Abstract
Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights.

Abstract
An autonomous vehicle can sense its environment and operate without human involvement. Its adequate management in an intelligent transportation system could significantly reduce traffic congestion and overall travel time in a network. Adaptive traffic signal controller (ATSC) based on multi-agent systems using state-action-reward-state-action (SARSA ( lambda )) are well-known state-of-the-art models to manage autonomous vehicles within urban areas. However, this study found inefficient weights updating mechanisms of the conventional SARSA ( lambda ) models. Therefore, it proposes a Gaussian function to regulate the eligibility trace vector's decay mechanism effectively. On the other hand, an efficient understanding of the state of the traffic environment is crucial for an agent to take optimal actions. The conventional models feed the state values to the agents through the MinMax normalization technique, which sometimes shows less efficiency and robustness. So, this study suggests the MaxAbs scaled state values instead of MinMax to address the problem. Furthermore, the combination of the A-star routing algorithm and proposed model demonstrated a good increase in performance relatively to the conventional SARSA ( lambda )-based routing algorithms. The proposed model and the baselines were implemented in a microscopic traffic simulation environment using the SUMO package over a complex real-world-like 21 -intersections network to evaluate their performance. The results showed a reduction of the vehicle's average total waiting time and total stops by a mean value of 59.9 % and 17.55 % compared to the considered baselines. Also, the A-star combined with the proposed controller outperformed the conventional approaches by increasing the vehicle's average trip speed by 3.4 %.

Abstract
User-centred design is an iterative process that involves ideation, testing, and refinement to enhance usability, functionality, and overall user experience. However, there is still a need to code the final design. This transition from design to implementation is a time-consuming task. In this paper, we propose an approach and its supporting tool to generate a web application's front-end code from a prototype of its user interface. Our goal is to generate code that can be easily integrated into the overall application development. The results demonstrate that the proposed tool can achieve visual fidelity comparable to that of existing commercial plugins, while producing higher-quality code that better reflects the behaviour specified in the prototype.

Abstract
Decred is a cryptocurrency with its own blockchain and has several similarities with bitcoin but implements a governance model that resembles a company with thousands of investors. These stakeholders invest their coins, receive the right to direct the project as they see fit and are rewarded for doing so. Everyone else not invested may use the coin as means of exchange, trading it for goods or services or consuming other services provided by the blockchain as the digital notary. This paper investigates how Decred project created its own version of money and implemented security measures to improve governance and remove trusted third parties from money issuance and e-voting. This topic is particularly relevant to understand how blockchain technologies improve governance and avoid the tyranny of the majority. In order to reach our goal, we use multi-agent simulation and statistical modelling to verify to what extent Decred is capable of providing a predictable, scarce, trustworthy digital asset. We show that Decred increased blockchain security with its hybrid proof-of-work+proof-of-stake (PoW + PoS) security mechanism, making an attack more expensive. © 2025 Inderscience Enterprises Ltd.

Abstract

Abstract