Software systems, and in particular, Object-Oriented systems are models of the real world that manipulate representations of its entities through models of its processes. Software systems however suffer of two important issues: first, the world changes continuously and imposes to constantly revise the model itself; second, because details and multiple abstraction levels result in a high level of complexity, completely analyzing real software systems is impractical. For example, the Windows operating system consists of more than 60 millions lines of code (500,000 pages printeddouble-face, about 16 times the Encyclopedia Universalis). Different programming paradigms have been invented to cope with changes: late-binding, the cornerstone of object-oriented programming, is a typical illustration. Nowadays large object-oriented applications suffer from evolution problems and often require to be restructured or remodularized for example to fit memory constraints or different business cases. Whereas software (re-)modularization is a relatively old research field in the context of C or Cobol, it is still really important and requires innovative approaches to deal with the complexity of modern systems developed in OOP languages. The mismatch between current practices and the challenges software companies are facing is due to: (1) the complexity of the manipulated concepts (subsystems, packages, classes, class hierarchies, late-binding, various import relationships....) as well as (2) the use of one type of algorithms (clustering) and one kind of system representation. CUTTER addresses these issues by recognizing and accepting the complexity and multi-faceted nature of software remodularization, through developing, combining, and evaluating new techniques for analyzing and modularizing code. The innovation of CUTTER is to: - combine different package decomposition techniques (graph decomposition, Formal Concept Analysis, program visualization, etc.); - support different levels of abstractions (system, packages, classes) ; - be directed by the quality of the resulting remodularisation and take into account expert input. Several package decomposition techniques that will be used are: algorithmic approaches for facing to graph decomposition complexity (polynomial-time approximation algorithms, heuristics, and exacts methods), Formal Concept Analysis (FCA) algorithms, statistical clustering algorithms, and visualization based algorithms. Each of these techniques has already been used in software (re-) modularization, however, taken individually they failed to give satisfaction, offering results that are difficult to understand by software maintainers, or do not fit experts' views of the systems. The results will include: (i) a better understanding of how to best model software systems and modules for software remodularization, considering the intended users (software maintainers); (ii) a better understanding of the strengths and weaknesses of graph algorithms, FCA, clustering algorithms, and visualizations as package decomposition techniques; (iii) new package decomposition methods combining the four techniques; (iv) new class restructuration techniques and (v) practical tools implementing these results. Project management will be led by the team RMoD-INRIA. CUTTER will use an iterative and incremental approach allowing the two teams RMoD-INRIA and D’OC/APR-LIRMM to integrate other's advancements and profit from them. We plan to hold three workshops per year with all partners. Communication will be fostered by the use of concrete case studies to learn from, assess and steer the project results. To share results and analyses developed through the project, CUTTER will use a common source code model and format (a meta-model). The leader of each team knows each other personally and they enjoy working together, so the interaction will be dealt with efficiently.

The main objective of this project is to develop an innovative strategy based on models for helping decision-making process during surgical planning in Deep Brain Stimulation (DBS) by simulating the surgical procedure. Two types of models will be made available to the surgeon: patient specific models and generic models. The project will develop methods for 1) building these models and 2) automatically computing optimal electrodes trajectories from these models taking into account possible simulated deformations occurring during surgery. Parkinson Disease (PD) prevalence is about 1% in adults over 60 years old. High frequency Deep Brain Stimulation (DBS) has been demonstrated as an efficient minimally invasive surgical treatment for treating Parkinson or motor related diseases and recently severe neuropsychological diseases. It was originally developed in France by Pr. Benabid (Grenoble). As demonstrated in the literature, the quality of the clinical improvement, as well as the existence of motor, neuropsychological or psychiatric post operative side effects strongly depend on the location of the electrode, and therefore on the quality of the surgical planning. We aim at developing a new approach including data and methods for increasing the accuracy and precision of DBS surgical planning when defining the electrode trajectory. To the data already used by the surgeon, we will add vessels and cortical sulci extracted from MR images, and fiber tracts computed from Diffusion Tensor MRI (DTI). To the information already used by the surgeon, we will add a histological atlas aiming at modelling anatomical knowledge and anatomo-clinical atlases aiming at modelling the knowledge related to the surgical experience. The histological atlas, we developed, has been recognized by the international community as one of the best available for DBS. The anatomo-clinical atlases will gather the locations of the electrodes computed from post operative CT images and registered to a common anatomical referential coordinate system consisted in a MR template, and the pre and post operative clinical scores. We will develop an adapted non linear image registration methods for allowing accurate submillimeter transformation of information from atlas to patient data. We will develop a method for automatic computation of the possible electrode trajectories, taken into account the rules expressed by the surgeons, the knowledge available in the atlas, and the patient specific data and information. For better accuracy and precision, we will also simulate the possible deformations of the final electrode and anatomical structures during surgery and integrate this simulation into the trajectory computation. Additionally, an important effort will be assigned on the validation of the proposed tools. We will validate the proposed deformation models with rigorous studies on realistic physical phantoms. We will validate the image registration method on retrospective clinical data sets. We will quantitatively and qualitatively validate the computation of optimal trajectories in a large population of retrospective clinical data sets available in the two clinical centers associated to this project. The main expected output of this project will consist in a DBS planning software made available to the neurosurgical and neurological communities, as a software suite compatible with the French open source medical imaging software platforms. This project will be able to address the issue of accurate DBS targeting from an innovative approach. It aims at putting the French research in the front of the scene concerning surgical planning in DBS, from the technological point of view.

