Computer Sciences Commons^™

Bullynet: Unmasking Cyberbullies On Social Networks, Aparna Sankaran

Boise State University Theses and Dissertations

Social media has changed the way people communicate with each other, and consecutively affected people's ability to empathize in both positive and negative ways. One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying given that online records typically live on the internet for quite a long time and are hard to control. In this thesis, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network. BullyNet analyzes …

Falcon: Framework For Anomaly Detection In Industrial Control Systems, Subin Sapkota

Boise State University Theses and Dissertations

Industrial Control Systems (ICS) are used to control physical processes in the nation's critical infrastructures. They are composed of subsystems that control physical processes by analyzing the information received from the sensors. Based on the state of the process, the controller issues control commands to the actuators. These systems are utilized in a wide variety of operations such as water treatment plants, power, and manufacturing, etc. While the safety and security of these systems are of high concern, recent reports have shown an increase in targeted attacks that are aimed at manipulating the physical processes to cause catastrophic consequences. This …

Applying Formal Methods For Integrating Advanced Algorithms In Safety Critical Systems, Milton Stafford

Theses and Dissertations

In software engineering it is essential that updates are deployed for continual improvement. While software updates bring new functionality, updates also may introduce instability. This leads to failures of various kinds. This is especially problematic in safety-critical systems where there is a potential for injury or loss of life. However, newer and more sophisticated software carries potential advantages, including higher performance and reliability. Therefore, there are benefits in adopting newer software if the integration process is assured. In this thesis, I present a framework for assured integration; one that links requirements, design, and implementation. The proposed framework includes a new …

An Agile And Rapidly Reconfigurable Test Bed For Hardware-Based Security Features, Daniel Smith Beard

Theses and Dissertations

Current general-purpose computing hardware and the software that runs on it have evolved over more than a half century from large mainframe systems in corporate, military, and research use to interconnected commodity devices more common than wrist watches. Computational power, storage capacity, and communication capabilities have increased in wonderful and staggering ways; however, when we read about the latest vulnerability or data breach it seems that cybersecurity is stuck somewhere between 1983 when Matthew Broderick first heard a synthesized voice ask “Shall we play a game?”, [93] and 1988 when the Morris worm hit the Internet [116]. Multics [82] and …

Comparison Of Rl Algorithms For Learning To Learn Problems, Adolfo Gonzalez Iii

Theses and Dissertations

Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization.

Gesture-Based Profiling Of Commonplace Lifestyle And Physical Activity Behaviors, Meeralakshmi Radhakrishnan

Dissertations and Theses Collection (Open Access)

The widespread availability of sensors on personal devices (e.g., smartphones, smartwatches) and other cheap, commoditized IoT devices in the environment has opened up the opportunity for developing applications that capture and enhance various lifestyle-driven daily activities of individuals. Moreover, there is a growing trend of leveraging ubiquitous computing technologies to improve physical health and wellbeing. Several of the lifestyle monitoring applications rely primarily on the capability of recognizing contextually relevant human movements, actions and gestures. As such, gesture recognition techniques, and gesture-based analytics have emerged as a fundamental component for realizing personalized lifestyle applications.

This thesis explores how such wealth …

Leveraging Defects Life-Cycle For Labeling Defective Classes, Bailey R. Vandehei

Master's Theses

Data from software repositories are a very useful asset to building dierent kinds of

models and recommender systems aimed to support software developers. Specically,

the identication of likely defect-prone les (i.e., classes in Object-Oriented systems)

helps in prioritizing, testing, and analysis activities. This work focuses on automated

methods for labeling a class in a version as defective or not. The most used methods

for automated class labeling belong to the SZZ family and fail in various circum-

stances. Thus, recent studies suggest the use of aect version (AV) as provided by

developers and available in the issue tracker such as …

Testing Isomorphism Of Graded Algebras, Peter A. Brooksbank, James B. Wilson, Eamonn A. O'Brien

Faculty Journal Articles

We present a new algorithm to decide isomorphism between finite graded algebras. For a broad class of nilpotent Lie algebras, we demonstrate that it runs in time polynomial in the order of the input algebras. We introduce heuristics that often dramatically improve the performance of the algorithm and report on an implementation in Magma.

