Computer Sciences Commons^™

Signal Structure For A Class Of Nonlinear Dynamic Systems, Meilan Jin

Theses and Dissertations

The signal structure is a partial structure representation for dynamic systems. It characterizes the causal relationship between manifest variables and is depicted in a weighted graph, where the weights are dynamic operators. Earlier work has defined signal structure for linear time-invariant systems through dynamical structure function. This thesis focuses on the search for the signal structure of nonlinear systems and proves that the signal structure reduces to the linear definition when the systems are linear. Specifically, this work: (1) Defines the complete computational structure for nonlinear systems. (2) Provides a process to find the complete computational structure given a state …

Fm Radio Signal Propagation Evaluation And Creating Statistical Models For Signal Strength Prediction In Differing Topographic Environments, Timothy Land

Electronic Theses and Dissertations

Radio wave signal strength and associated propagation models are rarely analyzed across individual geographic provinces. This study evaluates the effectiveness of the Radio Mobile model to predict radio wave signal strength in the Blue Ridge and Valley and Ridge physiographic provinces. A spectrum analyzer was used on 19 FM transmitters to determine model accuracy. Statistical analysis determined the significance between different terrain factors and signal strength. Field signal strength was found to be related to test site elevation, transmitter azimuth, elevation angle, transmitter elevation, path loss, and distance. Using 76 signal strength receiver sites, Ordinary Least Square regression models predicted …

Distributed Coordination For Autonomous Guided Vehicles In Multi-Agent Systems With Shared Resources, Atefeh Mahdavi

Theses and Dissertations

The decentralized path planning technique proposed in this thesis solves major challenges in the domain of MAS. These challenges are trajectory planning and collision avoidance. Generally, in a shared infrastructure where several agents aim to use limited capacity resources, finding a set of optimal and conflict-free paths for each single agent is the most critical part. The purpose of this research is designing a decentralized framework to coordinate the behavior of a number of agents in dynamic environments where continual planning and scheduling are required. In this research, a set of tasks will be assigned to the agents. Based on …

Learning Latent Characteristics Of Locations Using Location-Based Social Networking Data, Thanh Nam Doan

Dissertations and Theses Collection (Open Access)

This dissertation addresses the modeling of latent characteristics of locations to describe the mobility of users of location-based social networking platforms. With many users signing up location-based social networking platforms to share their daily activities, these platforms become a gold mine for researchers to study human visitation behavior and location characteristics. Modeling such visitation behavior and location characteristics can benefit many use- ful applications such as urban planning and location-aware recommender sys- tems. In this dissertation, we focus on modeling two latent characteristics of locations, namely area attraction and neighborhood competition effects using location-based social network data. Our literature survey …

Exploring Relationship Between Indistinguishability-Based And Unpredictability-Based Rfid Privacy Models, Anjia Yang, Yunhui Zhuang, Jian Weng, Gerhard Hancke, Duncan S. Wong, Guomin Yang

Research Collection School Of Computing and Information Systems

A comprehensive privacy model plays a vital role in the design of privacy-preserving RFID authentication protocols. Among various existing RFID privacy models, indistinguishability-based (ind-privacy) and unpredictability-based (unp-privacy) privacy models are the two main categories. Unp*-privacy, a variant of unp-privacy has been claimed to be stronger than ind-privacy. In this paper, we focus on studying RFID privacy models and have three-fold contributions. We start with revisiting unp*-privacy model and figure out a limitation of it by giving a new practical traceability attack which can be proved secure under unp*-privacy model. To capture this kind of attack, we improve unp*-privacy model to …

Effect Of Probable And Guaranteed Monetary Value Gains And Losses On Cybersecurity Behavior Of Users, S. Ravindran, Fiona Fui-Hoon Nah, M. Cheng

Research Collection School Of Computing and Information Systems

The objective of this research is to examine users’ cybersecurity behavior in monetary gain and loss scenarios. Using Prospect Theory, we hypothesize that users are more likely to engage in risky cybersecurity behavior to avoid monetary losses than to benefit from monetary gains. We also hypothesize that guaranteed gains have a greater effect on a user’s risk-taking behavior than potential gains, and potential losses have a greater effect on a user’s risk-taking behavior than guaranteed losses. An experimental study is proposed to test the research hypotheses.

