Matching Items (51)
Filtering by
- All Subjects: Computer Science
- Creators: Turaga, Pavan
Description
In the age of artificial intelligence, Machine Learning (ML) has become a pervasive force, impacting countless aspects of our lives. As ML’s influence expands, concerns about its reliability and trustworthiness have intensified, with security and robustness emerging as significant challenges. For instance, it has been demonstrated that slight perturbations to a stop sign can cause ML classifiers to misidentify it as a speed limit sign, raising concerns about whether ML algorithms are suitable for real-world deployments. To tackle these issues, Responsible Machine Learning (Responsible ML) has emerged with a clear mission: to develop secure and robust ML algorithms. This dissertation aims to develop Responsible Machine Learning algorithms under real-world constraints. Specifically, recognizing the role of adversarial attacks in exposing security vulnerabilities and robustifying the ML methods, it lays down the foundation of Responsible ML by outlining a novel taxonomy of adversarial attacks within real-world settings, categorizing them into black-box target-specific, and target-agnostic attacks. Subsequently, it proposes potent adversarial attacks in each category, aiming to obtain effectiveness and efficiency. Transcending conventional boundaries, it then introduces the notion of causality into Responsible ML (a.k.a., Causal Responsible ML), presenting the causal adversarial attack. This represents the first principled framework to explain the transferability of adversarial attacks to unknown models by identifying their common source of vulnerabilities, thereby exposing the pinnacle of threat and vulnerability: conducting successful attacks on any model with no prior knowledge.
Finally, acknowledging the surge of Generative AI, this dissertation explores Responsible ML for Generative AI. It introduces a novel adversarial attack that unveils their adversarial vulnerabilities and devises a strong defense mechanism to bolster the models’ robustness against potential attacks.
ContributorsMoraffah, Raha (Author) / Liu, Huan (Thesis advisor) / Yang, Yezhou (Committee member) / Xiao, Chaowei (Committee member) / Turaga, Pavan (Committee member) / Carley, Kathleen (Committee member) / Arizona State University (Publisher)
Created2024
Description
Human movement is a complex process influenced by physiological and psychological factors. The execution of movement is varied from person to person, and the number of possible strategies for completing a specific movement task is almost infinite. Different choices of strategies can be perceived by humans as having different degrees of quality, and the quality can be defined with regard to aesthetic, athletic, or health-related ratings. It is useful to measure and track the quality of a person's movements, for various applications, especially with the prevalence of low-cost and portable cameras and sensors today. Furthermore, based on such measurements, feedback systems can be designed for people to practice their movements towards certain goals. In this dissertation, I introduce symmetry as a family of measures for movement quality, and utilize recent advances in computer vision and differential geometry to model and analyze different types of symmetry in human movements. Movements are modeled as trajectories on different types of manifolds, according to the representations of movements from sensor data. The benefit of such a universal framework is that it can accommodate different existing and future features that describe human movements. The theory and tools developed in this dissertation will also be useful in other scientific areas to analyze symmetry from high-dimensional signals.
ContributorsWang, Qiao (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Srivastava, Anuj (Committee member) / Sha, Xin Wei (Committee member) / Arizona State University (Publisher)
Created2018
Description
Generating real-world content for VR is challenging in terms of capturing and processing at high resolution and high frame-rates. The content needs to represent a truly immersive experience, where the user can look around in 360-degree view and perceive the depth of the scene. The existing solutions only capture and offload the compute load to the server. But offloading large amounts of raw camera feeds takes longer latencies and poses difficulties for real-time applications. By capturing and computing on the edge, we can closely integrate the systems and optimize for low latency. However, moving the traditional stitching algorithms to battery constrained device needs at least three orders of magnitude reduction in power. We believe that close integration of capture and compute stages will lead to reduced overall system power.
We approach the problem by building a hardware prototype and characterize the end-to-end system bottlenecks of power and performance. The prototype has 6 IMX274 cameras and uses Nvidia Jetson TX2 development board for capture and computation. We found that capturing is bottlenecked by sensor power and data-rates across interfaces, whereas compute is limited by the total number of computations per frame. Our characterization shows that redundant capture and redundant computations lead to high power, huge memory footprint, and high latency. The existing systems lack hardware-software co-design aspects, leading to excessive data transfers across the interfaces and expensive computations within the individual subsystems. Finally, we propose mechanisms to optimize the system for low power and low latency. We emphasize the importance of co-design of different subsystems to reduce and reuse the data. For example, reusing the motion vectors of the ISP stage reduces the memory footprint of the stereo correspondence stage. Our estimates show that pipelining and parallelization on custom FPGA can achieve real time stitching.
