The paper is about how we can use python for writing tools for reverse engineering and assembly code analysis it will talk about basic and modules those are available for doing reverse engineering.
To be uploaded
+ +For now two decades, functional brain imaging has provided a tool for +building models of cognitive processes. However, these models are +ultimately introduced without a formal data analysis step. Indeed, +cognition arise from the interplay of many elementary functions. There +are an exponential amount of competing possible models, that cannot be +discriminated with a finite amount of data. This data analysis problem is +common in many experimental science settings, although seldom diagnosed. +In statistics, it is known as the curse of dimensionality, and can be +tackled efficiently with machine learning tools.
++For these reasons, imaging neuroscience has recently seen a +multiplication of complex data analysis methods. Yet, machine learning is +a rapidly-evolving research field, often leading to impenetrable +publication and challenging algorithms, of which neuroscience data +analysts only scratch the surface. +
++I will present our efforts to foster a general-purpose machine-learning +Python module, scikit-learn, for scientific data analysis. As it aims +to bridge the gap between machine-learning researchers and end-users, the +package is focused on ease of use and high-quality documentation while +maintaining state-of-the-art performance. It is enjoying a growing +success in research laboratories, but also in communities with strong +industrial links such as web-analytics or natural language processing. +
++We combine this module with high-end interactive +visualization using Mayavi and neuroimaging-specific tools in nipy to +apply state of the art machine learning techniques to neuroscience: +learning from the data new models of brain activity, focused on +predictive or descriptive power. These models can be used to perform +"brain reading": predicting behavior our thoughts from brain images. This +is a well-posed supervised learning problem. In unsupervised +settings, that is in the absence of behavioral observations, we focus on +learning probabilistic models of the signal. For instance, interaction +graphs between brain regions at rest reveal structures well-known to be +recruited in tasks. +
++Optimal use of the data available from a brain imaging session raises +computational challenges that are well-known in large data analytics. The +scipy stack, including Cython and scikit-learn, used with care, can +provide a high-performance environment, matching dedicated solutions. I +will highlight how the *scikit-learn* performs efficient data analysis in +Python. +
++The challenges discussed here go beyond neuroscience. Imaging +neuroscience is a test bed for advanced data analysis in science, as it +faces the challenge of integrating new data without relying on +well-established fundamental laws. However, with the data available in +experimental sciences growing rapidly, high-dimensional statistical +inference and data processing are becoming key in many other fields. +Python is set to provide a thriving ecosystem for these tasks, as it +unites scientific communities and web-based industries. +
+To be uploaded
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