Computer Sciences Commons^™

An Ordinance-Tweet Mining App To Disseminate Urban Policy Knowledge For Smart Governance, Christina Varghese, Aparna S. Varde, Xu Du

Department of Computer Science Faculty Scholarship and Creative Works

This paper focuses on how populations by the use of technology, more specifically an app, can comprehend the enactment of ordinances (local laws) in an urban area along with their public reactions expressed as tweets. Furthermore, they can understand how well the area is developing and enhancing as a Smart City. The main goal of this research is to develop an Ordinance-Tweet Mining App that disseminates the results of analyzing ordinances and tweets about them, especially related to Smart City Characteristics such as Smart Environment, Smart Mobility etc. This app would be beneficial to various users such as environmental scientists, …

I Am Guessing You Can't Recognize This: Generating Adversarial Images For Object Detection Using Spatial Commonsense, Anurag Garg, Niket Tandon, Aparna S. Varde

Department of Computer Science Faculty Scholarship and Creative Works

Can we automatically predict failures of an object detection model on images from a target domain? We characterize errors of a state-of-the-art object detection model on the currently popular smart mobility domain, and find that a large number of errors can be identified using spatial commonsense. We propose CSK-SNIFFER, a system that automatically identifies a large number of such errors based on commonsense knowledge. Our system does not require any new annotations and can still find object detection errors with high accuracy (more than 80% when measured by humans). This work lays the foundation to answer exciting research questions on …

Fault Identification On Electrical Transmission Lines Using Artificial Neural Networks, Christopher W. Asbery

Theses and Dissertations--Electrical and Computer Engineering

Transmission lines are designed to transport large amounts of electrical power from the point of generation to the point of consumption. Since transmission lines are built to span over long distances, they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. When faults occur, protection systems installed near the faulted transmission lines will isolate these faults from the …

Differential Recurrent Neural Networks For Human Activity Recognition, Naifan Zhuang

Electronic Theses and Dissertations, 2020-2023

Human activity recognition has been an active research area in recent years. The difficulty of this problem lies in the complex dynamical motion patterns embedded through the sequential frames. The Long Short-Term Memory (LSTM) recurrent neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model various time-series data, where the current hidden state has to be considered in the context of the past hidden states. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient …

Machine Learning Based Rf Transmitter Characterization In The Presence Of Adversaries, Debashri Roy

Electronic Theses and Dissertations, 2020-2023

The advances in wireless technologies have led to autonomous deployments of various wireless networks. As these networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can learn and adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize the RF environment. In this dissertation, we address some of the fundamental challenges on how to effectively apply different learning techniques in the …

Efficient String Algorithms With Applications In Bioinformatics, Sahar Hooshmand

Electronic Theses and Dissertations, 2020-2023

The work presented in this dissertation deals with establishing efficient methods for solving some algorithmic problems, which have applications to Bioinformatics. After a short introduction in Chapter 1, an algorithm for genome mappability problem is presented in Chapter 2. Genome mappability is a measure for the approximate repeat structure of the genome with respect to substrings of specific length and a tolerance to define the number of mismatches. The similarity between reads is measured by using the Hamming distance function. Genome mappability is computed for each position in the string and has several applications in designing high-throughput short-read sequencing experiments. …

Learning Context-Sensitive Human Emotions In Categorical And Dimensional Domains, Pooyan Balouchian

Electronic Theses and Dissertations, 2020-2023

Still image emotion recognition (ER) has been receiving increasing attention in recent years due to the tremendous amount of social media content on the Web. Many works offer both categorical and dimensional methods to detect image sentiments, while others focus on extracting the true social signals, such as happiness and anger. Deep learning architectures have delivered great suc- cess, however, their dependency on large-scale datasets labeled with (1) emotion, and (2) valence, arousal and dominance, in categorical and dimensional domains respectively, introduce challenges the community tries to tackle. Emotions offer dissimilar semantics when aroused in different con- texts, however "context-sensitive" …