As proposed by initiatives in Europe and worldwide, enabling an open, general-purpose, and sustainable large-scale shared experimental facility will foster the emergence of the Future Internet. There is an increasing demand among researchers and production system architects to federate testbed resources from multiple autonomous organizations into a seamless/ubiquitous resource pool, thereby giving users standard interfaces for accessing the widely distributed and diverse collection of resources they need to conduct their experiments. The F-Lab project builds on a leading prototype for such a facility: the OneLab federation of testbeds. OneLab pioneered the concept of testbed federation, providing a federation model that has been proven through a durable interconnection between its flagship testbed PlanetLab Europe (PLE) and the global PlanetLab infrastructure, mutualising over five hundred sites around the world. One key objective of F-Lab is to further develop an understanding of what it means for autonomous organizations operating heterogeneous testbeds to federate their computation, storage and network resources, including defining terminology, establishing universal design principles, and identifying candidate federation strategies. On the operational side, F-Lab will enhance OneLab with the contribution of the unique sensor network testbeds from SensLAB, and LTE based cellular systems. In doing so, F-Lab continues the expansion of OneLab’s capabilities through federation with an established set of heterogeneous testbeds with high international visibility and value for users, developing the federation concept in the process, and playing a major role in the federation of national and international testbeds. F-Lab will also develop tools to conduct end-to-end experiments using the OneLab facility enriched with SensLAB and LTE. F-Lab is a “platform” type of project, fully compliant with the ANR VERSO call’s definition of a platform (technically challenging, open, shared and sustainable). It already involves a large and vibrant community of researchers and users; it is open and its operation is sustainable. It helps to structure the community with strong connections at the international level and develops common best practices in testbed operation, management, and experiments. It is fully aligned with the call in the areas of Future Internet, sensor and cellular networks, federation of networks and testbeds. F-Lab is a unique opportunity for the French community to play a stronger role in the design of federation systems, a topic of growing interest; for the SensLAB testbed to reach an international visibility and use; and for pioneering testbeds on LTE technology.

Access and metropolitan networks are much more limited in capacity than core networks. While the latter operate in over-provisioning mode, access and metropolitan networks may experience high overload due to evolution of the traffic or failures. In wired networks, some failures (but not all) are handled by rerouting the traffic through a backup network already in place. In developed countries, backup networks are adopted wherever possible (note that this is generally not the case for the links between end users and their local DSLAM). Such a redundant strategy may not be possible in emerging countries because of cost issues. When dedicated backup networks are not available, some operators use their 3G infrastructure to recover some specific failures; although such an alternative helps avoid full network outage, it is a costly solution. Furthermore, availability of 3G coverage is still mainly concentrated in metropolitan zones. When no backup networks are available, it would be interesting to deploy, for a limited time corresponding to the period of the problem (i.e., failure or traffic overload), a *substitution network* to help the base network keep providing services to users. In the RESCUE project, we will *investigate both the underlying mechanisms and the deployment of a substitution network composed of a fleet of dirigible wireless mobile routers*. Unlike many projects and other scientific works that consider mobility as a drawback, in RESCUE we use the controlled mobility of the substitution network to help the base network reduce contention or to create an alternative network in case of failure. The advantages of an on-the-fly substitution network are manifold: 1) Reusability and cost reduction. Substitution resources are only used when needed compared to a permanent backup network which may be not used very often. Furthermore, substitution nodes can be redeployed at different parts on the network at different times. 2) Deployability. Substitution network may help some parts of the base network where there is no redundancy. It is important to underline that deploying substitution networks is not orthogonal to having traditional backup networks. Instead, it should be seen as complementary. 3) Adaptability. The topology of the substitution network may be adapted to the context, i.e. to the environment as to the on-going traffic so that an efficient delivery service may be provided. Note that a fundamental aspect of the project is the *decision* strategy, as deploying a substitution network has some counterpart cost. By decision, we mean the judgment concerning the right time a substitution should be deployed (or undeployed when the system estimates that the substitution network is no further needed). To this end, the RESCUE project addresses both the *theoretical* and the *practical* aspects of the deployment of a substitution network. From a theoretical point of view, we will propose a two-tiered architecture including the base network and the substitution network. This architecture will describe the deployment procedures of the mobile routing devices, the communication stack, the protocols, and the services. The design of this architecture will take into account some constraints such as quality of service and energy consumption (since mobile devices are autonomous), as we want the substitution network to provide more than a best effort service. From a practical point of view, we will provide a proof of concept, the architecture linked to this concept, and the necessary tools (e.g., traffic monitoring, protocols) to validate the concept and mechanisms of on-the-fly substitution networks. At last but not least, we will validate the proposed system both in laboratory testbeds and in a real-usage scenario.