Tania Martin

María Miró Maestre, Iván Martínez-Murillo, Tania J. Martin et al.

Generative Artificial Intelligence has grown exponentially as a result of Large Language Models (LLMs). This has been possible because of the impressive performance of deep learning methods created within the field of Natural Language Processing (NLP) and its subfield Natural Language Generation (NLG), which is the focus of this paper. Within the growing LLM family are the popular GPT-4, Bard and more specifically, tools such as ChatGPT have become a benchmark for other LLMs when solving most of the tasks involved in NLG research. This scenario poses new questions about the next steps for NLG and how the field can adapt and evolve to deal with new challenges in the era of LLMs. To address this, the present paper conducts a review of a representative sample of surveys recently published in NLG. By doing so, we aim to provide the scientific community with a research roadmap to identify which NLG aspects are still not suitably addressed by LLMs, as well as suggest future lines of research that should be addressed going forward.

José Luis Hernández-Ramos, Georgios Karopoulos, Dimitris Geneiatakis et al.

This work proposes a scalable, blockchain-based platform for the secure sharing of COVID-19 or other disease vaccination certificates. As an indicative use case, we simulate a large-scale deployment by considering the countries of the European Union. The proposed platform is evaluated through extensive simulations in terms of computing resource usage, network response time and bandwidth. Based on the results, the proposed scheme shows satisfactory performance across all major evaluation criteria, suggesting that it can set the pace for real implementations. Vis-à-vis the related work, the proposed platform is novel, especially through the prism of a large-scale, full-fledged implementation and its assessment.

Tania Martin, Georgios Karopoulos, José L. Hernández-Ramos et al.

The coronavirus pandemic is a new reality and it severely affects the modus vivendi of the international community. In this context, governments are rushing to devise or embrace novel surveillance mechanisms and monitoring systems to fight the outbreak. The development of digital tracing apps, which among others are aimed at automatising and globalising the prompt alerting of individuals at risk in a privacy-preserving manner is a prominent example of this ongoing effort. Very promptly, a number of digital contact tracing architectures has been sprouted, followed by relevant app implementations adopted by governments worldwide. Bluetooth, and specifically its Low Energy (BLE) power-conserving variant has emerged as the most promising short-range wireless network technology to implement the contact tracing service. This work offers the first to our knowledge, full-fledged review of the most concrete contact tracing architectures proposed so far in a global scale. This endeavour does not only embrace the diverse types of architectures and systems, namely centralised, decentralised, or hybrid, but it equally addresses the client side, i.e., the apps that have been already deployed in Europe by each country. There is also a full-spectrum adversary model section, which does not only amalgamate the previous work in the topic, but also brings new insights and angles to contemplate upon.