Providing adapted e-health services, applications, and platforms responds to a growing need for medical institutions like hospitals or even homes

For improving e-health services, we propose a context-aware framework to monitor the activities of daily living of dependent persons. We define a strategy for generating long-term realistic scenarios and a framework containing an adaptive monitoring algorithm based on three approaches for optimizing resource usage. The used approaches provide a deep knowledge about the person's context by considering: the person's profile, the activities and the relationships between activities. We evaluate the performances of our framework and show its adaptability and significant reduction in network, energy and processing usage over a traditional monitoring implementation.

Extracting information from geographic images and text is crucial for autonomous vehicles to determine in advance the best cell stations to connect to along their future path. Multiple artificial neural network models can address this challenge; however, there is no definitive guidance on the selection of an appropriate model for such use cases. Therefore, we experimented two architectures to solve such a task: a first architecture with chained models where each model in the chain addresses a sub-task of the task; and a second architecture with a single model that addresses the whole task. Our results showed that these two architectures achieved the same level performance with a root mean square error (RMSE) of 0.055 and 0.056; The findings further revealed that when the task can be decomposed into sub-tasks, the chain architecture exhibits a twelve-fold increase in training speed compared to the composite model. Nevertheless, the composite model significantly alleviates the burden of data labeling.

Cooperation is fundamental for society's viability, as it enables the emergence of structure within heterogeneous groups that seek collective well-being. However, individuals are inclined to defect in order to benefit from the group's cooperation without contributing the associated costs, thus leading to unfair situations. In game theory, social dilemmas entail this dichotomy between individual interest and collective outcome. The most dominant approach to multi-agent cooperation is the utilitarian welfare which can produce efficient highly inequitable outcomes. This paper proposes a novel framework to foster fairer cooperation by replacing the standard utilitarian objective with Proportional Fairness. We introduce a fair altruistic utility for each agent, defined on the individual log-payoff space and derive the analytical conditions required to ensure cooperation in classic social dilemmas. We then extend this framework to sequential settings by defining a Fair Markov Game and deriving novel fair Actor-Critic algorithms to learn fair policies. Finally, we evaluate our method in various social dilemma environments.

In this paper, we present a new framework that links the two worlds of wired and cellular users sharing systems. The approach is to propose an easy gateway that enables the use of cellular networks based services by wireline users and applications. The idea is to use a mobile terminal or wireless equipment for sharing cellular services, available thanks to its cellular network, to other users that use the wireline Internet. The software application acts as a gateway between the cellular and the wired network; it is responsible for supporting the services provided by the wireless network and make them accessible and usable, in a standard and easy way, by anyone on the wireline network. The gateway software can be integrated easily on any complex architecture since it can interact with any cellular modem. The paper describes an implementation prototype where some examples of services, such as the ability of using messaging services and calls streaming, are experimented. The proposed platform combines different standards to guarantee the use of our gateway in heterogeneous environments.