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- All Subjects: Computer Science
- Genre: Masters Thesis
- Creators: Bansal, Ajay
- Creators: Walker, Erin
Description
Machine learning methodologies are widely used in almost all aspects of software engineering. An effective machine learning model requires large amounts of data to achieve high accuracy. The data used for classification is mostly labeled, which is difficult to obtain. The dataset requires both high costs and effort to accurately label the data into different classes. With abundance of data, it becomes necessary that all the data should be labeled for its proper utilization and this work focuses on reducing the labeling effort for large dataset. The thesis presents a comparison of different classifiers performance to test if small set of labeled data can be utilized to build accurate models for high prediction rate. The use of small dataset for classification is then extended to active machine learning methodology where, first a one class classifier will predict the outliers in the data and then the outlier samples are added to a training set for support vector machine classifier for labeling the unlabeled data. The labeling of dataset can be scaled up to avoid manual labeling and building more robust machine learning methodologies.
ContributorsBatra, Salil (Author) / Femiani, John (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2017
Description
Image processing has changed the way we store, view and share images. One important component of sharing images over the networks is image compression. Lossy image compression techniques compromise the quality of images to reduce their size. To ensure that the distortion of images due to image compression is not highly detectable by humans, the perceived quality of an image needs to be maintained over a certain threshold. Determining this threshold is best done using human subjects, but that is impractical in real-world scenarios. As a solution to this issue, image quality assessment (IQA) algorithms are used to automatically compute a fidelity score of an image.
However, poor performance of IQA algorithms has been observed due to complex statistical computations involved. General Purpose Graphics Processing Unit (GPGPU) programming is one of the solutions proposed to optimize the performance of these algorithms.
This thesis presents a Compute Unified Device Architecture (CUDA) based optimized implementation of full reference IQA algorithm, Visual Signal to Noise Ratio (VSNR) that uses M-level 2D Discrete Wavelet Transform (DWT) with 9/7 biorthogonal filters among other statistical computations. The presented implementation is tested upon four different image quality databases containing images with multiple distortions and sizes ranging from 512 x 512 to 1600 x 1280. The CUDA implementation of VSNR shows a speedup of over 32x for 1600 x 1280 images. It is observed that the speedup scales with the increase in size of images. The results showed that the implementation is fast enough to use VSNR on high definition videos with a frame rate of 60 fps. This work presents the optimizations made due to the use of GPU’s constant memory and reuse of allocated memory on the GPU. Also, it shows the performance improvement using profiler driven GPGPU development in CUDA. The presented implementation can be deployed in production combined with existing applications.
However, poor performance of IQA algorithms has been observed due to complex statistical computations involved. General Purpose Graphics Processing Unit (GPGPU) programming is one of the solutions proposed to optimize the performance of these algorithms.
This thesis presents a Compute Unified Device Architecture (CUDA) based optimized implementation of full reference IQA algorithm, Visual Signal to Noise Ratio (VSNR) that uses M-level 2D Discrete Wavelet Transform (DWT) with 9/7 biorthogonal filters among other statistical computations. The presented implementation is tested upon four different image quality databases containing images with multiple distortions and sizes ranging from 512 x 512 to 1600 x 1280. The CUDA implementation of VSNR shows a speedup of over 32x for 1600 x 1280 images. It is observed that the speedup scales with the increase in size of images. The results showed that the implementation is fast enough to use VSNR on high definition videos with a frame rate of 60 fps. This work presents the optimizations made due to the use of GPU’s constant memory and reuse of allocated memory on the GPU. Also, it shows the performance improvement using profiler driven GPGPU development in CUDA. The presented implementation can be deployed in production combined with existing applications.
ContributorsGupta, Ayush (Author) / Sohoni, Sohum (Thesis advisor) / Amresh, Ashish (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2017
Description
Traditional sports coaching involves face-to-face instructions with athletes or playingback 2D videos of athletes’ training. However, if the coach is not in the same area
as the athlete, then the coach will not be able to see the athlete’s full body and thus
cannot give precise guidance to the athlete, limiting the athlete’s improvement. To
address these challenges, this paper proposes Augmented Coach, an augmented reality
platform where coaches can view, manipulate and comment on athletes’ movement
volumetric video data remotely via the network. In particular, this work includes a).
Capturing the athlete’s movement video data with Kinects and converting it into point
cloud format b). Transmitting the point cloud data to the coach’s Oculus headset via
5G or wireless network c). Coach’s commenting on the athlete’s joints. In addition,
the evaluation of Augmented Coach includes an assessment of its performance from
five metrics via the wireless network and 5G network environment, but also from the
coaches’ and athletes’ experience of using it. The result shows that Augmented Coach
enables coaches to instruct athletes from a distance and provide effective feedback for
correcting athletes’ motions under the network.
ContributorsQiao, Yunhan (Author) / LiKamWa, Robert (Thesis advisor) / Bansal, Ajay (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Feedback represents a vital component of the learning process and is especially important for Computer Science students. With class sizes that are often large, it can be challenging to provide individualized feedback to students. Consistent, constructive, supportive feedback through a tutoring companion can scaffold the learning process for students.
