Abstract
Location privacy is a major concern in the current digital society, due to the sensitive information that can be inferred from location data. This has led smartphones' Operating Systems (OSs) to strongly tighten access to location information in the last few years. The same tightening has, however, not yet happened when it comes to our second most carried around device: the laptop. In this work, we demonstrate the privacy risks resulting from the fact that major laptop OSs still expose WiFi data to installed software, thus enabling to infer location information from WiFi Access Points (APs). Using data collected in a real-world experiment, we show that laptops are often carried along with smartphones and that a large fraction of our mobility profile can be inferred from WiFi APs accessed on laptops, thus concluding on the need to protect the access to WiFi data on laptops.

Abstract
Introduction: Urban centers face increasing congestion and pollution due to population growth driven by jobs, education, and entertainment. Promoting active modes like walking and cycling offers healthier and less polluting alternatives. Understanding perceptions of comfort (green areas, commercial areas, crowd density, noise, thermal sensation, air quality, allergenics), safety and security (street illumination, traffic volume, surveillance, visual appearance, and speed limits) are crucial for encouraging active modes adoption. This study categorizes user groups based on these indicators, supporting policymakers in the development of targeted strategies. Methods: We developed a questionnaire to support our empirical study and collected 653 responses. We have analyzed the data using clustering methods such as Affinity Propagation, BIRCH, Bisecting K-means, HAC, K-means, Mini-Batch K-means, and Spectral clustering. The best performing method (K-means) was used to identify the user groups while a random forest model evaluated the relative importance of indicators for each group. Results: The study identified five user groups based on urban mobility indicators for safety and security, comfort, and distance and time. Conclusions: These groups, distinguished by sociodemographic features, include: Street Aesthetes (young men valuing visual appeal), Safety Seekers (employed men prioritizing speed limits), Working Guardians (employed men focused on surveillance and green spaces), Urban Explorers (young women valuing air quality and low traffic), and Comfort Connoisseurs (employed women prioritizing noise reduction and aesthetics).

Abstract
In order to facilitate the adoption of deep learning in areas where decisions are of critical importance, understanding the model's internal workings is paramount. Nevertheless, since most models are considered black boxes, this task is usually not trivial, especially when the user does not have access to the network's intermediate outputs. In this paper, we propose IBISA, a model-agnostic attribution method that reaches stateof-the-art performance by optimizing sampling masks using the Information Bottleneck Principle. Our method improves on the previously known RISE and IBA techniques by placing the bottleneck right after the image input without complex formulations to estimate the mutual information. The method also requires only twenty forward passes and ten backward passes through the network, which is significantly faster than RISE, which needs at least 4000 forward passes. We evaluated IBISA using a VGG-16 and a ResNET-50 model, showing that our method produces explanations comparable or superior to IBA, RISE, and Grad-CAM but much efficiently.

Abstract
Road safety is a global issue, with road-related accidents being one of the biggest leading causes of death. Motorcyclists are especially susceptible to injuries and death when there is an accident, due to the inherent characteristics of motorcycles. Accident prevention is paramount. To improve motorcycle safety, this paper discusses and proposes a preliminary architecture of a system composed of various sensors, to assist and warn the rider of potentially dangerous situations such as front and back collision warnings, pedestrian collision warnings, and road monitoring.

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
Digital twins are increasingly used, as they allow the creation of detailed virtual representations of physical products and systems. They face, however, significant challenges such as heterogeneous data integration and high costs. This article presents an innovative methodology that uses Large Language Models to unify information and automate the generation of Digital Twin models. The proposal comprises several modules, covering the stages of data collection, semantic processing, modular construction and validation of the Digital Twin. In this way, the proposed model guarantees interoperability, efficiency and scalability for various domains.