554 research outputs found

    Crowdsourcing for Engineering Design: Objective Evaluations and Subjective Preferences

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    Crowdsourcing enables designers to reach out to large numbers of people who may not have been previously considered when designing a new product, listen to their input by aggregating their preferences and evaluations over potential designs, aiming to improve ``good'' and catch ``bad'' design decisions during the early-stage design process. This approach puts human designers--be they industrial designers, engineers, marketers, or executives--at the forefront, with computational crowdsourcing systems on the backend to aggregate subjective preferences (e.g., which next-generation Brand A design best competes stylistically with next-generation Brand B designs?) or objective evaluations (e.g., which military vehicle design has the best situational awareness?). These crowdsourcing aggregation systems are built using probabilistic approaches that account for the irrationality of human behavior (i.e., violations of reflexivity, symmetry, and transitivity), approximated by modern machine learning algorithms and optimization techniques as necessitated by the scale of data (millions of data points, hundreds of thousands of dimensions). This dissertation presents research findings suggesting the unsuitability of current off-the-shelf crowdsourcing aggregation algorithms for real engineering design tasks due to the sparsity of expertise in the crowd, and methods that mitigate this limitation by incorporating appropriate information for expertise prediction. Next, we introduce and interpret a number of new probabilistic models for crowdsourced design to provide large-scale preference prediction and full design space generation, building on statistical and machine learning techniques such as sampling methods, variational inference, and deep representation learning. Finally, we show how these models and algorithms can advance crowdsourcing systems by abstracting away the underlying appropriate yet unwieldy mathematics, to easier-to-use visual interfaces practical for engineering design companies and governmental agencies engaged in complex engineering systems design.PhDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133438/1/aburnap_1.pd

    Probabilistic latent variable models for knowledge discovery and optimization

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    I conduct a systematic study of probabilistic latent variable models (PLVMs) with applications to knowledge discovery and optimization. Probabilistic modeling is a principled means to gain insight of data. By assuming that the observed data are generated from a distribution, we can estimate its density, or the statistics of our interest, by either Maximum Likelihood Estimation or Bayesian inference, depending on whether there is a prior distribution for the parameters of the assumed data distribution. One of the primary goals of various machine learning/data mining models is to reveal the underlying knowledge of observed data. A common practice is to introduce latent variables, which are modeled together with the observations. Such latent variables compute, for example, the class assignments (labels), the cluster membership, as well as other unobserved measurements of the data. Besides, proper exploitation of latent variables facilities the optimization itself, which leads to computationally efficient inference algorithms. In this thesis, I describe a range of applications where latent variables can be leveraged for knowledge discovery and efficient optimization. Works in this thesis demonstrate that PLVMs are a powerful tool for modeling incomplete observations. Through incorporating latent variables and assuming that the observations such as citations, pairwise preferences as well as text are generated following tractable distributions parametrized by the latent variables, PLVMs are flexible and effective to discover knowledge in data mining problems, where the knowledge is mathematically modelled as continuous or discrete values, distributions or uncertainty. In addition, I also explore PLVMs for deriving efficient algorithms. It has been shown that latent variables can be employed as a means for model reduction and facilitates the computation/sampling of intractable distributions. Our results lead to algorithms which take advantage of latent variables in probabilistic models. We conduct experiments against state-of-the-art models and empirical evaluation shows that our proposed approaches improve both learning performance and computational efficiency

    Active Learning of Ordinal Embeddings: A User Study on Football Data

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    Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset. We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples. Our user study shows that our approach improves the quality of the information retrieval compared to a previous deep metric learning approach that relies on a Siamese network. Specifically, we shed light on the strengths and weaknesses of passive sampling heuristics and active learners alike by analyzing the participants' response efficacy. To this end, we collect accuracy, algorithmic time complexity, the participants' fatigue and time-to-response, qualitative self-assessment and statements, as well as the effects of mixed-expertise annotators and their consistency on model performance and transfer-learning.Comment: 23 pages, 17 figure

    Domain knowledge, uncertainty, and parameter constraints

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    Ph.D.Committee Chair: Guy Lebanon; Committee Member: Alex Shapiro; Committee Member: Alexander Gray; Committee Member: Chin-Hui Lee; Committee Member: Hongyuan Zh

    Learning To Scale Up Search-Driven Data Integration

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    A recent movement to tackle the long-standing data integration problem is a compositional and iterative approach, termed “pay-as-you-go” data integration. Under this model, the objective is to immediately support queries over “partly integrated” data, and to enable the user community to drive integration of the data that relate to their actual information needs. Over time, data will be gradually integrated. While the pay-as-you-go vision has been well-articulated for some time, only recently have we begun to understand how it can be manifested into a system implementation. One branch of this effort has focused on enabling queries through keyword search-driven data integration, in which users pose queries over partly integrated data encoded as a graph, receive ranked answers generated from data and metadata that is linked at query-time, and provide feedback on those answers. From this user feedback, the system learns to repair bad schema matches or record links. Many real world issues of uncertainty and diversity in search-driven integration remain open. Such tasks in search-driven integration require a combination of human guidance and machine learning. The challenge is how to make maximal use of limited human input. This thesis develops three methods to scale up search-driven integration, through learning from expert feedback: (1) active learning techniques to repair links from small amounts of user feedback; (2) collaborative learning techniques to combine users’ conflicting feedback; and (3) debugging techniques to identify where data experts could best improve integration quality. We implement these methods within the Q System, a prototype of search-driven integration, and validate their effectiveness over real-world datasets

    Human-in-the-Loop Learning From Crowdsourcing and Social Media

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    Computational social studies using public social media data have become more and more popular because of the large amount of user-generated data available. The richness of social media data, coupled with noise and subjectivity, raise significant challenges for computationally studying social issues in a feasible and scalable manner. Machine learning problems are, as a result, often subjective or ambiguous when humans are involved. That is, humans solving the same problems might come to legitimate but completely different conclusions, based on their personal experiences and beliefs. When building supervised learning models, particularly when using crowdsourced training data, multiple annotations per data item are usually reduced to a single label representing ground truth. This inevitably hides a rich source of diversity and subjectivity of opinions about the labels. Label distribution learning associates for each data item a probability distribution over the labels for that item, thus it can preserve diversities of opinions, beliefs, etc. that conventional learning hides or ignores. We propose a humans-in-the-loop learning framework to model and study large volumes of unlabeled subjective social media data with less human effort. We study various annotation tasks given to crowdsourced annotators and methods for aggregating their contributions in a manner that preserves subjectivity and disagreement. We introduce a strategy for learning label distributions with only five-to-ten labels per item by aggregating human-annotated labels over multiple, semantically related data items. We conduct experiments using our learning framework on data related to two subjective social issues (work and employment, and suicide prevention) that touch many people worldwide. Our methods can be applied to a broad variety of problems, particularly social problems. Our experimental results suggest that specific label aggregation methods can help provide reliable representative semantics at the population level

    PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

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    The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE
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