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22nd International Seminar on Current Trends in Theory and Practice of Informatics, SOFSEM 1995

作者： Marshall, Victoria A. Advanced Hypermedia Systems Group Computing and Information Systems Department Rutherford Appleton Laboratory Chilton DIDCOTOX11 0QX United Kingdom

ISBN: (纸本)3540606092

Over the last few years there has been a phenomenal growth in the use of WWW (World Wide Web) for a wide variety of purposes from advertising and publicity, to collaborative work and teaching. Because it is so easy to set up a server, there are almost as many web philosophies, Uses and page designs as there are web pages. Of course some of these are better than others and within CISD we believe that our corporate-wide style and practices are particularly effective. This talk will give a brief history of WWW, and then go on to describe the philosophy and technical theory of the design and structure of the CISD web, and explain how it fits into the CCLRC corporate web and philosophy. It will also touch on more advanced uses of the web. © Springer-Verlag Berlin Heidelberg 1995.

关键词： Teaching

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HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection

HybridCRS-TMS: Integrating Collaborative Recommender System ...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Rekik, Mouna Grati, Rima Benmohamed, Ichrak Boukadi, Khouloud Higher Institute of Transport and Logistics of Sousse Tunisia University of Sfax Multimedia Information systems and Advanced Computing Laboratory Tunisia Zayed University College of Technological Innovation Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897587061

The pervasive influence of smartphones and mobile internet has revolutionized journey planning, particularly transportation. With navigation applications delivering real-time information, recommender systems have emerged as crucial tools for enhancing the travel experience. This paper introduces HybridCRS-TMS, a unique Hybrid Collaborative Recommender System for Transport Mode Selection, leveraging a dataset of 260 passengers. Through advanced data mining techniques, specifically k-Nearest Neighbors (k-NN) for collaborative recommendations and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis for objective evaluation, the system provides personalized transportation mode recommendations. The model not only demonstrates exceptional performance but also showcases the synergy between collaborative and objective decision-making approaches, contributing to efficient, personalized, and well-informed travel solutions. This study underscores the system’s versatility, illustrating its ability to optimize travel choices through a hybrid recommendation framework that integrates both collaborative and objective criteria. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Nearest neighbor search

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Hand Gesture Recognition System Based on Local Binary Pattern Approach for Mobile Devices 1

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

作者： Lahiani, Houssem Kherallah, Monji Neji, Mahmoud National School of Electronics and Telecommunications University of Sfax Sfax Tunisia Faculty of Sciences University of Sfax Sfax Tunisia Faculty of Economics and Management University of Sfax Sfax Tunisia Multimedia Information Systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (数字)9783319763484

ISBN: (纸本)9783319763477

Since the appearance of mobile devices, gesture recognition is being a challenging task in the field of computer vision. In this paper, a simple and fast algorithm for static hand gesture recognition for mobile device is described. The hand pose is recognized by using gentle AdaBoost learning algorithm and Local Binary Pattern features. The system is developed on an Android OS platform. The method used consists of two steps: a real-time gesture captured by a smartphone’s camera and the recognition of the hand gestures. It presents a system based on a real-time hand posture recognition algorithm for mobile devices. The aim of this work is to allow the mobile device interpreting the sign made by the user without the need to touch the screen. In this system, the device is able to perform all necessary steps to recognize hand posture without the need to connect to any distant device. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Local binary pattern

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A New Method for the Construction of Evolving Embedded Representations of Words

A New Method for the Construction of Evolving Embedded Repre...

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ACS/IEEE International Conference on Computer systems and Applications

作者： Amal Bouraoui Salma Jamoussi Abdelmajid Ben Hamadou Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL Sfax-Tunisia Technopole of Sfax Sfax Tunisia

There are several natural language processing tasks such as natural language understanding, information retrieval and social network analysis which require dynamic update of the word embeddings representation so as to keep pace with the words meanings evolving. Thanks to their ability to capture both syntactic and semantic information within language, the continuous bag-of-words, a learning word representation model, has gained a lot of attention from researchers and has been successfully applied to different NLP tasks. However, this model does not adequately capture the change of word semantics over time. This paper provides a new perspective to determine the semantic similarity of words over time by constructing several representations of words that reflect the semantic change of words based on latest embeddings. We examine the performance of our improved CBOW on an evolved data base collected from English Wikipedia pages. Our learned embeddings illustrate the semantic evolution of words better than the original CBOW.

