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Title: Semi=supervised learning with side information : graph-based approaches
Creator: Liu, Yi
Date: 2007
Collection: Electronic Theses & Dissertations

Title: Some contributions to semi-supervised learning
Creator: Mallapragada, Paven Kumar
Date: 2010
Collection: Electronic Theses & Dissertations

Title: Evolution of distributed behavior
Creator: Knoester, David B.
Date: 2011
Collection: Electronic Theses & Dissertations
Description: In this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of...
Show moreIn this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of distributed behavior that require communication: data-driven behaviors, where semantically meaningful data is transmitted between individuals; temporal behaviors, which are based on the relative timing of individuals' actions; and structural behaviors, which are responsible for maintaining the underlying communication network connecting individuals. Our results demonstrate that evolutionary algorithms can discover groups of individuals that exhibit each of these different classes of distributed behavior, and that these behaviors can be discovered both in isolation (e.g., evolving a purely data-driven algorithm) and in concert (e.g., evolving an algorithm that includes both data-driven and structural behaviors). As part of this research, we show that evolutionary algorithms can discover novel heuristics for distributed computing, and hint at a new class of distributed algorithm enabled by such studies.The majority of this research was conducted with the Avida platform for digital evolution, a system that has been proven to aid researchers in understanding the biological process of evolution by natural selection. For this reason, the results presented in this dissertation provide the foundation for future studies that examine how distributed behaviors evolved in nature. The close relationship between evolutionary biology and evolutionary algorithms thus aids our study of evolving algorithms for the next generation of distributed computing systems.
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Title: Algorithms for deep packet inspection
Creator: Patel, Jignesh
Date: 2012
Collection: Electronic Theses & Dissertations
Description: The core operation in network intrusion detection and prevention systems is Deep Packet Inspection (DPI), in which each security threat is represented as a signature, and the payload of each data packet is matched against the set of current security threat signatures. DPI is also used for other networking applications like advanced QoS mechanisms, protocol identification etc.. In the past, attack signatures were specified as strings, and a great deal of research has been done in string...
Show moreThe core operation in network intrusion detection and prevention systems is Deep Packet Inspection (DPI), in which each security threat is represented as a signature, and the payload of each data packet is matched against the set of current security threat signatures. DPI is also used for other networking applications like advanced QoS mechanisms, protocol identification etc.. In the past, attack signatures were specified as strings, and a great deal of research has been done in string matching for network applications. Today most DPI systems use Regular Expression (RE) to represent signatures. RE matching is more diffcult than string matching, and current string matching solutions don't work well for REs. RE matching for networking applications is diffcult for several reasons. First, the DPI application is usually implemented in network devices, which have limited computing resources. Second, as new threats are discovered, size of the signature set grows over time. Last, the matching needs to be done at network speeds, the growth of which out paces improvements in computing speed; so there is a need for novel solutions that can deliver higher throughput. So RE matching for DPI is a very important and active research area.In our research, we investigate the existing methods proposed for RE matching, identify their limitations, and propose new methods to overcome these limitations. RE matching remains a fundamentally challenging problem due to the diffculty in compactly encoding DFA. While the DFA for any one RE is typically small, the DFA that corresponds to the entire set of REs is usually too large to be constructed or deployed. To address this issue, many alternative automata implementations that compress the size of the final automaton have been proposed. However, previously proposed automata construction algorithms employ a “Union then Minimize” framework where the automata for each RE are first joined before minimization occurs. This leads to expensive minimization on a large automata, and a large intermediate memory footprint. We propose a “Minimize then Union” framework for constructing compact alternative automata, which minimizes smaller automata first before combining them. This approach required much less time and memory, allowing us to handle a much larger RE set. Prior hardware based RE matching algorithms typically use FPGA. The drawback of FPGA is that resynthesizing and updating FPGA circuitry to handle RE updates is slow and diffcult. We propose the first hardware-based RE matching approach that uses Ternary Content Addressable Memory (TCAM). TCAMs have already been widely used in modern networking devices for tasks such as packet classification, so our solutions can be easily deployed. Our methods support easy RE updates, and we show that we can achieve very high throughput. The main reason combined DFAs for multiple REs grow exponentially in size is because of replication of states. We developed a new overlay automata model which exploit this replication to compress the size of the DFA. The idea is to group together the replicated DFA structures instead of repeating them multiple times. The result is that we get a final automata size that is close to that of a NFA (which is linear in the size of the RE set), and simultaneously achieve fast deterministic matching speed of a DFA.
