Computer Sciences Commons^™

Man-In-The-Middle Attacks On Mqtt Based Iot Networks, Henry C. Wong

Masters Theses

“The use of Internet-of-Things (IoT) devices has increased a considerable amount in recent years due to decreasing cost and increasing availability of transistors, semiconductor, and other components. Examples can be found in daily life through smart cities, consumer security cameras, agriculture sensors, and more. However, Cyber Security in these IoT devices are often an afterthought making these devices susceptible to easy attacks. This can be due to multiple factors. An IoT device is often in a smaller form factor and must be affordable to buy in large quantities; as a result, IoT devices have less resources than a typical computer. …

Robot Guided Exercise Training: The Role Of The Human Model, Selena R. Richards

Honors Theses and Capstones

Physical therapy after an injury can be difficult for patients to access, whether it be due to location, finances, or other factors. To make physical therapy more accessible, robot-guided exercise training can be used. Commercial anthropomorphic robots have been created and have human-like movement, but may still lack qualities that make it easy for a human to understand its intentions intuitively. A human model that mimics the movement of a robot performing human exercises can be used to complement the robot and increase human understanding. The ideal model would be viewable from different angles and could visibly show the difference …

Distributed Partial Differential Equation Solving With Julia Fast Fourier Transform Library, Christopher E. Mottola

Honors Theses and Capstones

Scientific computing relies on advanced computational and mathematical techniques to solve complex problems in scientific domains. For the numerical rendering of spectral, nonlinear, and dynamic phenomena, there is a growing need for greater availability of a broad class of Fourier-based algorithms to perform large scale operations on multidimensional data in distributed and optimized ways. To this effect, the Julia programming language is new and has significant advantages compared to other common languages used in scientific computing. The research presented here formulates a basis for further development in high-performance scientific computing of periodic partial differential equations through the application of distributed …

Temporally Sliced Photon Primitives For Volumetric Time-Of-Flight Rendering, Yang Liu

Dartmouth College Master’s Theses

Traditional steady-state rendering assumes that the light transport has already reached equilibrium. In contrast, time-of-flight rendering removes this assumption and recovers the pattern of light at extremely high temporal resolutions. This novel rendering modality not only provides a way to visualize the propagation of light, but can also empower the advances in time-of-flight imaging and its corresponding applications.

Building on previous work in steady-state volumetric rendering, this thesis introduces a novel framework for deriving new Monte Carlo estimators for solving the time-of-flight rendering problem in participating media. Conceptually, our method starts with any steady-state photon primitive, like a photon plane …

Spoken Language Interaction With Robots: Recommendations For Future Research, Casey Kennington

Computer Science Faculty Publications and Presentations

With robotics rapidly advancing, more effective human–robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. In this article, based on the report of an interdisciplinary workshop convened by the National Science Foundation, we identify key scientific and engineering advances needed to enable effective spoken language interaction with robotics. We make 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods …

Developers Perception Of Peer Code Review In Research Software Development, Nasir U. Eisty, Jeffrey C. Carver

Computer Science Faculty Publications and Presentations

Context Research software is software developed by and/or used by researchers, across a wide variety of domains, to perform their research. Because of the complexity of research software, developers cannot conduct exhaustive testing. As a result, researchers have lower confidence in the correctness of the output of the software. Peer code review, a standard software engineering practice, has helped address this problem in other types of software.

Objective Peer code review is less prevalent in research software than it is in other types of software. In addition, the literature does not contain any studies about the use of peer code …

Drones, Virtual Reality, And Modeling: Communicating Catastrophic Dam Failure, H. R. Spero, I. Vazquez-Lopez, K. Miller, R. Joshaghani, S. Cutchin, J. Enterkine

Computer Science Faculty Publications and Presentations

Dam failures occur worldwide and can be economically and ecologically devastating. Communicating the scale of these risks to the general public and decision-makers is imperative. Two-dimensional (2D) dam failure hydraulic models inform owners and floodplain managers of flood regimes but have limitations when shared with non-specialists. This study addresses these limitations by constructing a 3D Virtual Reality (VR) environment to display the 1976 Teton Dam disaster case study using a pipeline composed of (1) 2D hydraulic model data (extrapolated into 3D), (2) a 3D reconstructed dam, and (3) a terrain model processed from UAS (Uncrewed Airborne System) imagery using Structure …

