The exponential growth of the Internet of Things (IoT) necessitates robust, scalable, and secure identity management solutions to handle the vast number of interconnected devices. Traditional centralized identity systems are increasingly inadequate due to their vulnerabilities, such as single points of failure, scalability issues, and limited user control over data. This study explores a decentralized identity management model leveraging the IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger technology, to address these challenges. By integrating Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and IOTA-specific technologies like IOTA Identity, IOTA Streams, and IOTA Stronghold, we propose a proof-of-concept framework that enhances security, scalability, and privacy in IoT ecosystems. Our implementation on resource-constrained IoT devices demonstrates the feasibility of this approach, highlighting significant improvements in transaction efficiency, real-time data exchange, and cryptographic key management. Furthermore, this research aligns with Web 3.0 principles, emphasizing decentralization, user autonomy, and data sovereignty. The findings suggest that IOTA-based solutions can effectively advance secure and user-centric identity management in IoT, paving the way for broader applications in various domains, including smart cities and healthcare

Quantum kernel methods are promising for near-term quantum machine learning, yet their behavior under data corruption remains insufficiently understood. We analyze how quantum feature constructions degrade under controlled additive noise. We introduce Spectral Phase Encoding (SPE), a hybrid construction combining a discrete Fourier transform (DFT) front-end with a diagonal phase-only embedding. Within a unified framework, we compare the resulting DFT-based quantum-kernel pipeline (QK-DFT) against alternative quantum variants based on principal component analysis (PCA) and random projections (RP), as well as classical support vector machine (SVM) baselines, under identical clean-data hyperparameter selection. Robustness is quantified via dataset fixed-effects regression with wild cluster bootstrap inference across heterogeneous real-world datasets. Across the matched quantum family, DFT-based preprocessing yields the smallest degradation rate as noise increases, with statistically supported slope differences relative to PCA and RP. Compared with classical baselines, QK-DFT shows degradation comparable to linear SVM and more favorable than radial basis function (RBF) SVM under the present tuning protocol. Hardware experiments further confirm that the resulting overlap-estimation primitive remains executable and numerically stable. Taken together, these results support a robustness-first view of quantum kernel evaluation and show that, within the controlled pipeline family studied here, structure-aligned spectral preprocessing combined with a shared diagonal quantum embedding can yield slower degradation under corruption.

Serverless computing is one of the latest paradigms in cloud computing. It offers a framework for the development of event-driven applications whose functions are executed in a scalable environment provided by the corresponding cloud platform. In this way, resources are obtained on demand, paying only for the time the function is running. This new model has new vulnerabilities and, therefore, new types of cybersecurity attacks. However, there are still not enough transaction datasets for serverless systems with a sufficient amount of data to develop advanced detection methods for this type of threat. Therefore, we present this dataset that has been built with generative AI to advance the development of models that can effectively deal with these threats.

The Mobile Cloud Computing paradigm has revolutionized the concepts of mobile computing and the Internet of Things (IoT). This paradigm allows outsourcing the workload of mobile devices, or other connected “things,” to be computed in the Cloud. Currently, outsourcing possibilities have been widely developed making available computing platforms at different network layers. In a consequence of that, a virtual increasing of the performance and a homogenization of the computing capabilities of the devices are produced. The research described in this work presents a review of the state of the art about recent works, the main operational concerns, challenges, and open issues of this paradigm in order to update the border of knowledge on this topic. As a result, a critical analysis is conducted, and new research directions are discussed. The findings provide value-added to the scientific community and, therefore it could be helpful for other researches in these topics, especially given the rising popularity of IoT platforms.