Factors Influencing The Adoption Of Smart Wearable Devices, Apurva Adapa, Fiona Fui-Hoon Nah, Richard H. Hall, Keng Siau, Samuel Smith

Research Collection School Of Computing and Information Systems

This article examined factors associated with the adoption of smart wearable devices. More specifically, this research explored the contributing and inhibiting factors that influence the adoption of wearable devices through in-depth interviews. The laddering approach was used in the interviews to identify not only the factors but also their relationships to underlying values. The wearable devices examined were a Smart Glass (Google Glass) and a Smart Watch (Sony Smart Watch 3). Two user groups, college students and working professionals, participated in the study. After the participants had the opportunity to try out each of the two devices, the factors that …

Evidence Aggregation For Answer Re-Ranking In Open-Domain Question Answering, Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell

Research Collection School Of Computing and Information Systems

A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from …

Big Data For Climate Change Actions And The Paradox Of Citizen Informedness, Kustini Lim-Wavde, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Advanced sensor technology, social media, and other information technologies have provided us with “big data” on climate change. Due to the World Meteorological Organization’s Global Climate Observing System, climate observations and records, as well as discussions on climate-related concerns such as measurement of air temperature, are widely available now. The United Nations’ Global Pulse visualises public engagement on climate change globally, with data such as the volume of climate-related tweets. Big data, data analytics, and the sharing of scientific results in the popular press have created, as a result, an unprecedented level of citizen informedness—the degree to which citizens have …

Analyzing Requirements And Traceability Information To Improve Bug Localization, Michael Rath, David Lo, Patrick Mader

Research Collection School Of Computing and Information Systems

Locating bugs in industry-size software systems is time consuming and challenging. An automated approach for assisting the process of tracing from bug descriptions to relevant source code benefits developers. A large body of previous work aims to address this problem and demonstrates considerable achievements. Most existing approaches focus on the key challenge of improving techniques based on textual similarity to identify relevant files. However, there exists a lexical gap between the natural language used to formulate bug reports and the formal source code and its comments. To bridge this gap, state-of-the-art approaches contain a component for analyzing bug history information …

Understanding The Effects Of Taxi Ride-Sharing: A Case Study Of Singapore, Yazhe Wang, Baihua Zheng, Ee Peng Lim

Research Collection School Of Computing and Information Systems

This paper studies the effects of ride-sharing among those calling on taxis in Singapore for similar origin and destination pairs at nearly the same time of day. It proposes a simple yet practical framework for taxi ride-sharing and scheduling, to reduce waiting times and travel times during peak demand periods. The solution method helps taxi users save money while helping taxi drivers serve multiple requests per day, thus increasing their earnings. A comprehensive simulation study is conducted, based on real taxi booking data for the city of Singapore, to evaluate the effect of various factors of the ride-sharing practice, e.g., …

A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau

Research Collection School Of Computing and Information Systems

The age of artificial intelligence is here! Artificial Intelligence, robotics, machine learning, and automation are impacting the field of marketing and sales in an unprecedented way. In this study, the qualitative research methodology will be used to better understand the revolution and evolution of marketing and sales field in the AI age. Multiple case studies will be performed in various marketing and sales units in different organizations. This research is of value to both academics and practitioners as it aims to provide a detailed analysis and documentation of the changes in marketing and sales functionalities and job markets as AI …

Breathing-Based Authentication On Resource-Constrained Iot Devices Using Recurrent Neural Networks, Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee

Research Collection School Of Computing and Information Systems

Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor smart homes. However, the feasibility and performance ofthese inferences on resource-constrained devices remain largelyunexplored. The authors compare traditional machine-learningmodels with deep-learning RNN models for an end-to-endauthentication system based on breathing acoustics.

Discovering Hidden Topical Hubs And Authorities In Online Social Networks, Roy Ka-Wei Lee, Tuan-Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combines the two process …

Wisdom In Sum Of Parts: Multi-Platform Activity Prediction In Social Collaborative Sites, Roy Ka-Wei Lee, David Lo

Research Collection School Of Computing and Information Systems

In this paper, we proposed a novel framework which uses user interests inferred from activities (a.k.a., activity interests) in multiple social collaborative platforms to predict users’ platform activities. Included in the framework are two prediction approaches: (i) direct platform activity prediction, which predicts a user’s activities in a platform using his or her activity interests from the same platform (e.g., predict if a user answers a given Stack Overflow question using the user’s interests inferred from his or her prior answer and favorite activities in Stack Overflow), and (ii) cross-platform activity prediction, which predicts a user’s activities in a platform …

