Computer Sciences Commons^™

Anflo: Detecting Anomalous Sensitive Information Flows In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for “Health” apps to share heart rate information through the Internet …

Learning From Mutants: Using Code Mutation To Learn And Monitor Invariants Of A Cyber-Physical System, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the …

Sotorrent: Reconstructing And Analyzing The Evolution Of Stack Overflow Posts, Sebastian Baltes, Lorik Dumani, Christoph Treude, Stephan Diehl

Research Collection School Of Computing and Information Systems

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of code snippets and free-form text on a wide variety of topics. Like other software artifacts, questions and answers on SO evolve over time, for example when bugs in code snippets are fixed, code is updated to work with a more recent library version, or text surrounding a code snippet is edited for clarity. To be able to analyze how content on SO evolves, we built SOTorrent, an open dataset based on the official SO data dump. SOTorrent provides access to the version history …

Empirical Risk Landscape Analysis For Understanding Deep Neural Networks, Pan Zhou, Jiashi Feng

Research Collection School Of Computing and Information Systems

This work aims to provide comprehensive landscape analysis of empirical risk in deep neural networks (DNNs), including the convergence behavior of its gradient, its stationary points and the empirical risk itself to their corresponding population counterparts, which reveals how various network parameters determine the convergence performance. In particular, for an l-layer linear neural network consisting of di neurons in the i-th layer, we prove the gradient of its empirical risk uniformly converges to the one of its population risk, at the rate of O(r 2l p l √ maxi dis log(d/l)/n). Here d is the total weight dimension, s is …

Neural Correlates Of States Of User Experience In Gaming Using Eeg And Predictive Analytics, Chandana Mallapragada, Fiona Fui-Hoon Nah, Keng Siau, Langtao Chen, Tejaswini Yelamanchili

Research Collection School Of Computing and Information Systems

In this research, we will analyze EEG signals to obtain neural correlate classifications of user experience by applying predictive analytics. Boredom, flow, and anxiety are three states experienced by users interacting with a computer-based system. A within-subjects experiment was used to collect EEG data for these three states and a baseline. We will apply predictive analytics including linear regression, support vector machine, and neural networks to analyze and classify the EEG data for these three states of user experience.

Assessing Classical And Expressive Aesthetics Of Web Pages Using Machine Learning, Ang Chen, Fiona Fui-Hoon Nah, Langtao Chen

Research Collection School Of Computing and Information Systems

Aesthetics plays a key role in web design. However, most websites are developed based on designers’ "inspirations" or "educated guesses" (Liu, 2003). While perceptions of aesthetics are intuitive abilities of humankind, the underlying principles for assessing aesthetics are not well understood. In this research, we propose using machine learning techniques to explore and more fully understand the patterns and underlying principles of aesthetics. We propose using machine learning techniques to develop predictive models for two aesthetic dimensions – classical aesthetics and expressive aesthetics – as well as for overall aesthetics of web pages in order to evaluate the aesthetic quality …

Improving Swarm Performance By Applying Machine Learning To A New Dynamic Survey, John Taylor Jackson

Master's Theses

A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is …

Secure Multiparty Protocol For Differentially-Private Data Release, Anthony Harris

Boise State University Theses and Dissertations

In the era where big data is the new norm, a higher emphasis has been placed on models which guarantees the release and exchange of data. The need for privacy-preserving data arose as more sophisticated data-mining techniques led to breaches of sensitive information. In this thesis, we present a secure multiparty protocol for the purpose of integrating multiple datasets simultaneously such that the contents of each dataset is not revealed to any of the data owners, and the contents of the integrated data do not compromise individual’s privacy. We utilize privacy by simulation to prove that the protocol is privacy-preserving, …

Evaluating Sequence Discovery Systems In An Abstraction-Aware Manner, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to evaluating activity discovery systems. Pre-annotated ground truths, often used to evaluate the performance of such systems on existing datasets, may exist at different levels of abstraction to the output of the output produced by the system. We propose a method for detecting and dealing with this situation, allowing for useful ground truth comparisons. This work has applications for activity discovery, and also for related fields. For example, it …

Complex Neural Networks For Audio, Andy M. Sarroff

Dartmouth College Ph.D Dissertations

Audio is represented in two mathematically equivalent ways: the real-valued time domain (i.e., waveform) and the complex-valued frequency domain (i.e., spectrum). There are advantages to the frequency-domain representation, e.g., the human auditory system is known to process sound in the frequency-domain. Furthermore, linear time-invariant systems are convolved with sources in the time-domain, whereas they may be factorized in the frequency-domain. Neural networks have become rather useful when applied to audio tasks such as machine listening and audio synthesis, which are related by their dependencies on high quality acoustic models. They ideally encapsulate fine-scale temporal structure, such as that encoded in …

Universal Mobile Service Execution Framework For Device-To-Device Collaborations, Minh Le

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or significantly overburdened, such as on a battlefield, disaster zones, isolated rural areas, or crowded public venues. To enable collaboration among the devices in opportunistic networks, code offloading and Remote Method Invocation are the two major mechanisms to ensure code portions of applications are successfully transmitted to and executed on the remote platforms. Although these domains are highly enjoyed in research for a decade, the limitations of multi-device connectivity, system error handling or cross platform compatibility prohibit …