We approach the problem by building a hardware prototype and characterize the end-to-end system bottlenecks of power and performance. The prototype has 6 IMX274 cameras and uses Nvidia Jetson TX2 development board for capture and computation. We found that capturing is bottlenecked by sensor power and data-rates across interfaces, whereas compute is limited by the total number of computations per frame. Our characterization shows that redundant capture and redundant computations lead to high power, huge memory footprint, and high latency. The existing systems lack hardware-software co-design aspects, leading to excessive data transfers across the interfaces and expensive computations within the individual subsystems. Finally, we propose mechanisms to optimize the system for low power and low latency. We emphasize the importance of co-design of different subsystems to reduce and reuse the data. For example, reusing the motion vectors of the ISP stage reduces the memory footprint of the stereo correspondence stage. Our estimates show that pipelining and parallelization on custom FPGA can achieve real time stitching.
ContributorsGunnam, Sridhar (Author) / LiKamWa, Robert (Thesis advisor) / Turaga, Pavan (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the development of modern technological infrastructures, such as social networks or the Internet of Things (IoT), data is being generated at a speed that is never before seen. Analyzing the content of this data helps us further understand underlying patterns and discover relationships among different subsets of data, enabling intelligent decision making. In this thesis, I first introduce the Low-rank, Win-dowed, Incremental Singular Value Decomposition (SVD) framework to inclemently maintain SVD factors over streaming data. Then, I present the Group Incremental Non-Negative Matrix Factorization framework to leverage redundancies in the data to speed up incremental processing. They primarily tackle the challenges of using factorization models in the scenarios with streaming textual data. In order to tackle the challenges in improving the effectiveness and efficiency of generative models in this streaming environment, I introduce the Incremental Dynamic Multiscale Topic Model framework, which identifies multi-scale patterns and their evolutions within streaming datasets. While the latent factor models assume the linear independence in the latent factors, the generative models assume the observation is generated from a set of latent variables with various distributions. Furthermore, some models may not be accessible or their underlying structures are too complex to understand, such as simulation ensembles, where there may be thousands of parameters with a huge parameter space, the only way to learn information from it is to execute real simulations. When performing knowledge discovery and decision making through data- and model-driven simulation ensembles, it is expensive to operate these ensembles continuously at large scale, due to the high computational. Consequently, given a relatively small simulation budget, it is desirable to identify a sparse ensemble that includes the most informative simulations, while still permitting effective exploration of the input parameter space. Therefore, I present Complexity-Guided Parameter Space Sampling framework, which is an intelligent, top-down sampling scheme to select the most salient simulation parameters to execute, given a limited computational budget. Moreover, I also present a Pivot-Guided Parameter Space Sampling framework, which incrementally maintains a diverse ensemble of models of the simulation ensemble space and uses a pivot guided mechanism for future sample selection.
ContributorsChen, Xilun (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Pedrielli, Giulia (Committee member) / Sapino, Maria Luisa (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
ContributorsKoneripalli Seetharam, Kaushik (Author) / Turaga, Pavan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019
Description
The spatial databases are used to store geometric objects such as points, lines, polygons. Querying such complex spatial objects becomes a challenging task. Index structures are used to improve the lookup performance of the stored objects in the databases, but traditional index structures cannot perform well in case of spatial databases. A significant amount of research is made to ingest, index and query the spatial objects based on different types of spatial queries, such as range, nearest neighbor, and join queries. Compressed Spatial Bitmap Index (cSHB) structure is one such example of indexing and querying approach that supports spatial range query workloads (set of queries). cSHB indexes and many other approaches lack parallel computation. The massive amount of spatial data requires a lot of computation and traditional methods are insufficient to address these issues. Other existing parallel processing approaches lack in load-balancing of parallel tasks which leads to resource overloading bottlenecks.
