This paper explores the integration of quantum computing into service ecosystems to enhance manufacturing simulations.

Introduces quantum feature embeddings for improving the performance of graph neural networks.

In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a novel approach using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate these responses. We developed QuestSecure, a system that interprets diverse document formats and generates precise responses by integrating large language models (LLMs) with an advanced retrieval system. Our experiments show that QuestSecure significantly improves response accuracy and operational efficiency. By employing advanced NLP techniques and tailored retrieval mechanisms, the system consistently produces contextually relevant and semantically rich responses, reducing cognitive load on security teams and minimizing potential errors. This research offers promising avenues for automating complex security management tasks, enhancing organizational security processes.

REAVER introduces an attention-based sliding-window spectogram approach for real-time earthquake prediction.

This paper presents a Self Attention-based Convolutional Neural Network (SACNN) designed to detect fraudulent behavior in sports data.

Uses semantic priming and knowledge graphs to address and mitigate errors in language models.

Manually diagnosing mechanical faults is highly laborious, and a lack of strategies to intuitivelyexplain model decisions has precluded effective automated diagnosis by unsupervised systems. Toremedy this, we propose an objectively interpretable probabilistic framework for analysing time-series data in grinding, using a Gaussian mixture model (GMM) trained only on normal processesto assign likelihoods to each 50-ms segment of a grinding signal. A dashboard visualises theselikelihoods in sequence, showing where during a process anomalies crop up and yielding diagramsthat could expedite visual fault diagnosis. Anomalous grinding runs are objectively defined bydeviations from normal training data, facilitating predictive maintenance. We show that ourframework allows various simple GMM-based architectures to outperform a more complexrecurrent one in stability of F2-scores on small training samples with simulated anomaly data.

Discusses the challenges and opportunities in requirements engineering for quantum computing in manufacturing.

Most people are aware of the huge benefits that Artificial Intelligence (AI) brings to humanity in terms of sustainable applications (AI for sustainability). Yet, the fewest face the environmental impacts caused by an AI over its complete lifecycle (Sustainability of AI), e.g., the energy consumption, regardless how beneficial its outputs are. This paper presents a systematic literature review on the existing approaches for conducting a Life Cycle Assessment (LCA) on AI applications, alongside the key factors influencing their environmental impact. The study identifies critical environmental impact drivers of an AI over its life cycle, like the energy and resource consumption of hardware devices which provide the needed computing power. It underscores the importance of a holistic LCA approach considering operational and embodied energy use and the lifecycle impacts of data centers and other physical devices required for AI. The results provide critical insights for stakeholders looking to assess and mitigate the environmental impact of AI applications.

Introduces a framework for generating synthetic black swan events to model unpredictable crises.

This paper presents a novel approach for using quantum graph neural networks in simulating laser cutting processes.

ADA introduces a novel approach for automatic data annotation in data ecosystems.

AI-based scenario planning to predict and address energy crises in process industries.

Introduces the cascading scenario technique for automating crisis management in production systems.

Explores a conceptual alignment method for enhancing data integration and interoperability.