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Title: Applying evolutionary computation techniques to address environmental uncertainty in dynamically adaptive systems
Creator: Ramirez, Andres J.
Date: 2013
Collection: Electronic Theses & Dissertations
Description: A dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of...
Show moreA dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of system and environmental conditions that a DAS might encounter throughout its lifetime. Nevertheless, a DAS must continuously satisfy its requirements despite the threat that this uncertainty poses to its adaptation capabilities. This dissertation proposes a model-based framework that supports the specification, monitoring, and dynamic reconfiguration of a DAS to explicitly address uncertainty. The proposed framework uses goal-oriented requirements models and evolutionary computation techniques to derive and fine-tune utility functions for requirements monitoring in a DAS, identify combinations of system and environmental conditions that adversely affect the behavior of a DAS, and generate adaptations on-demand to transition the DAS to a target system configuration while preserving system consistency. We demonstrate the capabilities of our model-based framework by applying it to an industrial case study involving a remote data mirroring network that efficiently distributes data even as network links fail and messages are dropped, corrupted, and delayed.
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Title: Finding optimized bounding boxes of polytopes in d-dimensional space and their properties in k-dimensional projections
Creator: Shahid, Salman (Of Michigan State University)
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Using minimal bounding boxes to encapsulate or approximate a set of points in d-dimensional space is a non-trivial problem that has applications in a variety of fields including collision detection, object rendering, high dimensional databases and statistical analysis to name a few. While a significant amount of work has been done on the three dimensional variant of the problem (i.e. finding the minimum volume bounding box of a set of points in three dimensions), it is difficult to find a...
Show moreUsing minimal bounding boxes to encapsulate or approximate a set of points in d-dimensional space is a non-trivial problem that has applications in a variety of fields including collision detection, object rendering, high dimensional databases and statistical analysis to name a few. While a significant amount of work has been done on the three dimensional variant of the problem (i.e. finding the minimum volume bounding box of a set of points in three dimensions), it is difficult to find a simple method to do the same for higher dimensions. Even in three dimensions existing methods suffer from either high time complexity or suboptimal results with a speed up in execution time. In this thesis we present a new approach to find the optimized minimum bounding boxes of a set of points defining convex polytopes in d-dimensional space. The solution also gives the optimal bounding box in three dimensions with a much simpler implementation while significantly speeding up the execution time for a large number of vertices. The basis of the proposed approach is a series of unique properties of the k-dimensional projections that are leveraged into an algorithm. This algorithm works by constructing the convex hulls of a given set of points and optimizing the projections of those hulls in two dimensional space using the new concept of Simultaneous Local Optimal. We show that the proposed algorithm provides significantly better performances than those of the current state of the art approach on the basis of time and accuracy. To illustrate the importance of the result in terms of a real world application, the optimized bounding box algorithm is used to develop a method for carrying out range queries in high dimensional databases. This method uses data transformation techniques in conjunction with a set of heuristics to provide significant performance improvement.
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Title: Non-coding RNA identification in large-scale genomic data
Creator: Yuan, Cheng
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Noncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes....
Show moreNoncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes. Identification of novel and known ncRNAs becomes increasingly important in order to understand their functionalities and the underlying communities.Next-generation sequencing (NGS) technology sheds lights on more comprehensive and sensitive ncRNA annotation. Lowly transcribed ncRNAs or ncRNAs from rare species with low abundance may be identified via deep sequencing. However, there exist several challenges in ncRNA identification in large-scale genomic data. First, the massive volume of datasets could lead to very long computation time, making existing algorithms infeasible. Second, NGS has relatively high error rate, which could further complicate the problem. Third, high sequence similarity among related ncRNAs could make them difficult to identify, resulting in incorrect output. Fourth, while secondary structures should be adopted for accurate ncRNA identification, they usually incur high computational complexity. In particular, some ncRNAs contain pseudoknot structures, which cannot be effectively modeled by the state-of-the-art approach. As a result, ncRNAs containing pseudoknots are hard to annotate.In my PhD work, I aimed to tackle the above challenges in ncRNA identification. First, I designed a progressive search pipeline to identify ncRNAs containing pseudoknot structures. The algorithms are more efficient than the state-of-the-art approaches and can be used for large-scale data. Second, I designed a ncRNA classification tool for short reads in NGS data lacking quality reference genomes. The initial homology search phase significantly reduces size of the original input, making the tool feasible for large-scale data. Last, I focused on identifying 16S ribosomal RNAs from NGS data. 16S ribosomal RNAs are very important type of ncRNAs, which can be used for phylogenic study. A set of graph based assembly algorithms were applied to form longer or full-length 16S rRNA contigs. I utilized paired-end information in NGS data, so lowly abundant 16S genes can also be identified. To reduce the complexity of problem and make the tool practical for large-scale data, I designed a list of error correction and graph reduction techniques for graph simplification.
