Abstract
The size of massive knowledge graphs (KGs) and the lack of prior information regarding the schemas, ontologies and vocabularies they use frequently makes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they represent. Particularly, IRI namespaces can be used to classify RDF resources. Namespaces, used in some RDF serialization formats as a shortening mechanism for resource IRIs, have no role in the semantics of RDF. Nevertheless, there is often a hidden meaning behind the decision of grouping resources under a common prefix and assigning an alias to it. We improved on previous work on a namespace-based approach to KG summarization that classifies resources using their namespaces. Producing the summary graph is fast, light on computing resources and requires no previous domain knowledge. The summary graph can be used to analyze the namespace interdependencies of the original graph. We also present chilon, a tool for calculating namespace-based KG summaries. Namespaces are gathered from explicit declarations in the graph serialization, community contributions or resource IRI prefix analysis. We applied chilon to publicly available KGs, used it to generate interactive visualizations of the summaries, and discuss the results obtained.

Abstract
The reading of the T650 glyph has been a puzzle for decades. Here, we analyze the semantic contexts in which the glyph appears together with available phonetic evidence to arrive at a phonetic reading of JOM. We provide grammatical reconstructions of the lexical contexts and discuss the rebuses involved in non semantic contexts.

Abstract
Lock-free data structures are an important tool for the development of concurrent programs as they provide scalability, low latency and avoid deadlocks, livelocks and priority inversion. However, they require some sort of additional support to guarantee memory reclamation. The Optimistic Access (OA) method has most of the desired properties for memory reclamation, but since it allows memory to be accessed after being reclaimed, it is incompatible with the traditional memory management model. This renders it unable to release memory to the memory allocator/operating system, and, as such, it requires a complex memory recycling mechanism. In this paper, we extend the lock-free general purpose memory allocator LRMalloc to support the OA method. By doing so, we are able to simplify the memory reclamation method implementation and also allow memory to be reused by other parts of the same process. We further exploit the virtual memory system provided by the operating system and hardware in order to make it possible to release reclaimed memory to the operating system.

Abstract

Abstract
Cybersecurity has a major impact on the healthcare sector, mainly due to the sensitive data and vital medical devices that, when an attack occurs, may compromise the life, safety, and well-being of the patients. However, those institutions fail on implementing correct system protection policies and providing adequate programs for cybersecurity training and raising cybersecurity awareness. Healthcare professionals develop their academic courses focusing on providing the best care for the patients, studying guidelines, treatment protocols, and diagnostic criteria. However, there are insufficient subjects dedicated to the development of digital literacy to match the requisites of the daily challenges of those professionals, with human error being the main cause of data breaches worldwide. So, developing training programs to face the cybersecurity day-to-day threats is mandatory. Broadly speaking, traditional training programs seem to fail on retaining students' motivation, engagement, and long-term knowledge acquisition, being time-consuming and challenging in scheduling and planning. To face this situation, new techniques, such as gamification, have emerged, with promising results on motivation and engagement, allowing the users to be the center of the training programs, matching the strategy to their levels of knowledge and preferences. This paper aims to identify the existing gamified approaches available, review the state-of-the-art related to gamification and cybersecurity training, and elaborates on how they can be successfully applied to training programs for healthcare professionals.

Abstract
Tampered digital multimedia content has been increasingly used in a wide set of cyberattacks, chal-lenging criminal investigations and law enforcement authorities. The motivations are immense and range from the attempt to manipulate public opinion by disseminating fake news to digital kidnapping and ransomware, to mention a few cybercrimes that use this medium as a means of propagation.Digital forensics has recently incorporated a set of computational learning-based tools to automatically detect manipulations in digital multimedia content. Despite the promising results attained by machine learning and deep learning methods, these techniques require demanding computational resources and make digital forensic analysis and investigation expensive. Applied statistics techniques have also been applied to automatically detect anomalies and manipulations in digital multimedia content by statisti-cally analysing the patterns and features. These techniques are computationally faster and have been applied isolated or as a member of a classifier committee to boost the overall artefact classification.This paper describes a statistical model based on Benford's Law and the results obtained with a dataset of 18000 photos, being 9000 authentic and the remaining manipulated.Benford's Law dates from the 18th century and has been successfully adopted in digital forensics, namely in fraud detection. In the present investigation, Benford's law was applied to a set of features (colours, textures) extracted from digital images. After extracting the first digits, the frequency with which they occurred in the set of values obtained from that extraction was calculated. This process allowed focusing the investigation on the behaviour with which the frequency of each digit occurred in comparison with the frequency expected by Benford's law.The method proposed in this paper for applying Benford's Law uses Pearson's and Spearman's corre-lations and Cramer-Von Mises (CVM) fitting model, applied to the first digit of a number consisting of several digits, obtained by extracting digital photos features through Fast Fourier Transform (FFT) method.The overall results obtained, although not exceeding those attained by machine learning approaches, namely Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), are promising, reaching an average F1-score of 90.47% when using Pearson correlation. With non-parametric approaches, namely Spearman correlation and CVM fitting model, an F1-Score of 56.55% and 76.61% were obtained respec-tively. Furthermore, the Pearson's model showed the highest homogeneity compared to the Spearman's and CVM models in detecting manipulated images, 8526, and authentic ones, 7662, due to the strong correlation between the frequencies of each digit and the frequency expected by Benford's law.The results were obtained with different feature sets length, ranging from 3000 features to the totality of the features available in the digital image. However, the investigation focused on extracting 1000 features since it was concluded that increasing the features did not imply an improvement in the results.The results obtained with the model based on Benford's Law compete with those obtained from the models based on CNN and SVM, generating confidence regarding its application as decision support in a criminal investigation for the identification of manipulated images.& COPY; 2023 Elsevier Ltd. All rights reserved.