Zobrazeno 1 - 10
of 7 898
pro vyhledávání: '"Kheir, A."'
Autor:
Lachemat, Houssam Eddine-Othman, Abbas, Akli, Oukas, Nourredine, Kheir, Yassine El, Haboussi, Samia, Shammur, Absar Chowdhury
The paper introduces and publicly releases (Data download link available after acceptance) CAFE -- the first Code-switching dataset between Algerian dialect, French, and english languages. The CAFE speech data is unique for (a) its spontaneous speaki
Externí odkaz:
http://arxiv.org/abs/2411.13424
Autor:
Hedar, Abdel-Rahman, Abdel-Hakim, Alaa E., Deabes, Wael, Alotaibi, Youseef, Bouazza, Kheir Eddine
Metaheuristic search methods have proven to be essential tools for tackling complex optimization challenges, but their full potential is often constrained by conventional algorithmic frameworks. In this paper, we introduce a novel approach called Dee
Externí odkaz:
http://arxiv.org/abs/2410.17042
This paper presents a novel Dialectal Sound and Vowelization Recovery framework, designed to recognize borrowed and dialectal sounds within phonologically diverse and dialect-rich languages, that extends beyond its standard orthographic sound sets. T
Externí odkaz:
http://arxiv.org/abs/2408.02430
Today, hate speech classification from Arabic tweets has drawn the attention of several researchers. Many systems and techniques have been developed to resolve this classification task. Nevertheless, two of the major challenges faced in this context
Externí odkaz:
http://arxiv.org/abs/2407.02448
Machines need data and metadata to be machine-actionable and FAIR (findable, accessible, interoperable, reusable) to manage increasing data volumes. Knowledge graphs and ontologies are key to this, but their use is hampered by high access barriers du
Externí odkaz:
http://arxiv.org/abs/2407.20007
Nowadays, topic classification from tweets attracts considerable research attention. Different classification systems have been suggested thanks to these research efforts. Nevertheless, they face major challenges owing to low performance metrics due
Externí odkaz:
http://arxiv.org/abs/2407.03253
Self-supervised models have revolutionized speech processing, achieving new levels of performance in a wide variety of tasks with limited resources. However, the inner workings of these models are still opaque. In this paper, we aim to analyze the en
Externí odkaz:
http://arxiv.org/abs/2406.16099
Autor:
Stocker, Markus, Snyder, Lauren, Anfuso, Matthew, Ludwig, Oliver, Thießen, Freya, Farfar, Kheir Eddine, Haris, Muhammad, Oelen, Allard, Jaradeh, Mohamad Yaser
Literature is the primary expression of scientific knowledge and an important source of research data. However, scientific knowledge expressed in narrative text documents is not inherently machine reusable. To facilitate knowledge reuse, e.g. for syn
Externí odkaz:
http://arxiv.org/abs/2405.13129
Autor:
Qian, Kun, Kheir, Mohamed
The main objective of this paper is to investigate the feasibility of employing Physics-Informed Neural Networks (PINNs) techniques, in particular KolmogorovArnold Networks (KANs), for facilitating Electromagnetic Interference (EMI) simulations. It i
Externí odkaz:
http://arxiv.org/abs/2405.11383
A machine-learned interatomic potential for Ge-rich Ge$_x$Te alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded phase chang
Externí odkaz:
http://arxiv.org/abs/2404.15128