Smart City Development with Urban Transfer Learning

Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with limited data?'. To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city planners and relevant practitioners with guidelines on how to apply this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies in public safety, transportation management, etc. We also summarize a few research opportunities and expect this article can attract more researchers to study urban transfer learning

DNS zones revisited

Recent research [Pap04b] suggests DNS reliability and performance is not up to the levels it should be due to misconfigurations. This paper checks the configuration of nameserver zones against additional requirements, recommendations and best-practices. It shows that almost one in four domains fails to pass one or more of these checks. During the checks an interesting correlation is established: a higher number of nameservers for a single zone usually decreases reliability and performance instead of increasing both

Does Uncertainty Matter: An Application to the Willingness to Pay to Reduce Swimming Bans in Chicago

Using a survey of Chicago beachgoers, this research examines the effect of uncertain response options on the willingness to pay to reduce swimming bans. Various recoding options are tested and implemented, as well as multinomial model for choice. Estimates are compared to those from a dataset with certainty, as well as to those from revealed preference methods. The reasons and sources for uncertainty are explored and compared across samples.Institutional and Behavioral Economics,

Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems

This paper presents the Frames dataset (Frames is available at http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues with an average of 15 turns per dialogue. We developed this dataset to study the role of memory in goal-oriented dialogue systems. Based on Frames, we introduce a task called frame tracking, which extends state tracking to a setting where several states are tracked simultaneously. We propose a baseline model for this task. We show that Frames can also be used to study memory in dialogue management and information presentation through natural language generation