Abstract
This article introduces a comprehensive frame-work for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we investigate an architectural solution and address the associated challenges to ensure superior performance in real-world disaster scenarios. The proposed framework is designed to provide comprehensive coverage of affected areas by utilizing a multi-tier swarm architecture with multi-modal sensing capabilities. By integrating data from var-ious sensors and applying machine learning for data fusion, the framework enhances detection accuracy and supports precise survivor identification, even in complex environments.

Abstract
Integrating Internet of Things (IoT) technologies into physical education (PE) presents opportunities for improving the methodologies for collecting, analysing, and managing student performance data. However, it also introduces technical challenges, particularly related to the real-time handling and protection of sensitive data in dynamic training environments. This paper presents a comprehensive solution outline based on a private local network architecture that supports scalable sensor data processing, real-time database integration, and mobile application interfaces. The proposed distributed system ensures data integrity, low-latency communication, and secure access while enabling educators to monitor student performance in real-time and review historical data. The system supports more personalised, data-driven training strategies by providing actionable insights for sports education.

Abstract
Predictive models often reinforce biases which were originally embedded in their training data, through skewed decisions. In such cases, mitigation methods are critical to ensure that, regardless of the prevailing disparities, model outcomes are adjusted to be fair. To assess this, datasets could be systematically generated with specific biases, to train machine learning classifiers. Then, predictive outcomes could aid in the understanding of this bias embedding process. Hence, an agent-based model (ABM), depicting a loan application process that represents various systemic biases across two demographic groups, was developed to produce synthetic datasets. Then, by applying classifiers trained on them to predict loan outcomes, we can assess how biased data leads to unfairness. This highlights a main contribution of this work: a framework for synthetic dataset generation with controllable bias injection. We also contribute with a novel explainability technique, which shows how mitigations affect the way classifiers leverage data features, via second-order Shapley values. In experiments, both offline and online learning approaches are employed. Mitigations are applied at different stages of the modelling pipeline, such as during pre-processing and in-processing.

Abstract

Abstract
Contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. Previous research has demonstrated the value of contracts in traditional software development. However, the adoption and impact of contracts in the context of mobile app development, particularly of Android apps, remain unexplored. To address this, we present the first large-scale empirical study on the use of contracts in Android apps, written in Java or Kotlin. We consider contract elements divided into five categories: conditional runtime exceptions, APIs, annotations, assertions, and other. We analyzed 2,390 Android apps from the F-Droid repository and processed 52,977 KLOC to determine 1) how and to what extent contracts are used, 2) which language features are used to denote contracts, 3) how contract usage evolves from the first to the last version, and 4) whether contracts are used safely in the context of program evolution and inheritance. Our findings include: 1) although most apps do not specify contracts, annotation-based approaches are the most popular; 2) apps that use contracts continue to use them in later versions, but the number of methods increases at a higher rate than the number of contracts; and 3) there are potentially unsafe specification changes when apps evolve and in subtyping relationships, which indicates a lack of specification stability. Finally, we present a qualitative study that gathers challenges faced by practitioners when using contracts and that validates our recommendations.

Abstract
The shift to AI-native 6G networks demands autonomous slicing strategies that can adapt to diverse and evolving edge and IoT service needs. Two paradigms have emerged: Learn to Slice (L2S), where AI optimizes network slicing for general services, and Slice to Learn (S2L), where slices support AI model training, often offloaded from Internet of Things (IoT) devices. Existing S2L approaches typically optimize communication or computation in isolation. This paper presents the first unified framework that jointly optimizes communication resources, computation capacity, and AI hyperparameters to maximize the average accuracy of multiple concurrent AI services. We address the complexity of this joint problem by applying L2S-inspired techniques to enhance S2L, introducing two autonomous agents: EXP3 from online convex optimization and DQN from deep reinforcement learning. Extensive experiments demonstrate and contrast the effectiveness of these agents in maximizing aggregated AI accuracy, supporting knowledge transfer, and sustaining robust performance under adversarial and long-term conditions, thereby enhancing the realization of zero-touch network management for AI services in 6G networks, supporting resource-constrained IoT.