Matching Items (31)
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- All Subjects: Data Mining
- All Subjects: deep learning
- Creators: Davulcu, Hasan
- Creators: Wu, Teresa
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Twitter is a micro-blogging platform where the users can be social, informational or both. In certain cases, users generate tweets that have no "hashtags" or "@mentions"; we call it an orphaned tweet. The user will be more interested to find more "context" of an orphaned tweet presumably to engage with his/her friend on that topic. Finding context for an Orphaned tweet manually is challenging because of larger social graph of a user , the enormous volume of tweets generated per second, topic diversity, and limited information from tweet length of 140 characters. To help the user to get the context of an orphaned tweet, this thesis aims at building a hashtag recommendation system called TweetSense, to suggest hashtags as a context or metadata for the orphaned tweets. This in turn would increase user's social engagement and impact Twitter to maintain its monthly active online users in its social network. In contrast to other existing systems, this hashtag recommendation system recommends personalized hashtags by exploiting the social signals of users in Twitter. The novelty with this system is that it emphasizes on selecting the suitable candidate set of hashtags from the related tweets of user's social graph (timeline).The system then rank them based on the combination of features scores computed from their tweet and user related features. It is evaluated based on its ability to predict suitable hashtags for a random sample of tweets whose existing hashtags are deliberately removed for evaluation. I present a detailed internal empirical evaluation of TweetSense, as well as an external evaluation in comparison with current state of the art method.
ContributorsVijayakumar, Manikandan (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Data imbalance and data noise often coexist in real world datasets. Data imbalance affects the learning classifier by degrading the recognition power of the classifier on the minority class, while data noise affects the learning classifier by providing inaccurate information and thus misleads the classifier. Because of these differences, data imbalance and data noise have been treated separately in the data mining field. Yet, such approach ignores the mutual effects and as a result may lead to new problems. A desirable solution is to tackle these two issues jointly. Noting the complementary nature of generative and discriminative models, this research proposes a unified model fusion based framework to handle the imbalanced classification with noisy dataset.
The phase I study focuses on the imbalanced classification problem. A generative classifier, Gaussian Mixture Model (GMM) is studied which can learn the distribution of the imbalance data to improve the discrimination power on imbalanced classes. By fusing this knowledge into cost SVM (cSVM), a CSG method is proposed. Experimental results show the effectiveness of CSG in dealing with imbalanced classification problems.
The phase II study expands the research scope to include the noisy dataset into the imbalanced classification problem. A model fusion based framework, K Nearest Gaussian (KNG) is proposed. KNG employs a generative modeling method, GMM, to model the training data as Gaussian mixtures and form adjustable confidence regions which are less sensitive to data imbalance and noise. Motivated by the K-nearest neighbor algorithm, the neighboring Gaussians are used to classify the testing instances. Experimental results show KNG method greatly outperforms traditional classification methods in dealing with imbalanced classification problems with noisy dataset.
The phase III study addresses the issues of feature selection and parameter tuning of KNG algorithm. To further improve the performance of KNG algorithm, a Particle Swarm Optimization based method (PSO-KNG) is proposed. PSO-KNG formulates model parameters and data features into the same particle vector and thus can search the best feature and parameter combination jointly. The experimental results show that PSO can greatly improve the performance of KNG with better accuracy and much lower computational cost.
The phase I study focuses on the imbalanced classification problem. A generative classifier, Gaussian Mixture Model (GMM) is studied which can learn the distribution of the imbalance data to improve the discrimination power on imbalanced classes. By fusing this knowledge into cost SVM (cSVM), a CSG method is proposed. Experimental results show the effectiveness of CSG in dealing with imbalanced classification problems.
The phase II study expands the research scope to include the noisy dataset into the imbalanced classification problem. A model fusion based framework, K Nearest Gaussian (KNG) is proposed. KNG employs a generative modeling method, GMM, to model the training data as Gaussian mixtures and form adjustable confidence regions which are less sensitive to data imbalance and noise. Motivated by the K-nearest neighbor algorithm, the neighboring Gaussians are used to classify the testing instances. Experimental results show KNG method greatly outperforms traditional classification methods in dealing with imbalanced classification problems with noisy dataset.
