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- Genre: Academic theses
- Creators: Davulcu, Hasan
- Creators: Ryan, Russell
- Member of: Theses and Dissertations
Description
The recent proliferation of online platforms has not only revolutionized the way people communicate and acquire information but has also led to propagation of malicious information (e.g., online human trafficking, spread of misinformation, etc.). Propagation of such information occurs at unprecedented scale that could ultimately pose imminent societal-significant threats to the public. To better understand the behavior and impact of the malicious actors and counter their activity, social media authorities need to deploy certain capabilities to reduce their threats. Due to the large volume of this data and limited manpower, the burden usually falls to automatic approaches to identify these malicious activities. However, this is a subtle task facing online platforms due to several challenges: (1) malicious users have strong incentives to disguise themselves as normal users (e.g., intentional misspellings, camouflaging, etc.), (2) malicious users are high likely to be key users in making harmful messages go viral and thus need to be detected at their early life span to stop their threats from reaching a vast audience, and (3) available data for training automatic approaches for detecting malicious users, are usually either highly imbalanced (i.e., higher number of normal users than malicious users) or comprise insufficient labeled data.
To address the above mentioned challenges, in this dissertation I investigate the propagation of online malicious information from two broad perspectives: (1) content posted by users and (2) information cascades formed by resharing mechanisms in social media. More specifically, first, non-parametric and semi-supervised learning algorithms are introduced to discern potential patterns of human trafficking activities that are of high interest to law enforcement. Second, a time-decay causality-based framework is introduced for early detection of “Pathogenic Social Media (PSM)” accounts (e.g., terrorist supporters). Third, due to the lack of sufficient annotated data for training PSM detection approaches, a semi-supervised causal framework is proposed that utilizes causal-related attributes from unlabeled instances to compensate for the lack of enough labeled data. Fourth, a feature-driven approach for PSM detection is introduced that leverages different sets of attributes from users’ causal activities, account-level and content-related information as well as those from URLs shared by users.
To address the above mentioned challenges, in this dissertation I investigate the propagation of online malicious information from two broad perspectives: (1) content posted by users and (2) information cascades formed by resharing mechanisms in social media. More specifically, first, non-parametric and semi-supervised learning algorithms are introduced to discern potential patterns of human trafficking activities that are of high interest to law enforcement. Second, a time-decay causality-based framework is introduced for early detection of “Pathogenic Social Media (PSM)” accounts (e.g., terrorist supporters). Third, due to the lack of sufficient annotated data for training PSM detection approaches, a semi-supervised causal framework is proposed that utilizes causal-related attributes from unlabeled instances to compensate for the lack of enough labeled data. Fourth, a feature-driven approach for PSM detection is introduced that leverages different sets of attributes from users’ causal activities, account-level and content-related information as well as those from URLs shared by users.
ContributorsAlvari, Hamidreza (Author) / Shakarian, Paulo (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Ruston, Scott (Committee member) / Arizona State University (Publisher)
Created2020
Description
In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior. On the other hand, from a very early age, humans are able to understand the relation between an agent and their ultimate goal even if the action gets disrupted or unintentional effects occur. Inculcating this ability in artificially intelligent agents would make them better social learners by not just learning from their own mistakes, i.e, reinforcement learning, but also learning from other's mistakes. For example, this could greatly reduce the search space for artificially intelligent agents for finding the correct action sequence when trying to achieve a new goal, since they would be able to learn from others what not to do as well as how/when actions result in undesired outcomes.To validate this ability of deep learning models to perform this task, the Weakly Augmented Oops (W-Oops) dataset is proposed, built upon the Oops dataset. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 33 unintentional video-level activity labels collected through human annotations.
Inspired by previous methods on tasks such as weakly supervised action localization which show promise for achieving good localization results without ground truth segment annotations, this paper proposes a weakly supervised algorithm for localizing the goal-directed as well as the unintentional temporal region of a video using only video-level labels. In particular, an attention mechanism based strategy is employed that predicts the temporal regions which contributes the most to a classification task, leveraging solely video-level labels. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity, while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method.
