Abstract
Practice is pivotal in learning programming. As many other automated assessment tools for programming assignments, Mooshak has been adopted by numerous educational practitioners to support them in delivering timely and accurate feedback to students during exercise solving. These tools specialize in the delivery and assessment of blank-sheet coding questions. However, the different phases of a student's learning path may demand distinct types of exercises (e.g., bug fix and block sorting) to foster new competencies such as debugging programs and understanding unknown source code or, otherwise, to break the routine and keep engagement. Recently, a format for describing programming exercises - YAPExIL -, supporting different types of activities, has been introduced. Unfortunately, no automated assessment tool yet supports this novel format. This paper describes a JavaScript library to transform YAPExIL packages into Mooshak problem packages (i.e., MEF format), keeping support for all exercise types. Moreover, its integration in an exercise authoring tool is described.

Abstract
Consuming Semantic Web data presents several challenges, from the number of datasets it is composed of, to the (very) large size of some of those datasets and the uncertain availability of querying endpoints. According to its core principles, accessing linked data can be done simply by dereferencing the IRIs of RDF resources. This is a light alternative both for clients and servers when compared to dataset dumps or SPARQL endpoints. The linked data interface does not support complex querying, but using it recursively may suffice to gather information about RDF resources, or to extract the relevant sub-graph which can then be processed and queried using other methods. We present Derzis1, an open source semantic web crawler capable of traversing the linked data cloud starting from a set of seed resources. Derzis maintains information about the paths followed while crawling, which allows to define property path-based restrictions to the crawling frontier.

Abstract

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 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. Our design is modular enough to allow different types of configurations aimed for different performances in memory usage and execution time and can be easily implemented in any type of language, library or within other complex data structures. We discuss in detail the key algorithms required to easily reproduce our implementation by others and we present a proof of correctness showing that our proposal is linearizable and lock-free for the search, insert and remove operations. Experimental results show that our proposal is quite competitive when compared against other state-of-the-art proposals implemented in Java.

Abstract
Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. Starting from a particular lock-free hash map data structure, named Lock-Free Hash Tries, we focus on solving the problem of memory reclamation without losing the lock-freedom property. To the best of our knowledge, outside garbage collected environments, there is no current implementation of hash maps that is able to reclaim memory in a lock-free manner. To achieve this goal, we propose an approach for memory reclamation specific to Lock-Free Hash Tries that explores the characteristics of its structure in order to achieve efficient memory reclamation with low and well-defined memory bounds. We present and discuss in detail the key algorithms required to easily reproduce our implementation by others. Experimental results show that our approach obtains better results when compared with other state-of-the-art memory reclamation methods and provides a competitive and scalable hash map implementation, if compared to lock-based implementations.

Abstract
A key aspect of any hash map design is the problem of dynamically resizing it in order to deal with hash collisions. In this context, elasticity refers to the ability to automatically resize the internal data structures that support the hash map operations in order to meet varying workloads, thus optimizing the overall memory consumption of the hash map. This work extends a previous lock-free hash trie design to support elastic hashing, i.e., expand saturated hash levels and compress unused hash levels, such that, at each point in time, the number of levels in a path matches the current demand as closely as possible. Experimental results show that elasticity effectively improves the search operation and, in doing so, our design becomes very competitive when compared to other state-of-the-art designs implemented in Java.