Numerical Analysis and Scientific Computing Commons^™

A Novel Representation And Compression For Queries On Trajectories In Road Networks, Xiaochun Yang, Bin Wang, Kai Yang, Chengfei Liu, Baihua Zheng

Research Collection School Of Computing and Information Systems

Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road mbox{networks}, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that the …

Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems

Computer Science and Engineering Theses and Dissertations

Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …

Octopus: An Online Topic-Aware Influence Analysis System For Social Networks, Ju Fan, Jiarong Qiu, Yuchen Li, Qingfei Meng, Dongxiang Zhang, Guoliang Li, Kian-Lee Tan, Xiaoyong Du

Research Collection School Of Computing and Information Systems

The wide adoption of social networks has brought a new demand on influence analysis. This paper presents OCTOPUS that offers social network users and analysts valuable insights through topic-aware social influence analysis services. OCTOPUS has the following novel features. First, OCTOPUS provides a user-friendly interface that allows users to employ simple and easy-to-use keywords to perform influence analysis. Second, OCTOPUS provides three powerful keyword-based topic-aware influence analysis tools: keyword-based influential user discovery, personalized influential keywords suggestion, and interactive influential paths exploration. These tools can not only discover influential users, but also provide insights on how the users influence the network. …

Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors Of Food Journalers, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek

Research Collection School Of Computing and Information Systems

Past research has shown the benefits of food journaling in promoting mindful eating and healthier food choices. However, the links between journaling and healthy eating have not been thoroughly examined. Beyond caloric restriction, do journalers consistently and sufficiently consume healthful diets? How different are their eating habits compared to those of average consumers who tend to be less conscious about health? In this study, we analyze the healthy eating behaviors of active food journalers using data from MyFitnessPal. Surprisingly, our findings show that food journalers do not eat as healthily as they should despite their proclivity to health eating and …

Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Which venue is a tweet posted from? We call this a fine-grained geolocation problem. Given an observed tweet, the task is to infer its discrete posting venue, e.g., a specific restaurant. This recovers the venue context and differs from prior work, which geolocats tweets to location coordinates or cities/neighborhoods. First, we conduct empirical analysis to uncover venue and user characteristics for improving geolocation. For venues, we observe spatial homophily, in which venues near each other have more similar tweet content (i.e., text representations) compared to venues further apart. For users, we observe that they are spatially focused and more likely …

Social Network Monitoring For Bursty Cascade Detection, Wei Xie, Feida Zhu, Jing Xiao, Jianzong Wang

Research Collection School Of Computing and Information Systems

Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks brings value to a wide range of real-life applications, such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this article, we …

A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~$2/h, and only 4% earned more than $7.25/h. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, …

Predicting Episodes Of Non-Conformant Mobility In Indoor Environments, Kasthuri Jayarajah, Archan Misra

Research Collection School Of Computing and Information Systems

Traditional mobility prediction literature focuses primarily on improved methods to extract latent patterns from individual-specific movement data. When such predictions are incorrect, we ascribe it to 'random' or 'unpredictable' changes in a user's movement behavior. Our hypothesis, however, is that such apparently-random deviations from daily movement patterns can, in fact, of ten be anticipated. In particular, we develop a methodology for predicting Likelihood of Future Non-Conformance (LFNC), based on two central hypotheses: (a) the likelihood of future deviations in movement behavior is positively correlated to the intensity of such trajectory deviations observed in the user's recent past, and (b) the …

Feature Engineering For Machine Learning And Data Analytics, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

This chapter provides an introduction on feature generation and engineering for software analytics. Specifically, we show how domain-specifc features can be designed and used to automate three software engineering tasks: (1) detecting defective software modules (defect prediction), (2) identifying crashing mobile app release (crash release prediction), and (3) predicting who will leave a software team (developer turnover prediction). For each of the three tasks, different sets of features are extracted from a diverse set of software artifacts, and used to build predictive models.

Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak

Research Collection School Of Computing and Information Systems

To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using eye-tracking and talk-aloud data collection. We coded the verbal data into classes of informativeness and confusion and correlated it with fixations and durations on the Areas of Interests recorded by the eye-tracking device. We used various analysis techniques, including Mann-Whitney, regression, and Levenshtein distance, to investigate how confused users differed …

Mining Sandboxes: Are We There Yet?, Lingfeng Bao, Tien Duy B. Le, David Lo

Research Collection School Of Computing and Information Systems

The popularity of Android platform on mobile devices has attracted much attention from many developers and researchers, as well as malware writers. Recently, Jamrozik et al. proposed a technique to secure Android applications referred to as mining sandboxes. They used an automated test case generation technique to explore the behavior of the app under test and then extracted a set of sensitive APIs that were called. Based on the extracted sensitive APIs, they built a sandbox that can block access to APIs not used during testing. However, they only evaluated the proposed technique with benign apps but not investigated whether …