ContributorsChakravarthy, Arnav (Author) / Yang, Yezhou (Thesis advisor) / Davulcu, Hasan (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2021
Description
Tensors are commonly used for representing multi-dimensional data, such as Web graphs, sensor streams, and social networks. As a consequence of the increase in the use of tensors, tensor decomposition operations began to form the basis for many data analysis and knowledge discovery tasks, from clustering, trend detection, anomaly detection to correlationanalysis [31, 38]. It is well known that Singular Value matrix Decomposition (SVD) [9] is used to extract latent semantics for matrix data. When apply SVD to tensors, which have more than two modes, it is tensor decomposition. The two most popular tensor decomposition algorithms are the Tucker [54] and the CP [19] decompositions. Intuitively, they both generalize SVD to tensors. However, one key problem with tensor decomposition is its computational complexity which may cause system bottleneck. Therefore, two phase block-centric CP tensor decomposition (2PCP) was proposed to partition the tensor into small sub-tensors, execute sub-tensor decomposition in parallel and combine the factors from each sub-tensor into final decomposition factors through iterative rerefinement process. Consequently, I proposed Sub-tensor Impact Graph (SIG) to account for inaccuracy propagation among sub-tensors and measure the impact of decomposition of sub-tensors on the other's decomposition, Based on SIG, I proposed several optimization strategies to optimize 2PCP's phase-2 refinement process. Furthermore, I applied SIG and optimization strategies for data focus, data evolution, and focus shifting in tensor analysis. Personalized Tensor Decomposition (PTD) is proposed to account for the users focus given the observations that in many applications, the user may have a focus of interest i.e., part of the data for which the user needs high accuracy and beyond this area focus, accuracy may not be as critical. PTD takes as input one or more areas of focus and performs the decomposition in such a way that, when reconstructed, the accuracy of the tensor is boosted for these areas of focus. A related challenge of data evolution in tensor analytics is incremental tensor decomposition since re-computation of the whole tensor decomposition with each update will cause high computational costs and incur large memory overheads. Especially for applications where data evolves over time and the tensor-based analysis results need to be continuouslymaintained. To avoid re-decomposition, I propose a two-phase block-incremental CP-based tensor decomposition technique, BICP, that efficiently and effectively maintains tensor decomposition results in the presence of dynamically evolving tensor data. I further extend the research focus on user focus shift. User focus may change over time as data is evolving along the time. Although PTD is efficient, re-computation for each user preference update can be the bottleneck for the system. Therefore I propose dynamic evolving user focus tensor decomposition which can smartly reuse the existing decomposition result to improve the efficiency of evolving user focus block decomposition.
ContributorsHuang, shengyu (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Tong, Hanghang (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2021
Description
Pulitzer Prize-winning composer Michael Colgrass wrote Tales of power: A Musical Drama for Solo Piano on the Writings of Carlos Castaneda in 1980. However, since the work’s premiere it has been overlooked, receiving little attention from pianists. This neglect is perhaps due, in part, to the absence of circulated recordings and writings. The present study includes the author’s recorded performance, found online at https://youtu.be/GqzMjgaSIDc. Because Tales of Power is a programmatic work about Carlos Castaneda’s study with Don Juan, an Indian sorcerer from Mexico, the author has inserted the score’s written program indications in the recording so that listeners may follow the narrative. This performance guide includes a concise biography of Colgrass, a review of the composer’s major works and general compositional styles, observations on the program and structure, insights regarding thematic transformation and recurring motives, and performance recommendations for accommodating troublesome sections. The author hopes that Tales of Power can be revived in concert performance and become part of the programmatic piano repertoire in the near future.