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Title: Multiple kernel and multi-label learning for image categorization
Creator: Bucak, Serhat Selçuk
Date: 2014
Collection: Electronic Theses & Dissertations
Description: "One crucial step towards the goal of converting large image collections to useful information sources is image categorization. The goal of image categorization is to find the relevant labels for a given an image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there remains much room for improvement in the image categorization task. In this dissertation, we develop efficient multiple kernel learning and multi-label learning...
Show more"One crucial step towards the goal of converting large image collections to useful information sources is image categorization. The goal of image categorization is to find the relevant labels for a given an image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there remains much room for improvement in the image categorization task. In this dissertation, we develop efficient multiple kernel learning and multi-label learning algorithms with high prediction performance for image categorization... " -- Abstract.
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Title: Example-Based Parameterization of Linear Blend Skinning for Skinning Decomposition (EP-LBS
Creator: Hopkins, Kayra M.
Date: 2017
Collection: Electronic Theses & Dissertations
Description: This thesis presents Example-based Parameterization of Linear Blend Skinning for Skinning Decomposition (EP-LBS), a unified and robust method for using example data to simplify and improve the development and parameterization of high quality 3D models for animation. Animation and three-dimensional (3D) computer graphics have quickly become a popular medium for education, entertainment and scientific simulation. In addition to film, gaming and research applications, recent advancements in...
Show moreThis thesis presents Example-based Parameterization of Linear Blend Skinning for Skinning Decomposition (EP-LBS), a unified and robust method for using example data to simplify and improve the development and parameterization of high quality 3D models for animation. Animation and three-dimensional (3D) computer graphics have quickly become a popular medium for education, entertainment and scientific simulation. In addition to film, gaming and research applications, recent advancements in augmented reality (AR) and virtual reality (VR) are driving additional demand for 3D content. However, the success of graphics in these arenas depends greatly on the efficiency of model creation and the realism of the animation or 3D image.A common method for figure animation is skeletal animation using linear blend skinning (LBS). In this method, vertices are deformed based on a weighted sum of displacements due to an embedded skeleton. This research addresses the problem that LBS animation parameter computation, including determining the rig (the skeletal structure), identifying influence bones (which bones influence which vertices), and assigning skinning weights (amounts of influence a bone has on a vertex), is a tedious process that is difficult to get right. Even the most skilled animators must work tirelessly to design an effective character model and often find themselves repeatedly correcting flaws in the parameterization. Significant research, including the use of example-data, has focused on simplifying and automating individual components of the LBS deformation process and increasing the quality of resulting animations. However, constraints on LBS animation parameters makes automated analytic computation of the values equally as challenging as traditional 3D animation methods. Skinning decomposition is one such method of computing LBS animation LBS parameters from example data. Skinning decomposition challenges include constraint adherence and computationally efficient determination of LBS parameters.The EP-LBS method presented in this thesis utilizes example data as input to a least-squares non-linear optimization process. Given a model as a set of example poses captured from scan data or manually created, EP-LBS institutes a single optimization equation that allows for simultaneous computation of all animation parameters for the model. An iterative clustering methodology is used to construct an initial parameterization estimate for this model, which is then subjected to non-linear optimization to improve the fitting to the example data. Simultaneous optimization of weights and joint transformations is complicated by a wide range of differing constraints and parameter interdependencies. To address interdependent and conflicting constraints, parameter mapping solutions are presented that map the constraints to an alternative domain more suitable for nonlinear minimization. The presented research is a comprehensive, data-driven solution for automatically determining skeletal structure, influence bones and skinning weights from a set of example data. Results are presented for a range of models that demonstrate the effectiveness of the method.
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Title: AN EVOLUTIONARY MULTI-OBJECTIVE APPROACH TO SUSTAINABLE AGRICULTURAL WATER AND NUTRIENT OPTIMIZATION
Creator: Kropp, Ian Meyer
Date: 2018
Collection: Electronic Theses & Dissertations
Description: One of the main problems that society is facing in the 21st century is that agricultural production must keep pace with a rapidly increasing global population in an environmentally sustainable manner. One of the solutions to this global problem is a system approach through the application of optimization techniques to manage farm operations. However, unlike existing agricultural optimization research, this work seeks to optimize multiple agricultural objectives at once via multi-objective...
