Computer Engineering Commons^™

Classification Of Arabic Social Media Texts Based On A Deep Learning Multi-Tasks Model, Ali A. Jalil, Ahmed H. Aliwy

Al-Bahir Journal for Engineering and Pure Sciences

The proliferation of social networking sites and their user base has led to an exponential increase in the amount of data generated on a daily basis. Textual content is one type of data that is commonly found on these platforms, and it has been shown to have a significant impact on decision-making processes at the individual, group, and national levels. One of the most important and largest part of this data are the texts that express human intentions, feelings and condition. Understanding these texts is one of the biggest challenges that facing data analysis. It is the backbone for understanding …

A Study Of Non-Datapath Cache Replacement Algorithms, Steven G. Lyons Jr.

FIU Electronic Theses and Dissertations

Conventionally, caching algorithms have been designed for the datapath — the levels of memory that must contain the data before it gets made available to the CPU. Attaching a fast device (such as an SSD) as a cache to a host that runs the application workload are recent developments. These host-side caches open up possibilities for what are referred to as non-datapath caches to exist. Non-Datapath caches are referred to as such because the caches do not exist on the traditional datapath, instead being optional memory locations for data. As these caches are optional, a new capability is available to …

Recipe For Disaster, Zac Travis

MFA Thesis Exhibit Catalogs

Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.

The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a …

Combining Algorithms For More General Ai, Mark Robert Musil

Undergraduate Research & Mentoring Program

Two decades since the first convolutional neural network was introduced the AI sub-domains of classification, regression and prediction still rely heavily on a few ML architectures despite their flaws of being hungry for data, time, and high-end hardware while still lacking generality. In order to achieve more general intelligence that can perform one-shot learning, create internal representations, and recognize subtle patterns it is necessary to look for new ML system frameworks. Research on the interface between neuroscience and computational statistics/machine learning has suggested that combined algorithms may increase AI robustness in the same way that separate brain regions specialize. In …

Policy-Preferred Paths In As-Level Internet Topology Graphs, Mehmet Engin Tozal

Theory and Applications of Graphs

Using Autonomous System (AS) level Internet topology maps to determine accurate AS-level paths is essential for network diagnostics, performance optimization, security enforcement, business policy management and topology-aware application development. One significant drawback that we have observed in many studies is simplifying the AS-level topology map of the Internet to an undirected graph, and then using the hop distance as a means to find the shortest paths between the ASes. A less significant drawback is restricting the shortest paths to only valley-free paths. Both approaches usually inflate the number of paths between ASes; introduce erroneous paths that do not conform to …

Proactive Service Migration For Long-Running Byzantine Fault-Tolerant Systems, Wenbing Zhao, H. Zhang

Wenbing Zhao

A proactive recovery scheme based on service migration for long-running Byzantine fault-tolerant systems is described. Proactive recovery is an essential method for ensuring the long-term reliability of fault-tolerant systems that are under continuous threats from malicious adversaries. The primary benefit of our proactive recovery scheme is a reduced vulnerability window under normal operation. This is achieved in two ways. First, the time-consuming reboot step is removed from the critical path of proactive recovery. Second, the response time and the service migration latency are continuously profiled and an optimal service migration interval is dynamically determined during runtime based on the observed …

Proactive Service Migration For Long-Running Byzantine Fault-Tolerant Systems, Wenbing Zhao, H. Zhang

Electrical and Computer Engineering Faculty Publications

A proactive recovery scheme based on service migration for long-running Byzantine fault-tolerant systems is described. Proactive recovery is an essential method for ensuring the long-term reliability of fault-tolerant systems that are under continuous threats from malicious adversaries. The primary benefit of our proactive recovery scheme is a reduced vulnerability window under normal operation. This is achieved in two ways. First, the time-consuming reboot step is removed from the critical path of proactive recovery. Second, the response time and the service migration latency are continuously profiled and an optimal service migration interval is dynamically determined during runtime based on the observed …

Nesting System With Quantization And Knowledge Base Applied, Leszek Koszalka, Grzegorz Chmaj

Electrical & Computer Engineering Faculty Research

Nesting algorithms deal with placing two dimensional shapes on the given canvas. In this paper a binary way of solving the nesting problem is proposed. Geometric shapes are quantized into binary form, which is used to operate on them. After finishing nesting they are converted back into original geometrical form. Investigations showed, that there is a big influence of quantization accuracy for the nesting effect. However, greater accuracy results with longer time of computation. The proposed knowledge base system is able to strongly reduce the computational time.

A Reconfigurable Superscalar Architecture, Christopher B. Mayer

Theses and Dissertations

The invention of the Field Programmable Gate Array (FPGA) has led to a number of interesting developments. One is the idea of providing custom hardware support for applications running on a computer. These reconfigurable computers have been shown to decrease the execution time for some applications. Based on past results, attention has subsequently turned to using reconfigurable computing in general-purpose computers (e.g. desktop and workstation environments). This thesis develops a design for just such a computer. The design, FPGADLX, is based on a hypothetical superscalar computer running the DLX instruction set and is generic enough in principle to be adapted …