ContributorsLi, Aoshuang (Author) / Hamilton, Robert (Thesis advisor) / Rockmaker, Jody Rockmaker (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2021
Description
The proliferation of semantic data in the form of RDF (Resource Description Framework) triples demands an efficient, scalable, and distributed storage along with a highly available and fault-tolerant parallel processing strategy. There are three open issues with distributed RDF data management systems that are not well addressed altogether in existing work. First is the querying efficiency, second is that solutions are optimized for certain types of query patterns and don’t necessarily work well for all types, and third is concerned with reducing pre-processing cost. Therefore, the rapid growth of RDF data raises the need for an efficient partitioning strategy over distributed data management systems to improve SPARQL (SPARQL Protocol and RDF Query Language) query performance regardless of its pattern shape with minimized pre-processing overhead. In this context, the first contribution of this work is a distributed RDF data partitioning schema called 3CStore that extends the existing VP (Vertical Partitioning) approach by using a subset of triples from the VP tables based on different join correlations. This approach speeds up queries at the cost of additional pre-processing overhead. To solve this, a relational partitioning schema called VPExp was developed by splitting predicates based on explicit type information of objects. This approach gains a significant query performance only for the specific type of query where the object is bound to a value for a particular predicate. To get efficient query performance on a wide range of query patterns, an improved solution is proposed by extending the existing Property Table approach to Subset-Property Table and combined with the VP approach. Further investigation on distributed RDF processing and querying systems based on typical use cases led to a novel relational partitioning schema called PTP (Property Table Partitioning) that further partitions the whole Property Table into the number of unique properties to minimize query input size and join operations during query evaluation. Finally, an RDF data management system based on the SPARQL-over-SQL approach called S3QLRDF is developed that generates the optimal query execution plan using statistics of PTP tables to provide efficient SPARQL query processing on a distributed system.
ContributorsHassan, P M Mahmudul Mahmudul (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Davulcu, Hasan (Committee member) / Sarwat Abdelghany Aly Elsayed, Mohamed (Committee member) / Arizona State University (Publisher)
Created2021
Description
The need of effective forecasting models for multi-variate time series has been underlined by the integration of sensory technologies into essential applications such as building energy optimizations, flight monitoring, and health monitoring. To meet this requirement, time series prediction techniques have been expanded from uni-variate to multi-variate. However, due to the extended models’ poor ability to capture the intrinsic relationships among variates, naïve extensions of prediction approaches result in an unwanted rise in the cost of model learning and, more critically, a significant loss in model performance. While recurrent models like Long Short-Term Memory (LSTM) and Recurrent Neural Network Network (RNN) are designed to capture the temporal intricacies in data, their performance can soon deteriorate. First, I claim in this thesis that (a) by exploiting temporal alignments of variates to quantify the importance of the recorded variates in relation to a target variate, one can build a more accurate forecasting model. I also argue that (b) traditional time series similarity/distance functions, such as Dynamic Time Warping (DTW), which require that variates have similar absolute patterns are fundamentally ill-suited for this purpose, and that should instead quantify temporal correlation in terms of temporal alignments of key “events” impacting these series, rather than series similarity. Further, I propose that (c) while learning a temporal model with recurrence-based techniques (such as RNN and LSTM – even when leveraging attention strategies) is challenging and expensive, the better results can be obtained by coupling simpler CNNs with an adaptive variate selection strategy. Putting these together, I introduce a novel Selego framework for variate selection based on these arguments, and I experimentally evaluate the performance of the proposed approach on various forecasting models, such as LSTM, RNN, and CNN, for different top-X% percent variates and different forecasting time in the future (lead), on multiple real-world data sets. Experiments demonstrate that the proposed framework can reduce the number of recorded variates required to train predictive models by 90 - 98% while also increasing accuracy. Finally, I present a fault onset detection technique that leverages the precise baseline forecasting models trained using the Selego framework. The proposed, Selego-enabled Fault Detection Framework (FDF-Selego) has been experimentally evaluated within the context of detecting the onset of faults in the building Heating, Ventilation, and Air Conditioning (HVAC) system.
ContributorsTiwaskar, Manoj (Author) / Candan, K. Selcuk (Thesis advisor) / Sapino, Maria Luisa (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2021
Description
More and more Taiwanese composers have been internationally recognizednowadays. However, their works have received scarce attention compared to their Western counterparts, even within Taiwan. Ming-Hsiu Yen (b. 1980) is a perfect example of an active composer whose works have been awarded many international prizes and performed worldwide, and yet there are few published studies that promote deeper understanding of her compositional language.
This paper is the explanatory document for a lecture recital presented by the author. Four of Yen’s compositions for piano are examined, namely The Forgotten Corner (2005), “The Core” and “Frozen March” from Movements (2002-2005), and Fantasy Amour (2013). These works are discussed from both compositional and performance perspectives. After a brief biography of Yen and an overview of her piano compositions, the four works are given detailed description focusing on their coherence created by specific elements. Performance suggestions for each work are provided as well.
The discussion offers insight into the musical organization of these pieces and Yen’s unifying techniques. It is hoped that this information will be beneficial to pianists interested in this composer and the repertoire of contemporary Taiwanese piano music.
