end, multiple courses across each of the core disciplines will be created based on California State Standards.
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Dharod, Vishal. "Web based internship management system: A collaborative coordinating tool." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2575.

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Laveaux, Michele Barbara. "Assisting students with concept acquisition in basic skills reading through the use of an interactive website." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2688.

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This project creates an interactive website on the literature surrounding reading and concept acquisition skills in adult learners. This website used in conjunction with the Basic Skills English 10A course given in community colleges will enhance reading and concept acquisitions skills enabling student the self cofidence, encouragement and motivation to complete the course.
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Vu, Dung Tien. "The web-based database management system for the computer science graduate program." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2557.

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The purpose of this project is to provide faculty and students a secure access to graduate student resources and it will also cover database design, web development, security, migration, and deployment of the new system.
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Cole, Jason. "Foreign language learning in the age of the internet : a comparison of informal acquirers and traditional classroom learners in central Brazil." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:db80473a-2075-4e91-bb07-a706bb6a433f.

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Several recent studies (e.g., Benson and Chik, 2010; Sockett, 2014) suggest that as a result of changes in technology and the concomitant emergence of a globalized culture, highly effective out-of-class, informal English acquisition is becoming more common. The present study compared high-level, well-motivated Central Brazilian classroom-trained learners (CTLs) with fully autonomous self-instructed learners (FASILs) of similar backgrounds. Using linguistic tests, a questionnaire and a structured interview, the study analysed group differences as well as individual differences in language proficiency, learner histories, behaviour, beliefs, and attitudes. The key research question asked whether there existed, in more than rare circumstances, FASILs who attained levels of proficiency at least as high as highly-motivated, well-trained CTLs? Furthermore, if the knowledge and skills of FASILs were, in some respects, superior to those of CTLs, what variables accounted for the advantage? FASILs significantly outperformed CTLs across a battery of linguistic tests measuring a range of knowledge and skills. Test results indicated that while CTLs tended to plateau at upper intermediate levels, FASILs generally improved through advanced levels, often achieving native-like levels of knowledge and use. The strongest contributing factor to proficiency was found to be self-determined motivation driven by a personalized relationship with English often marked by a transnational identity. The evidence suggests this type of motivation, significantly more associated with FASILs than CTLs, led users to engage deeply with the linguistic details of informal sources. The findings challenge dominant paradigms in several fields of SLA which prioritize expert regulation over independent discovery and controlled, collaborative environments over real-world contexts of use entered into for personal reasons. A hoped for consequence of this study is that SLA research and teaching practice will begin to recognize and promote rather than regulate or dismiss the unique learning arcs that more and more English learners experience in their everyday lives.
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Dullien, Starley Beatrix. "In time on time: Website for teachers of English to speakers of other languages." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2730.

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The purpose of the "In Time On Time TESOL" website for Teachers of English to Students of Other Languages (TESOL) is to provide adult-education teachers online access to classroom managing techniques, teaching and learning strategies, and online resources based on constructivism and adult-learning theory. The instructional design and navigation structure is based on Random Access Instruction (RAI) and hypertext theory.
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Shefchik, Michael James. "Effectively incorporating web tools into the community college composition curriculum." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2435.

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This project shows how web tools can be effectively integrated into the community college composition curriculum through staff development. It shows the need for adaptation of materials designed for K-12 education to the community college level and the development of grade-appropriate materials using web resources. The need for authentic assessment is explored and the means to prove it were supplied. Examples of web tools, sites for developing web tools and resources for accessing and applying authentic assessment tools were given.
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Yang, Dai Fei. "Improving Networked Learning in Higher Education: Language Functions and Design Patterns." University of Sydney, 2007. http://hdl.handle.net/2123/2465.

