Computer Engineering Commons^™

Experimental Evaluation Of Three Different Humidity Conditions To Physical And Mechanical Properties Of Three Different Mixtures Of Unfired Soil Bricks, Heru Purnomo, Srikandi Wahyu Arini

Makara Journal of Technology

Unfired brick is considered as a more environmentally friendly material than fired brick. It has lower mechanical properties than that of fired brick where humidity influences both bricks. Physical and mechanical properties of unfired bricks made of three kinds of mixtures were studied experimentally under three humidity conditions. The first kind of unfired brick was made only with soil and water while the second type was made of a mixture of soil, water and lime, and the third type was a mixture of soil, water, lime and uniform treated coir where 4% of lime mass was substituted with coir mass. …

Component Analysis And Antiangiogenic Activity Of Thailand Stingless Bee Propolis, Eriko Ishizu, Sari Honda, Tosihro Ohta, Boonyadist Vongsak, Shigenori Kumazawa

Makara Journal of Technology

Propolis is a natural resin produced by honey bees from certain plants, has gained popularity as a food and alternative medicine. However, to the best of our knowledge, few studies on native Thailand stingless bee propolis are available. Information on the chemical composition and biological activities of propolis is needed to investigate its potential utility. Recently we have reported the possible plant origin of Thailand stingless bee propolis, Garcinia mangostana. In this study, further component analysis, functional evaluation, and identification of the plant origin of Thailand stingless bee propolis are conducted. Nine xanthones, including α-mangostin, garcinone C, γ-mangostin, cochinchinone T, …

3d Fdtd Method For Modeling Of Seismo-Electromagnetics Disturbance On Crustal Earth, Nabila Husna Shabrina, Yasuhide Hobara, Achmad Munir

Makara Journal of Technology

The paper deals with the modelling of seismo-electromagnetics disturbance on the crustal earth by use of threedimensional (3D) finite-difference time-domain (FDTD) method. The model is built up by discretizing the frontier geographical region between Java Island and Sumatra Island in a cylindrical coordinate system-based 3D object. The proposed method is applied to compute and analyze electromagnetics (EM) fields of the observed very low frequency (VLF) wave used for the investigation. Boundary condition of uniaxial perfectly matched layer (UPML) are applied surrounding the area of computation for truncating the object of simulation. The investigation are focused on the propagation time of …

Dripline: A Distributed Experiment Control System, Eric Gonzalez, Ben Laroque, Noah Oblath

STAR Program Research Presentations

Project 8 is a nuclear physics experiment which seeks to measure the mass of neutrinos. The experiment requires the use of various pieces of hardware which need to be controlled, and from this need, a software system named Dripline is being developed. Because most researchers have some knowledge in Python and it is easier to understand the syntax of Python as opposed to C++, a Python application programming interface (API) is being created to allow any researcher working with Dripline ease of use. As development progresses, features may be added or removed as needed which requires constant testing, debugging, and …

Machine Learning-Based Network Vulnerability Analysis Of Industrial Internet Of Things, Maede Zolanvari, Marcio Teixeira, Lav Gupta, Khaled Khan, Raj Jain

Computer Science Faculty Works

No abstract provided.

Map My Murder: A Digital Forensic Study Of Mobile Health And Fitness Applications, Courtney Hassenfeldt, Shabana Baig, Ibrahim Baggili, Xiaolu Zhang

Electrical & Computer Engineering and Computer Science Faculty Publications

The ongoing popularity of health and fitness applications catalyzes

the need for exploring forensic artifacts produced by them. Sensitive

Personal Identifiable Information (PII) is requested by the applications

during account creation. Augmenting that with ongoing

user activities, such as the user’s walking paths, could potentially

create exculpatory or inculpatory digital evidence. We conducted

extensive manual analysis and explored forensic artifacts produced

by (n = 13) popular Android mobile health and fitness applications.

We also developed and implemented a tool that aided in the timely

acquisition and identification of artifacts from the examined applications.

