The Business Model Canvas

Painel do The Business Model generationThe Business Model Canva

Generating Sentences Using a Dynamic Canvas

We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language. It uses a recurrent neural network with a dynamic attention and canvas memory mechanism to iteratively construct sentences. By viewing the state of the memory at intermediate stages and where the model is placing its attention, we gain insight into how it constructs sentences. We demonstrate that AUTR learns a meaningful latent representation for each sentence, and achieves competitive log-likelihood lower bounds whilst being computationally efficient. It is effective at generating and reconstructing sentences, as well as imputing missing words.Comment: AAAI 201

AMC Native WebRTC Client

Traditional call center and telecommunication hardware is being replaced by thin, browser-based, cloud enabled web services. Industry standards for web based communication protocols, such as WebRTC, are being established. AMC needed to address this new technology, while maintaining a hybrid approach of server-based capabilities, taking advantage of the web-based communication channel, while broadcasting events to the Contact Canvas Server. Contact Canvas Agent Palette is the editing platform of the AMC adapter for Salesforce.com, allowing agents to communicate with customers through the AMC adapter/ Softphone. Using Agent Palette, the task was to integrate Video Chat using WebRTC into the AMC toolbar. Two agents use a peer-to-peer connection to establish communication with one another. The connected two can communicate through video chat which supports screen pop. The components that were provided and used were the AMC adapter for salesforce.com, the Agent Palette, and the salesforce.com Customer Relation Management (CRM) database. The AMC adapter is an HTML Softphone that can be used to voice enable salesforce.com, while Socket.io and Node.js were used to communicate with the server side. Eventually this video chat will advance to the point where communication will be established between agents and their customers.https://scholarscompass.vcu.edu/capstone/1162/thumbnail.jp

Towards accurate detection of obfuscated web tracking

Web tracking is currently recognized as one of the most important privacy threats on the Internet. Over the last years, many methodologies have been developed to uncover web trackers. Most of them are based on static code analysis and the use of predefined blacklists. However, our main hypothesis is that web tracking has started to use obfuscated programming, a transformation of code that renders previous detection methodologies ineffective and easy to evade. In this paper, we propose a new methodology based on dynamic code analysis that monitors the actual JavaScript calls made by the browser and compares them to the original source code of the website in order to detect obfuscated tracking. The main advantage of this approach is that detection cannot be evaded by code obfuscation. We applied this methodology to detect the use of canvas-font tracking and canvas fingerprinting on the top-10K most visited websites according to Alexa's ranking. Canvas-based tracking is a fingerprinting method based on JavaScript that uses the HTML5 canvas element to uniquely identify a user. Our results show that 10.44% of the top-10K websites use canvas-based tracking (canvas-font and canvas fingerprinting), while obfuscation was used in 2.25% of them. These results confirm our initial hypothesis that obfuscated programming in web tracking is already in use. Finally, we argue that canvas-based tracking can be more present in secondary pages than in the home page of websites.Peer ReviewedPostprint (author's final draft

Modeling On-Line Art Auction Dynamics Using Functional Data Analysis

In this paper, we examine the price dynamics of on-line art auctions of modern Indian art using functional data analysis. The purpose here is not just to understand what determines the final prices of art objects, but also the price movement during the entire auction. We identify several factors, such as artist characteristics (established or emerging artist; prior sales history), art characteristics (size; painting medium--canvas or paper), competition characteristics (current number of bidders; current number of bids) and auction design characteristics (opening bid; position of the lot in the auction), that explain the dynamics of price movement in an on-line art auction. We find that the effects on price vary over the duration of the auction, with some of these effects being stronger at the beginning of the auction (such as the opening bid and historical prices realized). In some cases, the rate of change in prices (velocity) increases at the end of the auction (for canvas paintings and paintings by established artists). Our analysis suggests that the opening bid is positively related to on-line auction price levels of art at the beginning of the auction, but its effect declines toward the end of the auction. The order in which the lots appear in an art auction is negatively related to the current price level, with this relationship decreasing toward the end of the auction. This implies that lots that appear earlier have higher current prices during the early part of the auction, but that effect diminishes by the end of the auction. Established artists show a positive relationship with the price level at the beginning of the auction. Reputation or popularity of the artists and their investment potential as assessed by previous history of sales are positively related to the price levels at the beginning of the auction. The medium (canvas or paper) of the painting does not show any relationship with art auction price levels, but the size of the painting is negatively related to the current price during the early part of the auction. Important implications for auction design are drawn from the analysis.Comment: Published at http://dx.doi.org/10.1214/088342306000000196 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org