Students spend most of their daytime in classrooms. Indoor air quality of classrooms is essential to students’ health and learning efficiency. In 2019, we continuously monitored indoor air temperature, relative humid...
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this study focuses on quantifying the microbial volatile organic compounds (MVOCs) emitted from several strains of indoor mold. the mold was collected via house dust from houses around the state of Ohio and tested at ...
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Although people spend over 85% of their time indoors, the oxidative potential (OP) of particulate matter (PM) emitted from indoor sources are largely unknown. We collected PM emissions from commonly used household sou...
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Geometry problem solving always attracts much attention of research and engineering. Solving geometry problems requires the capacity of parsing multi-modal information and utilizing theorem knowledge for comprehensive...
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Context. Modern software systems increasingly commonly contain one or multiple machine learning (ML) components. Current development practices are generally on a trial-and-error basis, posing a significant risk of int...
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ISBN:
(纸本)9798400706226
Context. Modern software systems increasingly commonly contain one or multiple machine learning (ML) components. Current development practices are generally on a trial-and-error basis, posing a significant risk of introducing bugs. One type of bug is the "conceptual design bug," referring to a misunderstanding between the properties of input data and prerequisites imposed by ML algorithms (e.g., using unscaled data in a scale-sensitive algorithm). these bugs are challenging to test at design time, causing problems at runtime through crashes, noticeably poor model performance, or not at all, threatening the system's robustness and transparency. Objective. In this work, I propose the line of research I intend to pursue during my PhD, addressing conceptual design bugs in complex ML software from a prevention-oriented perspective. I intend to build open-source tooling for ML engineers that can be used to detect conceptual design bugs, enabling them to make quality assurances about their system design's robustness. Approach. We need to understand conceptual bugs beyond the status quo, identifying their types, prevalence, impacts, and structural elements in the code. We operationalize this knowledge into a tool that detects them at design time, allowing ML engineers to resolve them before running their code and wasting resources. We anticipate this tool will leverage contract-based validation applied to partial ML software models. Evaluation. We plan to evaluate the built tool two-fold using professional (industrial) ML software. First, we will study its effectiveness regarding bug detection at design time, identifying whether it fulfills its functional objective. Second, we will study its usability, identifying whether ML engineers benefit when tools like this are introduced into their ML engineering workflow.
Anti-lock Braking Systems (ABS) has been a big improvement in car safety in recent years. their job is to keep the slip ratio below an ideal value so that the wheels don't lock up when the car comes to a stop. To ...
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In the present paper, a comprehensive review was performed on the application of technology acceptance models (TAM) in the healthcare sector. the important post-2019 published papers were searched and extracted by the...
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In the context of CO2 monitoring and mechanical ventilation gaining increasing popularity in schools, this study investigates the variations in classroom CO2 concentrations across 24 primary schools in Switzerland, ai...
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Coffee aroma is characterized by its composition of different volatile organic compounds (VOCs) such as pyridines, furans, aldehydes, and organic acids. Some of these compounds are classified as air toxins. Using a Vo...
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In this study we monitored indoor carbon monoxide (CO) levels for ~50 homes over two seasons in the Denver metropolitan area as part of the Social, Justice, and Environmental Quality-Denver initiative (SJEQ-D). these ...
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