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Proteins play many roles in the body, and the task of understanding how proteins function is very challenging. Determining a protein’s location within the cell (also referred to as the subcellular location) helps shed light on the function of that protein. Protein subcellular location can be inferred through experimental methods or predicted using computational systems. In particular, we focus on two existing computational systems, namely EpiLoc and HomoLoc, that use features derived from text (abstracts of technical papers), and apply a support vector machine (SVM) classifier to classify proteins into their respective locations. Both EpiLoc and HomoLoc’s prediction accuracy is comparable to that of state-of-the-art protein location prediction systems. However, in addition to accuracy, other factors such as training efficiency must be considered in evaluating the quality of a location prediction system. In this thesis, we replace the SVM classifier in EpiLoc and HomoLoc, by a naïve Bayes classifier and by a novel classifier which we call the Mean Weight Text classifier. The Mean Weight Text classifier and the naïve Bayes classifier are simple to implement and execute efficiently. In addition, naïve Bayes classifiers have been shown effective in the context of protein location prediction and are considered preferable to SVM due to clarity in explaining the process used to derive the results. Evaluating the performance of these classifiers on existing data sets, we find that SVM classifiers have a slightly higher accuracy than naïve Bayes and Mean Weight Text classifiers. This slight advantage is offset by the simplicity and efficiency offered by naïve Bayes and Mean Weight Text classifiers. Moreover, we find that the Mean Weight Text classifier has a slightly higher accuracy than the naïve Bayes classifier. / Thesis (Master, Computing) -- Queen's University, 2010-07-06 11:06:47.613

The purpose of this thesis is to propose an alternative type of interface for mobile software being used while walking or running. Our work addresses the problem of visual user interfaces for mobile software be- ing potentially unsafe for pedestrians, and not being very immersive when used for location-based games. In addition, location-based games and applications can be dif- ficult to develop when directly interfacing with the sensors used to track the user’s location. These problems need to be addressed because portable computing devices are be- coming a popular tool for navigation, playing games, and accessing the internet while walking. This poses a safety problem for mobile users, who may be paying too much attention to their device to notice and react to hazards in their environment. The difficulty of developing location-based games and other location-aware applications may significantly hinder the prevalence of applications that explore new interaction techniques for ubiquitous computing. We created the TREC toolkit to address the issues with tracking sensors while developing location-based games and applications. We have developed functional location-based applications with TREC to demonstrate the amount of work that can be saved by using this toolkit.In order to have a safer and more immersive alternative to visual interfaces, we have developed ambient audio interfaces for use with mobile applications. Ambient audio uses continuous streams of sound over headphones to present information to mobile users without distracting them from walking safely. In order to test the effectiveness of ambient audio, we ran a study to compare ambient audio with handheld visual interfaces in a location-based game. We compared players’ ability to safely navigate the environment, their sense of immersion in the game, and their performance at the in-game tasks. We found that ambient audio was able to significantly increase players’ safety and sense of immersion compared to a visual interface, while players performed signifi- cantly better at the game tasks when using the visual interface. This makes ambient audio a legitimate alternative to visual interfaces for mobile users when safety and immersion are a priority. / Thesis (Master, Computing) -- Queen's University, 2012-01-31 23:35:28.946

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Post-Stack inversion of reflection data in seismic exploration can be used to obtain detailed information about lithology variations in the zone of interest. Generalized Linear Inversion (GLI) has previously been applied as a useful tool to achieve this. The purpose of my investigation is to apply GLI to data from the Coal Measures. It is known that in the Coal Measures the most strongly reflecting horizons are the coal seams, which are the exploration targets. In the seismic bandwidth they are thin beds, which causes particular problems associated with vertical resolution for the inversion. The method is applied to a seismic line from the Belvoir Coalfield supplied by British Coal. In order to get better relative amplitudes and to keep the same bandwidth down the whole section, the data were carefully reprocessed using the ProMAX software. Wireline log data from two boreholes intersected by the seismic line were edited to generate acoustic impedance logs as functions of time. Software was developed to implement GLI, and tested on synthetic data before applying it to the reprocessed data. The initial guesses for earth and wavelet models at the boreholes were obtained after systematic studies to determine the best strategy. The construction of the initial guess for the boundary locations elsewhere on the section is very critical for the success of the search for the global minimum. A combination of structural interpretation and the inversion results obtained from the previous trace was found to do the best job. I have tried to invert separately for the boundary locations, acoustic impedances and the wavelet, with the wavelet parameterized in the frequency domain. I found that, provided that the wavelet extracted at a borehole is a good estimate with low error energy, the most successful strategy is just to invert for the boundary locations, keeping the acoustic impedances and the extracted wavelet fixed. If the extracted wavelet is not a good estimate, then parameterizing the wavelet in the frequency domain and optimizing those parameters at the borehole is a useful approach. None of the implemented inversion strategies produced a perfect result. Discrepancies were due to the difficulty in obtaining true relative amplitude values on the processed section. The inversion results and systematic studies on the field dataset indicate that the assumptions of the convolutional model are not satisfied by the processed section.