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Exploring The Relationship Between Intrinsic Motivation And Receptivity To Mhealth Interventions, Sarah Hong 2021 Dartmouth College

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 ...


Deterring Intellectual Property Thieves: Algorithmic Generation Of Adversary-Aware Fake Knowledge Graphs, Snow Kang 2021 Dartmouth College

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 ...


Grand-Vision: An Intelligent System For Optimized Deployment Scheduling Of Law Enforcement Agents, Jonathan CHASE, Tran PHONG, Kang LONG, Tony LE, Hoong Chuin LAU 2021 Singapore Management University

Grand-Vision: An Intelligent System For Optimized Deployment Scheduling Of Law Enforcement Agents, Jonathan Chase, Tran Phong, Kang Long, Tony Le, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Law enforcement agencies in dense urban environments, faced with a wide range of incidents to handle and limited manpower, are turning to data-driven AI to inform their policing strategy. In this paper we present a patrol scheduling system called GRAND-VISION: Ground Response Allocation and Deployment - Visualization, Simulation, and Optimization. The system employs deep learning to generate incident sets that are used to train a patrol schedule that can accommodate varying manpower, break times, manual pre-allocations, and a variety of spatio-temporal demand features. The complexity of the scenario results in a system with real world applicability, which we demonstrate through simulation ...


Pier Ocean Pier, Brandon J. Nowak 2021 California Polytechnic State University, San Luis Obispo

Pier Ocean Pier, Brandon J. Nowak

Computer Engineering

Pier Ocean Peer is a weatherproof box containing a Jetson Nano, connected to a cell modem and camera, and powered by a Lithium Iron Phosphate battery charged by a 50W solar panel. This system can currently provide photos to monitor the harbor seal population that likes to haul out at the base of the Cal Poly Pier, but more importantly it provides a platform for future expansion by other students either though adding new sensors directly to the Jetson Nano or by connecting to the jetson nano remotely through a wireless protocol of their choice.


Pedestrian Attribute Recognition Using Two-Branch Trainable Gabor Wavelets Network, Imran N. Junejo 2021 Zayed University

Pedestrian Attribute Recognition Using Two-Branch Trainable Gabor Wavelets Network, Imran N. Junejo

All Works

Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This multi-label problem is extremely challenging even for human observers and has rightly garnered attention from the computer vision community. Towards a solution to this problem, in this paper, we adopt trainable Gabor wavelets (TGW) layers and cascade them with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed ...


Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah 2021 Zayed University

Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah

All Works

The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a ...


Niching Grey Wolf Optimizer For Multimodal Optimization Problems, Rasel Ahmed, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, Jahedul Islam 2021 Universiti Teknologi Petronas

Niching Grey Wolf Optimizer For Multimodal Optimization Problems, Rasel Ahmed, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, Jahedul Islam

All Works

Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails ...


The Long-Term Cost Of Cyber Overreaction, Jan Kallberg 2021 Army Cyber Institute

The Long-Term Cost Of Cyber Overreaction, Jan Kallberg

ACI Journal Articles

The default modus operandi when facing negative cyber events is to overreact. It is essential to highlight the cost of overreaction, which needs to be a part of calculating when to engage and how. For an adversary probing cyber defenses, reactions provide information that can aggregate a clear picture of the defendant’s capabilities and preauthorization thresholds.

Ideally, potential adversaries cannot assess our strategic and tactical cyber capacities, but over time and numerous responses, the information advantage evaporates. A reactive culture triggered by cyberattacks provides significant information to a probing adversary, which seeks to understand underlying authorities and tactics, techniques ...


Challenges And Success Factors Of Scaled Agile Adoption – A South African Perspective, Lucas Khoza, Carl Marnewick 2021 University of Johannesburg

Challenges And Success Factors Of Scaled Agile Adoption – A South African Perspective, Lucas Khoza, Carl Marnewick

The African Journal of Information Systems

Agile methods and Agile scaling frameworks have become a solution for software-developing organizations striving to improve the success of software projects. Agile methods were developed for small projects, but due to their benefits, even large software-developing organizations have adopted them to scale their software projects. This quantitative study was undertaken to deepen the researchers’ understanding of the critical success factors and challenges of Scaled Agile from the South African perspective. A simple random sampling method was used. Data was collected with the use of an online structured questionnaire and the response rate was 70%. The results reveal that customer satisfaction ...


Towards A Machine Learning Based Generalizable Framework For Detecting Covid-19 Misinformation On Social Media, Yuanzhi Chen 2021 University of Nebraska - Lincoln

Towards A Machine Learning Based Generalizable Framework For Detecting Covid-19 Misinformation On Social Media, Yuanzhi Chen

Computer Science and Engineering: Theses, Dissertations, and Student Research

Since the beginning of the COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, online social media has become a conduit for the rapid propagation of misinformation. The misinformation is a type of fake news that is created inadvertently without the intention of causing harm. Yet COVID-19 misinformation has caused serious social disruptions including accidental death and destruction of public property. Timely prevention of the propagation of online misinformation requires the development of automated detection tools. Machine learning (ML) based models have been used to automate techniques for identifying fake news. These techniques involve converting text data ...