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Doctor of Philosophy
The thesis of this study is that two seemingly disparate research disciplines can be coalesced to develop an effective pedagogical framework for educational design in the context of networked learning. That contention is grounded in, and inspired by, the rapid developments in educational technologies which have greatly changed the landscape in teaching and learning in higher education over the last decade. The study attempts to add to the corpus of contemporary learning theory which sees students not merely as passive recipients of knowledge, but as active participants in the learning process, having much greater control over their selection of technological learning tools, learning resources and learning methodologies. This is very much in line with the shift from the traditional focus on content design and knowledge transmission towards a more student-centred design for knowledge co-construction, a development which demands the type of new thinking about the design of learning tasks and learning resources contained in this study. Also set out are new lines of action for the fashioning of a collaborative learning environment, for community interaction and the sharing of knowledge, and for promoting good teaching and learning practice. The central argument of the study is that such pedagogical goals may be attained by juxtaposing the theories of Systemic Functional Linguistics (hereafter SFL) and pattern languages. These have not, thus far, been used in combination. SFL is a well established theory in the study of language, and is used in this thesis to help analyse and classify discourses produced and shared by teachers and students in networked learning. Pattern languages have their origin in architecture. Design patterns can be used as a means of representing and sharing important and specific empirical research results and design experiences. This new knowledge can be used to support and improve the quality of educational design. The study has two central components. The first uses the SFL theoretical framework to demonstrate how text is used as a key medium in networked learning. In other words, it is argued in this section that the quality of texts has a direct impact on the quality of learning and learning outcomes. The quality of text is assessed by means of a detailed discourse analysis of selected texts. This process involves deconstructing, identifying and capturing the linguistic resources and language strategies used in the texts. The detailed discourse analysis also illustrates and reveals how language is used in the construction of knowledge and the promotion of collaboration in teaching and learning. The second component centres on the argument that SFL provides valuable language knowledge which can be represented by using Alexander’s design patterns. New knowledge encoded in these design patterns can be used by teachers and designers as reusable and shared resources to help them improve their design work. The empirical research was carried out in three phases. The first involved a) the identification of text patterns of discourses used in networked learning based on detailed discourse analysis; b) Interviewing experienced academic staff to identify their perspectives on good online teaching practices and success factors. The second phase involved using the data which emerged from these interviews and discourse analysis to model illustrative patterns. (Here, illustrative means that due to the scope of the study, it is only possible to develop a limited number of patterns to illustrate the methods used for pattern development. It is not the intention to develop a full repository of design patterns in this study). In the third (validation) phase the patterns were reviewed by two groups of academic staff, with the aim of improving these patterns. Improved patterns were then tested on a group of educational design students for their usefulness and application. It is concluded from this research that it is possible to develop design patterns which ensure the best use of linguistic resources in both the teaching and learning process. Finally, it is argued that the combination of SFL and pattern languages provides a promising theoretical framework for the complex and demanding task of educational design. Future research could make use of such a framework to explore a fuller application of the pattern- based approach for the representation of new knowledge for educational design. Suggested additional research directions include finding new ways of capturing a new pedagogical approach to mobile learning and blended learning. Also, a promising direction could be the use of SFL Appraisal theory (Martin, 2000) for the investigation on how students construct interpersonal relationships (appraise peer work) in online joint projects. In the conclusion, it is contended that through its exploration of new ground in the use of SFL and pattern language theory in the construction of education design patterns, the study makes a significant contribution to knowledge in the field of networked learning.
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Salomao, Ricardo. "Portugues em linha : o desafio da internet ao ensino do Portugues lingua estrangeira." Thesis, University of Macau, 1998. http://umaclib3.umac.mo/record=b1636625.

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Books on the topic "Arabic language – Computer network resources"

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Bodomo, Adams. Computer-mediated communication for linguistics and literacy: Technology and natural language education. Hershey PA: Information Science Reference, 2009.

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1975-, Salaita Steven, ed. The internet discourse of Arab-American groups: A study in web linguistics. Lewiston, N.Y: Edwin Mellen Press, 2009.

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Mark, Warschauer, and Kern Richard, eds. Network-based language teaching: Concepts and practice. Cambridge, UK: Cambridge University Press, 2000.

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Branscomb, H. Eric. Quick guide to the internet for composition. Boston: Allyn and Bacon, 1998.

