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Full-Text Articles in Physical Sciences and Mathematics
Temporal Sentiment Mapping System For Time-Synchronized Data, Jiachen Ma
Temporal Sentiment Mapping System For Time-Synchronized Data, Jiachen Ma
Dissertations (1934 -)
Temporal sentiment labels are used in various multimedia studies. They are useful for numerous classification and detection tasks such as video tagging, segmentation, and labeling. However, generating a large-scale sentiment dataset through manual labeling is usually expensive and challenging. Some recent studies explored the possibility of using online Time-Sync Comments (TSCs) as the primary source of their sentiment maps. Although the approach has positive results, existing TSCs datasets are limited in scale and content categories. Guidelines for generating such data within a constrained budget are yet to be developed and discussed. This dissertation tries to address the above issues by …
Modeling Cost Of Interruption (Coi) To Manage Unwanted Interruptions For Mobile Devices, Sina Zulkernain
Modeling Cost Of Interruption (Coi) To Manage Unwanted Interruptions For Mobile Devices, Sina Zulkernain
Master's Theses (2009 -)
Unwanted and untimely interruptions have been a major cause in the loss of productivity in recent years. It has been found that they are mostly detrimental to the immediate task at hand. Multiple approaches have been proposed to address the problem of interruption by calculating cost of it. The Cost Of Interruption (COI) gives a measure of the probabilistic value of harmfulness of an inopportune interruption. Bayesian Inference stands as the premier model so far to calculate this COI. However, Bayesian-based models suffer from not being able to model context accurately in situations where a priori, conditional probabilities and uncertainties …
Computational Modeling Of Biological Neural Networks On Gpus: Strategies And Performance, Byron Galbraith
Computational Modeling Of Biological Neural Networks On Gpus: Strategies And Performance, Byron Galbraith
Master's Theses (2009 -)
Simulating biological neural networks is an important task for computational neuroscientists attempting to model and analyze brain activity and function. As these networks become larger and more complex, the computational power required grows significantly, often requiring the use of supercomputers or compute clusters. An emerging low-cost, highly accessible alternative to many of these resources is the Graphics Processing Unit (GPU) - specialized massively-parallel graphics hardware that has seen increasing use as a general purpose computational accelerator thanks largely due to NVIDIA's CUDA programming interface. We evaluated the relative benefits and limitations of GPU-based tools for large-scale neural network simulation and …