315 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Platform work in municipal contexts: a multi-level governance analysis of Madrid, Milan, and San Francisco

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    This thesis makes an original contribution to the emergent field of platform urbanism by analysing platform work governance in three cities using a multi-level governance framework. Multi-level governance is operationalised in the form of four interrelated indicators; it is used to conduct qualitative policy analysis in Madrid (Spain), Milan (Italy), and San Francisco (USA). The four indicators capture 1. the roles of non-state actors, 2. the relationship between governmental levels regarding platform work, 3. the availability of relevant competences on municipal level, and 4. the involvement of the municipality in the policy debate on platform work. Each indicator raises different questions that guide data collection and analysis as well as the consideration of the contexts within which a municipality responds to platform work. Empirical data is generated from 17 extensive semistructured interviews with 19 local participants from academia, trade unions, and municipal and regional governments – including elite interview participants – and from documentary analysis of 14 municipal policy documents. The thesis produces several significant findings. Above all, multi-level governance generates new evidence on why municipalities govern platform work in distinct ways. Municipal responses in San Francisco and Madrid are influenced by other governmental levels and respective legislation targeting workers’ misclassification as self-employed. The perception of platform work as remedy against poverty by officials in San Francisco and as source of precarity in Madrid reinforces openness and resistance to the phenomenon, respectively. In Milan, tensions between a desire to promote innovative platform services and a commitment to workers’ rights result in municipal engagement with workers and representatives of digital labour platforms. During the Covid-19 pandemic, the recognition of platform work as essential service contributed to a continuation of earlier municipal responses. Moreover, the thesis presents evidence demonstrating the difference between platform governance and platform work governance: city governments often treat platform work differently than other aspects of the platform economy. Altogether, the thesis strongly suggests that even in uncertain regulatory environments, city governments can play a decisive role in mitigating workers’ precarity or promoting platform work

    Learning Reserve Prices in Second-Price Auctions

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    This paper proves the tight sample complexity of Second-Price Auction with Anonymous Reserve, up to a logarithmic factor, for each of all the value distribution families studied in the literature: [0,1]-bounded, [1,H]-bounded, regular, and monotone hazard rate (MHR). Remarkably, the setting-specific tight sample complexity poly(?^{-1}) depends on the precision ? ? (0, 1), but not on the number of bidders n ? 1. Further, in the two bounded-support settings, our learning algorithm allows correlated value distributions. In contrast, the tight sample complexity ??(n) ? poly(?^{-1}) of Myerson Auction proved by Guo, Huang and Zhang (STOC 2019) has a nearly-linear dependence on n ? 1, and holds only for independent value distributions in every setting. We follow a similar framework as the Guo-Huang-Zhang work, but replace their information theoretical arguments with a direct proof

    Exploring Animal Behavior Through Sound: Volume 1

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    This open-access book empowers its readers to explore the acoustic world of animals. By listening to the sounds of nature, we can study animal behavior, distribution, and demographics; their habitat characteristics and needs; and the effects of noise. Sound recording is an efficient and affordable tool, independent of daylight and weather; and recorders may be left in place for many months at a time, continuously collecting data on animals and their environment. This book builds the skills and knowledge necessary to collect and interpret acoustic data from terrestrial and marine environments. Beginning with a history of sound recording, the chapters provide an overview of off-the-shelf recording equipment and analysis tools (including automated signal detectors and statistical methods); audiometric methods; acoustic terminology, quantities, and units; sound propagation in air and under water; soundscapes of terrestrial and marine habitats; animal acoustic and vibrational communication; echolocation; and the effects of noise. This book will be useful to students and researchers of animal ecology who wish to add acoustics to their toolbox, as well as to environmental managers in industry and government

    CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

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    This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship

    Reducing the labeling effort for entity resolution using distant supervision and active learning

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    Entity resolution is the task of identifying records in one or more data sources which refer to the same real-world object. It is often treated as a supervised binary classification task in which a labeled set of matching and non-matching record pairs is used for training a machine learning model. Acquiring labeled data for training machine learning models is expensive and time-consuming, as it typically involves one or more human annotators who need to manually inspect and label the data. It is thus considered a major limitation of supervised entity resolution methods. In this thesis, we research two approaches, relying on distant supervision and active learning, for reducing the labeling effort involved in constructing training sets for entity resolution tasks with different profiling characteristics. Our first approach investigates the utility of semantic annotations found in HTML pages as a source of distant supervision. We profile the adoption growth of semantic annotations over multiple years and focus on product-related schema.org annotations. We develop a pipeline for cleansing and grouping semantically annotated offers describing the same products, thus creating the WDC Product Corpus, the largest publicly available training set for entity resolution. The high predictive performance of entity resolution models trained on offer pairs from the WDC Product Corpus clearly demonstrates the usefulness of semantic annotations as distant supervision for product-related entity resolution tasks. Our second approach focuses on active learning techniques, which have been widely used for reducing the labeling effort for entity resolution in related work. Yet, we identify two research gaps: the inefficient initialization of active learning and the lack of active learning methods tailored to multi-source entity resolution. We address the first research gap by developing an unsupervised method for initializing and further assisting the complete active learning workflow. Compared to active learning baselines that use random sampling or transfer learning for initialization, our method guarantees high anytime performance within a limited labeling budget for tasks with different profiling characteristics. We address the second research gap by developing ALMSER, the first active learning method which uses signals inherent to multi-source entity resolution tasks for query selection and model training. Our evaluation results indicate that exploiting such signals for query selection alone has a varying effect on model performance across different multi-source entity resolution tasks. We further investigate this finding by analyzing the impact of the profiling characteristics of multi-source entity resolution tasks on the performance of active learning methods which use different signals for query selection
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