Real-time Arabic scene text detection using fully convolutional neural networks

The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed

A new algorithm for approaching Nash equilibrium and Kalai Smoridinsky solution

International audienceIn the present paper, a new formulation of Nash games is proposed for solving general multi-objective optimization problems. The main idea of this approach is to split the optimization variables which allow us to determine numerically the strategies between two players. The first player minimizes his cost function using the variables of the first table P, the second player, using the second table Q. The original contribution of this work concerns the construction of the two tables of allocations that lead to a Nash equilibrium on the Pareto front. On the other hand, we search P and Q that lead to a solution which is both a Nash equilibrium and a Kalai Smorodinsky solution. For this, we proposed and tried out successfully two algorithms which calculate P, Q and their associated Nash equilibrium, by using some extension of Normal Boundary Intersection approach (NBI)

A new algorithm for approaching Nash equilibrium and Kalai Smoridinsky solution

In the present paper, a new formulation of Nash games is proposed for solving general multi-objective optimization problems. The main idea of this approach is to split the optimization variables which allow us to determine numerically the strategies between two players. The first player minimizes his cost function using the variables of the first table P, the second player, using the second table Q. The original contribution of this work concerns the construction of the two tables of allocations that lead to a Nash equilibrium on the Pareto front. On the other hand, we search P and Q that lead to a solution which is both a Nash equilibrium and a Kalai Smorodinsky solution. For this, we proposed and tried out successfully two algorithms which calculate P, Q and their associated Nash equilibrium, by using some extension of Normal Boundary Intersection approach (NBI)

The Mean-CVaR Model for Portfolio Optimization Using a Multi-Objective Approach and the Kalai-Smorodinsky Solution

International audienceThe purpose of this work is to present a model for portfolio multi-optimization, in which distributions are compared on the basis of tow statistics: the expected value and the Conditional Value-at-Risk (CVaR), to solve such a problem many authors have developed several algorithms, in this work we propose to find the efficient boundary by using the Normal Boundary Intersection approach (NBI) based on our proposed hybrid method SASP, since the considered problem is multi-objective, then we find the Kalai-smorodinsky solution