text:
Isn't it time for music to catch up to modern technologies? Write and save music in your browser. Read more
Isn't it time for music to catch up to modern technologies? Write and save music in your browser.
About this project
We Love Composing Music!
We also love technology, which has seen great advancements in the past decade. We want to see music composition catch up, even if we have to push it along ourselves.
Do Enough of YOU Care About Music Composition?
If the world doesn't have 1,500 people willing to drop a buck for music composition, that's a strong indication that there aren't enough musicians out there to make composition software matter. Meanwhile, old software models are becoming obsolete, showing their age and inefficiency, while corporate investors are focused on distribution channels that don't focus on creation. Traditional notation is becoming as scarce in software as it is in schools.
What You're Getting:
Liederboard will give you notation software that runs in your browser. You'll be able to write notes and rests in C, 4/4 time, play it back, print the music out and save/load that song locally to/from personal computers. Other improvements will be implemented as time and funds permit.
How We'll Do It:
The funds will be to used finish the Los Angeles-based development of the following features: playback, load/save and pitches. Development blog is here.
Oh, You Want More?!
We haven't had the time or resources to develop many features musicians are accustomed to.
Let's get you those features!
For every $1500 above the goal, two features can be completed in a month. It's like a cheap contract with an engineer, except the most enthused supporters will vote to prioritize the new features.
All backers get a version of the software with the three features we're funding. Every month henceforth, they'll get a version with (at least) whichever two features were chosen.
The backlog is located here, but if you have requests, we want to hear from you! ♪
Kickstarter has rules against funding a web service, but we plan to do much more with Liederboard in the near future.
Eventually, our free service will store the compositions in the cloud and let other musicians judge those pieces, creating an online collection of well-respected musical compositions. What floats to the top will be the cream-- the best piece of music at that time, as determined by musicians and listeners like you. Think of it as a competitive game in which you write awesome music instead of shooting things, micromanaging, collecting loot or getting dates. (You might save the world from bad music, though.)
All anyone will ever need in order to participate are an HTML5-enabled browser and an internet connection. Nothing to download! Ever!
Where Are the Big Corporate Investors?
They're busy spamming you (and the 30 seconds before your favorite video) with information about artists with extensive financial backing. Liederboard is a homegrown, grassroots project born of a desire to give creation and quality as much importance as distribution.
Short Answer: Unfortunately, no.
Long Answer:
Yes, for a length of time corresponding to what is pledged and only if at least a certain amount is pledged. The front-end Javascript code that you see linked on http://liederboard.com will be released under an open source license for one year on Github, matching feature development on Liederboard until September 22, 2013 (one year after our Kickstarter closes) if we raise $90,000.This project is the first step in what will be a network of musicians composing, judging, and competing to provide the world's best music for anyone to hear-- that system as a whole is valuable. Beyond the front end, we can't expect to repeatedly Kickstart (guidelines prevent this!) our next batches of money to maintain the service forever. To do that, we want to start a viable business.
Because a business isn't (and shouldn't be) something you Kickstart, and because recent trends around litigation tend to be really scary, it would take a little less than 9 Mozart levels to make us feel comfortable with letting go of our proprietary code. That seems fair in a business sense since that's what we want to pay an engineer for a year's worth of work.
Luckily, this fits in with what we want to become: an organization that listens to and acts on feedback from our community. We're in if you're in! Let's do this!
We're using the HTML5 Web Audio API to play your music back to you. It's the one described here: http://www.html5rocks.com/en/tutorials/webaudio/intro/ .
We're recording sounds ourselves, so expect a set of home-brewed synthesizer sounds. Let us know, in the comments, if more voices/instruments is something you really need to be added to the backlog!
That's exactly the kind of idea we want from the most forward-thinking musicians!
MIDI input support will be added to the backlog and will be worked on when our biggest supporters want us to. When work starts on it, feel free to contact us if you own MIDI hardware so we can develop exactly what you need (we like developing while seeing exactly what users are doing, as you can see here: http://trivialsoftware.blogspot.com/2012/06/week-9-sessions-with-our-users-sheet.html).
Pledge $1 or more
132 backers Limited (1368 left of 1500)
A copy of Liederboard that saves your music locally on your computer. This version of the software will only be sent to supporters!
Estimated delivery: -
Pledge $5 or more
100 backers
In addition to lower tier rewards, you can have your name added to a section of the supporters' page at http://liederboard.com. Upon launch of the online service, your Liederboard account will be given a badge that denotes this contribution level.
Estimated delivery: -
Pledge $25 or more
19 backers
In addition to lower tier rewards, you can have your name added to a more prominent section of the supporters' page at http://liederboard.com. Upon launch of the online service, your Liederboard account will be given a shiny badge that denotes a higher level contribution.
Estimated delivery: -
Pledge $50 or more
8 backers
In addition to lower tier rewards and a shinier badge, your name at the more prominent section of the supporters' page can link to a static "safe-for-work" page of your choice. You will also get priority selection of your public username.
Estimated delivery: -
Pledge $100 or more
3 backers
In addition to lower tier rewards and a still shinier badge, your link on the supporters' page can include an icon and byline (140 chars, 150 x 150). Supporters will be listed by contribution amount, then alphabetically in the case of ties.
Estimated delivery: -
Pledge $1,000 or more
0 backers Limited (15 left of 15)
In addition to lower tier rewards, your Liederboard account will be given the shiniest badge you've ever seen in your life. You may also choose one of the following: (1) Founder Danny Favela and a motley crew of other musicians will perform and make an Internet video of one of your compositions! or (2) Founder Danny Favela will meet with you in a video conference, then write a song especially for you!
Estimated delivery:
Funding period
- (30 days)


text: Galaga REMIX gamesBookmark the permalink. Release date: Mar 31st, 2009 Price: 2.99 Version: 1.0.3 Seller: Namco Networks America Buy Now STP SCORE: Not yet reviewed Description This arcade remake has both classic and enhanced versions of the classic top-down shooter. Related Games LEGO Star Wars: Microfighters Anchorman 2: Scotchy Scotch Toss Double Dragon Trilogy Meltdown Trial By Survival Abyss Attack Monsters University Hyper Breaker Turbo Gravity Guy 2 Can Knockdown 3

subset: commoncrawl


text:
Elina Svitolina Cheering Thread!
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Sep 27th, 2013, 02:38 PM
Natural Joe
Senior Member
Join Date: Aug 2012
Location: In The Lost Woods
Posts: 12,909
Re: Elina Svitolina Cheering Thread!
Tough draw.
Hoping for a good match against Roberta!
Just so you know: I'm better than you because my fave is better than your fave. Your fave is a bitch and a shrieking BBB. Or was it pusher? Who cares. She's a classless brat anyway. But don't you dare insult my fave, idiot.
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Story Summary
After the quest the hobbits rest in Minas Tirith, passing the time with story-telling. One afternoon when Merry talks about Fangorn, he remembers a walk with his grandfather in the Old Forest.
Keywords: Frodo Baggins, Samwise Gamgee, Meriadoc Brandybuck, Peregrin Took, slash, Minas Tirith, post-quest, trees, Treebeard
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@APALondon #thefutureofadvertising Dave Trott "Bill Bernbach says creativity is the only legal advantage we have over the competition"
05:05 PM - 05 Mar 15We craft spectacular visual experiences in any space, on any screen.
We have been one of the global leaders in VFX for over 25 years; renowned for adding visual wonder and creative expertise to the film, advertising and entertainment industries.
Read More >


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Obama appeared on "Late Night with Jimmy Fallon," yesterday, where he surprised the audience by joining Fallon and The Roots in "slow jamming the news."
