Kobe web 2.0 Applications and Services

Kobe is code name given by Microsoft for planning architecting and implementing using web 2.0 applications and services.

Web 2.0 are web site applications that fosters active and social user engagement with in surround online community in the context of cba (cba -> could be anything, web app -> experience using a browser)

Web 2.0 service not restricted to browsers and it spreads across pcs, mobiles, gaming consols, mashup .

Web 2.0 Application can be explained technically as

OPEN

O- OPEN
P- programmable
E- Embeddable, Extensible, Everywhere
N- Neutral

more detailed information about planning, architecting and implementing using web 2.0 applications and services checkout Microsoft website which has series of videos with detailed demos

This is one of the previous absolete blog posts which i had .

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ABOD and its PyOD python module

Angle based detection By Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek Ludwig-Maximilians-Universität München Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By Yue Zhao Zain Nasrullah Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Zheng Li jk Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing. Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based sim...

TableSense: Spreadsheet Table Detection with Convolutional Neural Networks

- By Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, Dongmei Zhang Microsoft Research, Beijing 100080, China. Beihang University, Beijing 100191, China Paper Link Abstract Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for...

Why Should I Trust You?. . LIME

-By Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin University of Washington Seattle, WA 98105, USA Paper Link This is third post in the series of Explainable AI (XAI). Earlier post i shed light on Machine learning impact on models with respect to Biasing. Today's topic is one of the large number of elementary operations , "Linear Proxy Models" (LIME). TRUST I would like to quote Stephen M.R. Covey "THE SPEED OF TRUST" Statements here, which is relevant to Trust. Executive summary link Simply put, trust means confidence. The opposite of trust — distrust — is suspicion. Trust always affects two outcomes: speed and cost. When trust goes down, speed goes down and cost goes up. When trust goes up, speed goes up and cost goes down (Strategy x Execution) x Trust = Results Not trusting people is a greater risk. if the users do not trust a model or a prediction, they will not use it. ...

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