Computer Sciences Commons^™

A Distributed Semi-Supervised Platform For Dnase-Seq Data Analytics Using Deep Generative Convolutional Networks, Shayan Shams, Richard Platania, Joohyun Kim, Jian Zhang, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

A deep learning approach for analyzing DNase-seq datasets is presented, which has promising potentials for unraveling biological underpinnings on transcription regulation mechanisms. Further understanding of these mechanisms can lead to important advances in life sciences in general and drug, biomarker discovery, and cancer research in particular. Motivated by recent remarkable advances in the field of deep learning, we developed a platform, Deep Semi-Supervised DNase-seq Analytics (DSSDA). Primarily empowered by deep generative Convolutional Networks (ConvNets), the most notable aspect is the capability of semi-supervised learning, which is highly beneficial for common biological settings often plagued with a less sufficient number of …

The Example Guru: Suggesting Examples To Novice Programmers In An Artifact-Based Context, Michelle Ichinco

McKelvey School of Engineering Graduate Student Theses & Dissertations

Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young …

Concurrency Platforms For Real-Time And Cyber-Physical Systems, David Ferry

McKelvey School of Engineering Graduate Student Theses & Dissertations

Parallel processing is an important way to satisfy the increasingly demanding computational needs of modern real-time and cyber-physical systems, but existing parallel computing technologies primarily emphasize high-throughput and average-case performance metrics, which are largely unsuitable for direct application to real-time, safety-critical contexts. This work contrasts two concurrency platforms designed to achieve predictable worst case parallel performance for soft real-time workloads with millisecond periods and higher. One of these is then the basis for the CyberMech platform, which enables parallel real-time computing for a novel yet representative application called Real-Time Hybrid Simulation (RTHS). RTHS combines demanding parallel real-time computation with real-time …

Self-Powered Time-Keeping And Time-Of-Occurrence Sensing, Liang Zhou

McKelvey School of Engineering Graduate Student Theses & Dissertations

Self-powered and passive Internet-of-Things (IoT) devices (e.g. RFID tags, financial assets, wireless sensors and surface-mount devices) have been widely deployed in our everyday and industrial applications. While diverse functionalities have been implemented in passive systems, the lack of a reference clock limits the design space of such devices used for applications such as time-stamping sensing, recording and dynamic authentication. Self-powered time-keeping in passive systems has been challenging because they do not have access to continuous power sources. While energy transducers can harvest power from ambient environment, the intermittent power cannot support continuous operation for reference clocks. The thesis of this …

Suas: Cybersecurity Threats, Vulnerabilities, And Exploits, Philip Craiger, Gary Kessler, William Rose

National Training Aircraft Symposium (NTAS)

The FAA predicts that purchases of hobbyist small unmanned aerial systems (sUAS) will grow from 1.9 million in 2016 to 4.3 million by 2020, and commercial sUAS to increase from 600,000 in 2016 to 2.7 million by 2020. sUAS, often referred to as 'drones,' are comprised of aeronautical hardware, a CPU, RAM, onboard storage, radio frequency communications, sensors, a camera, and a controller used by the pilot-in-command (PIC). Some have argued that a sUAS is essentially a flying computer. As such, sUAS are sometimes susceptible to many of the types of attacks that are often used on PC-based computers attached …

The Transmuted Geometric-Quadratic Hazard Rate Distribution: Development, Properties, Characterizations And Applications, Fiaz Ahmad Bhatti, Gholamhossein Hamedani, Mustafa Ç. Korkmaz, Munir Ahmad

Mathematics, Statistics and Computer Science Faculty Research and Publications

We propose a five parameter transmuted geometric quadratic hazard rate (TG-QHR) distribution derived from mixture of quadratic hazard rate (QHR), geometric and transmuted distributions via the application of transmuted geometric-G (TG-G) family of Afify et al.(Pak J Statist 32(2), 139-160, 2016). Some of its structural properties are studied. Moments, incomplete moments, inequality measures, residual life functions and some other properties are theoretically taken up. The TG-QHR distribution is characterized via different techniques. Estimates of the parameters for TG-QHR distribution are obtained using maximum likelihood method. The simulation studies are performed on the basis of graphical results to illustrate the performance …

Transforming Learning With Information And Communication Technologies: Insights From Three Decades Of Research, Romina Jamieson-Proctor

