�ݺ�ߣshows by User: njss

�ݺ�ߣshows by User: njss / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: njss / Sat, 04 Aug 2018 20:11:57 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: njss Leveraging Eye-gaze and Time-series Features to Predict User Interests and Build a Recommendation Model for Visual Analysis /slideshow/leveraging-eyegaze-and-timeseries-features-to-predict-user-interests-and-build-a-recommendation-model-for-visual-analysis/108648998 a13-silva-180804201157
We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.]]>
We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.]]> Sat, 04 Aug 2018 20:11:57 GMT /slideshow/leveraging-eyegaze-and-timeseries-features-to-predict-user-interests-and-build-a-recommendation-model-for-visual-analysis/108648998 njss@slideshare.net(njss) Leveraging Eye-gaze and Time-series Features to Predict User Interests and Build a Recommendation Model for Visual Analysis njss We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/a13-silva-180804201157-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.

Leveraging Eye-gaze and Time-series Features to Predict User Interests and Build a Recommendation Model for Visual Analysis from Nelson J. S. Silva

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0 Optimal Forms /slideshow/optimal-forms/9750390 optimalformsamasterthesisonoptimizationofgenerativemodelsnelsonsilva-13189509786423-phpapp02-111018102051-phpapp02
A Master Thesis On Optimization Of Generative Models by Nelson de Jesus Silverio da Silva]]>
A Master Thesis On Optimization Of Generative Models by Nelson de Jesus Silverio da Silva]]> Tue, 18 Oct 2011 10:18:10 GMT /slideshow/optimal-forms/9750390 njss@slideshare.net(njss) Optimal Forms njss A Master Thesis On Optimization Of Generative Models by Nelson de Jesus Silverio da Silva <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/optimalformsamasterthesisonoptimizationofgenerativemodelsnelsonsilva-13189509786423-phpapp02-111018102051-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> A Master Thesis On Optimization Of Generative Models by Nelson de Jesus Silverio da Silva

Optimal Forms from Nelson J. S. Silva

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0 https://cdn.slidesharecdn.com/profile-photo-njss-48x48.jpg?cb=1730628814 Currently, co-responsible for Business Development in the area of Strategic Intelligence. Also, a research member of the Knowledge Visualization team. PhD student in Computer Science, at TU Graz, Austria. Current research includes time series and algorithms, visual analytics, eye-tracking, graphs, IoT. Moto: "One life is too short to know everything on any topic...". Working on projects related to: - Strategic Intelligence - text-mining, graph analytics and visual explorations. - Visual Analytics and Optimisation of ranking results in time series pattern analysis. - IoT, and HoloLens - Hierarchical data clustering exploration and visualisation supported by eye-tracking. www.know-center.at https://cdn.slidesharecdn.com/ss_thumbnails/a13-silva-180804201157-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/leveraging-eyegaze-and-timeseries-features-to-predict-user-interests-and-build-a-recommendation-model-for-visual-analysis/108648998 Leveraging Eye-gaze an... https://cdn.slidesharecdn.com/ss_thumbnails/optimalformsamasterthesisonoptimizationofgenerativemodelsnelsonsilva-13189509786423-phpapp02-111018102051-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/optimal-forms/9750390 Optimal Forms