关键词： Semantics Task analysis Vocabulary Computer architecture Natural language processing Encyclopedias

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Hand Pose Estimation System Based on a Cascade Approach for Mobile Devices 17th

Hand Pose Estimation System Based on a Cascade Approach for ...

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

ISBN: (纸本)9783319763477

The rise in the use of mobile devices requires finding new ways to interact with this type of devices. Gestures are an effective way to interact with the mobile device and to place order to it. However, gesture recognition in this context constitute a challenging task due the limited computational capacities of this type of devices. In this work, we present a hand pose estimation system for mobile device. The gesture is recognized by using a boosting algorithm and Haar-like features. The system is designed for Android devices. The method used consists of capturing gestures by a smartphone’s camera to recognize the hand sign. It presents a system based on a real-time hand posture recognition algorithm for mobile devices. The aim of this system is to allow the mobile device interpreting hand signs made by users without the need to touch the screen. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Adaptive boosting

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Graphical Models for Multi-dialect Arabic Isolated Words Recognition

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Procedia Computer Science 2015年 60卷 508-516页

作者： Elyes Zarrouk Yassine BenAyed Faiez Gargouri MIRACL Multimedia InfoRmation system and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia ISIMS University of Sfax Tunisia.

This paper presents the use of multiple hybrid systems for the recognition of isolated words from a large multi-dialect Arabic vocabulary. Such as the Hidden Markov models (HMM), Dynamic Bayesian networks (DBN) lack a discriminatory ability especially on speech recognition even if their progress is huge. Multi-Layer perceptrons (MLP) was applied in literature as an estimator of emission probabilities in HMM and proves it effectiveness. In order to ameliorate the results of recognition systems, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities since they are characterized by a high predictive power and discrimination. Moreover, they are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. In this work we have done a comparative study between three hybrid systems MLP/HMM, SVM/HMM and SVM/DBN and the standards models of HMM and DBN. In this paper, we describe the use of the hybrid model SVM/DBN for multi-dialect Arabic isolated words recognition. So, by using 67,132 speech files of Arabic isolated words, this work arises a comparative study of our acknowledgment system of it as the following: the use of especially the HMM standards leads to a recognition rate of 74.18%.as the average rate of 8 domains for everyone of the 4 dialects. Also, with the hybrid systems MLP/HMM and SVM/HMM we succeed in achieving the value of 77.74%.and 7806% respectively. Moreover, our proposed system SVM/DBN realizes the best performances, whereby, we achieve 87.67% as a recognition rate more than 83.01% obtained by GMM/DBN.

关键词： graphical models hidden markov models dynamic bayesian networks supports vecotrs machine multi-layer perceptrons isoalted words recognition.

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Translating verbs between MSA and arabic dialects through deep morphological analysis 20

Translating verbs between MSA and arabic dialects through de...

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20e Conference Traitement Automatique des Langues Naturelles et la 15e Rencontres des Etudiants Chercheurs en Informatique pour le Traitement Automatique des Langues, TALN-RECITAL 2013 - 20th Conference on Automatic Natural Language Processing and the 15th Meeting of Computer Science Student Researchers for Natural Language Processing, TALN-RECITAL 2013

作者： Hamdi, Ahmed Boujelbane, Rahma Habash, Nizar Nasr, Alexis Laboratoire d'Informatique Fondamentale de Marseille CNRS UMR 7279 Université Aix Marseille France Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax3021 Tunisia Center for Computational Learning Systems Columbia University New York NY10115 United States

The developpment of NLP tools for dialects faces the severe problem of lack of resources for such dialects. In the case of diglossia, as in arabic, a variant of arabic, Modern Standard Arabic, exists, for which many resources have been developped which can be used to build NLP tools. Taking advantage of the closeness of MSA and dialects, one way to solve the problem consist in performing a surfacic translation of the dialect into MSA in order to use the tools developped for MSA. We describe in this paper an achitecture for such a translation and we evaluate it on arabic verbs. © ATALA.

关键词： Natural language processing systems

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Deep architecture using multi-kernel learning and multi-classifier methods

Deep architecture using multi-kernel learning and multi-clas...

