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ACS/IEEE International Conference on Computer systems and Applications

作者： Rima Grati Khouloud Boukadi Hanene Ben-Abdallah Mir@cl - Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax Tunisia Faculty of Computing and Information Technology King Abdulaziz University Jeddah KSA

A cloud service is usually classified as Infrastructure as a Service, Platform as a Service or Software as a Service. This classification is not sufficient, when Business Processes are considered. Therefore, the next level of abstraction above SaaS is Business Process as a Service (BPaaS) [1]. BPaaS runs in a dynamic and volatile environment such as cloud computing and has hence very changing QoS (Quality of Service) parameters. Therefore, the BPaaS monitoring and adaptation are of a great importance to guarantee the QoS parameters defined in the Service Level Agreement (SLA). To address these issues, in this paper we proposed two Fuzzy systems for self-adaptive BPaaS that aim to prevent the SLA violations. The proposed approach objective is to satisfy two important issues: (i) the selection of service adaptation strategies and (ii) the cost as well as the impact of changes. We also presented the results of the experiments achieved to evaluate the proposed approach. These experimental results prove the effectiveness of the fuzzy systems.

关键词： Quality of service Fuzzy systems Time factors Context Fuzzy logic Adaptation models

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Ti interstitial flows giving rutile TiO2 reoxidation process enhanced in (001) surface

arXiv

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arXiv 2019年

作者： Ichibha, Tom Benali, Anouar Hongo, Kenta Maezono, Ryo School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Computational Science Division Argonne National Laboratory 9700 Cass Avenue LemontIL60439 United States Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

We revisited ab initio evaluations of the energy barriers along the possible diffusion paths of the defects in rutile TiO2. By using a method carefully considering the cancellation of the self-interaction, Ti interstitials hopping along c-axis are identified as the major diffusion directing to [001] surface. The conclusion is contradicting to any of previous theoretical works, and the discrepancy is explained by the overestimation of the radius of defects due to the poor cancellations in the previous works. The updated prediction here can explain the superior photocatalysis activity in [001] surface to [110]. Copyright © 2019, The Authors. All rights reserved.

关键词： Titanium dioxide

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MOT Knowledge Model Integration Rules for Knowledge Warehousing

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Procedia Computer Science 2017年 112卷 544-553页

作者： Rim Ayadi Yasser Hachaichi Jamel Feki 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 Faculty of Computing and IT University of Jeddah Jeddah Saudi Arabia

A knowledge warehousing process aims to build an intelligent decision support system. It collects, homogenizes, integrates and stores knowledge for a decision-making process. In this paper, we are interested in knowledge integration. More accurately, we propose an integration process for knowledge homogenized/modeled according to the MOT (Modeling with Object Types) knowledge model. This integration process consists of three ordered steps based on the type of schemas to integrate and their similarity. For this process, we define five integration rules based on semantic relationships between elements of MOT knowledge models, and then we develop an algorithm using these integration rules.

关键词： Knowledge warehouse Knowledge integration MOT Knowledge Semantic relationships Integration rules

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Novelty detection in data stream clustering using the artificial immune system 13

Novelty detection in data stream clustering using the artifi...

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13th European, Mediterranean and Middle Eastern Conference on information systems, EMCIS 2016

作者： Abid, Amai Jamoussi, Salma Multimedia Information systems and Advanced Computing Laboratory MIRACL Sfax University Tunisia

ISBN: (纸本)9789606897092

Data stream clustering poses many challenges to the data mining community where traditional data mining techniques become infeasible to mine such continuous flow of data. This paper presents a novel method for data stream clustering. In particular, we address the problem of novelty detection in data streams. For this purpose, the proposed method uses the Artificial Immune system meta-heuristic. The latter has been widely used for unsupervised data mining tasks and it owns the property of adaptability required by data stream clustering algorithms. Our method, called AIS-Clus (Artificial Immune system for data stream Clustering), is able to detect novel concepts using the performance of the learning process of AIS meta-heuristic. Furthermore, AIS-Clus has the ability to adapt its model to handle novel arriving items in textual data streams. Experimental results are given for two textual datasets and promising results are obtained. © Proceedings of the 13th European, Mediterranean and Middle Eastern Conference on information systems, EMCIS 2016. All rights reserved.

关键词： Clustering algorithms

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Adapted pruning scheme for the framework of imbalanced data-sets

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Procedia Computer Science 2017年 112卷 1542-1553页

作者： Ikram CHAABANE Radhouane GUERMAZI Mohamed HAMMAMI Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax University Tunis road Km 10 3021 Sfax Tunisia Saudi Electronic University Abu Bakr Street PO Box: 93499 11673 Riyad Kingdom of Saudi Arabia

Learning from imbalanced data is attracting an increasing interest by the machine learning community. This is mainly due to the high number of real applications that are affected by this situation. The adaptation of the standard decision trees to deal with imbalanced data represents one of the important number of approaches that have been developed to address this problem. This adaptation has been proposed under three different perspectives: splitting criterion, assignment rule and pruning. In this paper, we focus our attention to the pruning of decision trees. We propose an adaptation of the standard pruning algorithm MCCP to address the skewed-data problem. Our contribution affects two levels: adaption of the metric used in selecting nodes to be firstly pruned and change of the evaluation measure used in selecting the best decision-tree through the pruning set. Our goal is to show that, contrary to the popular belief in the literature enquiring into the uselessness of decision tree pruning, an adaptive pruning technique for imbalanced situations is more efficient and more accurate towards the minority class. A total of twelve binary class data-sets having different imbalance ratio are used to test the performance of the proposed method. Experimental results show that the proposed post-pruning approach can increase the performance of imbalanced decision trees in terms of evaluation measures that are recent and appropriate for the context of imbalanced classification.