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Barack Obama, Jimmy Fallon and The Roots joined forces last night to Slow Jam The News on Late Night with Jimmy Fallon. Read Post
On Friday, Mitt Romney joined Jimmy Fallon and the Roots on NBC's Late Night to "slow jam the news." The main topic of the segment was President Obama's upcoming State of the Union address. Highlights include Fallon making a 47-perc... Read Post
In the much anticipated appearance of Barack Obama on NBC's Late Night with Jimmy Fallon, the President got to slow jam his Democrat talking points while bashing Republicans. Not surprisingly, Fallon and his band were more than happ... Read Post


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Bikes & Gear
Mountain Bike Review
13 for 2013: This Year's Best New Mountain Bikes
Salsa Beargrease
12 / 13
Why Its Cool: A lightweight, nimble fatbike
Most fatback bikes are built for one thing—getting riders across snow or sand. In that regard, they're effective. But not many are fun to ride. They're heavy and slow turning. The Beargrease is different. The racy model weighs 28.5 pounds complete—that's 5 pounds less than the brand's Mukluk 2. The bike's aluminum frame is mated to an aluminum fork with tapered steerer. It's driven by a custom E-13 crank and corrosion-resistant, zinc-coated KMC chain. Expect to see the bike in specialty shops this winter. Complete bikes run about $3,000 while framesets cost a cool grand.
Related Content
- EDITORS' CHOICE : 2012 Mountain Bike Editors' Choice Winners
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When people first see a demo of BigML, there is often a sense that it is magical. The surprise likely stems from the fact that this isn't how people are accustomed to computers working; rather than acting like a calculator creating a fixed outcome, the process is able to generalize from patterns in the data and make seemingly sentient predictions.
However it is important to understand that predictive analytics is not magic, and although the algorithm is learning on a very basic level, it can only extract meaning from the data you give it. It does not have the wealth of intuition that a human has, whether that's good or bad, and subsequently the success of the algorithm can often hinge on how you engineer the input features.
Let's consider a very simple learning task — please keep in mind that this is a contrived example to make explaining the problem of feature engineering clear, and does not necessarily represent an actual useful end result itself.
Assume you are working on a navigational system, and at some point in the system you would like a way to predict the principal direction of a highway knowing only its assigned number. For example, if a user wants to go north, and there are two nearby highways, Interstate 5 and Interstate 84, which should they take?
Now, you could use a list of known highways, but this would require you to regularly update the list as new highways are built, or removed. Instead, if there was a pattern relating principal direction to highway number, this might be a useful thing for you device to know.
So, let's take a list of primary interstates in the US and let BigML train a model to predict the principal direction: East-West or North-South. The resulting tree looks like this:
In the highlighted node, you can see that the learning algorithm has discovered that if the highway number is greater than 96, then the highway is principally North-South. And indeed, if we look at the dataset there are only two highways that match this pattern, 97 and 99 and they are both North-South and so this pattern is relevant.
However, as you navigate around the tree it becomes obvious that each split is simply creating bounds that eventually isolate a single highway or a small group of highways, at which point the prediction is less of a generalization and more of a truism:
In other words, the model doesn't seem to be generalizing in a meaningful way from the highway number to the principal direction.
Now if you are familiar with the US highway numbering system then you might know that there is significance in whether the highway number is even or odd. Lets re-engineer our dataset to include this property and see if the model changes. We can do this by selecting the "Add Fields to Dataset" option:
We'll call the new field "isEven" and define it with the JSON s-expression:
[ "=", 0, [ "mod", [ "field", "Highway Number" ], 2 ]]
Reading from the inside brackets, we take the field named "Highway Number" and compute this value mod 2. If it equals 0, then this expression will return True meaning the highway number is an even integer, and False if odd:
Now we re-build the model including this new feature:
And now we get a very simple tree which generalizes to the following rules:
IF isEven = false THEN direction = North-South IF isEven != false THEN direction = East-West
This is a much more useful generalization! But what is happening here? Why didn't the machine learning algorithm find this pattern in the first tree?
Remember the first dataset: all we gave the algorithm to learn from was an integer. And the only thing the algorithm knows about integers is that they have a natural order. That's it. And so, it tried to find a pattern relating the natural order of the integers to the principal highway direction.
As humans, we potentially know a *lot* more about integers: some are squares, some are prime, some are perfect, and some are even. In the second dataset, we added some of this additional information about integers, specifically the even-ness, to the algorithm. By engineering this feature, we gave the algorithm the extra information it needed to find the pattern. In other words:
1) The "Feature Engineering" was adding the even/odd property.
2) The "Machine Learning" was the discovery that the even/odd property determines the principal direction.
The insight here is that learning algorithm can only discover the patterns that we provide in the data, either intentionally or accidentally.
In this rather contrived example, it might seem circular. That is, we start with an insight that even/odd has meaning, add that property, and then discover that even/odd has meaning. However, it is important to remember that this is a very simple example. When working with real data you may have hundreds or thousands of features and the patterns will be much more nuanced.
In that real world case, the importance of feature engineering is to use domain specific knowledge and human insight to ensure that the data contains relevant indicators for the prediction task. And, in that case, the beauty of machine learning is that it discovers the relevant patterns and filters out the incorrect human insights.
If you would like to run this example in your own account, here is a little python script which reproduces all the steps in development mode (FREE):
Arguments missing when calling a function,
mispelling keys setting some advanced option,
accessing attributes your object lacks:
These are a few of my favorite bugs.
Non-closing commas on deep JSON structures,
long late-night hacking that ends up in rapture:
Found a solution, that actually sucks.
These are a few of my favorite bugs.
Wrong file permissions on root system folders,
new model's methods that break on the olders,
out of range values on sliders and bars:
These are a few of my favorite bugs.
When the job fails,
and the app blocks,
or the cloud turns black,
I simply remember my favorite bugs
and then I don't feel so bad.
When the job fails,
and the app blocks,
or the cloud turns black,
I simply remember it's wintermute's fault
and then I don't care at all!
Predictive Lead Scoring is a crucial task to maximize the efforts of any sales organization.
There are a few applications on market today like Fliptop, KXEN, or Infer that already allow you to score your sales leads. These offerings have validated both the importance and market appetite for predictive scoring solutions.
However, you may want to have greater flexibility in choosing your CRM system, or perhaps you want to build your own predictive model to do the scoring, or you might want to integrate the scoring process within related services in your organization. In this post, I'm going to show you how to build an application to score your Salesforce.com leads using Talend Open Studio and BigML—three great tools working together to build a flexible predictive solution for a common business problem!
To complement this post I've created a complete step-by-step tutorial that will guide you on the implementation for this use case. No matter if you are a developer, a business analyst or a data scientist, this tutorial is made for you :-). At the end of each section of this post, you will find references to the related parts in the tutorial.
The Example
To illustrate the post, I am going to use a fictitious company named AllYouCanBuy that uses Salesforce and wants to prioritize their sales leads automatically. The objective is to provide AllYouCanBuy's sales team with an automated solution that provides a panel like the one below where leads can be sorted by priority score. Each lead should be automatically labeled with a score so that the top priority leads (green bars on the picture) represent the leads with higher confidence of becoming customers.
To implement a fully automated solution, we need to accomplish the following tasks:
- Automatically generate scores for sales leads. I'll solve this task with Machine Learning. We'll use historical data on which leads converted into customers to predict future scores. We need an engine or service that allows us to programmatically build predictive models and generate predictions. Obviously, I am going to use BigML but another machine learning package could be used in a similar fashion.
- Automatically extract historical data from the CRM and return it with new scores. I'll solve this task using an ETL tool. ETL tools are great helpers on the integration of disparate sources of data and services. They help Extract data from a multitude of sources including Salesforce, Transform them with using a programmable toolkit with many pre-built functions and techniques, and finally Load the results into other system or service. For this example, I am going to use the Talend Open Studio tool, from the Talend Company
The following picture shows a high-level architecture of the predictive lead scoring solution:
Next I'm going to describe each of the high-level components of the architecture with a little more detail, but first let me tell you about more Predictive Lead Scoring.
Predictive Lead Scoring
Predictive Lead Scoring is one of the most active fields today within the set of problems that can be solved using Machine Learning algorithms, and therefore with the use of BigML.