Understanding Intention For Machine Theory Of Mind: A Position Paper, Casey Kennington

Computer Science Faculty Publications and Presentations

Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention—choice with commitment—following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I …

Measuring Fairness In Ranked Results: An Analytical And Empirical Comparison, Amifa Raj, Michael D. Ekstrand

Computer Science Faculty Publications and Presentations

Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user's information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system's behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and …

Incremental Unit Networks For Distributed, Symbolic Multimodal Processing And Representation, Mir Tahsin Imtiaz, Casey Kennington

Computer Science Faculty Publications and Presentations

Incremental dialogue processing has been an important topic in spoken dialogue systems research, but the broader research community that makes use of language interaction (e.g., chatbots, conversational AI, spoken interaction with robots) have not adopted incremental processing despite research showing that humans perceive incremental dialogue as more natural. In this paper, we extend prior work that identifies the requirements for making spoken interaction with a system natural with the goal that our framework will be generalizable to many domains where speech is the primary method of communication. The Incremental Unit framework offers a model of incremental processing that has been …

Symbol And Communicative Grounding Through Object Permanence With A Mobile Robot, Josue Torres-Fonseca, Catherine Henry, Casey Kennington

Computer Science Faculty Publications and Presentations

Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration as it relates to symbol and communicative grounding in spoken dialogue systems. In this paper, we leverage SLAM as a module for tracking object permanence and use a robot platform to move around a scene where it discovers objects and learns how they are denoted. We evaluated by comparing our system’s effectiveness at learning words from human dialogue partners both with and without object permanence. …

Religious Violence And Twitter: Networks Of Knowledge, Empathy And Fascination, Samah Senbel, Carly Seigel, Emily Bryan

School of Computer Science & Engineering Faculty Publications

Twitter analysis through data mining, text analysis, and visualization, coupled with the application of actor-network-theory, reveals a coalition of heterogenous religious affiliations around grief and fascination. While religious violence has always existed, the prevalence of social media has led to an increase in the magnitude of discussions around the topic. This paper examines the different reactions on Twitter to violence targeting three religious communities: the 2015 Charleston Church shooting, the 2018 Pittsburgh Synagogue shooting, and the 2019 Christchurch Mosque shootings. The attacks were all perpetrated by white nationalists with firearms. By analyzing large Twitter datasets in response to the attacks, …

Impact Of Sleep And Training On Game Performance And Injury In Division-1 Women’S Basketball Amidst The Pandemic, Samah Senbel, S. Sharma, S. M. Raval, Christopher B. Taber, Julie K. Nolan, N. S. Artan, Diala Ezzeddine, Kaya Tolga

School of Computer Science & Engineering Faculty Publications

We investigated the impact of sleep and training load of Division - 1 women’s basketball players on their game performance and injury prediction using machine learning algorithms. The data was collected during a pandemic-condensed season with unpredictable interruptions to the games and athletic training schedules. We collected data from sleep monitoring devices, training data from coaches, injury reports from medical staff, and weekly survey data from athletes for 22 weeks.With proper data imputation, interpretable feature set, data balancing, and classifiers, we showed that we could predict game performance and injuries with more than 90% accuracy. More importantly, our F1 and …

Cybersecurity Logging & Monitoring Security Program, Thai H. Nguyễn

School of Computer Science & Engineering Undergraduate Publications

With ubiquitous computing becoming pervasive in every aspect of societies around the world and the exponential rise in cyber-based attacks, cybersecurity teams within global organizations are spending a massive amount of human and financial capital on their logging and monitoring security programs. As a critical part of global organizational security risk management processes, it is important that log information is aggregated in a timely, accurate, and relevant manner. It is also important that global organizational security operations centers are properly monitoring and investigating the security use-case alerting based on their log data. In this paper, the author proposes a model …

