Labelling 2-D geometric primitives using probabilistic relaxation:reducing the computational requirements

In previous work, an algorithm for matching geometric features was developed. Although the method worked well, it was demanding in computational resources when applied to large problems. The authors now present methods for reducing the computational requirements, without significantly affecting the reliability of the algorithm     

PBURC: a patterns-based,unsupervised requirements clustering framework for distributed agile software development

Agile software development methodologies are increasingly adopted by organizations because they focus on the client’s needs, thus safeguarding business value for the final product. At the same time, as the economy and society move toward globalization, more organizations shift to distributed development of software projects. From this perspective, while adopting agile techniques seems beneficial, there are still a number of challenges that need to be addressed; among these notable is the effective cooperation between the stakeholders and the geographically distributed development team. In addition, data collection and validation for requirements engineering demands efficient processing techniques in order to handle the volume of data as well as to manage different inconsistencies, when the data are collected using online tools. In this paper, we present “PBURC,” a patterns-based, unsupervised requirements clustering framework, which makes use of machine-learning methods for requirements validation, being able to overcome data inconsistencies and effectively determine appropriate requirements clusters for optimal definition of software development sprints.     

Formal verification of collaboration patterns in healthcare

We propose a computer-based framework for the formal verification of collaboration patterns in healthcare teams. In this, the patterns are constructed diagrammatically as compositions of keystones that are viewed as abstract processes. The approach provides mechanisms for ensuring that safety properties are enforced and exceptional events are handled systematically. Additionally, a fully verified, executable model is obtained as an end product, enabling a simulation of its associated collaboration scenarios.     

A k-anonymity privacy-preserving approach in wireless medical monitoring environments

With the proliferation of wireless sensor networks and mobile technologies in general, it is possible to provide improved medical services and also to reduce costs as well as to manage the shortage of specialized personnel. Monitoring a person’s health condition using sensors provides a lot of benefits but also exposes personal sensitive information to a number of privacy threats. By recording user-related data, it is often feasible for a malicious or negligent data provider to expose these data to an unauthorized user. One solution is to protect the patient’s privacy by making difficult a linkage between specific measurements with a patient’s identity. In this paper we present a privacy-preserving architecture which builds upon the concept of k-anonymity; we present a clustering-based anonymity scheme for effective network management and data aggregation, which also protects user’s privacy by making an entity indistinguishable from other k similar entities. The presented algorithm is resource aware, as it minimizes energy consumption with respect to other more costly, cryptography-based approaches. The system is evaluated from an energy-consuming and network performance perspective, under different simulation scenarios.     

On the Modelling and Monitoring of General Inflated Poisson Processes

In this work, we propose and study general inflated probability distributions that can be used for modelling and monitoring unusual count data. The considered models extend the well‐known zero‐inflated Poisson distribution because they allow the excess of values, other than zero. Four simple upper‐sided control schemes are considered for the monitoring of count data based on the proposed general inflated Poisson distributions, and their performance is evaluated under various out‐of‐control situations. The usefulness of the considered models and techniques is illustrated via two real‐data examples, while practical guidelines are provided as well. Copyright © 2015 John Wiley & Sons, Ltd.     

2.5D Elastic graph matching

In this paper, we propose novel elastic graph matching (EGM) algorithms for face recognition assisted by the availability of 3D facial geometry. More specifically, we conceptually extend the EGM algorithm in order to exploit the 3D nature of human facial geometry for face recognition/verification. In order to achieve that, first we extend the matching module of the EGM algorithm in order to capitalize on the 2.5D facial data. Furthermore, we incorporate the 3D geometry into the multiscale analysis used and build a novel geodesic multiscale morphological pyramid of dilations/erosions in order to fill the graph jets. We show that the proposed advances significantly enhance the performance of EGM algorithms. We demonstrate the efficiency of the proposed advances in the face recognition/verification problem using photometric stereo.     

Invariant image reconstruction from irregular samples and hexagonal grid splines

We present an algorithm for the reconstruction of images from irregularly placed samples, using linear splines with control points on a hexagonal grid. Several spline approximations are computed for different transformations of the control point grid (e.g. translations and rotations). These approximations are then merged together after compensation of the transformations, yielding a high-quality invariant image reconstruction. Evaluations show that the use of hexagonal grids of the “invariance by integration” principle improves reconstruction quality. An application to image coding is also presented.     

Providing advanced remote medical treatment services through pervasive environments

Advances on sensor technology, wireless environments and data mining introduce new possibilities in the healthcare sector, realizing the anytime-anywhere access to medical information. Towards this direction, integration of packet-switched networks and sensor devices can be effective in deploying assistive environments, such as home monitoring for elderly or patients. In this paper we describe a policy-based architecture that utilizes wireless sensor devices, advanced network topologies and software agents to enable remote monitoring of patients and elderly people; through the aforementioned technologies we achieve continuous monitoring of a patient’s condition and we can proceed when necessary with proper actions. We also present a software framework and network architecture that realizes the provision of remote medical services, in compliance with the imposed security and privacy requirements. A proof of concept prototype is also deployed, along with an evaluation of the overall architecture’s performance.