Show moreOne of the main problems that society is facing in the 21st century is that agricultural production must keep pace with a rapidly increasing global population in an environmentally sustainable manner. One of the solutions to this global problem is a system approach through the application of optimization techniques to manage farm operations. However, unlike existing agricultural optimization research, this work seeks to optimize multiple agricultural objectives at once via multi-objective optimization techniques. Specifically, the algorithm Unified Non-dominated Sorting Genetic Algorithm-III (U-NSGA-III) searched for irrigation and nutrient management practices that minimized combinations of environmental objectives (e.g., total irrigation applied, total nitrogen leached) while maximizing crop yield for maize. During optimization, the crop model named the Decision Support System for Agrotechnology Transfer (DSSAT) calculated the yield and nitrogen leaching for each given management practices. This study also developed a novel bi-level optimization framework to improve the performance of the optimization algorithm, employing U-NSGA-III on the upper level and Monte Carlo optimization on the lower level. The multi-objective optimization framework resulted in groups of equally optimal solutions that each offered a unique trade-off among the objectives. As a result, producers can choose the one that best addresses their needs among these groups of solutions, known as Pareto fronts. In addition, the bi-level optimization framework further improved the number, performance, and diversity of solutions within the Pareto fronts.
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Title: Gender-related effects of advanced placement computer science courses on self-efficacy, belongingness, and persistence
Creator: Good, Jonathon Andrew
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The underrepresentation of women in computer science has been a concern of educators for multiple decades. The low representation of women in the computer science is a pattern from K-12 schools through the university level and profession. One of the purposes of the introduction of the Advanced Placement Computer Science Principles (APCS-P) course in 2016 was to help broaden participation in computer science at the high school level. The design of APCS-P allowed teachers to present computer...
Show moreThe underrepresentation of women in computer science has been a concern of educators for multiple decades. The low representation of women in the computer science is a pattern from K-12 schools through the university level and profession. One of the purposes of the introduction of the Advanced Placement Computer Science Principles (APCS-P) course in 2016 was to help broaden participation in computer science at the high school level. The design of APCS-P allowed teachers to present computer science from a broad perspective, allowing students to pursue problems of personal significance, and allowing for computing projects to take a variety of forms. The nationwide enrollment statistics for Advanced Placement Computer Science Principles in 2017 had a higher proportion of female students (30.7%) than Advanced Placement Computer Science A (23.6%) courses. However, it is unknown to what degree enrollment in these courses was related to students’ plans to enroll in future computer science courses. This correlational study examined how students’ enrollment in Advanced Placement Computer Science courses, along with student gender, predicted students’ sense of computing self-efficacy, belongingness, and expected persistence in computer science. A nationwide sample of 263 students from 10 APCS-P and 10 APCS-A courses participated in the study. Students completed pre and post surveys at the beginning and end of their Fall 2017 semester regarding their computing self-efficacy, belongingness, and plans to continue in computer science studies. Using hierarchical linear modeling analysis due to the nested nature of the data within class sections, the researcher found that the APCS course type was not predictive of self-efficacy, belongingness, or expectations to persist in computer science. The results suggested that female students’ self-efficacy declined over the course of the study. However, gender was not predictive of belongingness or expectations to persist in computer science. Students were found to have entered into both courses with high a sense of self-efficacy, belongingness, and expectation to persist in computer science.The results from this suggests that students enrolled in both Advanced Placement Computer Science courses are already likely to pursue computer science. I also found that the type of APCS course in which students enroll does not relate to students’ interest in computer science. This suggests that educators should look beyond AP courses as a method of exposing students to computer science, possibly through efforts such as computational thinking and cross-curricular uses of computer science concepts and practices. Educators and administrators should also continue to examine whether there are structural biases in how students are directed to computer science courses. As for the drop in self-efficacy related to gender, this in alignment with previous research suggesting that educators should carefully scaffold students’ initial experiences in the course to not negatively influence their self-efficacy. Further research should examine how specific pedagogical practices could influence students’ persistence, as the designation and curriculum of APCS-A or APCS-P alone may not capture the myriad of ways in which teachers may be addressing gender inequity in their classrooms. Research can also examine how student interest in computer science is affected at an earlier age, as the APCS courses may be reaching students after they have already formed their opinions about computer science as a field.
