Physical Sciences and Mathematics Commons^™

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …

Blockchain Based Access Control For Enterprise Blockchain Applications, Lei Xu, Isaac Markus, Subhod I, Nikhil Nayab

Computer Science Faculty Publications and Presentations

Access control is one of the fundamental security mechanisms of IT systems. Most existing access control schemes rely on a centralized party to manage and enforce access control policies. As blockchain technologies, especially permissioned networks, find more applicability beyond cryptocurrencies in enterprise solutions, it is expected that the security requirements will increase. Therefore, it is necessary to develop an access control system that works in a decentralized environment without compromising the unique features of a blockchain. A straightforward method to support access control is to deploy a firewall in front of the enterprise blockchain application. However, this approach does not …

Gaze Collaboration Patterns Of Successful And Unsuccessful Programming Pairs Using Cross-Recurrence Quantification Analysis, Maureen Villamor, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

A dual eye tracking experiment was performed on pairs of novice programmers as they traced and debugged fragments of code. These programming pairs were categorized into successful and unsuccessful pairs based on their debugging scores. Cross-recurrence quantification analysis (CRQA), an analysis using cross-recurrence plots (CRP), was used to determine whether there are significant differences in the gaze collaboration patterns between these pair categories. Results showed that successful and unsuccessful pairs can be characterized distinctively based on their CRPs and CRQA metrics. This study also attempted to interpret the CRQA metrics in relation to how the pairs collaborated in order to …

A Hybrid And Scalable Error Correction Algorithm For Indel And Substitution Errors Of Long Reads, Arghya Kusum Das, Sayan Goswami, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Background: Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. Methods: In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the …

Data-Driven Multiscale Modeling Reveals The Role Of Metabolic Coupling For The Spatio-Temporal Growth Dynamics Of Yeast Colonies, Jukka Intosalmi, Adrian C. Scott, Michelle Hays, Nicholas Flann, Olli Yli-Harja, Harri Lähdesmäki, Aimée M. Dudley, Alexander Skupin

Computer Science Faculty and Staff Publications

Background: Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell–cell and metabolic coupling lead to functionally optimized structures is still limited.

Results: Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chu, Weihua Zhou

Faculty Publications

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

Results: Compared with the seven conventional machine learning …

Learning Nearest Neighbor Graphs From Noisy Distance Samples, Blake Mason, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and …

Maxgap Bandit: Adaptive Algorithms For Approximate Ranking, Sumeet Katariya, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits. The key novelty of the MaxGap bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or threshold. Estimating an arm's gap requires …

Computational Screening Of New Perovskite Materials Using Transfer Learning And Deep Learning, Xiang Li, Yabo Dan, Rongzhi Dong, Zhuo Cao, Chengcheng Niu, Yuqi Song, Shaobo Li, Jianjun Hu

Faculty Publications

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us …

Point Cloud Processing With Neural Networks, Stephanie Miller, Jiahao Li

All Computer Science and Engineering Research

In this project, we explore new techniques and architectures for applying deep neural networks when the input is point cloud data. We first consider applying convolutions on regular pixel and voxel grids, using polynomials of point coordinates and Fourier transforms to get a rich feature representation for all points mapped to the same pixel or voxel. We also apply these ideas to generalize the recently proposed "interpolated convolution", by learning continuous-space kernels as a combination of polynomial and Fourier basis kernels. Experiments on the ModelNet40 dataset demonstrate that our methods have superior performance over the baselines in 3D object recognition.

Overrepresentation Of The Underrepresented: Gender Bias In Wikipedia, Anna Marinina

Honors College Theses

The goal of our research is to determine if gender bias exists in Wikipedia. Wikipedia is a very large dataset that has been used to train artificial intelligence models. If a dataset that is being used for this purpose is biased, then the artificial intelligence model that was trained with it will be biased as well, therefore making biased decisions. For this reason, it is important to explore large datasets for any potential biases before they are used in machine learning. Since Wikipedia is ontologically structured, we used graph theory to create a network of all of the website’s categories …

Founding The Domain Of Ai Forensics, Ibrahim Baggili, Vahid Behzadan

Electrical & Computer Engineering and Computer Science Faculty Publications

With the widespread integration of AI in everyday and critical technologies, it seems inevitable to witness increasing instances of failure in AI systems. In such cases, there arises a need for technical investigations that produce legally acceptable and scientifically indisputable findings and conclusions on the causes of such failures. Inspired by the domain of cyber forensics, this paper introduces the need for the establishment of AI Forensics as a new discipline under AI safety. Furthermore, we propose a taxonomy of the subfields under this discipline, and present a discussion on the foundational challenges that lay ahead of this new research …

Using Machine Learning Classification Methods To Detect The Presence Of Heart Disease, Nestor Pereira

Dissertations

Cardiovascular disease (CVD) is the most common cause of death in Ireland, and probably, worldwide. According to the Health Service Executive (HSE) cardiovascular disease accounting for 36% of all deaths, and one important fact, 22% of premature deaths (under age 65) are from CVD.

