2,406 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

    An Inquiry into Supply Chain Strategy Implications of the Sharing Economy for Last Mile Logistics

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    As the prevalence of e-commerce and subsequent importance of effective and efficient omnichannel logistics strategies continues to rise, retail firms are exploring the viability of sourcing logistics capabilities from the sharing economy. Questions arise such as, “how can crowdbased logistics solutions such as crowdsourced logistics (CSL), crowdshipping, and pickup point networks (PPN) be leveraged to increase performance?” In this dissertation, empirical and analytical research is conducted that increases understanding of how firms can leverage the sharing economy to increase logistics and supply chain performance. Essay 1 explores crowdsourced logistics (CSL) by employing a stochastic discrete event simulation set in New York City in which a retail firm sources drivers from the crowd to perform same day deliveries under dynamic market conditions. Essay 2 employs a design science paradigm to develop a typology of crowdbased logistics strategies using two qualitative methodologies: web content analysis and Delphi surveys. A service-dominant logic theoretical perspective guides this essay and explains how firms co-create value with the crowd and consumer markets while presenting a generic design for integrating crowdbased models into logistics strategy. In Essay 3, a crowdsourced logistics strategy for home delivery is modeled in an empirically grounded simulation optimization to explore the logistics cost and responsiveness implications of sharing economy solutions on omnichannel fulfillment strategies

    A Study of Realtime Summarization Metrics

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    Unexpected news events, such as natural disasters or other human tragedies, create a large volume of dynamic text data from official news media as well as less formal social media. Automatic real-time text summarization has become an important tool for quickly transforming this overabundance of text into clear, useful information for end-users including affected individuals, crisis responders, and interested third parties. Despite the importance of real-time summarization systems, their evaluation is not well understood as classic methods for text summarization are inappropriate for real-time and streaming conditions. The TREC 2013-2015 Temporal Summarization (TREC-TS) track was one of the first evaluation campaigns to tackle the challenges of real-time summarization evaluation, introducing new metrics, ground-truth generation methodology and dataset. In this paper, we present a study of TREC-TS track evaluation methodology, with the aim of documenting its design, analyzing its effectiveness, as well as identifying improvements and best practices for the evaluation of temporal summarization systems

    Managing risks of crowdsourcing innovation: an action research in progress

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    Over the last few years, a number of academics and practitioners have emphasized the value of innovation as a main driver for firms to enhance their business performance and sustain a high profitability. Recent studies of innovation have pointed to the growing relevance of external sources of innovation and the firm's necessity of involving a wide range of internal and external actors and sources to help achieving and sustaining its business strategy. The company can become more innovative by implementing a process of cocreation. It can do this in two ways (1) by internally identifying the business problems and needs for innovation felt by individuals, teams and organizational units (seekers) and furthering the emergence of a community of specialists (within or outside the organization), or employees motivated to provide their knowledge and skills to address innovation problems, increasing their internal visibility and ensuring their empowerment across the company (solvers); (2) by placing its innovation problems and needs to a brokering service that can find the right people to present solutions. These two forms of open innovation is called Crowdsourcing Innovation. Innovation brings risks. Risk of Financial loss or of being unsuccessful. If innovation requires business or organizational change, the risk is even bigger because innovation implies newness and unknown. Any company that innovates must face the inherent risks. Facing the risks requires that the company manages them, understanding in advance their nature and impact, monitoring the relevant indicators to anticipate their occurrence, and being ready to act immediately at the first signs of trouble. The innovating company should consider managing risks as one of its core competences. Without this capability, any innovation project can become an opportunity to dramatically fail the business objectives and sustainability. Steady progress has been made over the last years in understanding open innovation strategy. This paper adds to that effort by focusing- (undefined

    Ensuring an Essential Supply of Allied Health Professions (AHP) Placements: Using Crowdsourcing to Develop a National Call to Action

