414 research outputs found
Book review of Diversity in Language: Contrastive studies in English and Arabic theoretical and applied linguistics. Edited by Zeinab M. Ibrahim, Sabiha T. Aydelott and Nagwa Kassabgy.
Get PDFBook review of Diversity in Language: Contrastive studies in English and Arabic theoretical and applied linguistics. Edited by Zeinab M. Ibrahim, Sabiha T. Aydelott and Nagwa Kassabgy
Architecture and FPGA Implementation of Digital Time-to-Digital Converter for Sensing Applications
No full textDeclarative Entity Resolution Via Matching Dependencies and Combining Matching Dependencies With Machine Learning for Entity Resolution
No full textEntity resolution (ER) is an important problem in data cleaning. It is about iden- tifying and merging records in a database that represent the same external entity. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. An ER process supported by MDs over a dirty instance may lead to multiple clean instances.In this thesis, we first present disjunctive answer set programs that capture through their models the class of alternative clean instances obtained after an ER process based on MDs. With these programs, we can obtain clean answers to queries by skeptically reasoning from the program. As an important practical case of ER, we provide a declarative reconstruction of the so-called union-case ER methodology, as presented through a generic approach to ER, the so-called Swoosh approach. We extend our ASP-based account of the union-case of Swoosh with negative rules.In this work, we extend MDs to relational MDs, which capture more application semantics, and identify classes of relational MDs for which the proposed declarative specifications for ER via MDs can be automatically rewritten into stratified Datalog programs.We also show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs using machine learn- ing (ML) techniques; (b) Use of relational MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language LogiQL -an extended form of Datalog supported by the LogicBlox platform- for all activities related to data processing, and the specification and enforcement of MDs
Balaustium akramii Noei 2017
No full text25. Balaustium akramii Noei, 2017: 2019 in Noei et al. (2017a). Holotype larva (ARS-20170305-4a), IRAN, SARI, Coll. Solmaz Asadollahzadeh Paratype larvae (ARS-20170305-4b, c, d), TURKEY, AYDIN, Coll. Ibrahim Çakmak, AVAILABLE.Published as part of Saboori, Alireza & Shirvani, Zeinab, 2021, A checklist of Acari type specimens deposited in the Jalal Afshar Zoological Museum, Karaj, Iran, pp. 289-311 in Zootaxa 4949 (2) on page 292, DOI: 10.11646/zootaxa.4949.2.4, http://zenodo.org/record/463640
Re-visiting the Park: Reviving the “Cultural Park for Children” in Sayyeda Zeinab in the shadows of Social Sustainability
No full textThis paper aims to question the level of success of one of Egypt’s contemporary architectural milestones which is the Aga-Khan Award-winning project of the Child Park in Sayyeda Zeinab - designed by the Egyptian architect Abdel-Halim Ibrahim; from a community participation perspective. Stemming from the fact that the level of successful community participation in architecture and urban design projects affects the sustainability of the added value, this paper tackles the current process of operation of the Park and the adjacent pedestrian street, as complementary aspects of a community participatory process, and evaluates the social sustainability of the project as well. The study sheds light on the operation of the park after twenty-eight years of the initiation of the project, it explores its functional and social role in the district of Sayyeda, located at the centre of Cairo city.In order to achieve this end, the paper adopts a two-fold methodology. Based on those two main research approaches, the paper concludes with a framework and several guidelines to enhance the social sustenance of the place through rephrasing the park’s role in relation to the changing needs of the community
Low Power Design of Approximate Adders Based on Inexact Full Adder
No full textThis paper presents the design of a low-power logic gate approximate adder based on an inexact full adder. Simulation results show that the proposed adder improves the accuracy over approximate adders by 27% in terms of mean square error (MSE) while achieving a power reduction of 8% demonstrated by synthesis. Moreover, when compared to the exact adder, the proposed circuit achieves a power reduction of up to 40% in the case of a 16-bit adder with 14 approximated bits
Erythraeus (Zaracarus) aydinicus Saboori, Cakmak & Nouri-Gonbalani 2004
No full text<p> 46. <b> <i>Erythraeus</i> (<i>Zaracarus</i>) <i>aydinicus</i></b> Saboori, Çakmak & Nouri-Gonbalani, 2004a: 131.</p> <p> <b>Holotype larva</b> (ARS-20030931-1a), TURKEY, AYDIN, Coll. Ibrahim Çakmak. AVAILABLE.</p> <p> <b>Paratype larva</b> (ARS-20030931-1b), same data as holotype. AVAILABLE.</p>Published as part of <i>Saboori, Alireza & Shirvani, Zeinab, 2021, A checklist of Acari type specimens deposited in the Jalal Afshar Zoological Museum, Karaj, Iran, pp. 289-311 in Zootaxa 4949 (2)</i> on page 294, DOI: 10.11646/zootaxa.4949.2.4, <a href="http://zenodo.org/record/4636405">http://zenodo.org/record/4636405</a>