Video games have evolved into a key part of modern culture and a major economic force, with the global market projected to reach $282.30 billion in 2024. As technology advances, video games increasingly demand high computing power, often requiring specialized hardware for optimal performance. Real-time strategy games, in particular, are computationally intensive, with complex artificial intelligence algorithms that simulate numerous units and behaviors in real-time. Specialized gaming PCs are use a dedicated GPU to run video games. Due to the usefulness of GPUs besides gaming, modern processors usually include an integrated GPU, specially in the laptop market. We propose a hybrid architecture that utilizes both the dedicated GPU and the integrated GPU simultaneously, to accelerate AI and physics simulations in video games. The hybrid approach aims to maximize the utilization of all available resources. The AI and physics computations are offloaded from the dedicated GPU to the integrated GPU. Therefore, the dedicated GPU can be used exclusively for rendering, resulting in improved performance. We implemented this architecture in a custom-built game engine using OpenGL for graphics rendering and OpenCL for general-purpose GPU computations. Experimental results highlight the performance characteristics of the hybrid architecture, including the challenges of working with the two devices and multi-tenant GPU interference.

In the era of cloud computing and serverless architectures, the security of applications and services has become a critical challenge. Serverless computing, often referred to as function as a service (FaaS), is a cloud computing model that allows developers to build and run applications without the need to manage traditional server infrastructure. Serverless architectures have gained popularity in cloud computing due to their flexibility and ability to scale automatically based on demand. These architectures are based on executing functions without the need to manage the underlying infrastructure. Denial of wallet (DoW) attacks refer to a type of cyberattack that aims to exploit and exhaust the financial resources of an organization by triggering excessive costs or charges within their cloud or serverless computing environment, exploiting characteristics such as the pay-as-you-go model, auto-scaling, limited control, and cost amplification. This research aims to assess existing methods for detecting distributed denial of service (DDoS) attacks and extend their application to detect denial of wallet (DoW) threats, leveraging a dataset tailored to serverless architectures. We investigate various strategies and techniques that employ entropy, machine learning and deep learning algorithms to enable early detection of DDoS and DoW attacks in serverless environments. This research provides insights into the options that are available for detecting DoW attacks in serverless environments, allowing security professionals and developers to make decisions on the most appropriate solutions to protect their applications and cloud services.

The Athlete Biological Passport (ABP), established by the World Anti-Doping Agency (WADA), serves as a cornerstone in the ongoing fight against doping in sports. By creating individual electronic records that track athletes’ biological markers and test results over time, the ABP enables the detection of abnormal variations that may suggest doping. Despite its effectiveness, the ABP faces significant challenges, particularly concerning data privacy, system interoperability, and the risk of unauthorized record manipulation. These issues underscore the need for more robust and transparent technological solutions. This article introduces a novel framework that integrates blockchain technology into the management of the ABP. Blockchain's decentralized and immutable ledger provides a secure and transparent platform for recording, sharing, and auditing anti-doping data among authorized stakeholders. Our proposal outlines a consortium-governed blockchain network, involving sports organizations and regulatory bodies, which ensures that data integrity is maintained while respecting athletes’ privacy rights and complying with international anti-doping regulations. We detail the technical architecture, governance model, and implementation roadmap for this blockchain-based system, emphasizing its capacity to prevent data tampering, streamline access control, and enhance the overall transparency of the anti-doping process. By leveraging blockchain's unique features, our approach aims to strengthen athlete integrity, increase trust in anti-doping measures, and support the evolution of fair and accountable sports governance. The integration of blockchain into the ABP represents a significant step forward in safeguarding the credibility of competitive sports and protecting athlete welfare.

In serverless architectures, applications are executed in environments where resources are dynamically allocated according to demand. Instead of having dedicated servers, functions are executed in response to specific events, allowing for high scalability and simplified infrastructure management. However, the lack of resources to monitor and protect these architectures makes them a ripe target for attackers. Denial of Wallet (DoW) attack is a term used to describe a situation in which an organization incurs financial losses by exceeding the service quota limits contracted with a cloud platform. The objective of this study is to detect Denial of Wallet (DoW) attacks using the concept of entropy. Entropy refers to the measure of uncertainty in a dataset and can be used as a metric to identify anomalous patterns. In this context, this study proposes an approach based on calculating the entropy of various features of executing serverless functions, including the number of invocations, and memory and CPU consumption. By applying this approach, it is expected to improve early detection of DoW attacks and reduce false negatives. By leveraging entropy as a measure of randomness in serverless function call data, an additional tool is provided to strengthen network security and mitigate the negative effects of denial-of-service and denial-of-wallet (DoW) attacks.

