Pervasive and Mobile Computing

The result that run under different number of shards.

Data for: "Free Floating Electric Car Sharing Design: Data Driven Optimisation". Anonymized datasaset of 2 months of trips of car sharing users in the city of Turin init_lon: longitude in which the car starts the actual rental. Coordinates reference system: EPSG:4326 init_lat: latitude in which the car starts the actual rental. Coordinates reference system: EPSG:4326 final_lon: longitude in which the car ends the actual rental. Coordinates reference system: EPSG:4326 final_lat: latitude in which the car ends the actual rental. Coordinates reference system: EPSG:4326 init_time: rental init timestamp, in ISODate final_time: rental init timestamp, in ISODate For tools and simulator refer to: https://github.com/michelelt/sim3.0.

The attachment contains two folders: code and data. The code folder contains the Python code implemented for the models proposed and compared by the paper "Effective Truth Discovery and Fair Reward Distribution for Mobile Crowdsensing Using Sensing Expertise from IoT Infrastructures". The data folder contains the real-life sensing data collected from 10 mobile devices, which cover illuminance, sound level and WiFi signal strength.

Emotion: A numeric real dataset

Non-verbal communication, such as eye contact between drivers and pedestrians, has been regarded as one way to reduce accident risk. So far, studies have assumed rather than objectively measured the occurrence of eye contact. We address this research gap by developing an eye contact detection method and testing it in an indoor experiment with scripted driver-pedestrian interactions at a pedestrian crossing. Thirty participants acted as a pedestrian either standing on an imaginary curb or crossing an imaginary one-lane road in front of a stationary vehicle with an experimenter in the driver’s seat. In half of the trials, pedestrians were instructed to make eye contact with the driver; in the other half, they were prohibited from doing so. Both parties’ gaze was recorded using eye trackers. An in-vehicle stereo camera recorded the car’s point of view, a head-mounted camera recorded the pedestrian’s point of view, and the location of the driver’s and pedestrian’s eyes was estimated using image recognition. We demonstrate that eye contact can be detected by measuring the angles between the vector joining the estimated location of the driver’s and pedestrian’s eyes, and the pedestrian’s and driver’s instantaneous gaze directions, respectively, and identifying whether these angles fall below a threshold of 4°. We achieved 100% correct classification of the trials involving eye contact and those without eye contact, based on measured eye contact duration. The proposed eye contact detection method may be useful for future research into eye contact.

Non-verbal communication, such as eye contact between drivers and pedestrians, has been regarded as one way to reduce accident risk. So far, studies have assumed rather than objectively measured the occurrence of eye contact. We address this research gap by developing an eye contact detection method and testing it in an indoor experiment with scripted driver-pedestrian interactions at a pedestrian crossing. Thirty participants acted as a pedestrian either standing on an imaginary curb or crossing an imaginary one-lane road in front of a stationary vehicle with an experimenter in the driver’s seat. In half of the trials, pedestrians were instructed to make eye contact with the driver; in the other half, they were prohibited from doing so. Both parties’ gaze was recorded using eye trackers. An in-vehicle stereo camera recorded the car’s point of view, a head-mounted camera recorded the pedestrian’s point of view, and the location of the driver’s and pedestrian’s eyes was estimated using image recognition. We demonstrate that eye contact can be detected by measuring the angles between the vector joining the estimated location of the driver’s and pedestrian’s eyes, and the pedestrian’s and driver’s instantaneous gaze directions, respectively, and identifying whether these angles fall below a threshold of 4°. We achieved 100% correct classification of the trials involving eye contact and those without eye contact, based on measured eye contact duration. The proposed eye contact detection method may be useful for future research into eye contact.

Non-verbal communication, such as eye contact between drivers and pedestrians, has been regarded as one way to reduce accident risk. So far, studies have assumed rather than objectively measured the occurrence of eye contact. We address this research gap by developing an eye contact detection method and testing it in an indoor experiment with scripted driver-pedestrian interactions at a pedestrian crossing. Thirty participants acted as a pedestrian either standing on an imaginary curb or crossing an imaginary one-lane road in front of a stationary vehicle with an experimenter in the driver’s seat. In half of the trials, pedestrians were instructed to make eye contact with the driver; in the other half, they were prohibited from doing so. Both parties’ gaze was recorded using eye trackers. An in-vehicle stereo camera recorded the car’s point of view, a head-mounted camera recorded the pedestrian’s point of view, and the location of the driver’s and pedestrian’s eyes was estimated using image recognition. We demonstrate that eye contact can be detected by measuring the angles between the vector joining the estimated location of the driver’s and pedestrian’s eyes, and the pedestrian’s and driver’s instantaneous gaze directions, respectively, and identifying whether these angles fall below a threshold of 4°. We achieved 100% correct classification of the trials involving eye contact and those without eye contact, based on measured eye contact duration. The proposed eye contact detection method may be useful for future research into eye contact.