Maximo is an IBM enterprise asset management for asset life-cycle and maintenance management. IBM Maximo® enterprise asset management solutions allow you to gain near real time visibility into asset usage, better govern assets, extend the useful life of capital equipment, improve return on assets and defer new purchases—while unifying processes for wide-ranging enterprising asset management functions across multiple sites.
Support enterprise asset management in key industries, including manufacturing, healthcare, life sciences, nuclear power, oil and gas, service providers, transportation and utilities.
Provide visibility and control over critical assets that affect compliance, risk and business performance.
Increase the useful life of physical assets with improved business processes for an increased return on assets and enhanced operational efficiency.
It has six major functions
Asset management – Achieve the control you need to more efficiently track and manage asset and location data throughout the asset lifecycle.
Work management – Manage both planned and unplanned work activities, from initial request through completion and recording of actuals.
Service management – Define service offerings, establish service level agreements (SLAs), more proactively monitor service level delivery and implement escalation procedures.
Contract management – Gain complete support for purchase, lease, rental, warranty, labor rate, software, master, blanket and user-defined contracts.
Inventory management – Know the details of asset-related inventory and its usage including what, when, where, how many and how valuable.
Procurement management – Support all phases of enterprise-wide procurement such as direct purchasing and inventory replenishment.
IBM MobileFirst Foundation, formerly known as IBM Worklight®, is a suite of software development products that allow developers to build and deliver mobile applications for the enterprise.
The MobileFirst Platform Foundation consists of:
MobileFirst Server – the middleware tier that provides a gateway between back-end systems and services and the mobile client applications.
MobileFirst API - both client and server-side APIs for developing and managing your enterprise mobile applications.
MobileFirst Studio - an optional all-inclusive development environment for developing enterprise apps on the MobileFirst platform. This is based on the Eclipse platform, and includes an integrated server, development environment, facilities to create and test all data adapters/services, a browser-based hybrid app simulator, and the ability to generate platform-specific applications for deployment.
MobileFirst Console – the console provides a dashboard and management portal for everything happening within your MobileFirst applications.
MobileFirst Application Center - a tool to make sharing mobile apps easier within an organization. Basically, it’s an app store for your enterprise.
BM talks about the MobileFirst Platform in two ways, based in its capabilities and also by its components. The capability areas are: Continuously Improve, Secure, Contextualize and Personalize, and Enrich with Data.
Continuously Improve - allows IT to manage application refresh cycles and collect in-app usage analytics. Secure - provides enterprise mobility management (EMM) capabilities. Contextualize and Personalize - allows developers to create mobile apps that are location- and context-aware. Enrich with Data - allows IT to join its mobile apps to internal and external data sources by connecting directly with IBM's Cloudant database as a service (DBaaS).
What is Seaborn? Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
A dataset-oriented API for examining relationships between multiple variables
Specialized support for using categorical variables to show observations or aggregate statistics
Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
Automatic estimation and plotting of linear regression models for different kinds dependent variables
Convenient views onto the overall structure of complex datasets
High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
Concise control over matplotlib figure styling with several built-in themes
Tools for choosing color palettes that faithfully reveal patterns in your data
Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.
mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users.
This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.
As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.
mlpack is developed by contributors from around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License: LGPL, version 3.)
mlpack was originally presented at the BigLearning workshop of NIPS 2011 [pdf] and later published in the Journal of Machine Learning Research [pdf], with version 3 being published in the Journal of Open Source Software [pdf]. Please cite mlpack in your work using this citation.
mlpack bindings for R are provided by the RcppMLPACK project.
Currently mlpack supports the following algorithms:
Decision stumps (one-level decision trees)
Density Estimation Trees
Euclidean Minimum Spanning Trees
Gaussian Mixture Models (GMMs)
Hidden Markov Models (HMMs)
Kernel Principal Component Analysis (KPCA)
Least-Angle Regression (LARS/LASSO)
Local Coordinate Coding
Locality-Sensitive Hashing (LSH)
Naive Bayes Classifier
Nearest neighbor search with dual-tree algorithms
Neighbourhood Components Analysis (NCA)
Non-negative Matrix Factorization (NMF)
Principal Components Analysis (PCA)
Independent component analysis (ICA)
Rank-Approximate Nearest Neighbor (RANN)
Simple Least-Squares Linear Regression (and Ridge Regression)
Sparse Coding, Sparse dictionary learning
For more detail visit here - http://mlpack.org/docs.html
PyShark is a wrapper for the Wireshark CLI interface, tshark, so all of the Wireshark decoders are available to PyShark!
Python wrapper for tshark, allowing python packet parsing using wireshark dissectors.
There are quite a few python packet parsing modules, this one is different because it doesn't actually parse any packets, it simply uses tshark's (wireshark command-line utility) ability to export XMLs to use its parsing.
This package allows parsing from a capture file or a live capture, using all wireshark dissectors you have installed. Tested on windows/linux.
Example Code for Reading a File
import pyshark cap = pyshark.FileCapture('/tmp/mycapture.cap') cap >>> <FileCapture /tmp/mycapture.cap> print cap Packet (Length: 698) Layer ETH: Destination: aa:bb:cc:dd:ee:ff Source: 00:de:ad:be:ef:00 Type: IP (0x0800) Layer IP: Version: 4 Header Length: 20 bytes Differentiated Services Field: 0x00 (DSCP 0x00: Default; ECN: 0x00: Not-ECT (Not ECN-Capable Transport)) Total Length: 684 Identification: 0x254f (9551) Flags: 0x00 Fragment offset: 0 Time to live: 1 Protocol: UDP (17) Header checksum: 0xe148 [correct] Source: 192.168.0.1 Destination: 192.168.0.2
FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. FastText builds on modern Mac OS and Linux distributions. Since it uses C++11 features, it requires a compiler with good C++11 support.
Steps for Installing
- git clone https://github.com/facebookresearch/fastText.git - cd fastText - make
Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.
What is text classification? The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed.
Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).