A product quality impacts of a mobile software product line: an empirical study.

Background: The software product lines (SPL) enable development teams to fully address a systematic reuse of shared assets to deliver a family of similar software products. Mobile applications are an obvious candidate for employing an SPL approach. This paper presents our research outcomes, based on empirical data from an industry-level development project.

A Model of Perception of Privacy, Trust, and Self-Disclosure on Online Social Networks.

Online Social Networks are used widely, raising new issues in terms of privacy, trust, and self-disclosure. For a better understanding of these issues for Facebook users, a model was built that includes privacy value, privacy risk, trust, privacy control, privacy concerns, and self-disclosure. A total of 602 respondents participated in an online survey, and structural equation modeling was used to evaluate the model.

An empirical evaluation of a hands-free computer interaction for users with motor disabilities.

Standard computer input devices such as a mouse or a keyboard are not well suited to the needs of users with severe motor disabilities in their interaction with standard computer interfaces. The emergence of contemporary human computer interfaces has allowed for the development of innovative solutions for hands-free Human-Computer Interaction (HCI), which can improve the quality and accessibility of Information and Communication Technology (ICT) for motor-impaired users. The objectives of this study were to design, develop and evaluate a solution for a hands-free HCI, based on the Emotiv EPOC+ device, which, among other capabilities, also enables controlling the computer with facial expressions and motion sensors.

Outsourcing medical data analyses: can technology overcome legal, privacy, and confidentiality issues?

Background: Medical data are gold mines for deriving the knowledge that could change the course of a single patient's life or even the health of the entire population. A data analyst needs to have full access to relevant data, but full access may be denied by privacy and confidentiality of medical data legal regulations, especially when the data analyst is not affiliated with the data owner.

Objective: Our first objective was to analyze the privacy and confidentiality issues and the associated regulations pertaining to medical data, and to identify technologies to properly address these issues.

Security analysis and improvements to the PsychoPass method.

Background: In a recent paper, Pietro Cipresso et al proposed the PsychoPass method, a simple way to create strong passwords that are easy to remember. However, the method has some security issues that need to be addressed.

Objective: To perform a security analysis on the PsychoPass method and outline the limitations of and possible improvements to the method.

Knowledge discovery with classification rules in a cardiovascular dataset.

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients.

Improving the reliability of medical software by predicting the dangerous software modules.

Software reliability analysis is inevitable for modern medical systems, since a large amount of medical system functionality is now dependent on software, and software does contribute to system failures. Most software reliability models are based on software failure data collected from the project. This creates a problem for the designers since, during the early stage, software failure data are not available.