The phase III study addresses the issues of feature selection and parameter tuning of KNG algorithm. To further improve the performance of KNG algorithm, a Particle Swarm Optimization based method (PSO-KNG) is proposed. PSO-KNG formulates model parameters and data features into the same particle vector and thus can search the best feature and parameter combination jointly. The experimental results show that PSO can greatly improve the performance of KNG with better accuracy and much lower computational cost.
ContributorsHe, Miao (Author) / Wu, Teresa (Thesis advisor) / Li, Jing (Committee member) / Silva, Alvin (Committee member) / Borror, Connie (Committee member) / Arizona State University (Publisher)
Created2014
Efficient processing of skyline queries on static data sources, data streams and incomplete datasets
Description
Skyline queries extract interesting points that are non-dominated and help paint the bigger picture of the data in question. They are valuable in many multi-criteria decision applications and are becoming a staple of decision support systems.
An assumption commonly made by many skyline algorithms is that a skyline query is applied to a single static data source or data stream. Unfortunately, this assumption does not hold in many applications in which a skyline query may involve attributes belonging to multiple data sources and requires a join operation to be performed before the skyline can be produced. Recently, various skyline-join algorithms have been proposed to address this problem in the context of static data sources. However, these algorithms suffer from several drawbacks: they often need to scan the data sources exhaustively to obtain the skyline-join results; moreover, the pruning techniques employed to eliminate tuples are largely based on expensive tuple-to-tuple comparisons. On the other hand, most data stream techniques focus on single stream skyline queries, thus rendering them unsuitable for skyline-join queries.
Another assumption typically made by most of the earlier skyline algorithms is that the data is complete and all skyline attribute values are available. Due to this constraint, these algorithms cannot be applied to incomplete data sources in which some of the attribute values are missing and are represented by NULL values. There exists a definition of dominance for incomplete data, but this leads to undesirable consequences such as non-transitive and cyclic dominance relations both of which are detrimental to skyline processing.
Based on the aforementioned observations, the main goal of the research described in this dissertation is the design and development of a framework of skyline operators that effectively handles three distinct types of skyline queries: 1) skyline-join queries on static data sources, 2) skyline-window-join queries over data streams, and 3) strata-skyline queries on incomplete datasets. This dissertation presents the unique challenges posed by these skyline queries and addresses the shortcomings of current skyline techniques by proposing efficient methods to tackle the added overhead in processing skyline queries on static data sources, data streams, and incomplete datasets.
An assumption commonly made by many skyline algorithms is that a skyline query is applied to a single static data source or data stream. Unfortunately, this assumption does not hold in many applications in which a skyline query may involve attributes belonging to multiple data sources and requires a join operation to be performed before the skyline can be produced. Recently, various skyline-join algorithms have been proposed to address this problem in the context of static data sources. However, these algorithms suffer from several drawbacks: they often need to scan the data sources exhaustively to obtain the skyline-join results; moreover, the pruning techniques employed to eliminate tuples are largely based on expensive tuple-to-tuple comparisons. On the other hand, most data stream techniques focus on single stream skyline queries, thus rendering them unsuitable for skyline-join queries.
Another assumption typically made by most of the earlier skyline algorithms is that the data is complete and all skyline attribute values are available. Due to this constraint, these algorithms cannot be applied to incomplete data sources in which some of the attribute values are missing and are represented by NULL values. There exists a definition of dominance for incomplete data, but this leads to undesirable consequences such as non-transitive and cyclic dominance relations both of which are detrimental to skyline processing.
Based on the aforementioned observations, the main goal of the research described in this dissertation is the design and development of a framework of skyline operators that effectively handles three distinct types of skyline queries: 1) skyline-join queries on static data sources, 2) skyline-window-join queries over data streams, and 3) strata-skyline queries on incomplete datasets. This dissertation presents the unique challenges posed by these skyline queries and addresses the shortcomings of current skyline techniques by proposing efficient methods to tackle the added overhead in processing skyline queries on static data sources, data streams, and incomplete datasets.
