It took just minutes for a new Greek-made anti-drone system to show what it is capable of. On its first test run with a European Union patrol in the Red Sea a year ago, the Centauros system detected and swiftly brought down two aerial drones launched by Yemen's Houthis, who have been attacking merchant vessels in the busy shipping lane. Another two drones swiftly retreated: Centauros had jammed their electronics, said Kyriakos Enotiadis, electronics director at state-run Hellenic Aerospace Industry (HAI), which produces the anti-drone system.

Using agent-based social simulations can enhance our understanding of urban planning, public health, and economic forecasting. Realistic synthetic populations with numerous attributes strengthen these simulations. The Wasserstein Generative Adversarial Network, trained on census data like EU-SILC, can create robust synthetic populations. These methods, aided by external statistics or EU-SILC weights, generate spatial synthetic populations for agent-based models. The increased access to high-quality micro-data has sparked interest in synthetic populations, which preserve demographic profiles and analytical strength while ensuring privacy and preventing discrimination. This study uses national data from Finland and Greece for Helsinki and Thessaloniki to explore balanced spatial synthetic population generation. Results show challenges related to balancing data with or without aggregated statistics for the target population and the general under-representation of fringe profiles by deep generative methods. The latter can lead to discrimination in agent-based simulations.

In the field of modern robotics, robots are proving to be useful in tackling high-risk situations, such as navigating hazardous environments like burning buildings, earthquake-stricken areas, or patrolling crime-ridden streets, as well as exploring uncharted caves. These scenarios share similarities with maze exploration problems in terms of complexity. While several methods have been proposed for single-agent systems, ranging from potential fields to flood-fill methods, recent research endeavors have focused on creating methods tailored for multiple agents to enhance the quality and efficiency of maze coverage. The contribution of this paper is the implementation of established maze exploration methods and their comparison with a new cost-utility algorithm designed for multiple agents, which combines the existing methodologies to optimize exploration outcomes. Through a comprehensive and comparative analysis, this paper evaluates the performance of the new approach against the implemented baseline methods from the literature, highlighting its efficacy and potential advantages in various scenarios. The code and experimental results supporting this study are available in the following repository (https://github.com/manouslinard/multiagent-exploration/).

Abstract--This paper investigates citizens' counter-strategies to We further identified factors that increase the propensity for counter-strategies. These perceptions are linked to citizens' decisions about For instance, protesters may don uniform clothing, goggles and face masks I. Also, an increasing number of recommendations and tools emerge to obfuscate, Artificial Intelligence (AI) is a critical asset for law enforcement'confuse' or even'weaponize own data' against data collection agencies' (LEAs) efficiency and effectiveness, e.g., efforts [2], [5], [6]. Simultaneously, to avoid government entities collecting data about them [7] there are legitimate concerns about their usage, chief amongst and over half decided against products or services because them that algorithms can reinforce social inequalities (e.g., they worried about collection of personal information [8]. Such with respect to minority groups or genders), lead to faulty changes in mass-behaviors have operational consequences for decisions with dramatic real-life consequences and create LEAs [3], including training and long-term viability of AI inflexible, insensitive procedures that fail to take into account applications. In this paper, we investigate citizens' counter-strategies, This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 883596 (AIDA The information in this paper reflects only the authors' view and We further captured about current and future AI applications.

In this study, we propose an axiomatic system to define and quantify the precise memorization and in-context reasoning effects used by the large language model (LLM) for language generation. These effects are formulated as non-linear interactions between tokens/words encoded by the LLM. Specifically, the axiomatic system enables us to categorize the memorization effects into foundational memorization effects and chaotic memorization effects, and further classify in-context reasoning effects into enhanced inference patterns, eliminated inference patterns, and reversed inference patterns. Besides, the decomposed effects satisfy the sparsity property and the universal matching property, which mathematically guarantee that the LLM's confidence score can be faithfully decomposed into the memorization effects and in-context reasoning effects. Experiments show that the clear disentanglement of memorization effects and in-context reasoning effects enables a straightforward examination of detailed inference patterns encoded by LLMs.

Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to mitigate their devastating impact and safeguard Earth's natural resources. Robust Machine Learning methods combined with the abundance of high-resolution satellite imagery can provide accurate and timely mappings of the affected area in order to assess the scale of the event, identify the impacted assets and prioritize and allocate resources effectively for the proper restoration of the damaged region. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area). This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event, it contains information from Sentinel-2 and MODIS modalities with variable spatial and spectral resolution, and contains a large number of events where the corresponding burnt area ground truth has been annotated by domain experts. FLOGA covers the wider region of Greece, which is characterized by a Mediterranean landscape and climatic conditions. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task. We also compare the results to those obtained using standard specialized spectral indices for burnt area mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our benchmark results demonstrate the efficacy of the proposed technique in the automatic extraction of burnt areas, outperforming all other methods in terms of accuracy and robustness. Our dataset and code are publicly available at: https://github.com/Orion-AI-Lab/FLOGA.

What could be more delightful than a trip to Greece to meet Yanis Varoufakis, the charismatic leftwing firebrand who tried to stick it to the man, AKA the IMF, EU and entire global financial order? The mental imagery I have before the visit is roughly two parts Zorba the Greek to one part an episode of BBC series Holiday from the Jill Dando era: blue skies, blue sea, maybe some plate breaking in a jolly taverna. What I'm not expecting is a wall of flames rippling across a hillside next to the highway from the airport and a plume of black smoke billowing across the carriageway. Because even a modernist villa on a hillside on the island of Aegina – a fast ferry ride from the port of Piraeus and the summer bolthole of chic Athenians – is not the sanctuary from the modern world that it might once have been. The house is where Varoufakis and his wife, landscape artist Danae Stratou, live, year round since the pandemic, but in August 2023 at the end of a summer of heatwaves and extreme weather conditions across the world, it feels more than a little apocalyptic. The sun is a dim orange orb struggling to shine through a haze of smoke while a shower of fine ash falls invisibly from the sky.

In this paper, we extend our ensemble-based component model DEECo with the capability to use machine-learning and optimization heuristics in establishing and reconfiguration of autonomic component ensembles. We show how to capture these concepts on the model level and give an example of how such a model can be beneficially used for modeling access-control related problem in the Industry 4.0 settings. We argue that incorporating machine-learning and optimization heuristics is a key feature for modern smart systems which are to learn over the time and optimize their behavior at runtime to deal with uncertainty in their environment.