Dependency Graph (DG) structural pruning for enabling edge deployment of optimized Deep Learning (DL) models within the context of Non-Intrusive Load Monitoring (NILM) is introduced. DG pruning groups the DL model’s interdependencies between pruned and adjacent layers, enforcing structural consistency while simultaneously pruning all affected parameters. This addresses challenges in conventional structured pruning, related to maintaining model architecture integrity and performance when entire units are removed. By doing so, the DG structural pruning allows for a larger trade-off between pruning threshold and performance degradation when compared to its conventional counterpart. Experimental results using the Plegma dataset containing Mediterranean-based appliances, show that the DG structural pruning can achieve up to 90% model size, and up to 10× computational efficiency, calculated in terms of Multiply-and-Accumulate (MAC) units, all while exhibiting minimal performance degradation. The findings underscore the applicability of DG structural pruning to enable robust edge NILM solutions that enhance flexibility and energy efficiency, thereby facilitating broader adoption in real-world applications.

Segmentation of remote sensing data is a critical task in various environmental and geospatial applications. However, the presence of missing modalities, such as the Digital Elevation Model (DEM), poses significant challenges to achieving high segmentation accuracy. In this paper, we propose a novel approach for addressing this issue using Prototype Knowledge Distillation. Our methodology involves training a teacher model with access to all available modalities, including DEM, to generate high-quality segmentation results. Subsequently, we train a student model that performs segmentation without access to the DEM modality. The teacher model distills its learned knowledge into the student model through prototype representations, ensuring that the student model can achieve comparable or better performance despite the missing modality. Experimental results demonstrate the effectiveness of our approach, showing significant improvements in segmentation accuracy over baseline methods that do not utilize knowledge distillation. This work paves the way for robust segmentation of remote sensing data in scenarios where certain modalities are unavailable, enhancing the applicability and reliability of remote sensing analyses.

Climate change and extreme weather events diversly affect riverine areas. Since decades river monitoring techniques in combination with satellite images, are used to draw meaningful conclusions about rivers e.g., river banklines, islets, flooded areas etc. However, the majority of the existing methods rely on manual techniques especially for the analysis of river islets. In this paper we propose an automatic method to find the islets of a river and analyze them. Firstly, the proposed method uses deep learning and GIS algorithms, applied on satellite images, to find the islets. Then, the detected islets are stored as GIS layers in multiple geometries i.e., polygons, lines and points. Finally, the created layers are used to classify the islets into the "missing", "existing" and "new" ones resulting in an informative output for the users. In general, the proposed approach gives promising results and especially for the vectorization part which achieves an mIoU 94.9%.

An explainable AI framework for flood detection in SAR images is proposed. Compact encoder-decoder CNNs are used within the framework to achieve flood segmentation, with their output results fed to a Grad-CAM explainer so as to introduce trustworthiness to a stakeholder from naive thresholding selection during post-processing steps. The proposed framework is evaluated on the ETCi 2021 dataset using three different CNNs, resulting in more than 97% accuracy, while descriptive statistics on the Jaccard score are used to indicate the CNNs improper generalization towards the dataset. Edge cases highlight the importance of using Grad-CAM in complement with the CNN when the latter struggles to segment small regions due to thresholding.

EV charging optimization with utilization of decentralized renewable energy resources can be seen as a promising tool towards domestic EV fleet decarbonization. However, optimization is usually conducted on an individual household level, and community-driven approaches with shared resources are heavily underexplored. In this work, a community-driven smart EV charging optimization scheme with Multi-Agent Deep Reinforcement Learning is proposed. Compared to existing single-agent approaches, the employment of a multi-agent one allows for concurrent EV charging optimization for each household within a community, deployed in a centralized, shared, energy management system, while utilizing a community-owned solar photovoltaic (PV) panel. Our approach results in reduced cost barriers for domestic EV owners that desire to optimize their charging profile, as it eliminates the investment on an individual PV panel and energy management system. Experimental results on the Pecan Street dataset validate the effectiveness of our approach compared to individual household optimization, resulting in cost savings up to 17.65%, increase in PV power utilization of up to 133.10%, as well as network stress reduction of up to 18.75%.