A Model For Bioaugmented Anaerobic Granule, Amitesh Mahajan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In this study, we have created a simulation model which is concerned about digesting cellulose, as a major component of microalgae in a bioreactor. This model is designed to generate a computational model that simulates the process of granulation in anaerobic sludge and aims to investigate scenarios of possible granular bioaugmentation. Once a mature granule is formed, protein is used as an alternative substrate that will be supplied to a mature granule. Protein, being a main component of cyanobacteria, will promote growth and incorporation of a cell type that can degrade protein (selective pressure). The model developed in a cDynoMiCs …

Standard Machine Learning Techniques In Audio Beehive Monitoring: Classification Of Audio Samples With Logistic Regression, K-Nearest Neighbor, Random Forest And Support Vector Machine, Prakhar Amlathe

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder (CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation.

Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony behavior and health without invasive beehive inspections. Previously, significant …

In Situ Detection Of Road Lanes Using Raspberry Pi, Ashwani Chahal

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A self-driven car is a vehicle that can drive without human intervention by making correct decisions based on the environmental conditions. Since the innovation is in its beginning periods, totally moving beyond the human inclusion is still a long shot. However, rapid technological advancements are being made towards the safety of the driver and the passengers. One such safety feature is a Lane Detection System that empowers vehicle to detect road lane lines in various climate conditions.

This research provides a feasible and economical solution to detect the road lane lines while driving in a sunny, rainy, or snowy weather …

Code4her Spring 2018, Rebeccah Knoop

Honors Projects

CODE4her is a mentorship program with a goal of sparking interest in computer science organized by the BGSU Women in Computing (BGWIC) student organization. Participation is open to middle school girls (grades 5-8), and participants are paired with BGWIC members who serve as mentors.

Budgeting In Student Life: An Educational Website, Heather Grunden

Honors Projects

An applied honors project in the form of a website prototype. The purpose of this website is to introduce college students to the concept of budgeting and to teach them the core steps of creating their own budget, since many existing budgeting applications are pay-to-use, and the free options tend to have little to no instruction.

Design And Evaluation Of A Privacy Architecture For Crowdsensing Applications, Alfredo J. Perez, Sherali Zeadally

Computer Science Faculty Publications

By using consumer devices such as cellphones, wearables and Internet of Things devices owned by citizens, crowdsensing systems are providing solutions to the community in areas such as transportation, security, entertainment and the environment through the collection of various types of sensor data. Privacy is a major issue in these systems because the data collected can potentially reveal aspects considered private by the contributors of data. We propose the Privacy-Enabled ARchitecture (PEAR), a layered architecture aimed at protecting privacy in privacy-aware crowdsensing systems. We identify and describe the layers of the architecture. We propose and evaluate the design of MetroTrack, …

Characterizing Mental Health And Wellness In Students Across Engineering Disciplines, Andrew Danowitz, Kacey Beddoes

Computer Science and Software Engineering

Anecdotal evidence has long supported the idea that engineering students have lower levels of mental health and wellness than their peers. It is often posited that the large number of courses, low overall retention, difficult courses, and the abundance of intensive engineering projects lead to an unhealthy work-life balance and eventually lower levels of mental health for this population. To date, however, there has been no comprehensive study on the prevalence and types of mental health conditions that afflict engineering students, or any data on whether certain disciplines within engineering may see a greater prevalence of certain mental health conditions …

Transfer Information Energy: A Quantitative Indicator Of Information Transfer Between Time Series, Angel Caƫaron, Rǎzvan Andonie

All Faculty Scholarship for the College of the Sciences

We introduce an information-theoretical approach for analyzing information transfer between time series. Rather than using the Transfer Entropy (TE), we define and apply the Transfer Information Energy (TIE), which is based on Onicescu’s Information Energy. Whereas the TE can be used as a measure of the reduction in uncertainty about one time series given another, the TIE may be viewed as a measure of the increase in certainty about one time series given another. We compare the TIE and the TE in two known time series prediction applications. First, we analyze stock market indexes from the Americas, Asia/Pacific and Europe, …

Cerebral Necrosis Research With Machine Learning Techniques, Sangyu Shen

Creative Activity and Research Day - CARD

Cerebral necrosis after radiotherapy for patients with brain metastases is being recognized as a problem more common than previously estimated. To better understand the onset of necrosis and reduce its occurrence, we studied the relationships between features of patients and necrosis onset with machine learning techniques.

