University of the Aegean

About: University of the Aegean is a education organization based out in Mytilene, Greece. It is known for research contribution in the topics: Population & Context (language use). The organization has 2818 authors who have published 8100 publications receiving 179275 citations. The organization is also known as: UAEG.

Mapping the impact of alien species on marine ecosystems: the Mediterranean Sea case study

Abstract: Aim To develop a standardized, quantitative method for mapping cumulative impacts of invasive alien species on marine ecosystems. Location The methodology is applied in the Mediterranean Sea but is widely applicable. Methods A conservative additive model was developed to account for the Cumulative IMPacts of invasive ALien species (CIMPAL) on marine ecosystems. According to this model, cumulative impact scores are estimated on the basis of the distributions of invasive species and ecosystems, and both the reported magnitude of ecological impacts and the strength of such evidence. In the Mediterranean Sea case study, the magnitude of impact was estimated for every combination of 60 invasive species and 13 habitats, for every 10 × 10 km cell of the basin. Invasive species were ranked based on their contribution to the cumulative impact score across the Mediterranean. Results The CIMPAL index showed strong spatial heterogeneity. Spatial patterns varied depending on the pathway of initial introduction of the invasive species in the Mediterranean Sea. Species introduced by shipping gave the highest impact scores and impacted a much larger area than those introduced by aquaculture and the Suez Canal. Overall, invasive macroalgae had the highest impact among all taxonomic groups. These results represent the current best estimate of the spatial variation in impacts of invasive alien species on ecosystems, in the Mediterranean Sea. Main Conclusions A framework for mapping cumulative impacts of invasive alien species was developed. The application of this framework in the Mediterranean Sea provided a baseline that can be built upon with future improved information. Such analysis allows the identification of hotspots of highly impacted areas, and prioritization of sites, pathways and species for management actions.

Top-k Spatial Preference Queries

Abstract: A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, consider a real estate agency office that holds a database with available flats for lease. A customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within a distance range from them. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Our methods are experimentally evaluated for a wide range of problem settings.

Evaluation of anomaly‐based IDS for mobile devices using machine learning classifiers

Abstract: Mobile devices have evolved and experienced an immense popularity over the last few years. This growth however has exposed mobile devices to an increasing number of security threats. Despite the variety of peripheral protection mechanisms described in the literature, authentication and access control cannot provide integral protection against intrusions. Thus, a need for more intelligent and sophisticated security controls such as intrusion detection systems (IDSs) is necessary. Whilst much work has been devoted to mobile device IDSs, research on anomaly-based or behaviour-based IDS for such devices has been limited leaving several problems unsolved. Motivated by this fact, in this paper, we focus on anomaly-based IDS for modern mobile devices. A dataset consisting of iPhone users data logs has been created, and various classification and validation methods have been evaluated to assess their effectiveness in detecting misuses. Specifically, the experimental procedure includes and cross-evaluates four machine learning algorithms (i.e. Bayesian networks, radial basis function, K-nearest neighbours and random Forest), which classify the behaviour of the end-user in terms of telephone calls, SMS and Web browsing history. In order to detect illegitimate use of service by a potential malware or a thief, the experimental procedure examines the aforementioned services independently as well as in combination in a multimodal fashion. The results are very promising showing the ability of at least one classifier to detect intrusions with a high true positive rate of 99.8%. Copyright © 2011 John Wiley & Sons, Ltd.