THE CAD-GIS MANAGEMENT OF THE REGIONAL DIGITAL TECHNICAL MAP (CTRN) OF FRIULI VENEZIA GIULIA REGION

I nuovi strumenti software messi a disposizione dall’informatica consentono, anche in ambito cartografico, ampi miglioramenti nella gestione dei dati e delle rappresentazioni grafiche sia a video che in stampa. La procedura automatizzata qui presentata permette di gestire, in ambiente CAD – GIS, la cartografia numerica della regione Friuli Venezia Giulia; le sue caratteristiche principali sono: la gestione continua di tutto il territorio regionale, la visualizzazione delle singole sottoclassi della carta numerica, l’utilizzo di immagini raster di sfondo (ortofoto), la gestione dello “storico” della carta, nonché ampie possibilità di personalizzazione e di stampa. Pur non volendo essere un’alternativa alla produzione tradizionale di cartografia, risulta comunque un ottimo strumento operativo di lavoro.The new software tools from IT world concur to obtain large improvement in managing data and graphical representations on video and paper mapping. The automatic procedure here introduced concur in managing via CAD and GIS instruments the digital cartography of Friuli Venezia Giulia region. The main characteristics of this procedure deal with the continuous management of the regional territory, the visualization of the single features of the digital cartography, as well as the use of raster backgrounds like orthophotos and the possibility to manage and monitor the modifications occurred in the digital cartography in time. The system does not represent an alternative to the traditional cartography plotted on paper but represent a very efficient and useful working instrument

MONETIZATION ABUSE DETECTION BASED ON CO-WATCH SIMILARITY

A system for detecting monetization abuse by channels of content sharing networks is disclosed. The proposed system uses machine learning methods to generate a predictive model capable of being applied to channels to determine a likelihood that the channel is engaged in monetization abuse. Specifically, the proposed system may train a machine learning classifier using a plurality of training videos. The training may be based on generating clusters of videos and identifying which clusters are risky. The machine learning classifier may then be applied to existing and new channels to detect monetization abuse (e.g., channels uploading inappropriate content) and flag the potentially abusive channels for administrative review

Method to Identify Change based on Content-based Embedding

A method for identifying change based on content-based embedding is disclosed. The proposed method computes content similarity of content items uploaded to a user’s personal channel. The method then computes a channel risk score, which identifies whether the type(s) of content uploaded to a user’s personal channel has changed after a specific point in time. A channel in which the type(s) of content has changed after a specific point in time, for example after the channel was reviewed for a monetization program, may indicate potential abuse of the monetization program. Based on the risk score, the method may flag potentially abusive channels for administrative review, which may be reviewed by an automated system, a human reviewer, or a combination

Scalable Methods And Systems For Storing Matching Segments

Disclosed herein is a mechanism for providing a compact representation of match information. Such a mechanism can include (i) accessing a repository or database of match information or other information about a segment of a video being re-used by a segment of another video, (ii) dividing each video from the match information into multiple segments (e.g., segments of a fixed duration or segments of a fixed number), and (iii) selecting a representative segment from the multiple segments, where the representative segment is stored in a storage device. The representative segment can be a compact approximate representation of the match information from a match database. A real-time matcher can reconstruct match information from the compact approximate representation retrieved from the storage device, which can be transmitted for use by a real-time application

Flight Planning in Free Route Airspaces

We consider the problem of finding cheapest flight routes through free route airspaces in a 2D setting. We subdivide the airspace into regions determined by a Voronoi subdivision around the points from a weather forecast. This gives rise to a regular grid of rectangular regions (quads) with every quad having an associated vector-weight that represents the wind magnitude and direction. Finding a cheapest path in this setting corresponds to finding a piece-wise linear path determined by points on the boundaries of the quads. In our solution approach, we discretize such boundaries by introducing border points and only consider segments connecting border points belonging to the same quad. While classic shortest path graph algorithms are available and applicable to the graphs originating from these border points, we design an algorithm that exploits the geometric structure of our scenario and show that this algorithm is more efficient in practice than classic graph-based algorithms. In particular, it scales better with the number of quads in the subdivision of the airspace, making it possible to find more accurate routes or to solve larger problems

Local Ranking Problem on the BrowseGraph

The "Local Ranking Problem" (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a web-server has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8

Search Behaviour On Photo Sharing Platforms

The behaviour, goals, and intentions of users while searching for images in large scale online collections are not well understood, with image search log analysis providing limited insights, in part because they tend only to have access to user search and result click information. In this paper we study user search behaviour in a large photo-sharing platform, analyzing all user actions during search sessions (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image search. We show that search behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image search. © 2013 IEEE.published_or_final_versio