Research on multi-robot systems has flourished over the last decades with a number of theoretical and experimental results, based on the idea that proper coordination of many simple robots can lead to the fulfillment of arbitrarily complex tasks in a robust (to single robot failures) and highly flexible way. Autonomous search and rescue, firefighting, exploration and intervention in dangerous or inaccessible areas are some of the most promising multi-robot applications. An active research direction is that of decentralized formation control of multiple mobile robots based on only local (onboard) sensing and communication, with the aim of deploying highly autonomous robot teams in `non-trivial' environments (e.g., inside buildings, underwater, underground, or even in deep space) where centralized measuring/communication facilities (such as GPS) are not available. These effort are, therefore, aimed at increasing the autonomy and decision-making of a robot group for accomplishing missions in complex situations (e.g., outdoor in presence of obstacles, disturbances, limited sensing/communication, and so forth). At the same time, more and more attention is focused on the topic of human/multi-robot interfacing, i.e., how to interface a human operator with a team of multiple robot for sharing the load of autonomous decision-making and mission control. Nevertheless, it is truly challenging to design effective multi-robot teleoperation systems. First, the human operator should be able to single-handedly control the action of the robotic team in a natural and intuitive way. Second, the robotic team should be able to effectively and exhaustively provide the human with the large amount of feedback information coming from the remote environment. This topic is a very promising direction since human assistance is most often required for a successful completion of a mission for several reasons: (i) technological ones, as robot autonomy is still quite limited when needing to deal with uncertain and unstructured environments, and the human superior cognitive abilities are crucial for taking the right decisions assessing the situation, (ii) task-related ones, since in some missions the human operator needs to be interfaced with the robot team for taking part to the task itself (i.e., organizing a mapping/exploration mission for selecting areas of interest), (iii) safety ones, since in most cases the current legislation requires presence of a human operator for supervising a mission and taking responsibility of any unexpected outcome. Building upon the consolidated experience of the applicant and his team in the topics of multi-robot coordination and decision-making, AI for robotics, shared control and human-robot interaction, the goal of MULTISHARED is to significantly advance the state-of-the-art in multi-robot autonomy and human-multi-robot interaction for allowing a human operator to intuitively control the coordinated motion of multi-UAV group navigating in remote environments, with a strong emphasis on the division of roles between multi-robot autonomy (in controlling its motion/configuration and online decision-making) and human intervention/guidance for providing high-level commands to the group while being most aware of the group status via VR and haptics technology.

Governors are essential components of an operating system that manage energy consumption. They are in charge of increasing or decreasing the frequency at which a CPU core operates to achieve energy savings while preserving performance. Governors rely on the scheduler that keeps track of each thread running on each core and maintains metrics on the usage of each core. The metrics maintained by the scheduler are periodically used by the governor to adjust the frequency. We uncover a limitation of governors in the context of virtualization systems (angular stone to Cloud infrastructures). Concretely, we discovered that since the scheduler incorrectly accounts virtual machine (VMs) idle times due to their black box nature. Consequently, governors tend to decide to switch to an operating frequency higher than what should be, leading to energy waste. The goal of sGov is to provide a generic and non-intrusive mechanism to improve governors in such scenarios so that Cloud providers can directly benefit from it and achieve real energy savings.

The Internet of Things (IoT) will influence the majority of our daily life’s infrastructure. The IoT is still only in its early stages, but the number of internet-enabled devices is beginning to explode (likely to hit 50 billion by 2020). While efficiency and diffusion of IoT are increasing, security threats are becoming a far-reaching problem. Here we are particularly concentrating on ensuring the security of IoT nodes against malware threats, which may seriously disrupt daily life and economic activity or even reveal privacy critical data of users. As state-of-the-art software monitoring techniques (static or dynamic) can still be circumvented by sophisticated attackers, we propose an automated hardware malware analysis (AHMA) framework that is non-intrusive and cannot easily be controlled or hidden by the malware attacker. AHMA uses side-channel information of the underlying hardware IoT device to detect if a device is infected by malware or in its typical running state. The framework includes supervised and unsupervised machine learning techniques to classify already known (mutated) malware as well as unknown malware. As side-channel sources we will first concentrate on power consumption and/or electromagnetic emanation which is captured after a teardown of the device. In this proposal we cover three real-world case studies: 1) Dedicated IoT devices This first case study covers home routers and dedicated IoT devices which are designed to make our daily life easier and simpler. These devices often do not have any user interface and typically do not run standard operating systems that support the commonly used security tools (e.g. antivirus, firewall) or just do not have enough resources. In this study we will rely on published malware samples as open-source and/or collaborate with researchers collecting IoT malware samples through honey pots. One of the most predominant Distributed Denial of Service (DDoS) IoT botnet in recent times is Mirai, which source code has been published as open- source. At its peak, Mirai infected 4000 IoT devices per hour and in the beginning of 2017 it was estimated to have more than half a million infected active IoT devices. 2) Connected cars Early in 2018 another new variant of Mirai, called Mirai Okiru, targeted ARC-based IoT devices, which are widely used also in automotive applications. Therefore also devices inside connected cars could be enslaved to perform DDoS attacks. Moreover, malware may directly attack the automotive system. In this context the motivation of attackers could be to breach drivers privacy, ransom, theft, sabotage, harm people and properties, disrupt transportation, and/or fun and publicity. Any network interface, physical or wireless, could be exploited by malware to infect a vehicle. Given the large interconnectivity and multiple different architectures in this context of connected cars, additional to power consumption/ electromagnetic emanation we may observe new side-channel sources to detect malware which have not been considered and studied before. 3) Mobile phones/devices In the last decade, Android became the most popular operating environment for smart devices, with almost 85% of the market share in the first quarter of 2017. This popularity makes it a very attractive target for malware attackers. However, its open application market and lax review mechanism have led to a rapid proliferation of Android malware as well as security threats. In this case study, we aim at detecting recent malware samples on modern devices such as Nexus = 4 or Galaxy = S III using power consumption/ electromagnetic emanation. Our novel framework is of high importance and impact for industries, and thus for users benefitting from increasing protection.

