Search results for "artificial intelligence"

showing 10 items of 6122 documents

PTNet: An efficient and green data center network

2017

International audience; In recent years, data centers have witnessed an exponential growth for hosting hundreds of thousands of servers as well as to accommodating a very large demand for resources. To fulfill the required level of demand, some approaches tackled network aspects so to host a huge number of servers while others focused on delivering rapid services to the clients by minimizing the path length between any two servers. In general, network devices are often designed to achieve 1:1 oversubscription. Alternatively, in a realistic data center environment, the average utilization of a network could vary between 5% and 25%, and thus the energy consumed by idle devices is wasted. This…

[ INFO ] Computer Science [cs]Computer Networks and CommunicationsComputer scienceDistributed computing02 engineering and technologyNetwork topology[ INFO.INFO-AO ] Computer Science [cs]/Computer ArithmeticTheoretical Computer Science03 medical and health sciences[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]0302 clinical medicineArtificial IntelligenceRobustness (computer science)Energy savingServerArchitecture0202 electrical engineering electronic engineering information engineering[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO][INFO]Computer Science [cs]ComputingMilieux_MISCELLANEOUSAverage path lengthInterconnectionNetwork topologyEnergy[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industry[INFO.INFO-AO]Computer Science [cs]/Computer ArithmeticScalability020206 networking & telecommunicationsData center networkAverage path lengthNetworking hardware[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]Hardware and Architecture030220 oncology & carcinogenesis[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]ScalabilityData center[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessSoftwareComputer network
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Toward a virtual reconstruction of an antique three-dimensional marble puzzle

2017

International audience; Abstract | Introduction | Related Work | Acquisition Setup, Proposed Prototype: Calibration and Visibility | Preprocessing of Scanned Three-Dimensional Fragment Data | Processing of Scanned Three-Dimensional Surface Data: Matching | Conclusion and Future Works | Appendices | Acknowledgments | ReferencesAbstract. The reconstruction of broken objects is an important field of research for many applications, such as art restoration, surgery, forensics, and solving puzzles. In archaeology, the reconstruction of broken artifacts is a very time-consuming task due to the handling of fractured objects, which are generally fragile. However, it can now be supported by three-dim…

[ INFO ] Computer Science [cs]Computer scienceAntique[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]02 engineering and technology[SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/SurgeryField (computer science)Task (project management)Domain (software engineering)Data acquisitionComputer graphics (images)Clouds[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingVirtual reconstruction0202 electrical engineering electronic engineering information engineering[INFO.INFO-IM]Computer Science [cs]/Medical ImagingComputer visionScanning[INFO]Computer Science [cs][ SDV.IB ] Life Sciences [q-bio]/BioengineeringComputing systems[ SDV.MHEP.CHI ] Life Sciences [q-bio]/Human health and pathology/SurgeryElectrical and Electronic EngineeringScanners[ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]Image segmentation[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryLasers020207 software engineeringImage segmentation3D modelingCamerasAtomic and Molecular Physics and OpticsComputer Science Applications[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Calibration020201 artificial intelligence & image processingSurgery[SDV.IB]Life Sciences [q-bio]/BioengineeringArtificial intelligencebusinessAlgorithms
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An Image Segmentation Algorithm based on Community Detection

2016

International audience; With the recent advances in complex networks, image segmentation becomes one of the most appropriate application areas. In this context, we propose in this paper a new perspective of image segmentation by applying two efficient community detection algorithms. By considering regions as communities, these methods can give an over-segmented image that has many small regions. So, the proposed algorithms are improved to automatically merge those neighboring regions agglomerative to achieve the highest modularity/stability. To produce sizable regions and detect homogeneous communities, we use the combination of a feature based on the Histogram of Oriented Gradients of the …

[ INFO ] Computer Science [cs]Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Minimum spanning tree-based segmentationImage texture0202 electrical engineering electronic engineering information engineeringcommunity detection[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Segmentation[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]modularityImage segmentationSegmentation-based object categorizationbusiness.industry[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]Pattern recognitionImage segmentationcomplex networksHistogram of oriented gradientsRegion growing020201 artificial intelligence & image processingArtificial intelligencebusiness
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Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context

2016

International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…

[ INFO ] Computer Science [cs]Computer scienceMaintenanceBig dataAdaptive learningContext (language use)Multi-label classification02 engineering and technologyOntology (information science)[INFO] Computer Science [cs]Machine learningcomputer.software_genreAdaptive LearningData modeling[SPI.AUTO]Engineering Sciences [physics]/AutomaticMachine LearningCold start020204 information systems[ SPI.AUTO ] Engineering Sciences [physics]/AutomaticMachine learning0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Multi-Label ClassificationMulti-label classificationbusiness.industryOntologyOntology-based data integration[SPI.AUTO] Engineering Sciences [physics]/Automatic020201 artificial intelligence & image processingAdaptive learningArtificial intelligencebusinesscomputer
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Automated Characterization of Mouth Activity for Stress and Anxiety Assessment

2016

International audience; Non-verbal information portrayed by human facial expression, apart from emotional cues also encompasses information relevant to psychophysical status. Mouth activities in particular have been found to correlate with signs of several conditions; depressed people smile less, while those in fatigue yawn more. In this paper, we present a semi-automated, robust and efficient algorithm for extracting mouth activity from video recordings based on Eigen-features and template-matching. The algorithm was evaluated for mouth openings and mouth deformations, on a minimum specification dataset of 640x480 resolution and 15 fps. The extracted features were the signals of mouth expa…

