Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
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Computer Science↗
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Scalable and efficient learning from crowds with Gaussian processes
Over the last few years, multiply-annotated data has become a very popular source of information. Online platforms such as Amazon Mechanical Turk have revolutionized the labelling process needed for any classification task, sharing the effort between a number of annotators (instead of the classical single expert). This crowdsourcing approach has introduced new challenging problems, such as handling disagreements on the annotated samples or combining the unknown expertise of the annotators. Probabilistic methods, such as Gaussian Processes (GP), have proven successful to model this new crowdsourcing scenario. However, GPs do not scale up well with the training set size, which makes them prohibitive for medium-to-large datasets (beyond 10K training instances). This constitutes a serious limitation for current real-world applications. In this work, we introduce two scalable and efficient GP-based crowdsourcing methods that allow for processing previously-prohibitive datasets. The first one is an efficient and fast approximation to GP with squared exponential (SE) kernel. The second allows for learning a more flexible kernel at the expense of a heavier training (but still scalable to large datasets). Since the latter is not a GP-SE approximation, it can be also considered as a whole new scalable and efficient crowdsourcing method, useful for any dataset size. Both methods use Fourier features and variational inference, can predict the class of new samples, and estimate the expertise of the involved annotators. A complete experimentation compares them with state-of-the-art probabilistic approaches in synthetic and real crowdsourcing datasets of different sizes. Finally, they stand out as the best performing approach for large scale problems. Moreover, the second method is competitive with the current state-of-the-art for small datasets.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Scalable and Actionable Performance Measures for Traffic Signal Systems using Probe Vehicle Trajectory Data
Scalable and actionable performance measures for traffic signal systems provide opportunities for practitioners to measure and improve the transportation network. Historically, traffic signal improvements have relied on scheduled signal retiming based on limited data collection, or on the public to call and alert engineers of an issue. This inefficient method of improving signal timing led to the creation of automated traffic signal performance measures (ATSPMs). These metrics rely on expensive infrastructure, including detection and communications, which has produced barriers for numerous agencies to fully adopt. Recently, third-party data providers have begun to release vehicle trajectory data, which allows for enhanced signal metrics with no investment in physical equipment. The purpose of this study is to demonstrate the use of these data and summarize the scalability of the created metrics. This work builds on previous efforts to quantify signal performance on nine intersections in Michigan, U.S. Ten signalized corridors in Columbus, Ohio, were chosen to scale a performance assessment using crowdsourced trajectory data. A total of 136 intersections were assessed in 2-h intervals using data from all weekdays in 2017. High-level corridor summary metrics including average percent of vehicles stopping (18%–32%), average delay (9.4–20.5 s), and level of travel time reliability (1.23–2.73) were calculated for each corridor direction. Intersection-level metrics were also introduced, which can be used by practitioners to identify problems, improve signal timings, and prioritize future infrastructure investments.
99 GENERAL AND MISCELLANEOUS↗
Crowdsourced Microfinance for Energy Efficiency in Underserved Communities
BlocPower’s mission is to provide access to energy efficiency financing for underserved communities across the United States. This project, “Crowdsourced Microfinance for Energy Efficiency in Underserved Communities,” is an extension of that goal and is grounded in the principles of providing engineering and financing services to those in need. The project is based on the creation of a BlocPower Marketplace as a central hub for connecting shovel-ready green buildings to institutional investors. This ‘connection’ entails using online crowdfunding to aggregate debt and equity capital from institutional investors to connect to customers (building owners) across various financial portfolios. BlocPower Marketplace is intended to bring social, environmental, and financial returns to investors while also decreasing investor risk by loaning out funds for energy installations in individual buildings. In detail, the intended benefits of crowdsourcing are two-sided. Firstly, for building owners, clean energy retrofit installations improve building operations, reduce utility costs, and reduce harmful impacts to their surrounding environment. Secondly, for institutional investors, they gain access to a new market of energy efficiency and are able to provide debt or equity capital with high financial returns. This gives investors the opportunity to create social and environmental impact in communities around the country as well. With this in mind, BlocPower designed the marketplace to specifically answer exploratory research questions with respect to the pricing of energy financing. Institutional investors typically charge high rates on project financing solutions in the energy space, particularly in low and middle-income communities, because of fears that required debt service will not be made. This makes access to energy capital exorbitantly difficult for those that need it the most. Through this project, BlocPower tested investor appetite to determine if crowdsourcing would lower prices and subsequently lower barriers to entry for underserved communities’ access to energy capital. BlocPower’s results in this project were extremely informative for the industry. The project demonstrates that the marketplace is a scalable tool to help overcome barriers to entry for small building owners in underserved communities to access energy efficiency financing, but that crowdfunding by itself does not necessarily lower interest rates and make energy efficiency projects feasible. For that, we need a repayment mechanism that lowers perceived risk. That mechanism is on bill repayment.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)
Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.
59 BASIC BIOLOGICAL SCIENCES↗
Automatic Lane-Level Road Network Extraction from Aerial Imagery for Transportation Digital Twins
Accurate road networks are essential for credible traffic microsimulation and transportation digital twins, yet high-definition maps are often difficult to obtain due to limited availability, high cost, or proprietary restrictions. Some build networks from crowdsourced data, such as OpenStreetMap, but these sources often contain geometric and semantic inconsistencies. Others create networks manually, a process that is labor-intensive and difficult to scale. To address these limitations, this work presents an end-to-end pipeline that automatically extracts georeferenced, lane-level road networks from publicly available high-resolution satellite imagery and converts them into simulation-ready assets. The developed end-to-end pipeline has three primary modules: (1) A computer-vision-based module first detects directed lane geometries and intersection layouts. (2) A heuristic-based topology construction module then identifies approach and exit legs and establishes conflict-free lane-to-lane connections. (3) Finally, an automatic simulation-building module converts the extracted network into standard formats, e.g., OpenDRIVE, and generates routable SUMO networks. The framework supports both complete network construction from scratch and local-scale refinement of existing networks through lane-count correction, transition recovery, and geometric regularization. The proposed pipeline provides a practical pathway to generate traffic simulation networks from satellite imagery, significantly reducing manual reconstruction effort and enabling scalable, continuously updated transportation digital twins.
Guo, Hetian [University of Georgia, Athens] (ORCID↗