Additionally, our work explored the type of …

Tutorial: Are You My Neighbor?: Bringing Order To Neighbor Computing Problems, David Anastasiu, Huzefa Rangwala, Andrea Tagarelli

Faculty Publications

Finding nearest neighbors is an important topic that has attracted much attention over the years and has applications in many fields, such as market basket analysis, plagiarism and anomaly detection, community detection, ligand-based virtual screening, etc. As data are easier and easier to collect, finding neighbors has become a potential bottleneck in analysis pipelines. Performing pairwise comparisons given the massive datasets of today is no longer feasible. The high computational complexity of the task has led researchers to develop approximate methods, which find many but not all of the nearest neighbors. Yet, for some types of data, efficient exact solutions …

Fqstat: A Parallel Architecture For Very High-Speed Assessment Of Sequencing Quality Metrics, Sree K. Chanumolu, Mustafa Albahrani, Hasan H. Otu

Department of Electrical and Computer Engineering: Faculty Publications

Background: High throughput DNA/RNA sequencing has revolutionized biological and clinical research. Sequencing is widely used, and generates very large amounts of data, mainly due to reduced cost and advanced technologies. Quickly assessing the quality of giga-to-tera base levels of sequencing data has become a routine but important task. Identification and elimination of low-quality sequence data is crucial for reliability of downstream analysis results. There is a need for a high-speed tool that uses optimized parallel programming for batch processing and simply gauges the quality of sequencing data from multiple datasets independent of any other processing steps.

Results: FQStat is a …

Asap: A Source Code Authorship Program, Matthew F. Tennyson Phd

Faculty & Staff Research and Creative Activity

Source code authorship attribution is the task of determining who wrote a computer program, based on its source code, usually when the author is either unknown or under dispute. Areas where this can be applied include software forensics, cases of software copyright infringement, and detecting plagiarism. Numerous methods of source code authorship attribution have been proposed and studied. However, there are no known easily accessible and user-friendly programs that perform this task. Instead, researchers typically develop software in an ad hoc manner for use in their studies, and the software is rarely made publicly available. In this paper, we present …

Stamina: Stochastic Approximate Model-Checker For Infinite-State Analysis, Thakur Neupane

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Reliable operation of every day use computing system, from simple coffee machines to complex flight controller system in an aircraft, is necessary to save time, money, and in some cases lives. System testing can check for the presence of unwanted execution but cannot guarantee the absence of such. Probabilistic model checking techniques have demonstrated significant potential in verifying performance and reliability of various systems whose execution are defined with likelihood. However, its inability to scale limits its applicability in practice.

This thesis presents a new model checker, STAMINA, with efficient and scalable model truncation for probabilistic verification. STAMINA uses a …

An Optimized Encoding Algorithm For Systematic Polar Codes, Xiumin Wang, Zhihong Zhang, Jun Li, Yu Wang, Haiyan Cao, Zhengquan Li, Liang Shan

Publications and Research

Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation …

A Simulation Tool For Evaluating The Environmental Impacts Of Management Scenarios For Modern Broiler Production Systems, Martin Andrew Christie

Graduate Theses and Dissertations

The purpose of this work is to provide a simulation tool that allows broiler production practitioners and researchers to simulate the effects of farm design and management practices on resource consumption and environmental impacts. This tool allows the user to design unique farms and simulates on farm processes required to raise broiler chicks to a marketable age. The use can input data such as farm location, broiler breed, flock size, ration type, barn dimensions, and climate control equipment specifications. The algorithms used to simulate broiler breed specific feed intake, broiler weight gain, and other on farm processes such as heating, …

Fake Review Detection Using Data Mining, Md Forhad Hossain

Graduate Theses/Dissertations

Online spam reviews are deceptive evaluations of products and services. They are often carried out as a deliberate manipulation strategy to deceive the readers. Recognizing such reviews is an important but challenging problem. In this work, I try to solve this problem by using different data mining techniques. I explore the strength and weakness of those data mining techniques in detecting fake review. I start with different supervised techniques such as Support Vector Ma- chine (SVM), Multinomial Naive Bayes (MNB), and Multilayer Perceptron. The results attest that all the above mentioned supervised techniques can successfully detect fake review with more …

Cooperative Learning For The Consensus Of Multi-Agent Systems, Qishuai Liu

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

Due to a lot of attention for the multi-agent system in recent years, the consensus algorithm gained immense popularity for building fault-tolerant systems in system and control theory. Generally, the consensus algorithm drives the swarm of agents to work as a coherent group that can reach an agreement regarding a certain quantity of interest, which depends on the state of all agents themselves. The most common consensus algorithm is the average consensus, the final consensus value of which is equal to the average of the initial values. If we want the agents to find the best area of the particular …