Algorithmic Trading For Cryptocurrencies, Michael Ward

Undergraduate Honors Capstone Projects

This project takes several common strategies for algorithmic stock trading and tests them on the cryptocurrency market. The three strategies used are moving average crossover, mean reversion, and pairs trading. Data was collected every five minutes for the top one hundred cryptocurrencies between October 5, 2017, and January 24, 2018. Due to the high volatility of the market, the data includes various market situations. Three noted situations are a rising market, falling market, and relatively stable market. The three strategies were modified to optimally follow each market situation. Modifications include adjusting parameters used in each strategy as well as mixing …

Glocalizing The Composition Classroom With Google Apps For Education, Daniel L. Hocutt, Maury Elizabeth Brown

School of Professional and Continuing Studies Faculty Publications

Composing practices in a digitally networked world are inherently intercultural, and situate local needs and constraints within global opportunities and concerns. Global technologies like Google Apps for Education (GAFE) allow students to compose collaboratively across place and time; to do so, students and teachers must navigate a complex local network of institutional policy, learning outcomes, situational needs, and composing practices while also being aware of the global implications of using the interface to compose, review, edit, and share with others. The chapter describes using GAFE in locally situated composition classes. Using such technologies requires a focus on glocalization and an …

Bayesian Network Modeling And Inference Of Gwas Catalog, Qiuping Pan

Graduate Theses and Dissertations

Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. The Bayesian network has been proposed as a powerful tool for modeling single-nucleotide polymorphism (SNP)-trait associations due to its advantage in addressing the high computational complex and high dimensional problems. Most current works learn the interactions among genotypes and phenotypes from the raw genotype data. However, due to the privacy issue, genotype information is sensitive and should be handled by complying with specific restrictions. In this work, we aim to build Bayesian networks from publicly released GWAS statistics to explicitly reveal the conditional dependency between SNPs and …

Disruptive Technology: Do Robots Want Your Job?, Martin Ford

Promotional Materials

Keynote talk with Martin Ford, author of Rise of the Robots. Part of the “Deep Humanities,” One-Day Symposium: FrankenSTEM? Technology Ethics in Silicon Valley, organized by Dr. Revathi Krishnaswamy & Dr. Katherine D. Harris, Department of English and Comparative Literature, San Jose State University.

May 1, 2018, 7pm, The Tech Museum of Innovation, San Jose.

Will Artificial Intelligence Have Free-Will?, Guadalupe Rodriguez

Frankenstein @ 200: Student Posters

Will Artificial Intelligence have free will the way the Creature did?

Analysis Of 2016-17 Major League Soccer Season Data Using Poisson Regression With R, Ian D. Campbell

Undergraduate Theses and Capstone Projects

To the outside observer, soccer is chaotic with no given pattern or scheme to follow, a random conglomeration of passes and shots that go on for 90 minutes. Yet, what if there was a pattern to the chaos, or a way to describe the events that occur in the game quantifiably. Sports statistics is a critical part of baseball and a variety of other of today’s sports, but we see very little statistics and data analysis done on soccer. Of this research, there has been looks into the effect of possession time on the outcome of a game, the difference …

Finding All Nearest Neighbors With A Single Graph Traversal, Yixin Xu, Qi Jianzhong, Borovica‐Gajic Renata, Kulik Lars

Research Collection School Of Computing and Information Systems

Finding the nearest neighbor is a key operation in data analysis and mining. An important variant of nearest neighbor query is the all nearest neighbor (ANN) query, which reports all nearest neighbors for a given set of query objects. Existing studies on ANN queries have focused on Euclidean space. Given the widespread occurrence of spatial networks in urban environments, we study the ANN query in spatial network settings. An example of an ANN query on spatial networks is finding the nearest car parks for all cars currently on the road. We propose VIVET, an index-based algorithm to efficiently process ANN …

Artificial Intelligence: A Study On Governance, Policies, And Regulations, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) is displacing jobs and creating an upheaval in the world. It will change the way we work and the way we live. What should be the AI governance, policies, and regulations? How can AI governance, policies, and regulations mitigate and alleviate the negative aspects of AI advancement? How will AI governance, policies, and regulations impact the future of work and the future of humanity? This longitudinal multiple case studies research will study the evolution and revolution of AI governance, policies, and regulations, and how governance, policies and regulations impact AI advancement and are impacted by AI advancement. …

Low Latency Intrusion Detection In Smart Grids, Israel Zairi Akingeneye

Graduate Theses and Dissertations

The transformation of traditional power grids into smart grids has seen more new technologies such as communication networks and smart meters (sensors) being integrated into the physical infrastructure of the power grids. However, these technologies pose new vulnerabilities to the cybersecurity of power grids as malicious attacks can be launched by adversaries to attack the smart meters and modify the measurement data collected by these meters. If not timely detected and removed, these attacks may lead to inaccurate system state estimation, which is critical to the system operators for control decisions such as economic dispatch and other related functions.