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Michael, Keene, ed. The Mayfield quick view guide to the Internet for students of English. 2nd ed. Mountain View, Calif: Mayfield Pub. Co., 2000.

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Branscomb, H. Eric. Quick guide to the internet for college composition. Boston: Allyn and Bacon, 1997.

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Inc, Classroom Connect, ed. Web guide: Language arts : teacher-selected Internet resources, with ideas for classroom use. [El Segundo, Calif.?]: Classroom Connect, 1999.

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Uschi, Felix, ed. Beyond Babel: Language learning online. Melbourne, Vic: Language Australia, 2001.

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Kitao, Kenji. Internet resources: ELT, linguistics, and communication. Tokyo, Japan: Eichosha, 1998.

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Institut russkogo i͡azyka im. V.V. Vinogradova, ed. Sovremennyĭ russkiĭ i͡azyk v internete. Moskva: I͡Azyki slavi͡anskoĭ kulʹtury, 2014.

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Book chapters on the topic "Arabic language – Computer network resources"

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Dahmani, Habiba, Hussein Hussein, Burkhard Meyer-Sickendiek, and Oliver Jokisch. "Natural Arabic Language Resources for Emotion Recognition in Algerian Dialect." In Communications in Computer and Information Science, 18–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32959-4_2.

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Elons, A. Samir, and Magdy Aboull-Ela. "Arabic Sign Language Recognition System Based on Adaptive Pulse-Coupled Neural Network." In Communications in Computer and Information Science, 213–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35326-0_22.

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Webster, Thomas E. "The Need to Engage With Smartphones and Social Network Sites (SNSs) at Korean Universities." In Recent Developments in Technology-Enhanced and Computer-Assisted Language Learning, 122–43. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1282-1.ch006.

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The disparity between the pervasiveness of Smartphone and SNS use in Korean society, and the near absence of systematic engagement with educational approaches to their use could not be starker. This is even more perplexing in second language learning where contemporary communicative practices are underpinned by the assumption that they will enable students to apply lessons to real life circumstances outside the classroom. As found in an extensive study on teachers' perceptions and uses of technology, teachers often lack motivation to make changes owing to a lack of reliable resources, training, and professional development support. This chapter, therefore, provides the underpinning for a new theoretical approach to promoting a more culturally holistic engagement with technology, Smartphones, and SNS use at Korean universities and education in general.
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Rossetti, Chip. "Translating Cairo’s Hidden Lines: The City as Visual Text in Magdy El Shafee’s Metro." In The City in Arabic Literature, edited by Nizar F. Hermes and Gretchen Head, 306–25. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474406529.003.0016.

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An examination of the visual language used in Magdy El Shafee’s 2008 graphic novel, which narrates an Egyptian computer programmer’s frustration with a rigged political and economic system in late-Mubarak-era Egypt, and his discovery of official corruption. In Metro, Cairo serves both as an urban canvas for text that visually assaults the reader and as a grid that organizes and reinforces the novel’s thematic elements. This essay analyses how Metro employs a language of verticality to emphasize the disparity between the powerful and powerless. Drawing on Walter Benjamin’s concept of the map as offering an “infrared,” abstracted knowledge of a city, this essay also examines the running motif of Cairo’s Metro stations as a symbol of the city’s hidden lines of power, a horizontal network of connections and corruption.
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Jasnau Caeiro, José, Henrique Oliveira, Margarida Goes, Manuel José Lopes, and César Fonseca. "Integrated Continuous Healthcare Team Computer System Architecture." In Exploring the Role of ICTs in Healthy Aging, 231–46. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1937-0.ch013.

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A computer-based pattern recognition system architecture destined to collect and process geographically referenced data about integrated continuous healthcare teams (ECCI) is presented and discussed in the chapter. These teams are part of Portugal's National Network of Integrated Continuous Care (RNCCI). The system is designed to collect data about the displacement of each team during healthcare assistance. The pattern recognition system handles information about the costs related to the provided healthcare. The architecture is designed around open source software resources. Virtual machines and container-based technologies provide hardware independence. The Python programming language ecosystem is chosen for all the main components of the system.
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Malik, Kaleem Razzaq, and Tauqir Ahmad. "Technique for Transformation of Data From RDB to XML Then to RDF." In Information Retrieval and Management, 1078–96. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch046.

