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- All Subjects: Computer Science
- Genre: Masters Thesis
- Creators: Li, Baoxin
- Creators: Sen, Arunabha
- Member of: Theses and Dissertations
Description
With the advent of social media (like Twitter, Facebook etc.,) people are easily sharing their opinions, sentiments and enforcing their ideologies on others like never before. Even people who are otherwise socially inactive would like to share their thoughts on current affairs by tweeting and sharing news feeds with their friends and acquaintances. In this thesis study, we chose Twitter as our main data platform to analyze shifts and movements of 27 political organizations in Indonesia. So far, we have collected over 30 million tweets and 150,000 news articles from RSS feeds of the corresponding organizations for our analysis. For Twitter data extraction, we developed a multi-threaded application which seamlessly extracts, cleans and stores millions of tweets matching our keywords from Twitter Streaming API. For keyword extraction, we used topics and perspectives which were extracted using n-grams techniques and later approved by our social scientists. After the data is extracted, we aggregate the tweet contents that belong to every user on a weekly basis. Finally, we applied linear and logistic regression using SLEP, an open source sparse learning package to compute weekly score for users and mapping them to one of the 27 organizations on a radical or counter radical scale. Since, we are mapping users to organizations on a weekly basis, we are able to track user's behavior and important new events that triggered shifts among users between organizations. This thesis study can further be extended to identify topics and organization specific influential users and new users from various social media platforms like Facebook, YouTube etc. can easily be mapped to existing organizations on a radical or counter-radical scale.
ContributorsPoornachandran, Sathishkumar (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2013
Description
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
We solve the problem of activity verification in the context of sustainability. Activity verification is the process of proving the user assertions pertaining to a certain activity performed by the user. Our motivation lies in incentivizing the user for engaging in sustainable activities like taking public transport or recycling. Such incentivization schemes require the system to verify the claim made by the user. The system verifies these claims by analyzing the supporting evidence captured by the user while performing the activity. The proliferation of portable smart-phones in the past few years has provided us with a ubiquitous and relatively cheap platform, having multiple sensors like accelerometer, gyroscope, microphone etc. to capture this evidence data in-situ. In this research, we investigate the supervised and semi-supervised learning techniques for activity verification. Both these techniques make use the data set constructed using the evidence submitted by the user. Supervised learning makes use of annotated evidence data to build a function to predict the class labels of the unlabeled data points. The evidence data captured can be either unimodal or multimodal in nature. We use the accelerometer data as evidence for transportation mode verification and image data as evidence for recycling verification. After training the system, we achieve maximum accuracy of 94% when classifying the transport mode and 81% when detecting recycle activity. In the case of recycle verification, we could improve the classification accuracy by asking the user for more evidence. We present some techniques to ask the user for the next best piece of evidence that maximizes the probability of classification. Using these techniques for detecting recycle activity, the accuracy increases to 93%. The major disadvantage of using supervised models is that it requires extensive annotated training data, which expensive to collect. Due to the limited training data, we look at the graph based inductive semi-supervised learning methods to propagate the labels among the unlabeled samples. In the semi-supervised approach, we represent each instance in the data set as a node in the graph. Since it is a complete graph, edges interconnect these nodes, with each edge having some weight representing the similarity between the points. We propagate the labels in this graph, based on the proximity of the data points to the labeled nodes. We estimate the performance of these algorithms by measuring how close the probability distribution of the data after label propagation is to the probability distribution of the ground truth data. Since labeling has a cost associated with it, in this thesis we propose two algorithms that help us in selecting minimum number of labeled points to propagate the labels accurately. Our proposed algorithm achieves a maximum of 73% increase in performance when compared to the baseline algorithm.