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Title: Energy Conservation in Heterogeneous Smartphone Ad Hoc Networks
Creator: Mariani, James
Date: 2018
Collection: Electronic Theses & Dissertations
Description: In recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped...
Show moreIn recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped with both Bluetooth and Wifi Direct, where Wifi Direct has a better transmission range and rate and Bluetooth is more energy efficient. However only one or the other is used in a smartphone ad hoc network. We propose a Heterogeneous Smartphone Ad Hoc Network, HSNet, a framework to enable the automatic switching between Wifi Direct and Bluetooth to emphasize minimizing energy consumption while still maintaining an efficient network. We develop an application to evaluate the HSNet framework which shows significant energy savings when utilizing our switching algorithm to send messages by a less energy intensive technology in situations where energy conservation is desired. We discuss additional features of HSNet such as load balancing to help increase the lifetime of the network by more evenly distributing slave nodes among connected master nodes. Finally, we show that the throughput of our system is not affected due to technology switching for most scenarios. Future work of this project includes exploring energy efficient routing as well as simulation/scale testing for larger and more diverse smartphone ad hoc networks.
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Title: Network analysis with negative links
Creator: Derr, Tyler Scott
Date: 2020
Collection: Electronic Theses & Dissertations
Description: As we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively extract insightful patterns. We can then seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis...
Show moreAs we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively extract insightful patterns. We can then seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis. Traditionally, network analysis has focused on networks having only positive links, or unsigned networks. However, in many real-world systems, relations between nodes in a graph can be both positive and negative, or signed networks. For example, in online social media, users not only have positive links such as friends, followers, and those they trust, but also can establish negative links to those they distrust, towards their foes, or block and unfriend users.Thus, although signed networks are ubiquitous due to their ability to represent negative links in addition to positive links, they have been significantly under explored. In addition, due to the rise in popularity of today's social media and increased polarization online, this has led to both an increased attention and demand for advanced methods to perform the typical network analysis tasks when also taking into consideration negative links. More specifically, there is a need for methods that can measure, model, mine, and apply signed networks that harness both these positive and negative relations. However, this raises novel challenges, as the properties and principles of negative links are not necessarily the same as positive links, and furthermore the social theories that have been used in unsigned networks might not apply with the inclusion of negative links.The chief objective of this dissertation is to first analyze the distinct properties negative links have as compared to positive links and towards improving network analysis with negative links by researching the utility and how to harness social theories that have been established in a holistic view of networks containing both positive and negative links. We discover that simply extending unsigned network analysis is typically not sufficient and that although the existence of negative links introduces numerous challenges, they also provide unprecedented opportunities for advancing the frontier of the network analysis domain. In particular, we develop advanced methods in signed networks for measuring node relevance and centrality (i.e., signed network measuring), present the first generative signed network model and extend/analyze balance theory to signed bipartite networks (i.e., signed network modeling), construct the first signed graph convolutional network which learns node representations that can achieve state-of-the-art prediction performance and then furthermore introduce the novel idea of transformation-based network embedding (i.e., signed network mining), and apply signed networks by creating a framework that can infer both link and interaction polarity levels in online social media and constructing an advanced comprehensive congressional vote prediction framework built around harnessing signed networks.
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Title: Achieving reliable distributed systems : through efficient run-time monitoring and predicate detection
Creator: Tekken Valapil, Vidhya
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Runtime monitoring of distributed systems to perform predicate detection is critical as well as a challenging task. It is critical because it ensures the reliability of the system by detecting all possible violations of system requirements. It is challenging because to guarantee lack of violations one has to analyze every possible ordering of system events and this is an expensive task. In this report, wefocus on ordering events in a system run using HLC (Hybrid Logical Clocks) timestamps,...
