This Research to Practice Work-In-Progress paper discussed the incorporation of machine learning (ML) concepts in data structure education. The thriving of the ML especially deep learning techniques has led to an incr...
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ISBN:
(纸本)9781728189611
This Research to Practice Work-In-Progress paper discussed the incorporation of machine learning (ML) concepts in data structure education. The thriving of the ML especially deep learning techniques has led to an increased demand for trained professionals with ML skills to solve challenging engineering problems in many fields. Getting students familiar with ML as early as from CS2 (the data structure course) could benefit them in many aspects, but this direction has not been explored yet. In this paper, we discussed possible ways to integrate the ML concepts into data structure (DS) course. First, after introducing the concept of tensor in DS classroom teaching, we propose a practical experiment to implement the forward propagation of a pretrained convolutional neural network (CNN) aiming at classifying handwritten digits. Second, an experiment of decision tree based classification is set to give students an illuminating context via practicing the usage of tree structure. Finally, we design the experiment of computing graph to help the understanding of Directed Acyclic graph (DAG), in which the students are required to implement the calculation of a multiple-variable function and its gradient based on DAG. Practicing DS knowledge in interesting ML-related problem contexts would intrigue the study enthusiasm of students and give them a general understanding of the application of DS knowledge in frontier technology, which could benefit the education of both DS and ML-related courses.
The usage of electronic health data from different sources for statistical analysis requires a toolset where the legal, security and privacy concerns have been taken into consideration. The health data are typically l...
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ISBN:
(纸本)9781479966448
The usage of electronic health data from different sources for statistical analysis requires a toolset where the legal, security and privacy concerns have been taken into consideration. The health data are typically located at different general practices and hospitals. The data analysis consists of local processing at these locations, and the locations become nodes in a computing graph. To support the legal, security and privacy concerns, the proposed toolset for statistical analysis of health data uses a combination of secure multi-party computation (SMC) algorithms, symmetric and public key encryption, and public key infrastructure (PKI) with certificates and a certificate authority (CA). The proposed toolset should cover a wide range of data analysis with different data distributions. To achieve this, large set of possible SMC algorithms and computing graphs have to be supported.
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