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مظفر شاہ خاں یوسفی

مظفر شاہ خاں یوسفی
سخت افسوس ہے کہ گزشتہ مہینہ ہمارے عزیز دوست اورندوۃ المصنفین کے پُرانے رفیق مظفر شاہ خاں صاحب یوسفی اچانک راہی ملک بقا ہوگئے۔موصوف کی عمر پچاس سال کے لگ بھگ ہوگی۔صحت بہت عمدہ تھی اور مضبوط توانا جسم رکھتے تھے۔شب کے کسی حصّہ میں کچھ کرب و بے چینی محسوس ہوئی، پلنگ سے اُٹھ کر صحن میں ٹہلنے لگے، اسی عالم میں گرپڑے اور بیہوش ہوگئے اورصبح تک جان جان آفریں کے سپرد کردی۔ مرحوم بڑے قابل اور لائق فائق تھے۔ اُردو اور ہندی دونوں میں ایم۔ اے تھے، روسی زبان کاامتحان بھی اعلیٰ نمبروں میں پاس کیا تھا۔ اُردو اور ہندی کے شگفتہ نگار ادیب تھے۔تقسیم سے پہلے اُن کی دوکتابیں ادارہ سے شائع ہوئی تھیں ،ایک عرصہ سے ماہنامہ’’ آجکل ‘‘ کے عملہ ادارت سے وابستہ تھے۔ اسی درمیان میں ایک اسکالر شپ پردو برس امریکہ میں بھی رہ آئے تھے اور وہاں سے واپسی پر کناڈا، تمام یورپ اور مشرقِ وسطیٰ کی سیاحت کرتے ہوئے وطن پہنچے تھے اور اب پھر دوبارہ ڈاکٹریٹ کے لیے امریکہ جانے والے تھے کہ آخرت کاسفر پیش آگیا۔ اخلاقی اعتبارسے نہایت شریف، بڑے دوست نواز، ملنسار اورخلیق ومتواضع اور شائستہ اطوار تھے۔اﷲ تعالیٰ انہیں مغفرت وبخشش کی نعمتوں سے سرفراز فرمائے اوراُن کے بچّوں وبیوہ کاحامی وناصر ہو۔آمین۔
[اگست۱۹۶۸ء]

 

Voices Unheard: Analyzing Marginalization and Resistance in Rabbani's 'Invisible People' through a Spivakian Lens

This scholarly research offers a comprehensive analysis of Rabbani's seminal work, 'Invisible People,' through the theoretical lens of Gayatri Chakraborty Spivak's postcolonial feminist perspective. The study investigates the experiences and challenges faced by marginalized populations in contemporary society, aiming to elucidate effective strategies for resistance against marginalization. Key focal points include Spivak's subaltern concept and the imperative of amplifying marginalized voices. The analysis critically examines Rabbani's portrayal of 'invisible people' (2017) as individuals bereft of agency and influence due to their marginalized status, exploring multiple forms of exclusion encompassing economic, social, and political dimensions. Moreover, the research delves into Rabbani's nuanced treatment of identity construction and the intricate intersections of marginalization. Applying Spivak's theory of strategic essentialism, the study scrutinizes how these individuals navigate their identities and mobilize collective resistance. Additionally, the research evaluates notions of agency and empowerment within Rabbani's narrative, investigating methodologies employed by marginalized groups to challenge entrenched power dynamics. Through Spivak's concept of "subaltern counter publics," the study examines the significance of grassroots movements, community organizing, and the establishment of alternative platforms for collective action. Ethical considerations within Spivak and Rabbani's frameworks are also addressed, highlighting the complexities and ethical responsibilities associated with representing marginalized populations. This research underscores the importance of reflexive researcher positionality when engaging with these narratives. Overall, this study provides a nuanced and insightful analysis of Rabbani's 'Invisible People' from a Spivakian perspective, contributing to the ongoing discourse on resistance, marginality, and social justice within this theoretical framework.

Digital Watermarking Using Machine Learning Approaches

In recent years digital watermarking has gained substantial attraction by the research community. It promises the solution to many problems such as content piracy, illicit manipulation of medical/legal documents, content security and so on. Watermarked content is usually vulnerable to a series of attacks in real world scenario. These attacks may be legitimate, such as common signal processing operations, or illegitimate, such as a malicious attempt by an attacker to remove the watermark. A low strength watermark usually possesses high imperceptibility but weak robustness and vice versa. On the other hand, different set of attacks are associated with distinctive watermarking applications, which pose different requirements on a watermarking scheme. Therefore, intelligent approaches are needed to adaptively and judiciously structure the watermark in view of the current application. In addition, traditional watermarking techniques cause irreversible degradation of an image. Although the degradation is perceptually insignificant, it may not be admissible in applications like medical, legal, and military imagery. For applications such as these, it is desirable to extract the embedded information, as well as recover the sensitive host image. This leads us to the use of reversible watermarking. An efficient reversible watermarking scheme should be able to embed more information with less perceptual distortion, and equally, be able to restore the original cover content. Therefore, for reversible watermarking, capacity and imperceptibility are two important properties. However, if one increases the other decreases and vice versa. Hence, one needs to make an optimum choice between these two properties for reversible watermarking. 5The research in this work is two-fold. Firstly, we develop intelligent systems for making optimum robustness versus imperceptibility tradeoffs. The performance of the existing watermarking approaches is not up to the task when we consider watermark structuring in view of a sequence of attacks, which is much desirous in real world applications. In order to resist a series of attacks, we employ intelligent selection of both the frequency band as well as strength of alteration for watermark embedding using Genetic Programming. To further enhance the robustness of the watermarking system, Support Vector Machines and Artificial Neural Networks are applied to adaptively modify the decoding strategy in view of the anticipated sequence of attacks at the watermark extraction phase. Secondly, we devise an intelligent system capable of making optimum/ near optimum tradeoff between watermark payload and imperceptibility. In the context of reversible watermarking, we propose an intelligent scheme which selects suitable coefficients in different wavelet sub-bands and yields superior capacity versus imperceptibility tradeoff. Experimental results show that machine learning approaches are very promising in state of the art watermarking applications.
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