ContributorsDesai, Vaishnav (Author) / Sundaram, Hari (Thesis advisor) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contention based IEEE 802.11MAC uses the binary exponential backoff algorithm (BEB) for the contention resolution. The protocol suffers poor performance in the heavily loaded networks and MANETs, high collision rate and packet drops, probabilistic delay guarantees, and unfairness. Many backoff strategies were proposed to improve the performance of IEEE 802.11 but all ignore the network topology and demand. Persistence is defined as the fraction of time a node is allowed to transmit, when this allowance should take into account topology and load, it is topology and load aware persistence (TLA). We develop a relation between contention window size and the TLA-persistence. We implement a new backoff strategy where the TLA-persistence is defined as the lexicographic max-min channel allocation. We use a centralized algorithm to calculate each node's TLApersistence and then convert it into a contention window size. The new backoff strategy is evaluated in simulation, comparing with that of the IEEE 802.11 using BEB. In most of the static scenarios like exposed terminal, flow in the middle, star topology, and heavy loaded multi-hop networks and in MANETs, through the simulation study, we show that the new backoff strategy achieves higher overall average throughput as compared to that of the IEEE 802.11 using BEB.
ContributorsBhyravajosyula, Sai Vishnu Kiran (Author) / Syrotiuk, Violet R. (Thesis advisor) / Sen, Arunabha (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2013
Description
Software has a great impact on the energy efficiency of any computing system--it can manage the components of a system efficiently or inefficiently. The impact of software is amplified in the context of a wearable computing system used for activity recognition. The design space this platform opens up is immense and encompasses sensors, feature calculations, activity classification algorithms, sleep schedules, and transmission protocols. Design choices in each of these areas impact energy use, overall accuracy, and usefulness of the system. This thesis explores methods software can influence the trade-off between energy consumption and system accuracy. In general the more energy a system consumes the more accurate will be. We explore how finding the transitions between human activities is able to reduce the energy consumption of such systems without reducing much accuracy. We introduce the Log-likelihood Ratio Test as a method to detect transitions, and explore how choices of sensor, feature calculations, and parameters concerning time segmentation affect the accuracy of this method. We discovered an approximate 5X increase in energy efficiency could be achieved with only a 5% decrease in accuracy. We also address how a system's sleep mode, in which the processor enters a low-power state and sensors are turned off, affects a wearable computing platform that does activity recognition. We discuss the energy trade-offs in each stage of the activity recognition process. We find that careful analysis of these parameters can result in great increases in energy efficiency if small compromises in overall accuracy can be tolerated. We call this the ``Great Compromise.'' We found a 6X increase in efficiency with a 7% decrease in accuracy. We then consider how wireless transmission of data affects the overall energy efficiency of a wearable computing platform. We find that design decisions such as feature calculations and grouping size have a great impact on the energy consumption of the system because of the amount of data that is stored and transmitted. For example, storing and transmitting vector-based features such as FFT or DCT do not compress the signal and would use more energy than storing and transmitting the raw signal. The effect of grouping size on energy consumption depends on the feature. For scalar features energy consumption is proportional in the inverse of grouping size, so it's reduced as grouping size goes up. For features that depend on the grouping size, such as FFT, energy increases with the logarithm of grouping size, so energy consumption increases slowly as grouping size increases. We find that compressing data through activity classification and transition detection significantly reduces energy consumption and that the energy consumed for the classification overhead is negligible compared to the energy savings from data compression. We provide mathematical models of energy usage and data generation, and test our ideas using a mobile computing platform, the Texas Instruments Chronos watch.
ContributorsBoyd, Jeffrey Michael (Author) / Sundaram, Hari (Thesis advisor) / Li, Baoxin (Thesis advisor) / Shrivastava, Aviral (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2014
Description
In the sport of competitive water skiing, the skill of a human boat driver can affect athletic performance. Driver influence is not necessarily inhibitive to skiers, however, it reduces the fairness and credibility of the sport overall. In response to the stated problem, this thesis proposes a vision-based real-time control system designed specifically for tournament waterski boats. The challenges addressed in this thesis include: one, the segmentation of floating objects in frame sequences captured by a moving camera, two, the identification of segmented objects which fit a predefined model, and three, the accurate and fast estimation of camera position and orientation from coplanar point correspondences. This thesis discusses current ideas and proposes new methods for the three challenges mentioned. In the end, a working prototype is produced.
ContributorsWalker, Collin (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Claveau, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
Splicing of digital images is a powerful form of tampering which transports regions of an image to create a composite image. When used as an artistic tool, this practice is harmless but when these composite images can be used to create political associations or are submitted as evidence in the judicial system they become more impactful. In these cases, distinction between an authentic image and a tampered image can become important.
