Abstract
The emergence of Near Field Communication technology added more functionality to mobile phones. People can now access to new mobile services such as payment, ticketing, couponing, and control access to cars, homes and hotels. Although these services seem to complement each other, most studies have focused on the development and evaluation of each mobile service individually. In this paper, we propose an integrated mobile service solution payment, ticketing and couponing - based on NFC. This solution was evaluated in a two-phase user study, which was run in real environment. Participants have largely appreciated the integration of tickets and discount coupons on the payment process. However, despite the integration of services being one of the strongest points of the solution, it also revealed to be one of the main challenges that any payment systems may have to address. Findings suggest that adding additional services to a solution may become very complex from a cognitive perspective.

Abstract
Mobile payments still remain essentially an emerging technology, seeking to fill the gap between the envisioned potential and widespread usage. In this paper, we present an integrated mobile service solution based on the near field communication (NFC) protocol that was developed under a research project called MobiPag. The most distinctive characteristic of Mobipag is its open architectural model that allows multiple partners to become part of the payment value-chain and create solutions that complement payments in many unexpected ways. We describe the Mobipag architecture and how it has been used to support a mobile payment trial. We identify a set of design lessons resulting from usage experiences associated with real-world payment situations with NFC-enabled mobile phones. Based on results from this trial, we identify a number of challenges and guidelines that may help to shape future versions of NFC-based payment systems. In particular, we highlight key challenges for the initial phases of payment deployments, where it is essential to focus on scenarios that can be identified as more feasible for early adoption. We also have identified a fundamental trade-off between the flexibility supported by the Mobipag solution and the respective implications for the payment process, particularly on the users' mental model.

Abstract
This paper addresses the problem of scheduling multi-user jobs on clusters, both homogeneous and heterogeneous. A user job is composed by a set of dependent tasks and it is described by a direct acyclic graph (DAG). The aim is to maximize the resource usage by allowing a floating mapping of processors to a given job, instead of the common mapping approach that assigns a fixed set of processors to a user for a period of time. The simulation results show a better cluster usage. The scheduling algorithm minimizes the total length of the schedule (makespan) of a given set of parallel jobs, whose priorities are represented in a DAG. The algorithm is presented as producing static schedules although it can be adapted to a dynamic behavior as discussed in the paper.

Abstract
This paper addresses the problem of scheduling parallel tasks, represented by a direct acyclic graph (DAG) on heterogeneous clusters. Parallel tasks, also called malleable tasks, are tasks that can be executed on any number of processors with its execution time being a function of the number of processors alloted to it. The scheduling of independent parallel tasks on homogeneous machines has been extensively studied and the case of parallel tasks with precedence constraints has been studied for tree-like graphs. For arbitrary precedence graphs and for heterogeneous machines, the optimization problem is more complex because the processing time of a given task depends on the number of processors and on the total processing capacity of those processors. This paper presents a list scheduling algorithm to minimize the total length of the schedule (makespan) of a given set of parallel tasks, whose dependencies are represented by a DAG.

Abstract
With the increase of Endoscopic Capsule (EC) exams, the amount of video data to be analyzed by automated image processing algorithms grows exponentially and standard desktop computers start to present limitations to complete that procedure in an acceptable "clinical time". This has motivated us to assess the potential of Grid computing paradigm to process and analyze EC data, using existing Grid infrastructures. We used our Automated Topografic Segmentation (ATS) algorithm as a case study to run experiments on the Ibergrid Grid infrastructure. As a result, we were able to port the existing ATS algorithm used in routine to run on the Grid seamlessly. A friendly portal environment was adopted, allowing for clinical end-users to harness from existing production Grids in a practical way. The processing time could be reduced in some cases but, when considering job processing overheads, the desktop version is preferable for small scale processing. These results show that Grid computing is a promising technology to process massive amounts of EC data, but current infrastructures present a high variability in jobs processing times, especially when compared with dedicated clusters.