Computer Sciences Commons^™

Relationships Between Social Network Characteristics, Alcohol Use, And Alcohol-Related Consequences In A Large Network Of First-Year College Students: How Do Peer Drinking Norms Fit In?, Graham T. Diguiseppi, Matthew K. Meisel, Sara G. Balestrieri, Miles Q. Ott, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

A burgeoning area of research is using social network analysis to investigate college students' substance use behaviors. However, little research has incorporated students' perceived peer drinking norms into these analyses. The present study investigated the association between social network characteristics, alcohol use, and alcohol-related consequences among first-year college students (N 1,342; 81% of the first-year class) at one university. The moderating role of descriptive norms was also examined. Network characteristics and descriptive norms were derived from participants' nominations of up to 10 other students who were important to them; individual network characteristics included popularity (indegree), network expansiveness (outdegree), relationship reciprocity, …

Usable Security And Privacy For Secure Messaging Applications, Elham Vaziripour

Theses and Dissertations

The threat of government and corporate surveillance around the world, as well as the publicity surrounding major cybersecurity attacks, have increased interest in secure and private end-to-end communications. In response to this demand, numerous secure messaging applications have been developed in recent years. These applications have been welcomed and publically used not just by political activists and journalists but by everyday users as well. Most of these popular secure messaging applications are usable because they hide many of the details of how encryption is provided. The strength of the security properties of these applications relies on the authentication ceremony, wherein …

Improving The Quality Of Neural Machine Translation Using Terminology Injection, Duane K. Dougal

Theses and Dissertations

Most organizations use an increasing number of domain- or organization-specific words and phrases. A translation process, whether human or automated, must also be able to accurately and efficiently use these specific multilingual terminology collections. However, comparatively little has been done to explore the use of vetted terminology as an input to machine translation (MT) for improved results. In fact, no single established process currently exists to integrate terminology into MT as a general practice, and especially no established process for neural machine translation (NMT) exists to ensure that the translation of individual terms is consistent with an approved terminology collection. …

Flow Adaptive Video Object Segmentation, Fanqing Lin

Theses and Dissertations

We tackle the task of semi-supervised video object segmentation, i.e, pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. Recently introduced online adaptation of convolutional neural networks for video object segmentation (OnAVOS) has achieved good results by pretraining the network, fine-tuning on the first frame and training the network at test time using its approximate prediction as newly obtained ground truth. We propose Flow Adaptive Video Object Segmentation (FAVOS) that refines the generated adaptive ground truth for online updates and utilizes temporal consistency between video frames with the help …

On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher

Articles

This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …

Revolution In Crime: How Cryptocurrencies Have Changed The Criminal Landscape, Igor Groysman

Student Theses

This thesis will examine the ways in which various cryptocurrencies have impacted certain traditional crimes. While crime is always evolving with technology, cryptocurrencies are a game changer in that they provide anonymous and decentralized payment systems which, while they can be tracked in a reactive sense via the blockchain, are seen by criminals as having better uses for them than traditional fiat currencies, such as the ability to send money relatively fast to another party without going through an intermediary, or the ability to obscure the origin of the money for money laundering purposes. Every week there are new cryptocurrencies …

Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang

Research Collection School Of Computing and Information Systems

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep …

Early Prediction Of Merged Code Changes To Prioritize Reviewing Tasks, Yuanrui Fan, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Modern Code Review (MCR) has been widely used by open source and proprietary software projects. Inspecting code changes consumes reviewers much time and effort since they need to comprehend patches, and many reviewers are often assigned to review many code changes. Note that a code change might be eventually abandoned, which causes waste of time and effort. Thus, a tool that predicts early on whether a code change will be merged can help developers prioritize changes to inspect, accomplish more things given tight schedule, and not waste reviewing effort on low quality changes. In this paper, motivated by the above …

Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their collective influence'' on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference …

Sybmatch: Sybil Detection For Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Kan Yang, Yinghui Zhang, Xiaohua Jia, Robert H. Deng

Research Collection School Of Computing and Information Systems

The past decade has witnessed the rise of crowdsourcing, and privacy in crowdsourcing has also gained rising concern in the meantime. In this paper, we focus on the privacy leaks and sybil attacks during the task matching, and propose a privacy-preserving task matching scheme, called SybMatch. The SybMatch scheme can simultaneously protect the privacy of publishers and subscribers against semi-honest crowdsourcing service provider, and meanwhile support the sybil detection against greedy subscribers and efficient user revocation. Detailed security analysis and thorough performance evaluation show that the SybMatch scheme is secure and efficient.