Using data from the Heart Disease UCI Data Set (UCI Machine Learning), we use machine learning techniques to detect the presence or absence of heart disease in the patient according to 14 features provide for this dataset. The different results are compared based on accuracy performance, confusion matrix and area under the Receiver Operating Characteristics (ROC) …

Designing Learning Activities For Experiential Learning In A Design Thinking Course, Benjamin Gan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

One experiential learning design challenge is the duration of learning activities. These learning activities take up time and effort for teachers to design and student to perform. Another design challenge is the minimum instructional guidance of these learning activities which potentially impact the learning effectiveness of novice students. In this paper, we describe our findings of applying experiential learning method in a design thinking course with a list of learning activities performed iteratively. Each of the learning activity varies in their duration required and level of instructional guidance. Our survey seeks to find out which of the learning activities are …

Optimal Management Of Virtual Infrastructures Under Flexible Cloud Service Agreements, Zhiling Guo, Jin Li, Ram Ramesh

Research Collection School Of Computing and Information Systems

A cloud service agreement entails the provisioning of a required set of virtual infrastructure resources at a specified level of availability to a client. The agreement also lays out the price charged to the client and a penalty to the provider when the assured availability is not met. The availability assurance involves backup resource provisioning, and the provider needs to allocate backups cost-effectively by balancing the resource-provisioning costs with the potential penalty costs. We develop stochastic dynamic optimization models of the backup resource-provisioning problem, leading to cost-effective resource-management policies in different practical settings. We present two sets of dynamic provisioning …

Factor Analysis Of Mixed Data (Famd) And Multiple Linear Regression In R, Nestor Pereira

Dissertations

In the previous projects, it has been worked to statistically analysis of the factors to impact the score of the subjects of Mathematics and Portuguese for several groups of the student from secondary school from Portugal.

In this project will be interested in finding a model, hypothetically multiple linear regression, to predict the final score, dependent variable G3, of the student according to some features divide into two groups. One group, analyses the features or predictors which impact in the final score more related to the performance of the students, means variables like study time or past failures. The second …

Dronescape:Distributed Rapid On-Site Network Self-Deploying Cellular Advanced Phone Environment, Daryl Johnson, Bill Stackpole

Presentations and other scholarship

When disasters happen, the speed with which first responders and emergency personnel can contact and be contacted by the people affected by the disaster during the first minutes or hours is critical. Early communications can make the difference between life and death. During a disaster communications infrastructure of the affected area is likely to be compromised. This project proposes an inexpensive, rapidly deployable cloud of autonomous drones, each coupled with a micro-cellular base station that deploys from a transportable deployment module. The goal is to temporarily restore communications for both first responders to communicate amongst themselves as well as for …

College Of Engineering Senior Design Competition Fall 2019, University Of Nevada, Las Vegas

Fred and Harriet Cox Senior Design Competition Projects

Part of every UNLV engineering student’s academic experience, the senior design project stimulates engineering innovation and entrepreneurship. Each student in their senior year chooses, plans, designs, and prototypes a product in this required element of the curriculum. A capstone to the student’s educational career, the senior design project encourages the student to use everything learned in the engineering program to create a practical, real world solution to an engineering challenge. The senior design competition helps focus the senior students in increasing the quality and potential for commercial application for their design projects. Judges from local industry evaluate the projects on …

The Privacy Preserving Framework With Virtual Ring And Identity-Based Cryptography For Smart Grid, Leonard Sutanto

Publications and Research

One of the main challenges in the smart grid is how to efficiently manage the high-volume data from smart meters and sensors and preserve the privacy from the consumption data to avoid potential attacks (e.g., identity theft) for the involved prosumers, retail electricity providers and other clusters of distributed energy resources. This poster proposes a two-layer framework with the cloud computing infrastructure. The virtual ring and identity-based cryptography are utilized in each layer to preserve privacy efficiently. The methods of the virtual ring and identity-based cryptography are introduced. The purposes and needs are discussed at the end of this poster.

Sensor Emulation With Physiolocal Data In Immersive Virtual Reality Driving Simulator, Jungsu Pak, Oliver Mathias, Ariane Guirguis, Uri Maoz

Student Scholar Symposium Abstracts and Posters

Can we enhance the safety and comfort of AVs by training AVs with physiological data of human drivers? We will train and compare AV algorithm with/without physiological data.

