Computer Sciences Commons^™

Endpoints And Interdependencies In Internet Of Things Residual Artifacts: Measurements, Analyses, And Insights Into Defenses, Jinchun Choi

Electronic Theses and Dissertations, 2020-2023

The usage of Internet of Things (IoT) devices is growing fast. Moreover, the lack of security measures among the IoT devices and their persistent online connection give adversaries an opportunity to exploit them for multiple types of attacks, such as distributed denial-of-service (DDoS). To understand the risks of IoT devices, we analyze IoT malware from an endpoint standpoint. We investigate the relationship between endpoints infected and attacked by IoT malware, and gain insights into the underlying dynamics in the malware ecosystem. We observe the affinities and different patterns among endpoints. Towards this, we reverse-engineer 2,423 IoT malware samples and extract …

Computational Methods For Discovery And Analysis Of Rna Structural Motifs, Shahidul Islam

Electronic Theses and Dissertations, 2020-2023

Understanding the 3D structural properties of RNAs will play a critical role in identifying their functional characteristics and designing new RNAs for RNA-based therapeutics and nanotechnology. In an attempt to achieve a better insight into RNAs, biochemical experiments have been conducted to produce data with positional details of atoms in RNA structures. This data has created opportunities for applying computational analysis to solve various biological problems. In this dissertation, we have addressed annotation issues of base-pairing interactions in the low-resolution structure data and presented new methods to analyze RNA structural motifs. Annotating base-pairing interactions is one of the critical steps …

Improving The Security Of Critical Infrastructure: Metrics, Measurements, And Analysis, Jeman Park

Electronic Theses and Dissertations, 2020-2023

In this work, we propose three important contributions needed in the process of improving the security of the critical infrastructure: metrics, measurement, and analysis. To improve security, metrics are key to ensuring the accuracy of the assessment and evaluation. Measurements are the core of the process of identifying the causality and effectiveness of various behaviors, and accurate measurement with the right assumptions is a cornerstone for accurate analysis. Finally, contextualized analysis essential for understanding measurements. Different results can be derived for the same data according to the analysis method, and it can serve as a basis for understanding and improving …

3d Localization Of Defects In Facility Inspections, Nicholas Califano

Electronic Theses and Dissertations, 2020-2023

Wind tunnels are crucial facilities that support the aerospace industry. However, these facilities are large, complex, and pose unique maintenance and inspection requirements. Manual inspections to identify defects such as cracks, missing fasteners, leaks, and foreign objects are important but labor and schedule intensive. The goal of this thesis is to utilize small Unmanned Aircraft Systems with onboard cameras and computer vision-based analysis to automate the inspection of the interior and exterior of NASA's critical wind tunnel facilities. Missing fasteners are detected as the defect class, and existing fasteners are detected to provide potential future missing fastener sites for preventative …

Analyzing User Behavior In Collaborative Environments, Samaneh Saadat

Electronic Theses and Dissertations, 2020-2023

Discrete sequences are the building blocks for many real-world problems in domains including genomics, e-commerce, and social sciences. While there are machine learning methods to classify and cluster sequences, they fail to explain what makes groups of sequences distinguishable. Although in some cases having a black box model is sufficient, there is a need for increased explainability in research areas focused on human behaviors. For example, psychologists are less interested in having a model that predicts human behavior with high accuracy and more concerned with identifying differences between actions that lead to divergent human behavior. This dissertation presents techniques for …

Novel Computational Approaches For Multidimensional Brain Image Analysis, Harish Raviprakash

Electronic Theses and Dissertations, 2020-2023

The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical …

Learning Transferable Representations For Visual Recognition, Yang Zhang

Electronic Theses and Dissertations, 2020-2023

In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks. It is believed that a combination of big curated data and novel deep learning techniques can lead to unprecedented results. However, the increasingly large training data is still a drop in the ocean compared with scenarios in the wild. In this literature, we focus on learning transferable representation in the neural networks to ensure the models stay robust, even given different data distributions. We present three exemplar topics in three chapters, respectively: zero-shot learning, domain adaptation, and generalizable …

Measuring Differential Forest Growth In The Sheepscot River Headwaters With Bitemporal Lidar, Soren Denlinger

Honors Theses

In recent years, lidar has proven itself as a forestry tool capable of accurate, large- scale inventories. Lidar has even shown utility in multitemporal analysis and growth assessment, given high-resolution or small-scale point clouds. However, lidar’s efficacy as a multitemporal tool with relatively low-resolution, large-scale datasets is comparatively unknown. In this study, I compared forest in Midcoast Maine bitemporally, with publicly available datasets from the years 2007 and 2012. Specifically, I compared differences in growth characteristics of riparian, wetland, and upland forests. Although the 2007 dataset (created for geomorphological research) and the 2012 dataset (statewide, general-purpose) possess varying point densities …