Embedded System Design Of Robot Control Architectures For Unmanned Agricultural Ground Vehicles, Ryan Humphrey

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

Engineering technology has matured to the extent where accompanying methods for unmanned field management is now becoming a technologically achievable and economically viable solution to agricultural tasks that have been traditionally performed by humans or human operated machines. Additionally, the rapidly increasing world population and the daunting burden it places on farmers in regards to the food production and crop yield demands, only makes such advancements in the agriculture industry all the more imperative. Consequently, the sector is beginning to observe a noticeable shift, where there exist a number of scalable infrastructural changes that are in the process of slowly …

Distributed Edge Bundling For Large Graphs, Yves Tuyishime

School of Computing: Dissertations, Theses, and Student Research

Graphs or networks are widely used to depict the relationships between data entities in diverse scientific and engineering applications. A direct visualization (such as node-link diagram) of a graph with a large number of nodes and edges often incurs visual clutter. To address this issue, researchers have developed edge bundling algorithms that visually merge similar edges into curved bundles and can effectively reveal high-level edge patterns with reduced visual clutter. Although the existing edge bundling algorithms achieve appealing results, they are mostly designed for a single machine, and thereby the size of a graph they can handle is limited by …

Exploring Eye Tracking Data On Source Code Via Dual Space Analysis, Li Zhang

School of Computing: Dissertations, Theses, and Student Research

Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a …

Cognitive Satellite Communications And Representation Learning For Streaming And Complex Graphs., Wenqi Liu

Electronic Theses and Dissertations

This dissertation includes two topics. The first topic studies a promising dynamic spectrum access algorithm (DSA) that improves the throughput of satellite communication (SATCOM) under the uncertainty. The other topic investigates distributed representation learning for streaming and complex networks. DSA allows a secondary user to access the spectrum that are not occupied by primary users. However, uncertainty in SATCOM causes more spectrum sensing errors. In this dissertation, the uncertainty has been addressed by formulating a DSA decision-making process as a Partially Observable Markov Decision Process (POMDP) model to optimally determine which channels to sense and access. Large-scale networks have attracted …

An Explainable Recommender System Based On Semantically-Aware Matrix Factorization., Mohammed Sanad Alshammari

Electronic Theses and Dissertations

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less …

Formally Designing And Implementing Cyber Security Mechanisms In Industrial Control Networks., Mehdi Sabraoui

Electronic Theses and Dissertations

This dissertation describes progress in the state-of-the-art for developing and deploying formally verified cyber security devices in industrial control networks. It begins by detailing the unique struggles that are faced in industrial control networks and why concepts and technologies developed for securing traditional networks might not be appropriate. It uses these unique struggles and examples of contemporary cyber-attacks targeting control systems to argue that progress in securing control systems is best met with formal verification of systems, their specifications, and their security properties. This dissertation then presents a development process and identifies two technologies, TLA+ and seL4, that can be …

Phishing Websites Detection Using Machine Learning, Arun D. Kulkarni, Leonard L. Brown, Iii

Computer Science Faculty Publications and Presentations

Tremendous resources are spent by organizations guarding against and recovering from cybersecurity attacks by online hackers who gain access to sensitive and valuable user data. Many cyber infiltrations are accomplished through phishing attacks where users are tricked into interacting with web pages that appear to be legitimate. In order to successfully fool a human user, these pages are designed to look like legitimate ones. Since humans are so susceptible to being tricked, automated methods of differentiating between phishing websites and their authentic counterparts are needed as an extra line of defense. The aim of this research is to develop these …

Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders

Faculty and Staff Scholarship

Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of …

Digital Marketing In The Artificial Intelligence And Machine Learning Age, Z. Ruan, Keng Siau

Research Collection School Of Computing and Information Systems

We are living in a period of profound change driven by digitization, information and communication technology, artificial intelligence, machine learning, and robotics (Gupta, Keen, Shah, and Verdier, 2017; Wang and Siau, 2019). Traditional marketing is shifting to digital marketing enabled by AI and machine learning. Customer consumption behavior has changed from traditional in-store shopping to online shopping (Thiraviyam, 2018). The large volume of transaction and demographic data enables business analytics, AI, and machine learning to analyze and predict customer behavior to improve customer satisfaction and enhance sales (Siau and Wang, 2018). For example, predictive analytics uses different algorithms to predict …

Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang

Research Collection School Of Computing and Information Systems

The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional …

Iot Ignorance Is Digital Forensics Research Bliss: A Survey To Understand Iot Forensics Definitions, Challenges And Future Research Directions, Tina Wu, Frank Breitinger, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

Interactions with IoT devices generates vast amounts of personal data that can be used as a source of evidence in digital investigations. Currently, there are many challenges in IoT forensics such as the difficulty in acquiring and analysing IoT data/devices and the lack IoT forensic tools. Besides technical challenges, there are many concepts in IoT forensics that have yet to be explored such as definitions, experience and capability in the analysis of IoT data/devices and current/future challenges. A deeper understanding of these various concepts will help progress the field. To achieve this goal, we conducted a survey which received 70 …

Modeling Climate Driven Urban Migration In The United States, Julia Beckwith

REU Final Reports

Though research on climate driven migration has become more prevalent, the majority of recent studies model migration patterns in the Global South. While these inquiries are rightfully focused on populations that will be disproportionately affected by climate change, countries in the Global North are not impervious to these effects. As global population distributions shift, it will be necessary to know which urban areas in the United States might be best equipped to handle influxes of people. Drawing on existing climate-migration frameworks, the agent-based model detailed in this paper utilizes available demographic and climate data to simulate climate-driven migration between key …

Shoal: Large-Scale Hierarchical Taxonomy Via Graph-Based Query Coalition In E-Commerce, Zhao Li, Xia Chen, Xuming Pan, Pengcheng Zou, Yuchen Li, Guoxian Yu

Research Collection School Of Computing and Information Systems

E-commerce taxonomy plays an essential role in online retail business. Existing taxonomy of e-commerce platformsorganizes items into an ontology structure. However, theontology-driven approach is subject to costly manual maintenance and often does not capture user’s search intention,particularly when user searches by her personalized needsrather than a universal definition of the items. Observingthat search queries can effectively express user’s intention,we present a novel large-Scale Hierarchical taxOnomy viagrAph based query coaLition (SHOAL) to bridge the gapbetween item taxonomy and user search intention. SHOALorganizes hundreds of millions of items into a hierarchicaltopic structure. Each topic that consists of a cluster of itemsdenotes a …

Investigating Semantic Properties Of Images Generated From Natural Language Using Neural Networks, Samuel Ward Schrader

Boise State University Theses and Dissertations

This work explores the attributes, properties, and potential uses of generative neural networks within the realm of encoding semantics. It works toward answering the questions of: If one uses generative neural networks to create a picture based on natural language, does the resultant picture encode the text's semantics in a way a computer system can process? Could such a system be more precise than current solutions at detecting, measuring, or comparing semantic properties of generated images, and thus their source text, or their source semantics?

This work is undertaken in the hope that detecting previously unknown properties, or better understanding …

Incremental Processing For Improving Conversational Grounding In A Chatbot, Aprajita Shukla

Boise State University Theses and Dissertations

Current Digital Personal Assistants can be quite efficient while performing routine tasks like setting up reminders and looking up information. However, they do not attempt to establish common ground–the process of establishing and building mutual understanding–and require a significant amount of initial data to learn how to understand user intent. In this thesis, an incremental processing framework is leveraged through a chatbot interface which updates its understanding state at each inputted word, asks the user to clarify input when the system is unsure and prompts user to give feedback several times during an interaction, all of which are instrumental in …

Minos: Unsupervised Netflow-Based Detection Of Infected And Attacked Hosts, And Attack Time In Large Networks, Mousume Bhowmick

Boise State University Theses and Dissertations

Monitoring large-scale networks for malicious activities is increasingly challenging: the amount and heterogeneity of traffic hinder the manual definition of IDS signatures and deep packet inspection. In this thesis, we propose MINOS, a novel fully unsupervised approach that generates an anomaly score for each host allowing us to classify with high accuracy each host as either infected (generating malicious activities), attacked (under attack), or clean (without any infection). The generated score of each hour is able to detect the time frame of being attacked for an infected or attacked host without any prior knowledge. MINOS automatically creates a personalized traffic …