关键词： imbalanced class decision-tree pruning skewed data-sets IBA B42

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A Survey on Learner Models in Adaptive E-learning systems

A Survey on Learner Models in Adaptive E-learning Systems

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IEEE/ACS International Conference of Computer systems and Applications

作者： Fedia Hlioui Nadia Alioui Faiez Gargouri Multimedia InfoRmation system and Advanced Computing Laboratory University of Sfax

ISBN: (纸本)9781509043217

E-learning became an omnipresent concept in modern education thanks to the rapid development of information and Communication Technology for Education. Along with this exponential evolution, the learner plays a central role in both traditional and computer-assisted learning. Accordingly, the learner's individual differences have a remarkable potential to improve his/her performance and motivation. This is ensured by providing an adaptive learning support. The present paper presents a survey of learner modeling in adaptive E-learning systems while describing the main methods for extracting of the personalization parameters. The learner's modeling does not only interest researchers and developers, but also pedagogues, course' designer and tutors.

关键词： Learner model Adaptive E-learning system Personalization parameters Learner behavior

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Sentiment lexicon enrichment using emotional vector representation

Sentiment lexicon enrichment using emotional vector represen...

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IEEE/ACS International Conference on Computer systems and Applications

作者： Hanen Ameur Salma Jamoussi Abdelmajid Ben Hamadou Technopole of Sfax Sfax Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL-Sfax University Sfax Tunisia Multimedia Inf. Syst. & Adv. Comput. Lab. Universite de Sfax Sfax TN

With the explosive growth of social media sites, an enormous amount of opinionated data has become numerically documented online. The explosion of this kind of data gives rise of new areas: sentiment analysis and opinion mining. Our purpose in this paper is to determine the polarity of Facebook comments "positive or negative" using machine learning-based approach. This approach requires an adequate vector representation of linguistic units (words or sentences) to obtain better classification performance. For this reason, we present a new emotional vector representation of words which can faithfully translate their semantic and sentimental characteristics, based on emotion symbols. Furthermore, we propose an enrichment step that aims to enlarge the lexicon and dynamically update word representation according to new contexts. Finally, by using these word representations, we present the comments in the same feature space to classify them and predict their polarity. We use Support Vector Machines to show that our emotional vector representation of lexicon's word and its enrichment significantly improves accuracy for sentiment analysis problem compared with the well-known bag-of-words vector representations, using dataset derived from Facebook. Our results are also consistent and effective.

关键词： Sentiment analysis Semantics Support vector machines Facebook Task analysis Electronic mail

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A property grammar-based method to enrich the arabic treebank ATB 7

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7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015

作者： Bahloul, Raja Bensalem Haddar, Kais Blache, Philippe Multimedia InfoRmation Systems and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia Sfax Tunisia Laboratoire Parole et Langage CNRS Université de Provence Marseille France

ISBN: (纸本)9783319527574

We present a method based on the formalism of Property Grammars to enrich the Arabic treebank ATB with syntactic constraints (so-called properties). The Property Grammar formalism is an effectively constraint-based approach that directly specifies the constraints on information categories. This can facilitate the enrichment process. The latter is based on three phases: the problem formalization, the Property Grammar induction from the ATB and the treebank regeneration with a new syntactic property-based representation. The enrichment of the ATB can make it more useful for many NLP applications such as the ambiguity resolution. This allows also the acquisition of new linguistic resources and the ease of the probabilistic parsing process. This enrichment process is purely automatic and independent from any language and source corpus formalism. This motivates its reuse. We obtained good and encouraging experiment results and various properties of different types. © Springer International Publishing AG 2016.

关键词： Syntactics

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GLOBAL CLIMATE

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Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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Semi-hierarchical naive Bayes classifier

Semi-hierarchical naive Bayes classifier

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International Joint Conference on Neural Networks

作者： Hasna Njah Salma Jamoussi Walid Mahdi Multimedia InfoRmation system and Advanced Computing Laboratory (MIRACL) University of Sfax College of Computers and Information Technology Taif University

ISBN: (纸本)9781509006212

The classification of high dimensional data is an arduous task especially with the emergence of high quality data acquisition techniques. This problem is accentuated when the whole set of features is needed to learn a classifier such as the case of genomic data. The Bayesian approach is suitable for these applications because it represents graphically and statistically the dependencies between the features. Unfortunately, learning a Bayesian classifier using a high number of features does not ensure a tradeoff between the dimensions' reduction, the semantic of the model and the predictive performance. We propose a new semi-hierarchical naive Bayes that uses the latent variables for abstracting the features of a given dataset in order to reduce the dimensionality. These variables are suitable for finding graphically and semantically analyzable models. We combined them with the observed variables in a tree-augmented naive Bayes structure in order to improve the prediction accuracy. An excessive experimental study showed that our method is suitable for high dimensional data and overcomes the existing methods.

关键词： Bayesian classifier Latent variable High dimensional data Bayesian classifier dimensional data graphically Bayes tradeoffs

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