No matter what type of business you’re in, boosting your bottom line is always in the back of your mind. In a rough economy, it can be tempting to focus too much on pulling in more money and not enough on containing it from within.

That’s right, containing it. You have the potential to lose hundreds of thousands of dollars every year right under your nose from something as simple as lack of or ineffective computer training.

How much do the employees you have working for you right now really understand about technology? How good is your IT tech department? Technology changes faster than a blink of an eye and oftentimes, businesses struggle to keep their top employees trained.

With millions of dollars being lost to Internet espionage, file corruption and other computer crimes, staying on top of changes is essential. Recently, online learning centers have been emerging as the go-to method for quick and inexpensive learning. Unfortunately, this type of computer learning isn’t always the best solution.

Not too long ago, Apple added something phenomenal to the iPhone OS: a dashboard screen. If you have a Macintosh computer, you may be familiar with the dashboard that is available (regularly) by pressing F4. Otherwise, you can draw similarities to your Windows 7 Dashboard on the right hand side of your desktop, that shows you updates on your applications and widgets you add to it. Finding your dashboard on your iPhone is just as easy: just put your finger on the top of your iPhone screen, and drag down.

Here, in your dashboard, you will see all of the updates that has been pushed into such by your applications that desire to send you messages: things like new text messages, new updates to your subscribed magazines, your messages on payment applications. If you have reviewed a message set by an application by tapping on it, that message will automatically become deleted. However, if you don’t desire to go into the application to delete it, simply tap in the top right on the bar that categorizes that particular application, and tap again to clear all of the messages set by that application, and clear up your dashboard.

But, your dashboard isn’t all about your application. You not only get your messages, but you get important information set by default applications, such as the weather. If you don’t feel like scouting out your weather application amidst all your applications you have downloaded, simply go into your dashboard, and find out the forecast for the whole week, just by a simple swipe. Not only that, tickers for your stocks are displayed near the bottom of the dashboard.

Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.


Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.

Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.

The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.


Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.

Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.

Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.

The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.

Related:

 What Are The 10 Most Famous Software Programs Written in Python?

Python, a Zen Poem

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”