This …

Multi-Stop Routing Optimization: A Genetic Algorithm Approach, Abbas Hommadi

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In this research, we investigate and propose new operators to improve Genetic Algorithm’s performance to solve the multi-stop routing problem. In a multi-stop route, a user starts at point x, visits all destinations exactly once, and then return to the same starting point. In this thesis, we are interested in two types of this problem. The first type is when the distance among destinations is fixed. In this case, it is called static traveling salesman problem. The second type is when the cost among destinations is affected by traffic congestion. Thus, the time among destinations changes during the day. In …

Expressive Query Over Outsourced Encrypted Data, Yang Yang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Data security and privacy concerns in cloud storage services compel data owners to encrypt their sensitive data before outsourcing. Standard encryption systems, however, hinder users from issuing search queries on encrypted data. Though various systems for search over encrypted data have been proposed in the literature, existing systems use different encrypted index structures to conduct search on different search query patterns and hence are not compatible with each other. In this paper, we propose a query over encrypted data system which supports expressive search query patterns, such as single/conjunctive keyword query, range query, boolean query and mixed boolean query, all …

Finding Small-Bowel Lesions: Challenges In Endoscopy-Image-Based Learning Systems, Jungmo Ahn, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee, Jeonggil Ko

Research Collection School Of Computing and Information Systems

Capsule endoscopy identifies damaged areas in a patient's small intestine but often outputs poor-quality images or misses lesions, leading to either misdiagnosis or repetition of the lengthy procedure. The authors propose applying deep-learning models to automatically process the captured images and identify lesions in real time, enabling the capsule to take additional images of a specific location, adjust its focus level, or improve image quality. The authors also describe the technical challenges in realizing a viable automated capsule-endoscopy system.

Recommending Frequently Encountered Bugs, Yun Zhang, David Lo, Xin Xia, Jing Jiang, Jianling Sun

Research Collection School Of Computing and Information Systems

Developers introduce bugs during software development which reduce software reliability. Many of these bugs are commonly occurring and have been experienced by many other developers. Informingdevelopers, especially novice ones, about commonly occurring bugsin a domain of interest (e.g., Java), can help developers comprehendprogram and avoid similar bugs in the future. Unfortunately, information about commonly occurring bugs are not readily available. Toaddress this need, we propose a novel approach named RFEB whichrecommends frequently encountered bugs (FEBugs) that may affectmany other developers. RFEB analyzes Stack Overflow which is thelargest software engineering-specific Q&A communities. Amongthe plenty of questions posted in Stack Overflow, many …

Deep Code Comment Generation, Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin

Research Collection School Of Computing and Information Systems

During software maintenance, code comments help developerscomprehend programs and reduce additional time spent on readingand navigating source code. Unfortunately, these comments areoften mismatched, missing or outdated in the software projects.Developers have to infer the functionality from the source code.This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generatedcomments aim to help developers understand the functionalityof Java methods. DeepCom applies Natural Language Processing(NLP) techniques to learn from a large code corpus and generatescomments from learned features. We use a deep neural networkthat analyzes structural information of Java methods for bettercomments generation. We conduct …

Doas: Efficient Data Owner Authorized Search Over Encrypted Cloud Data, Yibin Miao, Jianfeng Ma, Ximeng Liu, Zhiquan Liu, Junwei Zhang, Fushan Wei

Research Collection School Of Computing and Information Systems

Data outsourcing service can shift the local data storage and maintenance to cloud service provider (CSP) to ease the burden from data owner, but it brings the data security threats as CSP is always considered to honest-but-curious. Therefore, searchable encryption (SE) technique which allows cloud clients (including data owner and data user) to securely search over ciphertext through keywords and selectively retrieve files of interest is of prime importance. However, in practice, data user’s access permission always dynamically varies with data owner’s preferences. Moreover, existing SE schemes which are based on attribute-based encryption (ABE) incur heavy computational burden through attribution …