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Full-Text Articles in Computer Engineering
Brunet: Disruption-Tolerant Tcp And Decentralized Wi-Fi For Small Systems Of Vehicles, Nicholas Brunet
Brunet: Disruption-Tolerant Tcp And Decentralized Wi-Fi For Small Systems Of Vehicles, Nicholas Brunet
Master's Theses
Reliable wireless communication is essential for small systems of vehicles. However, for small-scale robotics projects where communication is not the primary goal, programmers frequently choose to use TCP with Wi-Fi because of their familiarity with the sockets API and the widespread availability of Wi-Fi hardware. However, neither of these technologies are suitable in their default configurations for highly mobile vehicles that experience frequent, extended disruptions. BRUNET (BRUNET Really Useful NETwork) provides a two-tier software solution that enhances the communication capabilities for Linux-based systems. An ad-hoc Wi-Fi network permits decentralized peer-to-peer and multi-hop connectivity without the need for dedicated network infrastructure. …
Decentralized Machine Learning On Blockchain: Developing A Federated Learning Based System, Nikhil Sridhar
Decentralized Machine Learning On Blockchain: Developing A Federated Learning Based System, Nikhil Sridhar
Master's Theses
Traditional Machine Learning (ML) methods usually rely on a central server to per-
form ML tasks. However, these methods have problems like security risks, data
storage issues, and high computational demands. Federated Learning (FL), on the
other hand, spreads out the ML process. It trains models on local devices and then
combines them centrally. While FL improves computing and customization, it still
faces the same challenges as centralized ML in security and data storage.
This thesis introduces a new approach combining Federated Learning and Decen-
tralized Machine Learning (DML), which operates on an Ethereum Virtual Machine
(EVM) compatible blockchain. The …
Contextually Dynamic Quest Generation Using In-Session Player Information In Mmorpg, Shangwei Lin
Contextually Dynamic Quest Generation Using In-Session Player Information In Mmorpg, Shangwei Lin
Master's Theses
Massively multiplayer online role-playing games (MMORPGs) are one of the most
popular genres in video games that combine massively multiplayer online genres with
role-playing gameplay. MMORPGs’ featured social interaction and forms of level pro-
gression through quest completion are the core for gaining players’ attention. Varied
and challenging quests play an essential part in retaining that attention. However,
well-crafted content takes much longer to develop with human efforts than it does to
consume, and the dominant procedural content generation models for quests suffer
from the drawback of being incompatible with dynamic world changes and the feeling
of repetition over time. …
Analysis And Usage Of Natural Language Features In Success Prediction Of Legislative Testimonies, Marine Cossoul
Analysis And Usage Of Natural Language Features In Success Prediction Of Legislative Testimonies, Marine Cossoul
Master's Theses
Committee meetings are a fundamental part of the legislative process in which
constituents, lobbyists, and legislators alike can speak on proposed bills at the
local and state level. Oftentimes, unspoken “rules” or standards are at play in
political processes that can influence the trajectory of a bill, leaving constituents
without a political background at an inherent disadvantage when engaging with
the legislative process. The work done in this thesis aims to explore the extent to
which the language and phraseology of a general public testimony can influence a
vote, and examine how this information can be used to promote civic …
Shelfaware: Accelerating Collaborative Awareness With Shelf Crdt, John C. Waidhofer
Shelfaware: Accelerating Collaborative Awareness With Shelf Crdt, John C. Waidhofer
Master's Theses
Collaboration has become a key feature of modern software, allowing teams to work together effectively in real-time while in different locations. In order for a user to communicate their intention to several distributed peers, computing devices must exchange high-frequency updates with transient metadata like mouse position, text range highlights, and temporary comments. Current peer-to-peer awareness solutions have high time and space complexity due to the ever-expanding logs that each client must maintain in order to ensure robust collaboration in eventually consistent environments. This paper proposes an awareness Conflict-Free Replicated Data Type (CRDT) library that provides the tooling to support an …
Group-Invariant Reinforcement Learning, Fnu Ankur
Group-Invariant Reinforcement Learning, Fnu Ankur
Master's Theses
