This work documents the recent rise in popularity of messaging bots: chatterbot-like agents with simple, textual interfaces that allow users to access information, make use of services, or provide entertainment through online messaging platforms. Conversational interfaces have been often studied in their many facets, including natural language processing, artificial intelligence, human-computer interaction, and usability. In this work we analyze the recent trends in chatterbots and provide a survey of major messaging platforms, reviewing their support for bots and their distinguishing features. We then argue for what we call “Botplication”, a bot interface paradigm that makes use of context, history, and structured conversation elements for input and output in order to provide a conversational user experience while overcoming the limitations of text-only interfaces

Mobile devices present several features which make them attractive as enabling technology for crowdsensing systems. In particular, their spectrum of sensing capabilities, together with consolidated diffusion and ease of use contribute to an increasing adoption in different mobility-based sensing scenarios. On the other hand, the availability of massive volumes of geospatial data provided by large-scale distributed sensing systems prompts the need for innovative approaches to efficient data gathering and processing. Data reduction strategies are often necessary in order to cope with challenges posed by these volumes, for instance when dealing with real-time visualization of query results. In this paper we present a data reduction and aggregation approach for mitigating the impact of data size in a vehicular sensing application aimed at monitoring the roughness of road surfaces. Data collected by smartphones on board of vehicles is progressively thinned at different levels of the proposed architecture through sampling and spatial/temporal aggregation. Preliminary results show that the proposed methodology provides substantial benefits in terms of reduced impact of data while, at the same time, it enables full exploitation of statistical error compensation.

This paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the participation of engaged citizens into communities that contribute towards a shared goal. In providing people with the tools needed to positively impact society, crowd-based initiatives can be seen as purposeful drivers of social innovation from the bottom. Mobile crowdsensing (MCS), in particular, takes advantage of the ubiquitous nature of mobile devices with on-board sensors to allow large-scale inexpensive data collection campaigns. This paper illustrates MCS in the context of road surface quality monitoring, presenting results from several pilots adopting a public crowdsensing mobile application for systematic data collection. Evaluation of collected information, its quality, and its relevance to road sustainability and maintenance are discussed, in comparison to authoritative data from a variety of other sources.

Information about the paper titled "BRINGING AN UNPLUGGED CODING CARD GAME TO AUGMENTED REALITY" at IATED Digital Library