انج تاں توں ڈکیندا نہیں ہائیں، ڈکیا ہنجواں ہاہواں نال
انج تاں توں ٹھلیہندا نہیں ہائیں، ٹھلیا ٹھنڈیاں ساہواں نال
بدل ماحول گیا اے سارا نویاں قدراں بدلن نال
گولاں اج وناں تے نہیں نے، نہیں نے بور اکاہواں نال
سر دا بھار اوڑک نوں اپنے پیراں اتے اونا ایں
اپنے بھار نے چونے پوندے ٹٹیاں ہویاں باہواں نال
ہک دوجے نال مل کے سارے لوک ترقی کر دے نیں
بندے نکل جاندے نیں اگے، اپنیاں اپنیاں ٹھاہواں نال
پٹھے وڈھ کے چھیڑ مجھیں دا اج رجونا پوندا اے
ڈھور کدے وی رج دے نہیں نیں، بنیوں پٹے گھاہواں نال
نازک جان ملوک تیری اے، اوکھا پیار دا پینڈا ای
ساڈی ریس ناں کر توں جھلیا، اسیں ہاں حال تباہواں نال
بھانویں اوگنہار ہاں میں، پاک نبیؐ دی امت ہاں
مینوں ساڑ دوزخ نہیں سکدا اگاں اتے بھاہواں نال
The Benazir Income Support Program (BISP), introduced in 2008-09, is a unique cash support scheme for economically stressed families. Its uniqueness arises from several facets. The cash transfers are provided only to women aged over 18 years and have been ever married. It is unconditional and aimed at supplementing income as opposed to alleviating poverty. It was politically neutral, given that the facility to identify potential beneficiaries was extended to all parliamentarians, irrespective of party affiliation. A set of filters, applied electronically, ensured objectivity in beneficiary selection. Disbursement mechanism was automated to ensure minimal leakage. This paper outlines the process of the preparatory work that went into designing BISP – the conceptual debates, the beneficiary identification and disbursement procedures, etc. – involving a combination of high quality research with political decision making. It also addresses the debates surrounding BISP, cites independent empirical studies that show that the parliamentarian-based beneficiary selection mechanism was efficient and equitable and did indeed cover the deserving, and also responds to the variety of criticisms. ______
Image clustering deals with the optimal partitioning of images into different groups. Using linear discriminant analysis (LDA) criterion, optimal partitioning of images is obtained by maximizing the ratio of between-class scatter matrix (Sb) to within-class scatter matrix (Sw). In global learning based clustering models, scatter matrices (Sb and Sw) were evaluated on whole image datasets. Owing to which, nonlinear manifold in image datasets may not be effectively handled. For manifold learning, local neighborhood information in data objects were utilized in local learning based clustering models. Further, for high-dimensional data, Sw is singular which corresponds to under-sampled or small-sample-size (SSS) problem of LDA. Owing to which, almost all global learning and local learning based clustering models are based on regularized discriminant analysis (RDA), a variant of LDA. In RDA, the singularity problem of Sw was solved by perturbing it with regularization parameter λ > 0. However, tuning for optimal value of parameter λ is required. Further, for optimal clustering performance in existing state-of-the-art local learning based clustering models, one has to tune a number of clustering parameters from a large candidate set. In this thesis, we propose a novel local learning based image clustering model. Our proposed clustering model is inspired from exponential discriminant analysis (EDA). EDA is another variant of LDA in which SSS problem of LDA was handled using matrix exponential properties. Owing to which, EDA is less parameterized as compared with RDA. Number of nearest neighbor images k is the only clustering parameter in our proposed clustering model as compared with existing state-of-the-art local learning based clustering models. Image clustering performances on 12 benchmark image datasets are comparable over near competitor RDA based image clustering model. Performances are comparable because no discriminant information of LDA is lost in EDA. However, well separated images may not be achieved at local level for image datasets that contain images with pose, illumination, or xiv occlusion variations. Owing to which, local learning based image clustering models may face limitations in such variations. For this problem, various clustering models were proposed in which both global learning and local learning approaches were utilized. However, almost all existing local and global learning based clustering models are based on RDA. Owing to which, tuning of clustering parameters is extensive in almost all state-of-the-art local and global learning based image clustering models. We propose novel local and global learning based image clustering models that are inspired from EDA. Our proposed image clustering models are less-parameterized and computationally efficient where image clustering performances are comparable with existing local and global learning based clustering models. However, performances of all state-of-the-art clustering models are not optimal for challenging image datasets that contain images with illumination and occlusion changes. We explore the challenges in image clustering problem. We show that variation from one image to another image in a class (within-class variation) of an image dataset may vary from nominal to significant due to images with different facial expressions, pose, illumination, or occlusion changes. Using pixel intensity values as image features, we obtain histogram of within-class variation for each image datasets. On the basis of histogram, we categorize image datasets as Gaussian-like or multimodal. We show that image clustering performances of state-of-the-art clustering models are optimal for Gaussian-like image datasets and it degrade significantly for multimodal image datasets. We achieved significant overall performance improvement on 13 benchmark image datasets by employing optimal image descriptors with our proposed clustering model. Our study shows that there is no direct correlation between image clustering performance and local neighborhood structure. However, image clustering performance has correlation with the distribution of within-class variation in image datasets.