2009 - 2019 ACER Research Conferences

Since computers first appeared in classrooms, educators have sought to integrate information communication technologies (ICT) into teaching and learning. In Australia, as elsewhere, ICT are widely regarded as critical facilitators of student learning. The ability to use ICT effectively is specified in Australia’s national curriculum as a required general capability. However, despite the educational environment being replete with ICT related programs, our understanding of how students use ICT for learning is still limited. This paper presents insights from the past 30 years of research, which suggest that even though the current ‘climate’ in Australian schools is favourable, teacher confidence and …

Automatic Knowledge Extraction From Ocr Documents Using Hierarchical Document Analysis, Mohammad Masum, Sai Kosaraju, Tanju Bayramoglu, Girish Modgil, Mingon Kang

Published and Grey Literature from PhD Candidates

Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently, there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries …

An Examination Of Factors Correlating With Course Failure In A High School Computer Science Course, Steven Mcgee, Ronald I. Greenberg, Lucia Dettori, Andrew M. Rasmussen, Randi Mcgee-Tekula, Jennifer Duck, Erica Wheeler

Computer Science: Faculty Publications and Other Works

Across the United States, enrollment in high school computer science (CS) courses is increasing. These increases, however, are not spread evenly across race and gender. CS remains largely an elective class, and fewer than three-fourths of the states allow it to count towards graduation. The Chicago Public Schools has sought to ensure access for all students by recently enacting computer science as a high school graduation requirement. The primary class that fulfills the graduation requirement is Exploring Computer Science (ECS), a high school introductory course and professional development program designed to foster deep engagement through equitable inquiry around CS …

Imapsplice: Alleviating Reference Bias Through Personalized Rna-Seq Alignment, Xinan Liu, James N. Macleod, Jinze Liu

Computer Science Faculty Publications

Genomic variants in both coding and non-coding sequences can have functionally important and sometimes deleterious effects on exon splicing of gene transcripts. For transcriptome profiling using RNA-seq, the accurate alignment of reads across exon junctions is a critical step. Existing algorithms that utilize a standard reference genome as a template sometimes have difficulty in mapping reads that carry genomic variants. These problems can lead to allelic ratio biases and the failure to detect splice variants created by splice site polymorphisms. To improve RNA-seq read alignment, we have developed a novel approach called iMapSplice that enables personalized mRNA transcriptome profiling. The …

Automating Mobile Device File Format Analysis, Richard A. Dill

Theses and Dissertations

Forensic tools assist examiners in extracting evidence from application files from mobile devices. If the file format for the file of interest is known, this process is straightforward, otherwise it requires the examiner to manually reverse engineer the data structures resident in the file. This research presents the Automated Data Structure Slayer (ADSS), which automates the process to reverse engineer unknown file for- mats of Android applications. After statically parsing and preparing an application, ADSS dynamically runs it, injecting hooks at selected methods to uncover the data structures used to store and process data before writing to media. The resultant …

Writing A Moral Code: Algorithms For Ethical Reasoning By Humans And Machines, James F. Mcgrath, Ankur Gupta

Scholarship and Professional Work - LAS

The moral and ethical challenges of living in community pertain not only to the intersection of human beings one with another, but also our interactions with our machine creations. This article explores the philosophical and theological framework for reasoning and decision-making through the lens of science fiction, religion, and artificial intelligence (both real and imagined). In comparing the programming of autonomous machines with human ethical deliberation, we discover that both depend on a concrete ordering of priorities derived from a clearly defined value system.

Work-In-Progress: Rws - A Roulette Wheel Scheduler For Preventing Execution Pattern Leakage, Ying Zhang, Lingxiang Wang, Wei Jiang, Zhishan Guo

Computer Science Faculty Research & Creative Works

Many real-time systems are safety-critical, where reliability is crucial. Under traditional scheduling mechanism, the execution patterns of the tasks on such system can be easily derived from side-channel attacks, such that attackers can launch short high-priority tasks at critical instants which may cause deadline miss for high-critical tasks. In order to protect the system from such kind of attacks, this paper proposes the roulette wheel scheduler (RWS) to randomize the task execution pattern. Under RWS, probabilities will be assigned to each task at predefined scheduling points, and the choice for execution is randomized, such that the execution pattern is no …

Cloud Service Reliability And Usability Measurement, Abdullah Bokhary

Computer Science and Engineering Theses and Dissertations

Cloud computing has become a major resource for fulfilling people's computational and storage needs. Investing in these services requires measuring and assuring its quality in general, and reliability and usability are primary concerns. However, using traditional reliability models can be challenging because of the environmental constraints and limited data availability due to the heterogeneous environment and diverse stakeholders. Also, the quality of cloud service Application Programming Interfaces (APIs) has a direct impact on the usability and reliability of the service.

