As humans advance toward a higher level of artificial intelligence, it is always at the cost of escalating computational resource consumption, which requires developing novel solutions to meet the exponential growth of AI computing demand. Neuromorphic hardware takes inspiration from how the brain processes information and promises energy-efficient computing of AI workloads. Despite its potential, neuromorphic hardware has not found its way into commercial AI data centers. In this article, we try to analyze the underlying reasons for this and derive requirements and guidelines to promote neuromorphic systems for efficient and sustainable cloud computing: We first review currently available neuromorphic hardware systems and collect examples where neuromorphic solutions excel conventional AI processing on CPUs and GPUs. Next, we identify applications, models and algorithms which are commonly deployed in AI data centers as further directions for neuromorphic algorithms research. Last, we derive requirements and best practices for the hardware and software integration of neuromorphic systems into data centers. With this article, we hope to increase awareness of the challenges of integrating neuromorphic hardware into data centers and to guide the community to enable sustainable and energy-efficient AI at scale.

Demonstrates how newspaper signals can be leveraged for predicting crises using natural language processing techniques.

Planning for complex tasks is a key task for knowledge workers that is often time-consuming and depends on the manual extraction of knowledge from documents. In this research, we propose an end-to-end method, called PlanKG, that: (1) extracts knowledge graphs from full-text plan descriptions (FTPD); and (2) generates novel FTPD according to plan requirements and context information provided by users. From the knowledge graphs, activity sequences are obtained and projected into embedding spaces. We show that compressed activity sequences are sufficient for the search and generation of plan descriptions. The PlanKG method uses a pipeline consisting of decoder-only transformer models and encoder-only transformer models. To evaluate the PlanKG method, we conducted an experimental study for movie plot descriptions and compared our method with original FTPDs and FTPD summarizations. The results of this research has significant potential for enhancing efficiency and precision when searching and generating plans.

Public transport can be a sustainable and efficient way to provide mobility. However, its use is declining while the number of car owners is escalating. Previous research overlooks the dual nature of public transport in rural areas, as it can be both a mobility enabler and disenabler, and the question of how digitalisation can influence this is also not sufficiently considered. This paper attempts to fill this gap through a comprehensive literature review, including the consideration of regional characteristics and system design. For this purpose, 21 papers were analysed and the challenges and opportunities for improving public transport in rural areas were identified. The findings underscore the need for holistic approaches integrating technological, organizational, and societal dimensions to maximize the benefits of rural public transport and address mobility challenges effectively.

Quantum computing (QC) and machine learning (ML), taken individually or combined into quantum-assisted ML (QML), are ascending computing paradigms whose calculations come with huge potential for speedup, increase in precision, and resource reductions. Likely improvements for numerical simulations in engineering imply the possibility of a strong economic impact on the manufacturing industry. In this project report, we propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing, consisting of various layers ranging from hardware to algorithms to service and organizational layers. In addition, we give insight into the current state of the art of applications research based on QC and QML, both from a scientific and an industrial point of view. We further analyze two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially relevant settings.

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.