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The Effect Of Data Marshalling On Computation Offloading Decisions, Julio Alberto Reyes Muñoz

Open Access Theses & Dissertations

Computation offloading consists in allowing resource constrained computers, such as smartphones and other mobile devices, to use the network for the remote execution of resource intensive computing tasks in powerful computers. However, deciding whether to offload or not is not a trivial problem, and it depends in several variables related to the environment conditions, the computing devices involved in the process, and the nature of the task to be remotely executed. Furthermore, it comprises the optimal solution to some questions, like how to partition the application and where to execute the tasks.

The computation offloading decision problem has been widely …

Essays On Empirical Operations Management In Retail Sales And Service, Youran Fu

Publicly Accessible Penn Dissertations

This dissertation studies empirical operational problems in retail sales and service systems through three essays. We are in the middle of a remarkable rise in data analytics as available data, as well as the capability of artificial intelligence grows exponentially. This dissertation demonstrates how we can use traditional econometrics and cutting-edge machine learning models to provide data analytics for retail operations management. The first essay studies the value of unstructured social media text data in forecasting future fashion demands several months out, at a granular style-color level. Using recent advancement in natural language processing and machine learning techniques, we show …

Learning Convolutional Neural Network For Face Verification, Elaheh Rashedi

Wayne State University Dissertations

Convolutional neural networks (ConvNet) have improved the state of the art in many applications. Face recognition tasks, for example, have seen a significantly improved performance due to ConvNets. However, less attention has been given to video-based face recognition. Here, we make three contributions along these lines.

First, we proposed a ConvNet-based system for long-term face tracking from videos. Through taking advantage of pre-trained deep learning models on big data, we developed a novel system for accurate video face tracking in the unconstrained environments depicting various people and objects moving in and out of the frame. In the proposed system, we …