Abstract
The deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character. The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial. Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations.

Abstract
Nowadays an important issue, for an organization is to be able to implement relevant anti-bribery risk management systems with mandatory laws. Managers strive to reach an equilibrium between a pure mandatory rule oriented organization and people freedom of choice to mitigate bribery on organization. The problem is how to develop and manage efficiently anti-bribery system in an organization without putting at risk its day by day operation. They are concerned how to balance between deep control and flexible way of people work on organization. The purpose of this document is to introduce a decision-making way of defining a context to establish an anti-bribery risk management system in accordance with the best practices. To address this matter, we will support our work in a theoretical framework for the analysis of human work and introduce anti-bribery as non-functional requirement (generic qualities of services) of organization information systems.

Abstract

Abstract
In this poster, we are addressing the topic of "system's evaluation" from the point of view of assessing the usability of a gamified experience with 20 teenagers aged 15-17 years. The currently tested experience was ideally designed for children 9-10 years. In order to adapt the application to teenagers, we tested it with 20 targeted users. In this poster, we share the results and encourage a discussion among the researchers about how to adapt the gamified experience designed for children to a teenage audience.

Abstract
Charging performance of Plug-in Hybrid Electric Vehicles (PHEVs) under various charging approaches brings new challenges for the distribution grid, such as feeder overloading and increment of loss. In this paper, the impact of charging PHEVs on the load profile is evaluated considering Demand Response (DR) programs in order to minimize the impact of charging PHEVs on load profile. In the proposed DR strategy, all consumers can determine and control their loads. Also, the proposed DR strategy can satisfy the function of reducing peak with regard to different PHEV penetration, while preserving consumer comfort levels. For performing DR programs, time of use and real time pricing models are used to evaluate the impacts of charging of PHEVs on distribution system operations. A 33-node test feeder has been studied to assess different penetration schemes of PHEVs including 11.3%, 35% and 45% PHEV penetration in residential loads. Moreover, uncertainties associated with PHEV charging performances are handled with Monte Carlo simulation. Results validate that the amount of peak loss, and Peak to Average Ratio (PAR) increase owing to PHEVs penetration growth. Also, the results illustrate that scheduling for the operation of PHEVs with DR program reduces the distribution losses and PAR.

Abstract
The multirotor UAVs are being integrated into a wide range of application scenarios due to maneuverability in 3D, versatility and reasonable payload of sensors. One of the application scenarios is the inspection of structures where the human intervention is difficult or unsafe and the UAV can provide an improvement of the collected data. At the same time introduce challenges due to low altitude missions and also the fact of being manually operated without line of sight. In order to overcome these issues, this paper presents a LiDAR-based realtime collision avoidance algorithm, denoted by Escape Elliptical Search Point with the ability to be integrated into autonomous and manned modes of operation. The algorithm was validated in a simulation environment developed in Gazebo and also in a mixed environment composed by a real robot in an outdoor scenario and simulated obstacle and LiDAR.