Pribioauth: Privacy-Preserving Biometric-Based Remote User Authentication, Yangguang Tian, Yingjiu Li, Ximeng Liu, Robert H. Deng, Binanda Sengupta

Research Collection School Of Computing and Information Systems

Biometric-based remote user authentication (BRUA) is a useful primitive that allows an authorized user to remotely authenticate to a cloud server using biometrics. However, the existing BRUA solutions in the client-server setting lack certain privacy considerations. For example, authorized user's multiple sessions should not be linked while his identity remains anonymous to cloud server. In this work, we introduce an identity-concealed and unlinkable biometric-based remote user authentication framework, such that authorized users authenticate to an honest-but-curious server in an anonymous and unlinkable manner. In particular, we employ two non-colluding cloud servers to perform the complex biometrics matching. We formalize two …

Message From The Chairs, Toshiki Hirao, Yutaro Kashiwa, Christoph Treude, Raula Gaikovina Kula

Research Collection School Of Computing and Information Systems

It is our great pleasure to welcome everyone to the 2018 International Workshop on Empirical Software Engineering in Practice (IWESEP 2018). Our workshop aims to foster the development of the area by providing a forum where researchers and practitioners can report on and discuss new research results and applications in the area of empirical software engineering. The workshop encourages the exchange of ideas within the international community so as to be able to understand, from an empirical viewpoint, the strengths and weaknesses of technology in use and new technologies, with the expectation of advancing the field of software engineering in …

Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, Arumugam Thendramil Pavai

Electronic Theses, Projects, and Dissertations

Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons with 4 …

Active Matting, Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin, Rynson Lau

Research Collection School Of Computing and Information Systems

Image matting is an ill-posed problem. It requires a user input trimap or some strokes to obtain an alpha matte of the foreground object. A fine user input is essential to obtain a good result, which is either time consuming or suitable for experienced users who know where to place the strokes. In this paper, we explore the intrinsic relationship between the user input and the matting algorithm to address the problem of where and when the user should provide the input. Our aim is to discover the most informative sequence of regions for user input in order to produce …

Efficient Stochastic Gradient Hard Thresholding, Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

Research Collection School Of Computing and Information Systems

Stochastic gradient hard thresholding methods have recently been shown to work favorably in solving large-scale empirical risk minimization problems under sparsity or rank constraint. Despite the improved iteration complexity over full gradient methods, the gradient evaluation and hard thresholding complexity of the existing stochastic algorithms usually scales linearly with data size, which could still be expensive when data is huge and the hard thresholding step could be as expensive as singular value decomposition in rank-constrained problems. To address these deficiencies, we propose an efficient hybrid stochastic gradient hard thresholding (HSG-HT) method that can be provably shown to have sample-size-independent gradient …

Ensuring Data Confidentiality Via Plausibly Deniable Encryption And Secure Deletion: A Survey, Qionglu Zhang, Shijie Jia, Bing Chang, Bo Chen

Research Collection School Of Computing and Information Systems

Ensuring confidentiality of sensitive data is of paramount importance, since data leakage may not only endanger data owners’ privacy, but also ruin reputation of businesses as well as violate various regulations like HIPPA and Sarbanes-Oxley Act. To provide confidentiality guarantee, the data should be protected when they are preserved in the personal computing devices (i.e., confidentiality during their lifetime); and also, they should be rendered irrecoverable after they are removed from the devices (i.e., confidentiality after their lifetime). Encryption and secure deletion are used to ensure data confidentiality during and after their lifetime, respectively. This work aims to perform a …

The Living Wall Display: Physical Augmentation Of Interactive Content Using An Autonomous Mobile Display, Yuki Onishi, Yoshiki Kudo, Kazuki Takashima, Anthony Tang, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

The Living Wall Display displays interactive content on a mobile wall screen that moves in concert with content animation. To augment the interaction experience, the display dynamically changes its position and orientation, responding to the content animation triggered by user interactions. We implement three proof of concept prototypes that represent pseudo force impact of the interactive content using physical screen movement. Pilot studies show that the Living Wall augments content expressiveness, and increases the sense of presence of the screen content.

An Essential Applied Statistical Analysis Course Using Rstudio With Project-Based Learning For Data Science, Aldy Gunawan, Michelle L. F. Cheong, Johnson Poh

Research Collection School Of Computing and Information Systems

This paper presents a newpostgraduate level course, named Applied Statistical Analysis with R. Wepresent the course structure, teaching methodology including the assessmentframework and student feedback. The course covers the basic concepts ofstatistics, the knowledge of applying statistical theory in analyzing real dataand the skill of developing statistical applications with R programminglanguage. The first half of each lesson is dedicated to teaching students thestatistical concepts while the second half focuses on the practical aspects ofimplementing the concepts within the RStudio console. The Project-BasedLearning (PBL) approach is adopted to encourage students to apply the knowledgegained to solve real world problems, answer complex …

Data Mining Approach To The Identification Of At-Risk Students, Li Chin Ho, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS ® Enterprise Miner (EM) and SAS ® JMP Pro.

Data Mining Approach To The Detection Of Suicide In Social Media: A Case Study Of Singapore, Jane H. K. Seah, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In this research, we focus on the social phenomenon of suicide. Specifically, we perform social sensing on digital traces obtained from Reddit. We analyze the posts and comments in that are related to depression and suicide. We perform natural language processing to better understand different aspects of human life that relate to suicide.

