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

Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests

A typical approach to building a feature set for a conditionalrandom field model is to build a large set of conjunctions of atomic tests, all of which adhere to a small number of relatively simple templates. Building more complex features in this way can be difficult, as the more complex templates needed to do this can result in a combinatoric explosion in the number of features. We use the inherent instability of decision trees to produce a small set of more complex conjunctions that are particularly suitable for the problem to be solved, using the same techniques used in generating random forest ensemble classifiers, andbuild a CRF on these features. We apply this method to an activityrecognition problem on a dataset from the CASAS smart home project, in which we predict activities of daily living from sensor activations

Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare

Passive radio frequency (RF) sensing and monitoring of human daily activities in elderly care homes is an emerging topic. Micro-Doppler radars are an appealing solution considering their non-intrusiveness, deep penetration, and high-distance range. Unsupervised activity recognition using Doppler radar data has not received attention, in spite of its importance in case of unlabelled or poorly labelled activities in real scenarios. This study proposes two unsupervised feature extraction methods for the purpose of human activity monitoring using Doppler-streams. These include a local Discrete Cosine Transform (DCT)-based feature extraction method and a local entropy-based feature extraction method. In addition, a novel application of Convolutional Variational Autoencoder (CVAE) feature extraction is employed for the first time for Doppler radar data. The three feature extraction architectures are compared with the previously used Convolutional Autoencoder (CAE) and linear feature extraction based on Principal Component Analysis (PCA) and 2DPCA. Unsupervised clustering is performed using K-Means and K-Medoids. The results show the superiority of DCT-based method, entropy-based method, and CVAE features compared to CAE, PCA, and 2DPCA, with more than 5\%-20\% average accuracy. In regards to computation time, the two proposed methods are noticeably much faster than the existing CVAE. Furthermore, for high-dimensional data visualisation, three manifold learning techniques are considered. The methods are compared for the projection of raw data as well as the encoded CVAE features. All three methods show an improved visualisation ability when applied to the encoded CVAE features