The increasing volume of multimedia news content ranging from written articles and press websites to televised video segments and radio broadcasts has created a growing need for robust and adaptable summarization tools. However, most existing approaches rely on supervised training and task-specific fine-tuning, which limits their ability to generalize across media formats, languages, and domains. In this work, we present a few-shot prompting framework tailored to news summarization from unstructured, multimodal inputs. The system leverages Large Language Models (LLMs) to produce bilingual outputs in French and Arabic without requiring labeled training data. Our method combines Retrieval-Augmented Generation (RAG) with a rewriting stage guided by professional examples. It first extracts and embeds content from various media sources, retrieves the most relevant items through semantic search, and generates initial summaries using prompt-based generation aligned with journalistic conventions. In a second step, the summaries are rewritten using support examples selected based on content similarity, and then translated into Arabic to produce final bilingual outputs. Experimental results demonstrate that integrating a support set during the rewriting phase leads to more relevant and well-structured summaries.

Side-channel attacks and deep learning have both attracted significant attention in recent years. As deep learning continues to advance, its application in side-channel analysis has opened new possibilities for cryptographic key recovery. This paper presents a clear approach for recovering AES keys from side-channel traces using deep learning and introduces an efficient Neural Architecture Search (NAS) method based on random search to enhance the performance of standard Convolutional Neural Networks (CNNs) in side-channel analysis. Specifically, we apply NAS to the VGG16 and ResNet18 architectures, resulting in two optimized models, referred to as NAS-VGG and NAS-ResNet. Our method significantly reduces training time, achieving improved performance after only 10 epochs, compared to the original models, which required 50 or more epochs. Furthermore, the NAS-VGG model not only outperforms the original VGG16 in terms of guessing entropy (GE), but also surpasses a well-established CNN-based approach from the literature, reaching GE = 0 with as few as ∼450 traces. These results demonstrate the effectiveness of random search-based NAS in discovering compact, high-performing architectures with minimal computational cost.

Abstract Pattern matching in big graphs is important for different modern applications. Recently, this problem was defined in terms of multiple extensions of graph simulation, to reduce complexity and capture more meaningful results. These results were achieved through the relaxation of commonly used constraint in subgraph isomorphism pattern matching. Nevertheless, these graph simulation variant models are still too strict to provide results in many cases, especially when analyzed graphs contain anomalies and incomplete information. To deal with this issue, we introduce a new graph pattern matching (GPM) method, called partial simulation, capable of retrieving matches despite missing parts of the pattern graph, such as vertices and/or edges. Furthermore, considering the number and inequality of the outputs, we define a relevance function to compute a value expressing how each match vertex respects the pattern graph. Similarly, we define partial dual simulation GPM that returns vertices that satisfy a part of the dual simulation constraints and assigns a relevance value to them. Additionally, we provide distributed scalable algorithms to evaluate the proposed partial simulation methods based on the distributed vertex-centric programming paradigm. Finally, our experiments on real-world data graphs demonstrate the effectiveness of the proposed models and the efficiency of their associated algorithms.

Graph Edit Distance (GED) is a well-known measure used in the graph matching to measure the similarity/dissimilarity between two graphs by computing the minimum cost of edit operations needed to transform one graph into another. This process, Which appears to be simple, is known NP-hard and time consuming since the search space is increasing exponentially. One way to optimally solve this problem is by using Branch and Bound (B&B) algorithms, Which reduce the computation time required to explore the whole search space by performing an implicit enumeration of the search space instead of an exhaustive one based on a pruning technique. nevertheless, They remain inefficient when dealing with large problem instances due to the impractical running time needed to explore the whole search space. To overcome this issue, We propose in this paper three parallel B&B approaches based on shared memory to exploit the multi-core CPU processors: First, a work-stealing approach where several instances of the B&B algorithm explore a single search tree concurrently achieving speedups up to 24 × faster than the sequential version. Second, a tree-based approach where multiple parts of the search tree are explored simultaneously by independent B&B instances achieving speedups up to 28 × . Finally, Due to the irregular nature of the GED problem, two load-balancing strategies are proposed to ensure a fair workload between parallel processes achieving impressive speedups up to 300 × . all experiments have been carried out on well-known datasets

Topology Control (TC) is a well known technique used in wireless ad hoc and sensor networks to reduce energy consumption. This technique coordinates the decisions of network nodes about their transmission power to save energy, prolong network lifetime, and mitigate MAC-level medium contention, while maintaining network connectivity. In order to ease the implementation and the study in systematic way of proposed TC protocols, in terms of energy usage and network graph properties, we propose a new framework based on NS-2 simulator. The framework is named TopCoF and composed of two main parts. The first one consists of a set of NS-2 extensions to support TC, while the second is a graphical user interface for statistical analysis and visualization of simulation results traced by the first part. TopCoF is modular and generic since it implements a set of basic components used by TC protocols.