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- Artificial intelligence (5)
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- Machine learning algorithms (2)
- Online Learning (2)
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- Research Collection School Of Computing and Information Systems (23)
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Articles 1 - 30 of 33
Full-Text Articles in Databases and Information Systems
Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen
Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen
Student Theses
The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the …
Design And Implementation Of A Stand-Alone Tool For Metabolic Simulations, Milad Ghiasi Rad
Design And Implementation Of A Stand-Alone Tool For Metabolic Simulations, Milad Ghiasi Rad
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
In this thesis, we present the design and implementation of a stand-alone tool for metabolic simulations. This system is able to integrate custom-built SBML models along with external user’s input information and produces the estimation of any reactants participating in the chain of the reactions in the provided model, e.g., ATP, Glucose, Insulin, for the given duration using numerical analysis and simulations. This tool offers the food intake arguments in the calculations to consider the personalized metabolic characteristics in the simulations. The tool has also been generalized to take into consideration of temporal genomic information and be flexible for simulation …
A Novel Density Peak Clustering Algorithm Based On Squared Residual Error, Milan Parmar, Di Wang, Ah-Hwee Tan, Chunyan Miao, Jianhua Jiang, You Zhou
A Novel Density Peak Clustering Algorithm Based On Squared Residual Error, Milan Parmar, Di Wang, Ah-Hwee Tan, Chunyan Miao, Jianhua Jiang, You Zhou
Research Collection School Of Computing and Information Systems
The density peak clustering (DPC) algorithm is designed to quickly identify intricate-shaped clusters with high dimensionality by finding high-density peaks in a non-iterative manner and using only one threshold parameter. However, DPC has certain limitations in processing low-density data points because it only takes the global data density distribution into account. As such, DPC may confine in forming low-density data clusters, or in other words, DPC may fail in detecting anomalies and borderline points. In this paper, we analyze the limitations of DPC and propose a novel density peak clustering algorithm to better handle low-density clustering tasks. Specifically, our algorithm …
Btci: A New Framework For Identifying Congestion Cascades Using Bus Trajectory Data, Meng-Fen Chiang, Ee Peng Lim, Wang-Chien Lee, Agus Trisnajaya Kwee
Btci: A New Framework For Identifying Congestion Cascades Using Bus Trajectory Data, Meng-Fen Chiang, Ee Peng Lim, Wang-Chien Lee, Agus Trisnajaya Kwee
Research Collection School Of Computing and Information Systems
The knowledge of traffic health status is essential to the general public and urban traffic management. To identify congestion cascades, an important phenomenon of traffic health, we propose a Bus Trajectory based Congestion Identification (BTCI) framework that explores the anomalous traffic health status and structure properties of congestion cascades using bus trajectory data. BTCI consists of two main steps, congested segment extraction and congestion cascades identification. The former constructs path speed models from historical vehicle transitions and design a non-parametric Kernel Density Estimation (KDE) function to derive a measure of congestion score. The latter aggregates congested segments (i.e., those with …
Nbpmf: Novel Network-Based Inference Methods For Peptide Mass Fingerprinting, Zhewei Liang
Nbpmf: Novel Network-Based Inference Methods For Peptide Mass Fingerprinting, Zhewei Liang
Electronic Thesis and Dissertation Repository
Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches. MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches …
The Complexity Of Resilience, Cibele Matos Freire
The Complexity Of Resilience, Cibele Matos Freire
Doctoral Dissertations
One focus area in data management research is to understand how changes in the data can affect the output of a view or standing query. Example applications are explaining query results and propagating updates through views. In this thesis we study the complexity of the Resilience problem, which is the problem of finding the minimum number of tuples that need to be deleted from the database in order to change the result of a query. We will see that resilience is closely related to the well-studied problems of deletion propagation and causal responsibility, and that analyzing its complexity offers important …
Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao
Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label querybudget. Unlike conventional online learning tasks, online active learning is considerably more challenging because of two reasons.Firstly, it is difficult to design an effective query strategy to decide when is appropriate to query the label of an incoming instance givenlimited query budget. Secondly, it is also challenging to decide how to update the predictive models effectively whenever the true labelof an instance is queried. Most existing approaches for online active learning are often based on a family of first-order online …
Scalable Online Kernel Learning, Jing Lu
Scalable Online Kernel Learning, Jing Lu
Dissertations and Theses Collection (Open Access)
One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose …
Large Scale Kernel Methods For Online Auc Maximization, Yi Ding, Chenghao Liu, Peilin Zhao, Steven C. H. Hoi
Large Scale Kernel Methods For Online Auc Maximization, Yi Ding, Chenghao Liu, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Learning to optimize AUC performance for classifying label imbalanced data in online scenarios has been extensively studied in recent years. Most of the existing work has attempted to address the problem directly in the original feature space, which may not suitable for non-linearly separable datasets. To solve this issue, some kernel-based learning methods are proposed for non-linearly separable datasets. However, such kernel approaches have been shown to be inefficient and failed to scale well on large scale datasets in practice. Taking this cue, in this work, we explore the use of scalable kernel-based learning techniques as surrogates to existing approaches: …
