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IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises

作者： Mouna Rekik Khouloud Boukadi Hanane Ben Abdallah Multimedia InfoRmation systems & Advanced Computing Laboratory (Mir@cl) University of Sfax

ISBN: (纸本)9781479904051

Business process outsourcing has regained further attention with the emergence of Cloud computing. In fact, enterprises can benefit from the cloud at the service (i.e., business), the platform and/or the infrastructure levels. Face to these various benefits, an enterprise that desires to outsource and/or deploy parts of its business process in the Cloud must resolve two decisional points: which part of its business process to outsource, which Cloud and which level of the Cloud environment are the most beneficial. The resolution of these decisional points must take into account several contextual factors that are specific to each enterprise. This paper presents the elements of enterprise context that influence the decision whether to outsource or not in a Cloud environment. In addition, it shows how AHP can be used to assist manager in taking their decision.

关键词： BPO Cloud models Enterprise context Decision method AHP

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Transducers Cascades for an Automatic Recognition of Arabic Named Entities in Order to Establish Links to Free Resources

Transducers Cascades for an Automatic Recognition of Arabic ...

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International Conference on Arabic Computational Linguistics (ACLing)

作者： Fatma Ben Mesmia Nathalie Friburger Kais Haddar Denis Maurel University of Sfax Laboratory MIRACL Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax Tunisia University of François Rabelais of Tours LI Computer laboratory Tours France

Arabic named entities (ANE) are often sources of information. That is why they are used by several applications of natural language processing (NLP) mainly in information retrieval. In order to improve the relevance of the information obtained, links to free resources can be established. Indeed, the recognition of these entities requires the use of adequate formalisms. In this paper, we propose an approach based on transducer cascades which allows the recognition of ANE more precisely the dates. This categorycan be an integral part in the events and the names of places. The implementation of the developed transducers cascades elaborated by using the CasSys tool is available under the Unitex platform. The results are motivating.

关键词： Transducers Automata Pragmatics Dictionaries Hidden Markov models Natural language processing

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Towards a modeling of video grammar based on a priori knowledge for the optimization of the audiovisual documents structuring

Towards a modeling of video grammar based on a priori knowle...

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International Conference on Computer Technology and Development (ICCTD)

作者： Tarek Zlitni Walid Mahdi Hanene Ben-Abdallah Multimedia Information Systems and Advanced Computing Laboratory Sfax MIR CI Tunisia

The emergence of the TV channels number and the appearance of the Internet led to an exponential growth of the audiovisual documents. Several works were proposed in the literature for automatic video segmentation. However, the effectiveness of these works depends on video type. So, for a better quality of the segmentation, it is necessary to consider a priori knowledge concerning video types. Besides, TV channels use a set of rules during the production and the streaming of their programs such as the respect of the graphic charter, the recurring of studios... The exploitation of these rules, as a priori knowledge for the temporal and/or semantic structuring of the video documents, would be then advantageous for the quality of the indexing and the performance of the multimedia systems. In this context, we propose a method for the extraction and the formalization of descriptors based on a priori knowledge in order to conceive a video grammar model. This model will be used thereafter for a multitude of interesting applications like automatic structuring of TV streams or TV news segmentation. Video grammar; video grammar modeling; multimedia descriptors; video indexing.

关键词： Biological system modeling Nonvolatile memory Random access memory multimedia communication Ferroelectric films TV

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Evaluation of Stationary Wavelet Transforms in Reconstruction of Pure High Frequency Oscillations (HFOs) 18th

Evaluation of Stationary Wavelet Transforms in Reconstructio...

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18th International Conference on Smart Homes and Health Telematics, ICOST 2020

作者： Guesmi, Thouraya Hadriche, Abir Jmail, Nawel Amar, Chokri Ben Institut Supérieur d’Informatique et de Multimédia Gabes University Teboulbou Tunisia Research Groups on Intelligent Machines Laboratory REGIM ENIS Sfax University Sfax Tunisia Digital Research Centre of Sfax Sfax University Sfax Tunisia Multimedia Information Systems and Advanced Computing Laboratory MIRACL Sfax University Sfax Tunisia