C2 Microservices Api: Ch4rl3sch4l3m4gn3, Thai H. Nguyễn

School of Computer Science & Engineering Undergraduate Publications

In the 21^st century, cyber-based attackers such as advance persistent threats are leveraging bots in the form of botnets to conduct a plethora of cyber-attacks. While there are several social engineering techniques used to get targets to unknowingly download these bots, it is the command-and-control techniques advance persistent threats use to control their bots that is of critical interest to the author. In this research paper, the author aims to develop a command-and-control microservice application programming interface infrastructure to facilitate botnet command-and-control attack simulations. To achieve this the author will develop a simple bot skeletal framework, utilize the latest …

Formal Modeling And Verification Of A Blockchain-Based Crowdsourcing Consensus Protocol, Hamra Afzaal, Muhammad Imran, Muhammad Umar Janjua, Sarada Prasad Gochhayat

VMASC Publications

Crowdsourcing is an effective technique that allows humans to solve complex problems that are hard to accomplish by automated tools. Some significant challenges in crowdsourcing systems include avoiding security attacks, effective trust management, and ensuring the system’s correctness. Blockchain is a promising technology that can be efficiently exploited to address security and trust issues. The consensus protocol is a core component of a blockchain network through which all the blockchain peers achieve an agreement about the state of the distributed ledger. Therefore, its security, trustworthiness, and correctness have vital importance. This work proposes a Secure and Trustworthy Blockchain-based Crowdsourcing (STBC) …

Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick

Complex Systems Faculty Publications and Presentations

An introduction to Reconstructability Analysis for the Discrete Multivariate Modeling course and for other purposes.

Supporting Stylized Language Models Using Multi-Modality Features, Chengxi Li

Theses and Dissertations--Computer Science

As AI and machine learning systems become more common in our everyday lives, there is an increased desire to construct systems that are able to seamlessly interact and communicate with humans. This typically means creating systems that are able to communicate with humans via natural language. Given the variance of natural language, this can be a very challenging task. In this thesis, I explored the topic of humanlike language generation in the context of stylized language generation. Stylized language generation involves producing some text that exhibits a specific, desired style. In this dissertation, I specifically explored the use of multi-modality …

Smart Decision-Making Via Edge Intelligence For Smart Cities, Nathaniel Hudson

Theses and Dissertations--Computer Science

Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and …

Matrix Interpretations And Tools For Investigating Even Functionals, Benjamin Stringer

Theses and Dissertations--Computer Science

Even functionals are a set of polynomials evaluated on the terms of hollow symmetric matrices. Their properties lend themselves to applications such as counting subgraph embeddings in generic (weighted or unweighted) host graphs and computing moments of binary quadratic forms, which occur in combinatorial optimization. This research focuses primarily on counting subgraph embeddings, which is traditionally accomplished with brute-force algorithms or algorithms curated for special types of graphs. Even functionals provide a method for counting subgraphs algebraically in time proportional to matrix multiplication and is not restricted to particular graph types. Counting subgraph embeddings can be accomplished by evaluating a …

Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System, Andrew L. Sanders

College of Graduate Studies: Theses & Dissertations

Academic instructors and institutions desire the ability to accurately and autonomously measure the attentiveness of students in the classroom. Generally, college departments use unreliable direct communication from students (i.e. emails, phone calls), distracting and Hawthorne effect-inducing observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. Young et al. discuss that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, …

License Plate Image Quality Enhancement Utilizing Super Resolution Generative Adversarial Networks, Mark Moelter

College of Graduate Studies: Theses & Dissertations

This thesis focuses primarily on enhancing the image quality of blurred license plates through the use of Super-Resolution Generative Adversarial Networks (SRGANs) [1]. We propose a synthetic dataset with SRGAN model to promote blurred image quality enhancement, and allow for model evaluation on a multitude of image input and output size combinations. SRGAN is mainly used for low-resolution image enhancement, but by heavily blurring the input images, the model is tested on its ability to blindly deblur and upsample images to the desired super-resolution (SR) size. The model enhances the image quality to nearly that of the reference images. The …

