Abstract
The paper discusses an ongoing project that aims to enhance the UX of teachers while using e-learning systems. Specifically, the project focuses on developing the teacher’s user interface (UI) for Agni, a web-based code playground for learning JavaScript. The goal is to design an intuitive UI with valuable features that will encourage more teachers to use the system. To achieve this goal, the paper explores the use of a headless Content Management System (CMS) called Strapi. The primary research question the paper seeks to answer is whether a headless CMS, specifically Strapi, can provide a good UX to teachers. A usability evaluation of the built-in Strapi UI for content creation and management reveals it to be generally consistent and user-friendly but challenging and unintuitive to create courses with programming exercises. As a result, the decision was made to develop a new teacher’s UI based on the existing Agni UI for students in an editable version. Once the development is complete, a new usability evaluation of the fully developed teacher’s UI will be conducted with the Strapi UI evaluation as a baseline for comparison. © Yannik Bauer, José Paulo Leal, and Ricardo Queirós; licensed under Creative Commons License CC-BY 4.0.

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

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.