The present contribution constitutes the first comprehensive attempt to (a) record the spatial characteristics of the beaches of the Aegean archipelago (Greece), a critical resource for both the local and national economy, and (b) provide a rapid assessment of the impacts of the long-term and episodic sea level rise (SLR) under different scenarios. Spatial information and other attributes (e.g., presence of coastal protection works and backshore development) of the beaches of the 58 largest islands of the archipelago were obtained on the basis of remote-sensed images available on the web. Ranges of SLR-induced beach retreats under different morphological, sedimentological and hydrodynamic forcing, and SLR scenarios were estimated using suitable ensembles of cross-shore (1-D) morphodynamic models. These ranges, combined with empirically derived estimations of wave run-up induced flooding, were then compared with the recorded maximum beach widths to provide ranges of retreat/erosion and flooding at the archipelago scale. The spatial information shows that the Aegean pocket beaches may be particularly vulnerable to mean sea level rise (MSLR) and episodic SLRs due to (i) their narrow widths (about 59 % of the beaches have maximum widths < 20 m), (ii) their limited terrestrial sediment supply, (iii) the substantial coastal development and (iv) the limited existing coastal protection. Modeling results indeed project severe impacts under mean and episodic SLRs, which by 2100 could be devastating. For example, under MSLR of 0.5 m – representative concentration pathway (RCP) 4.5 of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate change (IPCC) – a storm-induced sea level rise of 0.6 m is projected to result in a complete erosion of between 31 and 88 % of all beaches (29–87 % of beaches are currently fronting coastal infrastructure and assets), at least temporarily. Our results suggest a very considerable risk which will require significant effort, financial resources and policies/regulation in order to protect/maintain the critical economic resource of the Aegean archipelago.

Opinion mining techniques, investigating if text is expressing a positive or negative opinion, continuously gain in popularity, attracting the attention of many scientists from different disciplines. Specific use cases, however, where the expressed opinion is indisputably positive or negative, render such solutions obsolete and emphasize the need for a more in-depth analysis of the available text. Emotion analysis is a solution to this problem, but the multi-dimensional elements of the expressed emotions in text along with the complexity of the features that allow their identification pose a significant challenge. Machine learning solutions fail to achieve a high accuracy, mainly due to the limited availability of annotated training datasets, and the bias introduced to the annotations by the personal interpretations of emotions from individuals. A hybrid rule-based algorithm that allows the acquisition of a dataset that is annotated with regard to the Plutchik’s eight basic emotions is proposed in this paper. Emoji, keywords and semantic relationships are used in order to identify in an objective and unbiased way the emotion expressed in a short phrase or text. The acquired datasets are used to train machine learning classification models. The accuracy of the models and the parameters that affect it are presented in length through an experimental analysis. The most accurate model is selected and offered through an API to tackle the emotion detection in social media posts.

This paper investigates the potential of using a polynomial radial basis function (RBF) neural network to extract the shoreline position from coastal video images. The basic structure of the proposed network encompasses a standard RBF network module,

In this paper, a novel method to modify color images for the protanopia and deuteranopia color vision deficiencies is proposed. The method admits certain criteria, such as preserving image naturalness and color contrast enhancement. Four modules are employed in the process. First, fuzzy clustering-based color segmentation extracts key colors (which are the cluster centers) of the input image. Second, the key colors are mapped onto the CIE 1931 chromaticity diagram. Then, using the concept of confusion line (i.e., loci of colors confused by the color-blind), a sophisticated mechanism translates (i.e., removes) key colors lying on the same confusion line to different confusion lines so that they can be discriminated by the color-blind. In the third module, the key colors are further adapted by optimizing a regularized objective function that combines the aforementioned criteria. Fourth, the recolored image is obtained by color transfer that involves the adapted key colors and the associated fuzzy clusters. Three related methods are compared with the proposed one, using two performance indices, and evaluated by several experiments over 195 natural images and six digitized art paintings. The main outcomes of the comparative analysis are as follows. (a) Quantitative evaluation based on nonparametric statistical analysis is conducted by comparing the proposed method to each one of the other three methods for protanopia and deuteranopia, and for each index. In most of the comparisons, the Bonferroni adjusted p-values are <0.015, favoring the superiority of the proposed method. (b) Qualitative evaluation verifies the aesthetic appearance of the recolored images. (c) Subjective evaluation supports the above results.