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 …

Decentralized Adaptable Task Allocation For Ongoing Tasks, Vera Kazakova

Electronic Theses and Dissertations, 2020-2023

This thesis extends an existing bio-inspired model for decentralized task allocation and benchmarks it against alternative approaches to assess robustness in dynamic conditions, applicability to domains with ongoing and hierarchical tasks, and scalability to large teams of agents. The work addresses decentralized task allocation of simple non-communicating agents in dynamic environments of multiple tasks. Multi-area patrolling is used as the sample domain: specific number of agents is required to successfully patrol each area on each timestep, indefinitely, until the system's security needs. Agents must individually decide whether to patrol and where (i.e., task availability is not limited by task demand …

The Dollar General: Continuous Custom Gesture Recognition Techniques At Everyday Low Prices, Eugene Taranta

Electronic Theses and Dissertations, 2020-2023

Humans use gestures to emphasize ideas and disseminate information. Their importance is apparent in how we continuously augment social interactions with motion—gesticulating in harmony with nearly every utterance to ensure observers understand that which we wish to communicate, and their relevance has not escaped the HCI community's attention. For almost as long as computers have been able to sample human motion at the user interface boundary, software systems have been made to understand gestures as command metaphors. Customization, in particular, has great potential to improve user experience, whereby users map specific gestures to specific software functions. However, custom gesture recognition …

Local Alignment Of Frame Of Reference Assignment In English And Swedish Dialogue, Simon Dobnik, John D. Kelleher, Christine Howes

Conference papers

In this paper we examine how people assign, interpret, negotiate and repair the frame of reference (FoR) in online text-based dialogues discussing spatial scenes in English and Swedish. We describe our corpus and data collection which involves a coordination experiment in which dyadic dialogue participants have to identify differences in their picture of a visual scene. As their perspectives of the scene are different, they must coordinate their FoRs in order to complete the task. Results show that participants do not align on a global FoR, but tend to align locally, for sub-portions (or particular conversational games) in the dialogue. …

Lung Cancer Subtype Differentiation From Positron Emission Tomography Images, Oğuzhan Ayyildiz, Zafer Aydin, Bülent Yilmaz, Seyhan Karaçavuş, Kübra Şenkaya, Semra İçer, Erdem Arzu Taşdemi̇r, Eser Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Lung cancer is one of the deadly cancer types, and almost 85 % of lung cancers are nonsmall cell lung cancer (NSCLC). In the present study we investigated classification and feature selection methods for the differentiation of two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The major advances in understanding the effects of therapy agents suggest that future targeted therapies will be increasingly subtype specific. We obtained positron emission tomography (PET) images of 93 patients with NSCLC, 39 of which had ADC while the rest had SqCC. Random walk segmentation was applied to delineate three-dimensional tumor …

On The Automorphisms And Isomorphisms Of Mds Matrices And Their Efficient Implementations, Muharrem Tolga Sakalli, Sedat Akleylek, Kemal Akkanat, Vincent Rijmen

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we explicitly define the automorphisms of MDS matrices over the same binary extension field. By extending this idea, we present the isomorphisms between MDS matrices over $\mathbb{F}_{2^{m}}$ and MDS matrices over $\mathbb{F}_{2^{mt}}$, where $t \ge 1$ and $m>1$, which preserves the software implementation properties in view of XOR operations and table lookups of any given MDS matrix over $\mathbb{F}_{2^{m}}$. Then we propose a novel method to obtain distinct functions related to these automorphisms and isomorphisms to be used in generating isomorphic MDS matrices (new MDS matrices in view of implementation properties) using the existing ones. The …

Wideband Patch Array Antenna Using Superstrate Configuration For Future 5gapplications, Sidra Farhat, Farzana Arshad, Yasar Amin, Jonathan Loo

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, four distinct antenna configurations for future-centric 5G applications are proposed. Initially, a single rectangular patch is designed to operate at the frequency of 28 GHz while maintaining a wide operational band. Performance of the antenna is improved by incorporating an array of identical rectangular elements resulting in a higher gain and wider bandwidth. The proposed arrangement consists of three rectangular elements realized using 0.508-mm thick Rogers RT/Duroid 5880 laminate. The bandwidth is further enhanced by increasing the number of radiating elements in the array from three to five. Evolution of the proposed design is concluded by stacking …

Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …

Multi-Modal Medical Imaging Analysis With Modern Neural Networks, Gongbo Liang

Theses and Dissertations--Computer Science

Medical imaging is an important non-invasive tool for diagnostic and treatment purposes in medical practice. However, interpreting medical images is a time consuming and challenging task. Computer-aided diagnosis (CAD) tools have been used in clinical practice to assist medical practitioners in medical imaging analysis since the 1990s. Most of the current generation of CADs are built on conventional computer vision techniques, such as manually defined feature descriptors. Deep convolutional neural networks (CNNs) provide robust end-to-end methods that can automatically learn feature representations. CNNs are a promising building block of next-generation CADs. However, applying CNNs to medical imaging analysis tasks is …

Analiza E Trafikut Në Rrjete Të Enkriptuara, Faton Bekteshi

Theses and Dissertations

Kjo tezë, është punuar në bashkëpunim me Profesor Selman Haxhijaha MSc dhe Profesor Betan Karahoda PHd dhe është testuar përmes simulatorit të rrjetës GNS3, si dhe në laboratorin e Universitetit për Biznes dhe Teknologji në Prishtinë (http://www.ubt-uni.net/). Objektivi kryesor i kësaj teze, është shfaqja e përparësive dhe mangësive të procesit të analizës së një trafiku të dendur të komunikimit dhe bartjes së të dhënave në rrjetën e enkriptuar, duke analizuar dhe krahasuar disa parametra si ai: i kohës, gabimeve, vonesave dhe mbi të gjitha rëndësisë së sigurisë. Do të krijojmë një topologji të rrjetit të ngjajshëm me organizatën tonë, me …

A Cool Brisk Walk Through Discrete Mathematics, Stephen Davies

Computer Science Articles

A Cool Brisk Walk Through Discrete Mathematics - and its companion site "allthemath" - are completely-and-forever-free-and-open-source educational materials dedicated to the mathematics that budding computer science practitioners actually need to know. They feature the fun and addictive teaching of award-winning lecturer Dr. Stephen Davies of the University of Mary Washington in Fredericksburg, Virginia!

Initiating A Collaborative Cybersecurity Governance Framework At The State Level, Autum C. Pylant

West Chester University Doctoral Projects

Cybersecurity risk is dynamic and rapidly evolving. Today, cyber incidents can significantly impair government operations, erode public confidence, undermine operations of critical infrastructures, and put citizen data and whole industries at risk. Managing these risks to cyber assets must be part of a state’s overall risk management portfolio. To do this successfully, state leaders must have effective cybersecurity governance. To achieve this, governors and state legislatures must ensure that their states have the essential governance mechanisms necessary for understanding and managing cybersecurity risk and for translating awareness about cyber threats into action. This dissertation gives an overview of three different …

Computation And Research In Data Science (Cards) Report - 2020, Computation And Research In Data Science, East Tennessee State University

Computation and Research in Data Science (CaRDS) Board Meeting Minutes

No abstract provided.

Power-Over-Tether Uas Leveraged For Nearly Indefinite Meteorological Data Acquisition In The Platte River Basin, Daniel Rico, Carrick Detweiler, Francisco Munoz-Arriola

School of Computing: Conference and Workshop Papers

The integration of unmanned aerial systems (UASs) has increased in the field of agriculture. These systems can provide data that was previously difficult to obtain to help increase efficiency and production. Typical commercial off the shelf (COTS) UASs have significant limitations in the form of small payloads, and short flight times which inhibit their ability to provide significant quantities of useful data. We present the development of a novel power-over-tether UAS that leverages the physical presence of the tether to integrate sensors at multiple altitudes along the tether. The UAS can acquire data nearly indefinitely to sense atmospheric conditions and …

Special Topics In Natural Language Processing: Mwe Identification, Opinion Question Answering And Summarization, Aishwarya Ashok

Computer Science and Engineering Dissertations - Archive

Natural Language Processing (NLP) is the use of computers for the analysis of text. The approaches used to solve problems in NLP has transitioned from human tagging to statistical methods and currently many of the approaches make use of techniques from Machine Learning (ML) and Artificial Intelligence (AI). Some of the text-based problems in NLP include question answering, summarization, and text-based recommendation systems. The amount of text data that is available is increasing by the day. With the growing size of text data, the research in NLP needs more sophisticated methods than traditional statistical methods. Current day approaches make use …

Supp & Mapp: Adaptable Structure-Based Representations For Mir Tasks, Claire Savard, Erin H. Bugbee, Melissa R, Mcguirl, Katherine M. Kinnaird