Show moreRuntime monitoring of distributed systems to perform predicate detection is critical as well as a challenging task. It is critical because it ensures the reliability of the system by detecting all possible violations of system requirements. It is challenging because to guarantee lack of violations one has to analyze every possible ordering of system events and this is an expensive task. In this report, wefocus on ordering events in a system run using HLC (Hybrid Logical Clocks) timestamps, which are O(1) sized timestamps, and present some efficient algorithms to perform predicate detection using HLC. Since, with HLC, the runtime monitor cannot find all possible orderings of systems events, we present a new type of clock called Biased Hybrid Logical Clocks (BHLC), that are capable of finding more possible orderings than HLC. Thus we show that BHLC based predicate detection can find more violations than HLC based predicate detection. Since predicate detection based on both HLC and BHLC do not guarantee detection of all possible violations in a system run, we present an SMT (Satisfiability Modulo Theories) solver based predicate detection approach, that guarantees the detection of all possible violations in a system run. While a runtime monitor that performs predicate detection using SMT solvers is accurate, the time taken by the solver to detect the presence or absence of a violation can be high. To reduce the time taken by the runtime monitor, we propose the use of an efficient two-layered monitoring approach, where the first layer of the monitor is efficient but less accurate and the second layer is accurate but less efficient. Together they reduce the overall time taken to perform predicate detection drastically and also guarantee detection of all possible violations.
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Title: SOCIAL MECHANISMS OF LEADERSHIP EMERGENCE : A COMPUTATIONAL EVALUATION OF LEADERSHIP NETWORK STRUCTURES
Creator: Griffin, Daniel Jacob
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Leadership emergence is a topic of immense interest in the organizational sciences. One promising recent development in the leadership literature focuses on the development and impact of informal leadership structures in a share leadership paradigm. Despite its theoretical importance, the network perspective of leadership emergence is still underdeveloped, largely due to the complexity of studying and theorizing about network-level phenomena. Using computational modeling techniques, I...
Show moreLeadership emergence is a topic of immense interest in the organizational sciences. One promising recent development in the leadership literature focuses on the development and impact of informal leadership structures in a share leadership paradigm. Despite its theoretical importance, the network perspective of leadership emergence is still underdeveloped, largely due to the complexity of studying and theorizing about network-level phenomena. Using computational modeling techniques, I evaluate the network-level implications of two existing theories that broadly represent social theories of leadership emergence. I derive formal representations for both foundational theories and expand on this theory to develop a synthesis theory describing how these two processes work in parallel. Results from simulated experiments indicate that group homogeneity is associated with vastly different leadership network structures depending on which theoretical process mechanisms are in play. This thesis contributes significantly to the literature by 1) advancing a network-based approach to leadership emergence research, 2) testing the implications of existing theory, 3) developing new theory, and 4) providing a strong foundation and tool kit for future leadership network emergence research.
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Title: Fast edit distance calculation methods for NGS sequence similarity
Creator: Islam, A. K. M. Tauhidul
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Sequence fragments generated from targeted regions of phylogenetic marker genes provide valuable insight in identifying and classifying organisms and inferring taxonomic hierarchies. In recent years, significant development in targeted gene fragment sequencing through Next Generation Sequencing (NGS) technologies has increased the necessity of efficient sequence similarity computation methods for very large numbers of pairs of NGS sequences.The edit distance has been widely used to determine...
Show moreSequence fragments generated from targeted regions of phylogenetic marker genes provide valuable insight in identifying and classifying organisms and inferring taxonomic hierarchies. In recent years, significant development in targeted gene fragment sequencing through Next Generation Sequencing (NGS) technologies has increased the necessity of efficient sequence similarity computation methods for very large numbers of pairs of NGS sequences.The edit distance has been widely used to determine the dissimilarity between pairs of strings. All the known methods for the edit distance calculation run in near quadratic time with respect to string lengths, and it may take days or weeks to compute distances between such large numbers of pairs of NGS sequences. To solve the performance bottleneck problem, faster edit distance approximation and bounded edit distance calculation methods have been proposed. Despite these efforts, the existing edit distance calculation methods are not fast enough when computing larger numbers of pairs of NGS sequences. In order to further reduce the computation time, many NGS sequence similarity methods have been proposed using matching k-mers. These methods extract all possible k-mers from NGS sequences and compare similarity between pairs of sequences based on the shared k-mers. However, these methods reduce the computation time at the cost accuracy.In this dissertation, our goal is to compute NGS sequence similarity using edit distance based methods while reducing the computation time. We propose a few edit distance prediction methods using dataset independent reference sequences that are distant from each other. These reference sequences convert sequences in datasets into feature vectors by computing edit distances between the sequence and each of the reference sequences. Given sequences A, B and a reference sequence r, the edit distance, ed(A.B) 2265 (ed(A, r) 0303ed(B, r)). Since each reference sequence is significantly different from each other, with sufficiently large number of reference sequences and high similarity threshold, the differences of edit distances of A and B with respect to the reference sequences are close to the ed(A,B). Using this property, we predict edit distances in the vector space based on the Euclidean distances and the Chebyshev distances. Further, we develop a small set of deterministically generated reference sequences with maximum distance between each of them to predict higher edit distances more efficiently. This method predicts edit distances between corresponding sub-sequences separately and then merges the partial distances to predict the edit distances between the entire sequences. The computation complexity of this method is linear with respect to sequence length. The proposed edit distance prediction methods are significantly fast while achieving very good accuracy for high similarity thresholds. We have also shown the effectiveness of these methods on agglomerative hierarchical clustering.We also propose an efficient bounded exact edit distance calculation method using the trace [1]. For a given edit distance threshold d, only letters up to d positions apart can be part of an edit operation. Hence, we generate pairs of sub-sequences up to length difference d so that no edit operation is spilled over to the adjacent pairs of sub-sequences. Then we compute the trace cost in such a way that the number of matching letters between the sub-sequences are maximized. This technique does not guarantee locally optimal edit distance, however, it guarantees globally optimal edit distance between the entire sequences for distance up to d. The bounded exact edit distance calculation method is an order of magnitude faster than that of the dynamic programming edit distance calculation method.
