The true cost of unusable password policies: password use in the wild

HCI research published 10 years ago pointed out that many users cannot cope with the number and complexity of passwords, and resort to insecure workarounds as a consequence. We present a study which re-examined password policies and password practice in the workplace today. 32 staff members in two organisations kept a password diary for 1 week, which produced a sample of 196 passwords. The diary was followed by an interview which covered details of each password, in its context of use. We find that users are in general concerned to maintain security, but that existing security policies are too inflexible to match their capabilities, and the tasks and contexts in which they operate. As a result, these password policies can place demands on users which impact negatively on their productivity and, ultimately, that of the organisation. We conclude that, rather than focussing password policies on maximizing password strength and enforcing frequency alone, policies should be designed using HCI principles to help the user to set an appropriately strong password in a specific context of use

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.This research was partially funded by Fundación Tecnalia Research & Innovation, and J.O.-M. also wants to recognise the support obtained from the EU RFCS program through project number 793505 ‘4.0 Lean system integrating workers and processes (WISEST)’ and from the grant PRX18/00036 given by the Spanish Secretaría de Estado de Universidades, Investigación, Desarrollo e Innovación del Ministerio de Ciencia, Innovación y Universidades