Grey-Wolf Optimizer (GWO) and Salp-Swarm Algorithm (SSA) are among the recent metaheuristic algorithms that have shown considerable potential in solving practical problems. Nevertheless, these optimisation methods usually suffer from a poor performance, especially when dealing with multimodal and high-dimensional problems. To overcome drawbacks of these algorithms, a new population-based global optimisation algorithm inspired by GWO and SSA is proposed in this paper. The basic idea behind the proposed approach is to divide the population into two groups that adopt new position updating strategies. The movement method of the first group is inspired by the hunting mechanism used in GWO algorithm. On the other hand, the population of the second group moves into the search space using a new strategy based on the position updating method employed by the follower salps in SSA. The performances of the proposed algorithm, named GW-SSA, have been studied through 23 benchmark functions of different types and dimensions. The obtained results were statistically analysed using Wilcoxon signed-rank. Furthermore, GW-SSA algorithm has been applied to solve four real engineering design problems. The experimental results show the superior performance of GW-SSA compared to GWO, SSA and other recently proposed optimisation algorithms.

Sign language recognition (SLR) is a promising research field that aims to blur boundaries between Deaf and hearing people by creating a system that can transcribe signs into a written or vocal language. There is a growing body of literature that investigates the recognition of different sign languages, especially American sign language. So far, to the best of our knowledge, no study has considered the Algerian SLR. This is mainly due to the lack of datasets. To address this issue, we created the Alabib-65, the first Algerian Sign Language dataset. It consists of up to 6,238 Videos recorded from 41 native signers under realistic settings. This dataset is challenging due to a variety of reasons. First, there is a little inter-class variability. The 65 sign classes are similar in terms of hands’ configuration, placement, or movement and can share the same sub-parts. Second, there is a large intra-class variability. Furthermore, compared to other SL datasets that were collected from an indoor environment with a static and simple background, our videos were recorded from both indoor and outdoor environments with 22 backgrounds varying from simple to cluttered, and from static to dynamic. To underpin future research, we provided baseline results on this new dataset using state-of-the-art machine learning methods, namely: IDTFs with Fisher vector and SVM-classifier, VGG16-GRU, I3D, I3D-GRU, and I3D-GRU-Attention. The results show the validity and the challenges of our dataset.

Over the past few years, multicore systems have become increasingly powerful and thereby very useful in high-performance computing. However, many applications, such as some linear algebra algorithms, still cannot take full advantage of these systems. This is mainly due to the shortage of optimization techniques dealing with irregular control structures. In particular, the well-known polyhedral model fails to optimize loop nests whose bounds and/or array references are not affine functions. This is more likely to occur when handling sparse matrices in their packed formats. In this article, we propose using 2d-packed layouts and simple affine transformations to enable optimization of triangular and banded matrix operations. The benefit of our proposal is shown through an experimental study over a set of linear algebra benchmarks.

The polyhedral model is a well-known compiler optimization framework for the analysis and transformation of affine loop nests. We present a new method to solve a difficult geometric operation that is raised by this model: the integer affine transformation of parametric ℤ-polytopes. The result of such a transformation is given by a worst-case exponential union of ℤ-polytopes. We also propose a polynomial algorithm (for fixed dimension), to count points in arbitrary unions of a fixed number of parametric ℤ-polytopes. We implemented these algorithms and compared them to other existing algorithms, for a set of applications to loop nest analysis and optimization.

In this paper, we propose a novel SVD-based RGB color image watermarking, in which the watermark is assumed to be an RGB color image whose detection and extraction are performed in a blind manner. The performance of the proposed watermarking method is investigated by measuring its imperceptible and robust capabilities. For the imperceptible capability, a quantitative index, Peek Signal-to-Noise Ratio (PSNR), is employed to evaluate the difference between an original image and its corresponding watermarked image. For the robust capability, the Correlation Coefficient (CC) is also used to evaluate the similarity between the original watermark and the extracted one. Experimental results indicate that our technique is robust and produces high-quality watermarked images.

Memory size reduction and memory accesses optimization are crucial issues for embedded systems. In the context of affine programs, these two challenges are classically tackled by array linearization, cache access optimization and memory size computation. Their formalization in the polyhedral model reduce to solving the following problem: count the number of solutions of a Presburger formula. In this paper we propose a novel algorithm that answers this question. We solve the Presburger formula whose solution is a union of parametric Z-polytopes and we propose an algorithm to count points in such a union of parametric Z-polytopes. These algorithms were implemented and we compare them to other existing methods.

Many optimization techniques, including several targeted specifically at embedded systems, depend on the ability to calculate the number of elements that satisfy certain conditions. If these conditions can be represented by linear constraints, then such problems are equivalent to counting the number of integer points in (possibly) parametric polytopes. It is well known that this parametric count can be represented by a set of Ehrhart polynomials. Previously, interpolation was used to obtain these polynomials, but this technique has several disadvantages. Its worst-case computation time for a single Ehrhart polynomial is exponential in the input size, even for fixed dimensions. The worst-case size of such an Ehrhart polynomial (measured in bits needed to represent the polynomial) is also exponential in the input size. Under certain conditions this technique even fails to produce a solution.Our main contribution is a novel method for calculating Ehrhart polynomials analytically. It extends an existing method, based on Barvinok's decomposition, for counting the number of integer points in a non-parametric polytope. Our technique always produces a solution and computes polynomially-sized Ehrhart polynomials in polynomial time (for fixed dimensions).