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Rancang Bangun Aplikasi Status Gizi Bayi Berbasis Android, Ima Kurniastuti, Ahmad Syafiq Kamil

Elinvo (Electronics, Informatics, and Vocational Education)

Penelitian ini bertujuan untuk menghasilkan Aplikasi Status Gizi Bayi Berbasis Android yang mempermudah petugas kesehatan dalam menentukan status gizi bayi. Proses pembuatan dilakukan menggunakan Android Studio. Fitur dalam Android Studio yang dimanfaatkan adalah fitur TextView, fitur Radiogroup dan fitur Button. Input dalam aplikasi terdiri dari lima macam yaitu usia, berat badan, panjang bayi, lingkar kepala memanfaatkan fitur TextView sedangkan input jenis kelamin menggunakan fitur Radiogroup. Aplikasi memiliki dua tombol yaitu tombol hitung dan tombol reset. Tombol hitung berfungsi untuk memulai proses pemeriksaan dan tombol reset berfungsi untuk menghapus semua input yang dilakukan oleh pengguna sebelumnya sehingga memudahkan pengguna dalam melakukan …

Sistem Informasi Geografi (Sig) Pencarian Lokasi Tambal Ban Dengan Pemanfaatan Teknologi Gps, Desy Ika Puspitasari, Zaenuddin Zaenuddin, Fitrah Yuridka

Elinvo (Electronics, Informatics, and Vocational Education)

The occurrence of a tire leak in the middle of a trip can often be caused by many things, such as being hit by a sharp object, the age of the tire is too old, leaking in the former patches, or it can be caused by others. These factors make vehicle users panic and think to immediately find the nearest tire repair location. Almost everyone has a smartphone where there is a Google Map application by utilizing GPS technology so as not to get lost when driving, with GPS technology can be estimated the distance between one location and another. …

Analisis Dan Desain Sistem Informasi Penelitian Dan Pengabdian Masyarakat (Simpenmas) Politeknik Negeri Lhokseumawe, Hari Toha Hidayat

Elinvo (Electronics, Informatics, and Vocational Education)

Information systems of research and community service need to be developed to improve the management of research outputs and community service outcomes by lecturers while making it easier to evaluate tertiary accreditation. This system was developed using the waterfall model. This article aims to describe the stages of system development consisting of: (1) investigating user needs; (2) data and needs analysis in the process of making a system; (3) design; and (4) implementation. The result is the need for a system that specifically can provide facilities for uploading research outputs and community service such as journal publications, textbooks / texts, …

Using The Analytic Hierarchy Process For Decision Making In Smart Traffic Light For High Priority Vehicle, Muhammad Izzuddin Mahali, Eko Marpanaji, Muhammad Adi Febri Setiawan

Elinvo (Electronics, Informatics, and Vocational Education)

Kemacetan sering terjadi di banyak persimpangan jalan kota-kota besar di Indonesia. Sesuatu yang penting seperti kendaraan prioritas sering pula berada pada kemaccetan tersebut. Untuk mengatasi permasalahan tersebut terdapat inovasi baru yaitu Intelligent Traffic Light yang dibekali dengan Aplikasi "Bang Jopin". Namun terdapat permasalahan baru ketika ada kendaraan prioritas melakukan request emergency secara bersamaan pada traffic light yang sama. Penentuan prioritas tidak dapat dilakukan dengan pengurutan saja karena ketika memprioritaskan kendaraan pada traffic light harus mempertimbangkan karakteristik traffic light dan kebiasaan pengendara. Oleh kerena itu, Metode Analitical Hierarchy Process (AHP) merupakan solusi yang tepat dalam menentukan kendaraan prioritas yang didahulukan ketika …

Integrated Mobile And Web-Based Application For Enhancing Delivery Of Hiv/Aids Healthcare Information In Tanzania, Ibrahim Mwammenywa

Tanzania Journal of Engineering and Technology (TJET)

This study aims to develop an integrated e-health platform for enhancing delivery of HIV/AIDS healthcare information in Tanzania, which consists of a mobile application and a web-based system. The study is based on the system's functional and non-functional requirements for an e-health system for delivery of HIV/AIDS healthcare information. The Rapid Application Development (RAD) model was adopted during the system development. The system requirements were modelled into Data Flow Diagram (DFD) in order to obtain the clear flow of the HIV/AIDS healthcare information between the clients and HIV/AIDS healthcare practitioners. With the use of different software development tools and environment …

