No abstract

Computer security experts recommend that people use two-factor authentication (2FA) on password protected systems to help keep hackers out. Providing two pieces of information to verify a person’s identity adds extra security to an account. However, it is not clear if the added security and procedures impact system usability. This paper aims to answer this question by assessing per ISO 9241-11’s suggested measurements the usability of Google’s optional 2FA methods. We found few differences across four different 2FA methods when comparing efficiency, effectiveness and satisfaction measures—illustrating that one method is not necessarily more or less usable then another. Overall, the measures indicated that the systems’ usability needed to be improved, especially with regard to the initial setup of 2FA. In conclusion, developers need to focus more attention on making 2FA easier and faster to use, especially since it is often optional for password users, yet makes accounts significantly more secure.

The research presented in this paper develops a novel approach for a risk-based authentication system that takes into account mobile user movement patterns. Inspired by the concept of Shannon's information theory, we introduce a variant version of spatial entropy vectors embedded with time information as a mathematical modeling tool to evaluate regular movement patterns, and spatial entropy vectors derived from user movements range and paces. To support the approach, several algorithms have been designed and implemented. A prototype iPhone application was developed as a proof-of-concept, user movement data has been collected over a predetermined timeframe by accumulating, merging, and saving spatial entropy vectors in a database on the application. The application simulates risk-based authentication by calculating risk factors based on the similarity between current spatial entropy vectors calculated on demand, and historical distributions of movement patterns. Data collected on the field shows that the risk factor is relatively low for similar moving patterns, while different patterns can yield a higher risk factor. Rather than modeling this process by directly storing GPS location data with complicated pathmatching algorithms, the spatial entropy model developed uses sampled location data, but does not keep it, preserving user privacy. Practical applications can be used, for example, to adjust fingerprint authentication threshold in iPhone when combining with the risk factor calculated in real time.

Queer in AI is an organization that aims to combat the harms faced by queer researchers within AI. Several inclusion initiatives are outlined, including those centered on policy and financial aid.

Unsupervised image segmentation is a challenge task, since a high-quality segmented image should perceive not only local object structures but also certain semantics without any annotations. In this paper, we propose a novel encoder-decoder pixel clustering framework with dual constraints to incorporate local structure and global semantic information for guiding pixel feature learning in a self-supervised manner. On one hand, a Local Structure Constraint (LStC) is constructed based on fine-grained superpixels, which improves the boundary perception of pixel features by keeping intrasuperpixel feature consistency and largening inter-superpixel feature distance. On the other hand, a new Global Semantic Constraint (GSeC) is proposed via adapting the mutual information maximization technique to the single-image setting, and it strengthens the global semantic perception of pixel features and thus improves the segmenting integrity of objects. Finally, based on the learned pixel features, a smoothing component is employed to achieve semantically meaningful pixel clustering. The experimental evaluation on BSDS500 and PASCAL Context datasets show the superiority of our method on region and boundary qualities.