Preservation of non-mineralized structures (including plants) and articulated skeletons results from extraordinary hydrographic, sedimentational early diagenetic conditions. The corresponding chief causative effects (stagnation, obrution bacterial sealing) define a conceptual continuum into which individual occurrences may be mapped. A more pragmatic, typological classification conservation deposits, using standard questionnaire, reveals ecological replacements, as well trends related to the...

The integration of blockchain and digital twins (DT) for better building-lifecycle data management has recently received much attention from researchers in the field. In this respect, adoption enabling technologies such as artificial intelligence (AI) machine learning (ML), Internet Things (IoT), cloud edge computing, Big Data analytics, etc., also been investigated an abundance studies. present review inspects recent studies to shed light on foremost among those their scope, challenges,...

In the context of document image analysis, binarization is an important preprocessing step for other analysis algorithms, but also relevant on its own by improving readability images historical documents. While challenging due to common degradations, such as bleedthrough, faded ink or stains, achieving good performance in a timely manner worthwhile goal facilitate efficient information extraction from this paper, we propose recurrent neural network based algorithm using Grid Long Short-Term...

Extensive use of drainage ditches in European boreal forests and some parts North America has resulted a major change wetland soil hydrology impacted the overall ecosystem functions these regions. An increasing understanding environmental risks associated with forest makes mapping priority for sustainable land management. Here, we present first rigorous deep learning–based methodology to map at regional scale. A neural network was trained on airborne laser scanning data (ALS) 1,607 km...

This article compares novel and existing uncertainty quantification approaches for semantic segmentation used in remote sensing applications. We compare the probability estimates produced by a neural network with Monte Carlo dropout-based approaches, including predictive entropy mutual information, conformal prediction-based feature prediction (FCP) approach based on regression. The chosen task focuses identifying ditches natural streams LiDAR derived digital elevation models. found that...

Water is the primary medium through which society will experience effects of climate change. Altered precipitation patterns, enhanced evapotranspiration rates, loss snow and ice, declines in groundwater storage, increased risk flooding droughts all have important implications for our water resources. In fact, we are already experiencing climate-related perturbations to cycle, as manifested increases temperatures frequency weather extremes across globe (IPCC, 2021). However, a critical but...

Forests worldwide contain unique cultural traces of past human land use. Increased pressure on forest ecosystems and intensive modern management methods threaten these ancient monuments remains. In northern Europe, older forests often very old traces, such as millennia-old hunting pits indigenous Sami hearths. Investigations have repeatedly found that owners fail to protect remains many are damaged by forestry operations. Current maps incomplete, the locations known poor spatial accuracy....

With the growth of virtualization and cloud computing, more forensic investigations rely on being able to perform live forensics a virtual machine using introspection (VMI). Inspecting through its hypervisor enables investigation without risking contamination evidence, crashing computer, etc. To further access these techniques for investigator/researcher we have developed new VMI monitoring language. This language is based review most commonly used VMI-techniques date, it user monitor...

In the context of historical document analysis, image binarization is a first important step, which separates foreground from background, despite common degradations, such as faded ink, stains, or bleed-through. Fast has great significance when analyzing vast archives images, since even small inefficiencies can quickly accumulate to years wasted execution time. Therefore, efficient especially relevant companies and government institutions, who want analyze their large collections images. The...

Digital Twins (DTs), enriched with Artificial Intelligence (AI) and Blockchain technology, promise a revolutionary breakthrough in smart asset management predictive maintenance the built environment. This study aims to portray conceptual framework of AI-based DTs outline its key characteristics, requirements, system architecture by composing functional model using IDEF0. Such an approach is expected enhance building facilities, simplify operation environments, ultimately deliver valuable...

Since this is a HP today contribution I understand that it should not have an abstract

The current state-of-the-art, in terms of performance, for solving document image binarization is training artificial neural networks on pre-labelled ground truth data. As such, it faces the same issues as other, more conventional, classification problems; requiring a large amount However, unlike those conventional problems, involves having to either manually craft or estimate binarized data, which can be error-prone and time-consuming. This where sample selection, act selecting samples...

In IPv6 networks, two security mechanisms are available at the network-layer; SEcure Neighbor Discovery (SEND) and IP (IPsec). Although both provide authentication, neither subsumes other; SEND IPsec should be deployed together to protect networks. However, when a node uses IPsec, authentication has done twice, which increases burden on decreases its performance. this paper, we propose an approach enable them work under mediation of Authentication Management Block, where public-private keys...

This study explores the effects of incorporating demonstrations as pre-training an improved Deep Q-Network (DQN). Inspiration is taken from methods such Q-learning Demonstrations (DQfD), but instead retaining throughout training, performance and behavioral policy when using solely are studied. A comparative experiment performed on two game environments, Gymnasium's Car Racing Atari Space Invaders. While demonstration in shows learning efficacy, indicated by higher evaluation training...

This paper proposes a pre-training method for neural network-based character recognizers to reduce the required amount of training data, and thus human labeling effort. The proposed transfers knowledge about similarities between graph representations characters recognizer by predict edit distance. We show that convolutional networks trained with this outperform traditional supervised learning if only ten or less labeled images per class are available. Furthermore, we our approach performs up...

There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle tasks, but mostly focused single aspects postural control. This study finding relevant factors by examining the entire over sequences time. A data collection was performed with 13 human participants carrying out live scenarios while being recorded multiple tracking...