Front Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1

Tanzania Journal of Engineering and Technology (TJET)

Worldwide, every day human beings generate millions of tons of Faecal Sludge (FS), which is rich in water, nutrients, energy, and organic compounds. Yet FS is not being managed in a way that permits us to derive value from its reuse, while at the same time, millions of farmers struggle with depleted soils and lack of water. In most of the developing countries, energy for cooking is mainly derived from cutting of trees, either as wood or charcoal. Resource recovery and reuse from FS can create livelihoods, enhance food security, support green economies, reduce waste and contribute to cost recovery …

Modeling It Of Discourse Analysis And The Ussues Machine Translation, Nilufar Z. Abdurakhmonova, Allayorzhon G. Teshabayev

Scientific Bulletin. Physical and Mathematical Research

The aim of the research work is to analyze theories on the formation of linguistic database of the translation program of simple texts from English into Uzbek and to create program foundations. The object of the research work is word combinations and simple sentences of English and Uzbek languages, grammatical expressions as well. Scientific novelty of the research work is as follows: Drawn conclusions provide exactness of translation on creating linguistic database of machine translation. created linguistic database of phrasal verbs, morphological lexicon, affixes of English and Uzbek languages and their morphological and syntactic models; identified coordination of simple sentence …

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 …

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 …

Mixed Mode Device-To-Device Communication Scheme For Congestion Reduction And Channel Usage Optimization In 5g Cellular Networks, Chiza M. Christophe, Omar F. Hamad, Libe V. Massawe, Abdi T. Abdalla

Tanzania Journal of Science

Device-to-Device (D2D) communication schemes have gained more attention in cellular networks particularly in normalization process of the upcoming 5G networks. They have been investigated in core network offloading, congestion reduction and channel usage optimization.  The two last cases are among the major constraints in current cellular networks and are the main concerns of this paper. The paper presents a mixed mode D2D communication scheme to decentralize data collection between devices and the base station in order to reduce the number of direct connections at the base station of ultra-dense cells characterized by different levels of channel utilizations or target data …

Total Electron Content Prediction Model Using The Artificial Neural Networks Over The Eastern Africa Region, Emmanuel D. Sulungu, Christian B.S. Uiso

Tanzania Journal of Science

In this paper, development of a model using NN technique for prediction of GPS TEC over the Eastern Africa region is presented. TEC data was obtained from the Africa array and IGS network of ground based dual-frequency GPS receivers from 18 stations within the East African region. It covers approximately the area from ~2.6°N to ~26.9°S in magnetic latitudes and from ~95°E to ~112oE in magnetic longitudes. The input layer of the developed model consisted of seven neurons which were selected by considering the parameters that are known to affect the TECv data. The results showed that when the number …

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

Computer Science Faculty Publications

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 …

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.

Automating Software Changes Via Recommendation Systems, Xiaoyu Liu

Computer Science and Engineering Theses and Dissertations

As the complexity of software systems is growing tremendously, it came with increasingly sophisticated data provided during development. The systematic and large-scale accumulation of software engineering data opened up new opportunities that infer information appropriately can be helpful to software development in a given context. This type of intelligent software development tools came to be known as recommendation systems.

Recommendation Systems in Software Change (RSSCs) share commonalities with conventional recommendation systems: mainly in their usage model, the usual reliance on data mining, and in the predictive nature of their functionality. So a major challenge for designing RSSCs is to automatically …

Smart Factories, Dumb Policy? Managing Cybersecurity And Data Privacy Risks In The Industrial Internet Of Things, Scott J. Shackelford

Minnesota Journal of Law, Science & Technology

No abstract provided.

The Effects Of Automated Grading On Computer Science Courses At The University Of New Orleans, Jerod F A Dunbar

LSU New Orleans Theses and Dissertations

This is a study of the impacts of the incorporation, into certain points of the Computer Science degree program at the University of New Orleans, of Course Management software with an Autograding component. The software in question, developed at Carnegie Mellon University, is called “Autolab.” We begin by dissecting Autolab in order to gain an understanding of its inner workings. We can then take out understanding of its functionality and apply that to an examination of fundamental changes to courses in the time since they incorporated the software. With that, we then compare Drop, Failure, Withdrawal rate data from before …

Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia

LSU New Orleans Theses and Dissertations

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By …