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Title: LIDAR AND CAMERA CALIBRATION USING A MOUNTED SPHERE
Creator: Li, Jiajia
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Extrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically...
Show moreExtrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically localize the sphere relative to the camera and the lidar. Then using the localization covariance models, the relative pose between the camera and lidar is derived. To evaluate the accuracy of our method, we record image and lidar data of a sphere at a set of known grid positions by using two rails mounted on a wall. The accurate calibration results are demonstrated by projecting the grid centers into the camera image plane and finding the error between these points and the hand-labeled sphere centers.
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Title: Semi-Adversarial Networks for Imparting Demographic Privacy to Face Images
Creator: Mirjalili, Vahid
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Face recognition systems are being widely used in a number of applications ranging from user authentication in hand-held devices to identifying people of interest from surveillance videos. In several such applications, face images are stored in a central database. In such cases, it is necessary to ensure that the stored face images are used for the stated purpose and not for any other purposes. For example, advanced machine learning methods can be used to automatically extract age, gender,...
Show moreFace recognition systems are being widely used in a number of applications ranging from user authentication in hand-held devices to identifying people of interest from surveillance videos. In several such applications, face images are stored in a central database. In such cases, it is necessary to ensure that the stored face images are used for the stated purpose and not for any other purposes. For example, advanced machine learning methods can be used to automatically extract age, gender, race and so on from the stored face images. These cues are often referred to as demographic attributes. When such attributes are extracted without the consent of individuals, it can lead to potential violation of privacy. Indeed, the European Union's General Data Protection and Regulation (GDPR) requires the primary purpose of data collection to be declared to individuals prior to data collection. GDPR strictly prohibits the use of this data for any purpose beyond what was stated. In this thesis, we consider this type of regulation and develop methods for enhancing the privacy accorded to face images with respect to the automatic extraction of demogrpahic attributes. In particular, we design algorithms that modify input face images such that certain specified demogrpahic attributes cannot be reliably extracted from them. At the same time, the biometric utility of the images is retained, i.e., the modified face images can still be used for matching purposes. The primary objective of this research is not necessarily to fool human observers, but rather to prevent machine learning methods from automatically extracting such information. The following are the contributions of this thesis. First, we design a convolutional autoencoder known as a semi-adversarial neural network, or SAN, that perturbs input face images such that they are adversarial with respect to an attribute classifier (e.g., gender classifier) while still retaining their utility with respect to a face matcher. Second, we develop techniques to ensure that the adversarial outputs produced by the SAN are generalizable across multiple attribute classifiers, including those that may not have been used during the training phase. Third, we extend the SAN architecture and develop a neural network known as PrivacyNet, that can be used for imparting multi-attribute privacy to face images. Fourth, we conduct extensive experimental analysis using several face image datasets to evaluate the performance of the proposed methods as well as visualize the perturbations induced by the methods. Results suggest the benefits of using semi-adversarial networks to impart privacy to face images while still retaining the biometric utility of the ensuing face images.
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Title: Sequence learning with side information : modeling and applications
Creator: Wang, Zhiwei
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Sequential data is ubiquitous and modeling sequential data has been one of the most long-standing computer science problems. The goal of sequence modeling is to represent a sequence with a low-dimensional dense vector that incorporates as much information as possible. A fundamental type of information contained in sequences is the sequential dependency and a large body of research has been devoted to designing effective ways to capture it. Recently, sequence learning models such as recurrent...
