Computer Sciences Commons^™

Simulation And Experimental Verification Of Precision Grinding Of Micro-Groove Structure, Haoyang Cao

Journal of System Simulation

Abstract: In order to study the influence of micro-groove structure precision grinding, based on grinding wheel dressing and grinding kinematics, a grinding simulation model for the micro-groove structural surface is established. The influence of dressing, grinding parameters and pitch length on the profile of micro-groove structure was analyzed systematically, the grinding conditions for forming complete or interfering micro-groove profile are described, the profile and size of micro-groove are predicted, and the grinding test is carried out on carbon steel. The test results are consistent with the trend of the simulation model. The profile and size of the micro-groove …

Multi-Objective Optimization Of Multi-Task Parallel Motorcycle Suspension System Parameters, Xiansheng Ran, Yang Jing, Luo Ling, Chen Kai

Journal of System Simulation

Abstract: Aiming at the comprehensive problem of wobble of front suspension system and weave of rear suspension system of large displacement motorcycle at medium and high speed, a multi-objective optimization scheme based on sensitivity analysis and approximate modeling is proposed. The motorcycle model is established and the dynamics simulation is carried out. The lateral acceleration of front wheel's centroid position, the yaw rate and roll rate of whole vehicle's centroid position, which characterize the wobble and weave are the targets. The sensitivity analysis of suspension system parameters and the approximate modeling are carried out. Based on the analysis results, …

Research On Variable Swept Wing Mode Of Missile Based On Flutter Characteristics, Gao Yang, Yanbin Li, Wang Ying, Zhang Ze, Lü Rui

Journal of System Simulation

Abstract: The variable sweep angle of missile wing is an important variant missile design scheme. In the process of variant design, the aerodynamic and flutter characteristics of missile will be significantly different by using different variable sweep methods. Referring to the shape characteristics of Tomahawk missile, the geometric model of variable sweep wing missile is established. The flutter characteristics of variable sweep wing missile under different variable sweep angle modes are calculated and analyzed by fluid structure coupling method, and the selection scheme of variable sweep wing mode based on flutter characteristics is explored. The results show that the …

Examining Dimensions Of Patient Satisfaction With Telemedicine, Robert Garcia

College of Computing and Digital Media Dissertations

During the outbreak of the novel coronavirus (COVID-19) medical institutions and practitioners have drastically increased their adoption of telemedicine. The proliferation of telemedicine systems has sparked renewed interest among IS researchers in evaluating its usage. One of the main indicators used to measure the success of telemedicine services is patient satisfaction. Yet several problems exist with current methods used to evaluate telemedicine satisfaction. Patient satisfaction with telemedicine is frequently evaluated using either single question items or handmade instruments that are seldom assessed for validity. While telemedicine satisfaction is typically evaluated through single measures, satisfaction is considered a complex and multidimensional …

Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz

All Works

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or …

A Method For Comparative Analysis Of Trusted Execution Environments, Stephano Cetola

Dissertations and Theses

The problem of secure remote computation has become a serious concern of hardware manufacturers and software developers alike. Trusted Execution Environments (TEEs) are a solution to the problem of secure remote computation in applications ranging from "chip and pin" financial transactions to intellectual property protection in modern gaming systems. While extensive literature has been published about many of these technologies, there exists no current model for comparing TEEs. This thesis provides hardware architects and designers with a set of tools for comparing TEEs. I do so by examining several properties of a TEE and comparing their implementations in several technologies. …

Counting And Sampling Small Structures In Graph And Hypergraph Data Streams, Themistoklis Haris

Dartmouth College Undergraduate Theses

In this thesis, we explore the problem of approximating the number of elementary substructures called simplices in large k-uniform hypergraphs. The hypergraphs are assumed to be too large to be stored in memory, so we adopt a data stream model, where the hypergraph is defined by a sequence of hyperedges.

First we propose an algorithm that (ε, δ)-estimates the number of simplices using O(m^1+1/k / T) bits of space. In addition, we prove that no constant-pass streaming algorithm can (ε, δ)- approximate the number of simplices using less than O( m ^1+1/k / T ) bits of space. Thus …

Bountychain: Toward Decentralizing A Bug Bounty Program With Blockchain And Ipfs, Alex Hoffman, Phillipe Austria, Chol Hyun Park, Yoohwan Kim

Computer Science Faculty Research

Bug Bounty Programs (BBPs) play an important role in providing and maintaining security in software applications. These programs allow testers to discover and resolve bugs before the general public is aware of them, preventing incidents of widespread abuse. However, they have shown problems such as organizations providing accountability of reporting bugs and nonrecognition of testers. In this paper, we discuss Bountychain, a decentralized application using Ethereum-based Smart Contracts (SCs) and the Interplanetary File System (IPFS), a distributed file storage system. Blockchain and SCs provide a safe, secure and transparent platform for a BBP. Testers can submit bug reports and organizations …