Use Of The Proof-Of-Stake Algorithm For Distributed Consensus In Blockchain Protocol For Cryptocurrency, Spencer J. Hosack

Honors Scholar Theses

Recent attention to Bitcoin and other cryptocurrencies has opened investors and the public to the realm of digital currency. Greater exposure around the world has led to a frenzy of entry into the market and a test into the long-term feasibility of Bitcoin being able to remain a functioning peer-to-peer (P2P), decentralized currency. Its main structure is supported by the Proof-of-Work (PoW) protocol in which users can elect to participate in determining transaction approval and ensuring an honest blockchain. This system relies on elected users to expend computational power and energy to solve puzzles to prove the accuracy of the …

Forecasting Smart Meter Energy Usage Using Distributed Systems And Machine Learning, Feiran Ji, Chris Dong, Lingzhi Du, Zizhen Song, Yuedi Zheng, Paul Intrevado

Creative Activity and Research Day - CARD

In this research, we explore the technical and computational merits of a machine learning algorithm on a large data set, employing distributed systems. Using 167 million(10 GB) energy consumption observations collected by smart meters from residential consumers in London, England, we predict future residential energy consumption using a Random Forest machine learning algorithm. Distributed systems such as AWS S3 and EMR, MongoDB and Apache Spark are used. Computational times and predictive accuracy are evaluated. We conclude that there are significant computational advantages to using distributed systems when applying machine learning algorithms on large-scale data. We also observe that distributed systems …

An Optimization Approach To Automate The Generation Of Radiotherapy Treatment Plans, Qian Li

Creative Activity and Research Day - CARD

The main goal of radiotherapy is to deliver a specified dose of radiation directly to the tumor while minimizing radiation damage to healthy tissues. Currently, the treatment plan is being developed by professional planners using a commercial treatment planning system. In this treatment planning system, the planner modifies the objectives and weights of the objectives until an ideal combination of doses is achieved. This arbitrary process can cost a few hours or a day to finish. My research aims to automate the generation of the plans by implementing an optimization algorithm on top of the treatment planning system using gradient …

Fit Buddy Prototype And Ksugo Mobile App, Albert Lim

KSU Journey Honors College Capstones and Theses

The purpose of my Honors Capstone project is to deliver a mobile app prototype that is focused on improving the student experience at Kennesaw State University (KSU), called Fit Buddy. There are three key concepts that will be covered in this Proof of Concept project: social networking, fitness, and IoT (Internet of Things) usages.

Additionally, the purpose of the CS Capstone project is to deliver a mobile app that is focused on improving the faculty, student, and guest experience at KSU, called KSUGo. The primary objective and purpose in creating this stems from KSU’s many resourceful outlets, which we seek …

Weaponizing Twitter Litter: Abuse-Forming Networks And Social Media, Hal Berghel

Computer Science Faculty Research

Instead of liberating us from the biases of the educated among us, the Internet has saddled us with the biases of the unreasoned among us.

Microfluidic Chip For Non-Invasive Analysis Of Tumor Cells Interaction With Anti-Cancer Drug Doxorubicin By Afm And Raman Spectroscopy, Han Zhang, Lifu Xiao, Qifei Li, Xiaojun Qi, Anhong Zhou

Biological Engineering Faculty Publications

Raman spectroscopy has been playing an increasingly significant role for cell classification. Here, we introduce a novel microfluidic chip for non-invasive Raman cell natural fingerprint collection. Traditional Raman spectroscopy measurement of the cells grown in a Polydimethylsiloxane (PDMS) based microfluidic device suffers from the background noise from the substrate materials of PDMS when intended to apply as an in vitro cell assay. To overcome this disadvantage, the current device is designed with a middle layer of PDMS layer sandwiched by two MgF2slides which minimize the PDMS background signal in Raman measurement. Three cancer cell lines, including a human lung cancer …

Cross Campus Collaborations, Sarah Asp Olson

Frontiers

As a computer scientist and a champion for accessibility, Dr. Guario Salivia works across campus to come up with tech solutions that work for all.

Creating A Reproducible Metadata Transformation Pipeline Using Technology Best Practices, Cara Key, Mike Waugh

Digital Initiatives Symposium

Over the course of two years, a team of librarians and programmers from LSU Libraries migrated the 186 collections of the Louisiana Digital Library from OCLC's CONTENTdm platform over to the open-source Islandora platform.

Early in the process, the team understood the value of creating a reproducible metadata transformation pipeline, because there were so many unknowns at the beginning of the process along with the certainty that mistakes would be made. This presentation will describe how the team used innovative and collaborative tools, such as Trello, Ansible, Vagrant, VirtualBox, git and GitHub to accomplish the task.

Seismology And Volcanology: Exploration Of Volcanoes, Long-Periods, And Machines - Predicting Volcano Eruption Using Signature Seismic Data, Kyle Killion, Rajeev Kumar, Celia J. Taylor, Gabriele Morra

SMU Data Science Review

Abstract. Seismo-volcanologists manually isolate and verify long-period waves and Strombolian events using seismic and acoustic waves. This is a very detailed and time-consuming process. This project is to employ machine learning algorithms to find models which locate long-period and Strombolian signatures automatically. By comparing the timing of seismic and acoustic waves, clustering techniques effectively isolated big volcanic events and aided in the further refinement of techniques to capture the hundreds of typical daily Strombolian events at Villarrica volcano. Within the research, we utilized the unsupervised machine learning environment to locate a group of signatures for customizing machine learned long-period signature …

Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels

SMU Data Science Review

In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …