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1. Automating the Classification of Urban Issue Reports: an Optimal Stopping Approach

   https://doi.org/10.1109/ICASSP.2019.8682778  

   Liyanage, Yasitha Warahena; Zois, Daphney-Stavroula; Chelmis, Charalampos; Yao, Mengfan (May 2019, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   Empowering citizens to interact directly with their local governments through civic engagement platforms has emerged as an easy way to resolve urban issues. However, for authorities to manually process reported issues is both impractical and inefficient; accurate, online and near-real-time processing methods are necessary to maintain citizens' satisfaction with their local governments. Herein, an optimal stopping framework is proposed to process urban issue requests quickly and accurately. The optimal classification and stopping rules are derived, and significant reduction in time-to-decision without sacrificing accuracy is demonstrated on a real-world dataset from SeeClickFix. 

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2. Improving Monitoring of Participatory Civil Issue Requests through Optimal Online Classification

   https://doi.org/10.1109/ACSSC.2018.8645177  

   Zois, Daphney-Stavroula; Yong, Christopher; Chelmis, Charalampos; Kapodistria, Angeliki; Lee, Wonhyung (October 2018, 52nd Asilomar Conference on Signals, Systems, and Computers)

   Participatory civil issue monitoring has emerged as an easy way for concerned citizens to report problems to their local government. For reported issues to be timely processed and addressed however, accurate, online and real-time processing methods to infer issue types are necessary. To address this challenge, we propose a computational, near-real-time civil issue reports processing method to estimate the actual issue from ambiguous and/or complementary information accurately and efficiently. We demonstrate the effectiveness of the proposed approach using a real-world dataset from SeeClickFix. We show that our approach is both highly accurate and scalable. 

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3. Improving Monitoring of Participatory Civil Issue Requests through Optimal Online Classification

   Zois, Daphney-Stavroula; Yong, Christopher; Chelmis, Charalampos; Kapodistria, Angeliki; Lee, Wonhyung (October 2018, Asilomar Conference on Signals, Systems, and Computers)

   Participatory civil issue monitoring has emerged as an easy way for concerned citizens to report problems to their local government. For reported issues to be timely processed and addressed however, accurate, online and real–time processing methods to infer issue types are necessary. To address this challenge, we propose a computational, near–real–time civil issue reports processing method to estimate the actual issue from ambiguous and/or complementary information accurately and efficiently. We demonstrate the effectiveness of the proposed approach using a real-world dataset from SeeClickFix. We show that our approach is both highly accurate and scalable. 

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4. Understanding online civic engagement: a multi-neighborhood study of SeeClickFix

   https://doi.org/10.1145/3341161.3345330  

   Yong, Christopher; Chelmis, Charalampos; Lee, Wonhyung; Zois, Daphney-Stavroula (August 2019, 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM))

   The relationship between local governments and the general public is being redefined by the increasing use of online platforms that enable participatory reporting of non-emergency urban issues, such as potholes and illegal graffiti by concerned citizens to their local authorities. In this work, we study, for the first time, participatory reporting data together with neighborhood-level demographics, socioeconomic indicators, and pedestrian friendliness and transit and bike scores, across multiple neighborhoods in the Capital District of the New York State. Our data-driven approach offers a large-scale, low-cost alternative to traditional survey methods, and provides insights on citizen participation and satisfaction, and public value creation on such platforms. Our findings can be used to guide government service departments to work more closely with each neighborhood to improve the offline and online communication channels through which citizens can report urban issues. 

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5. Optimal Stopping of Adaptive Dose-Finding Trials

   https://doi.org/10.1287/serv.2020.0261  

   Nasrollahzadeh, Amir Ali; Khademi, Amin (June 2020, Service Science)

   null (Ed.)

   The primary objective of this paper is to develop computationally efficient methods for optimal stopping of an adaptive Phase II dose-finding clinical trial, where the decision maker may terminate the trial for efficacy or abandon it as a result of futility. We develop two solution methods and compare them in terms of computational time and several performance metrics such as the probability of correct stopping decision. One proposed method is an application of the one-step look-ahead policy to this problem. The second proposal builds a diffusion approximation to the state variable in the continuous regime and approximates the trial’s stopping time by optimal stopping of a diffusion process. The secondary objective of the paper is to compare these methods on different dose-response curves, particularly when the true dose-response curve has no significant advantage over a placebo. Our results, which include a real clinical trial case study, show that look-ahead policies perform poorly in terms of the probability of correct decision in this setting, whereas our diffusion approximation method provides robust solutions. 

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