Statistical and Data Sciences: Faculty Publications

Accurate and flexible representations of music data are paramount to addressing MIR tasks, yet many of the existing approaches are difficult to interpret or rigid in nature. This work introduces two new song representations for structure-based retrieval methods: Surface Pattern Preservation (SuPP), a continuous song representation, and Matrix Pattern Preservation (MaPP), SuPP’s discrete counterpart. These representations come equipped with several user-defined parameters so that they are adaptable for a range of MIR tasks. Experimental results show MaPP as successful in addressing the cover song task on a set of Mazurka scores, with a mean precision of 0.965 and recall of …

Creating A Sample Of Off-Color Galaxies Using Big Data Tools, Christopher Becker

Honors Program Theses

This thesis begins an investigation into the presence of off-colored galaxies in the Sloan Digital Sky Survey. Through establishing the emergence and history of Astroinformatics, the thesis introduces the concepts surrounding both off-color galaxies and the Big Data tools helpful in analyzing the data to find them. A discussion of initial implementation methods and revised implementation due to difficulties with previous plans follows. Results are presented, with well in excess of 500,000 candidates for off-color galaxies present in the sample. Conclusions are then drawn regarding such a large sample and the implications this may have on the conventional understanding of …

Machines Finding Injustice, Hannah S. Lacqueur, Ryan W. Copus

Faculty Works

With rising caseloads, review systems are increasingly taxed, stymieing traditional methods of case screening. We propose an automated solution: predictive models of legal decisions can be used to identify and focus review resources on outlier decisions—those decisions that are most likely the product of biases, ideological extremism, unusual moods, and carelessness and thus most at odds with a court’s considered, collective judgment. By using algorithms to find and focus human attention on likely injustices, adjudication systems can largely sidestep the most serious objections to the use of algorithms in the law: that algorithms can embed racial biases, deprive parties of …

Towards A General Solution For Layout Of Visual Goal Models With Actors: Supplemental Material, Yilin Lucy Wang, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper:

"Towards a General Solution for Layout of Visual Goal Models with Actors"

This paper presents early results and lays a foundation for discussion within our GORE community.

Reconstructing The Past: The Case Of The Spadina Expressway, Alicia M. Grubb, Marsha Chechik

Computer Science: Faculty Publications

In order to build resilient systems that can be operational for a long time, it is important that analysts are able to model the evolution of the requirements of that system. The Evolving Intentions framework models how stakeholders’ goals change over time. In this work, our aim is to validate applicability and effectiveness of this technique on a substantial case. In the absence of ground truth about future evolutions, we used historical data and rational reconstruction to understand how a project evolved in the past. Seeking a well-documented project with varying stakeholder intentions over a substantial period of time, we …

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …

Fusion Of 3d Lidar And Camera Data For Object Detection In Autonomous Vehicle Applications, Xiangmo Zhao, Pengpeng Sun, Zhigang Xu, Haigen Min, Hongkai Yu

Computer Science Faculty Publications

It’s critical for an autonomous vehicle to acquire accurate and real-time information of the objects in its vicinity, which will fully guarantee the safety of the passengers and vehicle in various environment. 3D LIDAR can directly obtain the position and geometrical structure of the object within its detection range, while vision camera is very suitable for object recognition. Accordingly, this paper presents a novel object detection and identification method fusing the complementary information of two kind of sensors. We first utilize the 3D LIDAR data to generate accurate object-region proposals effectively. Then, these candidates are mapped into the image space …

Poisoning Attacks On Learning-Based Keystroke Authentication And A Residue Feature Based Defense, Zibo Wang

Doctoral Dissertations

Behavioral biometrics, such as keystroke dynamics, are characterized by relatively large variation in the input samples as compared to physiological biometrics such as fingerprints and iris. Recent advances in machine learning have resulted in behaviorbased pattern learning methods that obviate the effects of variation by mapping the variable behavior patterns to a unique identity with high accuracy. However, it has also exposed the learning systems to attacks that use updating mechanisms in learning by injecting imposter samples to deliberately drift the data to impostors’ patterns. Using the principles of adversarial drift, we develop a class of poisoning attacks, named Frog-Boiling …