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Secure Browsing from Mobile and PC ( Tor ,PeerNet, WasteAgain)

While Tor remains the tool of choice with pseudo-techie hacker wannabes , there is enough juice and smoke and mirrors on the market to confuse your average Joe.

For a secure browsing experience on Mobile – do NOT use either Apple or Windows OS

Use Android  and this app called Orbot in particular

Installing Tor with a QR code

Orbot is easy to install by simply scanning the following QR code with your Android Barcode scanner.

Android QR code

Installing Tor from the Android Market

Orbot is available in the Android Market.

ENTER PEERNET

If you have a Dell PC, well just use PeerNet to configure and set up your own network around the neighbourhood. This is particularly applicable if you are in country that is both repressive and not so technologically advanced. Wont work in China or USA.

http://support.dell.com/support/edocs/network/p70008/EN/vista_7/peernet.htm

What is a peer network?

A peer network is a network in which one computer can connect directly to another computer. This capability is accomplished by enabling access point (AP) functionality on one of the computers. Other computers can then connect to this computer in the same way that they would connect to a physical AP. If Internet Connection Sharing is enabled on the computer that has the AP functionality, computers that connect to that computer have Internet connectivity as well.

A basic peer network, which requires no networking knowledge or experience to set up, should meet the needs of most home users and small businesses. By default, a basic peer network is configured with the strongest available security (see How do I set up a basic peer network?).

For users who are familiar with wireless networking technology, advanced configuration features are available to do the following:

Change security settings (see How do I configure my peer network?)
Choose which method (push button or PIN) computers with Wi-Fi Protected Setup™ capability can join your peer network (see How do I allow peer devices to join my peer network using Wi-Fi Protected Setup technology?)
Change the DHCP Server IP address (see How do I configure my peer network?).
Change the channel on which to operate your peer network (see How do I configure my peer network?)

 If you are really really in a need for secure browsing (like you are maybe a big hot shot in the tech world), I suggest go over to VMWare

http://www.vmware.com/products/player/

create a seperate Linux (Ubuntu for ease) virtual disc, then download the Tor Browser Bundle from

https://www.torproject.org/projects/torbrowser.html.en for surfing and a Peernet (above) or  a prepaid one time use disposable mobile pre-paid wireless card. It is also quite easy to delete your virtual disc in times of emergencies (but it is best to use encryption even when in Ubuntu https://help.ubuntu.com/community/EncryptedHome)

IRC chat is less secure than you think it is thanks to BOT  Trawlers- so I am hoping someone in the open source community updates Waste Again for encrypted chats http://wasteagain.sourceforge.net/

What is “WASTE again”?

“WASTE again” enables you to create a decentralized and secure private mesh network using an unsecure network, such as the internet. Once the public encryption keys are exchanged, sending messages, creating groupchats and transferring files is easy and secure.

Creating a mesh

To create a mesh you need at least two computers with “WASTE again” installed. During installation, a unique pair of public and private keys for each computer is being generated. Before the first connection can be established, you need to exchange these public keys. These keys enable “WASTE again” to authenticate every connection to other “WASTE again” clients.

After exchanging the keys, you simply type in the computers IP address to connect to. If that computer is located behind a firewall or a NAT-router, you have to create a portmap first to enable incoming connections.

At least one computer in your mesh has to be able to accept incoming connections, making it a “public node”. If no direct connection between two firewalled computers can be made, “WASTE again” automatically routes your traffic through one or more of the available public nodes.

Every new node simply has to exchange keys with one of the connected nodes and then connect to it. All the other nodes will exchange their keys automatically over the mesh.

Augustus- a PMML model producer and consumer. Scoring engine.

A Bold GNU Head

Image via Wikipedia

I just checked out this new software for making PMML models. It is called Augustus and is created by the Open Data Group (http://opendatagroup.com/) , which is headed by Robert Grossman, who was the first proponent of using R on Amazon Ec2.

