Yes / Smart contracts have recently attracted interest from diverse fields including law and finance. Ethereum in particular has grown rapidly to accommodate an entire ecosystem of contracts which run using its own crypto-currency. Smart contract developers can opt to verify their contracts so that any user can inspect and audit the code before executing the contract. However, the huge numbers of deployed smart contracts and the lack of supporting tools for the analysis of smart contracts makes it very challenging to get insights into this eco-environment, where code gets executed through transactions performing value transfer of a crypto-currency. We address this problem and report on the use of unsupervised clustering techniques and a seed set of verified contracts, in this work we propose a framework to group together similar contracts within the Ethereum network using only the contracts publicly available compiled code. We report qualitative and quantitative results on a dataset and provide the dataset and project code to the research community. / Link to conference webpage: http://icccn.org/icccn17/workshop/

The human cerebral cortex is a highly foliated structure that supports the complex cognitive abilities of humans. The cortex is divided by its cytoarchitectural characteristics that can be approximated by the folding pattern of the cortex. Psychiatric and neurological diseases, such as Huntington's disease or schizophrenias, are often related with structural changes in the cerebral cortex. Detecting structural changes in different regions of cerebral cortex can provide insight into disease biology, progression and response to treatment. The delineation of anatomical regions on the cerebral cortex is time intensive if performed manually, therefore automated methods are needed to perform this delineation. Magnetic Resonance Imaging (MRI) is commonly used to explore the structural change in patients with psychiatric and neurological diseases. This dissertation proposes a fast and reliable method to automatically parcellate the cortical surface generated from MR images. A fully automated pipeline has been built to process MR images and generate cortical surfaces associated with parcellation labels. First, genus zero cortical surfaces for each hemisphere of a subject are generated from MR images. The surface is generated at the parametric boundary between gray matter and white matter. Geometry features are calculated for each cortical surface to as scalar values to drive a multi-resolution spherical registration that can align two cortical surfaces together in the spherical domain. Then, the labels on a subject's cortical surface are evaluated by registering a subject's cortical surface with a population atlas and combining the information of prior probabilities on the atlas with the subject's geometry features. The automated parcellation has been tested on a group of subjects with various cerebral cortex structures. It shows that the proposed method is fast (takes about 3 hours to parcellate at one hemisphere) and accurate (with the weighted average Dice ~0.86). The framework of this dissertation will be as follows: the first chapter is about the introduction, including motivation, background, and significance of the study. The second chapter describes the whole pipeline of the automated surface parcellation and focuses on technical details of every method used in the pipeline. The third chapter presents results achieved in this study and the fourth chapter discusses the results and draws a conclusion.