Physical Sciences and Mathematics Commons^™

Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li

Research Collection School Of Computing and Information Systems

A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the one-vs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks. Moreover, this framework makes active learning practically feasible for multi-class problems, while the one-vs.-all strategy cannot. Specifically, we employ a novel randomized query technique to prioritize the informative instances. This query technique based on the hybrid criterion of "margin" and "uncertainty" can achieve a comparable mistake bound …

Skewer: Sentiment Knowledge Extraction With Entity Recognition, Christopher James Wu

Master's Theses

The California state legislature introduces approximately 5,000 new bills each legislative session. While the legislative hearings are recorded on video, the recordings are not easily accessible to the public. The lack of official transcripts or summaries also increases the effort required to gain meaningful insight from those recordings. Therefore, the news media and the general population are largely oblivious to what transpires during legislative sessions.

Digital Democracy, a project started by the Cal Poly Institute for Advanced Technology and Public Policy, is an online platform created to bring transparency to the California legislature. It features a searchable database of state …

Collaborative Development Of A Small Business Emergency Planning Model, Arthur Henry Hendela

Dissertations

Small businesses, which are defined by the US Small Business Administration as entities with less than 500 employees, suffer interruptions from diverse risks such as financial events, legal situations, or severe storms exemplified by Hurricane Sandy. Proper preparations can help lessen the length of the interruption and put employees and owners back to work. Large corporations generally have large budgets available for planning, business continuity, and disaster recovery. Small businesses must decide which risks are the most important and how best to mitigate those risks using minimal resources.

This research uses a series of surveys followed by mathematical modeling to …

Mediating Chance Encounters Through Opportunistic Social Matching, Julia M. Mayer

Dissertations

Chance encounters, the unintended meeting between people unfamiliar with each other, serve as an important social lubricant helping people to create new social ties, such as making new friends or finding an activity, study or collaboration partner. Unfortunately, social barriers often prevent chance encounters in environments where people do not know each other and people have to rely on serendipity to meet or be introduced to interesting people around them. Little is known about the underlying dynamics of chance encounters and how systems could utilize contextual data to mediate chance encounters. This dissertation addresses this gap in research literature by …

Efficient Pair-Wise Similarity Computation Using Apache Spark, Parineetha Gandhi Tirumali

Master's Projects

Entity matching is the process of identifying different manifestations of the same real world entity. These entities can be referred to as objects(string) or data instances. These entities are in turn split over several databases or clusters based on the signatures of the entities. When entity matching algorithms are performed on these databases or clusters, there is a high possibility that a particular entity pair is compared more than once. The number of comparison for any two entities depend on the number of common signatures or keys they possess. This effects the performance of any entity matching algorithm. This paper …

Hybrid Similarity Function For Big Data Entity Matching With R-Swoosh, Vimal Chandra Gorijala

Master's Projects

Entity Matching (EM) is the problem of determining if two entities in a data set refer to the same real-world object. For example, it decides if two given mentions in the data, such as “Helen Hunt” and “H. M. Hunt”, refer to the same real-world entity by using different similarity functions. This problem plays a key role in information integration, natural language understanding, information processing on the World-Wide Web, and on the emerging Semantic Web. This project deals with the similarity functions and thresholds utilized in them to determine the similarity of the entities. The work contains two major parts: …

Library Writers Reward Project, Saravana Kumar Gajendran

Master's Projects

Open-source library development exploits the distributed intelligence of participants in Internet communities. Nowadays, contribution to the open-source community is fading [16] (Stackalytics, 2016) as there is not much recognition for library writers. They can start exploring ways to generate revenue as they actively contribute to the open-source community.

This project helps library writers to generate revenue in the form of bitcoins for their contribution. Our solution to generate revenue for library writers is to integrate bitcoin mining with existing JavaScript libraries, such as jQuery. More use of the library leads to more revenue for the library writers. It uses the …

The Mexican Water Forest: Benefits Of Using Remote Sensing Techniques To Assess Changes In Land Use And Land Cover, Maria F. Lopez Ornelas

Master's Projects and Capstones

In the past 30 years, anthropogenic activities like urbanization, agriculture, road fragmentation and deforestation have resulted in changes in the land use and land cover (LULC) in the Mexican Water Forest. Due to the important ecosystem services, and the natural resources this forest provides, in Mexico, it has become increasingly necessary to use new technologies and tools to support the planning, implementation and integration of forest management and conservation plans, as well as ecological and socioeconomic analysis of this ecosystem. Remote Sensing techniques and Geographic Information Systems (GIS) have been a true technological and methodological revolution in the acquisition, management …

Processing Posting Lists Using Opencl, Radha Kotipalli

Master's Projects

One of the main requirements of internet search engines is the ability to retrieve relevant results with faster response times. Yioop is an open source search engine designed and developed in PHP by Dr. Chris Pollett. The goal of this project is to explore the possibilities of enhancing the performance of Yioop by substituting resource-intensive existing PHP functions with C based native PHP extensions and the parallel data processing technology OpenCL. OpenCL leverages the Graphical Processing Unit (GPU) of a computer system for performance improvements.