This work contributes to the construction of a tutoring companion designed to provide this feedback to students. It aims to bridge the gap between the messages the compiler delivers, and the support required for a novice student to understand the problem and fix their code. Particularly, it provides support for students learning about recursion in a beginning university Java programming course. Besides also providing affective support, a tutoring companion could be more effective when it is embedded into the environment that the student is already using, instead of an additional tool for the student to learn. The proposed Tutoring Companion is embedded into the Eclipse Integrated Development Environment (IDE).
This thesis focuses on the reasoning model for the Tutoring Companion and is developed using the techniques of a neural network. While a student uses the IDE, the Tutoring Companion collects 16 data points, including the presence of certain key words, cyclomatic complexity, and error messages from the compiler, every time it detects an event, such as a run attempt, debug attempt, or a request for help, in the IDE. This data is used as inputs to the neural network. The neural network produces a correlating single output code for the feedback to be provided to the student, which is displayed in the IDE.
The effectiveness of the approach is examined among 38 Computer Science students who solve a programming assignment while the Tutoring Companion assists them. Data is collected from these interactions, including all inputs and outputs for the neural network, and students are surveyed regarding their experience. Results suggest that students feel supported while working with the Companion and promising potential for using a neural network with an embedded companion in the future. Challenges in developing an embedded companion are discussed, as well as opportunities for future work.
This work contributes to the construction of a tutoring companion designed to provide this feedback to students. It aims to bridge the gap between the messages the compiler delivers, and the support required for a novice student to understand the problem and fix their code. Particularly, it provides support for students learning about recursion in a beginning university Java programming course. Besides also providing affective support, a tutoring companion could be more effective when it is embedded into the environment that the student is already using, instead of an additional tool for the student to learn. The proposed Tutoring Companion is embedded into the Eclipse Integrated Development Environment (IDE).
This thesis focuses on the reasoning model for the Tutoring Companion and is developed using the techniques of a neural network. While a student uses the IDE, the Tutoring Companion collects 16 data points, including the presence of certain key words, cyclomatic complexity, and error messages from the compiler, every time it detects an event, such as a run attempt, debug attempt, or a request for help, in the IDE. This data is used as inputs to the neural network. The neural network produces a correlating single output code for the feedback to be provided to the student, which is displayed in the IDE.
The effectiveness of the approach is examined among 38 Computer Science students who solve a programming assignment while the Tutoring Companion assists them. Data is collected from these interactions, including all inputs and outputs for the neural network, and students are surveyed regarding their experience. Results suggest that students feel supported while working with the Companion and promising potential for using a neural network with an embedded companion in the future. Challenges in developing an embedded companion are discussed, as well as opportunities for future work.
ContributorsDay, Melissa (Author) / Gonzalez-Sanchez, Javier (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2019
Description
SLAM (Simultaneous Localization and Mapping) is a problem that has existed for a long time in robotics and autonomous navigation. The objective of SLAM is for a robot to simultaneously figure out its position in space and map its environment. SLAM is especially useful and mandatory for robots that want to navigate autonomously. The description might make it seem like a chicken and egg problem, but numerous methods have been proposed to tackle SLAM. Before the rise in the popularity of deep learning and AI (Artificial Intelligence), most existing algorithms involved traditional hard-coded algorithms that would receive and process sensor information and convert it into some solvable sensor-agnostic problem. The challenge for these sorts of methods is having to tackle dynamic environments. The more variety in the environment, the poorer the results. Also due to the increase in computational power and the capability of deep learning-based image processing, visual SLAM has become extremely viable and maybe even preferable to traditional SLAM algorithms. In this research, a deep learning-based solution to the SLAM problem is proposed, specifically monocular visual SLAM which is solving the problem of SLAM purely with a singular camera as the input, and the model is tested on the KITTI (Karlsruhe Institute of Technology & Toyota Technological Institute) odometry dataset.
ContributorsRupaakula, Krishna Sandeep (Author) / Bansal, Ajay (Thesis advisor) / Baron, Tyler (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2023
Description
Since the early 2000s the Rubik’s Cube has seen growing usage at speedsolving competitions and as an effective tool to teach Science, Technology, Engineering, Mathematics (STEM) topics at hundreds of schools and universities across the world. Recently, cube manufacturers have begun embedding sensors to enable digital face tracking. The live feedback from these so called “smartcubes” enables a new wave of immersive solution tutorials and interactive educational games using the cube as a controller. Existing smartcube software has several limitations. Manufacturers’ applications support only a narrow set of puzzle form factors and application platforms, fragmenting the ecosystem. Most apps require an active internet connection for key features, limiting where users can practice with a smartcube. Finally, existing applications focus on a single 3x3x3connection, losing opportunities afforded by new form factors. This research demonstrates an open-source smartcube application which mitigates these limitations. Particular attention is given to creating an Application Programming Interface (API) for smartcube communication and building representative solve analysis tools. These innovations have included successful negotiations to re-license existing open-source Rubik’sCube software projects to support deployment on multiple platforms, particularly iOS. The resulting application supports smartcubes from three manufacturers, runs on two platforms (Android and iOS), functions entirely offline after an initial download of remote assets, demonstrates concurrent connections with up to six smartcubes, and supports all current and anticipated smartcube form factors. These foundational elements can accelerate future efforts to build smartcube applications, including automated performance feedback systems and personalized gamification of learning experiences. Such advances will hopefully enhance the Rubik’s Cube’s value both as a competitive toy and as a pedagogical tool in educational institutions worldwide.
