Centre dedocumentation
scientifique

Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks / Hang Zhang in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 10 Titre : Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks Type de document : Article/Communication Auteurs : Hang Zhang, Auteur ; Mingxin Gan, Auteur ; Xi Sun, Auteur Année de publication : 2021 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] approche participative [Termes IGN] comportement [Termes IGN] filtrage d'information [Termes IGN] interprétation (psychologie) [Termes IGN] mémoire [Termes IGN] mobilité [Termes IGN] point d'intérêt [Termes IGN] réseau social géodépendant [Termes IGN] tourisme Résumé : (auteur) In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods. Numéro de notice : A2021-148 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010036 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/ijgi10010036 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97056 [article]

How do species and data characteristics affect species distribution models and when to use environmental filtering? / Lukáš Gábor in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 8 (August 2020) . - pp 1567 - 1584 Titre : How do species and data characteristics affect species distribution models and when to use environmental filtering? Type de document : Article/Communication Auteurs : Lukáš Gábor, Auteur ; Vítězslav Moudrý, Auteur ; Vojtěch Barták, Auteur ; Vincent Lecours, Auteur Année de publication : 2020 Article en page(s) : pp 1567 - 1584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] données environnementales [Termes IGN] données localisées [Termes IGN] échantillonnage (statistique) [Termes IGN] entropie maximale [Termes IGN] erreur d'échantillon [Termes IGN] filtrage d'information [Termes IGN] interaction spatiale Résumé : (auteur) Species distribution models (SDMs) are widely used in ecology and conservation. However, their performance is known to be affected by a variety of factors related to species occurrence characteristics. In this study, we used a virtual species approach to overcome the difficulties associated with testing of combined effects of those factors on performance of presence-only SDMs when using real data. We focused on the individual and combined roles of factors related to response variable (i.e. sample size, sampling bias, environmental filtering, species prevalence, and species response to environmental gradients). Results suggest that environmental filtering is not necessarily helpful and should not be performed blindly, without evidence of bias in species occurrences. The more gradual the species response to environmental gradients is, the greater is the model sensitivity to an inappropriate use of environmental filtering, although this sensitivity decreases with higher species prevalence. Results show that SDMs are affected to the greatest degree by the species response to environmental gradients, species prevalence, and sample size. Models’ accuracy decreased with sample size below 300 presences. Furthermore, a high level of interactions among individual factors was observed. Ignoring the combined effects of factors may lead to misleading outcomes and conclusions. Numéro de notice : A2020-414 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615070 Date de publication en ligne : 14/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615070 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95465 [article]

Learning evolving user’s behaviors on location-based social networks / Ruizhi Wu in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 3 (July 2020) . - pp 713 – 743 Titre : Learning evolving user’s behaviors on location-based social networks Type de document : Article/Communication Auteurs : Ruizhi Wu, Auteur ; Guangchun Luo, Auteur ; Qi jin, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 713 – 743 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] comportement [Termes IGN] données localisées des bénévoles [Termes IGN] filtrage d'information [Termes IGN] géopositionnement [Termes IGN] interaction homme-milieu [Termes IGN] modèle dynamique [Termes IGN] réseau social géodépendant [Termes IGN] utilisateur Résumé : (auteur) With the popularity of smart phones, users’ activities on location-based social networks (LBSNs) evolve faster than traditional social networks. Existing models focus on modeling users’ long-term preferences, leveraging social collaborative filtering to enhance prediction performance. However, the dynamic mobility mechanism of user’s check-in behaviors on LBSNs is seldom considered. In this paper, we propose a new dynamic model that considers both geo-aware user preferences and the social interaction excitation arising from social connections to learn the dynamic mobility mechanism of user’s behaviors on LBSNs. Geo-aware location features, such as semantic features, latent features and dynamic features, are utilized to characterize the location information and reveal the evolution of the geographical impact of location. These geo-aware location features enable us to exploit user’s personal preferences. Meanwhile, we integrate a user’s social connections and friends’ preferences for modeling social interaction excitations. Finally, we jointly incorporate geo-aware user preference learning and social interaction excitation modeling to create a conditional intensity function for temporal point processes with which to explore the dynamic mobility mechanism of evolving user’s check-in behaviors on LBSNs. Extensive experiments on several real-world check-in datasets confirm that our proposed algorithm performs better than existing state-of-the-art methods. Numéro de notice : A2020-372 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00400-3 Date de publication en ligne : 16/03/2020 En ligne : https://doi.org/10.1007/s10707-020-00400-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95267 [article]

A Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2516 - 2529 Titre : A Single Model CNN for Hyperspectral Image Denoising Type de document : Article/Communication Auteurs : Alessandro Maffei, Auteur ; Juan Mario Haut, Auteur ; Mercedes Eugenia Paoletti, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2516 - 2529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bande spectrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] filtrage d'information [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] information géographique [Termes IGN] signature spectrale Résumé : (auteur) Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this article considers deep learning models—such as convolutional neural networks (CNNs)—to perform spectral–spatial HSI denoising. The proposed model, called HSI single denoising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN Numéro de notice : A2020-199 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2952062 Date de publication en ligne : 26/11/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2952062 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94869 [article]

Using real polar ground gravimetry data to solve the GOCE polar gap problem in satellite-only gravity field recovery / Biao Lu in Journal of geodesy, Vol 94 n°3 (March 2020)  

Public [article]  inJournal of geodesy > Vol 94 n°3 (March 2020) Titre : Using real polar ground gravimetry data to solve the GOCE polar gap problem in satellite-only gravity field recovery Type de document : Article/Communication Auteurs : Biao Lu, Auteur ; Christoph Förste, Auteur ; Franz Barthelmes, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] anomalie de pesanteur [Termes IGN] Antarctique [Termes IGN] Arctique [Termes IGN] champ de gravitation [Termes IGN] données GOCE [Termes IGN] données GRACE [Termes IGN] Earth Gravity Model 2008 [Termes IGN] filtrage d'information [Termes IGN] levé gravimétrique [Termes IGN] modèle de géopotentiel [Termes IGN] zone polaire Résumé : (auteur) With the successful completion of European Space Agency (ESA)’s PolarGAP campaign, ground gravity data are now available for both polar regions. Therefore, it is now possible to solve the GOCE polar gap problem in satellite-only gravity field recovery by using additional polar ground gravity data instead of some regularization methods. However, ground gravimetry data need to be filtered to remove the short-wavelength information beyond a certain harmonic degree to avoid spectral leakage when inferring satellite-only gravity field models. For the Arctic, the ArcGP data set was successfully applied when inferring the high-resolution gravity field model EGM2008 which could be used for this filtering there. For Antarctica, a combination of latest airborne gravimetry data from ESA’s PolarGap campaign and some previous gravity data was recently published which was irregularly distributed in space and still had some small gaps within the GOCE south polar gap. Therefore, we proposed a point mass modeling method for this filtering which was similar to the way using EGM2008 for such filtering to the ground gravity data in the Arctic. Furthermore, a variance component estimation was applied to combine the normal equations from the different sources to build a global gravity field model called IGGT_R1C. Then, this model’s accuracy was evaluated by comparison with other gravity field models in terms of difference degree amplitudes, gravity anomaly differences as well as external checking by obit adjustment and gravity data in the GOCE polar gap areas. This gravity field model performed well globally according to these checking results; especially, the RMS of the residuals between the filtered gravity data and that calculated from IGGT_R1C was the smallest (2.6 mGal in the Arctic and 5.4 mGal in Antarctica) compared with that of the relevant satellite-only gravity field models, e.g., GOCO05s. Therefore, the disturbing impact of the GOCE polar data gap problem could be solved by adding the polar ground gravity data. Numéro de notice : A2020-155 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01361-z Date de publication en ligne : 25/02/2020 En ligne : https://doi.org/10.1007/s00190-020-01361-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94805 [article]

Développement d’outils web de data visualization / Edson Olivares Medina (2020) 

Permalink

PerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica [en ligne], vol 21 n° 3 (July - September 2017)  

Permalink

Constrained clustering by constraint programming / Thi-Bich-Hanh Dao in Artificial intelligence, vol 244 (March 2017)  

Permalink

Using seal trajectories in biological early warning system for real-time zone tracking / Rouaa Wannous in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 4 (juillet - août 2016)  

Permalink

Vérification automatique d’exigences pour les politiques d’échange d’information. Exigences de diffusion et de non-diffusion d'information / Rémi Delmas in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 2 (mars - avril 2016)  

Permalink

Assessment of the quality of digital terrain models / Joachim Höhle (December 2011)  

Permalink

Evaluating selected visualization methods for exploring VGI / Rob Feick in Geomatica, vol 64 n° 4 (December 2010)

Permalink

Analytical 3D views and virtual globes: scientific results in a familiar spatial context / D. Tiede in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 3 (May - June 2010)

Permalink