Static Taint Analysis Of Binary Executables Using Architecture-Neutral Intermediate Representation, Elaine Cole

All Computer Science and Engineering Research

Ghidra, National Security Agency’s powerful reverse engineering framework, was recently released open-source in April 2019 and is capable of lifting instructions from a wide variety of processor architectures into its own register transfer language called p-code. In this project, we present a new tool which leverages Ghidra’s specific architecture-neutral intermediate representation to construct a control flow graph modeling all program executions of a given binary and apply static taint analysis. This technique is capable of identifying the information flow of malicious input from untrusted sources that may interact with key sinks or parts of the system without needing access to …

Compositional Verification Of Heap-Manipulating Programs Through Property-Guided Learning, Long H. Pham, Jun Sun, Quang Loc Le

Research Collection School Of Computing and Information Systems

Analyzing and verifying heap-manipulating programs automatically is challenging. A key for fighting the complexity is to develop compositional methods. For instance, many existing verifiers for heap-manipulating programs require user-provided specification for each function in the program in order to decompose the verification problem. The requirement, however, often hinders the users from applying such tools. To overcome the issue, we propose to automatically learn heap-related program invariants in a property-guided way for each function call. The invariants are learned based on the memory graphs observed during test execution and improved through memory graph mutation. We implemented a prototype of our approach …

Advanced Security Analysis For Emergent Software Platforms, Mohannad Alhanahnah

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems.

This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the …

The Trolley Problem In Virtual Reality, Jungsu Pak, Ariane Guirguis, Nicholas Mirchandani, Scott Cummings, Uri Maoz

Student Scholar Symposium Abstracts and Posters

Would people react to the Trolley problem differently based on the medium? Immersive Virtual Reality Driving Simulator was used to examine participants respond to the trolley problem in a realistic and controlled simulated environment.

Food Scan: A Yelp For Dietary Restrictions, Andrew M. Bauer, Charlie Story, Curren Taber

Student Scholar Symposium Abstracts and Posters

Food restrictions pose a serious issue for Chapman students, with consequences as extreme as anaphylactic shock and death. Our team started the Food Scan project with a simple goal of streamlining how individuals with dietary restrictions find safe places to eat or buy food. The project name highlights our intention to clarify restaurant menu items for users by displaying important allergen and dietary information. Using Human-Computer Interaction methods, our team chose to develop a technology that is usable, effective, enjoyable, and inclusive by involving users during the entire design process. By integrating multimodal interfaces (including speech to text inputs), we …

Exploring Emotion Recognition For Vr-Ebt Using Deep Learning On A Multimodal Physiological Framework, Nicholas Dass

Faculty of Applied Science and Technology - Exceptional Student Work, Applied Computing Theses

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality …

Improving Video Game Recommendations Using A Hybrid, Neural Network And Keyword Ranking Approach, Nicholas Crawford

Faculty of Applied Science and Technology - Exceptional Student Work, Applied Computing Theses

Recommendations systems are software solutions for finding high-quality and relevant content for a given user type ranging from online shoppers, to music listeners, to video game players. Traditional recommendation systems use user review data to make recommendations, but we still want recommendations to perform well for new users with no review data. Currently, one of the problems that exists in recommendations is poor recommendation accuracy when only a small amount of data exists for a user, called the cold start problem. In this research we investigate solutions for the cold start problem in video game recommendations and we propose a …

Deepfuzzer: Accelerated Deep Greybox Fuzzing, Jie Liang, Yu Jiang, Mingzhe Wang, Houbing Song, Kim-Kwang Raymond Choo

Publications

Fuzzing is one of the most effective vulnerability detection techniques, widely used in practice. However, the performance of fuzzers may be limited by their inability to pass complicated checks, inappropriate mutation frequency, arbitrary mutation strategy, or the variability of the environment. In this paper, we present DeepFuzzer, an enhanced greybox fuzzer with qualified seed generation, balanced seed selection, and hybrid seed mutation. First, we use symbolic execution in a lightweight approach to generate qualified initial seeds which then guide the fuzzer through complex checks. Second, we apply a statistical seed selection algorithm to balance the mutation frequency between different seeds. …

Finitely Generated Sets Of Fuzzy Values: If "And" Is Exact, Then "Or" Is Almost Always Approximate, And Vice Versa -- A Theorem, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In the traditional fuzzy logic, experts' degrees of confidence are described by numbers from the interval [0,1]. Clearly, not all the numbers from this interval are needed: in the whole history of the Universe, there will be only countably many statements and thus, only countably many possible degree, while the interval [0,1] is uncountable. It is therefore interesting to analyze what is the set S of actually used values. The answer depends on the choice of "and"-operations (t-norms) and "or"-operations (t-conorms). For the simplest pair of min and max, any finite set will do -- as long as it is …

Fuzzy Logic Explains The Usual Choice Of Logical Operations In 2-Valued Logic, Julio Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In the usual 2-valued logic, from the purely mathematical viewpoint, there are many possible binary operations. However, in commonsense reasoning, we only use a few of them: why? In this paper, we show that fuzzy logic can explain the usual choice of logical operations in 2-valued logic.