Following the growth of multisource data with high spatial, spectral, and temporal resolutions, the problem of complex image information mining in remote sensing of environment becomes a great challenge, with many potential applications raising. However, there is no or only a few methodological frameworks for dealing with data with multiple spatial and temporal scales: recognition methods are most often straight applications of standard classification and modelisation methods. Besides, dealing with spatial and temporal neighborhood, with various kinds of data, is expected to improve significantly resulting performances. The goal of the ASTERIX project (Spatio-Temporal Analysis by Recognition within Complex Images for Remote Sensing of Environment) and its originality is to bring new methods, algorithms, softwares in the field of image analysis and machine learning in order to support recognition within complex image, by explicitly dealing with the specificity of remote sensing complex images. In this context, main challenges are related to high dimensionality, heterogeneity, volume and spatio-temporal behaviour of images. Besides methodological achievements supporting the development of the state-of-the-art in image processing and machine learning in the context of recognition within complex images, expected results from the ASTERIX project consist in a set of concrete solutions to crucial problems in remote sensing of environment, and especially in two environment: coastal and montains. More precisely, applications considered are related to the dynamic of environmental objects which help to understand coastal evolution, the dynamic of ash tree colonization in an agricultural mountain landscape, and the dynamic of geological process.

The pathological processes leading to Alzheimer’s (AD) and Parkinson’s (PD) diseases start decades before the onset of the typical clinical symptoms. However, current diagnosis comes quite late in the course of the disease, while evidence underlines the multiple benefits that would be associated with earlier diagnosis. Notably, early care can delay the onset of dementia, therefore reducing the overall prevalence, and current therapeutics targets require early treatment to prove their efficacy. An outstanding challenge for clinical neurosciences is therefore to provide reliable, non-invasive, affordable and easy-to-track biomarkers able to improve both the early detection and the monitoring of neurodegenerative diseases that can be applied at an individual patient level. It is well acknowledged that AD and PD display a progressive multifactorial disruption of cerebral networks, all along the course of the diseases, which is highly related to the clinical phenotype. In the search for those biomarkers, the connectome neuroimaging technology has represented a helpful technique to characterize the brain changes, more specifically structural and functional brain networks. However, prior studies have largely focused on the comparison between patients suffering from AD or PD versus healthy subjects. As a result, the relevance of the reported alterations in brain network may be limited due to a lack of specificity. Indeed, the extracted features that are sensitive to AD or PD may well reflect common neurodegenerative processes, therefore lacking specificity for the disease-related physiopathology at the individual level. A recent framework called Graph signal Processing (GSP) is promising to shed new light on the complex interplay between brain function and structure, by jointly analyzing functional activity and the underlying structural connectome. For the first time, in sharp contrast with the traditional approach emphasizing single-modality data and clinical assessment, our proposal will extend GSP to the development of innovative and more sensitive features of AD and PD able not only to identify modifications associated with each disease but also with each disease stage, based on the cerebral functional-structural coupling. In the NODAL project, we will develop a new multimodal and multi-stage approach using innovative machine learning methods, adapted for GSP-based features, to provide non-invasive, reliable and easy-to-track candidate biomarkers for AD and PD. We will first apply this approach on two large patients’ cohorts from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Parkinson’s Progression Markers Initiative (PPMI). Then, we will assess the effectiveness of candidate’s disease-specific biomarkers on a new dedicated local multimodal cohort including patients with and without cognitive impairment, at various stages of the diseases. Based on these new methodological developments, we hypothesize that the NODAL project may yield the estimation of specific and prognostic biomarkers of AD and PD, suitable for both diagnosis and differentiation among diseases’ stages, i.e. disease monitoring.