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IEEE/ACS International Conference on Computer systems and Applications

作者： Ilyes Rebai Yassine BenAyed Walid Mahdi Multimedia InfoRmation system and Advanced Computing Laboratory University of Sfax Tunisia

ISBN: (纸本)9781509004799

Kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. Multiple Kernel Learning (MKL) and Multilayer Multiple Kernel Learning (MLMKL), as new families of kernel methods, consist of learning the optimal kernel from a set of predefined kernels by using an optimization algorithm. However, learning this optimal combination is considered to be an arduous task. Furthermore, existing algorithms often do not converge to the optimal solution (i.e., weight distribution). They achieve worse results than the simplest method, which is based on the average combination of base kernels, for some real-world applications. In this paper, we present a hybrid model that integrates two methods: Support Vector Machine (SVM) and Multiple Classifier (MC) methods. More precisely, we propose a multiple classifier framework of deep SVMs for classification tasks. We adopt the MC approach to train multiple SVMs based on multiple kernel in a multi-layer structure in order to avoid solving the complicated optimization tasks. Since the average combination of kernels gives high performance, we train multiple models with a predefined combination of kernels. Indeed, we apply a specific distribution of weights for each model. To evaluate the performance of the proposed method, we conducted an extensive set of classification experiments on a number of benchmark data sets. Experimental results show the effectiveness and efficiency of the proposed method as compared to various state-of-the-art MKL and MLMKL algorithms.

关键词： Deep Multiple Kernel Learning Kernel methods Multiple Classifier Support Vector Machine

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Indexing and classifiying Video Genres using Support Vector Machines

Indexing and classifiying Video Genres using Support Vector ...

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IEEE/ACS International Conference on Computer systems and Applications

作者： Nouha Dammak Yassine BenAyed Multimedia InfoRmation system and Advanced Computing Laboratory MIRACL Sfax Tunisia

ISBN: (纸本)9781509004799

In this paper, classifying and indexing hierarchical video genres using Support Vector Machines (SVMs) are based on only audio features. In fact, segmentation parameters are extracted at block levels, which have a major benefit by capturing local temporal information. The main contribution of our study is to present a powerful combination between the two employed audio descriptors;Mel Frequency Cepstral Coefficients (MFCC) and signal energy in order to classify a big YouTube dataset that includes multi-Arabic dialects video genres and even sub-genres: several sports analysis and various matches categories (foot-ball, basket-ball, hand-ball and volley-ball), both studio and fields news scenes over and above various multi-singer and multi-instruments music clips. Validation of this approach was carried out on over 18 hours of video span yielding a classification accuracy of 98,5% for genres, 97% for sports subgenres and 76% for music sub-genres. Finally we discuss SVM kernels performance on our proposed dataset.

关键词： audio feature classification hierarchical segmentation support vector machine video genre

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Speech Processing for Early Alzheimer Disease Diagnosis: Machine Learning Based Approach

Speech Processing for Early Alzheimer Disease Diagnosis: Mac...

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ACS/IEEE International Conference on Computer systems and Applications

作者： Randa Ben Ammar Yassine Ben Ayed Multimedia Information System and Advanced Computing Laboratory Sfax university Tunisia

Alzheimer's disease (AD) is a neurodegenerative disease characterized by the insidious onset of cognitive, emotional and language disorders. These attacks are sufficiently intense to affect the daily social and professional lives of patients. Today, in the absence of a reliable diagnosis and effective curative treatments, fighting this disease is becoming a real public health issue, prompting research to consider non-drug techniques. Among these techniques, speech processing is proving to be a relevant and innovative field of investigation. Several Machine Learning algorithms achieved promising results in distinguishing AD from healthy control subjects. Alternatively, many other factors such as feature extraction, the number of attributes for feature selection, used classifiers, may affect the prediction accuracy evaluation. To surmount these weaknesses, a model is suggested which include a feature extraction step followed by imperative attribute selection and classification is achieved using a machine learning classifiers. The current findings show that the proposed model can be strongly recommended for classifying Alzheimer's patient from healthy individuals with an accuracy of 79%.

关键词： Alzheimer's disease Machine learning Feature extraction Speech processing Machine learning algorithms

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