Abstract
In many organizations with a distributed operation, not only is data collection distributed, but models are also developed and deployed separately. Understanding the combined knowledge of all the local models may be important and challenging, especially in the case of a large number of models. The automated development of consensus models, which aggregate multiple models into a single one, involves several challenges, including fidelity (ensuring that aggregation does not penalize the predictive performance severely) and completeness (ensuring that the consensus model covers the same space as the local models). In this paper, we address the latter, proposing two measures for geometrical and distributional completeness. The first quantifies the proportion of the decision space that is covered by a model, while the second takes into account the concentration of the data that is covered by the model. The use of these measures is illustrated in a real-world example of academic management, as well as four publicly available datasets. The results indicate that distributional completeness in the deployed models is consistently higher than geometrical completeness. Although consensus models tend to be geometrically incomplete, distributional completeness reveals that they cover the regions of the decision space with a higher concentration of data.

Abstract
The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of Service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users' behavior and mobile networks. Thus, this paper proposes an online learning framework to determine the allocation of computational and communication resources to AI services, to optimize their accuracy as one of their unique key performance indicators (KPIs), while abiding by resources, learning latency, and cost constraints. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity. Additionally, our solution outperforms State-of-the-Art techniques in adapting to diverse environmental dynamics and excels under varying levels of resource availability.

Abstract
In today's rapidly evolving business environment, digitalization has emerged not only as a technological trend but also as a strategic imperative. This paper develops a conceptual framework that examines how Industry 4.0 (I4.0) technologies and tools drive strategic innovation and enable the transformation of business models. Based on a systematic literature review, the framework identifies a set of organizational and contextual preconditions (strategic vision, organizational culture, digital skills, infrastructure, financial resources, and regulatory conditions) that can act as either enablers or barriers to innovation. The analysis reveals that these preconditions give rise to two contrasting innovation cycles: a virtuous cycle, where favourable conditions amplify the adoption of digital technologies and foster business model transformation, and a vicious cycle, where unfavourable conditions reinforce technological inertia and hinder strategic development. By integrating insights from innovation management, digital transformation, and business model theory, the framework offers a nuanced understanding of how technology and strategy intersect and provides actionable guidance for managers seeking to move beyond operational improvements toward reimagining value creation, delivery, and capture in the digital age.

Abstract
The paradigm of Device as a Service (DaaS) is one where devices are used as part of a service, with the user having no ownership over them. A centralised, web-based approach can be envisioned to support such a business model, but such lacks transparency, availability, and global scalability. A blockchain-based solution is proposed to support such a business model. The concept of a blockchain-assisted DaaS is novel and, by using smart contracts to support key interactions between relevant entities, marks a shift in device ownership, management, and revenue generation.

Abstract

Abstract
There is currently a lack of objective, quantifiable metrics to evaluate children's health and athletic performance during Physical Education classes. To address this gap, the TexP@ct Consortium is developing a Smart Vest for Physical Education (SV4PE)-a textile engineered wearable solution that integrates a single triaxial Inertial Measurement Unit (IMU) and electrocardiogram (ECG) sensors, embedded at the T8 spinal level. Designed for comfortable and unobtrusive use, the SV4PE enables recording and analysis of biomechanical and physiological data during physical activity. This paper presents the preliminary system validation and algorithm development for the SV4PE system, detailing the sensor fusion and signal processing methods used to extract metrics from live and recorded data, along with results from experimental and prototype datasets. The algorithms designed measure an athlete's heart rate, movement intensity, and effort, with additional post-exercise metrics to characterize fundamental movements such as walking, running, and jumping. Sensor fusion packages were implemented, combining acceleration and angular velocity, to correct sensor drifts and remove gravity components. Following filtering and resampling, walking and running metrics, such as cadence, distance and velocity, are extracted through gait event identification, using wavelet transforms. Jumping characteristics are derived from vertical acceleration using projectile motion equations to estimate jump height, take-off force, and power output. Lastly, heart rate is calculated from QRS peak detection in the ECG signal, and associated with subject metadata to evaluate exercise intensity and effort levels. Additional algorithms are under-development to assess fitness tests (e.g., mile run, shuttle run, push-ups, etc.), for team sport motion classification using machine learning, and for player localization within a playfield for detailed performance analysis. Ultimately, this work seeks to provide teachers and trainers with practical tools to objectively monitor and assess children's performance during sports and physical activities.