We developed a framework to measure reliability with alternative available information that most cloud providers offer in three stages: 1) …

Assessing Apache Spark Streaming With Scientific Data, Janak Dahal

LSU New Orleans Theses and Dissertations

Processing real-world data requires the ability to analyze data in real-time. Data processing engines like Hadoop come short when results are needed on the fly. Apache Spark's streaming library is increasingly becoming a popular choice as it can stream and analyze a significant amount of data. To showcase and assess the ability of Spark various metrics were designed and operated using data collected from the USGODAE data catalog. The latency of streaming in Apache Spark was measured and analyzed against many nodes in the cluster. Scalability was monitored by adding and removing nodes in the middle of a streaming job. …

Semantic-Aware Stealthy Control Logic Infection Attack, Sushma Kalle

LSU New Orleans Theses and Dissertations

In this thesis work we present CLIK, a new, automated, remote attack on the control logic of a programmable logic controller (PLC) in industrial control systems. The CLIK attack modifies the control logic running in a remote target PLC automatically to disrupt a physical process. We implement the CLIK attack on a real PLC. The attack is initiated by subverting the security measures that protect the control logic in a PLC. We found a critical (zero-day) vulnerability, which allows the attacker to overwrite password hash in the PLC during the authentication process. Next, CLIK retrieves and decompiles the original logic …

Manana: A Generalized Heuristic Scoring Approach For Concept Map Analysis As Applied To Cybersecurity Education, Sharon Elizabeth Blake Gatto

LSU New Orleans Theses and Dissertations

Concept Maps (CMs) are considered a well-known pedagogy technique in creating curriculum, educating, teaching, and learning. Determining comprehension of concepts result from comparisons of candidate CMs against a master CM, and evaluate "goodness". Past techniques for comparing CMs have revolved around the creation of a subjective rubric. We propose a novel CM scoring scheme called MAnanA based on a Fuzzy Similarity Scaling (FSS) score to vastly remove the subjectivity of the rubrics in the process of grading a CM. We evaluate our framework against a predefined rubric and test it with CM data collected from the Introduction to …

Forensic Analysis Of Immersive Virtual Reality Social Applications: A Primary Account, Ananya Yarramreddy, Peter Gromkowski, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

Our work presents the primary account for exploring the forensics of immersive Virtual Reality (VR) systems and their social applications. The Social VR applications studied in this work include Bigscreen, Altspace VR, Rec Room and Facebook Spaces. We explored the two most widely adopted consumer VR systems: the HTC Vive and the Oculus Rift. Our tests examined the efficacy of reconstructing evidence from network traffic as well as the systems themselves. The results showed that a significant amount of forensically relevant data such as user names, user profile pictures, events, and system details may be recovered. We anticipate that this …

Cyber Anomaly Detection: Using Tabulated Vectors And Embedded Analytics For Efficient Data Mining, Robert J. Gutierrez, Kenneth W. Bauer, Bradley C. Boehmke, Cade M. Saie, Trevor J. Bihl

Faculty Publications

Firewalls, especially at large organizations, process high velocity internet traffic and flag suspicious events and activities. Flagged events can be benign, such as misconfigured routers, or malignant, such as a hacker trying to gain access to a specific computer. Confounding this is that flagged events are not always obvious in their danger and the high velocity nature of the problem. Current work in firewall log analysis is manual intensive and involves manpower hours to find events to investigate. This is predominantly achieved by manually sorting firewall and intrusion detection/prevention system log data. This work aims to improve the ability of …

Gpu-Accelerated Large-Scale Genome Assembly, Sayan Goswami, Kisung Lee, Shayan Shams, Seung Jong Park

Computer Science Faculty Research & Creative Works

Spurred by a widening gap between hardware accelerators and traditional processors, numerous bioinformatics applications have harnessed the computing power of GPUS and reported substantial performance improvements compared to their CPU-based counterparts. However, most of these GPU-based applications only focus on the read alignment problem, while the field of de novo assembly still relies mostly on CPU-based solutions. This is primarily due to the nature of the assembly workload which is not only compute-intensive but also extremely data-intensive. Such workloads require large memories, making it difficult to adapt them to use GPUS with their limited memory capacities. To the best of …

Mass Spectrometry Image Creator (Msic): Ion Mobility / Mass Spectrometry Imaging Workflow In Python, Stephen Creger, Julia Laskin, Daniela Mesa Sanchez