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    Sustainable growth in the Allied Health Professions (AHP) workforce is an ambition of the United Kingdom’s NHS Long Term Plan. However historically, access to good quality placements has been a barrier to increasing pre-registration training numbers. This article focuses on work carried out by Health Education England (HEE) to gain insights on the impact of the COVID-19 pandemic on capacity. Using a pragmatic, embedded mixed-methods approach, insights were gathered using an online workshop, crowdsourcing, open for two weeks in the summer of 2020. AHP placement stakeholders could vote, share ideas or comment. Descriptive data were extracted, and comments made were analysed using inductive thematic analysis. Participants (N = 1,800) made over 8,500 comments. The themes identified included: diversity of placement opportunity, improved placement coordination, a more joined-up system, supervision models and educator capacity. Alongside considering the challenges to placement capacity, several areas of innovative practice owing to the pandemic were highlighted. Generated insights have shaped the aims and objectives of the Health Education (HEE) pre-registration AHP student practice learning programme for 2020/2021 and beyond. The COVID-19 pandemic has disrupted the delivery of AHP placements. In the absence of face-to-face activities, crowdsourcing provided an online data collection tool offering stakeholders an opportunity to engage with the placement capacity agenda and share learning. Findings have shaped the HEE approach to short-term placement recovery and long-term growth

    On Classification in Human-driven and Data-driven Systems

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    Classification systems are ubiquitous, and the design of effective classification algorithms has been an even more active area of research since the emergence of machine learning techniques. Despite the significant efforts devoted to training and feature selection in classification systems, misclassifications do occur and their effects can be critical in various applications. The central goal of this thesis is to analyze classification problems in human-driven and data-driven systems, with potentially unreliable components and design effective strategies to ensure reliable and effective classification algorithms in such systems. The components/agents in the system can be machines and/or humans. The system components can be unreliable due to a variety of reasons such as faulty machines, security attacks causing machines to send falsified information, unskilled human workers sending imperfect information, or human workers providing random responses. This thesis first quantifies the effect of such unreliable agents on the classification performance of the systems and then designs schemes that mitigate misclassifications and their effects by adapting the behavior of the classifier on samples from machines and/or humans and ensure an effective and reliable overall classification. In the first part of this thesis, we study the case when only humans are present in the systems, and consider crowdsourcing systems. Human workers in crowdsourcing systems observe the data and respond individually by providing label related information to a fusion center in a distributed manner. In such systems, we consider the presence of unskilled human workers where they have a reject option so that they may choose not to provide information regarding the label of the data. To maximize the classification performance at the fusion center, an optimal aggregation rule is proposed to fuse the human workers\u27 responses in a weighted majority voting manner. Next, the presence of unreliable human workers, referred to as spammers, is considered. Spammers are human workers that provide random guesses regarding the data label information to the fusion center in crowdsourcing systems. The effect of spammers on the overall classification performance is characterized when the spammers can strategically respond to maximize their reward in reward-based crowdsourcing systems. For such systems, an optimal aggregation rule is proposed by adapting the classifier based on the responses from the workers. The next line of human-driven classification is considered in the context of social networks. The classification problem is studied to classify a human whether he/she is influential or not in propagating information in social networks. Since the knowledge of social network structures is not always available, the influential agent classification problem without knowing the social network structure is studied. A multi-task low rank linear influence model is proposed to exploit the relationships between different information topics. The proposed approach can simultaneously predict the volume of information diffusion for each topic and automatically classify the influential nodes for each topic. In the third part of the thesis, a data-driven decentralized classification framework is developed where machines interact with each other to perform complex classification tasks. However, the machines in the system can be unreliable due to a variety of reasons such as noise, faults and attacks. Providing erroneous updates leads the classification process in a wrong direction, and degrades the performance of decentralized classification algorithms. First, the effect of erroneous updates on the convergence of the classification algorithm is analyzed, and it is shown that the algorithm linearly converges to a neighborhood of the optimal classification solution. Next, guidelines are provided for network design to achieve faster convergence. Finally, to mitigate the impact of unreliable machines, a robust variant of ADMM is proposed, and its resilience to unreliable machines is shown with an exact convergence to the optimal classification result. The final part of research in this thesis considers machine-only data-driven classification problems. First, the fundamentals of classification are studied in an information theoretic framework. We investigate the nonparametric classification problem for arbitrary unknown composite distributions in the asymptotic regime where both the sample size and the number of classes grow exponentially large. The notion of discrimination capacity is introduced, which captures the largest exponential growth rate of the number of classes relative to the samples size so that there exists a test with asymptotically vanishing probability of error. Error exponent analysis using the maximum mean discrepancy is provided and the discrimination rate, i.e., lower bound on the discrimination capacity is characterized. Furthermore, an upper bound on the discrimination capacity based on Fano\u27s inequality is developed
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