Feature-Based Transfer Learning In Natural Language Processing, Jianfei Yu

Dissertations and Theses Collection (Open Access)

In the past few decades, supervised machine learning approach is one of the most important methodologies in the Natural Language Processing (NLP) community. Although various kinds of supervised learning methods have been proposed to obtain the state-of-the-art performance across most NLP tasks, the bottleneck of them lies in the heavy reliance on the large amount of manually annotated data, which is not always available in our desired target domain/task. To alleviate the data sparsity issue in the target domain/task, an attractive solution is to find sufficient labeled data from a related source domain/task. However, for most NLP applications, due to …

Reinforcement Learning For Collective Multi-Agent Decision Making, Duc Thien Nguyen

Dissertations and Theses Collection (Open Access)

In this thesis, we study reinforcement learning algorithms to collectively optimize decentralized policy in a large population of autonomous agents. We notice one of the main bottlenecks in large multi-agent system is the size of the joint trajectory of agents which quickly increases with the number of participating agents. Furthermore, the noiseof actions concurrently executed by different agents in a large system makes it difficult for each agent to estimate the value of its own actions, which is well-known as the multi-agent credit assignment problem. We propose a compact representation for multi-agent systems using the aggregate counts to address …

Modeling Movement Decisions In Networks: A Discrete Choice Model Approach, Larry Lin Junjie

Dissertations and Theses Collection (Open Access)

In this dissertation, we address the subject of modeling and simulation of agents and their movement decision in a network environment. We emphasize the development of high quality agent-based simulation models as a prerequisite before utilization of the model as an evaluation tool for various recommender systems and policies. To achieve this, we propose a methodological framework for development of agent-based models, combining approaches such as discrete choice models and data-driven modeling.

The discrete choice model is widely used in the field of transportation, with a distinct utility function (e.g., demand or revenue-driven). Through discrete choice models, the movement decision …

Social Software Development: Insights And Solutions, Abhishek Sharma

Dissertations and Theses Collection (Open Access)

Over last few decades, the way software is developed has changed drastically. From being an activity performed by developers working individually to develop standalone programs, it has transformed into a highly collaborative and cooperative activity. Software development today can be considered as a participatory culture, where developers coordinate and engage together to develop software while continuously learning from one another and creating knowledge.

In order to support their communication and collaboration needs, software developers often use a variety of social media channels. These channels help software developers to connect with like-minded developers and explore collaborations on software projects of interest. …

Privacy-Preserving Genomic Data Publishing Via Differential Privacy, Tanya Khatri

Boise State University Theses and Dissertations

Privacy-preserving data publishing is a mechanism for sharing data while ensuring the privacy of individuals is preserved in the published data and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health research. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data.

In this thesis, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a …

Strong Mutation-Based Test Generation Of Xacml Policies, Roshan Shrestha

Boise State University Theses and Dissertations

There exist various testing methods for XACML policies which vary in their overall fault detection ability and none of them can detect all the (killable) injected faults except for the simple policies. Further, it is unclear that what is essential for the fault detection of XACML policies. To address these issues, we formalized the fault detection conditions in the well-studied fault model of XACML policies so that it becomes clear what is essential for the fault detection. We formalized fault detection conditions in the form of reachability, necessity and propagation constraint. We, then, exploit these constraints to generate a mutation-based …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

An Adaptive Memory-Based Reinforcement Learning Controller, Keith August Cissell

Graduate Theses/Dissertations

Recently, the use of autonomous robots for exploration has drastically expanded--largely due to innovations in both hardware technology and the development of new artificial intelligence methods. The wide variety of robotic agents and operating environments has led to the creation of many unique control strategies that cater to each specific agent and their goal within an environment. Most control strategies are single purpose, meaning they are built from the ground up for each given operation. Here we present a single, reinforcement learning control solution for autonomous exploration intended to work across multiple agent types, goals, and environments. The solution presented …

Agent Based Terrain Generator: Cruthú, Lawrence L. O'Boyle

Masters Theses

Terrain generation models are applied in different industries and fields of study. Current techniques assist in planning transportation networks, visualizing population migrations, conducting epidemiology research, and training self-driving cars and drones. Many current applications and research models generate complex realistic artifacts, e.g., trees, rivers, coastlines, populations, and even cities. Unfortunately, most of these techniques are described and implemented separately. Techniques do not work together to generate a complete holistic view. This thesis proposes a model, Cruthú (Gaelic for "creation"), that provides a novel platform allowing for complete world generation by integrating existing research and algorithms. The model is inspired by …

First Steps In Retrofitting A Versatile Software Testing Infrastructure To Android, Carol Anne Oliver

Theses and Dissertations

Are existing software testing technologies satisfying the needs of mobile software testing practitioners? This dissertation provides evidence that the existing technologies fall short of practitioner’s actual needs and analyzes some reasons why this is so. The first contribution presents a framework for analyzing the software testing needs of practitioners, which is then used to assess the existing software testing tools and body of research, revealing significant gaps. The second contribution proposes a vision for a new software testing technology for mobile apps, which is then applied to a real-world problem causing problems for software testing practitioners (namely, the verification of …