Protecting lightweight networked devices against manipulation or cloning is an important aspect in critical infrastructure, especially when processing and transmitting sensitive data such as in industry, medical devices, or smart home systems. However, these devices often have a lightweight structure and limited resource capacity, which makes it difficult or impossible to implement complex security measures. Physical unclonable functions (PUFs) can be used as lightweight security primitives to generate unique signals for device identification within a network or for key generation without relying on memory components. However, internal and external factors can influence the behavior of PUFs, which can affect their performance and, consequently, their security. To reduce this problem, error-correction code (ECC) algorithms are used in addition to PUFs to correct bit errors. In this paper, we combine PUFs with standardized low-density parity-check (LDPC) codes to improve reliability. For this purpose, we present a statistical model based on our previously implemented double arbiter PUF (DAPUF) designs in order to simulate different conditions and test scenarios. Our experimental results show that under normal operating conditions, reliability can be improved to the ideal value of 100% using LDPC codes. However, under extreme conditions, by adding more instabilities and bit errors, reliability is compromised, making the PUF unsuitable for security applications. Our experiments show that in order to increase reliability, larger LDPC codes with low code rates must be used however, this increases the complexity, processing time, and resource requirements of the hardware.
Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community. While LLMs offer unique avenues for learners and educators, using LLMs comes with computational and environmental costs. These costs are mostly hidden due to a lack of standardised procedures to measure and report these impacts. To address this gap, we first conducted a literature review of all N = 396 papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported. Most projects use LLMs, but few report computational resources used and almost none discuss environmental impacts of LLMs as an ethical concern. To address this lack of standardised reporting practices, we propose an open-source method for systematically measuring and reporting the computational expense of LLMs and environmental impact of running Machine Learning (ML) AIED systems. We provide software solutions to measure the carbon footprint for both local and cloud based hardware. We also provide an easy-to-use formula to calculate the computational expense of frontier LLMs even when the exact number of parameters is not known. Overall, we hope to motivate colleagues to use our method to strive for more transparent reporting of hidden costs of using LLMs in the AIED community.
Over the past decade, Extended Reality (XR), including Virtual, Augmented, and Mixed Reality, gained attention as a research instrument in human-robot interaction studies, but remains underexplored in empirical investigations of social robotics. To map the field, we systematically reviewed empirical studies from 2015 to 2025. Of 6,527 peer-reviewed articles, only 33 met strict inclusion criteria. We examined (1) how XR and virtual social robots are used, focusing on the software and hardware employed and the application contexts in which they are deployed, (2) data collection and analysis methods, (3) demographics of the researchers and participants, and (4) the challenges and future directions. Our findings show that social XR-HRI research is still driven by laboratory simulations, while crucial specifications - such as the hardware, software, and robots used - are often not reported. Robots typically act as passive and hardly interactive visual stimulus, while the rich biosignal (e.g., eye-tracking) and logging (e.g. motion capturing) functions of modern head-mounted displays remain largely untapped. While there are gaps in demographic reporting, the research teams and samples are predominantly tech-centric, Western, young, and male. Key limitations include hardware delays, small homogeneous samples, and short study cycles. We propose a four-phase roadmap to establish social XR-HRI as a reliable research medium, which includes (1) strengthen application contexts, (2) more robust and testable technological iterations, (3) embedding diversity in samples and research teams, and (4) the need for reporting standards, e.g., in form of a suitable taxonomy. Advancing in these directions is essential for XR to mature from a lab prototype into an ecologically valid research instrument for social robotics.
In this note, we propose to study the rate at which the weak pair correlation statistic with parameter 0 < α≤ 1 converges to its limit in the Poissonian case. As an application we show that if (x_n)_n ∈ℕ has weak α -Poissonian pair correlations and the rate of convergence is locally uniform of order cN^-κ , then it also has weak β -Poissonian pair correlations for some β > α depending on κ . This may be regarded as a partial converse of the well-known fact that β -Poissonian pair correlations imply α -Poissonian pair correlations if β > α .
This study introduces an interdisciplinary framework for benchmarking robots deployed in public environments, addressing the gap between traditional laboratory metrics and real-world benchmarking requirements. We evaluate three distinct robots across diverse use cases - outdoor park cleaning, pedestrian underpass cleaning, and interactive library assistance - each representing unique challenges in public daily life. Over a three-year benchmarking process (2023-2025) comprising seven benchmarking events, a consensus workshop and six on-site evaluations (two per use case), we utilized realistic indoor and outdoor test environments to assess not only technical performance but also the broader implications of deploying robots in unstructured, human-centric settings. An expert panel, spanning robotics, human-robot interaction, safety, and economics, systematically developed and refined an evaluation concept to analyze the transition from laboratory prototypes to operational systems. Our findings highlight critical factors for successful deployment, including task fulfillment, interaction quality, safety, and economic feasibility. This work provides actionable insights for researchers and practitioners aiming to bridge the gap between robotic innovation and real-world applicability.