Probably someone like Zementis ( http://adapasupport.zementis.com/ ) can use this to further test , enhance or benchmark on the Ec2. They did have a joint webinar with Revolution Analytics recently.

https://code.google.com/p/augustus/

Recent News

  • Augustus v 0.4.3.1 has been released
  • Added a guide (pdf) for including Augustus in the Windows System Properties.
  • Updated the install documentation.
  • Augustus 2010.II (Summer) release is available. This is v 0.4.2.0. More information is here.
  • Added performance discussion concerning the optional cyclic garbage collection.

See Recent News for more details and all recent news.

Augustus

Augustus is a PMML 4-compliant scoring engine that works with segmented models. Augustus is designed for use with statistical and data mining models. The new release provides Baseline, Tree and Naive-Bayes producers and consumers.

There is also a version for use with PMML 3 models. It is able to produce and consume models with 10,000s of segments and conforms to a PMML draft RFC for segmented models and ensembles of models. It supports Baseline, Regression, Tree and Naive-Bayes.

Augustus is written in Python and is freely available under the GNU General Public License, version 2.

See the page Which version is right for me for more details regarding the different versions.

PMML

Predictive Model Markup Language (PMML) is an XML mark up language to describe statistical and data mining models. PMML describes the inputs to data mining models, the transformations used to prepare data for data mining, and the parameters which define the models themselves. It is used for a wide variety of applications, including applications in finance, e-business, direct marketing, manufacturing, and defense. PMML is often used so that systems which create statistical and data mining models (“PMML Producers”) can easily inter-operate with systems which deploy PMML models for scoring or other operational purposes (“PMML Consumers”).

Change Detection using Augustus

For information regarding using Augustus with Change Detection and Health and Status Monitoring, please see change-detection.

Open Data

Open Data Group provides management consulting services, outsourced analytical services, analytic staffing, and expert witnesses broadly related to data and analytics. It has experience with customer data, supplier data, financial and trading data, and data from internal business processes.

It has staff in Chicago and San Francisco and clients throughout the U.S. Open Data Group began operations in 2002.


Overview

The above example contains plots generated in R of scoring results from Augustus. Each point on the graph represents a use of the scoring engine and a chart is an aggregation of multiple Augustus runs. A Baseline (Change Detection) model was used to score data with multiple segments.

Typical Use

Augustus is typically used to construct models and score data with models. Augustus includes a dedicated application for creating, or producing, predictive models rendered as PMML-compliant files. Scoring is accomplished by consuming PMML-compliant files describing an appropriate model. Augustus provides a dedicated application for scoring data with four classes of models, Baseline (Change Detection) ModelsTree ModelsRegression Models and Naive Bayes Models. The typical model development and use cycle with Augustus is as follows:

  1. Identify suitable data with which to construct a new model.
  2. Provide a model schema which proscribes the requirements for the model.
  3. Run the Augustus producer to obtain a new model.
  4. Run the Augustus consumer on new data to effect scoring.

Separate consumer and producer applications are supplied for Baseline (Change Detection) models, Tree models, Regression models and for Naive Bayes models. The producer and consumer applications require configuration with XML-formatted files. The specification of the configuration files and model schema are detailed below. The consumers provide for some configurability of the output but users will often provide additional post-processing to render the output according to their needs. A variety of mechanisms exist for transmitting data but user’s may need to provide their own preprocessing to accommodate their particular data source.

In addition to the producer and consumer applications, Augustus is conceptually structured and provided with libraries which are relevant to the development and use of Predictive Models. Broadly speaking, these consist of components that address the use of PMML and components that are specific to Augustus.

Post Processing

Augustus can accommodate a post-processing step. While not necessary, it is often useful to

  • Re-normalize the scoring results or performing an additional transformation.
  • Supplements the results with global meta-data such as timestamps.
  • Formatting of the results.
  • Select certain interesting values from the results.
  • Restructure the data for use with other applications.

Interview Luis Torgo Author Data Mining with R

Example of k-nearest neighbour classification

Image via Wikipedia

Here is an interview with Prof Luis Torgo, author of the recent best seller “Data Mining with R-learning with case studies”.

Ajay- Describe your career in science. How do you think can more young people be made interested in science.