Our work introduces a way to learn an optimal reinforcement learning agent accompanied by intrinsic properties of the environment. The extracted properties helps the agent to extrapolate the learning to unseen states efficiently. Out of all the various types of properties, we are intrigued towards equivariant and invariant properties, which essentially translates to symmetry. Contrary to many approaches, we do not assume the symmetry, rather learn them, making the approach agnostic to the environment and the property. The learned properties offers multiple perspective of the environment to exploit it to benefit decision making while interacting with the environment. By building …
Automatic Presentation Slide Generation Using Llms, Tanya Gupta
Automatic Presentation Slide Generation Using Llms, Tanya Gupta
Master's Theses
Presentation slides are widely used for conveying information in academic and professional contexts. However, manual slide creation can be time-consuming. Our research focuses on automated slide generation, specifically for scientific research papers. Automating the creation of presentation slides for scientific documents is a rather novel task and hence, there’s limited training data available and there also exists the token constraints of language models like BERT, with a maximum sequence length of 512 tokens. In this study, we fine-tune large language models, including Longformer-Encoder-Decoder (supporting sequences up to 16,834 tokens) and BIGBIRD-Pegasus (supporting sequences up to 4,096 tokens). We tackle this …
Intrinsic Motivation By The Principles Of Non-Linear Dynamical Systems, Phu C. Nguyen
Intrinsic Motivation By The Principles Of Non-Linear Dynamical Systems, Phu C. Nguyen
Master's Theses
The design of appropriate control rules for the stabilization of dynamical systems can require quite substantial domain knowledge. Modern AI methodologies, such as Reinforcement Learning, are often used to mitigate the need for such knowledge. However, these can be slow and often rely on at least some hand-designed reward structure, and thus human input, to be more effective. Here, we propose an alternative route to construct rewards requiring only minimal domain knowledge, essentially relying on the structure of the dynamical system itself. For this, we use truncated Lyapunov exponents as rewards to calculate the stabilizing controller from samples. Concretely, the …
Controllability-Constrained Deep Neural Network Models For Enhanced Control Of Dynamical Systems, Suruchi Sharma
Controllability-Constrained Deep Neural Network Models For Enhanced Control Of Dynamical Systems, Suruchi Sharma
Master's Theses
Control of a dynamical system without the knowledge of dynamics is an important and challenging task. Modern machine learning approaches, such as deep neural networks (DNNs), allow for the estimation of a dynamics model from control inputs and corresponding state observation outputs. Such data-driven models are often utilized for the derivation of model-based controllers. However, in general, there are no guarantees that a model represented by DNNs will be controllable according to the formal control-theoretical meaning of controllability, which is crucial for the design of effective controllers. This often precludes the use of DNN-estimated models in applications, where formal controllability …
Deep Learning In Ai Medical Imaging For Stroke Diagnosis, James Mario Guzman
Deep Learning In Ai Medical Imaging For Stroke Diagnosis, James Mario Guzman
Master's Theses
Enhancing medical imaging stroke diagnosis applications with artificial intelligence (AI) tools to determine lesion volume, location and clinical metadata is vital toward guiding patient treatment and procedure. A major hardship in developing stroke diagnosis AI tools is the scarcity of publicly available clinical 3D stroke datasets. Through working with Johns Hopkins University, University of Michigan’s ICPSR data repository and SJSU research, we gained access to potentially the largest 3D MRI stroke dataset with clinical metadata annotated by neuroradiologists known as ICPSR 38464. With the ICPSR 38464 dataset recently being available through institutional review board (IRB) approval or exemption, we were …
Detecting The Onion Routing Traffic In Real-Time By Using Reinforcement Learning, Dazhou Liu
Detecting The Onion Routing Traffic In Real-Time By Using Reinforcement Learning, Dazhou Liu
Master's Theses
Anonymous networks have been popularly utilized to protect user anonymity and facilitate network security for a decade. However, such networks have been a platform for adversarial affairs and various network attacks including suspicious traffic generators. As a result, detecting anonymous network traffic is one critical task to defend a network against unpredictable attacks. Many new methods using machine learning and deep learning techniques have been proposed. However, many of them rely heavily on a vast amount of labeled data and have complicated architectures. Since network traffic always fluctuates under different network environments, those techniques may degrade in performance due to …