Identifying Optimal Course Structures Using Topic Models, Tehut Tesfaye Biru

Dartmouth College Undergraduate Theses

This research project investigates whether there exists an optimal way to structure topics in educational course content that results in higher levels of engagement among students. It is implemented by fitting topic models to transcripts of educational videos contained in the Khan Academy platform. The fitted models were used to extract topic trajectories across time for each video and subsequently clustered based on whether they have similar “shapes”. The differences in mean engagement metrics per cluster suggest that some course shapes are more palatable to students regardless of subject matter. Additionally, the topic trajectories suggest a constant progression of topics …

U-Net And Its Variants For Medical Image Segmentation: A Review Of Theory And Applications, Nahian Siddique, Paheding Sidike, Colin P. Elkin, Vijay Devabhaktuni

Michigan Tech Publications, Part 1

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines …

Examining The Effect Of Explanation On Satisfaction And Trust In Ai Diagnostic Systems, Lamia Alam, Shane Mueller

Michigan Tech Publications, Part 1

Background: Artificial Intelligence has the potential to revolutionize healthcare, and it is increasingly being deployed to support and assist medical diagnosis. One potential application of AI is as the first point of contact for patients, replacing initial diagnoses prior to sending a patient to a specialist, allowing health care professionals to focus on more challenging and critical aspects of treatment. But for AI systems to succeed in this role, it will not be enough for them to merely provide accurate diagnoses and predictions. In addition, it will need to provide explanations (both to physicians and patients) about why the diagnoses …

Line Sampling In Participating Media, Hsu Cheng

Dartmouth College Master’s Theses

Participating media, such as fog, fire, dust, and smoke, surrounds us in our daily life. Rendering participating media efficiently has always been a challenging task in physically based rendering. Line sampling has been derived to be an alternative method in direct lighting recently. Since line sampling takes visibility into account, it could reduce variance in the same render time compared to point sampling. We leverage the benefits of line sampling in the context of evaluating direct lighting in participating media. We express the direct lighting as a three-dimensional integral and perform line sampling in any one of them. We show …

Pandemic Pivot: Designing A Participatory Simulation To Support Social Distancing And Remote Learning, K. K. Lamberty, Paul Friederichsen, Audrey Le Meur, Joseph Moonan Walbran

Computer Science Publications

Participatory simulations usually aim to bring simulations off screen into a shared physical space with people acting as agents in the simulation. In this paper, we describe considerations and design decisions related to creating a participatory simulation for use in learning settings with restrictions imposed due to the COVID-19 pandemic where typical classroom interactions were no longer allowed. We describe how our design decisions might help children both “dive in” and “step out” to understand more about pollinators and the prairie in spite of various restrictions on how exactly they can interact with each other. Our simulation, Buzz About, uses …

A Novel Color Image Encryption Scheme Based On Arnold’S Cat Map And 16-Byte S-Box, Tariq Shah, Ayesha Qureshi, Muhammad Usman

Applications and Applied Mathematics: An International Journal (AAM)

The presented work sets out to subsidize to the general body of knowledge in the field of cryptography application by evolving color image encryption and decryption scheme based on the amalgamation of pixel shuffling and efficient substitution. Arnold’s cat map is applied to snap off the correlation in pixels of image and the shuffled image is encrypted by 16-byte S-box substitution. Computer simulations with a standard test image and the outcome is presented to scrutinize the competence of the projected system. Several image-quality measures and security analyses have been made out for the encrypted image to estimate the statistical and …

Why Cauchy Membership Functions: Efficiency, Javier Viana, Stephan Ralescu, Kelly Cohen, Anca Ralescu, Vladik Kreinovich

Departmental Technical Reports (CS)

Fuzzy techniques depend heavily on eliciting meaningful membership functions for the fuzzy sets used. Often such functions are obtained from data. Just as often they are obtained from experts knowledgable of the domain and the problem being addressed. However, there are cases when neither is possible, for example because of insufficient data, or unavailable experts. What functions should one choose and what should guide such choice? This paper argues in favor of using Cauchy membership functions, thus named because their expression is similar to that of the Cauchy distributions. The paper provides a theoretical explanation for this choice.