ContributorsChang, Wen-Huei (Author) / Pagano, Caio (Thesis advisor) / Holbrook, Amy (Thesis advisor) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2021
Description
Jaime Mendoza-Nava (1925-2005) was an important Bolivian composer. In addition to writing music for the concert stage, he worked as a composer of film music in Los Angeles during the second half of the twentieth century. His life and work remain greatly unstudied, with the majority of his compositions existing only in manuscript form. The present study surveys the available biographical information on the composer and supplements it with new data collected through interviews with the composer’s family. The information presented here focuses on the composer’s American period as well as his personality traits. The study also examines the development of musical nationalism in Bolivia and other important aspects of Bolivian culture and society, thus creating a historical context through which key influences on the composer are identified. This historical and cultural information also contributes to an examination of Mendoza-Nava’s song cycle País de sombra (1988). A close study of this work reveals Mendoza-Nava’s sensitive setting of the poetry of Ricardo Jaimes Freyre (1868-1933) and his musical references to his Bolivian heritage. A recording of the song cycle by soprano Andrea Ramos and the current author and an edited copy of the musical score conclude the study.
ContributorsSakuma, Masaru (Author) / Ryan, Russell (Thesis advisor) / Holbrook, Amy (Committee member) / Campbell, Andrew (Committee member) / Arizona State University (Publisher)
Created2021
Description
Many real-world problems, such as model- and data-driven computer simulation analysis, social and collaborative network analysis, brain data analysis, and so on, benefit from jointly modeling and analyzing the underlying patterns associated with complex, multi-relational data. Tensor decomposition is an ideal mathematical tool for this joint modeling, due to its simultaneous analysis of such multi-relational data, which is made possible by the data's multidimensional, array-based nature. A major challenge in tensor decomposition lies with its computational and space complexity, especially for dense datasets. While the process is comparatively faster for sparse tensors, decomposition is still a major bottleneck for many applications. The tensor decomposition process results in dense (hence, large) intermediate results, even when the input tensor is sparse (or small). Noise is another challenge for most data mining techniques, and many tensor decomposition schemes are sensitive to noisy datasets; this is an inevitable problem for real-world data, which can lead to false conclusions. In this dissertation, I develop innovative tensor decomposition algorithms for mining both sparse and dense multi-relational data in a noise-resistant way. I present novel, scalable, parallelizable tensor decomposition algorithms, specifically tuned to be effective for dense, noisy tensors, and which maintain the quality of the resulting analysis. Furthermore, I present results on multi-relational data applications focusing on model- and data-driven computer simulation analysis, as well as social network and web mining, which demonstrate the effectiveness of these tensor decompositions.
ContributorsLi, Xinsheng (Author) / Candan, Kasim S (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria L (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Emerging trends in cyber system security breaches in critical cloud infrastructures show that attackers have abundant resources (human and computing power), expertise and support of large organizations and possible foreign governments. In order to greatly improve the protection of critical cloud infrastructures, incorporation of human behavior is needed to predict potential security breaches in critical cloud infrastructures. To achieve such prediction, it is envisioned to develop a probabilistic modeling approach with the capability of accurately capturing system-wide causal relationship among the observed operational behaviors in the critical cloud infrastructure and accurately capturing probabilistic human (users’) behaviors on subsystems as the subsystems are directly interacting with humans. In our conceptual approach, the system-wide causal relationship can be captured by the Bayesian network, and the probabilistic human behavior in the subsystems can be captured by the Markov Decision Processes. The interactions between the dynamically changing state graphs of Markov Decision Processes and the dynamic causal relationships in Bayesian network are key components in such probabilistic modelling applications. In this thesis, two techniques are presented for supporting the above vision to prediction of potential security breaches in critical cloud infrastructures. The first technique is for evaluation of the conformance of the Bayesian network with the multiple MDPs. The second technique is to evaluate the dynamically changing Bayesian network structure for conformance with the rules of the Bayesian network using a graph checker algorithm. A case study and its simulation are presented to show how the two techniques support the specific parts in our conceptual approach to predicting system-wide security breaches in critical cloud infrastructures.
ContributorsNagaraja, Vinjith (Author) / Yau, Stephen S. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015