Toward a Human-Centered Uml for Risk Analysis

Safety is now a major concern in many complex systems such as medical robots. A way to control the complexity of such systems is to manage risk. The first and important step of this activity is risk analysis. During risk analysis, two main studies concerning human factors must be integrated: task analysis and human error analysis. This multidisciplinary analysis often leads to a work sharing between several stakeholders who use their own languages and techniques. This often produces consistency errors and understanding difficulties between them. Hence, this paper proposes to treat the risk analysis on the common expression language UML (Unified Modeling Language) and to handle human factors concepts for task analysis and human error analysis based on the features of this language. The approach is applied to the development of a medical robot for teleechography

Bayesian semiparametric analysis for two-phase studies of gene-environment interaction

The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected subsample. It is natural to apply such a strategy for collecting genetic data in a subsample enriched for exposure to environmental factors for gene-environment interaction (G x E) analysis. In this paper, we consider two-phase studies of G x E interaction where phase I data are available on exposure, covariates and disease status. Stratified sampling is done to prioritize individuals for genotyping at phase II conditional on disease and exposure. We consider a Bayesian analysis based on the joint retrospective likelihood of phases I and II data. We address several important statistical issues: (i) we consider a model with multiple genes, environmental factors and their pairwise interactions. We employ a Bayesian variable selection algorithm to reduce the dimensionality of this potentially high-dimensional model; (ii) we use the assumption of gene-gene and gene-environment independence to trade off between bias and efficiency for estimating the interaction parameters through use of hierarchical priors reflecting this assumption; (iii) we posit a flexible model for the joint distribution of the phase I categorical variables using the nonparametric Bayes construction of Dunson and Xing [J. Amer. Statist. Assoc. 104 (2009) 1042-1051].Comment: Published in at http://dx.doi.org/10.1214/12-AOAS599 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Design-time Models for Resiliency

Resiliency in process-aware information systems is based on the availability of recovery flows and alternative data for coping with missing data. In this paper, we discuss an approach to process and information modeling to support the specification of recovery flows and alternative data. In particular, we focus on processes using sensor data from different sources. The proposed model can be adopted to specify resiliency levels of information systems, based on event-based and temporal constraints

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

Designing Systems for Risk Based Decision Making

A common challenge security analysts face, is in making decisions on risk when facing uncertain conditions especially when risk management procedures are inapplicable. Though analysts may have experience and their intuition to depend upon, these have sometimes proven to be insufficient and it has also been identified that risk and uncertainty may be magnified by personal, system and environmental factors. Risk decision making is an integral part of security analysis, a role facilitated by system automation and design. However, designing for usable security has not sufficiently considered the implications of design to risk decision making and more so in relation to security analysts. The research aims to address this by coming up with recommendations for design, for risk based decision making

A diversity-based approach to requirements tracing in new product development.

Production models emerged in recent times have stressed the need to face complex production contexts, characterized in particular by the rise in internal and environmental variability. In this work, a stylization of some elements concerning analysis and design of new products is given, and in particular those that involve definition and transfer phases in the development of innovative goods, where change and variability in requirements along development process are often high. This analysis has a twofold goal: first, to supply a conceptual frame for the close examination of some dynamics of requirement's integration into an artifact's design, in order to give account of their variability along development cycle; on the other side, to propose an approach based on simple similarity metrics, to be applied to linguistic descriptions of artifacts in the early phases of development process, in order to identify components in an artifact that undergo larger variability and therefore are to be paid more attention in the subsequent phases of life cycle.