A Simple Algorithm For Generating A New Two Sample Type-Ii Progressive Censoring With Applications, E. M. Shokr, Rashad Mohamed El-Sagheer, Mahmoud Mansour, H. M. Faied, B. S. El-Desouky

Basic Science Engineering

In this article, we introduce a simple algorithm to generating a new type-II progressive censoring scheme for two samples. It is observed that the proposed algorithm can be applied for any continues probability distribution. Moreover, the description model and necessary assumptions are discussed. In addition, the steps of simple generation algorithm along with programming steps are also constructed on real example. The inference of two Weibull Frechet populations are discussed under the proposed algorithm. Both classical and Bayesian inferential approaches of the distribution parameters are discussed. Furthermore, approximate confidence intervals are constructed based on the asymptotic distribution of the maximum …

Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman

Honors Theses and Capstones

Machine learning models can be trained to classify time series based sports motion data, without reliance on assumptions about the capabilities of the users or sensors. This can be applied to predict the count of occurrences of an event in a time period. The experiment for this research uses lacrosse data, collected in partnership with SPAITR - a UNH undergraduate startup developing motion tracking devices for lacrosse. Decision Tree and Support Vector Machine (SVM) models are trained and perform with high success rates. These models improve upon previous work in human motion event detection and can be used a reference …

Three Major Instructional Approaches For Requirements Engineering, Marian Daun, Alicia M. Grubb, Bastian Tenbergen

Computer Science: Faculty Publications

In this talk, we report on our findings from the paper A Survey of Instructional Approaches in the Requirements Engineering Education Literature [DGT21], which has been accepted at and published in the proceedings of the 2021 IEEE International Conference on Requirements Engineering. The paper reports the findings of a systematic literature review to define and investigate the current state of research on requirements engineering education.

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous

Dissertations

Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …

Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen

Dissertations

Dark patterns are user interfaces purposefully designed to manipulate users into doing something they might not otherwise do for the benefit of an online service. This study investigates the impact of dark patterns on overall user experience and site revisitation in the context of airline websites. In order to assess potential dark pattern effects, two versions of the same airline website were compared: a dark version containing dark pattern elements and a bright version free of manipulative interfaces. User experience for both websites were assessed quantitatively through a survey containing a User Experience Questionnaire (UEQ) and a System Usability Scale …

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy

Dissertations

Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …

กระบวนการและแบบจำลองสำหรับการคัดกรองเพื่อการจัดการคุณภาพข้อมูลในคราวด์ซอร์สซิงแพลตฟอร์ม, กฤตย์ กังวาลพงศ์พันธุ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การเก็บรวบรวมข้อมูลด้วยคราวด์ซอร์สซิงเป็นวิธีที่โดยทั่วไปมีความเร็วมากกว่า มีต้นทุนต่ำกว่า และมีความหลากหลายมากกว่าวิธีการเก็บรวบรวมข้อมูลแบบอื่น ๆ อย่างไรก็ตาม คราวด์ซอร์สซิงอาจเผชิญกับปัญหาคุณภาพ เช่น การติดป้ายกำกับผิดหรือการนำมาใช้ในทางที่ไม่เหมาะสม ดังนั้น กระบวนการควบคุมคุณภาพเป็นสิ่งที่จำเป็นสำหรับแพลตฟอร์มคราวด์ซอร์สซิง วิทยานิพนธ์นี้ศึกษาค้นคว้าอุปสรรคและวิธีการแก้ไขที่เป็นไปได้ในการจัดการคุณภาพของผู้ใช้งานแพลตฟอร์มคราวด์ซอร์สซิง ส่วนแรกเน้นวิธีการเพิ่มกระบวนการในคราวด์ซอร์สซิง โดยศึกษา 3 วิธี ได้แก่ 1. งานที่จำเป็นต้องทำก่อน 2. คำถามมาตรฐานแบบทองคำ และ 3. การทำซ้ำของข้อมูล พบว่างานที่จำเป็นต้องทำก่อนเป็นสิ่งจำเป็นเพื่อคัดกรองให้ได้ผู้ปฏิบัติงานที่มีคุณภาพสูง โดยควรเน้นไปที่ลักษณะเฉพาะและรายละเอียดของงาน คำถามที่ตรวจสอบความสอดคล้องระหว่างงานดีกว่าคำถามแบบชัดเจนในการตรวจสอบด้วยคำถามมาตรฐานทองคำ ผู้ตรวจสอบข้อมูลคนเดียวอาจนำไปสู่การปรับปรุงคุณภาพข้อมูลได้มากที่สุด ส่วนที่สองคือ การใช้แบบจำลองการเรียนรู้ของเครื่องที่ใช้ข้อมูลพฤติกรรมในการทำนายคุณภาพของข้อมูล ซึ่งวิธีนี้ยังช่วยคัดกรองข้อมูลคุณภาพต่ำออกไปได้โดยไม่เสียทรัพยากรเพิ่มเติม

