Abstract
Diabetes has significant effects on the human body, one of which is the increase in the blood pressure and when not diagnosed early, can cause severe vision complications and even lead to blindness. Early screening is the key to overcoming such issues which can have a significant impact on rural areas and overcrowded regions. Mobile systems can help bring the technology to those in need. Transfer learning based Deep Learning algorithms combined with mobile retinal imaging systems can significantly reduce the screening time and lower the burden on healthcare workers. In this paper, several efficiency factors of Diabetic Retinopathy detection systems based on Convolutional Neural Networks are tested and evaluated for mobile applications. Two main techniques are used to measure the efficiency of DL based DR detection systems. The first method evaluates the effect of dataset change, where the base architecture of the DL model remains the same. The second method measures the effect of base architecture variation, where the dataset remains unchanged. The results suggest that the inclusivity of the datasets, and the dataset size significantly impact the DR detection accuracy and sensitivity. Amongst the five chosen lightweight architectures, EfficientNet-based DR detection algorithms outperformed the other transfer learning models along with APTOS Blindness Detection dataset.

Abstract
Facility Location Problems (FLP) are complex combinatorial optimization problems whose general goal is to locate a set of facilities that serve a particular set of customers with minimum cost. Being NP-Hard problems, using exact methods to solve large instances of these problems can be seriously compromised by the high computational times required to obtain the optimal solution. To overcome this difficulty, a significant number of heuristic algorithms of various types have been proposed with the aim of finding good quality solutions in reasonable computational times. We propose a Scatter Search approach to solve effectively the Uncapacitated Facility Location Problem (UFLP). The algorithm was tested on the standard testbed for the UFLP obtained state-of-the-art results. Comparisons with current best-performing algorithms for the UFLP show that our algorithm exhibits excellent performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
We study principles and models for reasoning inductively about properties of distributed systems, based on programmed atomic handlers equipped with contracts. We present the Why3-do library, leveraging a state of the art software verifier for reasoning about distributed systems based on our models. A number of examples involving invariants containing existential and nested quantifiers (including Dijsktra's self-stabilizing systems) illustrate how the library promotes contract-based modular development, abstraction barriers, and automated proofs.

Abstract
Crowdsourced social media data has become popular for assessing cultural ecosystem services (CES). Nevertheless, social media data analyses in the context of CES can be time consuming and costly, particularly when based on the manual classification of images or texts shared by people. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use freely available deep learning models, i.e., Convolutional Neural Networks, for automating the classification of natural and human (e.g., species and human structures) elements relevant to CES from Flickr and Wikiloc images. Our approach is developed for Peneda-Ger <^>es (Portugal) and then applied to Sierra Nevada (Spain). For Peneda-Ger <^>es, image classification showed promising results (F1-score ca. 80%), highlighting a preference for aesthetics appreciation by social media users. In Sierra Nevada, even though model performance decreased, it was still satisfactory (F1-score ca. 60%), indicating a predominance of people's pursuit for cultural heritage and spiritual enrichment. Our study shows great potential from deep learning to assist in the automated classification of human-nature interactions and elements from social media content and, by extension, for supporting researchers and stakeholders to decode CES distributions, benefits, and values.

Abstract
Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are being used to provide on-demand wireless connectivity in extreme scenarios. Despite ongoing research, the optimization of UAVs' positions according to dynamic users' traffic demands remains challenging. We propose the Traffic-aware UAV Placement Algorithm (TUPA), which positions a UAV acting as FAP according to the users' traffic demands, in order to maximize the network utility. Using a DRL approach enables the FAP to autonomously learn and adapt to dynamic conditions and requirements of networking scenarios. Moreover, the proposed DRL methodology allows TUPA to generalize knowledge acquired during training to unknown combinations of users' positions and traffic demands, with no additional training. TUPA is trained and evaluated using network simulator ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the network utility, compared to baseline solutions, increasing the average network utility up to 4x in scenarios with heterogeneous traffic demands.