Luis- My interest in science only started after I’ve finished my degree. I’ve entered a research lab at the University of Porto and started working on Machine Learning, around 1990. Since then I’ve been involved generally in data analysis topics both from a research perspective as well as from a more applied point of view through interactions with industry partners on several projects. I’ve spent most of my career at the Faculty of Economics of the University of Porto, but since 2008 I’m at the department of Computer Science of the Faculty of Sciences of the same university. At the same time I’ve been a researcher at LIAAD / Inesc Porto LA (www.liaad.up.pt).

I like a lot what I do and like science and the “scientific way of thinking”, but I cannot say that I’ve always thought of this area as my “place”. Most of all I like solving challenging problems through data analysis. If that translates into some scientific outcome than I’m more satisfied but that is not my main goal, though I’m kind of “forced” to think about that because of the constraints of an academic career.

That does not mean I’m not passionate about science, I just think there are many more ways of “doing science” than what is reflected in the usual “scientific indicators” that most institutions seem to be more and more obsessed about.

Regards interesting young people in science that is a hard question that I’m not sure I’m qualified to answer. I do tend to think that young people are more sensible to concrete examples of problems they think are interesting and that science helps in solving, as a way of finding a motivation for facing the hard work they will encounter in a scientific career. I do believe in case studies as a nice way to learn and motivate, and thus my book ;-)

Ajay- Describe your new book “Data Mining with R, learning with case studies” Why did you choose a case study based approach? who is the target audience? What is your favorite case study from the book

Luis- This book is about learning how to use R for data mining. The book follows a “learn by doing it” approach to data mining instead of the more common theoretical description of the available techniques in this discipline. This is accomplished by presenting a series of illustrative case studies for which all necessary steps, code and data are provided to the reader. Moreover, the book has an associated web page (www.liaad.up.pt/~ltorgo/DataMiningWithR) where all code inside the book is given so that easy copy-paste is possible for the more lazy readers.

The language used in the book is very informal without many theoretical details on the used data mining techniques. For obtaining these theoretical insights there are already many good data mining books some of which are referred in “further readings” sections given throughout the book. The decision of following this writing style had to do with the intended target audience of the book.

In effect, the objective was to write a monograph that could be used as a supplemental book for practical classes on data mining that exist in several courses, but at the same time that could be attractive to professionals working on data mining in non-academic environments, and thus the choice of this more practically oriented approach.

Regards my favorite case study that is a hard question for an author… still I would probably choose the “Predicting Stock Market Returns” case study (Chapter 3). Not only because I like this challenging problem, but mainly because the case study addresses all aspects of knowledge discovery in a real world scenario and not only the construction of predictive models. It tackles data collection, data pre-processing, model construction, transforming predictions into actions using different trading policies, using business-related performance metrics, implementing a trading simulator for “real-world” evaluation, and laying out grounds for constructing an online trading system.

Obviously, for all these steps there are far too many options to be possible to describe/evaluate all of them in a chapter, still I do believe that for the reader it is important to see the overall picture, and read about the relevant questions on this problem and some possible paths that can be followed at these different steps.

In other words: do not expect to become rich with the solution I describe in the chapter !

Ajay- Apart from R, what other data mining software do you use or have used in the past. How would you compare their advantages and disadvantages with R

Luis- I’ve played around with Clementine, Weka, RapidMiner and Knime, but really only playing with teaching goals, and no serious use/evaluation in the context of data mining projects. For the latter I mainly use R or software developed by myself (either in R or other languages). In this context, I do not think it is fair to compare R with these or other tools as I lack serious experience with them. I can however, tell you about what I see as the main pros and cons of R. The main reason for using R is really not only the power of the tool that does not stop surprising me in terms of what already exists and keeps appearing as contributions of an ever growing community, but mainly the ability of rapidly transforming ideas into prototypes. Regards some of its drawbacks I would probably mention the lack of efficiency when compared to other alternatives and the problem of data set sizes being limited by main memory.

I know that there are several efforts around for solving this latter issue not only from the community (e.g. http://cran.at.r-project.org/web/views/HighPerformanceComputing.html), but also from the industry (e.g. Revolution Analytics), but I would prefer that at this stage this would be a standard feature of the language so the the “normal” user need not worry about it. But then this is a community effort and if I’m not happy with the current status instead of complaining I should do something about it!

Ajay- Describe your writing habit- How do you set about writing the book- did you write a fixed amount daily or do you write in bursts etc

Luis- Unfortunately, I write in bursts whenever I find some time for it. This is much more tiring and time consuming as I need to read back material far too often, but I cannot afford dedicating too much consecutive time to a single task. Actually, I frequently tease my PhD students when they “complain” about the lack of time for doing what they have to, that they should learn to appreciate the luxury of having a single task to complete because it will probably be the last time in their professional life!

Ajay- What do you do to relax or unwind when not working?

Luis- For me, the best way to relax from work is by playing sports. When I’m involved in some game I reset my mind and forget about all other things and this is very relaxing for me. A part from sports I enjoy a lot spending time with my family and friends. A good and long dinner with friends over a good bottle of wine can do miracles when I’m too stressed with work! Finally,I do love traveling around with my family.

Luis Torgo

Short Bio: Luis Torgo has a degree in Systems and Informatics Engineering and a PhD in Computer Science. He is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto. He is also a researcher of the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) belonging to INESC Porto LA. Luis Torgo has been an active researcher in Machine Learning and Data Mining for more than 20 years. He has lead several academic and industrial Data Mining research projects. Luis Torgo accompanies the R project almost since its beginning, using it on his research activities. He teaches R at different levels and has given several courses in different countries.

For reading “Data Mining with R” – you can visit this site, also to avail of a 20% discount the publishers have generously given (message below)-

For more information and to place an order, visit us at http://www.crcpress.com.  Order online and apply 20% Off discount code 907HM at checkout.  CRC is pleased to offer free standard shipping on all online orders!

link to the book page  http://www.crcpress.com/product/isbn/9781439810187

Price: $79.95
Cat. #: K10510
ISBN: 9781439810187
ISBN 10: 1439810184
Publication Date: November 09, 2010
Number of Pages: 305
Availability: In Stock
Binding(s): Hardback 

Interview John F Moore CEO The Lab

Social Media Landscape

Here is an interview with John F Moore, social media adviser,technologist and founder and CEO of The Lab.

Ajay-  The internet seems to be crowded by social media experts with everyone who spends a lot of time on the internet claiming to be one? How  does a small business owner on a budget distinguish for the correct value proposition that social media can give them. 

John- You’re right.  It seems like everytime I turn around I bump into more social media “experts”.  The majority of these self-proclaimed experts are not adding a great deal of value.  When looking to spend money for help ask the person a few questions about their approach. Things you should be hearing include:

  • The expert should be seeking to fully understand your business, your goals, your available resources, etc..
  • The expert should be seeking to understand current management thinking about social media and related technologies.

If the expert is purely focused on tools they are the wrong person.  Your solution may require tools alone but they cannot know this without first understanding your business.

Ajay- Facebook has 600 million people, with people preferring to play games and connect to old acquaintances rather than use social media for tangible career or business benefit..

John- People are definitely spending time playing games, looking at photos, and catching up with old friends.  However, there are many businesses seeing real value from Facebook (primarily by tying it into their e-mail marketing and using coupons and other incentives).  For example, I recently shared a small case study (http://thejohnfmoore.com/2010/10/07/email-social-media-and-coupons-makes-the-cfo-smile/) where a small pet product company achieved a 22% bump in monthly revenue by combining Facebook and coupons together.  In fact,45% of this bump in revenue came from new clients.  Customer acquisition and increased revenue were accomplished by using Facebook for their business.
Ajay-  How does a new social media convert (individual) go on selecting communities to join (Facebook,Twitter,Linkedin,Ning, Ping,Orkut, Empire Avenue etc etc.
How does a small business owner take the same decision.

John- It always starts with taking the time to define your goals and then determine how much time and effort you are willing to invest.  For example:
  • LinkedIn. A must have for individuals as it is one of the key social networking communities for professional networking.  Individuals should join groups that are relevant to their career and invest an hour a week.  Businesses should ensure they have a business profile completed and up to date.
  • Facebook can be a challenge for anyone trying to walk the personal/professional line.  However, from a business standpoint you should be creating a Facebook page that you can use to compliment your other marketing channels.
  • Twitter.  It is a great network to learn of, to meet, and to interact with people from around the world.  I have met thousands of interesting people, many of which I have had the pleasure to meet with in real life.  Businesses need to invest in listening on twitter to determine if their customers (current or potential) or competitors are already there discussing them, their marketplace, or their offerings.
In all cases I would encourage businesses to setup social media accounts on LinkedIn, Facebook, Twitter, YouTube, and Flickr.  You want to ensure your brand is protected by owning these accounts and ensuring at least the base information is accurate.
Ajay- Name the top 5 points that you think make a social media community successful.  What are the top 5 points for a business to succeed in their social media strategy.

John-
  • Define your goals up front.  Understand why you are building a community and keep this goal in mind.
  • Provide education.  Ideally you want to become a thought leader in your space, the trusted resource that people can turn to even if they are not using your product or services today.
  • Be honest.  We all make mistakes.  When you do, be honest with your community and engage them in any fall-out that may be coming out of your mistake.
  • Listen to them.  Use platforms like BubbleIdeas to gather feedback on what your community is looking for from the relationship.
  • Measure.  Are you on track with your goals?  Do your goals need to change?
Ajay- What is the unique value proposition that “The Lab” offers

John- The Lab understands the strategic importance of leveraging social media, management and leadership best practices, and our understanding of local government and small and medium business to help people in these areas achieve their goals.  Too many consultants come to the table with a predefined solution that really misses the mark as it lacks understanding of the client’s goals.
Ajay-  What is “CityCamp in Boston” all about.

John- CityCamp is a FREE unconference focused on innovation for municipal governments and community organizations (http://www.citycampboston.org/what-is-citycamp-boston/).  It brings together politicians, local municipal employees, citizens, vendors, developers, and journalist to build a common understanding of local government challenges and then works to deliver measurable outcomes following the event.  The key is the focus on change management, driving change as opposed to just in the moment education.
Biography-

John F Moore is the Founder and CEO of The Lab (http://thelabinboston.com).  John has experience working with local governments and small and medium business owners to achieve their goals.  His experience with social media strategies, CRM, and a plethora of other solutions provides immense value to all of our clients.   He has built engineering organizations, learned sales and marketing, run customer service teams, and built and executed strategies for social media thought leadership and branding.  He is also a prolific blogger as you can see by checking out his blog at http://thejohnfmoore.com.

Good Night Baghdad

Good night Baghdad

We met as soul mates
On Green Zone Island
We left as inmates
From an asylum

And we were sharp
As sharp as knives
And we were so gung ho
To lay down our lives

We came in spastic
Like tameless horses
We left in plastic
As numbered corpses

And we learned fast
To travel and fight
Our armored cars were heavy
But our bellies were tight

We had no home front
We had no one
They sent us John Mccain
They gave us Jessica Simpson

We dug in deep
And shot on sight
And prayed to Jesus Christ
With all of our might

We had video cameras
To shoot the landscape
We passed the hash pipe
And played our Eminem tapes

 

And it was dark
So dark in our humvee

And we held on to each other
Like brother to brother

We promised our mothers we’d write
And we would all go down together
We said we’d all go down together
Yes we would all go down together

Remember the Sunnis and Shias

Remember the imprisoned Iraqi

They left their childhood
On every acre

And who was wrong?
And who was right?
It didn’t matter in the thick of the fight

We held the day
In the palm
Of our hand
They ruled the night

And the night
Seemed to last as long as six weeks
On Green zone Island

We held the street corners
They held the rooftops
And they were sharp
As sharp as knives

They heard the hum of our convoys
They counted the humvees
And waited for us to arrive

And we would all go down together
We said we’d all go down together
Yes we would all go down together

 bush_codpiece.jpg

(with credit to Billy Joel from Good Night Saigon)

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