Approaches for extending logic to deal with uncertainty immanent to many real-world problems are often on the one side purely qualitative, such as modal logics, or on the other side quantitative, such as probabilistic logics. Research on combinations of qualitative and quantitative extensions to logic which put qualitative constraints on probability distributions, has mainly remained theoretical until now. In this paper, we propose a practically useful logic, which supports qualitative as well as quantitative uncertainty and can be extended with modalities with varying level of quantitative precision. This language has a solid semantic foundation based on imprecise probability theory. While in general imprecise probabilistic inference is much harder than the precise case, this is the first expressive imprecise probabilistic formalism for which probabilistic inference is shown to be as hard as corresponding precise probabilistic problems. A second contribution of this paper is an inference algorithm for this language based on the translation to a weighted model counting (WMC) problem, an approach also taken by state-of-the-art probabilistic inference methods for precise problems.

Nowadays the maritime operational picture is characterised by a growing number of entities whose interactions and activities are constantly changing. To provide timely support in this dynamic environment, automated systems need to be equipped with tools— lacking in existing systems—for real-time prioritisation of the application tasks (missions), selection and alignment of relevant information, and efficient reasoning at a situation level. In this paper, we present METIS—an industrial prototype system for supporting real-time, actionable maritime situational awareness. In particular, we focus on the innovation of METIS, which lies in the employment and integration of several state-of-the-art AI technologies to build the overall system's intelligence. These include reconfiguration of multi-context systems, natural language processing of heterogeneous (un)structured data and probabilistic reasoning of uncertain information. The capabilities of the system have been demonstrated in a proof of concept, which is deployed as a situational awareness plugin in the Tacticos command-and-control platform of our industrial partner. The principles exploited by METIS are giving valuable insights into what is considered to become the next generation of situational awareness systems.

MoSHCA is a mHealth project designed to improve patient-doctor interaction and to promote the self-management of chronic diseases by the patients themselves. The number of people with a chronic disease is dramatically increasing worldwide. This is becoming a major obstacle for economic stability and growth and the sustainability of national health care systems. The introduction of self-management by patients with a chronic disease seems inevitable as a countermeasure against these developments. MoSHCA provides intelligent, user-friendly and secure, medical and well-being decision support through embedded software in mobile devices by utilizing specific sensors and data from customized information systems.

Performing safety and security tasks requires the continuous gathering and interpretation of information about objects to detect and predict events of interest. Especially, reasoning about objects' identities and intentions is crucial, but requires making use of heterogeneous information inherent with uncertainty. This makes such tasks very challenging for human operators and decision support systems are clearly needed. In this paper, we present a generic probabilistic logic model for the systematic representation and uncertainty reasoning about the identity and intentions of monitored objects. We apply the model to the area of maritime safety and security after incorporating domain specific knowledge. Experiments with simulated and real-world vessel data demonstrate the model's capabilities to draw conclusions about objects using uncertain information. The first-order probabilistic logic we use shows to be a very powerful tool to deal with dynamic amounts of information and objects. To our knowledge it is one of the few real-world applications of probabilistic logic.

In this paper we present innovative research for the automatic interpretation of biochemical test strip color by a smartphone using image processing techniques. Urinalysis is the current application for these techniques. Our mobile application captures images of the color pads on strips using the camera phone, then analyzes automatically the images within the device itself and compares these against reference color pads to obtain a final classification. As a test scenario we focus on the detection of proteinuria, i.e., leakage of protein into the urine, for the detection of which strips for protein and creatinine are used. We performed an initial laboratory evaluation using specially prepared concentrations to check the accuracy and precision of detection. The results obtained are encouraging and show that the proposed technique has a good potential for the development of cheap, mobile and smart home-based readers for the early detection of health problems.

In many classification and prediction problems it is known that the response variable depends on certain explanatory variables. Monotone neural networks can be used as powerful tools to build monotone models with better accuracy and lower variance compared to ordinary nonmonotone models. Monotonicity is usually obtained by putting constraints on the parameters of the network. In this paper, we will clarify some of the theoretical results on monotone neural networks with positive weights, issues that are sometimes misunderstood in the neural network literature. Furthermore, we will generalize some of the results obtained by Sill for the so-called min-max networks to the case of partially monotone problems. The method is illustrated in practical case studies.

The goal of breast cancer screening programs is to detect cancers at an early (preclinical) stage, by using periodic mammographic examinations in asymptomatic women. In evaluating cases, mammographers insist on reading multiple images (at least two) of each breast as a cancerous lesion tends to be observed in different breast projections (views). Most computer-aided detection (CAD) systems, on the other hand, only analyze single views independently, and thus fail to account for the interaction between the views. In this paper, we propose a Bayesian framework for exploiting multi-view dependencies between the suspected regions detected by a single-view CAD system. The results from experiments with real-life data show that our approach outperforms the singleview CAD system in distinguishing between normal and abnormal cases. Such a system can support screening radiologists to improve the evaluation of breast cancer cases.

Often, in economic decision problems such as credit loan approval or risk analysis, data mining models are required to be monotone with respect to the decision variables involved. If the model is obtained by a blind search through the data, it does mostly not have this property, even if the underlying database is monotone. In this correspondence, we present methods to enforce monotonicity of decision models. We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone. In addition, it is shown that decision trees derived from cleaned data perform better compared to trees derived from raw data