An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop

Sim Kee Boon Institute for Financial Economics

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ" role="presentation" style="box-sizing: border-box; display: inline; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">λλ) of a linear quantile lasso regression. The FRM is calculated by taking the average …

Grounding Size Predictions For Answer Set Programs, Nicholas Hippen

Student Work

Answer set programming is a declarative programming paradigm geared towards solving difficult combinatorial search problems. Logic programs under answer set semantics can typically be written in many different ways while still encoding the same problem. These different versions of the program may result in diverse performances. Unfortunately, it is not always easy to identify which version of the program performs the best, requiring expert knowledge on both answer set processing and the problem domain. More so, the best version to use may even vary depending on the problem instance. One measure that has been shown to correlate with performance is …

Server Assignment With Time-Varying Workloads In Mobile Edge Computing, Quynh Vo

Graduate Doctoral Dissertations

Mobile Edge Computing (MEC) has emerged as a viable technology for mobile operators to push computing resources closer to the users so that requests can be served locally without long-haul crossing of the network core, thus improving network efficiency and user experience. In MEC, commodity servers are deployed in the edge to form a distributed network of mini datacenters. A consequential task is to partition the user cells into groups, each to be served by an edge server, to maximize the offloading to the edge. The conventional setting for this problem in the literature is: (1) assume that the interaction …

A Machine Learning Assessment To Predict The Sediment Transport Rate Under Oscillating Sheet Flow Conditions, Huy Vu

Senior Honors Theses

The two-phase flow approach has been the conventional method designed to study the sediment transport rate. Due to the complexity of sediment transport, the precisely numerical models computed from that approach require initial assumptions and, as a result, may not yield accurate output for all conditions. This research work proposes that Machine Learning algorithms can be an alternative way to predict the processes of sediment transport in two-dimensional directions under oscillating sheet flow conditions, by utilizing the available dataset of the SedFoam multidimensional two-phase model. The assessment utilized linear regression and gradient boosting algorithm to analyze the lowest average mean …

When Keystroke Meets Password: Attacks And Defenses, Ximing Liu

Dissertations and Theses Collection (Open Access)

Password is a prevalent means used for user authentication in pervasive computing environments since it is simple to be deployed and convenient to use. However, the use of password has intrinsic problems due to the involvement of keystroke. Keystroke behaviors may emit various side-channel information, including timing, acoustic, and visual information, which can be easily collected by an adversary and leveraged for the keystroke inference. On the other hand, those keystroke-related information can also be used to protect a user's credentials via two-factor authentication and biometrics authentication schemes. This dissertation focuses on investigating the PIN inference due to the side-channel …

Multimodal Mobile Sensing Systems For Physiological And Psychological Assessment, Nguyen Phan Sinh Huynh

Dissertations and Theses Collection (Open Access)

Sensing systems for monitoring physiological and psychological states have been studied extensively in both academic and industry research for different applications across various domains. However, most of the studies have been done in the lab environment with controlled and complicated sensor setup, which is only suitable for serious healthcare applications in which the obtrusiveness and immobility can be compromised in a trade-off for accurate clinical screening or diagnosing. The recent substantial development of mobile devices with embedded miniaturized sensors are now allowing new opportunities to adapt and develop such sensing systems in the mobile context. The ability to sense physiological …

Enhanced Gesture Sensing Using Battery-Less Wearable Motion Trackers, Huy Vu Tran

Dissertations and Theses Collection (Open Access)

Wearable devices are gaining in popularity, but are presently used primarily for productivity-related functions (such as calling people or discreetly receiving notifications) or for physiological sensing. However, wearable devices are still not widely used for a wider set of sensing-based applications, even though their potential is enormous. Wearable devices can enable a variety of novel applications. For example, wrist-worn and/or finger-worn devices could be viable controllers for real-time AR/VR games and applications, and can be used for real-time gestural tracking to support rehabilitative patient therapy or training of sports personnel. There are, however, a key set of impediments towards realizing …

Event Reconstruction In The Advanced Particle-Astrophysics Telescope, Emily Ramey