In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-R tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against over-fitting. Experimental results on real-world data from UNESCO protected areas indicate the superiority of the proposed scheme compared to conventional deep learning models.

Links between environmental conditions (e.g., meteo-rological factors and air quality) and COVID-19 infection/mortality have been reported worldwide. However, the existing statistical frameworks are insufficient to investigate the factors that increase the risk for COVID-19 in urban areas. In this paper, we extend the concept of machine learning-based predictive modelling for COVID-19 spread, proposing an explainable AI approach in order to i) prioritize the risk factors, ii) define the interconnections between them and iii) detect positive or negative influence of the factors with respect to COVID-19 morbidity and mortality.

In this paper, we present a deep learning scheme for classification of choreographic primitives from RGB images. The proposed framework combines the representational power of feature maps, extracted by Convolutional Neural Networks, with the long-term dependency modeling capabilities of Long Short-Term Memory recurrent neural networks. In addition, it uses AutoRegressive and Moving Average (ARMA) filter into the convolutionally enriched LSTM filter to face dance dynamic characteristics. Finally, an adaptive weight updating strategy is introduced for improving classification modeling performance The framework is used for the recognition of dance primitives (basic dance postures) and is experimentally validated with real-world sequences of traditional Greek folk dances.

Performing arts is an essential aspect of Intangible Cultural Heritage (ICH), requiring tools for its modelling. In this paper, we introduce a Bayesian Optimized Bi-directional LSTM model, called BOBi-LSTM, that automatically estimates dancers' poses through 3D skeleton data processing. Bi-directionality models non-causal relationships occurred in a dance performance, in the sense that future dancer's steps depend on previous/current steps. Additionally, long-range dependence correlates choreographic primitives on a long time (memory) window. To model the aforementioned principles, we modify the conventional LSTM networks under a Bayesian Optimized framework in order to define the best network structure. Experimental results and comparisons for different types of dances are given to showcase how the proposed BOBi-LSTM out-performs traditional classifiers.

In this paper, we propose a novel method to extract representative instances from choreographic sequences of 3D human motion data. The proposed key-frame extraction method implements a hierarchical scheme that exploits spatio-temporal variations of the dance movement features. The method is based on a hierarchical adaptation of the sparse modeling for representative selection algorithm (SMRS). Leveraging a joint -centric distance metric, summaries are provided at variable levels of granularity depending on the richness and complexity of the visual content at different sequence segments. The proposed method can contribute to addressing the need of organizing, indexing, archiving, retrieving and analyzing intangible (in this case, dance-related) cultural content in a tractable fashion and with lower computational and storage resource requirements. The approach is evaluated on real-world dance sequences, as well as on theatrical kinesiology datasets (available by Carnegie Mellon University). Comparisons with traditional video summarization methods show that the proposed hierarchical spatio- temporal decomposition scheme achieves promising results.

Capturing, documenting and storing Intangible Cultural Heritage content has been recently enabled at unprecedented volume and quality levels through a variety of sensors and devices. When it comes to the performing arts, and mainly dance and kinesiology, the massive amounts of RGB-D and 3D skeleton data produced by video and motion capture devices the huge number of different types of existing dances and variations thereof, dictate the need for organizing, indexing, archiving, retrieving and analyzing dance-related cultural content in a tractable fashion and with lower computational and storage resource requirements. In this context, we present a novel framework based on kinematics modeling for the extraction of salient 3D human motion data from real-world choreographic sequences. Two approaches are proposed: a clustering-based method for the selection of the basic primitives of a choreography, and a kinematics-based method that generates meaningful summaries at hierarchical levels of granularity. The dance summarization framework has been successfully validated and evaluated with two real-world datasets and with the participation of dance professionals and domain experts.