Recent Trends in Programming languages
Volume 1, Issue 3 (2014)
Published
Table of contents
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Masters of Deception: A Group That Ruled Cyberspace
Abstract: While widely used, the term hacker is not always applied as it should be. In the hacking community in particular, there can be stark divisions between hackers - people immensely skilled at navigating computer systems and diagnosing security flaws - and crackers - those who use their hacking knowledge for malicious gain. This same dichotomy is sometimes represented by the terms white hat and black hat. A white hat hacker, …
Published in Recent Trends in Programming languages · Vol. 1, Issue 3, 2014 · pp. 1–3 Read article
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Unsupervised Probabilistic Debugging
Abstract: We presented an unsupervised probabilistic approach for the debugging of the programs; where the focus is on diagnosing the wrong answers based on the proposed unsupervised semantic parsing algorithm. This approach can be implemented over other programming language. The parsing of the test cases are heuristically employed with the proposed parsing algorithm which reduces the computational complexity and rationally give better performing algorithms.
Published in Recent Trends in Programming languages · Vol. 1, Issue 3, 2014 · pp. 13–15 Read article
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A Complete Workflow of Multilingual Indian Regional Languages
Abstract: The system MLOCR is an effective way to convert the document images into editable text; this process is very tedious and time consuming. To make it easier, the MLOCR comes with a solution. It provides the one click solution to all the problems. Here the basic scanned image is taken and preprocessed to be converted by the OCR engine and the image is then recognized by the engine to be …
Published in Recent Trends in Programming languages · Vol. 1, Issue 3, 2014 · pp. 4–6 Read article
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Decomposition of Dynamic Graphs in Memory Saving with Effective Programming
Abstract: This study proposes a simple and effective heuristic to save memory in effective programming on tree decompositions when solving a graph optimization problem. The introduced “anchor technique” is based on a tree-like set covering problem. We substantiated our findings by experimental results. Our strategy has negligible computational overhead concerning running time but achieves memory savings for nice tree decompositions and path decompositions between 60% and 98%.
Published in Recent Trends in Programming languages · Vol. 1, Issue 3, 2014 · pp. 7–12 Read article