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A challenge of the computer vision community is to understand the semantics of an image that will allow for higher quality image generation based on existing high-level features and better analysis of (semi-) labeled datasets. Categorical labels aggregate a huge amount of information into a binary value which conceals valuable high-level concepts from the Machine Learning models. Towards addressing this challenge, this paper introduces a method, called Occlusion-based Latent Representations (OLR), for converting image labels to meaningful representations that capture a significant amount of data semantics. Besides being informationrich, these representations compose a disentangled low-dimensional latent space where each image label is encoded into a separate vector. We evaluate the quality of these representations in a series of experiments whose results suggest that the proposed model can capture data concepts and discover data interrelations. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

As the Internet of Things (IoT) penetrates dierent domains and application areas, it has recently entered also the world of robotics. Robotics constitutes a modern and fast-evolving technology, increasingly being used in industrial, commercial and domestic settings. IoT, together with the Web of Things (WoT) could provide many benefits to robotic systems. Some of the benefits of IoT in robotics have been discussed in related work. This paper moves one step further, studying the actual current use of IoT in robotics, through various real-world examples encountered through a bibliographic research. The paper also examines the potential ofWoT, together with robotic systems, investigating which concepts, characteristics, architectures, hardware, software and communication methods of IoT are used in existing robotic systems, which sensors and actions are incorporated in IoT-based robots, as well as in which application areas. Finally, the current application of WoT in robotics is examined and discussed. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

Estimating the height of buildings and vegetation in single aerial images is a challenging 12 problem. A task-focused Deep Learning (DL) model which combines architectural features from 13 successful DL models (U-NET and Residual Networks) and learns the mapping from single aerial 14 imagery to a normalized Digital Surface Model (nDSM) is proposed. The model is trained on aerial 15 images whose corresponding DSM and Digital Terrain Maps (DTM) are available and is then used 16 to infer the nDSM of images with no elevation information. The model is evaluated with a dataset 17 covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR 18 dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses 19 other state-of-the-art DL approaches by a large margin. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

A weakly supervised change detection method is proposed for remotely sensed multi-temporal images, by utilizing a Siamese neural network architecture. The architecture of the Siamese network is a combination of two multi-filter multi-scale deep convolutional neural networks (MFMS DCNN). Initially, the Siamese network is trained by utilizing the image-level semantic labels of the image pairs in the dataset. The features of the image pairs are obtained using the trained network to generate the difference image (DI). Then, a combination of the PCA and the K-means algorithms has been used to produce the change map for the pair of images. Experiments were carried out using two remotely sensed image datasets. The weakly supervised method proposed in this paper offers better results in comparison to both weakly supervised- and unsupervised-based state-of-the-art models and techniques. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

The ability to automatically count animals is important to design appropriate environmental policies and to monitor their populations in relation to biodiversity and maintain balance among species. Out of all living mammals on Earth, 60% are livestock, 36% humans, and only 4% are animals that live in the wild. In a relatively short period, development of human civilization caused a loss of 83% of wildlife and 50% of plants. The rate of species extinction is accelerating. Traditional wildlife surveys provide rough population estimates. However, emerging technologies, such as aerial photography, allow to perform large-scale surveys in a short period of time with high accuracy. In this paper, we propose the use of computer vision, through deep learning (DL) architecture, together with aerial photography and density maps, to count the population of Steller sea lions and African elephants with high precision. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

Blockchain is an emerging digital technology allowing ubiquitous financial transactions among distributed untrusted parties, without the need of intermediaries such as banks. This chapter examines the impact of blockchain technology in agriculture and food supply chain, presents existing ongoing projects and initiatives, and discusses overall implications, challenges and potential, with a critical view over the maturity of these projects. Our findings indicate that blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues, but many barriers and challenges still exist, which hinder its wider popularity among farmers and systems. These challenges involve technical aspects, education, policies and regulatory frameworks. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

