Confluence is project management tool which provide you best platform to create, organize, and discuss work with your team. Confluence makes team to spend less time hunting things down and more time making things happen. Organize your work, create documents, and discuss everything in one place.
Below operation we can perform under Jira confluence tool:
Create anything your team needs - meeting notes, product requirements, knowledge base articles - on the web so everyone can contribute.
Give feedback on any Confluence page or file with inline and pinned comments. No more ridiculous file_names_with_dates.doc or messy track changes.
Capture all the information that's scattered among email inboxes and countless apps in the same place.
Give every team, project, or department its own space to store work. Confluence keeps everything organized and accessible.
Confluence gives you the power to create anything - meeting notes, project plans, product requirements, etc. – thanks to a simple, but powerful editor.
You work with files every day – images, PDFs, spreadsheets, and presentations. You can give feedback directly on your files in Confluence, and it keeps tracks of versions automatically, so you're always working on the right one.
Create reports and charts
Reporting on information stored in JIRA is simple in Confluence. In addition to the JIRA Issues Macro, you can use the JIRA Report blueprint or JIRA Chart macro to show information from your JIRA application visually. It's the best way to give your stakeholders a snapshot of your team or project's progress.
Use the JIRA report blueprint to create a Change Log or Status report.
Use the JIRA Chart Micros to display data as a chart, including pie charts, created vs resolved, and two dimensional charts.
Use JIRA Gadgets to display detailed JIRA reports and charts on pages.
MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
Regression: generalized linear regression, survival regression,...
Decision trees, random forests, and gradient-boosted trees
Recommendation: alternating least squares (ALS)
Clustering: K-means, Gaussian mixtures (GMMs),...
Topic modeling: latent Dirichlet allocation (LDA)
Frequent itemsets, association rules, and sequential pattern mining
Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.
Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface.
Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries. Statmodels in free software released under the Modified BSD (3-clause) license.
Linear regression models:
Mixed Linear Model with mixed effects and variance components
GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
Bayesian Mixed GLM for Binomial and Poisson
GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
Nonparametric statistics: Univariate and multivariate kernel density estimators
Datasets: Datasets used for examples and in testing
Statistics: a wide range of statistical tests
Imputation with MICE, regression on order statistic and Gaussian imputation
Tools for reading Stata .dta files, but pandas has a more recent version
Scapy is a powerful interactive packet manipulation program. It is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, match requests and replies, and much more. It can easily handle most classical tasks like scanning, tracerouting, probing, unit tests, attacks or network discovery (it can replace hping, 85% of nmap, arpspoof, arp-sk, arping, tcpdump, tethereal, p0f, etc.).
It also performs very well at a lot of other specific tasks that most other tools can’t handle, like sending invalid frames, injecting your own 802.11 frames, combining technics
Scapy is a packet manipulation tool for computer networks, written in Python by Philippe Biondi. It can forge or decode packets, send them on the wire, capture them, and match requests and replies. It can also handle tasks like scanning, tracerouting, probing, unit tests, attacks, and network discovery.
Scapy provides a Python interface into libpcap, (WinPCap/Npcap on Windows), in a similar way to that in which Wireshark provides a view and capture GUI. It can interface with a number of other programs to provide visualization including Wireshark for decoding packets, GnuPlot for providing graphs, graphviz or VPython for visualisation, etc.
The concept behind Scapy is that it is cable of sending and receiving packets and it can sniff packets. The packets to be sent can be created easily using the built-in options and the received packets can be dissected. Sniffing of packets helps in understanding what communication is taking place on the network.
TopoJSON is an extension of GeoJSON that encodes topology. Rather than representing geometries discretely, geometries in TopoJSON files are stitched together from shared line segments called arcs. This technique is similar to Matt Bloch’s MapShaper and the Arc/Info Export format, .e00.
TopoJSON eliminates redundancy, allowing related geometries to be stored efficiently in the same file. For example, the shared boundary between California and Nevada is represented only once, rather than being duplicated for both states. A single TopoJSON file can contain multiple feature collections without duplication, such as states and counties. Or, a TopoJSON file can efficiently represent both polygons (for fill) and boundaries (for stroke) as two feature collections that share the same arc mesh.
A TopoJSON file format is a format that encodes topology. TopoJSON is an extension of geoJSON. This format contains both geospatial data (arcs) and attribute data. In contrast to other GIS formats topoJSON uses arcs. Arcs are sequences of points, while line strings and polygons are defined as sequences of arcs.
Each arc is defined only once, but can be referenced several times by different shapes, thus reducing redundancy and decreasing the file size. The topoJSON format is a format that is used by software like Microsoft PowerBI.
EOS is a blockchain platform for the development of decentralized applications (dapps), similar to Ethereum in function. It provides a complete operating system for decentralized applications focused on the web with services like user authentication, cloud storage, and server hosting.
EOSIO is a free, open-source blockchain software protocol that provides developers and entrepreneurs with a platform on which to build, deploy and run high-performing decentralized applications (DAPPs)
EOSIO based blockchains execute user-generated applications and code using WebAssembly (WASM). WASM is an emerging web standard with widespread support of Google, Microsoft, Apple, and industry leading companies.
At the moment the most mature toolchain for building applications that compile to WASM is clang/llvm with their C/C++ compiler. For best compatibility, it is recommended that you use the EOSIO toolchain.
Other toolchains in development by 3rd parties include: Rust, Python, and Solidity. While these other languages may appear simpler, their performance will likely impact the scale of application you can build. We expect that C++ will be the best language for developing high-performance and secure smart contracts and plan to use C++ for the foreseeable future.