Show moreSequential data is ubiquitous and modeling sequential data has been one of the most long-standing computer science problems. The goal of sequence modeling is to represent a sequence with a low-dimensional dense vector that incorporates as much information as possible. A fundamental type of information contained in sequences is the sequential dependency and a large body of research has been devoted to designing effective ways to capture it. Recently, sequence learning models such as recurrent neural networks (RNNs), temporal convolutional networks, and Transformer have gained tremendous popularity in modeling sequential data. Equipped with effective structures such as gating mechanisms, large receptive fields, and attention mechanisms, these models have achieved great success in many applications of a wide range of fields.However, besides the sequential dependency, sequences also exhibit side information that remains under-explored. Thus, in the thesis, we study the problem of sequence learning with side information. Specifically, we present our efforts devoted to building sequence learning models to effectively and efficiently capture side information that is commonly seen in sequential data. In addition, we show that side information can play an important role in sequence learning tasks as it can provide rich information that is complementary to the sequential dependency. More importantly, we apply our proposed models in various real-world applications and have achieved promising results.
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Title: Quantitative methods for calibrated spatial measurements of laryngeal phonatory mechanisms
Creator: Ghasemzadeh, Hamzeh
Date: 2020
Collection: Electronic Theses & Dissertations
Description: The ability to perform measurements is an important cornerstone and the prerequisite of any quantitative research. Measurements allow us to quantify inputs and outputs of a system, and then to express their relationships using concise mathematical expressions and models. Those models would then enable us to understand how a target system works and to predict its output for changes in the system parameters. Conversely, models would enable us to determine the proper parameters of a system for...
Show moreThe ability to perform measurements is an important cornerstone and the prerequisite of any quantitative research. Measurements allow us to quantify inputs and outputs of a system, and then to express their relationships using concise mathematical expressions and models. Those models would then enable us to understand how a target system works and to predict its output for changes in the system parameters. Conversely, models would enable us to determine the proper parameters of a system for achieving a certain output. Putting these in the context of voice science research, variations in the parameters of the phonatory system could be attributed to individual differences. Thus, accurate models would enable us to account for individual differences during the diagnosis and to make reliable predictions about the likely outcome of different treatment options. Analysis of vibration of the vocal folds using high-speed videoendoscopy (HSV) could be an ideal candidate for constructing computational models. However, conventional images are not spatially calibrated and cannot be used for absolute spatial measurements. This dissertation is focused on developing the required methodologies for calibrated spatial measurements from in-vivo HSV recordings. Specifically, two different approaches for calibrated horizontal measurements of HSV images are presented. The first approach is called the indirect approach, and it is based on the registration of a specific attribute of a common object (e.g. size of a lesion) from a calibrated intraoperative still image to its corresponding non-calibrated in-vivo HSV recording. This approach does not require specialized instruments and can be implemented in many clinical settings. However, its validity depends on a couple of assumptions. Violation of those assumptions could lead to significant measurement errors. The second approach is called the direct approach, and it is based on a laser-projection flexible fiberoptic endoscope. This approach would enable us to make accurate calibrated spatial measurements. This dissertation evaluates the accuracy of the first approach indirectly, and by studying its underlying fundamental assumptions. However, the accuracy of the second approach is evaluated directly, and using benchtop experiments with different surfaces, different working distances, and different imaging angles. The main significances and contributions of this dissertation are the following: (1) a formal treatment of indirect horizontal calibration is presented, and the assumptions governing its validity and reliability are discussed. A battery of tests is presented that can indirectly assess the validity of those assumptions in laryngeal imaging applications; (2) recordings from pre- and post-surgery from patients with vocal fold mass lesions are used as a testbench for the developed indirect calibration approach. In that regard, a full solution is developed for measuring the calibrated velocity of the vocal folds. The developed solution is then used to investigate post-surgery changes in the closing velocity of the vocal folds from patients with vocal fold mass lesions; (3) the method for calibrated vertical measurement from a laser-projection fiberoptic flexible endoscope is developed. The developed method is evaluated at different working distances, different imaging angles, and on a 3D surface; (4) a detailed analysis and investigation of non-linear image distortion of a fiberoptic flexible endoscope is presented. The effect of imaging angle and spatial location of an object on the magnitude of that distortion is studied and quantified; (5) the method for calibrated horizontal measurement from a laser-projection fiberoptic flexible endoscope is developed. The developed method is evaluated at different working distances, different imaging angles, and on a 3D surface.
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