Intensive livestock production might have a negative environmental impact, by producing large amounts of animal manure, which, if not properly managed, can contaminate nearby water bodies with nutrient excess. However, if animal manure is exported to nearby crop fields, to be used as organic fertilizer, pollution can be mitigated. It is a single-objective optimization problem, in regards to finding the best solution for the logistics process of satisfying nutrient needs of crops by means of livestock manure. This paper proposes three different approaches to solve the problem: a centralized optimal algorithm (COA), a decentralized nature-inspired cooperative technique, based on the foraging behaviour of ants (AIA), as well as a naive neighbour-based method (NBS), which constitutes the existing practice used today in an ad hoc, uncoordinated manner in Catalonia. Results show that the COA approach is 8.5% more efficient than the AIA. However, the AIA approach is fairer to the farmers and more balanced in terms of average transportation distances that need to be covered by each livestock farmer, while it is 1.07 times more efficient than the NBS. Our work constitutes the first application of a decentralized AIA to this interesting real-world problem, in a domain where swarm intelligence methods are still under-exploited. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

Halloumi is a semi-hard cheese produced in Cyprus for centuries and its popularity has significantly risen over the past years. High-throughput sequencing (HTS) was applied in the present research to characterize traditional Cyprus Halloumi bacterial diversity. Eighteen samples made by different milk mixtures and produced in different areas of the country were analyzed, to reveal that Halloumi's microbiome was mainly comprised by lactic acid bacteria (LAB), including Lactobacillus, Leuconostoc, and Pediococcus, as well as halophilic bacteria, such as Marinilactibacillus and Halomonas. Additionally, spore forming bacteria and spoilage bacteria, were also detected. Halloumi produced with the "traditional" method, had significantly richer bacterial diversity compared to Halloumi produced with the "industrial" method. Variations detected among the bacterial communities highlight the contribution of the initial microbiome that existed in milk and survived pasteurization, as well as factors associated with Halloumi manufacturing conditions, in the final microbiota composition shaping. Identification and characterization of Halloumi microbiome provides an additional, useful tool to characterize its typicity and probably safeguard it from fraud products that may appear in the market. Also, it may assist producers to further improve its quality and guarantee consumers' safety. ; This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

The Web of Things (WoT) enables information gathered by sensors deployed in urban environments to be easily shared utilizing open Web standards and semantic technologies, creating easier integration with other Web-based information, towards advanced knowledge. Besides WoT, an essential aspect of understanding dynamic urban systems is artificial intelligence (AI). Via AI, data produced byWoT-enabled sensory observations can be analyzed and transformed into meaningful information, which describes and predicts current and future situations in time and space. This paper examines the impact of WoT and AI in smart cities, considering a real-world problem, the one of predicting parking availability. Traffic cameras are used as WoT sensors, together with weather forecasting Web services. Machine learning (ML) is employed for AI analysis, using predictive models based on neural networks and random forests. The performance of the ML models for the prediction of parking occupancy is better than the state of the art work in the problem under study, scoring an MSE of 7.18 at a time horizon of 60 minutes. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

Contact Tracing (CT) is an effective mitigation measure against the Covid-19 pandemic that is typically performed manually. Its technological counterpart, i.e., Digital Contact Tracing (DCT), has emerged as potential, yet controversial due to privacy concerns, complementary tool to identify exposed individuals automatically. Among DCT solutions, privacy-preserving DCT mobile apps that rely on a Bluetooth-based protocol recently developed jointly by Google and Apple are becoming increasingly popular and have already been deployed in 67 territories around the world. Recent studies indicate that such apps are promising in controlling the spread of infections, yet their adoption is still low. This work presents CovTracer-EN, the national privacy-preserving DCT app in Cyprus that triggers Exposure Notifications (EN) to users that had contact with another infected user who shared this information anonymously with his/her consent. Performance evaluation results and lessons learned aim to offer useful insights and practical guidelines to public health authorities that consider deploying DCT apps for reducing the impact of Covid-19 and future pandemics. ; The CovTracer-EN project has received funding from the European Union's Emergency Support Instrument programme under grant agreement No. CYPRUS-LC-01591248. ©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. P. Isaia, C. Laoudias, A. Kamilaris and C. G. Panayiotou, "CovTracer-EN: The Journey of Covid-19 Digital Contact Tracing in Cyprus," 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2021, pp. 1-8, doi:10.1109/IPIN51156.2021.9662621.

Presentation slides for the publication P. Isaia, C. Laoudias, A. Kamilaris and C. G. Panayiotou, "CovTracer-EN: The Journey of Covid-19 Digital Contact Tracing in Cyprus," 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2021, pp. 1-8, doi:10.1109/IPIN51156.2021.9662621. ; The CovTracer-EN project has received funding from the European Union's Emergency Support Instrument programme under grant agreement No. CYPRUS-LC-01591248.