ISBN: (纸本)9783030515164

High frequency oscillations (HFO) from, MEG (magnetoencephalography) and intracerebral EEG are considered as effective tools to identify cognitive status and several cortical disorders especially in epilepsy diagnosis. The aim of our study is to evaluate stationary wavelet transform (SWT) technique performance in efficient reconstruction of pure epileptic high frequency oscillations, reputed as biomarkers of epileptogenic zones: generators of inter ictal epileptic discharges, and offhand seizures. We applied SWT on simulated and real database to detect non-contaminated HFO by spiky element. For simulated data, we computed the GOF of reconstruction that reaches for all studied constraint (relative amplitude, frequency, SNR and overlap) a promising results. For real data we used time frequency domain to evaluate SWT robustness of HFO reconstruction. We proved that SWT is an efficient filtering technique for separation HFO from spiky events. Our results would have an important impact on the definition of epileptogenic zones. © 2020, The Author(s).

关键词： Magnetoencephalography

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Association Rules Transformation for Knowledge Integration and Warehousing 17th

Association Rules Transformation for Knowledge Integration a...

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17th International Conference on Intelligent systems Design and Applications, ISDA 2017

作者： Ayadi, Rim Hachaichi, Yasser Feki, Jamel Multimedia Information Systems and Advanced Computing Laboratory University of Sfax Sfax Tunisia Department of Computer Science and Quantitative Methods Higher Institute of Business Administration of Sfax Sfax Tunisia Faculty of Computing and IT University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9783319763477

Knowledge management process is a set of procedures and tools applied to facilitate capturing, sharing and effectively using knowledge. However, knowledge collected from organizations is generally expressed in various formalisms, therefore it is heterogeneous. Thus, a Knowledge Warehouse (KW), which is a solution for implementing all phases of the knowledge management process, should solve this structural heterogeneity before loading and storing knowledge. In this paper, we are interested in knowledge normalization. More accurately, we firstly introduce our proposed architecture for a KW, and then we present the MOT (Modeling with Object Types) language for knowledge representation. Since our objective is to transform heterogeneous knowledge into MOT, as a pivot model, we suggest a meta-model for the MOT and another for the explicit knowledge extracted through the association rules technique. Thereafter, we define eight transformation rules and an algorithm to transform an association rules model into the MOT model. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Data mining

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New Dynamic Bayesian Network for Time-To-Event Prediction 21

New Dynamic Bayesian Network for Time-To-Event Prediction

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2024 IEEE/ACS 21st International Conference on Computer systems and Applications, AICCSA 2024

作者： Ben Abdallah, Walid Njah, Hasna Jamoussi, Salma National Engineering School of Gabes Gabes University Tunisia Sfax University Multimedia InfoRmation Systems and Advanced Computing Laboratory Tunisia Higher Institute of Computer Sciences and Multimedia of Gabes Gabes University Tunisia Higher Institute of Computer Sciences and Multimedia of Sfax Sfax University Tunisia

ISBN: (纸本)9798331518240

Time-To-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the context of chronic diseases such as multiple sclerosis (MS), precisely modelling time to disease progression, adverse events, or therapeutic responses can have a significant impact on patient management strategies and clinical results. This study investigate the use of Bayesian networks(BN), augmented with a temporal factor, for time-To-event prediction in predicting disease progression in MS patients. We suggest using dynamic Bayesian networks (DBNs) as a versatile temporal modeling framework to capture the complex dynamics and inter-dependencies found in time-To-event processes. © 2024 IEEE.

关键词： Prediction models

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Bimodal person recognition using dorsal-vein and finger-vein images

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Procedia Computer Science 2020年 176卷 1121-1130页

作者： Amal Abbes Randa Boukhris Trabelsi Yassine Ben Ayed Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax Tunisia Computers Imaging Electronics and Systems Group (CIELS) from Advanced Control and Energy Management Laboratory(CEM-Lab) University of Sfax Sfax Engineering School BP W 3038 Sfax Tunisia

Nowadays, human recognition with biometric characteristic have been investigated in many researchers. However, traditional biometric methods are not always robust against counterfeit and spoof attacks. The challenge of this work is to develop a bimodal biometric system, based on more reliable and robust characteristics against counterfeiting. Indeed, biometric system based on the venous network of the hand gives higher recognition rate compared to other systems. Furthermore, the fusion of two prints minimums the error rate and counterfeits. In our biometric system, the most challenging phase is the feature extraction step. For this, we propose a new feature extraction approach based on the concatenation of the pyramid of Difference of Gaussian (DoG) and Local Line Binary Patterns (LLBP) histograms (H-DoG_LLBP). To evaluate the proposed system, we opted for Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers. The experimental study is based on the dorsal hand vein BOSPHORUS database and finger vein MMCBNU_6000 database. Our system presents an area under curve (AUC) equal to 0.99, and 0.001 mean square error (MSE) with ANN, and 0.0042 equal error rate (EER) with SVM.