[ INFO ] Computer Science [cs]Computer scienceSpeech recognitionFeature extractionautomatic assessmentComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processing02 engineering and technologymouth gesture recognition[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Yawn[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Correlation03 medical and health sciencesstress0302 clinical medicineRobustness (computer science)Stress (linguistics)[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringmedicine[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Facial expression[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]anxietyimage processingRecognition[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][SPI.OPTI]Engineering Sciences [physics]/Optics / PhotonicAnxiety020201 artificial intelligence & image processing[ SPI.OPTI ] Engineering Sciences [physics]/Optics / Photonicmedicine.symptom030217 neurology & neurosurgery
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Comparative Study of the Mobile Learning Architectures

2016

International audience; With the emergence of mobile devices (Smart Phone, PDA, UMPC, game consoles, etc.), learning is changing from electronic learning (e-Learning) to mobile learning (m-learning). In fact, due to the mobility feature, it seems that the m-learning have to be adapted with the change within the context. Several researches addressed this issue and implemented a mobile learning environment to prove its usefulness and feasibility in various domains. In this article, we conduct a comparative study between a list of mobile learning architectures and methods that are presented in the literature. The performance of these architectures is evaluated based on several criteria, such a…

[ INFO ] Computer Science [cs]Computer science[ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI]Mobile computingMobile TechnologyM-learning02 engineering and technology[INFO] Computer Science [cs]Context-change managementcomputer.software_genreRobot learning[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]020204 information systems0202 electrical engineering electronic engineering information engineeringMobile searchMobile technology[INFO]Computer Science [cs]AdaptationLearning methodMultimediaLearning environmentEducational technologyContextSynchronous learningE-LearningM-learning020201 artificial intelligence & image processingcomputer
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Privacy in Big Data

2016

International audience

[ INFO ] Computer Science [cs]Computer sciencebusiness.industryInternet privacyBig data02 engineering and technology[INFO] Computer Science [cs]World Wide Web020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]020201 artificial intelligence & image processingbusinessComputingMilieux_MISCELLANEOUS
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Overlapping community detection versus ground-truth in AMAZON co-purchasing network

2015

International audience; Objective evaluation of community detection algorithms is a strategic issue. Indeed, we need to verify that the communities identified are actually the good ones. Moreover, it is necessary to compare results between two distinct algorithms to determine which is most effective. Classically, validations rely on clustering comparison measures or on quality metrics. Although, various traditional performance measures are used extensively. It appears very clearly that they cannot distinguish community structures with different topological properties. It is therefore necessary to propose an alternative methodology more sensitive to the community structure variations in orde…

[ INFO ] Computer Science [cs]Computer sciencemedia_common.quotation_subject02 engineering and technologycomputer.software_genreMachine learning01 natural sciencesClique percolation method010104 statistics & probability[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics]0202 electrical engineering electronic engineering information engineeringQuality (business)[INFO]Computer Science [cs]0101 mathematicsCluster analysisnetwork analysismedia_commonGround truthoverlapping community networksbusiness.industryCommunity structurePurchasing[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsCommunity structure[SPI.TRON]Engineering Sciences [physics]/Electronicsdetection algorithmsoverlap- ping community networks020201 artificial intelligence & image processingAlgorithm designArtificial intelligenceData miningbusinesscomputerNetwork analysis
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Interpreting Heterogeneous Geospatial Data Using Semantic Web Technologies

2016

International audience; The paper presents work on implementation of semantic technologies within a geospatial environment to provide a common base for further semantic interpretation. The work adds on the current works in similar areas where priorities are more on spatial data integration. We assert that having a common unified semantic view on heterogeneous datasets provides a dimension that allows us to extend beyond conventional concepts of searchability, reusability, composability and interoperability of digital geospatial data. It provides contextual understanding on geodata that will enhance effective interpretations through possible reasoning capabilities. We highlight this through …

[ INFO ] Computer Science [cs]Geospatial analysisComputer scienceInteroperabilitySemantification02 engineering and technologySDIcomputer.software_genreSocial Semantic Web020204 information systems0202 electrical engineering electronic engineering information engineeringSemantic analyticsGeospatial PDF[INFO]Computer Science [cs]Web Coverage ServiceSemantic Web StackSemantic WebData WebR2RMLInformation retrievalLand usebusiness.industryCIPcomputer.file_formatGeoSPARQLInteroperabilityGeoSPARQLSemantic technology020201 artificial intelligence & image processingHeterogeneitybusinesscomputer
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Ontology and protocol secure for SCADA: Int. J. of Metadata, Semantics and Ontologies, 2014 Vol.9, No.2, pp.114 - 127

2014

International audience; In this work, we present a semantic cyber security system and we study its semantic intelligent systems vulnerabilities, focusing on the semantic attacks. For resolving semantic problems we propose a security global solution for the new generation of SCADA systems. The proposed solution aims at protecting critical semantic SCADA processes from the effects of major failures and semantic vulnerabilities in the modern IT-SCADA network. We used a security block in the global network access point, security protocols deployed in different network (OSI) levels and security ontologies deployed in security devices. We used our mixed coordinates (ECC) cryptography solution, th…

[ INFO ] Computer Science [cs]security protocolsComputer sciencedata acquisition[ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI]020209 energy[ INFO.INFO-CR ] Computer Science [cs]/Cryptography and Security [cs.CR]Cryptography02 engineering and technologysemantic security blockLibrary and Information SciencesComputer securitycomputer.software_genreSOAP protocolSecurity information and event management[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI][INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]SCADAModbus protocolECC cryptography0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]SCADAcontrol systemsCloud computing securityvulnerabilitiesTOMSONbusiness.industryencryption technologyembedded applicationscyber securitysupervisory controlsemantic attacksCryptographic protocolComputer security modelsecurity ontologyComputer Science ApplicationsOSI modellow latencySecurity service020201 artificial intelligence & image processingbusinesscomputerInformation SystemsComputer network
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