Semi-supervised learning and bidirectional decoding for effective grammar correction in low-resource scenarios
Get PDFThe correction of grammatical errors in natural language processing is a crucial task as it aims to enhance the accuracy and intelligibility of written language. However, developing a grammatical error correction (GEC) framework for low-resource languages presents significant challenges due to the lack of available training data. This article proposes a novel GEC framework for low-resource languages, using Arabic as a case study. To generate more training data, we propose a semi-supervised confusion method called the equal distribution of synthetic errors (EDSE), which generates a wide range of parallel training data. Additionally, this article addresses two limitations of the classical seq2seq GEC model, which are unbalanced outputs due to the unidirectional decoder and exposure bias during inference. To overcome these limitations, we apply a knowledge distillation technique from neural machine translation. This method utilizes two decoders, a forward decoder right-to-left and a backward decoder left-to-right, and measures their agreement using Kullback-Leibler divergence as a regularization term. The experimental results on two benchmarks demonstrate that our proposed framework outperforms the Transformer baseline and two widely used bidirectional decoding techniques, namely asynchronous and synchronous bidirectional decoding. Furthermore, the proposed framework reported the highest F1 score, and generating synthetic data using the equal distribution technique for syntactic errors resulted in a significant improvement in performance. These findings demonstrate the effectiveness of the proposed framework for improving grammatical error correction for low-resource languages, particularly for the Arabic language
Optimizing the impact of data augmentation for low-resource grammatical error correction
Get PDFGrammatical Error Correction (GEC) refers to the automatic identification and amendment of grammatical, spelling, punctuation, and word-positioning errors in monolingual texts. Neural Machine Translation (NMT) is nowadays one of the most valuable techniques used for GEC but it may suffer from scarcity of training data and domain shift, depending on the addressed language. However, current techniques (e.g., tuning pre-trained language models or developing spell-confusion methods without focusing on language diversity) tackling the data sparsity problem associated with NMT create mismatched data distributions. This paper proposes new aggressive transformation approaches to augment data during training that extend the distribution of authentic data. In particular, it uses augmented data as auxiliary tasks to provide new contexts when the target prefix is not helpful for the next word prediction. This enhances the encoder and steadily increases its contribution by forcing the GEC model to pay more attention to the text representations of the encoder during decoding. The impact of these approaches was investigated using the Transformer-based for low-resource GEC task, and Arabic GEC was used as a case study. GEC models trained with our data tend more to source information, are more domain shift robustness, and have less hallucinations with tiny training datasets and domain shift. Experimental results showed that the proposed approaches outperformed the baseline, the most common data augmentation methods, and classical synthetic data approaches. In addition, a combination of the three best approaches Misspelling, Swap, and Reverse achieved the best F1 score in two benchmarks and outperformed previous Arabic GEC approaches
Suicide as a Passage Through Silent Issues: A Case Study of Suicide Attempt Survivors in Tehran Zeinab Kouchakian Alireza Kaldi Alireza Mohsenitabrizi
No full textSuicide as a Passage Through Silent Issues: A Case Study of Suicide Attempt Survivors in Tehran Zeinab Kouchakian[1] , Alireza Kaldi[2] , Alireza Mohsenitabrizi[3] Received: 06/4/2018 Accepted: 22/12/2018 Abstract Suicide, as a conscious action in hurting oneself causing death, is among the social issues of human society that is increasing day by day. Suicide epidemiological studies inIran have shown that attempting suicide and committing an act of suicide is increasing. Using a qualitative approach, and based on the grounded theory of the social and cultural reasons behind suicide attempt, the current study aims to investigate the interaction between the person who commits suicide and his/her family, group and society as well as its relationship with suicide attempt. Through this, the present study seeks to find a more vivid image of the relationship between [1]. Ph.D. student of Cultural Sociology, Islamic Azad University, Science and Research Branch of Tehran. [email protected] [2]. Professor of Sociology, Faculty of Social Welfare and Rehabilitation Sciences, Tehran, Iran (Corresponding Author). [email protected] [3]. Professor of Sociology, Faculty of Social Sciences, University of Tehran, Iran [email protected]
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