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Advanced Particle-Astrophysics Telescope (APT) is a concept for a gamma-ray space telescope operating in the keV to MeV energy range. Due to the nature of the telescope and the physics of detection, reconstructing initial photon trajectories can be very computationally complex. This is a barrier to the real-time detection of astrophysical transient phenomena such as Gamma Ray Bursts (GRBs), and a faster reconstruction algorithm is needed in order to effectively study them. In this project, we develop such an algorithm based on Boggs & Jean (2000) and discuss the effects of certain algorithmic parameters on computational performance. For testing, …

Overlap Matrix Completion For Predicting Drug-Associated Indications, Menhyun Yang, Huimin Luo, Yaohang Li, Fang-Xiang Wu, Jianxin Wang

Computer Science Faculty Publications

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated …

Plug-And-Play Web Services, Arihant Jain

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The goal of this research is to make it easier to design and create web services for relational databases. A web service is a software service for providing data over computer networks. Web services provide data endpoints for many web applications. We adopt a plug-and-play approach for web service creation whereby a designer constructs a “plug,” which is a simple specification of the output produced by the service. If the plug can be “played” on the database then the web service is generated. Our plug-and-play approach has three advantages. First, a plug is portable. You can take the plug …

An Empirical Study Of Sms One-Time Password Authentication In Android Apps, Siqi Ma, Runhan Feng, Juanru Li, Yang Liu, Surya Nepal, Elisa Bertino, Robert H. Deng, Zhuo Ma, Sanjay Jha

Research Collection School Of Computing and Information Systems

A great quantity of user passwords nowadays has been leaked through security breaches of user accounts. To enhance the security of the Password Authentication Protocol (PAP) in such circumstance, Android app developers often implement a complementary One-Time Password (OTP) authentication by utilizing the short message service (SMS). Unfortunately, SMS is not specially designed as a secure service and thus an SMS One-Time Password is vulnerable to many attacks. To check whether a wide variety of currently used SMS OTP authentication protocols in Android apps are properly implemented, this paper presents an empirical study against them. We first derive a set …

Do My Students Understand? Automated Identification Of Doubts From Informal Reflections, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Traditionally teaching is usually one directional where the instructor imparts knowledge and there is minimal interaction between learners and instructor. With the focus on learner-centred pedagogy, it can be a challenge to provide timely and relevant guidance to individual learners according to their levels of understanding. One of the options available is to collect reflections from learners after each lesson to extract relevant and high-value feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived an approach to automate the identification of doubts from the informal reflections through features analysis and machine …

The Dynamic Impacts Of Online Healthcare Community On Physician Altruism: A Hidden Markov Model, Kai Luo, Qiu-Hong Wang, Hock Hai Teo

Research Collection School Of Computing and Information Systems

Physician altruism is not only a key foundation of modern medical professionalism, but also a critical component in the theoretical health economics study. There is considerable interest in understanding the impacts of contemporary healthcare technology on physician altruism. In this paper, we investigate the dynamic influence of multiple incentive mechanisms developed by an online healthcare community (OHC) on physician altruism. We model physician altruism as the degree of tendency to benefit the patients at the cost of oneself and focus on the incentive mechanisms that give physicians social and economic returns. The dynamics of physician altruism is characterized via a …

Examining The Theoretical Mechanisms Underlying Health Information Exchange Impact On Healthcare Outcomes: A Physician Agency Perspective, Fang Zhou, Qiu-Hong Wang, Hock Hai Teo

Research Collection School Of Computing and Information Systems

Health information exchange (HIE) is presumed to reduce medical costs by facilitating information sharing across healthcare providers. Existing studies focused on different medical costs or one set of costs, and resulted in mixed findings. We examine the effects of patient access to HIE on two of the most important medical costs of a hospitalization episode - test costs and medication costs - through a natural experiment and the discharge data of a hospital. Besides the negative direct effect of access to HIT on tests costs, we also find its positive spillover effect on medication costs, such that more patients having …

Appmod: Helping Older Adults Manage Mobile Security With Online Social Help, Zhiyuan Wan, Lingfeng Bao, Debin Gao, Eran Toch, Xin Xia, Tamir Mendel, David Lo