This technique seeks to improve the results obtained by sales teams during the qualification stages of their leads, helping them to predict leads that have a higher probability of success. This will improve sales results by focusing efforts first on the most important leads rather on those with lower chances of success—thereby helping teams organize their time and work. When one considers the cost associated with lead qualification it is very logical to spend time looking for ways to optimize the process.
Imagine a company that buys a database of 5,000 new leads. Without a tool that allows them to set priorities, they would be calling from first to last in the list without having any idea what will happen. Ad hoc intuition would be the guiding light on lead prioritization. What a waste of time, right?
However, with solutions like we are detailing here, you can solve this kind of business challenge quite easily—helping your sales team be more effective through the power of machine learning (as opposed to gut instinct).
AllYouCanBuy (our fictitious company) fortunately has been operating for some time and has been storing data about prior leads using Salesforce. Their data includes the following types of custom fields:
- Input fields: fields that hold information about each lead (city, sector, number of interactions, etc). These are the fields that we will use as input data for making prediction. This set of features can be as reach as you want and can use both specific data about the lead as well as data about the interactions kept with the lead.
- Fact field: this is field that the sales team has been using to label if a lead became a new customer or not. This is the label (in supervised learning parlance) that will be used to train and evaluate the predictive models. In a more sophisticated application, this field could be automatically computed.
- Output fields: these are the fields that we will use to store the output of the model, the confidence of the prediction returned by the model, and a priority field (the lead score). We compute the priority field based on the output of the model and its confidence. For example, leads with a 'true' prediction and a high confidence will become our top priority and leads with a 'false' prediction and high confidence will become our low priority leads. Priority fields will allow for a more user-friendly representation of the predictions.
In the tutorial, you can read more on how to create a Salesforce.com Developer Account, how create custom fields of leads object, how to customize leads objects in Salesforce.
BigML not only provides a great set of 1-click functions and visualizations for predictive modeling but also a REST API to programmatically run diverse sophisticated predictive modeling workflows.
To simplify the first version of our predictive lead scoring app, I am going to create the model directly in BigML and use it to make predictions. In a second iteration, I'll automate the model creation too. So basically, we only need to export Salesforce data to a CSV file, upload the file to BigML and let it do the data modeling. I will use BigML's 1-Click Ensemble to create a very robust predictive model ready to make predictions in a matter of seconds.
I the tutorial you will find more details on how to create an account in BigML and how to create a predictive model in BigML
Talend Open Studio
Talend Open Studio provides an extensible, highly-performant, open source set of tools to access, transform and integrate data from any business system in real time or batch to meet both operational and analytical data integration needs. It has more than 800 connectors, and can help you integrate almost any data source. The broad range of use cases addressed includes: massive scale integration (big data/ NoSQL), ETL for business intelligence and data warehousing, data synchronization, data migration, data sharing, data services, and now predictions!!!
We will use Talend Open Studio to not only perform the data transformations requested but also to communicate with Salesforce and BigML. The transformations will be only simple metadata mapping between the respective output data and input data of both services as in our case with AllYouCanBuy, all the information about our leads actually come from the same place: Salesforce. However, the transformations can be more sophisticated for more complex applications—for in the real world, you may want to get information from other internal and external sources that can help create richer predictive models.
Talend allows you to use a high-level visual component to design complex ETL processes without writing a single line of code! You can see what the Talend ETL process looks like below:
BigML has developed a Talend Component named tBigMLPredict that you can download here and incorporate in your own installation of Talend. This component will help you make predictions with a predictive model you have previously created in BigML.
Once installed, this component will be visible in the Palette of components, inside the Business Intelligence – BigML Components category.
The tBigMLPredict component allows us to set the following configuration parameters:
In the tutorial, you can read more on how to download and install Talend Open Studio, how to download and configure the BigML Talend Components, how to design the integration job in Talend, and how to execute it in Talend Open Studio.
We have outlined in this blog (and the tutorial + related documents!) how you can orchestrate a flow that automatically scores sales leads in Salesforce using Talend and BigML. It shouldn't be difficult to create similar flows for other CRM services or using other ETL platforms–please let us know which ETL and CRM tools we should work on next!
I hope this post inspired you to start building your own predictive lead scoring application or more sophisticated predictive flows.
Bad things happen, but thankfully they tend to happen rarely. For example, you'd expect a small fraction of network traffic to be hackers, and a minority of patients to have a serious disease. (I was going to add that we expect a small percentage of credit card transactions to be fraud, but these days that feels a bit optimistic.) We obviously want to identify and avert these rare bad events, and anomaly detection—which BigML just launched last week—is a powerful way to achieve this.
In the disease category, there's a well-known dataset of breast cancer biopsies from University of Wisconsin Hospitals, including measurements from each biopsy and the result of "benign" or "malignant". Of course, you can use BigML to train a highly accurate predictive model on this labeled data, but that's almost too easy. So here's a challenge: what if we remove the labels of "benign" and "malignant"? Can we still find useful patterns in the data?
BigML makes it simple to create a dataset with only the measurements and not the "benign" or "malignant" labels. We then train an anomaly detector on this unlabeled data, and BigML displays the 10 biopsies with the highest "outlier-ness":
That's interesting, but I want more insight into what makes a biopsy anomalous. To do this, I create anomaly scores for the entire dataset, give each biopsy a label of "high" or "low" (with "high" defined as the top third of anomaly scores), then train a model to predict this new label. (I'm working on a video for David's Corner that shows how this all takes just a few mouse clicks—which is exactly what we expect from BigML!)
This new model finds a striking pattern: most high-anomaly biopsies have "uniformity of cell size" greater than 2. Of the 231 high-anomaly biopsies in the entire dataset, a whopping 207 (almost 90%) are covered by this single rule. A higher "uniformity of cell size" means (unintuitively) that the size is less uniform, which is a feature of cancer cells, so experts would conclude that this pattern is worth investigating further.
And they would be right. Because if we let BigML use the labels of benign or malignant, it tells us that biopsies with "uniformity of cell size" higher than 2 are almost always malignant. Think about that: the anomaly detector, having no idea which examples are actually malignant, still managed to figure out that this cell size attribute is important, and specifically that it's important when it's greater than 2.
Another way to see the power of anomaly detection is to predict the outcome of a biopsy using only the high/low anomaly attribute. This correctly predicts the result 89% of the time, and detects 83% of malignant biopsies. Again, not bad considering the anomaly detector has no idea which examples are actually benign or malignant!
Finally, we can simply compare the histogram of anomaly scores for malignant and benign biopsies. This clearly shows how well the anomaly score lines up with the biopsy results!
Hopefully I've conveyed how insanely useful anomaly detection can be for finding patterns in unlabeled data, especially if you expect the data to contain a highly interesting (and often unwelcome) smaller class. This is particularly useful for large datasets where it is not feasible to label all the "bad" examples: millions of credit card transactions, for example, or billions of network events.
Moreover, you expect your adversary—fraudsters, hackers or even cancer—to change tactics over time. Because anomaly detection doesn't require you to know exactly what you're looking for, it can pick up on new types of attacks and warn you that something weird is going on.
Anomaly detection is like magic goggles for your data, helping you find patterns in a completely automated and unsupervised way. Of course, it's not really magic: we're just reaping the benefits of assuming, correctly, that there is a minority class to be found. And as long as we have adversaries, that will continue to be a good assumption.
For serious data enthusiasts, here are the ingredients for this analysis:
- The original source data hosted at UC Irvine
- This dataset imported into BigML
- A decision tree on 100% of labeled data
- An evaluation of this decision tree
- The unlabeled dataset that we feed the anomaly detector
- The anomaly detector trained on this unlabeled data
- The full dataset with anomaly score added
- The model trained on this data, predicting if a biopsy is in the upper tercile of anomalies
Machine Learning for everyone also means Machine Learning everywhere. This month we will get a little closer. We are crossing the Pacific and launching BigML in Australia and New Zealand. From today on, BigML users in Australia and New Zealand can enjoy BigML at https://bigml.com.au. This site will have identical functionality as https://bigml.com, only it will run directly on local cloud-based infrastructure. In addition, we're very excited to detail a unique alliance that BigML has launched with a leading data intelligence company in the region, GCS Agile.
