On the Incorporation of Psychologically-Driven 'Music' Preference Models for Music Recommendation

There are hundreds of millions of songs available to the public, necessitating the use of music recommendation systems to discover new music. Currently, such systems account for only the quantitative musical elements of songs, failing to consider aspects of human perception of music and alienating the listener’s individual preferences from recommendations. Our research investigated the relationships between perceptual elements of music, represented by the MUSIC model, with computational musical features generated through The Echo Nest, to determine how a psychological representation of music preference can be incorporated into recommendation systems to embody an individual’s music preferences. Our resultant model facilitates computation of MUSIC factors using The Echo Nest features, and can potentially be integrated into recommendation systems for improved performance.

FRAMEWORK SYNTHESIS FOR SYMBOLIC EXECUTION OF EVENT-DRIVEN FRAMEWORKS

Symbolic execution is a powerful program analysis technique, but it is very challenging to apply to programs built using event-driven frameworks, such as Android. The main reason is that the framework code itself is too complex to symbolically execute. The standard solution is to manually create a framework model that is simpler and more amenable to symbolic execution. However, developing and maintaining such a model by hand is difficult and error-prone. We claim that we can leverage program synthesis to introduce a high-degree of automation to the process of framework modeling. To support this thesis, we present three pieces of work. First, we introduced SymDroid, a symbolic executor for Android. While Android apps are written in Java, they are compiled to Dalvik bytecode format. Instead of analyzing an app’s Java source, which may not be available, or decompiling from Dalvik back to Java, which requires significant engineering effort and introduces yet another source of potential bugs in an analysis, SymDroid works directly on Dalvik bytecode. Second, we introduced Pasket, a new system that takes a first step toward automatically generating Java framework models to support symbolic execution. Pasket takes as input the framework API and tutorial programs that exercise the framework. From these artifacts and Pasket's internal knowledge of design patterns, Pasket synthesizes an executable framework model by instantiating design patterns, such that the behavior of a synthesized model on the tutorial programs matches that of the original framework. Lastly, in order to scale program synthesis to framework models, we devised adaptive concretization, a novel program synthesis algorithm that combines the best of the two major synthesis strategies: symbolic search, i.e., using SAT or SMT solvers, and explicit search, e.g., stochastic enumeration of possible solutions. Adaptive concretization parallelizes multiple sub-synthesis problems by partially concretizing highly influential unknowns in the original synthesis problem. Thanks to adaptive concretization, Pasket can generate a large-scale model, e.g., thousands lines of code. In addition, we have used an Android model synthesized by Pasket and found that the model is sufficient to allow SymDroid to execute a range of apps.

FLOW REGIME DRIVEN THERMAL ENHANCEMENT IN INTERNALLY-GROOVED TUBES

Internally-grooved refrigeration tubes maximize tube-side evaporative heat transfer rates and have been identified as a most promising technology for integration into compact cold plates. Unfortunately, the absence of phenomenological insights and physical models hinders the extrapolation of grooved-tube performance to new applications. The success of regime-based heat transfer correlations for smooth tubes has motivated the current effort to explore the relationship between flow regimes and enhanced heat transfer in internally-grooved tubes. In this thesis, a detailed analysis of smooth and internally-grooved tube data reveals that performance improvement in internally-grooved tubes at low-to-intermediate mass flux is a result of early flow regime transition. Based on this analysis, a new flow regime map and corresponding heat transfer coefficient correlation, which account for the increased wetted angle, turbulence, and Gregorig effects unique to internally-grooved tubes, were developed. A two-phase test facility was designed and fabricated to validate the newly-developed flow regime map and regime-based heat transfer coefficient correlation. As part of this setup, a non-intrusive optical technique was developed to study the dynamic nature of two-phase flows. It was found that different flow regimes result in unique temporally varying film thickness profiles. Using these profiles, quantitative flow regime identification measures were developed, including the ability to explain and quantify the more subtle transitions that exist between dominant flow regimes. Flow regime data, based on the newly-developed method, and heat transfer coefficient data, using infrared thermography, were collected for two-phase HFE-7100 flow in horizontal 2.62mm - 8.84mm diameter smooth and internally-grooved tubes with mass fluxes from 25-300 kg/m²s, heat fluxes from 4-56 kW/m², and vapor qualities approaching 1. In total, over 6500 combined data points for the adiabatic and diabatic smooth and internally-grooved tubes were acquired. Based on results from the experiments and a reinterpretation of data from independent researchers, it was established that heat transfer enhancement in internally-grooved tubes at low-to-intermediate mass flux is primarily due to early flow regime transition to Annular flow. The regime-based heat transfer coefficient outperformed empirical correlations from the literature, with mean and absolute deviations of 4.0% and 32% for the full range of data collected.

Data-Driven Wildfire Propagation Modeling with FARSITE-EnKF

The goal of this study is to provide a framework for future researchers to understand and use the FARSITE wildfire-forecasting model with data assimilation. Current wildfire models lack the ability to provide accurate prediction of fire front position faster than real-time. When FARSITE is coupled with a recursive ensemble filter, the data assimilation forecast method improves. The scope includes an explanation of the standalone FARSITE application, technical details on FARSITE integration with a parallel program coupler called OpenPALM, and a model demonstration of the FARSITE-Ensemble Kalman Filter software using the FireFlux I experiment by Craig Clements. The results show that the fire front forecast is improved with the proposed data-driven methodology than with the standalone FARSITE model.

Mathematical Programming Models for Influence Maximization on Social Networks

In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.