关键词： Bimodal Biometric system hand veins

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Statistical parametric speech synthesis for Arabic language using ANN

Statistical parametric speech synthesis for Arabic language ...

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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Rebai Ilyes Yassine Ben Ayed MIRACL: Multimedia InfoRmation system and Advanced Computing Laboratory Sfax University Sfax Tunisie Universite de Sfax Sfax TN

Statistical parametric approach for speech synthesis becomes more popular over the concatenative approach due to the low size of the system and the high-quality speech. Moreover, few researches have been done in the field of speech synthesis for Arabic language with a poor quality of speech. In this paper, we propose a statistical parametric synthesis system for Arabic based on Artificial Neural Networks (ANN). Mel frequency Cepstral coefficients (MFCC), F0, energy and duration are the main components of our system. Speech waveform is generated from the predicted parameters F0, energy and MFCC. Different methods are proposed for this development process. In addition, we propose a method to solve the problem of discontinuities between neighboring segment boundaries in order to improve the speech quality. Experimental results of cepstral and prosodic parameters are given in this paper as well as the subjective evaluation.

关键词： Speech Mel frequency cepstral coefficient Artificial neural networks Speech synthesis Databases

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Improving of Open-Set Language Identification by using Deep SVM and Thresholding Functions

Improving of Open-Set Language Identification by using Deep ...

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IEEE/ACS International Conference on Computer systems and Applications

作者： Ilyes Rebai Yassine BenAyed Walid Mahdi MIRACL: Multimedia InfoRmation System and Advanced Computing Laboratory Sfax Tunisia College of Computers and Information Technology Taif University Taif Saudi Arabia

State-of-the-art language identification (LID) systems are based on an iVector feature extractor front-end followed by a multi-class recognition back-end. Identification accuracy degrades considerably when LID systems face open-set languages. As compared to in-set identification task, the open-set task is adequate to mimic the real challenge of language identification. In this paper, we propose an approach to the problem of out-of-set (OOS) data detection in the context of open-set language identification with zero-knowledge for OOS languages. The main feature of this study is the emphasis on the in-set (target) language identification, on the one hand, and on OOS language detection, on the other hand. Accordingly, we propose a deep SVM based LID back-end system to improve the target languages identification. Along with that, we define three OOS thresholding formulations. These formulations are used to decide whether the speech segment is a target or OOS language. The experimental results demonstrate the effectiveness of the deep SVM back-end system as compared to state-of-the-art techniques. Besides that, the thresholding functions perfectly detect and reject the OOS data. A relative decrease of 6% in Equal Error Rate (EER) is reported over classical OOS detection methods, in discriminating target and OOS languages.

关键词： Support vector machines Kernel Speech Task analysis NIST Data models Training

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A re-ranking method based on irrelevant documents in ad-hoc retrieval 5

A re-ranking method based on irrelevant documents in ad-hoc ...

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5th Symposium on Languages, Applications and Technologies, SLATE 2016

作者： Mbarek, Rabeb Tmar, Mohamed Hattab, Hawete Boughanem, Mohand Sfax University Multimedia Information Systems and Advanced Computing Laboratory Sfax Tunisia Umm Al-Qura University Department of Mathematics Makkah Saudi Arabia University of Toulouse IRIT Lab France Toulouse France

ISBN: (纸本)9783959770064

In this paper, we propose a novel approach for document re-ranking, which relies on the concept of negative feedback represented by irrelevant documents. In a previous paper, a pseudo-relevance feedback method is introduced using an absorbing document d which best fits the user's need. The document d is orthogonal to the majority of irrelevant documents. In this paper, this document is used to re-rank the initial set of ranked documents in Ad-hoc retrieval. The evaluation carried out on a standard document collection shows the effectiveness of the proposed approach. © Rabeb Mbarek, Mohamed Tmar, Hawete Hattab, and Mohand Boughanem;licensed under Creative Commons License CC-BY.

关键词： Absorption of irrelevance Re-ranking Vector product

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