Some of the critical functions in search engines are resource-intensive in terms of processing power, …

Exploring Data Mining Techniques For Tree Species Classification Using Co-Registered Lidar And Hyperspectral Data, Julia K. Marrs

Theses and Dissertations

NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level.

Analyzing Proactive Fraud Detection Software Tools And The Push For Quicker Solutions, Kerri Aiken

Economic Crime Forensics Capstones

This paper focuses on proactive fraud detection software tools and how these tools can help detect and prevent possible fraudulent schemes. In addition to relying on routine audits, companies are designing proactive methods that involve the inclusion of software tools to detect and deter instances of fraud and abuse. This paper discusses examples of companies using ACL and SAS software programs and how the software tools have positively changed their auditing systems.

Novelis Inc., an aluminum and recycling company, implemented ACL into their internal audit software system. Competitive Health Analytics (Division of Humana) implemented SAS in order to improve their …

A Study Of Three Paradigms For Storing Geospatial Data: Distributed-Cloud Model, Relational Database, And Indexed Flat File, Matthew A. Toups

University of New Orleans Theses and Dissertations

Geographic Information Systems (GIS) and related applications of geospatial data were once a small software niche; today nearly all Internet and mobile users utilize some sort of mapping or location-aware software. This widespread use reaches beyond mere consumption of geodata; projects like OpenStreetMap (OSM) represent a new source of geodata production, sometimes dubbed “Volunteered Geographic Information.” The volume of geodata produced and the user demand for geodata will surely continue to grow, so the storage and query techniques for geospatial data must evolve accordingly.

This thesis compares three paradigms for systems that manage vector data. Over the past few decades …

Identifying Relationships Between Scientific Datasets, Abdussalam Alawini

Dissertations and Theses

Scientific datasets associated with a research project can proliferate over time as a result of activities such as sharing datasets among collaborators, extending existing datasets with new measurements, and extracting subsets of data for analysis. As such datasets begin to accumulate, it becomes increasingly difficult for a scientist to keep track of their derivation history, which complicates data sharing, provenance tracking, and scientific reproducibility. Understanding what relationships exist between datasets can help scientists recall their original derivation history. For instance, if dataset A is contained in dataset B, then the connection between A and B could be that A was …

Cest: City Event Summarization Using Twitter, Deepa Mallela

Computer Science Graduate Projects and Theses

Twitter, with 288 million active users, has become the most popular platform for continuous real-time discussions. This leads to huge amounts of information related to the real-world, which has attracted researchers from both academia and industry. Event detection on Twitter has gained attention as one of the most popular domains of interest within the research community. Unfortunately, existing event detection methodologies have yet to fully explore Twitter metadata and instead rely solely on identifying events based on prior information or focus on events that belong to specific categories. Given the heavy volume of tweets that discuss events, summarization techniques can …

Data Partitioning Methods To Process Queries On Encrypted Databases On The Cloud, Osama M. Omran

Graduate Theses and Dissertations

Many features and advantages have been brought to organizations and computer users by Cloud computing. It allows different service providers to distribute many applications and services in an economical way. Consequently, many users and companies have begun using cloud computing. However, the users and companies are concerned about their data when data are stored and managed in the Cloud or outsourcing servers. The private data of individual users and companies is stored and managed by the service providers on the Cloud, which offers services on the other side of the Internet in terms of its users, and consequently results in …

Exploring Interactive Survivorship Plans: Patient Perceived Value, Acceptance And Usability Evaluation Of An Online Breast Cancer Survivorship Tool, Akshat Kapoor

Theses and Dissertations

Introduction: Having recently been discharged from the hospital, several breast cancer survivors find themselves unable to adjust to the transition and take charge of their own health, away from the confines of the hospital.

