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Filtering by: Creator Gard, Steve R. Remove constraint Creator: Gard, Steve R. License All rights reserved Remove constraint License: All rights reserved Subject Human-computer interaction Remove constraint Subject: Human-computer interaction

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Personalizing machine learning systems with explanatory debugging

Creator:

Kulesza, Todd

Abstract:

How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation...

Resource Type:

Dissertation
Developing a 2D Cross-section Training Strategy for 3D Volume Segmentation by Analyzing Human Perception and Cognitive Tasks

Creator:

Sanandaji, Anahita

Abstract:

3D volume segmentation is a fundamental process in many scientific and medical applications. Producing accurate segmentations, in an efficient way, is challenging, in part due to low imaging data quality (e.g., noise and low image resolution), and ambiguity in the data that can only be resolved with higher-level knowledge of...

Resource Type:

Dissertation

Full Text:

: Questions Average Score and Level of Difficulty. Higher average scores go with lower difficulty levels (r
Supporting merge comprehension when resolving merge conflicts

Creator:

Nelson, Nicholas

Abstract:

Distributed version control allows developers to manage software evolution among distributed development teams. But it does not eliminate all consistency and concurrency issues, and instead introduces additional complexity when merging code. And resolving merge conflicts is nontrivial when automated merging fails. In such cases, developers are forced to inspect the...

Resource Type:

Dissertation
Helping end-user programmers find and fix performance problems in visual code

Creator:

Chambers, Chris

Abstract:

End-user programmers often struggle to create programs that run quickly and effectively, which can be a major deterrent in completing their tasks as desired. Current research has primarily focused on catching user mistakes, such as errors or misused formulas. However, end users deal with issues other than just correctness. In...

Resource Type:

Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science4

Advisor

Burnett, Margaret M.1
Grimm, Cindy1
Sarma, Anita1
Scaffidi, Christopher P.1

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2015 to 2024

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Committee Member

Jensen, Carlos2
Borradaile, Glencora1
De Amicis, Raffaele1
Groce, Alex1
Groce, Alex D.1
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Creator

Chambers, Chris1
Kulesza, Todd1
Nelson, Nicholas1
Sanandaji, Anahita1

Date

Date range begin – Date range end

Current results range from 2014 to 2024

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Decade

2020-20291
2010-20193

Degree Field

Computer Science4

Degree Level

Doctoral4

Degree Name

Doctor of Philosophy (Ph.D.)4

Language

English [eng]4

License

All rights reserved[remove]4

Non-Academic Affiliation

Oregon State University. Graduate School2

Peer Reviewed

No4

Resource Type

Dissertation4

Rights Statement

In Copyright4

Subject

Human-computer interaction[remove]4
End-user computing2
Computer-aided software engineering1
Debugging in computer science1
Machine learning1
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