Uncertainty Handling in Genetic Risk Assessment and Counseling

In this paper, we consider genetic risk assessment and genetic counseling for breast cancer from the point of view of reliable uncertainty handling. In medical practice, there exist fairly accurate numerical tools predicting breast cancer (or gene mutation) probability based on such factors as the family history of a patient. However, they are too complex to be applied in normal doctors’ offices, so that several simplified, questionnaire-type support tools appeared. This process is highly affected by uncertainty. At the same time, reliability of test interpretations and counseling conclusions is especially important since they have direct influence on humans and their decisions. We show how expert opinions on mutation probabilities can be combined using the Dempster-Shafer theory. Based on multi-criteria binary decision trees and interval analysis, we combine the referral screening tool designed to determine patients at risk of breast cancer (and recommend genetic counseling or testing for them) with three further risk assessment tools available for this purpose. A patient’s confidence in the outcome of a genetic counseling session can be heightened by the proposed method since it combines different sources to provide score ranges leading to more information. Finally, based on this approach, a decision tree for assigning a risk category is proposed which enhances the existing methodology. The great impact of epistemic uncertainty is reflected through large overlapping intervals for the risk classes.

HANDLING OF EXPLICIT UNCERTAINTY IN REQUIREMENTS CHANGE MANAGEMENT

Innovation projects are characterized by numerous uncertainties. Typical concepts in development management like the application of safety coefficients imply limitations of the solution space. In contrast, explicit handling of uncertainties can support engineers in understanding the problem space and in utilising the full potential of the design space along iterative product development steps. As a result from literature analysis, there is a lack of a support for product development that addresses the specific problem of uncertainty and risk in the context of requirement changes. The aim of the contribution at hand is to enhance the efficient development of complex interdisciplinary systems by enabling uncertainty handling in requirements change management. Based on a classification of uncertainty types resulting in a descriptive model, risk management measures are identified to support requirements engineers. The proposed method includes identification & modelling, analysis, treatment and monitoring of risks and counter-measures. By applying this method, engineers are supported in adopting agile approaches and enabling flexible Requirements Engineering.

Risk assessment of COVID-19 based on a new structure of neural fuzzy probabilistic model

The risk assessment of the COVID-19 infection can save so many lives, reduce treatment costs, and increase public health. The unknown nature of the COVID-19 infection, the high impreciseness of available information, and not simply recognizing the relevant factors and their effectiveness may cause overestimating and underestimating of factors. This paper puts forward a development of a model with fewer limitations that are more consistent with progressive knowledge about COVID-19. Dealing with the situation of updating the statistical dataset daily, the proposed approach can effectively use the subjectivity inherent in the fuzzy probability interpretation of risk factors using expert knowledge in addition to the statistical dataset. Second, to this uncertainty handling improvement, a specificity-based parameter learning based on the learning network is also added to deal with the complexity aspect of the COVID-19 infection. The learning process helps the proposed structure better adjust the effectiveness of factors. From the achieved results, it is verified that people with advanced age, those with chronic obstructive pulmonary disease, lung cancer, and those having cancer treatments are at higher risk of death if they are infected by COVID-19. Undoubtedly, for vaccination, these three groups should be considered in order to prevent death situations.

Uncertainty in Self-adaptive Systems: A Research Community Perspective

One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of uncertainty in self-adaptive systems is still insufficiently understood. Several taxonomies of uncertainty have been proposed, and a substantial body of work exists on methods to tame uncertainty. Yet, these taxonomies and methods do not fully convey the research community’s perception on what constitutes uncertainty in self-adaptive systems and on the key characteristics of the approaches needed to tackle uncertainty. To understand this perception and learn from it, we conducted a survey comprising two complementary stages in which we collected the views of 54 and 51 participants, respectively. In the first stage, we focused on current research and development, exploring how the concept of uncertainty is understood in the community and how uncertainty is currently handled in the engineering of self-adaptive systems. In the second stage, we focused on directions for future research to identify potential approaches to dealing with unanticipated changes and other open challenges in handling uncertainty in self-adaptive systems. The key findings of the first stage are: (a) an overview of uncertainty sources considered in self-adaptive systems, (b) an overview of existing methods used to tackle uncertainty in concrete applications, (c) insights into the impact of uncertainty on non-functional requirements, (d) insights into different opinions in the perception of uncertainty within the community and the need for standardised uncertainty-handling processes to facilitate uncertainty management in self-adaptive systems. The key findings of the second stage are: (a) the insight that over 70% of the participants believe that self-adaptive systems can be engineered to cope with unanticipated change, (b) a set of potential approaches for dealing with unanticipated change, (c) a set of open challenges in mitigating uncertainty in self-adaptive systems, in particular in those with safety-critical requirements. From these findings, we outline an initial reference process to manage uncertainty in self-adaptive systems. We anticipate that the insights on uncertainty obtained from the community and our proposed reference process will inspire valuable future research on self-adaptive systems.

Fuzzy Spreadsheet: Understanding and Exploring Uncertainties in Tabular Calculations

Spreadsheet-based tools provide a simple yet effective way of calculating values, which makes them the number-one choice for building and formalizing simple models for budget planning and many other applications.A cell in a spreadsheet holds one specific value and gives a discrete, overprecise view of the underlying model. Therefore, spreadsheets are of limited use when investigating the immanent uncertainties of such models and answering what-if questions. Existing extensions typically require a complex modeling process that cannot be smoothly embedded in a tabular layout.In Fuzzy Spreadsheet, a cell can hold and display the distribution of values. This integrated uncertainty handling immediately conveys sensitivity and robustness information. The fuzzification of the cells enables calculations not only with precise values but with distributions, and probabilities. We conservatively added and carefully crafted visuals to maintain the look and feel of a traditional spreadsheet while facilitating what-if analyses.Given a user-specified reference cell, Fuzzy Spreadsheet automatically extracts and visualizes contextually relevant information, such as impact, uncertainty, and degree of neighborhood, for the selected and related cells.To evaluate its usability and the perceived mental effort required, we conducted a user study.The results show that our approach outperforms traditional spreadsheets in terms of answer correctness, response time, and perceived mental effort for almost all tasks tested.