While BigML makes it easy for you to build predictive models and perform a variety of machine learning tasks, there are many other related activities that are required for enterprise-grade deployment of machine learning solutions. For example, data transformations, feature engineering, finding the best modeling and prediction strategies, and measuring the impact are key to maximizing the power of machine-learned models. GCS Agile's data intelligence team is uniquely qualified to support this type of holistic approach to machine learning, and BigML is pleased to announce that our companies have entered into a strategic alliance.
GCS Agile is comprised of seasoned leaders who have extensive experience providing data-driven solutions to leading companies in diverse sectors such as telecommunications, finance, and government. BigML will rest at the heart of GCS Agile's data intelligence practice. GCS Agile and BigML teams will work together to bring BigML private deployments to mid-sized and big companies in Australia and New Zealand.
In addition, the GCS Agile team has been busy organizing a series of public events where you will be able to meet face to face with GCS Agile and BigML leadership team members. If you want to know more please contact the GCS Agile team at email@example.com
See below an incomplete list of public events:
- Cloud-based Machine Learning [Open to everyone]
Tuesday, 14 October 2014, 10am – 4pm
- Why data needs to be considered a strategic asset by organisations in 2015? [By invite only]
Wednesday, 15 October 2014, 5:30pm – 8:00pm
- Machine Learning For Everyone [RSVP here]
Thursday, October 16, 2014
Stay tuned for further announcements from GCS Agile and BigML. In the interim, if you are in Australia or New Zealand don't wait to give the new site a spin…
Before your sunburns subside and the leaves begin to turn from green to brown, the BigML team is excited to share our Late Summer Release which includes a bunch of new functionality to empower many new predictive applications.
Headlining this release is Anomaly Detection, which can help automate a number of predictive tasks for fraud detection, security, quality control, diagnoses and more. Also included in the release are support for model clusters, missing splits, client-side predictions and more!
For starters, we're excited to announce that BigML now allows you to automatically create top performer Anomaly Detectors in just one click, or programmatically via BigML's REST API.
An anomaly detector is a predictive model that can help identify the instances within a dataset that do not conform to a regular pattern. This can be useful for tasks like data cleansing, identifying unusual instances, or, given a new data point, deciding whether a model is competent to make a prediction or not. Thus anomaly detectors are not only critical tools to work on fraud detection, medical diagnosis, or preventing defects but also they do a great job removing outliers, which in turn helps increase performance of other modeling tasks.
When you create a new anomaly detector, it automatically returns an anomaly score for the top n most anomalous instances. The newly created anomaly detector can later be used to create anomaly scores for new data points or batch anomaly scores for all the instances of a dataset.
BigML anomaly detectors are built using an unsupervised anomaly detection technique that helps isolate those instances that are unusual, and you do not need to explicitly label each instance in your dataset as "normal" or "abnormal." We'll be explaining the technique further in blog posts soon and also will be layering in added functionality (e.g., the ability to work with text fields). You can get started today with just about any dataset—and as always, you can work for free in BigML's Development Mode!
Model Clusters
Now you can automatically create a dataset and model for each cluster. This will not only help you better understand the cluster, but you can also use model clusters to classify new instances.
To use this functionality, be sure to click the "create model clusters" option when configuring your cluster. Then, if you want to build a model from one of your clusters, simply hit 'shift' on any cluster and then choose 'create a model of this cluster" from beneath the right-hand summary box.
And, voila—you'll have a new model comprised of your cluster's data, which you can then interpret to find key patterns associated with whether or not data is likely to be within that cluster. Check this section out to learn how to use this feature via BigML's API.
Missing Splits
As we know that cleaning up data can be hard and having all the input data handy at prediction time is important, we have built a new option to create models that will generate predicates that explicitly deal with missing values.
To leverage this capability, go into the "Configure" subpanel when configuring your model, and click on the "missing splits" icon as follows:
The model that is created will look the same as before, but now you can see new predicates that directly check for missing values. See the example in the picture below.
Online Predictions
New client-side predictions make it easier than ever to explore the influence of each field in your models, ensembles or clusters. Whereas you previously had to rebuild predictions for each set of variables, you can now simply change your fields' inputs and see the predicted output change in realtime! In addition, the prediction form also includes the relative importance of each field so you can quickly select / de-select them for your predictions:
Some added benefits of online predictions are that they're free to use–both for pay-as-you-go customers, and also for anyone predicting against a model that's been shared in BigML's gallery and/or through a private link.
Also, we are open sourcing the related Javascript libraries so that you can easily leverage this functionality to build very powerful and dynamic apps and web services.
Faster Ensembles
As you already know, ensembles provide greater generalization than single decision trees, and BigML makes it easy for you to tap into this functionality with just a few mouse clicks.
With our latest release, BigML's ensembles now run much faster than before–meaning that you can more quickly build fully actionable ensembles to underpin predictive analyses and applications. Basically, we have reengineered the way ensembles deal with all the data processing that BigML needs before creating a model.
And More..
You'll also notice a bunch of UI and workflow improvements, which we're constantly bringing into production. These typically have a "new" image next to them. One of the features that we like most is the new option to automatically generate a dataset using the output of of batch process. That is, when you request a batch prediction, a batch centroid, or batch anomaly score you can optionally request to build a new dataset with the results. This is particularly useful to implement iterative flows where you use the output (prediction, centroid, or score) as an additional input for building another model. We'll elaborate more on this in a future blog post.
The Late Summer Release features are available immediately–simply log into your account and get started today! And be sure to let us know your feedback both on these features, and on what you'd like to see next!
Interested in seeing these new features in action? Check out the archived video and slides from our Late Summer Release launch webinar.
In 2006, Strands, my previous company, organized and sponsored a Late Summer School on Recommender Systems in Bilbao. With a little help from our friends and a grant from the Basque Country Government, we invited a number of experts and promising Ph.D. students to a series of workshops to discuss the Present and Future of Recommender Systems. Before the workshops were over, a few of the attendees, including Professor John Riedl, were already talking about building an international conference on the wonderful interactions we were experiencing (some pictures here). One year later, the First ACM Recommender Systems conference (RecSys), was held in Minneapolis. In a few weeks, RecSys will get to its 8th edition. It's incredibly rewarding to see how companies like Netflix, Linkedin, Google, Yahoo!, Baidu, IBM, Comcast, and Facebook picked up the baton and they currently sponsor what we started 8 years ago. Today, we announce that we're launching a similar endeavor to create a new community on Predictive APIs and Applications.
Early this year, I electronically met Louis Dorard and Ali Syed. It turned out that we were all on the same mission to democratize machine learning with our respective companies. We also shared the vision that a predictive world will be a much better world. Louis and I later met in San Francisco. Shortly thereafter, we started discussing the need for a world-wide practical community that gathers annually to discuss the latest technical advancements and challenges on Predictive APIs and Applications. So I am thus very happy to announce that PAPIs 2014 – The First International Conference on Predictive Application and APIs will be held in Barcelona on November 17-18. We even have plans for repeating the conference in Sydney right before KDD next year and afterwards bringing it to the US in 2016 or wherever the community takes it to.
Over the last few weeks, we've been busy building up a highly technical and diverse Program Committee that will help select the first presenters at PAPIs 2014:
Erick Alphonse (Deloitte), Sébastien Arnaud (OpinionLab), Richard Benjamins (Telefonica), Misha Bilenko (Microsoft), Jason Brownlee (Machine Learning Mastery), Natalino Busa (ING), Eric Chen (NTT Innovation), Mike Cossy (IBM), Beau Cronin (Salesforce), Ricard Gavaldà (UPC), Andrés González (CleverTask), Matthew Grover (Walmart), Harlan Harris (Education Advisory Board), Jeroen Janssens (YPlan), Benedikt Koehler (dcore.de), Maite López (UB), Gideon Mann (Bloomberg), Jordi Nin (Barcelona Supercomputing Center), Mark Reid (ANU), Juan Antonio Rodriguez (IIIA), Marc Torrens (Strands), Jordi Torres (Barcelona Supercomputing Center), and Zygmunt Zając (FastML).
A first group of companies like BigML, CleverTask, Codole, Dataiku, GCS Agile, Persontyle, Strands, and Taiger, and much larger ones like Microsoft and IBM are helping us impulse PAPI's and create the community. If your company is interested in helping us in this endeavor please contact us here.
Problem-First Approach
We want PAPIs to become an open forum for technologists and researchers on distributed, large-scale machine learning services and developers of real-world predictive applications. We aim at seeding highly technical discussions on the way common and uncommon predictive problems are being solved. However, we want PAPIs to be an eminently hands-on conference.
One of the features that characterized the RecSys community was its original mix of researchers and practitioners. However, it had a clear imbalance on the academic side. In the 2009 edition in New York City, I gave a provocative opening keynote arguing that too much emphasis was given to new algorithms rather than to other under-represented topics like data, evaluations, and interfaces that were essential to build real-world recommender systems. I still remember the (positive and negative) reactions of many attendees. In my opinion, an incredible group of talented scientists were not paying that much attention to the crucial problems. A couple of years ago, Domonkos Tikk sent me a note that said:
I often cite your provocative statement on the 5% contrib of algorithms to the success of recsys. 3y ago I completely disagreed. Now I see that it might be 10 or 15%, but there are other major factors too. Well, we are also more in the industry than in academia…
In this first edition, we will focus on the pragmatic issues and challenges that companies on the trenches must face to make predictive APIs and applications a reality, and add academic tracks on future editions, once we understand them better.
So if you are working on an interesting Predictive API or Application and want to show the rest of the world your new advancements or discuss the challenges that you are facing please send us your proposal.
Predictive APIs and Applications cover a wider area of application than Recommender Systems. Therefore, their impact in our everyday's life will be orders of magnitude higher and affect more industries than we can now imagine. So please don't miss the opportunity to join this nascent community early on.
See you all at PAPIs in Barcelona!!!
In the first part of this series, we saw how to insert machine-learned model predictions into a webpage on-the-fly using a content script browser extension which grabs the relevant pieces of the webpage and feeds them into a BigML actionable model. At the conclusion of that post, I noted that actionable models were very straightforward to use, but their static nature would lead to lots of effort in maintaining the browser extension if the underlying model were constantly updated with new data. In this post, we'll see how to use BigML's powerful API to both keep your models up to date, and write a browser extension that stays current with your latest models.
Staying up to date
In keeping with our previous post, we will be working on a model to predict the repayment status of loans on the microfinance site Kiva. In the last post, we used a model that was trained nearly one year ago on a snapshot of the Kiva database. Hundreds of new loans become available every day, which means this model is starting to look a bit long in the tooth. We'll start off by learning a fresh model from the latest Kiva database snapshot, and in doing so, we'll get to use some of the new features we've added to BigML over the last year, such as objective weighting and text analysis. However, the Kiva database snapshot is several gigabytes of JSON-encoded data, much of which won't be relevant to our model, so we really don't want to relearn from a new snapshot several months down the line when our hot new model becomes stale. Fortunately, we can avoid this situation using multi-datasets. Periodically, say once a month, we'll grab the newest loan data from Kiva and build a BigML dataset. With multi-datasets, we can take our monthly dataset, our original snapshot dataset, and all the previous monthly datasets, concatenate them together and learn a model from the whole thing. With the BigML API, this all happens behind the scenes, and all we need to do is supply a list of the individual datasets. We can do all of this with a little Python script. Here is the main loop:
Every dataset created by this script will have "kiva-data" as its name, so we can use the BigML API to list the available datasets and filter by that name. If none show up, then we know we need to create a base dataset from a Kiva snapshot; otherwise, we'll use the Kiva API to create an incremental update dataset. In either case, we then proceed to create a new model using multi-datasets. All we need to do is pass a list of our desired dataset resource IDs. We assign the name "kiva-model" to the model so that it can be easily found by our browser extension. We employ a few other minor tricks in our script, such as avoiding throttling in the Kiva API. You can check out the whole thing at this handy git repo.
API-powered browser extension
Our first browser extension was a content script that fired whenever we navigated to a Kiva loan page. It would grab the loan information, feed it to the model, and then use jQuery to insert a status indicator into the webpage's DOM tree. Our new extension won't be very different with regards to loan data scraping and DOM manipulation; the main difference will be how the model predictions are generated. Whereas our first extension featured an actionable model, i.e. a series of nested if-else statements, our new extension will perform predictions with a live BigML model, using the REST-ful API. To interface with the model, our extension now needs to know about our BigML credentials. In order to store the BigML credentials, we create a simple configuration page for the extension, consisting of two input boxes and a Save button.
Behind the scenes, we have a little bit of JavaScript to store and fetch our credentials using the chrome.storage API.
Whenever we save a new set of credentials, we want to lookup the most recent Kiva loans model found in that BigML account. However, this isn't the only context in which we want to fetch models. For instance, it would be a good idea to get the latest model every time the web browser is started up. We'll implement our model fetching procedure inside an event page, so it can be accessed from any part of the extension.
At the bottom of event page, we see that it listens to the browser startup event, and any messages with the greeting "fetchmodel" to fire the model fetching procedure. We also see that it listens for the onInstalled
to open up the configuration page for the first time. With this extra infrastructure in place, we are ready to make our modifications to the content script. Here is the script in its entirety:
Compared to the previous version of the extension, the bulk of the differences lie in the predictStatus
function. Rather than evaluating a hard-coded nested if-then-else structure, the loan data is POSTed to the BigML prediction URL with AJAX, and most of the DOM manipulation has been refactored to occur in the AJAX callback function. One perk we get from using a live model is that we get a confidence value along with our prediction of the loan status. We've used that to add a nice little meter alongside our status indicator icon.
Better browsing through big data
You can grab the source code for this Chrome browser extension here, where it is also available as a Greasemonkey user script for Firefox. We hope that this post, and its prequel, have been able to show the relative ease with which BigML models can be incorporated into content scripts and browser extensions. Beyond this kiva.org example, there are a myriad of potential applications on the internet where machine learned models can be used to provide a richer and more informed web browsing experience. It's now up to you to use what you've learned here to go out and realize those applications!
I recently came across a 1995 Newsweek article titled "Why the Web Won't be Nirvana" in which author and astronomer Cliff Stoll posited the following:
"How about electronic publishing? Try reading a book on disc. At best, it's an unpleasant chore: the myopic glow of a clunky computer replaces the friendly pages of a book. And you can't tote that laptop to the beach. Yet Nicholas Negroponte, director of the MIT Media Lab, predicts that we'll soon buy books and newspapers straight over the Internet. Uh, sure."
Well, it turns out that Mr. Stoll was slightly off the mark (not to mention his bearish predictions on e-commerce and virtual communities). Electronic books have been a revelation, with the Kindle format being far and away the most popular. In fact, over 30% of books are now purchased and read electronically. And Kindle readers are diligent with providing reviews and ratings for the books that they consume (and of course the helpful prod at the end of each book doesn't hurt).
So that got us thinking: are there hidden factors in a Kindle book's data that are impacting its rating? Luckily, import.io makes it easy to grab data for analysis, and we did exactly that: pulling down over 58,000 kindle reviews which we could quickly import into BigML for more detailed analysis.
My premise going into the analysis was that author and words in the book's description, along with length of book, would have the greatest impact on the number of stars that a book receives in its rating. Let's see what I found out after putting this premise (and the data) to the machine learning test via BigML…
We uploaded over 58,000 Kindle reviews, capturing URL, title, author, price, save (whether or not the book was saved), pages, text description, size, publisher, language, text-to-speech enabled (y/n), x-ray enabled (y/n), lending enabled (y/n), number of reviews and stars (the rating).