Automating Data Analysis For Two-Dimensional Gas Chromatography/Time-Of-Flight Mass Spectrometry Non-Targeted Analysis Of Comparative Samples, Ivan A. Titaley, O. Maduka Ogba, Leah Chibwe, Eunha Hoh, Paul H.-Y. Cheong, Staci L. Massey Simonich

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Non-targeted analysis of environmental samples, using comprehensive two‐dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO^® ChromaTOF^® software and facilitates selection of analytes of interest based on peak area …

Compressive Representation For Device-Free Activity Recognition With Passive Rfid Signal Strength, Lina Yao, Quan Z. Sheng, Xue Li, Tao Gu, Mingkui Tan, Xianzhi Wang, Sen Wang, Wenjie Ruan

Research Collection School Of Computing and Information Systems

Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system …

Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis, which is often obtained by averaging the series of hypotheses over all the …

Unified Locally Linear Classifiers With Diversity-Promoting Anchor Points, Chenghao Liu, Teng Zhang, Peilin Zhao, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Locally Linear Support Vector Machine (LLSVM) has been actively used in classification tasks due to its capability of classifying nonlinear patterns. However, existing LLSVM suffers from two drawbacks: (1) a particular and appropriate regularization for LLSVM has not yet been addressed; (2) it usually adopts a three-stage learning scheme composed of learning anchor points by clustering, learning local coding coordinates by a predefined coding scheme, and finally learning for training classifiers. We argue that this decoupled approaches oversimplifies the original optimization problem, resulting in a large deviation due to the disparate purpose of each step. To address the first issue, …

Predicting The Next Us President By Simulating The Electoral College, Boyan Kostadinov

Publications and Research

We develop a simulation model for predicting the outcome of the US Presidential election based on simulating the distribution of the Electoral College. The simulation model has two parts: (a) estimating the probabilities for a given candidate to win each state and DC, based on state polls, and (b) estimating the probability that a given candidate will win at least 270 electoral votes, and thus win the White House. All simulations are coded using the high-level, open-source programming language R. One of the goals of this paper is to promote computational thinking in any STEM field by illustrating how probabilistic …

The Accuracy, Fairness, And Limits Of Predicting Recidivism, Julie Dressel, Hany Farid

Dartmouth Scholarship

Algorithms for predicting recidivism are commonly used to assess a criminal defendant’s likelihood of committing a crime. These predictions are used in pretrial, parole, and sentencing decisions. Proponents of these systems argue that big data and advanced machine learning make these analyses more accurate and less biased than humans. We show, however, that the widely used commercial risk assessment software COMPAS is no more accurate or fair than predictions made by people with little or no criminal justice expertise. We further show that a simple linear predictor provided with only two features is nearly equivalent to COMPAS with its 137 …

Expression Of The Microrna-143/145 Cluster Is Decreased In Hepatitis B Virus-Associated Hepatocellular Carcinoma And May Serve As A Biomarker For Tumorigenesis In Patients With Chronic Hepatitis B, Qi Zhao, Xiangfei Sun, Chao Liu, Tao Li, Juan Cui, Chengyong Qin

School of Computing: Faculty Publications

The aims of the present study were to identify the expression profile of microRNA (miR)‑143/145 in hepatitis B virus (HBV)‑associated hepatocellular carcinoma (HCC), explore its association with prognosis and investigate whether the serum miR‑143/145 expression levels may serve as a diagnostic indicator of HBV‑associated HCC. The microRNA (miRNA) chromatin immunoprecipitation dataset was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus databases, and analyzed using the Wilcoxon signed‑rank test. It was observed that the expression of miR‑143 and miR‑145 was decreased 1.5‑fold in HBV‑associated HCC samples compared with non‑tumor tissue in the TCGA and the GSE22058 datasets …

Developing A Cyberterrorism Policy: Incorporating Individual Values, Osama Bassam J. Rabie

Theses and Dissertations

Preventing cyberterrorism is becoming a necessity for individuals, organizations, and governments. However, current policies focus on technical and managerial aspects without asking for experts and non-experts values and preferences for preventing cyberterrorism. This study employs value focused thinking and public value forum to bare strategic measures and alternatives for complex policy decisions for preventing cyberterrorism. The strategic measures and alternatives are per socio-technical process.

Wildfire Emissions In The Context Of Global Change And The Implications For Mercury Pollution, Aditya Kumar

Dissertations, Master's Theses and Master's Reports

Wildfires are episodic disturbances that exert a significant influence on the Earth system. They emit substantial amounts of atmospheric pollutants, which can impact atmospheric chemistry/composition and the Earth’s climate at the global and regional scales. This work presents a collection of studies aimed at better estimating wildfire emissions of atmospheric pollutants, quantifying their impacts on remote ecosystems and determining the implications of 2000s-2050s global environmental change (land use/land cover, climate) for wildfire emissions following the Intergovernmental Panel on Climate Change (IPCC) A1B socioeconomic scenario.

A global fire emissions model is developed to compile global wildfire emission inventories for major atmospheric …