Evangelos Niforatos

Atmos: a hybrid crowdsourcing approach to weather estimation

Motivated by the novel paradigm of participatory sensing in collecting in situ automated data and human input we introduce the Atmos platform. Atmos leverages a crowd-sourcing network of mobile devices for the collection of in situ weather related sensory data, provided by available on-board sensors, along with human input, to generate highly localized information about current and future weather conditions. In this paper, we share our first insights of an 8-month long deployment of Atmos mobile app on Google Play that gathered data from a total of 9 countries across 3 continents. Furthermore, we describe the underlying system infrastructure and showcase how a hybrid people-centric and environment-centric approach to weather estimation could benefit forecasting. Finally, we present our preliminary results originating from questionnaires inquiring into how people perceive the weather, how they use technology to know about the weather and how it affects their habits.

PLBSD: a platform for proactive location-based service discovery

We introduce a platform for the rapid prototyping of proactive location-based service discovery, proactive location-based services are conceptualised along three broad categories: location-triggered services chain-triggered services, proximity-triggered services, and illustrated through a number of usage scenarios. We report on a workshop with designers and researchers in the area of location-based services that resulted in a set of initial requirements for the platform. We describe how the platform aims at addressing these requirements, and illustrate the implemented features through the development of a proactive location-based application.

Supporting interactivity on a ski lift

Today, a wide variety of technologies and devices are available for skiers. Those gadgets perform a number of tasks to improve the overall skiing experience, such as collecting personal performance data, recording memorable moments, or assisting in communication with group members. In this position paper we outline our empirical findings from unstructured interviews with skiers and ethnographical observations during several field trips to skiing resorts. In particular, we aim to understand the challenges and opportunities of using personal and situated devices on a ski lift. Based on the findings, we discuss appropriate interaction techniques and propose possible applications that could support user needs and enhance the overall skiing experience.

Measuring the Media Effects of a Tourism-Related Virtual Reality Experience Using Biophysical Data

Tourism is among the domains that might see in Virtual reality (VR) several advances for promoting its products and services. This study is one of the first that proposes the use of biophysical data for investigating the media effects of a tourism-related VR experience and discusses the use of such technique for studying media effects in VR settings. A VR experience proposed to the public by Ticino Tourism, a regional Swiss Destination Management Organization (DMO) was tested with 23 people who participated in an experiment in which biophysical reactions were tracked. Findings show that specific media characteristics of the VR experience, such as proposing an unusual horizon perspective to the VR viewer, hold the potential to lead to the formation of strong memories. These results might enable a better understanding of the effects of VR technology on consumer behaviour and eventually lead to the design of more engaging VR content.

SkiAR: Wearable Augmented Reality System for Sharing Personalized Content on Ski Resort Maps

Winter sports like skiing and snowboarding are often group activities. Groups of skiers and snowboarders traditionally use folded paper maps or board-mounted larger-scale maps near ski lifts to aid decision making: which slope to take next, where to have lunch, or what hazards to avoid when going off-piste. To enrich those static maps with personal content (e.g., pictures, prior routes taken, or hazards encountered), we developed SkiAR, a wearable augmented reality system that allows groups of skiers and snowboarders to share such content in-situ on a printed resort map while on the slope.

My good old kodak: understanding the impact of having only 24 pictures to take

Todays abundance of cheap digital storage in the form of tiny memory cards put literally no bounds on the number of images one can capture with ones digital camera or camera phone during an event. However, studies have shown that taking many pictures may actually make us remember less of a particular event. In this position paper, we propose to re-introduce the paradigm of old film camera in the context of modern smartphones. The purpose is to investigate how users will behave when a significant capture limitation is imposed in a picture-taking context, and in what kind of pictures this will result. Ultimately, we are interested in the effect on memory recall of such a limitation, and describe a potential study setup that will help us explore this question.

Understanding the potential of humanmachine crowdsourcing for weather data

Reliable weather estimation traditionally requires a dense network of meteorological measurement stations. The concept of participatory sensing promises to alleviate this requirement by crowdsourcing weather data from an ideally very large set of participating users instead. Participation may involve nothing more than downloading a corresponding app to enable the collection of such data, given that modern smartphones contain a plethora of weather-related sensors. To understand the potential of participatory sensing for weather estimation, and how humans can be put in the loop to further improve such sensing, we created Atmos a crowdsourcing weather app that not only periodically samples smartphones sensors for weather measurements, but also allows users to enter their own estimates of both current and future weather conditions. We present the results of a 32-month public deployment of Atmos on the Google Play Store, showing that a combination of both types of sensing results in accurate temperature estimates, featuring an average error rate of 2.7C, whereas when using only user inputs, the average error rate drops to 1.86C. Sensor readings revealed significant variations during users commuting times.User inputs were more accurate in estimating actual temperature than sensor inputs.Bagged decision trees with user reported temperature achieved the lowest error rate.A 32-month public deployment on Google Play Store.

Measuring the effect of cued recall on work meetings

Recent technological improvements allow us to capture an increasing share of our everyday experiences, e.g. holidays, shopping routines, or sports activities, and store them in a digital format. An interesting avenue to explore in this context is how reviewing such captured content can improve ones memories of the original events. In this position paper, we describe a planned experiment to investigate the impact of such captured recordings (and their subsequent review) on supporting work meetings. We provide the planned study procedure, explain the envisioned apparatus and metric, and describe the technology used to support the review activity.

Design and evaluation of a wearable AR system for sharing personalized content on ski resort maps

Winter sports like skiing and snowboarding are often group activities. Groups of skiers and snowboarders traditionally use paper maps or board-mounted larger-scale maps near ski lifts to aid decision making: which slope to take next, where to have lunch, or what hazards to avoid when going off-piste. To enrich those static maps with personal content (e.g., pictures, prior routes taken, or hazards encountered), we developed SkiAR - a wearable augmented reality system that allows groups of skiers and snowboarders to share such content on a printed panoramic resort map. The contribution of our work is twofold: (1) we developed a system that offers a novel way to review and share personal content in situ while on the slope using a resort map; (2) we report on the results from a qualitative analysis of two user studies to inform the design and validate the usability and perceived usefulness of our prototype.

WeatherUSI: Crowdsourcing Weather Experience on Public Displays

Motivated by the opportunity of using public displays to contribute to crowdsourcing, we developed WeatherUSI -- an application that allows passers-by to provide subjective information about the weather, both currently and in the near future. In contrast to the input collected by an already fielded mobile app called Atmos, we envision the public display component to provide not only highly localized information about current and future weather conditions, but also to better incentivize the reporting process, given its public nature. In our upcoming deployment of WeatherUSI, we plan to investigate how participation through the public display component differs from the mobile component, and if the quality of weather related input differs between the two. This work in progress presents our approach, describes the underlying system infrastructure and outlines our planned study design.