Many Known Quantum Algorithms Are Optimal: Symmetry-Based Proofs, Vladik Kreinovich, Oscar Galindo, Olga Kosheleva

Departmental Technical Reports (CS)

Many quantum algorithms have been proposed which are drastically more efficient that the best of the non-quantum algorithms for solving the same problems. A natural question is: are these quantum algorithms already optimal -- in some reasonable sense -- or they can be further improved? In this paper, we review recent results showing that many known quantum algorithms are actually optimal. Several of these results are based on appropriate invariances (symmetries).

Why Rectified Linear Neurons: Two Convexity-Related Explanations, Jonatan Contreras, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

At present, the most efficient machine learning technique is deep learning, in which non-linearity is attained by using rectified linear functions s(x)=max(0,x). Empirically, these functions work better than any other nonlinear functions that have been tried. In this paper, we provide a possible theoretical explanation for this empirical fact. This explanation is based on the fact that one of the main applications of neural networks is decision making, when we want to find an optimal solution. We show that the need to adequately deal with situations when the corresponding optimization problem is feasible -- i.e., for which the objective function …

Exploring The Relationship Between Intrinsic Motivation And Receptivity To Mhealth Interventions, Sarah Hong

Dartmouth College Undergraduate Theses

Recent research in mHealth has shown the promise of Just-in-Time Adaptive Interventions (JITAIs). JITAIs aim to deliver the right type and amount of support at the right time. Choosing the right delivery time involves determining a user's state of receptivity, that is, the degree to which a user is willing to accept, process, and use the intervention provided.

Although past work on generic phone notifications has found evidence that users are more likely to respond to notifications with content they view as useful, there is no existing research on whether users' intrinsic motivation for the underlying topic of mHealth …

A Configurable Social Network For Running Irb-Approved Experiments, Mihovil Mandic

Dartmouth College Undergraduate Theses

Our world has never been more connected, and the size of the social media landscape draws a great deal of attention from academia. However, social networks are also a growing challenge for the Institutional Review Boards concerned with the subjects’ privacy. These networks contain a monumental variety of personal information of almost 4 billion people, allow for precise social profiling, and serve as a primary news source for many users. They are perfect environments for influence operations that are becoming difficult to defend against. Motivated to study online social influence via IRB-approved experiments, we designed and implemented a flexible, scalable, …

Lexical Complexity Prediction With Assembly Models, Aadil Islam

Dartmouth College Undergraduate Theses

Tuning the complexity of one's writing is essential to presenting ideas in a logical, intuitive manner to audiences. This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-based model and a deep neural network model with an underlying Transformer architecture based on BERT. While BERT itself performs competitively, our feature engineering-based model helps in extreme cases, eg. separating instances of easy and neutral difficulty. Our handcrafted features comprise a breadth of lexical, semantic, syntactic, and novel phonetic measures. Visualizations of BERT …

Fine-Grained Detection Of Hate Speech Using Bertoxic, Yakoob Khan

Dartmouth College Undergraduate Theses

This thesis describes our approach towards the fine-grained detection of hate speech using deep learning. We leverage the transformer encoder architecture to propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text and utilizes additional post-processing steps to refine the prediction boundaries. The post-processing steps involve (1) labeling character offsets between consecutive toxic tokens as toxic and (2) assigning a toxic label to words that have at least one token labeled as toxic. Through experiments, we show that these two post-processing steps improve the performance of our model by 4.16% on …

Deterring Intellectual Property Thieves: Algorithmic Generation Of Adversary-Aware Fake Knowledge Graphs, Snow Kang

Dartmouth College Undergraduate Theses

Publicly available estimates suggest that in the U.S. alone, IP theft costs our economy between $225 billion and $600 billion each year. In our paper, we propose combating IP theft by generating fake versions of technical documents. If an enterprise system has n fake documents for each real document, any IP thief must sift through an array of documents in an attempt to separate the original from a sea of fakes. This costs the attacker time and money - and inflicts pain and frustration on the part of its technical staff.

Leveraging a graph-theoretic approach, we created the Clique-FakeKG algorithm …

Impulse Method For Shallow Water Simulation, Evan Muscatel

Dartmouth College Undergraduate Theses

The Shallow Water Equations is a simple method to simulate fluid in real-time. As a real-time model, the SWE is an excellent candidate for use in video games. However, the model is not often used in most fluid simulations because it does not preserve vorticity well, and therefore does not look very realistic. We present an improvement on the Shallow Water Equations by using a gauge method to preserve the vorticity of the fluid. We add a variable called impulse !, which is only weakly coupled with the velocity " of the simulation. We show that using this impulse method, …

The Discrete-Event Modeling Of Administrative Claims (Demac) System: Dynamically Modeling The U.S. Healthcare Delivery System With Medicare Claims Data To Improve End-Of-Life Care, Rachael Chacko

