The use of eXplainable Artificial Intelligence (XAI) systems has introduced a set of challenges that need resolution. The XAI robustness, or stability, has been one of the goals of the community from its beginning. Mu...
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Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1...
Forest fires significantly threaten ecosystems, human life, and property, necessitating rapid detection and response mechanisms. Traditional detection methods, such as satellite imagery and ground-based observation, o...
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ISBN:
(数字)9798350367928
ISBN:
(纸本)9798350367935
Forest fires significantly threaten ecosystems, human life, and property, necessitating rapid detection and response mechanisms. Traditional detection methods, such as satellite imagery and ground-based observation, often suffer from delays and limited accuracy. This paper presents a novel approach to enhancing forest fire detection and emergency response using crowdsourcing and smartphone sensors. By leveraging the widespread availability of smartphones equipped with various sensors, coupled with the power of collective human effort, this method aims to improve the timeliness and accuracy of fire detection and optimize emergency response. Moreover, the proposed system enables real-time fire detection and reporting. A dedicated mobile application allows users to submit fire sightings, including multimedia evidence and precise location data. The integration of crowdsourcing and smartphone sensors represents a promising solution for addressing the challenges of forest fire management, enhancing both detection and emergency response capabilities.
Data processing units (DPUs) as network co-processors are gaining momentum in the high-performance computing (HPC) community, offering numerous unexplored opportunities. Typically, DPUs have been utilized as domain-sp...
Data processing units (DPUs) as network co-processors are gaining momentum in the high-performance computing (HPC) community, offering numerous unexplored opportunities. Typically, DPUs have been utilized as domain-specific accelerators that remain transparent to application developers. In the realm of HPC, DPUs have been employed as MPI accelerators and for offloading certain tasks from general-purpose processors. However, the latter approach required application developers to deploy MPI ranks on DPUs, treating them as remote compute nodes, which significantly impacted code usability.
One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that ma...
One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that makes previously learned knowledge and model outdated. Therefore, concept drift must be efficiently detected in order to adapt the classification model. While there exists a plethora of drift detectors, with different mechanisms, selecting the most suitable for a new stream is a difficult task, since apriori knowledge may not be available and changes over time can affect the performance of the detector. This paper proposes a framework that exploits statistical and temporal meta-features from sliding windows to dynamically recommend a suitable drift detector in real-time for unseen chunks of streams according to its properties using Meta-Learning. We performed experiments on 10 real-world data streams and 18 synthetic generated data streams that were subject to concept drift and class imbalance in order to evaluate the performance of the proposed framework. Experiments exposed that the proposed approach was able to enhance the concept drift detection in a variety of scenarios demonstrating robustness to class imbalance and the advantages of dynamically selecting the drift detector.
As the development of wireless networks advances towards the deployment of 6G technology, ensuring robust security measures becomes crucial. In this paper, we propose 6G-SECUREIDS, a novel intrusion detection system d...
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The financial fraud risk assessment requires expertise concerning the audit methodology and the risk assessment of business processes. The assessment of financial fraud risks is a significant challenge for independent...
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Process mining collects a variety of techniques. To test and compare these techniques, we need event logs tailored to their specific mining purposes, e.g., process discovery and conformance checking. To this aim, we p...
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Terahertz generation and high-precision spectroscopy have a need for dual-wavelength narrow-linewidth lasers. We report a laser with such characteristics, with linewidths below 3 MHz for each line, which reduce to val...
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Professionals as well as the general public need effective help to access, understand and consume complex biomedical concepts. The existence of an interaction environment capable of automatically processing such infor...
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Professionals as well as the general public need effective help to access, understand and consume complex biomedical concepts. The existence of an interaction environment capable of automatically processing such information - thus replacing human intervention - such as chatbots, is however challenging. In this paper we propose a method of utilizing chatbots in the domain of biomedicine. In the implementation we choose to incorporate the BERT algorithm, so as to adopt a modern technique for natural language processing tasks. We use several pre-trained models (RoBERTa, XLM-R, BERT Large, and BioBert) in order to evaluate their ability to back the chatbot infrastructure. The data is retrieved from the PubMed repository, with the final set being formed into full sentences or potential chatbot responses, thus preserving their conceptual meaning. Response selection is performed using similarity metrics and F-score. The results create a ranking of the models placing related ones closely, recognizing the ability to always answer each question and highlighting the importance of the training previously applied to them. These are compared to the Count Vectorizer technique, which appears to perform better, but with several weaknesses, as many questions could not be answered.
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