In this thesis, I propose novel spatial partitioning techniques, Max Containment Clustering and Max Containment Clustering with Separation, to create load-balanced partitions of a range query workload. Each partition takes a similar amount of time to process the spatial queries and reduces the response latency by minimizing the disk access cost and optimizing the bitmap operations. The partitions created are processed in parallel using cSHB indexes. The proposed techniques utilize the block-based organization of bitmaps in the cSHB index and improve the performance of the cSHB index for processing a range query workload.
In this thesis, I propose novel spatial partitioning techniques, Max Containment Clustering and Max Containment Clustering with Separation, to create load-balanced partitions of a range query workload. Each partition takes a similar amount of time to process the spatial queries and reduces the response latency by minimizing the disk access cost and optimizing the bitmap operations. The partitions created are processed in parallel using cSHB indexes. The proposed techniques utilize the block-based organization of bitmaps in the cSHB index and improve the performance of the cSHB index for processing a range query workload.
ContributorsGadkari, Ashish (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2018
Description
There has been tremendous technological advancement in the past two decades. Faster computers and improved sensing devices have broadened the research scope in computer vision. With these developments, the task of assessing the quality of human actions, is considered an important problem that needs to be tackled. Movement quality assessment finds wide range of application in motor control, health-care, rehabilitation and physical therapy. Home-based interactive physical therapy requires the ability to monitor, inform and assess the quality of everyday movements. Obtaining labeled data from trained therapists/experts is the main limitation, since it is both expensive and time consuming.
Motivated by recent studies in motor control and therapy, in this thesis an existing computational framework is used to assess balance impairment and disease severity in people suffering from Parkinson's disease. The framework uses high-dimensional shape descriptors of the reconstructed phase space, of the subjects' center of pressure (CoP) tracings while performing dynamical postural shifts. The performance of the framework is evaluated using a dataset collected from 43 healthy and 17 Parkinson's disease impaired subjects, and outperforms other methods, such as dynamical shift indices and use of chaotic invariants, in assessment of balance impairment.
In this thesis, an unsupervised method is also proposed that measures movement quality assessment of simple actions like sit-to-stand and dynamic posture shifts by modeling the deviation of a given movement from an ideal movement path in the configuration space, i.e. the quality of movement is directly related to similarity to the ideal trajectory, between the start and end pose. The S^1xS^1 configuration space was used to model the interaction of two joint angles in sit-to-stand actions, and the R^2 space was used to model the subject's CoP while performing dynamic posture shifts for application in movement quality estimation.
Motivated by recent studies in motor control and therapy, in this thesis an existing computational framework is used to assess balance impairment and disease severity in people suffering from Parkinson's disease. The framework uses high-dimensional shape descriptors of the reconstructed phase space, of the subjects' center of pressure (CoP) tracings while performing dynamical postural shifts. The performance of the framework is evaluated using a dataset collected from 43 healthy and 17 Parkinson's disease impaired subjects, and outperforms other methods, such as dynamical shift indices and use of chaotic invariants, in assessment of balance impairment.
In this thesis, an unsupervised method is also proposed that measures movement quality assessment of simple actions like sit-to-stand and dynamic posture shifts by modeling the deviation of a given movement from an ideal movement path in the configuration space, i.e. the quality of movement is directly related to similarity to the ideal trajectory, between the start and end pose. The S^1xS^1 configuration space was used to model the interaction of two joint angles in sit-to-stand actions, and the R^2 space was used to model the subject's CoP while performing dynamic posture shifts for application in movement quality estimation.
ContributorsSom, Anirudh (Author) / Turaga, Pavan (Thesis advisor) / Krishnamurthi, Narayanan (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2016
Description
This thesis aims to explore the language of different bodies in the field of dance by analyzing
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
ContributorsIyengar, Varsha (Author) / Xin Wei, Sha (Thesis advisor) / Turaga, Pavan (Committee member) / Coleman, Grisha (Committee member) / Arizona State University (Publisher)
Created2016
Description
The amount of time series data generated is increasing due to the integration of sensor technologies with everyday applications, such as gesture recognition, energy optimization, health care, video surveillance. The use of multiple sensors simultaneously
for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis.
Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping. However, it has not been studied how these algorithms account for asynchronous in time series. Human gestures, for example, exhibit asynchrony in their patterns as different subjects perform the same gesture with varying movements in their patterns at different speeds. In this thesis, we propose several algorithms (some of which also leverage metadata describing the relationships among the variates). In particular, we present several techniques that leverage the contextual relationships among the variates when measuring multi-variate time series similarities. Based on the way correlation is leveraged, various weighing mechanisms have been proposed that determine the importance of a dimension for discriminating between the time series as giving the same weight to each dimension can led to misclassification. We next study the robustness of the considered techniques against different temporal asynchronies, including shifts and stretching.
Exhaustive experiments were carried on datasets with multiple types and amounts of temporal asynchronies. It has been observed that accuracy of algorithms that rely on data to discover variate relationships can be low under the presence of temporal asynchrony, whereas in case of algorithms that rely on external metadata, robustness against asynchronous distortions tends to be stronger. Specifically, algorithms using external metadata have better classification accuracy and cluster separation than existing state-of-the-art work, such as EROS, PCA, and naive dynamic time warping.
for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis.
Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping. However, it has not been studied how these algorithms account for asynchronous in time series. Human gestures, for example, exhibit asynchrony in their patterns as different subjects perform the same gesture with varying movements in their patterns at different speeds. In this thesis, we propose several algorithms (some of which also leverage metadata describing the relationships among the variates). In particular, we present several techniques that leverage the contextual relationships among the variates when measuring multi-variate time series similarities. Based on the way correlation is leveraged, various weighing mechanisms have been proposed that determine the importance of a dimension for discriminating between the time series as giving the same weight to each dimension can led to misclassification. We next study the robustness of the considered techniques against different temporal asynchronies, including shifts and stretching.
Exhaustive experiments were carried on datasets with multiple types and amounts of temporal asynchronies. It has been observed that accuracy of algorithms that rely on data to discover variate relationships can be low under the presence of temporal asynchrony, whereas in case of algorithms that rely on external metadata, robustness against asynchronous distortions tends to be stronger. Specifically, algorithms using external metadata have better classification accuracy and cluster separation than existing state-of-the-art work, such as EROS, PCA, and naive dynamic time warping.
ContributorsGarg, Yash (Author) / Candan, Kasim Selcuk (Thesis advisor) / Chowell-Punete, Gerardo (Committee member) / Tong, Hanghang (Committee member) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2015
Description
Similarity search in high-dimensional spaces is popular for applications like image
processing, time series, and genome data. In higher dimensions, the phenomenon of
curse of dimensionality kills the effectiveness of most of the index structures, giving
way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity
searches. In addition to range searches and k-nearest neighbor searches, there
is a need to answer negative queries formed by excluded regions, in high-dimensional
data. Though there have been a slew of variants of LSH to improve efficiency, reduce
storage, and provide better accuracies, none of the techniques are capable of
answering queries in the presence of excluded regions.
This thesis provides a novel approach to handle such negative queries. This is
achieved by creating a prefix based hierarchical index structure. First, the higher
dimensional space is projected to a lower dimension space. Then, a one-dimensional
ordering is developed, while retaining the hierarchical traits. The algorithm intelligently
prunes the irrelevant candidates while answering queries in the presence of
excluded regions. While naive LSH would need to filter out the negative query results
from the main results, the new algorithm minimizes the need to fetch the redundant
results in the first place. Experiment results show that this reduces post-processing
cost thereby reducing the query processing time.
processing, time series, and genome data. In higher dimensions, the phenomenon of
curse of dimensionality kills the effectiveness of most of the index structures, giving
way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity
searches. In addition to range searches and k-nearest neighbor searches, there
is a need to answer negative queries formed by excluded regions, in high-dimensional
data. Though there have been a slew of variants of LSH to improve efficiency, reduce
storage, and provide better accuracies, none of the techniques are capable of
answering queries in the presence of excluded regions.
This thesis provides a novel approach to handle such negative queries. This is
achieved by creating a prefix based hierarchical index structure. First, the higher
dimensional space is projected to a lower dimension space. Then, a one-dimensional
ordering is developed, while retaining the hierarchical traits. The algorithm intelligently
prunes the irrelevant candidates while answering queries in the presence of
excluded regions. While naive LSH would need to filter out the negative query results
from the main results, the new algorithm minimizes the need to fetch the redundant
results in the first place. Experiment results show that this reduces post-processing
cost thereby reducing the query processing time.
ContributorsBhat, Aneesha (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2016