With the rapid advancement in treatment methods and techniques, the rate of breast cancer survivors has grown exponentially. It is crucial to provide adequate means to support cancer survivors in an active manner. This includes regular monitoring for recurrence (or occurrence of new cancers), handling any related and non-related comorbidities, provide recommendations for preventive care as well as dealing with any long term side effects from …

Three Essays On The Effects Of Appraisal, Cultural, Emotional, And Cognitive Factors On Information Technologies Acceptance And Use, Chun-Lung Huang

Theses and Dissertations

In essay 1, we propose a model, which utilized Lazarus and Folkman’s Cognitive Appraisal Theory of Emotion or Appraisal Theory (1984, 1987) as a structural foundation to lay out the nomological relationships among a person’s personal, cognitive, and emotional factors in predicting technology use behaviors. Emotion, likes many social and psychological factors, is challenging to give a full-consensus definition, and has been treated as a polar counterpart of cognition. Lazarus and Folkman’s Appraisal Theory suggested that when a person is facing a (disruptive) event, he or she appraises the possible outcomes (we suppose that appraising is a form of cognitive …

Using Abstractions To Solve Opportunistic Crime Security Games At Scale, Chao Zhang, Victor Bucarey, Ayan Mukhopadhyay, Arunesh Sinha, Qian. Yundi, Yevgeniy Vorobeychik, Milind Tambe

Research Collection School Of Computing and Information Systems

In this paper, we aim to deter urban crime by recommending optimal police patrol strategies against opportunistic criminals in large scale urban problems. While previous work has tried to learn criminals' behavior from real world data and generate patrol strategies against opportunistic crimes, it cannot scale up to large-scale urban problems. Our first contribution is a game abstraction framework that can handle opportunistic crimes in large-scale urban areas. In this game abstraction framework, we model the interaction between officers and opportunistic criminals as a game with discrete targets. By merging similar targets, we obtain an abstract game with fewer total …

Temporal Kernel Descriptors For Learning With Time-Sensitive Patterns, Doyen Sahoo, Abhishek Sharma, Hoi, Steven C. H., Peilin Zhao

Research Collection School Of Computing and Information Systems

Detecting temporal patterns is one of the most prevalent challenges while mining data. Often, timestamps or information about when certain instances or events occurred can provide us with critical information to recognize temporal patterns. Unfortunately, most existing techniques are not able to fully extract useful temporal information based on the time (especially at different resolutions of time). They miss out on 3 crucial factors: (i) they do not distinguish between timestamp features (which have cyclical or periodic properties) and ordinary features; (ii) they are not able to detect patterns exhibited at different resolutions of time (e.g. different patterns at the …

A Key-Insulated Cp-Abe With Key Exposure Accountability For Secure Data Sharing In The Cloud, Hanshu Hong, Zhixin Sun, Ximeng Liu

Research Collection School Of Computing and Information Systems

ABE has become an effective tool for data protection in cloud computing. However, since users possessing the same attributes share the same private keys, there exist some malicious users exposing their private keys deliberately for illegal data sharing without being detected, which will threaten the security of the cloud system. Such issues remain in many current ABE schemes since the private keys are rarely associated with any user specific identifiers. In order to achieve user accountability as well as provide key exposure protection, in this paper, we propose a key-insulated ciphertext policy attribute based encryption with key exposure accountability (KI-CPABE-KEA). …

Joint Search By Social And Spatial Proximity [Extended Abstract], Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

The diffusion of social networks introduces new challengesand opportunities for advanced services, especially so with their ongoingaddition of location-based features. We show how applications like company andfriend recommendation could significantly benefit from incorporating social andspatial proximity, and study a query type that captures these twofold semantics.We develop highly scalable algorithms for its processing, and use real socialnetwork data to empirically verify their efficiency and efficacy.

Health In Your Hand: Assessment Of Clinicians’ Readiness To Adopt Mhealth Into Rural Patient Care, Bryan Weichelt

Theses and Dissertations

Introduction: Technology is as much rural as it is urban, but mobile health (mHealth) could have a unique impact on health and quality of life for rural populations. The adoption of mobile technologies has soared in recent decades leading to new possibilities for mHealth use. This project considers the impact of these technologies on rural populations. Specifically, it is focused on assessing the barriers of physicians and healthcare organizations to adopt mHealth into their care plans. Gaps in knowledge exist in assessing organizational readiness for mHealth adoption, the use of patient-reported data, and the impact on rural healthcare. This project …