The Summer Undergraduate Research Fellowship (SURF) Symposium

Mass spectrometry (MS) is a powerful characterization technique that enables identification of compounds in complex mixtures. Acquiring mass spectra in a spatially-resolved manner (i.e. over a grid), allows the data to be used to generate images that show the spatial distribution and relative intensities of every compound in a sample. These images can be used to monitor and identify biomarkers, explore the metabolism of compounds within tissues, and much more. However, the limitations of mass spectrometry can result in ambiguous compound identifications. Another characterization tool, ion mobility spectrometry (IM) can be integrated into existing MS routines to address this problem; …

Tool For Correlating Ebsd And Afm Data Arrays, Andrew Krawec, Matthew Michie, John Blendell

The Summer Undergraduate Research Fellowship (SURF) Symposium

Ceramic and semiconductor research is limited in its ability to create holistic representations of data in concise, easily-accessible file formats or visual data representations. These materials are used in everyday electronics, and optimizing their electrical and physical properties is important for developing more advanced computational technologies. There is a desire to understand how changing the composition of the ceramic alters the shape and structure of the grown crystals. However, few accessible tools exist to generate a dataset with the proper organization to understand correlations between grain orientation and crystallographic orientation. This paper outlines an approach to analyzing the crystal structure …

Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether or not the wind turbine experienced …

Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick

The Summer Undergraduate Research Fellowship (SURF) Symposium

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – …

Expected Length Of The Longest Chain In Linear Hashing, Pongthip Srivarangkul, Hemanta K. Maji

The Summer Undergraduate Research Fellowship (SURF) Symposium

Hash table with chaining is a data structure that chains objects with identical hash values together with an entry or a memory address. It works by calculating a hash value from an input then placing the input in the hash table entry. When we place two inputs in the same entry, they chain together in a linear linked list. We are interested in the expected length of the longest chain in linear hashing and methods to reduce the length because the worst-case look-up time is directly proportional to it.

The linear hash function used to calculate hash value is defined …

A Divide-And-Conquer Approach To Syntax-Guided Synthesis, Peiyuan Shen, Xiaokang Qiu

The Summer Undergraduate Research Fellowship (SURF) Symposium

Program synthesis aims to generate programs automatically from user-provided specifications. One critical research thrust is called Syntax-Guideds Synthesis. In addition to semantic specifications, the user should also provide a syntactic template of the desired program, which helps the synthesizer reduce the search space. The traditional symbolic approaches, such as CounterExample-Guided Inductive Synthesis (CEGIS) framework, does not scale to large search spaces. The goal of this project is to explore a compositional, divide-n-conquer approach that heuristically divides the synthesis task into subtasks and solves them separately. The idea is to decompose the function to be synthesized by creating a set of …

Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pump failure is a general concerned problem in the hydraulic field. Once happening, it will cause a huge property loss and even the life loss. The common methods to prevent the occurrence of pump failure is by preventative maintenance and breakdown maintenance, however, both of them have significant drawbacks. This research focuses on the axial piston pump and provides a new solution by the prognostic of pump failure using the classification of machine learning. Different kinds of sensors (temperature, acceleration and etc.) were installed into a good condition pump and three different kinds of damaged pumps to measure 10 of …

Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

With the increasing amount of information stored, there is a need for efficient database algorithms. One of the most important database operations is “join”. This involves combining columns from two tables and grouping common values in the same row in order to minimize redundant data. The two main algorithms used are hash join and sort merge join. Hash join builds a hash table to allow for faster searching. Sort merge join first sorts the two tables to make it more efficient when comparing values. There has been a lot of debate over which approach is superior. At first, hash join …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …

Exploring Confidentiality Issues In Hyperledger Fabric Business Applications, Shivam Bajpayi, Pedro Moreno-Sanchez, Donghang Lu, Sihao Yin

The Summer Undergraduate Research Fellowship (SURF) Symposium

The rise of Bitcoin and cryptocurrencies over the last decade have made its underlying technology (blockchain) come into the spotlight. Blockchain is a secure ledger of linked records called blocks. These records are cryptographically immutable and any tampering with the block is evident through a change in the cryptographic signature of the block. Among the blockchains deployed in practice today, Hyperledger Fabric is a platform that allows businesses to make use of blockchains in their applications. However, confidentiality issues arise with respects to the blocks in this blockchain network due to the fact that blocks might contain sensitive information accessible …