Benchmarking Single-Image Reflection Removal Algorithms, Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Benchmarking Single-Image Reflection Removal Algorithms, Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Research Collection School Of Computing and Information Systems
Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison of existing approaches using the same dataset has never been conducted due to the lack of suitable benchmark data with ground truth. This paper presents the first captured Single-image Reflection Removal dataset `SIR 2 ' with 40 controlled and 100 wild scenes, ground truth of background and reflection. For each controlled scene, we further provide …
Micro-Review Synthesis For Multi-Entity Summarization, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas
Micro-Review Synthesis For Multi-Entity Summarization, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas
Research Collection School Of Computing and Information Systems
Location-based social networks (LBSNs), exemplified by Foursquare, are fast gaining popularity. One important feature of LBSNs is micro-review. Upon check-in at a particular venue, a user may leave a short review (up to 200 characters long), also known as a tip. These tips are an important source of information for others to know more about various aspects of an entity (e.g., restaurant), such as food, waiting time, or service. However, a user is often interested not in one particular entity, but rather in several entities collectively, for instance within a neighborhood or a category. In this paper, we address the …
Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang
Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang
Department of Computer Science Faculty Scholarship and Creative Works
As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Research Collection School Of Computing and Information Systems
Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Research Collection School Of Computing and Information Systems
Feature selection (FS) is an important technique in machine learning and data mining, especially for large scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, …
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Research Collection School Of Computing and Information Systems
Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neura lNetwork (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to …
Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose …
Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan
Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
As graph analytics often involves compute-intensive operations,GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform are build of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper,we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore,we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing …
Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu
Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu
Research Collection School Of Computing and Information Systems
Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations …
Compress: A Comprehensive Framework Of Trajectory Compression In Road Networks, Yunheng Han, Weiwei Sun, Baihua Zheng
Compress: A Comprehensive Framework Of Trajectory Compression In Road Networks, Yunheng Han, Weiwei Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
More and more advanced technologies have become available to collect and integrate an unprecedented amount of data from multiple sources, including GPS trajectories about the traces of moving objects. Given the fact that GPS trajectories are vast in size while the information carried by the trajectories could be redundant, we focus on trajectory compression in this article. As a systematic solution, we propose a comprehensive framework, namely, COMPRESS (Comprehensive Paralleled Road-Network-Based Trajectory Compression), to compress GPS trajectory data in an urban road network. In the preprocessing step, COMPRESS decomposes trajectories into spatial paths and temporal sequences, with a thorough justification …
Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun
Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun
Research Collection School Of Computing and Information Systems
Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for the CTR model has some critical limitations: First of all, it is designed to work in a batch learning manner, making it unsuitable to deal with streaming data or big data in real-world recommender systems. Secondly, in the existing algorithm, the item-specific topic proportions of LDA are fed to the downstream PMF but the rating information is not exploited in discovering …
Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang
Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang
Research Collection School Of Computing and Information Systems
This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH is computed at each pixel location, and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the …
Improving Discovery And Patron Experience Through Data Mining, Boyuan Guan, Jamie Rogers
Improving Discovery And Patron Experience Through Data Mining, Boyuan Guan, Jamie Rogers
Works of the FIU Libraries
As information professionals, we know simple database searches are imperfect. With rich and expansive digital collections, patrons may not find content that is buried in a long list of results. So, how do we improve discovery of pertinent materials and offer serendipitous experience? Following the example of recommendation functionality in online applications like Netflix, we have developed a recommendation function for our digital library system that provides relevant content beyond the narrow scope of patrons' original search parameters. This session will outline the reasoning, methodology, and design of the recommendation system as well as preliminary results from implementation.
Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin Wang, Steven C. H. Hoi, Martin Ester, Jiajun Bu, Chun Chen
Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin Wang, Steven C. H. Hoi, Martin Ester, Jiajun Bu, Chun Chen
Research Collection School Of Computing and Information Systems
Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in …
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as …
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Research Collection School Of Computing and Information Systems
Given two object sets Q and O , a metric similarity join finds similar object pairs according to a certain criterion. This operation has a wide variety of applications in data cleaning, data mining, to name but a few. However, the rapidly growing volume of data nowadays challenges traditional metric similarity join methods, and thus, a distributed method is required. In this paper, we adopt a popular distributed framework, namely, MapReduce, to support scalable metric similarity joins. To ensure the load balancing, we present two sampling based partition methods. One utilizes the pivot and the space-filling curve mappings to cluster …
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Research Collection School Of Computing and Information Systems
Finding components with high connectivity is an important problem in component detection with a wide range of applications, e.g., social network analysis, web-page research and bioinformatics. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real applications present needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive and therefore allows overlapping between components. To find k-VCCs, a top-down framework is first developed to find the exact k-VCCs. To further reduce the high computational cost for input networks of large …
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Research Collection School Of Computing and Information Systems
The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between discrete trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering the pair of most similar subtrajectories, be them parts of the same or of different input trajectories.The identified pair of subtrajectories is called a motif.The adoption of DFD as the similarity measure in motif discovery,although semantically ideal, is hindered by the high computational complexity of DFD calculation. In this paper, we propose a suite …
Soal: Second-Order Online Active Learning, Shuji Hao, Peilin Zhao, Jing Lu, Steven C. H. Hoi, Chunyan Miao, Chi Zhang
Soal: Second-Order Online Active Learning, Shuji Hao, Peilin Zhao, Jing Lu, Steven C. H. Hoi, Chunyan Miao, Chi Zhang
Research Collection School Of Computing and Information Systems
This paper investigates the problem of online active learning for training classification models from sequentially arriving data. This is more challenging than conventional online learning tasks since the learner not only needs to figure out how to effectively update the classifier but also needs to decide when is the best time to query the label of an incoming instance given limited label budget. The existing online active learning approaches are often based on first-order online learning methods which generally fall short in slow convergence rate and suboptimal exploitation of available information when querying the labeled data. To overcome the limitations, …
Data Mining By Grid Computing In The Search For Extrasolar Planets, Oisin Creaner [Thesis]
Data Mining By Grid Computing In The Search For Extrasolar Planets, Oisin Creaner [Thesis]
Doctoral
A system is presented here to provide improved precision in ensemble differential photometry. This is achieved by using the power of grid computing to analyse astronomical catalogues. This produces new catalogues of optimised pointings for each star, which maximise the number and quality of reference stars available. Astronomical phenomena such as exoplanet transits and small-scale structure within quasars may be observed by means of millimagnitude photometric variability on the timescale of minutes to hours. Because of atmospheric distortion, ground-based observations of these phenomena require the use of differential photometry whereby the target is compared with one or more reference stars. …
Xic Clustering By Baseyian Network, Kyle J. Handy
Xic Clustering By Baseyian Network, Kyle J. Handy
Graduate Student Theses, Dissertations, & Professional Papers
No abstract provided.