Abstract
Diabetes management is a complex problem. The patient needs to monitor several parameters in order to react in the most appropriate way. Different situations require the diabetic to understand and evaluate different rules. The main source of knowledge for those rules arises from medical practice and is usually transmitted through medical appointments. Given this initial advice, most patient are on a continuous process of managing the disease, toward achieving the best possible quality of life. Motivated by recent aadvances in diabetes monitoring devices, we introduce a diabetes support system designed to accompany the user, advising her and providing early guidance to avoid some of the many complications associated with diabetes. To accomplish this goal, we incorporate standard medical protocols, advice and directives in a Rule Based System (RBS). This RBS which we call Advice Rule Based System (ARBS) is capable of advising and uncovering possible causes for different occurrences. We believe that this solution is not only beneficial to the patient, but may also may be of use to the clinitians advising the patient. The device has continuous contact with the patient, thus it can provide early response if/where needed, Moreover, the system can provide useful data, that an authorized medical expert can use while prescribing a particular treatment, or even when investingating this health problem. We have started to add data-mining algorithms and methods, to uncover hidden behavioural patterns that may lead to crisis situations. Ultimately, through refining the rule systems base don human and machine learning, our approach has the potential for personalising the system according to the habits and phenotype of its user. The system is to be incorporated in a currently developed diabetes management application for Android.

Abstract
Fast cardiac imaging requires a reduction of the number of transmit events. This is typically achieved through multiline-transmission and/or multiline-acquisition techniques but restricting the field-of-view to the anatomically relevant domain, e.g. the myocardium, can increase frame rate further. In the present work, an anatomical scan sequence was implemented and tested experimentally by performing real-time segmentation of the myocardium on conventional B-mode and feeding this information back to the scanner in order to define a fast myocardial scan sequence. Ultrasound imaging was performed using HD-PULSE, an experimental fully programmable 256 channel ultrasound system equipped with a 3.5MHz phased array. A univentricular polyvinyl alcohol phantom was connected to a pump to simulate the cardiac cycle to perform in vitro validation of this approach. Three volunteers were also imaged from an apical 4-chamber view to analyse the feasibility of this method in vivo. It is shown that this method is feasible to be applied in real-time and in vivo giving a minimum frame rate gain of 1.5. Although the anatomical image preferably excludes the apical cap of the ventricle, this region is often unanalyzable due to near field clutter anyway. The advantage of this method is that spatial resolution is maintained when compared to conventional ultrasound in contrast to other fast imaging approaches. © 2017 IEEE.

Abstract

Abstract
[Extract] It is our great pleasure to welcome you to the first issue of PACMHCI EICS, which features full papers appearing at the 9th ACM SIGCHI Symposium on Engineering Interactive Systems (EICS’17). The conference takes place in Lisbon, Portugal on 26-29 June, 2017. EICS gathers researchers that aim to improve the ways we build interactive systems. Building interactive systems is a multi-faceted and challenging activity, involving a plethora of different actors and roles. This is particularly true in the domain of HCI, where we continuously push the edge of what is possible, where there is a crucial need for adequate processes, tools and methods to build reliable, useful and usable systems that help people cope with the ever-increasing complexity of work and life. The primary goal of the EICS conference series is to provide a venue for novel and high quality contributions in this direction...

Abstract
Software testing and benchmarking is a key component of the software development process. Nowadays, a good practice in big software projects is the Continuous Integration (CI) software development technique. The key idea of CI is to let developers integrate their work as they produce it, instead of doing the integration at the end of each software module. In this paper, we extend a previous work on a benchmark suite for the Yap Prolog system and we propose a fully automated test bench environment for Prolog systems, named Yet Another Prolog Test Bench Environment (YAPTBE), aimed to assist developers in the development and CI of Prolog systems. YAPTBE is based on a cloud computing architecture and relies on the Jenkins framework and in a set of new Jenkins plugins to manage the underneath infrastructure. We present the key design and implementation aspects of YAPTBE and show its most important features, such as its graphical user interface and the automated process that builds and runs Prolog systems and benchmarks. © Ricardo Gonçalves, Miguel Areias, and Ricardo Rocha

Abstract
While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. Additionally, a growing number of developers wish to become more energy-aware when programming and feel a lack of tools and the knowledge to do so. In this paper we define a ranking of energy efficiency in programming languages. We consider a set of computing problems implemented in ten well-known programming languages, and monitored the energy consumed when executing each language. Our preliminary results show that although the fastest languages tend to be the lowest consuming ones, there are other interesting cases where slower languages are more energy efficient than faster ones.