Abstract

Abstract
Non-Intrusive Load Monitoring (NILM) aims to estimate appliance-level consumption from aggregate household power measurements without requiring intrusive sensing infrastructure. However, most existing NILM approaches rely on offline supervisedlearning and static inference pipelines, which often struggle to adapt to changing appliance behavior, uncertainty in aggregatesignals, and domain shifts across households. This paper proposes an interactive NILM framework that integrates probabilisticappliance-level classifiers with a deterministic constraint-based inference engine and adaptive user feedback acquisition strategies.The proposed system operates in an online setting and selectively queries users about appliance states only when predictions areuncertain. To optimize this interaction process, we introduce a cost-sensitive active querying strategy based on a contextual bandit(LinUCB) that dynamically balances the expected improvement in inference accuracy against the cost of user queries. The resultsdemonstrate that cost-sensitive active querying provides an effective mechanism for improving NILM robustness under uncertaintyand evolving operating conditions. We use the REFIT dataset as the primary benchmark for real-world validation, and additionallyevaluate the framework on a synthetic household dataset generated with the open-source RAMP library. Across both datasets, theproposed strategy consistently reduces the Normalised Error in Power and Mean Absolute Error relative to a passive inferencebaseline– by 11–50% on REFIT, depending on household, and by 30.2% and 28.0% respectively on RAMP, alongside a 61.7%reduction in Signal Aggregate Error– while also outperforming a non-adaptive heuristic strategy using an identical query budget

Abstract
Despite the recognised potential of horizontal collaboration in logistics to reduce inefficiencies, and the increasing academic interest in this topic, in practice many initiatives fail. One of the main reasons for this failure is the poor strategy planning and governance organisation. This paper addresses this gap proposing a comprehensive conceptual framework to support the design and implementation of a common strategy for the stakeholders of such partnerships. The research employs qualitative methods, drawing on interviews and the case analysis of existent initiatives. The proposed framework involves the main phases of the strategic formulation, deciding the stakeholder engagement, strategic formulation, operational implementation, and business model elaboration. It serves as a road map for stakeholders to avoid common mistakes and accelerate the deployment of cooperative partnerships.

Abstract

Abstract
Recommender systems (RS) on platforms like YouTube are often evaluated as if they operate in a closed environment. In practice, however, user consumption patterns are also shaped by a broader ecosystem of external sources. This paper investigates how external link interactions influence RS behavior. We conducted a controlled experiment with three intervention timings and found that a single external link exerts an immediate and significant impact on YouTube's recommendations, an influence that decays but persists over time. These findings contribute to our understanding of how external interactions shape RS outputs and their subsequent impact on content diversity.

Abstract
Students in computing education increasingly use large language models (LLMs) such as ChatGPT. Yet, the role of LLMs in supporting cognitively demanding tasks, like deductive program verification, remains poorly understood. This paper investigates how students interact with an LLM when solving formal verification exercises in Dafny, a language that supports functional correctness by allowing programmers to write formal specifications and automatically verifying that the implementation satisfies the specification. We conducted a mixed-methods study with master's students enrolled in a formal methods course. Each participant completed two verification problems, one with access to a custom ChatGPT interface that logged all interactions and the other without. We identified strategies used by successful students and assessed the level of trust students place in LLMs. Our findings show that students perform significantly better when using ChatGPT; however, performance gains are tied to prompt quality. We conclude with practical recommendations for integrating LLMs into formal methods courses more effectively, including designing LLM-aware challenges that promote learning.