Filtering by
- All Subjects: Machine Learning
- All Subjects: Imperative programming
- Creators: Bansal, Ajay
- Resource Type: Text
In this thesis, several different methods for detecting and removing satellite streaks from astronomic images were evaluated and compared with a new machine learning based approach. Simulated data was generated with a variety of conditions, and the performance of each method was evaluated both quantitatively, using Mean Absolute Error (MAE) against a ground truth detection mask and processing throughput of the method, as well as qualitatively, examining the situations in which each model performs well and poorly. Detection methods from existing systems Pyradon and ASTRiDE were implemented and tested. A machine learning (ML) image segmentation model was trained on simulated data and used to detect streaks in test data. The ML model performed favorably relative to the traditional methods tested, and demonstrated superior robustness in general. However, the model also exhibited some unpredictable behavior in certain scenarios which should be considered. This demonstrated that machine learning is a viable tool for the detection of satellite streaks in astronomic images, however special care must be taken to prevent and to minimize the effects of unpredictable behavior in such models.
are constantly changing, and adapting to these changes in an academic curriculum
can be challenging. Given a specific aspect of a domain, there can be various levels of
proficiency that can be achieved by the students. Considering the wide array of needs,
diverse groups need customized course curriculum. The need for having an archetype
to design a course focusing on the outcomes paved the way for Outcome-based
Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of
following a process [23]. According to D. Clark, the major reason for the creation of
Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking
in academia – incorporating not just the basic fact-learning and application, but also
to evaluate and analyze on the facts and its applications [7]. Instructional Module
Development System (IMODS) is the culmination of both these models – Bloom’s
Taxonomy and OBE. It is an open-source web-based software that has been
developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,
step-by-step, through an outcomes-based process as they define the learning
objectives, the content to be covered and develop an instruction and assessment plan.
The tool also provides the user with a repository of techniques based on the choices
made by them regarding the level of learning while defining the objectives. This helps
in maintaining alignment among all the components of the course design. The tool
also generates documentation to support the course design and provide feedback
when the course is lacking in certain aspects.
It is not just enough to come up with a model that theoretically facilitates
effective result-oriented course design. There should be facts, experiments and proof
that any model succeeds in achieving what it aims to achieve. And thus, there are two
research objectives of this thesis: (i) design a feature for course design feedback and
evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various
aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the
effectiveness of the tool in providing appropriate and efficient pedagogy and
assessment techniques; (c) the effectiveness of the tool in building the learning
objectives; (d) effectiveness of the tool in document generation; (e) Usability of the
tool; (f) the effectiveness of OBE on course design and expected student outcomes.
The thesis presents a detailed algorithm for course design feedback, its pseudocode, a
description and proof of the correctness of the feature, methods used for evaluation
of the tool, experiments for evaluation and analysis of the obtained results.
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 main focus of this thesis is to use visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. For this, an end-to-end framework has been built, to retrieve relevant results that are also diverse. Different retrieval re-ranking and diversification strategies are evaluated to find a balance between relevance and diversification. Clustering techniques are employed to improve divergence. A unique fusion approach has been adopted to overcome the dilemma of selecting an appropriate clustering technique and the corresponding parameters, given a set of data to be investigated. Extensive experiments have been conducted on the Flickr Div150Cred dataset that has 30 different landmark locations. The results obtained are promising when evaluated on metrics for relevance and diversification.
User interface development on iOS is in a major transitionary state as Apple introduces a declarative and interactive framework called SwiftUI. SwiftUI’s success depends on how well it integrates its new tooling for novice developers. This paper will demonstrate and discuss where SwiftUI succeeds and fails at carving a new path for user interface development for new developers. This is done by comparisons against its existing imperative UI framework UIKit as well as elaborating on the background of SwiftUI and examples of how SwiftUI works to help developers. The paper will also discuss what exactly led to SwiftUI and how it is currently faring on Apple's latest operating systems. SwiftUI is a framework growing and evolving to serve the needs of 5 very different platforms with code that claims to be simpler to write and easier to deploy. The world of UI programming in iOS has been dominated by a Storyboard canvas for years, but SwiftUI claims to link this graphic-first development process with the code programmers are used to by keeping them side by side in constant sync. This bold move requires interactive programming capable of recompilation on the fly. As this paper will discuss, SwiftUI has garnered a community of developers giving it the main property it needs to succeed: a component library.