ContributorsNagendra, Mithila (Author) / Candan, Kasim Selcuk (Thesis advisor) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Silva, Yasin N. (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2014
Description
Analysis of political texts, which contains a huge amount of personal political opinions, sentiments, and emotions towards powerful individuals, leaders, organizations, and a large number of people, is an interesting task, which can lead to discover interesting interactions between the political parties and people. Recently, political blogosphere plays an increasingly important role in politics, as a forum for debating political issues. Most of the political weblogs are biased towards their political parties, and they generally express their sentiments towards their issues (i.e. leaders, topics etc.,) and also towards issues of the opposing parties. In this thesis, I have modeled the above interactions/debate as a sentimental bi-partite graph, a bi-partite graph with Blogs forming vertices of a disjoint set, and the issues (i.e. leaders, topics etc.,) forming the other disjoint set,and the edges between the two sets representing the sentiment of the blogs towards the issues. I have used American Political blog data to model the sentimental bi- partite graph, in particular, a set of popular political liberal and conservative blogs that have clearly declared positions. These blogs contain discussion about social, political, economic issues and related key individuals in their conservative/liberal view. To be more focused and more polarized, 22 most popular liberal/conservative blogs of a particular time period, May 2008 - October 2008(because of high intensity of debate and discussions), just before the presidential elections, was considered, involving around 23,800 articles. This thesis involves solving the questions: a) which is the most liberal/conservative blogs on the web? b) Who is on which side of debate and what are the issues? c) Who are the important leaders? d) How do you model the relationship between the participants of the debate and the underlying issues?
ContributorsThirumalai, Dananjayan (Author) / Davulcu, Hasan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Muslim radicalism is recognized as one of the greatest security threats for the United States and the rest of the world. Use of force to eliminate specific radical entities is ineffective in containing radicalism as a whole. There is a need to understand the origin, ideologies and behavior of Radical and Counter-Radical organizations and how they shape up over a period of time. Recognizing and supporting counter-radical organizations is one of the most important steps towards impeding radical organizations. A lot of research has already been done to categorize and recognize organizations, to understand their behavior, their interactions with other organizations, their target demographics and the area of influence. We have a huge amount of information which is a result of the research done over these topics. This thesis provides a powerful and interactive way to navigate through all this information, using a Visualization Dashboard. The dashboard makes it easier for Social Scientists, Policy Analysts, Military and other personnel to visualize an organization's propensity towards violence and radicalism. It also tracks the peaking religious, political and socio-economic markers, their target demographics and locations. A powerful search interface with parametric search helps in narrowing down to specific scenarios and view the corresponding information related to the organizations. This tool helps to identify moderate Counter-Radical organizations and also has the potential of predicting the orientation of various organizations based on the current information.
ContributorsNair, Shreejay (Author) / Davulcu, Hasan (Thesis advisor) / Dasgpta, Partha (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the advent of Internet, the data being added online is increasing at enormous rate. Though search engines are using IR techniques to facilitate the search requests from users, the results are not effective towards the search query of the user. The search engine user has to go through certain webpages before getting at the webpage he/she wanted. This problem of Information Overload can be solved using Automatic Text Summarization. Summarization is a process of obtaining at abridged version of documents so that user can have a quick view to understand what exactly the document is about. Email threads from W3C are used in this system. Apart from common IR features like Term Frequency, Inverse Document Frequency, Term Rank, a variation of page rank based on graph model, which can cluster the words with respective to word ambiguity, is implemented. Term Rank also considers the possibility of co-occurrence of words with the corpus and evaluates the rank of the word accordingly. Sentences of email threads are ranked as per features and summaries are generated. System implemented the concept of pyramid evaluation in content selection. The system can be considered as a framework for Unsupervised Learning in text summarization.
ContributorsNadella, Sravan (Author) / Davulcu, Hasan (Thesis advisor) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
ContributorsVenkatesan, Ragav (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for decades in political science and communications research. Media framing offers an “interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
ContributorsAlashri, Saud (Author) / Davulcu, Hasan (Thesis advisor) / Desouza, Kevin C. (Committee member) / Maciejewski, Ross (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2018
Description
The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017