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.     

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.     

Estimation of the Water Resources Potential in the Island System of the Aegean Archipelago,Greece

Abstract

The present paper tries to estimate the surface water resources potential in some of the major Aegean islands in an effort to provide a means for the continuous development of the region, and, by extension, for similar areas around the world. The islands have to confront the challenge of surviving in a semiarid environment under the constraints of uneven water resources distribution both in space and time. In addition to these, tourism development, industrialization and highly water consumptive life styles have exacerbated perennial problems in water resources and water resources management. The framework of the present effort has a two-prong emphasis. In the first part, a simulation model is presented, which tries to estimate the potential surface runoff under physical, structural organizational, and institutional constraints. The methodology and the premises of the simulation process are delineated. In the second part, the results of the model's application in distinct cases are demarcated. The final product, namely the model and the resulting runoff coefficients, are presented in the form of a standard, which may provide practitioners in the field as well as decisionmakers the means for an initial reference in pertinent developmental efforts. Finally, the conclusions and recommendations raise the question of ecosystem resilience and point towards the urgent and continuous need for the application of integrated water resources management principles.     

Optimal controllers for spatially invariant systems with delayed communication requirements

We consider the problem of optimal design of semi-decentralized controllers for a special class of spatially distributed systems. This class includes spatially invariant and distributed discrete-time systems with an inherent temporal delay in the interaction of neighboring sites. We consider the problem of optimal design of distributed controllers that have the same information passing delay structure as the plant. We show how for stable plants, the YJBK parameterization of such stabilizing controllers yields a convex parameterization for this class. We then show how the optimal problem can be solved.     

Shape Connectivity: Multiscale Analysis and Application to Generalized Granulometries

This paper develops a multiscale connectivity theory for shapes based on the axiomatic definition of new generalized connectivity measures, which are obtained using morphology-based nonlinear scale-space operators. The concept of connectivity-tree for hierarchical image representation is introduced and used to define generalized connected morphological operators. This theoretical framework is then applied to establish a class of generalized granulometries, implemented at a particular problem concerning soilsection image analysis and evaluation of morphological properties such as size distributions. Comparative results demonstrate the power and versatility of the proposed methodology with respect to the application of typical connected operators (such as reconstruction openings). This multiscale connectivity analysis framework aims at a more reliable evaluation of shape/size information within complex images, with particular applications to generalized granulometries, connected operators, and segmentation.     

Temporal course of perfusion in human masseter muscle during isometric contraction assessed by arterial spin labeling at 3T

Object  

Examination of blood perfusion in the masseter muscle in the course of repetitive isometric contraction by arterial spin-labeling (ASL) MR imaging and additional T2 relaxation time measurements during and after masseter muscle activation.