Many proposed approaches to image splicing detection follow the model of extracting features from an authentic and tampered dataset and then classifying them using machine learning with the goal of optimizing classification accuracy. This thesis approaches splicing detection from a slightly different perspective by choosing a modern splicing detection framework and examining a variety of preprocessing techniques along with their effect on classification accuracy. Preprocessing techniques explored include Joint Picture Experts Group (JPEG) file type block line blurring, image level blurring, and image level sharpening. Attention is also paid to preprocessing images adaptively based on the amount of higher frequency content they contain.
This thesis also recognizes an identified problem with using a popular tampering evaluation dataset where a mismatch in the number of JPEG processing iterations between the authentic and tampered set creates an unfair statistical bias, leading to higher detection rates. Many modern approaches do not acknowledge this issue but this thesis applies a quality factor equalization technique to reduce this bias. Additionally, this thesis artificially inserts a mismatch in JPEG processing iterations by varying amounts to determine its effect on detection rates.
Many proposed approaches to image splicing detection follow the model of extracting features from an authentic and tampered dataset and then classifying them using machine learning with the goal of optimizing classification accuracy. This thesis approaches splicing detection from a slightly different perspective by choosing a modern splicing detection framework and examining a variety of preprocessing techniques along with their effect on classification accuracy. Preprocessing techniques explored include Joint Picture Experts Group (JPEG) file type block line blurring, image level blurring, and image level sharpening. Attention is also paid to preprocessing images adaptively based on the amount of higher frequency content they contain.
This thesis also recognizes an identified problem with using a popular tampering evaluation dataset where a mismatch in the number of JPEG processing iterations between the authentic and tampered set creates an unfair statistical bias, leading to higher detection rates. Many modern approaches do not acknowledge this issue but this thesis applies a quality factor equalization technique to reduce this bias. Additionally, this thesis artificially inserts a mismatch in JPEG processing iterations by varying amounts to determine its effect on detection rates.
ContributorsGubrud, Aaron (Author) / Li, Baoxin (Thesis advisor) / Candan, Kasim (Committee member) / Kadi, Zafer (Committee member) / Arizona State University (Publisher)
Created2015
Description
There has been extensive study of the target tracking problems in the recent years. Very little work has been done in the problem of continuous monitoring of all the mobile targets using the fewest number of mobile trackers, when the trajectories of all the targets are known in advance. Almost all the existing research discretized time (and/or space), or assume infinite tracker velocity. In this thesis, I consider the problem of covering (tracking) target nodes using a network of Unmanned Airborne Vehicles (UAV's) for the entire period of observation by adding the constraint of fixed velocity on the trackers and observing the targets in continuous time and space. I also show that the problem is NP-complete and provide algorithms for handling cases when targets are static and dynamic.
ContributorsVachhani, Harsh (Author) / Sen, Arunabha (Thesis advisor) / Saripalli, Srikanth (Committee member) / Shirazipour, Shahrzad (Committee member) / Arizona State University (Publisher)
Created2014
Description
Query Expansion is a functionality of search engines that suggest a set of related queries for a user issued keyword query. In case of exploratory or ambiguous keyword queries, the main goal of the user would be to identify and select a specific category of query results among different categorical options, in order to narrow down the search and reach the desired result. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. These empirical methods fail to cover all semantics of categories present in the query results. More importantly these methods do not consider the semantic relationship between the keywords featured in an expanded query. Contrary to a normal keyword search setting, these factors are non-trivial in an exploratory and ambiguous query setting where the user's precise discernment of different categories present in the query results is more important for making subsequent search decisions. In this thesis, I propose a new framework for keyword query expansion: generating a set of queries that correspond to the categorization of original query results, which is referred as Categorizing query expansion. Two approaches of algorithms are proposed, one that performs clustering as pre-processing step and then generates categorizing expanded queries based on the clusters. The other category of algorithms handle the case of generating quality expanded queries in the presence of imperfect clusters.
ContributorsNatarajan, Sivaramakrishnan (Author) / Chen, Yi (Thesis advisor) / Candan, Selcuk (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011