การใช้รถโดยสารประจำทางเป็นโหนดที่ขอบในเครือข่ายยานพาหนะ, ณัฐนนท์ มานพ

Chulalongkorn University Theses and Dissertations (Chula ETD)

การเติบโตของเครือข่ายไร้สายแบบแอดฮอกบนยานพาหนะได้ทำให้เกิดการพัฒนาแอปพลิเคชันบนยานพาหนะต่าง ๆ มากมาย เพื่อตอบรับสนองต่อการเติบโตนี้โครงร่างระบบการคำนวณแบบขอบบนยานพาหนะจึงถูกพัฒนาขึ้นเพื่อมุ่งเน้นไปที่การติดตั้งโหนดที่ขอบที่มักติดตั้งที่สถานีรับส่งสัญญาณข้างทาง อย่างไรก็ตามการติดตั้งสถานีในพื้นที่ขนาดใหญ่ต้องพิจารณาให้ครอบคลุมพื้นที่การให้บริการมากที่สุด งานวิจัยนี้จึงได้นำเสนอโครงร่างระบบใหม่ชื่อว่า Buses as an Infrastructure ซึ่งได้มีการใช้งานให้รถโดยสารประจำทางเป็นโหนดที่ขอบในการให้บริการทรัพยากรในการคำนวณและบริการอื่น ๆ แก่ผู้ใช้งาน โดยงานวิจัยนี้ได้มีการใช้ข้อได้เปรียบของระบบขนส่งสาธารณะที่มีอยู่แล้วเพื่อลดค่าใช้จ่ายในการติดตั้งโหนดที่ขอบแบบดั้งเดิม อีกทั้งงานวิจัยนี้ยังได้เสนอฮิวริสติกอัลกอรึทึมสำหรับการคำนวณหาการติดตั้งโหนดที่ขอบบนรถโดยสารประจำทางโดยให้ลำดับความสำคัญแก่จำนวนงานที่เกิดขึ้นคู่กับการใช้เทคนิคการเลือก N ลำดับสูงสุด โดยได้ทำการทดลองบนสภาพแวดล้อมจำลองและบนชุดข้อมูลจริง ผลการทดลองเมื่อเทียบกับรูปแบบที่โหนดที่ขอบติดตั้งอยู่กับสถานีรับส่งสัญญาณข้างทางแสดงให้เห็นว่าฮิวริสติกอัลกอรึทึมที่นำเสนอสามารถให้จำนวนยานพาหนะที่โหนดที่ขอบสามารถให้บริการได้สูงขึ้นกว่า 6.08% - 52.20% และสามารถให้จำนวนยานพาหนะที่โหนดที่ขอบสามารถให้บริการได้สูงขึ้น 15.23% เมื่อเทียบกับรูปแบบที่โหนดที่ขอบติดตั้งอยู่กับสถานีรับส่งสัญญาณข้างทางบนสภาพแวดล้อมจำลองและให้ผลรวมของระยะเวลาที่ติดต่อสื่อสารกันได้สูงขึ้น 54.24% เมื่อเทียบกับรูปแบบที่โหนดที่ขอบติดตั้งอยู่กับสถานีรับส่งสัญญาณข้างทางบนชุดข้อมูลจริง