Organizations operating cloud-based systems receive a continuous stream of technical security findings-vulnerabilities, misconfigurations and identity risks-expressed in specialized metrics such as CVSS v4, EPSS, KEV and CSPM/IAM alerts. However, these signals are rarely translated into decision-ready economic indicators, such as expected loss, mitigation effort or return on investment (ROI) of remediation options. This paper proposes an explainable Risk Translation Layer (RTL) that systematically maps heterogeneous technical metrics into economic and operational indicators suitable for executive decision-making. The RTL combines (i) standardized technical signals (CVSS v4, EPSS, KEV, CSPM, IAM), (ii) business parameters (service criticality, revenue per hour, SLA penalties, remediation costs), and (iii) a hybrid AI layer that integrates rule-based reasoning, probabilistic modelling and supervised learning with explainability (XAI). The work is positioned with respect to classical ec onomic models of security investment and quantitative frameworks such as FAIR and MAGIC, and to recent advances in vulnerability enrichment and hybrid AI for trustworthy decision-support. We formalize the translation problem, present a proof-of-concept pipeline on a representative cloud-service scenario, define research questions and hypotheses, and outline a validation strategy that leverages observable proxies-remediation behaviour, SLA impact and expert benchmarks-in lieu of directly observing realized financial losses. The contribution is intended as a step towards an automated, explainable and empirically grounded “FAIR-lite” approach for cloud-centric cyber risk quantification. This paper presents a work-in-progress towards an AI-assisted risk translation layer, focusing on architectural design, explainability requirements, and initial feasibility insights rather than finalized empirical results.

The rapid growth of data and the increasing complexity of models have driven the emergence of Distributed Machine Learning as a key paradigm for scalable, efficient, and privacy-aware model training across decentralised environments. This paper presents a comprehensive review of recent advances in this topic, highlighting the field’s fragmentation and the absence of unified frameworks. In order to address this issue, a structured taxonomy is proposed that categorises its approaches into four key dimensions: distribution topologies, aggregation methods, application domains, and performance and system challenges. In addition, fundamental concepts are formalised, including those belonging to model and data partitioning, with the aim to facilitate the development of future Distributed Machine Learning systems. Furthermore, the paper explores the concept of Federated Learning as a hybrid model, which is a subject that has attracted increasing interest in the field of Machine Learning. The study places particular emphasis on the role of Federated Learning in applications that are sensitive to privacy. By addressing key research questions and highlighting unresolved challenges, this work establishes a foundational reference point for researchers and practitioners seeking to navigate and contribute to the evolving landscape of Distributed Machine Learning.

Currently, urban transportation is a field that requires new models and optimization systems. One of the most important aspects is the ability to dynamically optimize transport routes. This paper conducts a systematic analysis of the current methods for counting passengers in open urban transport systems, evaluating their advantages and disadvantages. Finally, an innovative solution is proposed to count passengers and obtain an origin-destination matrix using smartphones. This solution leverages the high penetration of mobile devices in modern society and their ability to capture accurate and real-time data. By using wireless technologies such as Wi-Fi and Bluetooth, the system aims to detect and count passengers anonymously, without their intervention, contributing to greater acceptance by transport passengers.

Physics-Informed Neural Networks (PINNs) are an innovative approach that combines deep neural networks and physical laws to tackle problems governed by differential equations. This paper focuses on the harmonic oscillator, a fundamental problem in physics, engineering, and technology. A detailed review of the state-of-the-art PINNs is provided, covering their inception in 2017 to their latest variations. The architecture, applications, and results of PINNs are discussed in comparison with other numerical and analytical methods. PINNs are implemented in different harmonic oscillator scenarios to demonstrate their effectiveness, advantages, and limitations. The study concludes by emphasizing the emerging role of PINNs in physics and other disciplines, outlining future research directions.