شاہ ولی اللہ رحمہ اللہ کا نظریہ تشکیل اخلاقیات

This research article consist unique study regarding constitution of Human being character building in the thoughts of eminent philosopher Shah Wali Ullah (1703-1764). In present critique the focus has been made to explore how individual characters build in the specific environments? How surrounding effects on the character building? Moreover linkage of Islamic ‘IB└DA and its positive impact on the Muslim society has been explored. In interpretation of Shah Wali Ullah, All ‘IB└DA are like tools which lead to generate four basic ethics i.e purity and transparency capitulation, gainful and abstinence. These are the basic moral code which are the ultimate result of the four kind of ‘IB└DA i.e prayer, fasting, zakat and hajj. Muslim has inestimable inner power in the form of six lat┐’ef )اطلفئ, )which ultimately resulted upon the change of behavior. Character building are etiquettes, noble practices, decentness and good morality. It is generally refers to a code of conduct, that an individual group or society hold as authoritative in distinguishing right from wrong. Ethics are phenomenon values and can develop up to reasonable universal standards. Conduct in Islam governs all aspects of life and specifically addresses such principles as truthfulness, honesty, trust, sincerity, brotherhood and justice, while Islam forbid false, conspiracy, dodge, rude, irascibility, corruption. To materialize the virtues and disgrace the fake a role model prophet Muhammad (S.A.W) were deputed from Allah to guide the human being. So In present article character building in the theory of Shah Wali Ullah especially while in other Muslims scholars in general has diagnosed.

Architecture Recovery of Legacy Software Systems Using Unsupervised Machine Learning Techniques

Perhaps the most important aspect in maintaining software legacy systems is un- derstanding their architecture. Architectural documentation is often unavailable. Thus efforts need to be made to recover the architectural design from the source code. This thesis addresses the problem of recovering the architecture of software systems for greater understanding, and modularizing them for greater maintainability, using machine learning techniques. We use clustering to obtain a high-level view of a software’s architecture, by identify- ing major sub-systems within it. For this purpose, we analyze the behaviour of existing similarity and distance measures when applied to software artifacts and keeping in view software characteristics, yielding explanations to some previously unanswered questions. We develop two new hierarchical clustering algorithms that address the problem of ar- bitrary decisions taken by existing hierarchical algorithms. We also propose a similarity measure suitable for software clustering. The performance of the proposed algorithms and similarity measure is evaluated using internal and external assessment. Instead of using only one expert decomposition for external assessment, as is commonly done, we use decompositions prepared by 4-5 experts for each test system. Such an approach allows us to validate the idea of multiple views of a software system. Experiments carried out on five open source legacy software systems show that the performance of our proposed algorithm is better than previously used algorithms. Interpreting the results of clustering algorithms is often difficult. To make clusters easier to understand, we propose a labeling scheme for clusters and compare two alter- native ranking schemes that can be utilized for this purpose. We demonstrate how the labels assigned by our scheme aid understanding of the clustering process of clustering algorithms. We also provide a comparison between cluster analysis and concept analysis as modularization techniques, and give examples of their application to different software structures, thus indicating the strengths and limitations of the two techniques. Finally, we use association rule mining to gain insight into the low-level structure of software systems by examining relationships between architectural quarks i.e. functions, global variables and user defined types. Metarule-guided association rule mining is used to ividentify problems within structured legacy systems. Re-engineering patterns that present solutions to these problems are proposed. Results for the test systems reveal interesting characteristics which allow us to understand legacy systems and their evolution.