Matching Items (11)
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- All Subjects: artificial intelligence
- Genre: Masters Thesis
- Creators: Bansal, Ajay
- Creators: Liu, Huan
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer tools with the large class of machine learning algorithms which are highly iterative in nature. In this thesis project, I set about to implement a low-rank matrix completion algorithm (as an example of a highly iterative algorithm) within a popular Big Data framework, and to evaluate its performance processing the Netflix Prize dataset. I begin by describing several approaches which I attempted, but which did not perform adequately. These include an implementation of the Singular Value Thresholding (SVT) algorithm within the Apache Mahout framework, which runs on top of the Apache Hadoop MapReduce engine. I then describe an approach which uses the Divide-Factor-Combine (DFC) algorithmic framework to parallelize the state-of-the-art low-rank completion algorithm Orthogoal Rank-One Matrix Pursuit (OR1MP) within the Apache Spark engine. I describe the results of a series of tests running this implementation with the Netflix dataset on clusters of various sizes, with various degrees of parallelism. For these experiments, I utilized the Amazon Elastic Compute Cloud (EC2) web service. In the final analysis, I conclude that the Spark DFC + OR1MP implementation does indeed produce competitive results, in both accuracy and performance. In particular, the Spark implementation performs nearly as well as the MATLAB implementation of OR1MP without any parallelism, and improves performance to a significant degree as the parallelism increases. In addition, the experience demonstrates how Spark's flexible programming model makes it straightforward to implement this parallel and iterative machine learning algorithm.
ContributorsKrouse, Brian (Author) / Ye, Jieping (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (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
Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
ContributorsHenderson, Christopher (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2018
Description
Ensemble learning methods like bagging, boosting, adaptive boosting, stacking have traditionally shown promising results in improving the predictive accuracy in classification. These techniques have recently been widely used in various domains and applications owing to the improvements in computational efficiency and distributed computing advances. However, with the advent of wide variety of applications of machine learning techniques to class imbalance problems, further focus is needed to evaluate, improve and optimize other performance measures such as sensitivity (true positive rate) and specificity (true negative rate) in classification. This thesis demonstrates a novel approach to evaluate and optimize the performance measures (specifically sensitivity and specificity) using ensemble learning methods for classification that can be especially useful in class imbalanced datasets. In this thesis, ensemble learning methods (specifically bagging and boosting) are used to optimize the performance measures (sensitivity and specificity) on a UC Irvine (UCI) 130 hospital diabetes dataset to predict if a patient will be readmitted to the hospital based on various feature vectors. From the experiments conducted, it can be empirically concluded that, by using ensemble learning methods, although accuracy does improve to some margin, both sensitivity and specificity are optimized significantly and consistently over different cross validation approaches. The implementation and evaluation has been done on a subset of the large UCI 130 hospital diabetes dataset. The performance measures of ensemble learners are compared to the base machine learning classification algorithms such as Naive Bayes, Logistic Regression, k Nearest Neighbor, Decision Trees and Support Vector Machines.
ContributorsBahl, Neeraj Dharampal (Author) / Bansal, Ajay (Thesis advisor) / Amresh, Ashish (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2017
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
Generative Adversarial Networks are designed, in theory, to replicate the distribution of the data they are trained on. With real-world limitations, such as finite network capacity and training set size, they inevitably suffer a yet unavoidable technical failure: mode collapse. GAN-generated data is not nearly as diverse as the real-world data the network is trained on; this work shows that this effect is especially drastic when the training data is highly non-uniform. Specifically, GANs learn to exacerbate the social biases which exist in the training set along sensitive axes such as gender and race. In an age where many datasets are curated from web and social media data (which are almost never balanced), this has dangerous implications for downstream tasks using GAN-generated synthetic data, such as data augmentation for classification. This thesis presents an empirical demonstration of this phenomenon and illustrates its real-world ramifications. It starts by showing that when asked to sample images from an illustrative dataset of engineering faculty headshots from 47 U.S. universities, unfortunately skewed toward white males, a DCGAN’s generator “imagines” faces with light skin colors and masculine features. In addition, this work verifies that the generated distribution diverges more from the real-world distribution when the training data is non-uniform than when it is uniform. This work also shows that a conditional variant of GAN is not immune to exacerbating sensitive social biases. Finally, this work contributes a preliminary case study on Snapchat’s explosively popular GAN-enabled “My Twin” selfie lens, which consistently lightens the skin tone for women of color in an attempt to make faces more feminine. The results and discussion of the study are meant to caution machine learning practitioners who may unsuspectingly increase the biases in their applications.
ContributorsJain, Niharika (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Manikonda, Lydia (Committee member) / Arizona State University (Publisher)
Created2020
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
Frontend development often involves the repetitive and time-consuming task of transforming a Graphical User interface (GUI) design into Frontend Code. The GUI design could either be an image or a design created on tools like Figma, Sketch, etc. This process can be particularly challenging when the website designs are experimental and undergo multiple iterations before the final version gets deployed. In such cases, developers work with the designers to make continuous changes and improve the look and feel of the website. This can lead to a lot of reworks and a poorly managed codebase that requires significant developer resources. To tackle this problem, researchers are exploring ways to automate the process of transforming image designs into functional websites instantly. This thesis explores the use of machine learning, specifically Recurrent Neural networks (RNN) to generate an intermediate code from an image design and then compile it into a React web frontend code. By utilizing this approach, designers can essentially transform an image design into a functional website, granting them creative freedom and the ability to present working prototypes to stockholders in real-time. To overcome the limitations of existing publicly available datasets, the thesis places significant emphasis on generating synthetic datasets. As part of this effort, the research proposes a novel method to double the size of the pix2code [2] dataset by incorporating additional complex HTML elements such as login forms, carousels, and cards. This approach has the potential to enhance the quality and diversity of training data available for machine learning models. Overall, the proposed approach offers a promising solution to the repetitive and time-consuming task of transforming GUI designs into frontend code.
ContributorsSingh, Ajitesh Janardan (Author) / Bansal, Ajay (Thesis advisor) / Mehlhase, Alexandra (Committee member) / Baron, Tyler (Committee member) / Arizona State University (Publisher)
Created2023
Description
One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.
ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)
Created2021