ContributorsHale, Joseph (Author) / Bansal, Ajay (Thesis advisor) / Heinrichs, Robert (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Recommendation systems provide recommendations based on user behavior andcontent data. User behavior and content data are fed to machine learning algorithms to train
them and give recommendations to the users. These algorithms need a large amount of data
for a reasonable conversion rate. But for small applications, the available amount of data
is minimal, leading to high recommendation aberrations. Also, when an existing large
scaled application with a high amount of available data uses a new recommendation
system, it requires some time and testing to decide which recommendation algorithm is
best suited to get higher conversion rates. This learning curve costs highly when the user
base and data size are significantly high.
In this thesis, A/B testing is used with manual intervention in the decision-making
of recommendation systems. To understand the effectiveness of the recommendations, user
interaction data is compared to compare experiences. Based on the comparisons, the
experiments conclude the effectiveness of A/B testing for the recommendation system.
ContributorsVaidya, Yogesh Vinayak (Author) / Bansal, Ajay (Thesis advisor) / Findler, Michael (Committee member) / Chakravarthi, Bharatesh (Committee member) / Arizona State University (Publisher)
Created2023
Description
This paper introduces Zenith, a statically typed, functional programming language that compiles to Lua modules. The goal of Zenith is to be used in tandem with Lua, as a secondary language, in which Lua developers can transition potentially unsound programs into Zenith instead. Here developers will be ensured a set of guarantees during compile time, which are provided through Zenith’s language design and type system. This paper formulates the reasoning behind the design choices in Zenith, based on prior work. This paper also provides a basic understanding and intuitions on the Hindley-Milner type system used in Zenith, and the functional programming data types used to encode unsound functions. With these ideas combined, the paper concludes on how Zenith can provide soundness and runtime safety as a language, and how Zenith may be used with Lua to create safe systems.
ContributorsShrestha, Abhash (Author) / De Luca, Gennaro (Thesis advisor) / Bansal, Ajay (Thesis advisor) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2023
Description
A significant proportion of medical errors exist in crucial medical information, and most stem from misinterpreting non-standardized clinical notes. Clinical Skills exam offered by the United States Medical Licensing Examination (USMLE) was put in place to certify patient note-taking skills before medical students joined professional practices, offering the first line of defense in protecting patients from medical errors. Nonetheless, the exams were discontinued in 2021 following high costs and resource usage in scoring the exams. This thesis compares four transformer-based models, namely BERT (Bidirectional Encoder Representations from Transformers) Base Uncased, Emilyalsentzer Bio_ClinicalBERT, RoBERTa (Robustly Optimized BERT Pre-Training Approach), and DeBERTa (Decoding-enhanced BERT with disentangled attention), with the goal to map free text in patient notes to clinical concepts present in the exam rubric. The impact of context-specific embeddings on BERT was also studied to determine the need for a clinical BERT in Clinical Skills exam. This thesis proposes the use of DeBERTa as a backbone model in patient note scoring for the USMLE Clinical Skills exam after comparing it with three other transformer models. Disentangled attention and enhanced mask decoder integrated into DeBERTa were credited for the high performance of DeBERTa as compared to the other models. Besides, the effect of meta pseudo labeling was also investigated in this thesis, which in turn, further enhanced DeBERTa’s performance.
ContributorsGanesh, Jay (Author) / Bansal, Ajay (Thesis advisor) / Mehlhase, Alexandra (Committee member) / Findler, Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
Embedded assessment constantly updates a model of the student as the student works on instructional tasks. Accurate embedded assessment allows students, instructors and instructional systems to make informed decisions without requiring the student to stop instruction and take a test. This thesis describes the development and comparison of several student models for Dragoon, an intelligent tutoring system. All the models were instances of Bayesian Knowledge Tracing, a standard method. Several methods of parameterization and calibration were explored using two recently developed toolkits, FAST and BNT-SM that replaces constant-valued parameters with logistic regressions. The evaluation was done by calculating the fit of the models to data from human subjects and by assessing the accuracy of their assessment of simulated students. The student models created using node properties as subskills were superior to coarse-grained, skill-only models. Adding this extra level of representation to emission parameters was superior to adding it to transmission parameters. Adding difficulty parameters did not improve fit, contrary to standard practice in psychometrics.
ContributorsGrover, Sachin (Author) / VanLehn, Kurt (Thesis advisor) / Walker, Erin (Committee member) / Shiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2015