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- Creators: Colbourn, Charles
- Creators: Arizona State University
- Creators: Bevilacqua, Vincent Frank
Description
A community in a social network can be viewed as a structure formed by individuals who share similar interests. Not all communities are explicit; some may be hidden in a large network. Therefore, discovering these hidden communities becomes an interesting problem. Researchers from a number of fields have developed algorithms to tackle this problem.
Besides the common feature above, communities within a social network have two unique characteristics: communities are mostly small and overlapping. Unfortunately, many traditional algorithms have difficulty recognizing these small communities (often called the resolution limit problem) as well as overlapping communities.
In this work, two enhanced community detection techniques are proposed for re-working existing community detection algorithms to find small communities in social networks. One method is to modify the modularity measure within the framework of the traditional Newman-Girvan algorithm so that more small communities can be detected. The second method is to incorporate a preprocessing step into existing algorithms by changing edge weights inside communities. Both methods help improve community detection performance while maintaining or improving computational efficiency.
Besides the common feature above, communities within a social network have two unique characteristics: communities are mostly small and overlapping. Unfortunately, many traditional algorithms have difficulty recognizing these small communities (often called the resolution limit problem) as well as overlapping communities.
In this work, two enhanced community detection techniques are proposed for re-working existing community detection algorithms to find small communities in social networks. One method is to modify the modularity measure within the framework of the traditional Newman-Girvan algorithm so that more small communities can be detected. The second method is to incorporate a preprocessing step into existing algorithms by changing edge weights inside communities. Both methods help improve community detection performance while maintaining or improving computational efficiency.
ContributorsWang, Ran (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Colbourn, Charles (Committee member) / Arizona State University (Publisher)
Created2015
Description
As robots become more prevalent, the need is growing for efficient yet stable control systems for applications with humans in the loop. As such, it is a challenge for scientists and engineers to develop robust and agile systems that are capable of detecting instability in teleoperated systems. Despite how much research has been done to characterize the spatiotemporal parameters of human arm motions for reaching and gasping, not much has been done to characterize the behavior of human arm motion in response to control errors in a system. The scope of this investigation is to investigate human corrective actions in response to error in an anthropomorphic teleoperated robot limb. Characterizing human corrective actions contributes to the development of control strategies that are capable of mitigating potential instabilities inherent in human-machine control interfaces. Characterization of human corrective actions requires the simulation of a teleoperated anthropomorphic armature and the comparison of a human subject's arm kinematics, in response to error, against the human arm kinematics without error. This was achieved using OpenGL software to simulate a teleoperated robot arm and an NDI motion tracking system to acquire the subject's arm position and orientation. Error was intermittently and programmatically introduced to the virtual robot's joints as the subject attempted to reach for several targets located around the arm. The comparison of error free human arm kinematics to error prone human arm kinematics revealed an addition of a bell shaped velocity peak into the human subject's tangential velocity profile. The size, extent, and location of the additional velocity peak depended on target location and join angle error. Some joint angle and target location combinations do not produce an additional peak but simply maintain the end effector velocity at a low value until the target is reached. Additional joint angle error parameters and degrees of freedom are needed to continue this investigation.
ContributorsBevilacqua, Vincent Frank (Author) / Artemiadis, Panagiotis (Thesis director) / Santello, Marco (Committee member) / Trimble, Steven (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2013-05
Description
Quantum computers provide a promising future, where computationally difficult
problems can be executed exponentially faster than the current classical computers we have in use today. While there is tremendous research and development in the creation of quantum computers, there is a fundamental challenge that exists in the quantum world. Due to the fragility of the quantum world, error correction methods have originated since 1995 to tackle the giant problem. Since the birth of the idea that these powerful computers can crunch and process numbers beyond the limit of the current computers, there exist several mathematical error correcting codes that could potentially give the required stability in the fragile and fault tolerant quantum world. While there has been a multitude of possible solutions, there is no one single error correcting code that is the key to solving the problem. Almost every solution presented has shared with it a limiting factor or an issue that prevents it from becoming the breakthrough that is desperately needed.
This paper gives an introductory knowledge of what is the quantum world and why there is a need for error correcting topologies. Finally, it introduces one recent topology that could be added to the list of possible solutions to this central problem. Rather than focusing on the mathematical frameworks, the paper introduces the main concepts so that most readers even outside the major field of computer science can understand what the main problem is and how this topology attempts to solve it.
problems can be executed exponentially faster than the current classical computers we have in use today. While there is tremendous research and development in the creation of quantum computers, there is a fundamental challenge that exists in the quantum world. Due to the fragility of the quantum world, error correction methods have originated since 1995 to tackle the giant problem. Since the birth of the idea that these powerful computers can crunch and process numbers beyond the limit of the current computers, there exist several mathematical error correcting codes that could potentially give the required stability in the fragile and fault tolerant quantum world. While there has been a multitude of possible solutions, there is no one single error correcting code that is the key to solving the problem. Almost every solution presented has shared with it a limiting factor or an issue that prevents it from becoming the breakthrough that is desperately needed.
This paper gives an introductory knowledge of what is the quantum world and why there is a need for error correcting topologies. Finally, it introduces one recent topology that could be added to the list of possible solutions to this central problem. Rather than focusing on the mathematical frameworks, the paper introduces the main concepts so that most readers even outside the major field of computer science can understand what the main problem is and how this topology attempts to solve it.
ContributorsAhmed, Umer (Author) / Colbourn, Charles (Thesis director) / Zhao, Ming (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05