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Corruption is an age old phenomenon and can be seen everywhere now a days. It is like a cancer in public life, which has not become to rampant and perpetuated overnight, but is course of time. The word corruption means destruction, ruining or spoiling a society or nation. A corrupt society is characterized by immorality and lack of fear or respect for the law. It is the abuse of public power for private gain. Corruption comes under many different guises like bribery, extortion, fraud, embezzlement. Misappropriations of public goods, nepotism (favoring family members for jobs and contracts) cronyism and influence pending

We study learning in a modified EXACT model, where the oracles are corrupt and only few of the presented attributes are relevant. Both modifications were already studied in the literature [Dana Angluin, Mārtiņš Krikis, Learning with malicious membership queries and exceptions (extended abstract), in: COLT ’94: Proceedings of the Seventh Annual Conference on Computational Learning Theory, ACM Press, 1994, pp. 56–57 [3]; Dana Angluin, Mārtiņš Krikis, Robert H. Sloan, György Turán, Malicious omissions and errors in answers to membership queries, Machine Learning 28 (1997) 211–255; Laurence Bisht, Nader H. Bshouty, Lawrance Khoury, Learning with errors in answers to membership queries (extracted abstract), in: FOCS ’04: Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science, FOCS’04, IEEE Computer Society, 2004, pp. 611–620; Nader H. Bshouty, Lisa Hellerstein, Attribute-efficient learning in query and mistake-bound models, J. Comput. System Sci. 56 (3) (1998) 310–319 [12]; Nick Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, Machine Learning 2 (4) (1988) 285–318; Robert H. Sloan, Gyorgy Turan, Learning with queries but incomplete information (extended abstract), in: COLT ’94: Proceedings of the Seventh Annual Conference on Computational Learning Theory, ACM Press, 1994, pp. 237–245 [5]], and efficient solutions were found to most of their variants. Nonetheless, their reasonable combination is yet to be studied, and integrating the existing solutions either fails or works with a complexity that can be significantly improved. In this paper we prove the equivalence of EXACT learning attribute-efficiently with and without corrupt oracles. For each of the possible scenarios we describe a generic scheme that enables learning in these cases using modifications of the standard learning algorithms. We also generalize and improve previous non-attribute-efficient algorithms for learning with corrupt oracles.

This paper examines the design of social investment funds (SIFs) and explores the ways they affect agents incentives to propose, select, and implement good projects. Compared with other forms of decentralized service provision, SIFs possess features of administratively delegated authority and deep political devolution. Where existing political institutions fail to deliver assistance to vulnerable groups, a well-designed SIF may represent a useful administrative alternative. This article reviews several features that provide incentives for both SIF staff and project beneficiaries and concludes with practical guidelines for designing and appraising social investment funds.

The International Crisis Group has recognized the need for simultaneity rather than sequencing in security and development to contribute to conflict prevention, end conflict where it exists, and help ensure the success of post conflict reconstruction and stabilization.

MDTFs can enhance aid effectiveness by reducing transaction costs and by mitigating the high risk levels inherent in post-crisis situations.

Afghanistan presents complex and, in some respects, unique development challenges, such as building a strong civil society

Afghanistan presents complex and, in some respects, unique development challenges. The role of the international community in supporting state building.