Abstract
One common characteristic among current lock-free memory allocators is that they rely on the operating system to manage memory since they lack a lower-level mechanism capable of splitting and coalescing blocks of memory. In this paper, we discuss this problem and we propose a generic scheme for an efficient lock-free best-fit coalescing-capable mechanism that is able of satisfying memory allocation requests with desirable low fragmentation characteristics.

Abstract
This paper presents a new, heterogeneous CPU+GPU attacks against lattice-based (post-quantum) cryptosystems based on the Shortest Vector Problem (SVP), a central problem in lattice-based cryptanalysis. To the best of our knowledge, this is the first SVP-attack against lattice-based cryptosystems using CPUs and GPUs simultaneously. We show that Voronoi-cell based CPU+GPU attacks, algorithmically improved in previous work, are suitable for the proposed massively parallel platforms. Results show that 1) heterogeneous platforms are useful in this scenario, as they increment the overall memory available in the system (as GPU's memory can be used effectively), a typical bottleneck for Voronoi-cell algorithms, and we have also been able to increase the performance of the algorithm on such a platform, by successfully using the GPU as a co-processor, 2) this attack can be successfully accelerated using conventional GPUs and 3) we can take advantage of multiple GPUs to attack lattice-based cryptosystems. Experimental results show a speedup up to 7.6x for 2 GPUs hosted by an Intel Xeon E5-2695 v2 CPU (12 cores x2 sockets) using only 1 core and gains in the order of 20% for 2 GPUs hosted by the same machine using all 22 CPU threads (2 are reserved for orchestrating the GPUs), compared to single-CPU execution using the entire 24 threads available.

Abstract
The current requirements for open data in the EU are increasing the awareness of researchers with respect to data management and data publication. Metadata is essential in research data management, namely on data discovery and reuse. Current practices tend to either leave metadata definition to researchers, or to assign their creation to curators. The former typically results in ad-hoc descriptors, while the latter follows standards but lacks specificity. In this exploratory study, we adopt a researcher-curator collaborative approach in five data publication cases, involving researchers in data description and discussing the use of both generic and domain-oriented metadata. The study shows that researchers working on familiar datasets can contribute effectively to the definition of metadata models, in addition to the actual metadata creation. The cases also provide preliminary evidence of cross-disciplinary descriptor use. Moreover, the interaction with curators highlights the advantages of data management, making researchers more open to participate in the corresponding tasks. © Springer Nature Switzerland AG 2019.

Abstract
Peer-to-Peer (P2P) overlay networks have gained popularity due to their robustness, cost advantage, network efficiency and openness. Unfortunately, the same properties that foster their success, also make them prone to several attacks. To mitigate these attacks, several scalable security mechanisms which are based on the concepts of trust and reputation have been proposed. These proposed methods tend to ignore some core practical requirements that are essential to make them more useful in the real world. Some of such requirements include efficient bootstrapping of each newcomer's reputation, and mitigating seeder(s) exploitation. Additionally, although interaction among participating peers is usually the bases for reputation, the importance given to the frequency of interaction between the peers is often minimized or ignored. This can result in situations where barely known peers end-up having similar trust scores to the well-known and consistently cooperative nodes. After a careful review of the literature, this work proposes a novel and scalable reputation based security mechanism that addresses the aforementioned problems. The new method offers more efficient reputation bootstrapping, mitigation of bandwidth attack and better management of interaction rate, which further leads to improved fairness. To evaluate its performance, the new reputation model has been implemented as an extension of the BitTorrent protocol. Its robustness was tested by exposing it to popular malicious behaviors in a series of extensive PeerSim simulations. Results show that the proposed method is very robust and can efficiently mitigate popular attacks on P2P overlay networks.

Abstract
Any software system that has a considerable growing number of features will suffer from essential complexity, which makes the understanding of the software artifacts increasingly costly and time-consuming. A common approach for reducing the software understanding complexity is to use software visualizations techniques. There are already several approaches for visualizing software, as well as for extracting the information needed for those visualizations. This paper presents a novel approach to tackle the software complexity, delving into the common approaches for extracting information about software artifacts and common software visualization metaphors, allowing users to dive into the software system in a live way using virtual reality (VR). Experiments were carried out in order to validate the correct extraction of metadata from the software artifact and the corresponding VR visualization. With this work, we intend to present a starting point towards a Live Software Development approach.

Abstract
Algorithmic problem solving is a way of approaching and solving problems by using the advances that have been made in the principles of correct-by-construction algorithm design. The approach has been taught at first-year undergraduate level since September 2003 and, since then, a substantial amount of learning materials have been developed. However, the existing materials are distributed in a conventional and static way (e.g. as a textbook and as several documents in PDF format available online), not leveraging the capabilities provided by modern collaborative and open-source platforms. In this paper, we propose the creation of an online, open-source repository of interactive learning materials on algorithmic problem solving. We show how the existing framework Mathigon can be used to support such a repository. By being open and hosted on a platform such as GitHub, the repository enables collaboration and anyone can create and submit new material. Furthermore, by making the material interactive, we hope to encourage engagement with and a better understanding of the materials. © Springer Nature Switzerland AG 2020.