Research Collection School Of Computing and Information Systems

The rapid adoption of Smartphone devices has caused increasing security and privacy risks and breaches. Catching up with ever-evolving contemporary smartphone technology challenges leads older adults (aged 50+) to reduce or to abandon their use of mobile technology. To tackle this problem, we present AppMoD, a community-based approach that allows delegation of security and privacy decisions a trusted social connection, such as a family member or a close friend. The trusted social connection can assist in the appropriate decision or make it on behalf of the user. We implement the approach as an Android app and describe the results of …

Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo

Research Collection School Of Computing and Information Systems

Program comprehension is a fundamental task in software development and maintenance processes. Software developers often need to understand a large amount of existing code before they can develop new features or fix bugs in existing programs. Being able to process programming language code automatically and provide summaries of code functionality accurately can significantly help developers to reduce time spent in code navigation and understanding, and thus increase productivity. Different from natural language articles, source code in programming languages often follows rigid syntactical structures and there can exist dependencies among code elements that are located far away from each other through …

Online Content Consumption: Social Endorsements, Observational Learning And Word-Of-Mouth, Qian Tang, Tingting Song, Liangfei Qiu, Ashish Agarwal

Research Collection School Of Computing and Information Systems

The consumption of online content can occur through observational learning (OL) whereby consumers follow previous consumers’ choices or social endorsement (SE) wherein consumers receive content sharing from their social ties. As users consume content, they also generate post-consumption word-of-mouth (WOM) signals. OL, SE and WOM together shape the diffusion of the content. This study examines the drivers of SE and the effect of SE on content consumption and post-consumption WOM. In particular, we compare SE with OL. Using a random sample of 8,945 new videos posted on YouTube, we collected a multi-platform dataset consisting of data on video consumption and …

Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen

Research Collection School Of Computing and Information Systems

Vehicle Routing Problem (VRP) is a combinatorial problem where a certain set of nodes must be visited within a certain amount of time as well as the vehicle’s capacity. There are numerous variants of VRP such as VRP with time windows, where each node has opening and closing time, therefore, the visiting time must be during that interval. Another variant takes time-dependent constraint into account. This variant fits real-world scenarios, where at different period of time, the speed on the road varies depending on the traffic congestion. In this study, three objectives – total traveling time, total traveling distance, and …

Guest Editorial: Special Issue On Software Engineering For Mobile Applications, Sebastiano Panichella, Fabio Palomba, David Lo, Meiyappan Nagappan

Research Collection School Of Computing and Information Systems

As Andreessen stated “software is eating the world” (Andreessen 2011). Most of todays industries, from engineering, manufacturing, logistics to health, are run on enterprise software applications and can efficiently automate the analysis and manipulation of several, heterogeneous types of data. One of the most prominent examples of such software diffusion is represented by the widespread adoption of mobile applications. Indeed, during the recent years, the Global App Economy experienced unprecedented growth, driven by the increasing usage of apps and by the greater adoption of mobile devices (e.g., smartphone) around the globe. This mobile application market, which is expected in few …

Simulation Model To Evaluate Effectiveness Of Queue Management Tool In Supermarket Retail Chain, Michelle L. F. Cheong, Yong Qing Chia

Research Collection School Of Computing and Information Systems

As part of its continuous process to improve operational excellence and productivity, a retail company in South East Asia trialed the usage of a commercial market retail tool, known as Queue Buster, to improve queue management processes in its physical stores. We employed Queuing Theory to determine the effectiveness of implementing the Queue Buster. Specifically, we constructed a queuing simulation model based on input parameters derived from queuing data collected at a pilot store. Three main performance metrics - Wait Time, System Time and System Length were measured fortwo different queue systems, with and without the implementation of Queue Buster. …

Self-Organizing Neural Networks For Universal Learning And Multimodal Memory Encoding, Ah-Hwee Tan, Budhitama Subagdja, Di Wang, Lei Meng

Research Collection School Of Computing and Information Systems

Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions. Fusion ART extends the single-channel Adaptive Resonance Theory (ART) model to learn multimodal pattern associative mappings. As a natural extension of ART, various forms of fusion ART have been developed for a myriad of learning paradigms, ranging from unsupervised learning to supervised learning, semi-supervised learning, multimodal learning, reinforcement learning, and sequence learning. In addition, fusion ART models …

Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele

Research Collection School Of Computing and Information Systems

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for …