An Autonomous Agent For Learning Spatiotemporal Models Of Human Daily Activities, Shan Gao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Activities of Daily Living (ADLs) refer to activities performed by individuals on a daily basis. As ADLs are indicatives of a person’s habits, lifestyle, and well being, learning the knowledge of people’s ADL routine has great values in the healthcare and consumer domains. In this paper, we propose an autonomous agent, named Agent for Spatia-Temporal Activity Pattern Modeling (ASTAPM), being able to learn spatial and temporal patterns of human ADLs. ASTAPM utilises a self-organizing neural network model named Spatiotemporal - Adaptive Resonance Theory (ST-ART). ST-ART is capable of integrating multimodal contextual information, involving the time and space, wherein the ADL …

Learning Adversary Behavior In Security Games: A Pac Model Perspective, Arunesh Sinha, Debarun Kar, Milind Tambe

Research Collection School Of Computing and Information Systems

Recent applications of Stackelberg Security Games (SSG), from wildlife crime to urban crime, have employed machine learning tools to learn and predict adversary behavior using available data about defender-adversary interactions. Given these recent developments, this paper commits to an approach of directly learning the response function of the adversary. Using the PAC model, this paper lays a firm theoretical foundation for learning in SSGs (e.g., theoretically answer questions about the numbers of samples required to learn adversary behavior) and provides utility guarantees when the learned adversary model is used to plan the defender's strategy. The paper also aims to answer …

Fast Weighted Histograms For Bilateral Filtering And Nearest Neighbor Searching, Shengfeng He, Qingxiong Yang, Rynson W. H. Lau, Ming-Hsuan Yang

Research Collection School Of Computing and Information Systems

The locality sensitive histogram (LSH) injects spatial information into the local histogram in an efficient manner, and has been demonstrated to be very effective for visual tracking. In this paper, we explore the application of this efficient histogram in two important problems. We first extend the LSH to linear time bilateral filtering, and then propose a new type of histogram for efficiently computing edge-preserving nearest neighbor fields (NNFs). While the existing histogram-based bilateral filtering methods are the state of the art for efficient grayscale image processing, they are limited to box spatial filter kernels only. In our first application, we …

Modeling Human-Like Non-Rationality For Social Agents, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann

Research Collection School Of Computing and Information Systems

Humans are not rational beings. Deviations from rationality in human thinking are currently well documented [25] as non-reducible to rational pursuit of egoistic benefit or its occasional distortion with temporary emotional excitation, as it is often assumed. This occurs not only outside conceptual reasoning or rational goal realization but also subconsciously and often in certainty that they did not and could not take place ‘in my case’. Non-rationality can no longer be perceived as a rare affective abnormality in otherwise rational thinking, but as a systemic, permanent quality, ’a design feature’ of human cognition. While social psychology has systematically addressed …

Context-Aware Advertisement Recommendation For High-Speed Social News Feeding, Yuchen Li, Dongxiang Zhang, Ziquan Lan, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Social media advertising is a multi-billion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for each user based on their personal interests. However, as user interests often evolve slowly, the user may end up receiving repetitive ads. In this paper, we propose a context-aware advertising framework that takes into account the relatively static personal interests as well as the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we first propose …

Semantic Proximity Search On Graphs With Metagraph-Based Learning, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Kevin Chen-Chuan Chang, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Given ubiquitous graph data such as the Web and social networks, proximity search on graphs has been an active research topic. The task boils down to measuring the proximity between two nodes on a graph. Although most earlier studies deal with homogeneous or bipartite graphs only, many real-world graphs are heterogeneous with objects of various types, giving rise to different semantic classes of proximity. For instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct classes of proximity. Thus, it becomes inadequate to only measure a …

Learning To Query: Focused Web Page Harvesting For Entity Aspects, Yuan Fang, Vincent W. Zheng, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

As the Web hosts rich information about real-world entities, our information quests become increasingly entity centric. In this paper, we study the problem of focused harvesting of Web pages for entity aspects, to support downstream applications such as business analytics and building a vertical portal. Given that search engines are the de facto gateways to assess information on the Web, we recognize the essence of our problem as Learning to Query (L2Q) - to intelligently select queries so that we can harvest pages, via a search engine, focused on an entity aspect of interest. Thus, it is crucial to quantify …

Efficient Verifiable Computation Of Linear And Quadratic Functions Over Encrypted Data, Ngoc Hieu Tran, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

In data outsourcing, a client stores a large amount of data on an untrusted server; subsequently, the client can request the server to compute a function on any subset of the data. This setting naturally leads to two security requirements: confidentiality of input data, and authenticity of computations. Existing approaches that satisfy both requirements simultaneously are built on fully homomorphic encryption, which involves expensive computation on the server and client and hence is impractical. In this paper, we propose two verifiable homomorphic encryption schemes that do not rely on fully homomorphic encryption. The first is a simple and efficient scheme …