Prediction Of Financial Capacity Using Diffusion Compartment Imaging, Lok Yi Tai 2021 San Jose State University

Prediction Of Financial Capacity Using Diffusion Compartment Imaging, Lok Yi Tai

Master's Projects

Financial Capacity (FC) is the ability to manage one’s financial affairs, which is essential for autonomy and independence particularly for aging adults. Since dementia develops gradually, it is often difficult to detect the early signs that this cognitive dysfunction is developing This project aims to use Neurite orientation dispersion and density imaging (NODDI) to identify the white matter tracts that are associated with FC. Diffusion Tensor Images (DTI) and T1 Magnetic Resonance Images (MRI) of 18 Alzheimer’s Disease (AD) subjects, 47 Mild Cognitive Impaired (MCI) subjects, and 193 healthy control (CN) are compared to neuropsychological tests. Orientation Dispersion ...


Spaceflight And The Differential Gene Expression Of Human Stem Cell-Derived Cardiomyocytes, Eugenie Zhu 2021 San Jose State University

Spaceflight And The Differential Gene Expression Of Human Stem Cell-Derived Cardiomyocytes, Eugenie Zhu

Master's Projects

The National Aeronautics and Space Administration (NASA) has performed many experiments on the International Space Station (ISS) to further understand how conditions in space can affect life on Earth. This project analyzed GLDS-258, a gene set from NASA’s GeneLab repository which examines the impact of microgravity on human induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs). While many datasets have been run through NASA’s RNA-Seq Consensus Pipeline (RCP) to study differential gene expression in space, a Homo sapiens dataset has yet to be analyzed using the RCP. The aim of this project was to run the first Homo sapiens dataset, GLDS-258 ...


An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja 2021 CUNY Graduate Center

An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed ...


Tolkien: Scholar And Modern Game Pioneer, Alicia Breinke 2021 San Jose State University

Tolkien: Scholar And Modern Game Pioneer, Alicia Breinke

ART 108: Introduction to Games Studies

History can be a necessity, or necessary evil for some people when we want to comprehend real-time issues or trends. Gaming is a trend that applies to this since we often seem to be drawn in by the excitement of the graphics, music, and storylines, yet it seems like people seldomly try to uncover their origins. At the same time, though, a game’s historic foundation is essential to understand since it can help us gain a greater appreciation for these experiences. Role play games are an exceptional example of this since many renowned ones have external influences. J.R ...


Fake Malware Classification With Cnn Via Image Conversion: A Game Theory Approach, Yash Sahasrabuddhe 2021 San Jose State University

Fake Malware Classification With Cnn Via Image Conversion: A Game Theory Approach, Yash Sahasrabuddhe

Master's Projects

Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and study classification techniques using the ever-evolving field of machine learning. The goal of this research project is to identify similarities between malware families and to improve on classification of malwares within different malware families by implementing Convolutional Neural Networks (CNNs) on ...


Overlapping Community Detection In Social Networks, Akshar Panchal 2021 San Jose State University

Overlapping Community Detection In Social Networks, Akshar Panchal

Master's Projects

Social networking sites are important to connect with the world virtually. As the number of users accessing these sites increase, the data and information keeps on increasing. There are communities and groups which are formed virtually based on different factors. We can visualize these communities as networks of users or nodes and the relationships or connections between them as edges. This helps in evaluating and analyzing different factors that influence community formation in such a dense network. Community detection helps in revealing certain characteristics which makes these groups in the network unique and different from one another. We can use ...


Presentation Attack Detection In Facial Biometric Authentication, Hardik Kumar 2021 San Jose State University

Presentation Attack Detection In Facial Biometric Authentication, Hardik Kumar

Master's Projects

Biometric systems are referred to those structures that enable recognizing an individual, or specifically a characteristic, using biometric data and mathematical algorithms. These are known to be widely employed in various organizations and companies, mostly as authentication systems. Biometric authentic systems are usually much more secure than a classic one, however they also have some loopholes. Presentation attacks indicate those attacks which spoof the biometric systems or sensors. The presentation attacks covered in this project are: photo attacks and deepfake attacks. In the case of photo attacks, it is observed that interactive action check like Eye Blinking proves efficient in ...


Cyberbullying Classification Based On Social Network Analysis, Anqi Wang 2021 San Jose State University

Cyberbullying Classification Based On Social Network Analysis, Anqi Wang

Master's Projects

With the popularity of social media platforms such as Facebook, Twitter, and Instagram, people widely share their opinions and comments over the Internet. Exten- sive use of social media has also caused a lot of problems. A representative problem is Cyberbullying, which is a serious social problem, mostly among teenagers. Cyber- bullying occurs when a social media user posts aggressive words or phrases to harass other users, and that leads to negatively affects on their mental and social well-being. Additionally, it may ruin the reputation of that media. We are considering the problem of detecting posts that are aggressive. Moreover ...


Classifying Illegal Advertisements On The Darknet Using Nlp, Karan Shashin Shah 2021 San Jose State University

Classifying Illegal Advertisements On The Darknet Using Nlp, Karan Shashin Shah

Master's Projects

The Darknet has become a place to conduct various illegal activities like child labor, contract murder, drug selling while staying anonymous. Traditionally, international and government agencies try to control these activities, but most of those actions are manual and time-consuming. Recently, various researchers developed Machine Learning (ML) approaches trying to aid in the process of detecting illegal activities. The above problem can benefit by using different Natural Language Processing (NLP) techniques. More specifically, researchers have used various classical topic modeling techniques like bag of words, N-grams, Term Frequency, Term Frequency Inverse Document Frequency (TF-IDF) to represent features and train machine ...


Malware Classification With Bert, Joel Lawrence Alvares 2021 San Jose State University

Malware Classification With Bert, Joel Lawrence Alvares

Master's Projects

Malware Classification is used to distinguish unique types of malware from each other.

This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can ...


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