This data source includes both text, numeric and categorical fields. To optimize the text processing for authors, I selected "Full Terms only" as I don't think that first names would have any bearing on the results.
I then created a dataset with all of the fields, and from this view, I can see that several of the key fields have some missing values:
Since I am most interested in seeing how the impact of the book descriptions impact the model, I decide to filter my dataset so that only the instances that contain descriptions will be included. BigML makes it easy to do this by simply selecting "filter dataset":
and from there I can choose which fields to filter, and how I'd like them to be filtered. In this case I selected "if value isn't missing" so that the filtered dataset will only include instances where those fields have complete values:
And just like that, I now have a new dataset with roughly 50,000 rows of data called "Amazon Kindle – all descriptions" (you can clone this dataset here). I then take a quick look at the tag cloud for description, which is always interesting:
In the above image we see generic book-oriented terms like "book," "author," "story" and the like coming up most frequently – but we also see terms like "American," "secret," and "relationship" which may end up influencing ratings.
Building my model:
My typical approach is to build a model and see if there are any interesting patterns or findings. If there are, I'll then go back and do a training/test split on my dataset so I can evaluate the strength of said model. For my model, I tried various iterations of the data (this is where BigML's subscriptions are really handy!). I'll spare you the gory details of my iterations, but for the final model I used the following fields: price, pages, description, lending, and number of reviews. You can clone the model into your own dashboard here.
What we immediately see in looking at the tree is a big split at the top, based on description, with the key word being "god". By hovering over the nodes immediately following the root node, we see that any book that contains the description "god" has a likely review of 4.46 stars:
while those without "god" in the description have a rating of 4.27:
Going back to the whole tree, I selected the "Frequent Interesting Patterns" option in order to quickly see which patterns in the model are most relevant, and in the picture below we see six branches with confident, frequent predictions:
The highest predicted value is on the far right (zoomed below), where we predict 4.57 stars for a book that contains "god" in the description (but not "novel" or "mystery"), costs more than $3.45 and has lending enabled.
Conversely, the prediction with the lowest rating does not contain "god," "practical," "novel" or several other terms, is over 377 pages, cannot be loaned, and costs between $8.42 and $11.53:
Looking through the rest of the tree, you can find other interesting splits on terms like "inspired" and "practical" as well as the number of pages that a book contains.
Okay, so let's evaluate
Evaluating your model is an important step as data most certainly can lie (or mislead), so it is critical to test your model to see how strong it truly is. BigML makes this easy: with a single step I can create a training/test split (80/20), which will enable me to build a model with the same parameters from the training set, and then evaluate that against the 20% hold-out set. (You can read more about BigML's approach to evaluations here).
The results are as follows:
You can see that we have some lift over mean-based or random-based decisioning, albeit somewhat moderate. Just for kicks, I decided to see how a 100-model Ensemble would perform, and as you'll see below we have improvement across the board:
This was largely a fun exercise, but it demonstrates that machine learning and decision trees can be informative beyond the predictive models that they create. By simply mousing through the decision tree I was able to uncover a variety of insights on what datapoints lend themselves to positive or less positive Kindle book reviews. From a practical standpoint, a publisher could build a similar model and factor it into its decision-making before green-lighting a book.
Of course my other takeaway is that if I want to write a highly-rated Kindle title on Amazon, it's going to be have something to do with God and inspiration.
PS – in case you didn't catch the links in the post above, you can view and clone the original dataset, filtered dataset and model through BigML's public gallery.
This is a guest post by Andy Thurai (@AndyThurai). Andy has held multiple technology and business development leadership roles with large enterprise companies including Intel, IBM, BMC, CSC, Netegrity and Nortel for the past 20+ years. He's a strategic advisor to BigML.
Now that I got your attention about Germany's unfair advantage in the World Cup, I want to talk about how they used analytics to their advantage to win the World Cup—in a legal way.
I know the first thing that comes to everyone's mind talking about unfair advantage is either performance-enhancing drugs (baseball & cycling) or SpyCam (football, NFL kind). Being a Patriots fan, it hurts to even write about SpyCam, but there are ways a similar edge can be gained without recording the opposing coaches' signals or play calling.
It looks like Germany did a similar thing, legally, and had a virtual 12th man on the field all the time. For those who don't follow football (the soccer kind) closely, it is played with 11 players on the field.
So much has been spoken about Big Data, Analytics and Machine Learning from the technology standpoint. But the World Cup provided us all with an outstanding use case on the application of those technologies.
SAP (a German company) collaborated with the German Football Association to create Match Insights analytics software, dubbed as the ultimate solution for football/soccer. This co-innovation project allows teams to not only analyze their own players, but also learn about their opponents as well to create a game plan. The goal of the co-innovation project between SAP and the coaches of the German national football team was to build an innovative solution that enhanced the on-field performance leading up to and winning the World Cup.
Considering that in 10 minutes, 10 players who touch the ball can produce as high as 7 million data points, imagine how many data points will be created in one game. When you analyze multiple games played by a specific opponent, your tape study and napkin cheat sheets won't be that effective anymore. By being able to effectively harness insights from this massive collection of data points, the German team had a leg up heading into the World Cup.
The proof was in the pudding when Germany used this program to thump Brazil 7-1 in the World Cup and then went on to triumph in the finals against Argentina. In their 7-1 win against Brazil, during a 3 minute stretch in the first half, 3 goals were scored while Brazil owned the ball for 52% of the time during that period. If you watched the game, Germany's teamwork was apparent; but what was not apparent is that the software provided information on the possession time, when to pass, whom to hold the ball against vs. whom to pass against, etc. The Defensive Shadow analysis portion of the software shows teams exactly how to beat the opponent's defensive setup based on a specific opponent alignment and movement.
In a recent article, Sophie Curtis of Telegraph explains a tactic used by the Germans, which was to reduce their average possession time from 3.4 seconds in the 2010 World Cup to 1.1 seconds in 2014. This not only confused the defenders, but also made the defenders uncertain whom to defend and quickly tired from randomly chasing their opponents.
If you watched the match closely, the passes and ball advancement all seemed to be executed with clinical precision. I thought they were just well coached or figured it out during the game, but apparently they had outside help which decided the win – before they ever set foot on the field. While the same advantage can be gained by watching tapes (or videos), the software's uncanny prediction ability goes far beyond traditional mechanisms by converting all game data and player skills to actionable directives that can be implemented on the field. I don't think you can make a more powerful statement than thumping a popular, soccer crazy host nation which fielded a decent football team. I doubt even the legendary Pele would have done any better against Big Data analytics.
The Match Insights tool is exclusive to the German team right now, but according to SAP they have plans to sell it more broadly in the future. In the meanwhile, it is wise to choose Germany if you are betting with your friends.
Big Data is generally a massive collection of data points. Most companies that I deal with think just by collecting data, securing and storing it for later analysis, they are doing "Big Data." However, they are missing the key element of creating real-time actionable intelligence so they can make decisions on the fly about their processes. Most companies either don't realize this, or are not set up to do this. This is where companies like BigML can add a lot of value. BigML is a machine learning company (best of breed in my mind) which helps you do exactly that.
For example, in a recent blog post, Ravi Kankanala of Xtream IT Labs talks about how they were able to predict opening weekend box office revenues for a new movie using BigML's predictive modeling. The point that caught my attention was that they made 200,000 predictions with 90%+ accuracy. This means their clients can look at these results and decide the best time to release their movie (day of the week, day of the month and the month) rather than treating it as a guessing game. This insight also helps studios segment and concentrate their marketing campaigns to improve box office results.
In a blog posted 3 days before the Super Bowl (on Jan 31, 2014), Andrew Shikiar used BigML to predict a Seattle win with an uncanny 76% accuracy. In the same article he also predicted Seattle covering the spread with 72% accuracy. But, if you were watching the pundits on ESPN, they were split 50-50% even an hour before the game with everyone of them yapping about the best offense ever going against a solid Seattle defense so the results are unpredictable.