Dartmouth College Undergraduate Theses

The shift of the U.S. healthcare delivery system from the treatment of acute conditions to chronic diseases requires a new method of healthcare system analysis to properly assess end- of-life (EOL) quality throughout the country. In this paper, we propose the Discrete-Event Modeling of Administrative Claims (DEMAC) system, which relies on a hetero-functional graph theory and discrete event-driven framework to dynamically model EOL care on multiple levels. The heat map visualizations produced by the DEMAC system enable the elucidation of not only patient-specific EOL care but also broader treatment patterns among providers and hospitals. As a whole, the DEMAC system …

Exploring The Long Tail, Joseph H. Hajjar

Dartmouth College Undergraduate Theses

The migration of datasets online has created a near-infinite inventory for big name retailers such as Amazon and Netflix, giving rise to recommendation systems to assist users in navigating the massive catalog. This has also allowed for the possibility of retailers storing much less popular, uncommon items which would not appear in a more traditional brick-and-mortar setting due to the cost of storage. Nevertheless, previous work has highlighted the profit potential which lies in the so-called "long tail'' of niche, unpopular items. Unfortunately, due to the limited amount of data in this subset of the inventory, recommendation systems often struggle …

Object Manipulation With Modular Planar Tensegrity Robots, Maxine Perroni-Scharf

Dartmouth College Undergraduate Theses

This thesis explores the creation of a novel two-dimensional tensegrity-based mod- ular system. When individual planar modules are linked together, they form a larger tensegrity robot that can be used to achieve non-prehensile manipulation. The first half of this dissertation focuses on the study of preexisting types of tensegrity mod- ules and proposes different possible structures and arrangements of modules. The second half describes the construction and actuation of a modular 2D robot com- posed of planar three-bar tensegrity structures. We conclude that tensegrity modules are suitably adapted to object manipulation and propose a future extension of the modular 2D …

The R Journal (June 2021) 13(1): Complete Issue, The R Foundation

The R Journal

Editorial, Dianne Cook

Contributed Research Articles

SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares, Bo-Young Kim, Yunju Im, and Jae Keun Yoo

npcure: An R Package for Nonparametric Inference in Mixture Cure Models, Ana López-Cheda, M. Amalia Jácome, and Ignacio López-de-Ullibarri

A Method for Deriving Information from Running R Code, Mark P. J. van der Loo

JMcmprsk: An R Package for Joint Modelling of Longitudinal and Survival Data with Competing Risks, Hong Wang, Ning Li, Shanpeng Li, and Gang Li

Wide-to-tall Data Reshaping Using Regular Expressions and the nc Package, Toby Dylan Hocking

Linear Regression with …

Learning Medical Materials From Radiography Images, Carson Molder, Benjamin Lowe, Justin Zhan

Computer Science and Computer Engineering Faculty Publications and Presentations

Deep learning models have been shown to be effective for material analysis, a subfield of computer vision, on natural images. In medicine, deep learning systems have been shown to more accurately analyze radiography images than algorithmic approaches and even experts. However, one major roadblock to applying deep learning-based material analysis on radiography images is a lack of material annotations accompanying image sets. To solve this, we first introduce an automated procedure to augment annotated radiography images into a set of material samples. Next, using a novel Siamese neural network that compares material sample pairs, called D-CNN, we demonstrate how to …

Capstone Case Study Guide Mapfre, Apoorva Arbooj, Ahamad Waqas, K C Prabhat, Manju Jayam, Rashmi Sakleshpur Rajashekar

School of Professional Studies

Mapfre is a Top-Notch insurer and a competitive and fast-evolving insurance company. Clark team will help Mapfre to organize to secure systems availability and resilience to support the business process. Assist and recommend the IT team for further analysis and identify data, trends, and patterns and come up with to improve the services

Data Integrity Preservation Schemes In Smart Healthcare Systems That Use Fog Computing Distribution, Abdulwahab Fahad S. Alazeb, Brajendra Panda, Sultan Ahmed A Almakdi, Mohammed Saleh H. Alshehri

Computer Science and Computer Engineering Faculty Publications and Presentations

The volume of data generated worldwide is rapidly growing. Cloud computing, fog computing, and the Internet of things (IoT) technologies have been adapted to compute and process this high data volume. In coming years information technology will enable extensive developments in the field of healthcare and offer health care providers and patients broadened opportunities to enhance their healthcare experiences and services owing to heightened availability and enriched services through real-time data exchange. As promising as these technological innovations are, security issues such as data integrity and data consistency remain widely unaddressed. Therefore, it is important to engineer a solution to …