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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
An Application Programming Interface For Parliamentary Procedure, Grant David Bourque
An Application Programming Interface For Parliamentary Procedure, Grant David Bourque
Honors Theses
No abstract provided.
Social Media Network Data Mining And Optimization, Neha Clare Jose
Social Media Network Data Mining And Optimization, Neha Clare Jose
LSU Master's Theses
Many small social aid organizations could benefit from collaborating with other organizations on common causes, but may not have the necessary social relationships. We present a framework for a recommender system for the Louisiana Poverty Initiative that identifies member organizations with common causes and aims to forge connections between these organizations. Our framework employs a combination of graph and text analyses of the organizations' Facebook pages. We use NodeXL, a plugin to Microsoft Excel, to download the Facebook graph and to interface with SNAP, the Stanford Network Analysis Platform, for calculating network measurements. Our framework extends NodeXL with algorithms that …
Applying Deep Learning Techniques To The Analysis Of Android Apks, Robin Andrew Nix
Applying Deep Learning Techniques To The Analysis Of Android Apks, Robin Andrew Nix
LSU Master's Theses
Malware targeting mobile devices is a pervasive problem in modern life and as such tools to detect and classify malware are of great value. This paper seeks to demonstrate the effectiveness of Deep Learning Techniques, specifically Convolutional Neural Networks, in detecting and classifying malware targeting the Android operating system. Unlike many current detection techniques, which require the use of relatively rigid features to aid in detection, deep neural networks are capable of automatically learning flexible features which may be more resilient to obfuscation. We present a parsing for extracting sequences of API calls which can be used to describe a …
Fairness And Approximation In Multi-Version Transactional Memory., Basem Ibrahim Assiri
Fairness And Approximation In Multi-Version Transactional Memory., Basem Ibrahim Assiri
LSU Doctoral Dissertations
Shared memory multi-core systems bene_x000C_t from transactional memory implementations due to the inherent avoidance of deadlocks and progress guarantees. In this research, we examine how the system performance is a_x000B_ected by transaction fairness in scheduling and by the precision in consistency. We _x000C_rst explore the fairness aspect using a Lazy Snapshot (multi-version) Algorithm. The fairness of transactions scheduling aims to balance the load between read-only and update transactions. We implement a fairness mechanism based on machine learning techniques that improve fairness decisions according to the transaction execution history. Experimental analysis shows that the throughput of the Lazy Snapshot Algorithm is …
Probabilistic And Deep Learning Algorithms For The Analysis Of Imagery Data, Saikat Basu
Probabilistic And Deep Learning Algorithms For The Analysis Of Imagery Data, Saikat Basu
LSU Doctoral Dissertations
Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework …
A Novel Recurrent Convolutional Neural Network For Ocean And Weather Forecasting, Robert James Firth
A Novel Recurrent Convolutional Neural Network For Ocean And Weather Forecasting, Robert James Firth
LSU Doctoral Dissertations
Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but also the creation of a parameterization scheme to estimate sub-grid scale phenomenon. The proposed method is an alternative approach to developing a mesoscale meteorological model – a modified recurrent convolutional neural network that learns to simulate the solution to these equations. Along with an appropriate time integration scheme and learning algorithm, this method can be used to create multi-day forecasts for a large region. The learning method presented is an extended form of Backpropagation Through …
Garbage Collection For General Graphs, Hari Krishnan
Garbage Collection For General Graphs, Hari Krishnan
LSU Doctoral Dissertations
Garbage collection is moving from being a utility to a requirement of every modern programming language. With multi-core and distributed systems, most programs written recently are heavily multi-threaded and distributed. Distributed and multi-threaded programs are called concurrent programs. Manual memory management is cumbersome and difficult in concurrent programs. Concurrent programming is characterized by multiple independent processes/threads, communication between processes/threads, and uncertainty in the order of concurrent operations. The uncertainty in the order of operations makes manual memory management of concurrent programs difficult. A popular alternative to garbage collection in concurrent programs is to use smart pointers. Smart pointers can collect …