A Model for Personalized Keyword Extraction from Web Pages using Segmentation

The World Wide Web caters to the needs of billions of users in heterogeneous groups. Each user accessing the World Wide Web might have his / her own specific interest and would expect the web to respond to the specific requirements. The process of making the web to react in a customized manner is achieved through personalization. This paper proposes a novel model for extracting keywords from a web page with personalization being incorporated into it. The keyword extraction problem is approached with the help of web page segmentation which facilitates in making the problem simpler and solving it effectively. The proposed model is implemented as a prototype and the experiments conducted on it empirically validate the model's efficiency.Comment: 6 Pages, 2 Figure

Morpes: A Model for Personalized Rendering of Web Content on Mobile Devices

With the tremendous growth in the information communication sector, the mobile phones have become the prime information communication devices. The convergence of traditional telephony with the modern web enabled communication in the mobile devices has made the communication much effective and simpler. As mobile phones are becoming the crucial source of accessing the contents of the World Wide Web which was originally designed for personal computers, has opened up a new challenge of accommodating the web contents in to the smaller mobile devices. This paper proposes an approach towards building a model for rendering the web pages in mobile devices. The proposed model is based on a multi-dimensional web page segment evaluation model. The incorporation of personalization in the proposed model makes the rendering user-centric. The proposed model is validated with a prototype implementation.Comment: 10 Pages, 2 Figure

Multidimensional Web Page Evaluation Model Using Segmentation And Annotations

The evaluation of web pages against a query is the pivot around which the Information Retrieval domain revolves around. The context sensitive, semantic evaluation of web pages is a non-trivial problem which needs to be addressed immediately. This research work proposes a model to evaluate the web pages by cumulating the segment scores which are computed by multidimensional evaluation methodology. The model proposed is hybrid since it utilizes both the structural semantics and content semantics in the evaluation process. The score of the web page is computed in a bottom-up process by evaluating individual segment's score through a multi-dimensional approach. The model incorporates an approach for segment level annotation. The proposed model is prototyped for evaluation; experiments conducted on the prototype confirm the model's efficiency in semantic evaluation of pages.Comment: 11 Pages, 4 Figures; International Journal on Cybernetics & Informatics (IJCI), Vol.1, No.4, August 2012. arXiv admin note: substantial text overlap with arXiv:1203.361

Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction

Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the control effort for next-best-view planning. We present Pred-NBV, a realistic object shape reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model trained on the ShapeNet dataset, and an information and control effort-based next-best-view method to address these issues. Pred-NBV shows an improvement of 25.46% in object coverage over the traditional methods in the AirSim simulator, and performs better shape completion than PoinTr, the state-of-the-art shape completion model, even on real data obtained from a Velodyne 3D LiDAR mounted on DJI M600 Pro.Comment: 6 pages, 4 figures, 2 tables. Accepted to IROS 202

A personalized web page content filtering model based on segmentation

In the view of massive content explosion in World Wide Web through diverse sources, it has become mandatory to have content filtering tools. The filtering of contents of the web pages holds greater significance in cases of access by minor-age people. The traditional web page blocking systems goes by the Boolean methodology of either displaying the full page or blocking it completely. With the increased dynamism in the web pages, it has become a common phenomenon that different portions of the web page holds different types of content at different time instances. This paper proposes a model to block the contents at a fine-grained level i.e. instead of completely blocking the page it would be efficient to block only those segments which holds the contents to be blocked. The advantages of this method over the traditional methods are fine-graining level of blocking and automatic identification of portions of the page to be blocked. The experiments conducted on the proposed model indicate 88% of accuracy in filtering out the segments.Comment: 11 Pages, 6 Figure