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المبحث الرابع: تعليمها

المبحث الرابع: تعليمها
یقول الشاعر ’’نزار‘‘ عن شقیقتہ إنھا نشأت وسط عائلۃ أدبیۃ وراثیاً أباً عن جد، فقد کان والد الشاعرۃ صادق الملائکۃ شاعراً مشھوراً وکاتباً، وألف وحدہ ’’دائرۃ معارف الناس‘‘ وذلک سیر وشخصیات الأعلام المشھورین الراحلین من العرب۔ تتکون من 36 مجلداً، وھي ربما محفوظۃ إلی الآن لدی ھيئة حکومیۃ في بغداد، وإلی الآن لم تطبع، وکانت والدتہا شاعرۃ أیضاً ولھا دیوان ’’أنشودۃ المجد‘‘ وکانت مشھورۃ بإسم ’’ أم نزار الملائکۃ‘‘ ولکن اسمھا الحقیقي ھو سلمی عبدالرازق الملائکۃ۔
یتضح من ذلک ثقافۃ والدیھا، وعندما بلغت نازک الملائکۃ الخامسۃ من عمرھا فکر أبویھا علی أن یدخلاھا المدرسۃ، فاختارو لھا الروضۃ التابعۃ للإبتدائیۃ المرکزیۃ في العاقولیۃ۔ وبعد أن انتھت من الدراسۃ الثانویۃ التحقت بمعھد المعلمین العالي وتخرجت سنۃ 1942م۔
درست نازک العزف علی العود، والتمثیل، واللغۃ اللاتینیۃ، واللغۃ الفرنسیۃ، والأدب الانکلیزي، واتجھت الی کتابۃ النثر عام 1951م، ومرضت والدتھا مرضاً مفاجئاً عام 1953 فکتبت قصیدۃ سمتھا ’’ثلاث مرات لأمي‘‘ ودرست في وسکنسن عام 1954، وسافرت الی بیروت۔ وفي عام 1958م قامت في العراق ثورہ 14 تموزہ وقبل ذلک قد عُینت مدرسۃ معیدۃ في کلیۃ التربیۃ في بغداد، فلما عادت 1960م في بیروت الی بغداد تعرفت الی زمیل جدید في قسم اللغۃ العربیۃ في الکلیۃ ھو الدکتور عبدالھادي محبوبۃ وتزوجتہ ومعھا لیسانس بالتربیۃ منذ 1944م من جامعۃ بغداد وأیضاً دخلت معھد الفنون وتخرجت سنۃ 1949م من قسم الموسیقي، وأنھا حصلت علی شھادۃ الماجستیر في الأدب المقارن من جامعۃ مادسن وسکونس عام 1950م من الولایات المتحدۃ الأمریکیۃ، ثم عُینت أستاذۃ في جامعۃ بغداد وجامعۃ البصرۃ وأخیراً جامعۃ الکویت۔

Discursive Functioning of Parts of Speech in Political Parties’ Manifestos in Pakistani Election 2013

The political discourse plays an essential role in shaping the perceptions and ideologies of people. The present research, titled ‘Discursive Functioning of Parts of Speech in Political Parties’ Manifestos’, focuses on the role of the discursive use of the parts of speech in Pakistani political parties’ manifestos, pertaining to the general election of 2013. The research is aimed at the linguistic analysis of the political discourse presented in the parties’ manifestos, focusing on parts of speech. The Van Dijk Socio Cognitive Model (1998) and Tajfel’s and Turner’s Social Identity Approach (1979) have served as the theoretical framework for this study. Corpus-based methodology was used for the Critical Discourse Analysis of the five political parties selected on the basis of popularity. The research revealed that all political parties studied used different parts of speech as a discursive strategy to frame positive images of their own party and negative images of the other parties. This was done by the parties in order to transform people’s ideology and identities in their own favor.

Intelligent View Extrapolation for Dynamic Scenes

This thesis targets Artificial Intelligence - a fundamental branch of Computer Engineering striving to provide human-like capabilities and intelligence to the computer systems. More specifically, it deals with computer vision, which has gained a lot of attention by researchers due to its wide applicability in day-to-day tasks involving view generation, synthesizing animations and videos from static images, surveillance, medical imaging, tracking, object recognition and classification etc. This thesis investigates the problem areas of image synthesis, object recognition and object categorization. The problem of generating images at novel, arbitrary and unconstrained viewpoints covering interpolation and extrapolation is investigated by operating on a sparse set of basis images of a real scene. This image generation methodology is further incorporated to develop models for object recognition and categorization. First, an image synthesis strategy has been presented that generates virtual views at arbitrary points using interpolation and extrapolation from a sparse set of images. The traditional work on view synthesis using interpolation has been extended and it has been shown that view extrapolation can be done as easily as interpolation. Moreover, certain scenarios have been identified like planar and/or multi-planar scenes and pure rotational camera motion for image capture that allow direct retrieval of the underlying mapping function between the images and hence leading to even more simplified image extrapolation. The major issues and factors affecting the accuracy of generation have been explored and suggestions are presented to improve the virtual view quality. Next, an approach is presented to generate a model for multi-view object recognition. A view- centered model is generated using either a video sequence or a sparse set of images captured around the object following arbitrary and unconstrained camera trajectory. It does not require any prior knowledge of camera parameters and positioning or motion of object and/or camera. The model thus generated is quite dense with a lot of redundant images. Thus the virtual view generation strategy is applied to identify the redundant images and remove them. This results in a model that is computationally economical in terms of space and time. Next, for testing or recognition, the model is used in conjunction with a video sequence which provides information of multiple views of the object and thus increases the confidence measure of results. The model is robust in that it captures the topological structure of the objects from multiple viewpoints allowing the use of a video iiisequence rather than a single test image for object recognition. No constraint has been placed on camera and/or object motion while capturing the video. Next, an approach for video-based multi-view object classification is presented. For each object instance of a particular category, a neighborhood graph-based model is generated using the set of input images which are arranged in a manner that highlights the underlying topological structure. Again, no constraint is placed on the motion and placement of the object and camera during image capture. Moreover no prior knowledge of positioning or parameters of camera is desired. The view synthesis algorithm is used to identify the redundant images in the model and remove them to give a computationally economical model in terms of space and training time. The independent graphs of the different instances of the object category are then merged by automatically identifying the corresponding viewpoints across them. The strength of this approach is that it allows object categorization from multiple viewpoints while eliminating the need of manual alignment of common viewing angles across object instances. Another strength is that the video sequences have been used for object classification, instead of images, which increases precision of results.
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