Abstract
Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and scalable hash trie map design that fully supports the concurrent search, insert and remove operations on hash maps. To the best of our knowledge, our proposal is the first concurrent hash map design that puts together the following characteristics: (i) be lock-free; (ii) use fixed size data structures; and (iii) maintain the access to all internal data structures as persistent memory references. Experimental results show that our proposal is quite competitive when compared against other state-of-the-art proposals implemented in Java. Its design is modular enough to allow different types of configurations aimed for different performances in memory usage and execution time. © 2017 IEEE.

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
We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog. The most used inference method for ProbLog is exact inference combined with tabling. Tabled exact inference first collects a set of SLG derivations which contain the probabilistic structure of the ProbLog program including the cycles. At a second step, inference requires handling these cycles in order to create a noncyclic Boolean representation of the probabilistic information. Finally, the Boolean representation is compiled to a data structure where inference can be performed in linear time. Previous work has illustrated that there are two limiting factors for ProbLog’s exact inference. The first factor is the target compilation language and the second factor is the handling of the cycles. In this paper, we address the second factor by presenting an iterative deepening algorithm which handles cycles and produces solutions to problems that previously ProbLog was not able to solve. Our experimental results show that our iterative deepening approach gets approximate bounded values in almost all cases and in most cases we are able to get the exact result for the same or one lower scaling factor. © Springer International Publishing AG 2017.

Abstract
This special issue of Theory and Practice of Logic Programming (TPLP) contains the regular papers accepted for presentation at the 33rd International Conference on Logic Programming (ICLP 2017), held in Melbourne, Australia from the 28th of August to the 1st of September, 2017. ICLP 2017 was colocated with the 23rd International Conference on Principles and Practice of Constraint Programming (CP 2017) and the 20th International Conference on Theory and Applications of Satisfiability Testing (SAT 2017). Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Copyright © Cambridge University Press 2017.

Abstract
Medical data is particularly interesting as a subject for relational data mining due to the complex interactions which exist between different entities. Furthermore, the ambiguity of medical imaging causes interpretation to be complex and error-prone, and thus particularly amenable to improvement through automated decision support. Probabilistic Inductive Logic Programming (PILP) is a particularly well-suited tool for this task, since it makes it possible to combine the relational nature of this field with the ambiguity inherent in human interpretation of medical imaging. This work presents a PILP setting for breast cancer data, where several clinical and demographic variables were collected retrospectively, and new probabilistic variables and rules reflecting domain knowledge were introduced. A PILP predictive model was built automatically from this data and experiments show that it can not only match the predictions of a team of experts in the area, but also consistently reduce the error rate of malignancy prediction, when compared to other non-relational techniques. © Springer International Publishing AG, part of Springer Nature 2018.

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.