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This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.
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Malik, Kaleem Razzaq, and Tauqir Ahmad. "Technique for Transformation of Data From RDB to XML Then to RDF." In Web Semantics for Textual and Visual Information Retrieval, 70–91. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2483-0.ch004.

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This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.
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Conference papers on the topic "Arabic language – Computer network resources"

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Namly, Driss, Yasser Regragui, and Karim Bouzoubaa. "Interoperable Arabic language resources building and exploitation in SAFAR platform." In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945667.

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ElJundi, Obeida, Mohamad Dhaybi, Kotaiba Mokadam, Hazem Hajj, and Daniel Asmar. "Resources and End-to-End Neural Network Models for Arabic Image Captioning." In 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008881202330241.

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Bensghaier, Mabrouka, Wided Bakari, and Mahmoud Neji. "Natural Language Inference for Arabic using Recurrent Neural Network and Word Embedding." In 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2022. http://dx.doi.org/10.1109/aiccsa56895.2022.10017722.

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Li, Haoran, Juan Hou, and Natasa Nord. "A fast and effective method for modelling and optimizing district heating systems in the Modelica language." In 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022. Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp192046.

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A district heating system is a centralized energy system that supplies heat to end users such as buildings and industrial facilities. This centralized system may have multiple heat sources, a complex distribution network, and a large number of end users. Moreover, the heat distribution and utilization processes entail tumultuous thermal dynamics. Therefore, modelling and optimizing such a system generally demands arduous labour and necessitates powerful computing resources. To overcome these difficulties, this study introduced a fast and effective method for modelling and optimizing district heating systems using the Modelica language. Firstly, a simplified district heating system model was developed. This simplified model lumped all the end-users into a single thermal pinot with critical physical constraints. Meanwhile, the distribution network was simplified into two pipelines: supply and return. In addition, a one-dimensional discrete model was used to describe the behaviours of water tank thermal energy storage. Other essential components, like central and distributed heat sources, were modelled using basic mass and energy balance equations. Afterwards, two optimization frameworks were formulated, which incorporated the developed system model: a long-term optimal operation framework aimed at a yearly level open-loop optimization with a two-hour resolution, and a model predictive control framework aimed at a daily level close-loop optimization with a one-hour resolution. The proposed method was tested numerically on a university campus district heating system in Norway on a personal computer. Model validation showed that the proposed modelling approach could capture the key characteristics of the studied system. Optimization results demonstrated the effectiveness of the proposed optimization frameworks both for the long-term and short-term optimization.
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Camarena, Fernando, Miguel Gonzalez-Mendoza, Leonardo Chang, and Neil Hernandez-Gress. "Boosting Self-supervised Video-based Human Action Recognition Through Knowledge Distillation." In LatinX in AI at Neural Information Processing Systems Conference 2022. Journal of LatinX in AI Research, 2022. http://dx.doi.org/10.52591/lxai202211286.

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Deep learning architectures lead the state-of-the-art in several computer vision, natural language processing, and reinforcement learning tasks due to their ability to extract multi-level representations without human engineering. The model’s performance is affected by the amount of labeled data used in training. Hence, novel approaches like self-supervised learning (SSL) extract the supervisory signal using unlabeled data. Although SSL reduces the dependency on human annotations, there are still two main drawbacks. First, high-computational resources are required to train a large-scale model from scratch. Second, knowledge from an SSL model is commonly finetuning to a target model, which forces them to share the same parameters and architecture and make it task-dependent. This paper explores how SSL benefits from knowledge distillation in constructing an efficient and non-task-dependent training framework. The experimental design compared the training process of an SSL algorithm trained from scratch and boosted by knowledge distillation in a teacher-student paradigm using the video-based human action recognition dataset UCF101. Results show that knowledge distillation accelerates the convergence of a network and removes the reliance on model architectures.
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