Abstract

Abstract
The development and use of computerized decision-support systems in the domain of breast cancer has the potential to facilitate the early detection of disease as well as spare healthy women unnecessary interventions. Despite encouraging trends, there is much room for improvement in the capabilities of such systems to further alleviate the burden of breast cancer. One of the main challenges that current systems face is integrating and translating multi-scale variables like patient risk factors and imaging features into complex management recommendations that would supplement and/or generalize similar activities provided by subspecialty-trained clinicians currently. In this chapter, we discuss the main types of knowledge-objectattribute, spatial, temporal and hierarchical-present in the domain of breast image analysis and their formal representation using two popular techniques from artificial intelligence-Bayesian networks and first-order logic. In particular, we demonstrate (i) the explicit representation of uncertain relationships between low-level image features and high-level image findings (e.g., mass, microcalcifications) by probability distributions in Bayesian networks, and (ii) the expressive power of logic to generally represent the dynamic number of objects in the domain. By concrete examples with patient data we show the practical application of both formalisms and their potential for use in decision-support systems.

Abstract
Handwritten signature recognition is still the most widely accepted method to validate paper based documents. However, in the digital world, there is no readymade way to distinguish a real handwritten signature on a scanned document from a forged copy of another signature made by the same person on another document that is simply "pasted" into the forged document. In this paper we describe how we are using the touch screen of smartphones or tablets to collect handwritten signature images and associated biometric markers derived from the motion direction of handwritten signatures that are made directly into the device touchscreen. These time base biometric markers can then be converted into signaling time waves, by using the dragging or lifting movements the user makes with a touch screen omnidirectional tip stylus, when he handwrites is signature at the device touchscreen. These time/space signaling time waves can then be converted into a biometric bit stream that can be matched with previously enrolled biometric markers of the user's handwritten signature. In this paper we contend that the collection of these simple biometric features is sufficient to achieve a level of user recognition and authentication that is sufficient for the majority of online user authentication and digital documents authenticity.

Abstract
Introduction: The improvements made to healthcare IT systems made over the past years led to the creation of a multitude of different applications essential to the institutions daily operations. Aim: We aim to create and install a system capable of displaying production metrics for healthcare management with little requirements, efforts and software providers involved. Methods: We propose a system capable of displaying production metrics for healthcare facilities, by extracting HL7 messages and other eHealth relevant protocols directly from the institution's network infrastructure. Our system is then able to populate a knowledge database with meaningful information derived from the gathered data. Results: Our system is currently being tested on a large healthcare facility where it extracts and analyses a daily average of 44,000 HL7 messages. The system is currently capable of inferring and displaying the daily distribution of healthcare related activities such as laboratory orders or even relevant billing information. Conclusion: HL7 messages moving over the network contain valuable information that can then be used to assess many relevant production metrics for the entire facility and from otherwise non-interoperable production systems that, in most cases, can only be seen as black boxes by other system integrators.

Abstract
The development of eHealth technologies over the last few years has been pushing healthcare institutions to evolve their own infrastructures. Along with this evolution, critical systems now need to use communication standards such as HL7 or DICOM in order to exchange information in a more meaningful and efficient way. However, healthcare institutions often experience complications when different systems communicate directly even when using communication standards. We aim to assess the quality of the data present in HL7 messages exchanged between different critical systems in a large healthcare facility and therefore propose an integration infrastructure that allows a real time and centralized way to manage, route and monitor the integration flows between various systems.

Abstract
Determining the frequency of small subgraphs is an important graph mining primitive. One major class of algorithms for this task is based upon the enumeration of all sets of k connected nodes. These are known as network-centric algorithms. FAst Subgraph Enumeration (FaSE) is a exact algorithm for subgraph counting that contrasted with its past approaches by performing the isomorphism tests while doing the enumeration, encapsulating the topological information in a g-trie and thus largely reducing the number of required isomorphism tests. Our goal with this paper is to expand this approach by providing an approximate algorithm, which we called Rand-FaSE. It uses an unbiased sampling estimator for the number of subgraphs of each type, allowing an user to trade some accuracy for even faster execution times. We tested our algorithm on a set of representative complex networks, comparing it with the exact alternative, FaSE. We also do an extensive analysis by studying its accuracy and speed gains against previous sampling approaches. With all of this, we believe FaSE and Rand-FaSE pave the way for faster network-centric census algorithms.