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To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
The tool was developed following the incremental development process in order to quickly create a functional and testable tool. The incremental process also allowed for feedback from radio astronomers to help guide the project's development.
UVLabel provides both a functional product, and a modifiable and scalable code base for radio astronomer developers. This enables astronomers studying various astronomical interferometric data labelling capabilities. The tool can then be used to improve their filtering methods, pursue machine learning solutions, and discover new trends. Finally, UVLabel will be open source to put customization, scalability, and adaptability in the hands of these researchers.
considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data.
The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
Current IoT integration utilities attempt to help simplify this task, but most fail to satisfy one of the requirements many users want in such a system ‒ simplified integration with third party devices. This project seeks to solve this issue through the creation of an easily extendable, modular integrating utility. It is open-source and does not require the use of a cloud-based server, with users hosting the server themselves. With a server and data controller implemented in pure Python and a library for embedded ESP8266 microcontroller-powered devices, the solution seeks to satisfy both casual users as well as those interested in developing their own integrations.
The aim of this project is to understand the basic algorithmic components of the transformer deep learning architecture. At a high level, a transformer is a machine learning model based off of a recurrent neural network that adopts a self-attention mechanism, which can weigh significant parts of sequential input data which is very useful for solving problems in natural language processing and computer vision. There are other approaches to solving these problems which have been implemented in the past (i.e., convolutional neural networks and recurrent neural networks), but these architectures introduce the issue of the vanishing gradient problem when an input becomes too long (which essentially means the network loses its memory and halts learning) and have a slow training time in general. The transformer architecture’s features enable a much better “memory” and a faster training time, which makes it a more optimal architecture in solving problems. Most of this project will be spent producing a survey that captures the current state of research on the transformer, and any background material to understand it. First, I will do a keyword search of the most well cited and up-to-date peer reviewed publications on transformers to understand them conceptually. Next, I will investigate any necessary programming frameworks that will be required to implement the architecture. I will use this to implement a simplified version of the architecture or follow an easy to use guide or tutorial in implementing the architecture. Once the programming aspect of the architecture is understood, I will then Implement a transformer based on the academic paper “Attention is All You Need”. I will then slightly tweak this model using my understanding of the architecture to improve performance. Once finished, the details (i.e., successes, failures, process and inner workings) of the implementation will be evaluated and reported, as well as the fundamental concepts surveyed. The motivation behind this project is to explore the rapidly growing area of AI algorithms, and the transformer algorithm in particular was chosen because it is a major milestone for engineering with AI and software. Since their introduction, transformers have provided a very effective way of solving natural language processing, which has allowed any related applications to succeed with high speed while maintaining accuracy. Since then, this type of model can be applied to more cutting edge natural language processing applications, such as extracting semantic information from a text description and generating an image to satisfy it.
The seamless integration of autonomous vehicles (AVs) into highly interactive and dynamic driving environments requires AVs to safely and effectively communicate with human drivers. Furthermore, the design of motion planning strategies that satisfy safety constraints inherit the challenges involved in implementing a safety-critical and dynamics-aware motion planning algorithm that produces feasible motion trajectories. Driven by the complexities of arriving at such a motion planner, this thesis leverages a motion planning toolkit that utilizes spline parameterization to compute the optimal motion trajectory within a dynamic environment. Our approach is comprised of techniques originating from optimal control, vehicle dynamics, and spline interpolation. To ensure dynamic feasibility of the computed trajectories, we formulate the optimal control problem in relation to the intrinsic state constraints derived from the bicycle state space model. In addition, we apply input constraints to bound the rate of change of the steering angle and acceleration provided to the system. To produce collision-averse trajectories, we enforce extrinsic state constraints extracted from the static and dynamic obstacles in the circumambient environment. We proceed to exploit the mathematical properties of B-splines, such as the Convex Hull Property, and the piecewise composition of polynomial functions. Second, we focus on constructing a highly interactive environment in which the con- figured optimal control problem is deployed. Vehicle interactions are categorized into two distinct cases: Case 1 is representative of a single-agent interaction, whereas Case 2 is representative of a multi-agent interaction. The computed motion trajectories per each case are displayed in simulation.
With the recent focus of attention towards remote work and mobile computing, the possibility of taking a powerful workstation wherever needed is enticing. However, even emerging laptops today struggle to compete with desktops in terms of cost, maintenance, and future upgrades. The price point of a powerful laptop is considerably higher compared to an equally powerful desktop computer, and most laptops are manufactured in a way that makes upgrading parts of the machine difficult or impossible, forcing a complete purchase in the event of failure or a component needing an upgrade. In the case where someone already owns a desktop computer and must be mobile, instead of needing to purchase a second device at full price, it may be possible to develop a low-cost computer that has just enough power to connect to the existing desktop and run all processing there, using the mobile device only as a user interface. This thesis will explore the development of a custom PCB that utilizes a Raspberry Pi Computer Module 4, as well as the development of a fork of the Open Source project Moonlight to stream a host machine's screen to a remote client. This implementation will be compared against other existing remote desktop solutions to analyze it's performance and quality.
This paper will demonstrate that the Agile development process helps to ensure incremental work on an Unreal Engine game project is achieved by presenting a product produced in Unreal Engine along with my experience in utilizing Scrum to facilitate the game’s development. Section 2 discusses project goals and motivations for using Agile, using Unreal Engine, and for the choice of genre in the final product. Section 3 contextualizes these goals by presenting the history of Unreal Engine, the novel applications of Unreal Engine, and the use of Unreal Engine in the development of Heady Stuff. Section 4 presents findings from the project’s development by describing my use of Agile and by presenting the steps taken in learning Unreal Engine. Section 4 continues by highlighting important development considerations in the use of Blueprints, C++, and HLSL in Unreal Engine. The section ends with the presentation of project feedback, its incorporation in the final product, and the resources used to assist development. Section 5 compares the workflow, help resources, and applications of Unreal Engine with those of Unity, another highly popular game engine. Lastly, Section 6 performs a post-mortem on the overall development process by considering how well Agile development processes were upheld along with how much of the original plans in the Design Document was present in the final product. Additionally, the section presents the major challenges encountered during project development. These challenges will help in proposing possible best practices for game development in Unreal Engine.
Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.