While Moneyball, and Billy Beane, may have introduced the public to this concept of manipulating a sport based on statistical analysis, that approach was based on how overall statistical numbers can be applied to a team's roster composition. But these precision statistics by BigML can help teams adjust every pitch (as in baseball), every throw (as in football), and every pass (as in soccer).
And the goodness doesn't stop there. The BigML team is busy developing technology that underpins a growing number of predictive initiatives, including:
- Predicting the symptoms of customers thinking of discontinuing subscription services and the best way to target them with exclusive campaigns, promotions, or incentives to retain them before their customers think of switching. – Churn rate analysis.
- Predicting fraudster behavior amongst apparently normal customers.
- Predicting future life events based on changing shopping patterns or suggest different shopping patterns based on life event/style changes.
The key takeaway here is that you can do more than just collect data and store it: with the right strategy and software you can get meaningful insights. If you gain an edge over your competitors, you can win your business World Cup too.
It sure pays to have an edge!


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Celebrity Buzz
Your daily dose of hot news & gossip.
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Ashley Olsen splits from fashion boss
Actress-turned-designer Ashley Olsen has reportedly split from fashion boss boyfriend David Schulte. After less than a year of dating, the 27 year old has ended her romance with the CEO of luxury eyewear brand Oliver Peoples, according to Us Weekly. A source tells the publication, "He was a great guy and cute, but it didn't [...]
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Chris Hemsworth and his wife expecting twins
"Thor" star Chris Hemsworth and his wife, Spanish actress Elsa Pataky, are to become parents to twins, according to a new report. The couple announced the pregnancy late last year and editors at Spain's Hola! magazine are now reporting Pataky is expecting a double arrival. The babies are due in the spring, according to the [...]
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David O. Russell sorry for comparing Jennifer Lawrence's 'Hunger Games' schedule to slavery
"American Hustle" director David O. Russell has apologized for comparing his leading lady Jennifer Lawrence's "The Hunger Games" work schedule to slavery. The director made his remarks at the Australian Academy's AACTA Awards in Los Angeles, and he is now back-pedaling after accepting his choice of words should have been better. He told the New [...]
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Tom Ford showers Hayden Panettiere with flowers following Golden Globes
Designer Tom Ford sent Hayden Panettiere a bouquet of white roses after discovering she had paid to wear one of his creations to the Golden Globe Awards on Sunday. The actress walked the red carpet in a black-and-white Tom Ford number and was more than happy to reveal she had bought it "off-the-rack" rather than [...]
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Lil Za arrested for drug possession at Justin Bieber's house
Justin Bieber's friend Lil Za has been arrested for drug possession following a raid on the pop star's home in California on Tuesday, according to a report. California police executed a search warrant on Bieber's Calabasas home in connection to an alleged neighborhood "egging" incident. Bieber's neighbor, who has reportedly clashed with the pop star [...]
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Justin Bieber shows off three new tattoos
Pop star Justin Bieber indulged his love of body art over the weekend by getting three more tattoos. The star, who boasts more than 20 tattoos all over his body, showed off three new designs on his arms by posting snaps on Instagram. The latest additions to his body art collection include a jester's face, [...]
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Ciara confirms pregnancy
R&B star Ciara has confirmed reports she is pregnant with her first child by showing off her growing baby bump on "The View" on Tuesday. The "Goodies"singer got engaged to wed her rapper beau, Future, in October, and rumors soon surfaced suggesting the couple was set to become parents after Ciara appeared to be sporting [...]
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Rashida Jones and Zosia Mamet writing columns for Glamour
Actresses Rashida Jones and Zosia Mamet are set to serve up relationship and life advice in new columns for Glamour magazine. Jones and Mamet, who have written for the publication in the past, have joined the women's magazine as monthly contributors. The "Parks and Recreation" star's column, titled Love, Rashida, will be illustrated and include [...]
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Woody Allen's attorney defends filmmaker from son's Twitter remarks
Woody Allen's lawyer has jumped to defend the filmmaker after his former partner Mia Farrow and their son, Ronan, attacked him on Twitter following a Golden Globes tribute on Sunday night. Farrow posted a tweet as the director was honored with the Cecil B. DeMille Award for lifetime achievement at the ceremony, suggesting she would [...]
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Tamera Mowry-Housley suffered bullying over interracial marriage
Actress Tamera Mowry-Housley has opened up about the bullying she has been subjected to over her interracial marriage since tying the knot to newsman Adam Housley in 2011. The former "Sister, Sister" star, who is biracial herself, reveals she has been left shocked and hurt by the cruel comments some critics have made about her [...]
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So I'm looking to buy a stir plate. I don't want to make one, I want to buy one. I want to use it primarily for 2L, 5 gallon starters, however I do have a 5L flask and would like both to work with whatever stir plate I get. Any recommendations for me?
I use this one with my 2L and 3L flasks without issue. I haven't [yet] used my 5L flask on it. If you want the mother of all stirplates, you could get this one.
nikobrew stir plate sees ok to me and great price
Had the Hanna one we used in our lab at the water plant. Barely lasted one year.
Take a look at stirstarters.com http://www.homebrewtalk.com/f173/sti...rs-com-278756/


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"Myself and other employees have been sent, during city time, to pick up checks for her, to make deposits into her campaign account, to pick up campaign signs, to get proofs of campaign signs," recounts the stocky, goateed 36-year-old with a shaved head. "The majority of her employees worked diligently on her campaign on city time."
He adds that much of Oliveros's campaign literature from the 1997 election, including handbills and mailers, was produced within the walls of city hall, on the city's computers, printers, and copying machines, most of it by the mayor herself. Two other ex-employees confirm that Godwin and other city staffers regularly worked on Oliveros's election campaigns on city time. Oliveros denies she ever ordered city employees to conduct campaign work during business hours.
The mayor's alleged illegal activity, and Godwin's admitted part in it, didn't stop there. Before the 1997 mayoral election, Godwin says, he planned on moving from his mother's home in Hialeah to an apartment in Hialeah Gardens, but wasn't going to move in time to vote in the mayoral election. Godwin says the mayor gave him a voter's registration form to sign, which he did; he later learned the address on the form was that of Oliveros's neighbor, Hialeah Gardens parks director Ricardo Vasquez. County voter-registration records show he was indeed registered to vote from the Vasquez home. Godwin says he never lived at that address, and that when he voted in the 1997 election (for Oliveros, of course), he still resided in Hialeah. (Godwin claims several other people, at the mayor's urging, engaged in similar voter fraud. But the registration history of a half-dozen or so people he named does not show clearly suspicious change-of-address patterns.) Oliveros denies asking anyone to change their registration into or out of Hialeah Gardens.
Again Godwin claims he acceded to the mayor's orders out of fear. "When she became mayor [the second time], it was like you gave her this ultimate power, and she became this humongous monster. 'I'm unbeatable, I'm unchallengeable, and I can do whatever I want.' She has commented that Fidel Castro is nothing compared to her. That she rules stronger than he does, and that the city belongs to her."
Godwin's life of crime did weigh on his conscience, he says. But what drove him to leave the office in May was the continual verbal abuse from Oliveros, and her clumsy retaliation against him when he dared to display disloyalty. "She has a mouth like a barmaid," Godwin says. "She'd say, 'You have to do this, porque esa me sale de la papaya,'" he says with disgust. (The phrase translates to, "because it comes out of my pussy.") "She called me maricon de playa [faggot from the beach]. She'd always ask me if I was having trouble sitting down because I'd been having sex the night before. Just very unprofessional."
After his supervisor told him the mayor "didn't want him around," Godwin spent three weeks in the hospital for what he calls "a nervous breakdown." Once he pulled himself together, he hired an attorney and filed a discrimination complaint with the federal EEOC. The complaint makes no mention of election-law violations or voter fraud. He does charge that the mayor discriminated against him as a homosexual; that she forced him and others to participate in a Santería ritual; and that his transfer out of the mayor's office amounted to "a constructive discharge," a bit of legalese that means he was jerked around so much that any reasonable person would have walked.
