Hassina Aliane - Academia.edu (original) (raw)

Papers by Hassina Aliane

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS), Oct 12, 2022

2022 International Conference on Advanced Aspects of Software Engineering (ICAASE)

2018 21st Saudi Computer Society National Computer Conference (NCC), 2018

In this paper, we introduce an efficient linear similarity learning system for face verification.... more In this paper, we introduce an efficient linear similarity learning system for face verification. Humans can easily recognize each other by their faces and since the features of the face are unobtrusive to the condition of illumination and varying expression, the face remains as an access of active recognition technique to the human. The verification refers to the task of teaching a machine to recognize a pair of match and non-match faces (kin or No-kin) based on features extracted from facial images and to determine the degree of this similarity. There are real problems when the discriminative features are used in traditional kernel verification systems, such as concentration on the local information zones, containing enough noise in non-facing and redundant information in zones overlapping in certain blocks, manual adjustment of parameters and dimensions high vectors. To solve the above problems, a new method of robust face verification with combining with a large scales local fea...

Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications, 2011

Considering the importance of watermarking of compressed video, several watermarking methods have... more Considering the importance of watermarking of compressed video, several watermarking methods have been proposed for authentication, copyrights protection or simply for a secure data carrying through the Internet. Applied to the H.264/AVC video standard, in most of cases, these methods are based on the use of the quantized DCT coefficients often experimentally or randomly selected. In this paper, we introduce a watermarking method based on the DCT coefficients using two steps: the first one consists in a watermark pre-processing based on similarity measurement which can allow to adapt the best the watermark to the carrying coefficients of low frequencies. A second step takes advantage from the coefficients of high frequencies in order to maintain the video quality and reduce the bitrate. Results show that it is possible to achieve a very good compromise between video quality, embedding capacity and bitrate.

Modelling and Implementation of Complex Systems, 2020

Learning ontological relations is an important step on the way to automatically developing ontolo... more Learning ontological relations is an important step on the way to automatically developing ontologies. This paper introduces a novel way to exploit WordNet [16], the combination of pre-trained word embeddings and deep neural networks for the task of ontological relation classification. The data from WordNet and the knowledge encapsulated in the pre-trained word vectors are combined into an enriched dataset. In this dataset a pair of terms that are linked in WordNet through some ontological relation are represented by their word embeddings. A Deep Neural Network uses this dataset to learn the classification of ontological relations based on the word embeddings. The implementation of this approach has yielded encouraging results, which should help the ontology learning research community develop tools for ontological relation extraction.

Procedia Computer Science, 2021

2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016

With the recently increasing interest for opinion mining from different research communities, the... more With the recently increasing interest for opinion mining from different research communities, there is an evolving body of work on Arabic Sentiment Analysis. There are few available polarity annotated datasets for this language, so most existing works use these datasets to test the best known supervised algorithms for their objectives. Naïve Bayes and SVM are the best reported algorithms in the Arabic sentiment analysis literature. The work described in this paper shows that using a genetic algorithm to select features and enhancing the quality of the training dataset improve significantly the accuracy of the learning algorithm. We use the LABR dataset of book reviews and compare our results with LABR's authors' results.

Control Engineering and Applied Informatics

2013 International Conference on Information Science and Applications (ICISA), 2013

2010 International Conference on Information Science and Applications, 2010

Abstract: - Video watermarking describes the process of embedding information in video to satisfy... more Abstract: - Video watermarking describes the process of embedding information in video to satisfy applications such as the protection of intellectual property and the control of video authentication. In this field, researchers orient their investigations towards the new video ...

2020 4th International Symposium on Informatics and its Applications (ISIA), 2020

Semantic text similarity in NLP is the study of the degree of resemblance between texts using a c... more Semantic text similarity in NLP is the study of the degree of resemblance between texts using a certain metric. It has many applications in tasks such as question answering, information retrieval, document clustering, topic detection, topic tracking, questions generation, machine translation, text summarizing and others. Nowadays, neural models are outperforming existing state of the art approaches in major NLP tasks and it is not surprising to see the STS community researchers adopt these models although there are still few works for Arabic language. As Siamese neural architecture has proven recently its relevance for STS in other languages, we evaluate in this work three models within this architecture for Arabic Textual similarity and plagiarism detection: BiLSTM and CNN which we call basic models and a BERT Transformer model.

2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)

We present in this paper our project to building an ontology centered infrastructure for Arabic r... more We present in this paper our project to building an ontology centered infrastructure for Arabic resources and applications. The core of this infrastructure is a linguistic ontology that is founded on Arabic Traditional Grammar. The methodology we have chosen consists in reusing an existing ontology, namely the Gold linguistic ontology. We discuss the development of the ontology and present our vision for the whole project which aims at using this ontology for creating tools and resources for both linguists and NLP researchers. 1.

2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA), 2020

The speed with which new scientific articles are published and shared on academic social networks... more The speed with which new scientific articles are published and shared on academic social networks generated a situation of cognitive overload and the targeted access to the relevant information represents a major challenge for researchers. In this context, we propose a scientific article recommendation approach based on the discovery of thematic community structures, it focuses on the topological structure of the network combined with the analysis of the content of the social object (scientific article), a strategy that aims to mitigate the cold start problems and sparcity data in scoring matrix. A key element of our approach is the modeling of the researcher's thematic centers of interest derived from his corpus (a set of articles that interested him). In this perspective we use the technique of semantic exploration and extraction of latent topics in document corpora, LDA(Latent DirichletAllocation), an unsupervised learning method which offers the best solution of scalability ...