Usage and Consequences of Privacy Settings in Microblogs

Twitter facilitates borderless communication, informing us about real-life events and news. To address privacy needs, Twitter provides various security settings. However, users with protected profiles are limited to their friendship circles and thus might have less visibility from outside of their networks. Previous research on privacy reveals information leakage and security threats in social networks despite of privacy protection enabled. In this context, could protecting microblogging content be counterproductive for individual users? Would microbloggers use Twitter more effectively when opening their content for everyone rather than protecting their profiles? Are user profile protection features necessary? We wanted to address this controversy by studying how microbloggers exploit privacy and geo-location setting controls. We followed a set of user profiles during half of year and compared their usage of Twitter features including status updates, favorites, being listed, adding friends and follower contacts. Our findings revealed that protecting user accounts is not always detrimental to exploiting the main microblogging features. Additionally, we found that users across geographic regions have different privacy preferences. Our results enable us to get insights into privacy issues in microblogs, underlining the need of respecting user privacy in microblogs. We suggest to further research user privacy controls usage in order to understand user goals and motivations for sharing and disclosing their microblogging data online with the focus on user cultural origins

SUMMIT:Supporting Rural Tourism with Motivational Intelligent Technologies

Abstract — SUMMIT is a mobile app that aims to gamify the experience of walkers and hikers and benefit the local communities through which they perambulate. It encourages physical activity through gamification of the user experience by adding additional elements of social fun and motivation to walking and hiking activities. It rewards users for their physical effort by offering access to local resources, hence increasing awareness and appreciation of the local assets and heritage and contributing to the local economy. The evaluation results show that both businesses and walkers were very receptive to the idea

SUMMIT:Supporting Rural Tourism with Motivational Intelligent Technologies

Abstract — SUMMIT is a mobile app that aims to gamify the experience of walkers and hikers and benefit the local communities through which they perambulate. It encourages physical activity through gamification of the user experience by adding additional elements of social fun and motivation to walking and hiking activities. It rewards users for their physical effort by offering access to local resources, hence increasing awareness and appreciation of the local assets and heritage and contributing to the local economy. The evaluation results show that both businesses and walkers were very receptive to the idea

To Stir or Not to Stir:Online Estimation of Liquid Properties for Pouring Actions

Our brains are able to exploit coarse physical models of fluids to solve everyday manipulation tasks. There has been considerable interest in developing such a capability in robots so that they can autonomously manipulate fluids adapting to different conditions. In this paper, we investigate the problem of adaptation to liquids with different characteristics. We develop a simple calibration task (stirring with a stick) that enables rapid inference of the parameters of the liquid from RBG data. We perform the inference in the space of simulation parameters rather than on physically accurate parameters. This facilitates prediction and optimization tasks since the inferred parameters may be fed directly to the simulator. We demonstrate that our "stirring" learner performs better than when the robot is calibrated with pouring actions. We show that our method is able to infer properties of three different liquids -- water, glycerin and gel -- and present experimental results by executing stirring and pouring actions on a UR10. We believe that decoupling of the training actions from the goal task is an important step towards simple, autonomous learning of the behavior of different fluids in unstructured environments.Comment: Presented at the Modeling the Physical World: Perception, Learning, and Control Workshop (NeurIPS) 201