Oliveros, through her attorney Harriet Lewis of Adorno & Zeder, declined to comment for this story. Lewis describes the salient points of the city's response to Godwin's EEOC complaint: Godwin "had some problems in his ability to perform his work," and when he was transferred out of the mayor's office, some "homosexual pornography" was found in his computer. She adds that he was never discriminated against because of his sexual orientation, and maintains he was never asked to leave the city, but that he simply stopped showing up.
Godwin and others allege Oliveros's bigotry is not limited to homosexuals. On more than one occasion, according to three sources, she has declared that "her" city is no place for black people. In 1998 the Thunderwheels of Hialeah Gardens roller-skating rink held a series of "Soul Nights," heavily advertised on Power 96 (WPOW-FM 96.5), which attracted a crowd of mostly black teenagers. A former city employee remembers that some of the kids became unruly the first couple of nights, prompting Manuel Carrera, a paralegal in Oliveros's office, to "start using the word nigger."
"He said, 'What you have to do is take these molletos out of the city,'" the ex-employee says, using the derogatory Spanish term for blacks. The mayor's reaction, according to this former staffer? "She said she did not like having those people 'darkening' her city, because she did not want to have a riot," the ex-employee says. "She said they should go have that Soul Night in Liberty City, Overtown, Opa-locka, or Carol City, because she said black people cannot afford to go to a nice place to spend money."


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Bass Pro Shops Fisherman Icon Wireless Weather Station
Sale price: Clearance price: Starting at: Sale starting at: Clearance starting at: Reg. price:
Make surprise rainouts a thing of the past with this Bass Pro Shops® Fisherman Icon Wireless Weather Station. This Weather Station features an advanced fisherman icon with tendency arrow that reacts to changes in the temperature (current forecast). Features include sun rise/set, moon phase and latitude and longitude for 250 pre-selected US cities; indoor/outdoor temperature (°F/°C) and humidity (%RH) with minimum/maximum records; indoor comfort level icon; and absolute barometric pressure with 12 hour history graph (inHg or hPa). Stay organized with a 12/24 Hour time display, time alarm with snooze and calendar (month, date, weekday). This weather station has a wireless outdoor temperature range of -40°F to 140°F (-40°C to 60°C), indoor temperature range of 32°F to 122°F (0°C to 50°C) and humidity range of 1-99%. Up to 12 month battery life. Requires 3 AA Alkaline batteries (not included). Wireless Temperature Sensor (TX14TH) has 200-foot range from wireless receiver display and up to 24 month battery life. Requires 2 AA Alkaline batteries (not included).
Manufacturer model #: B86012.
- Advanced fisherman icon with tendency arrow
- Sun rise/set, moon phase, and latitude and longitude for 250 pre-selected US cities
- Indoor/outdoor temperature (°F/°C) and humidity (%RH)
- Indoor comfort level icon
- Temperature and frost alarm icons
- Absolute barometric pressure with 12-hour history graph (inHg or hPa)
- 12/24 Hour time display
- Atomic time and date sets automatically
- Updates automatically for Daylight Saving Time
- Time alarm with snooze
- LED Backlight
- Low battery indicators
Outdoor Answers
Get help about this item from fellow customers.
Ask your questions. Share your answers.
5.12" L x 2.36" W 5.12" H
2.5" L x 1.42" W 3.98" H
Does the back light remain on continuously at night or do you manually have to push a button to illuminate it?
Side-by-side test: Bring the outdoor transmitter in the house and place it next to the Forecast Station for 2 hours.
*Compare indoor and outdoor temperature. The temperatures should be within 4 degrees to be within tolerance. The humidity should be within 10% to be within tolerance.
*If the transmitter reads correctly when next to the Forecast Station then try a different location outside.
*Look for heat sources such as sunlight, door or window frames, or reflected heat.
Outdoor Temperature:
Operating temperature range = -40 F to 140F (-40C to 60C)
Accuracy ± 2 degrees Fahrenheit 32 F to 122 F (0C to 50C)
Accuracy ±4 degrees Fahrenheit(-40 F to32 F (-40C to 0C);122 F to140 F (50C to 60C))
Resolution = 1 degree F


text:
Updated: 03/11/2014 1:19 PM
Created: 03/11/2014 1:09 PM WHEC.com
The Churchville-Chili Board of Education has selected three finalists for the district's next superintendent.
The finalists are Joseph DioGuardi, Addison Central School District Superintendent, Carmen F. Gumina, Webster Central School District Deputy Superintendent of Schools and Loretta J. Orologio, Churchville-Chili Central School District Assistant Superintendent for Instruction.
The three finalists will each make an individual day-long school visit to the district. Each visit will include tours of Churchville-Chili school buildings and a finalist forum from 4:30-6 p.m. in the Churchville-Chili Middle School North Cafeteria. These forums will be open to the Churchville-Chili community and allow students, staff and community members the opportunity to meet each finalist and ask questions.
DioGuardi will visit the district on March 18. Orologio and Gumina will visit the district on March 20 and March 21 respectively.
To learn more about the finalists, click here
News10NBC/WHEC-TV welcomes your opinion and interaction on our website, but please remember to keep it clean. If you're advertising for another business, being profane, vulgar or slanderous your post will be deleted. By commenting on this website, you agree that anything you post may be used, along with your name and profile picture, in accordance with our Privacy Policy and the license you have granted pursuant to our Terms of Use. Comments are not pre-screened before they posted.


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Somebody needs to tell Rush Limbaugh that the question isn't what's a "phony soldier," but what's a phony right? Does the First Amendment apply to members of our armed forces who, ironically enough, are daily risking, and losing, their lives to defend it? Do they have the right to question the moral validity, and/or efficacy of a war without having to succumb to verbal evisceration by a glib, radically right pundit? Does the provision that protects free speech apply also to college students, like the one in Florida, who get tasered for asking unorthodox questions, and daring to imply that an election was rigged, or that a sitting president deserves impeachment?
And, what about those we've captured, and detained, without charge, or access to counsel at Guantanamo Bay, and at secret prisons around the world? What "rights," if any, do they have? Yesterday, the Defense Department announced that fourteen "high value" detainees at Gitmo have been offered "the right to request lawyers," a first step in challenging their "unlawful enemy combatant" designation before the Combatant Status Review Tribunal which might itself be described as a rigged process. (WaPo)
But, be that as it may, remember that those enemy combatants who were afforded the "right" to counsel at Guantanamo, in the past, were reportedly only allowed to see a lawyer if they confessed, or admitted guilt, first. Is it any different with these fourteen terror suspects? Did they, too, have to acknowledge crimes, and/or complicity, in order to be allowed to request representation? One wonders what, besides the obvious methods of torture that have leaked out, is taking place during these interrogations that now makes these men eligible for clearing their names?
Since their capture, the fourteen detainees, as well as more than three hundred others held at the naval base at Cuba, have had access only to their captors, and an occasional visit from members of the International Red Cross which visits have been labelled classified. They are only now being allowed the right to what amounts to military personal shoppers, those who will help them through the administrative hurdles necessary to obtain due process, something that both the Constitution, and international law, has, for more than 500 years, guaranteed them, but it appears that this process is more an administrative technicality than an opportunity for genuine adjudication.
The fact that the president of the American Bar Association, whose help in representing the alleged "terrorists" has been requested by the government, has said that he doesn't want the ABA to "lend support and credibility to such an inadequate review scheme" (WaPo) is something that should not be lost on anyone who labors under the delusion that we still have rights that would be recognizable, if only adroitly, by our founding fathers.
What can be more oxymoronic than the statement by an "anonymous" official that "the goal here is to have trials open and public to the greatest extent consistent with protecting classified information." But, clearly somebody's been reading too much Kafka, or maybe not enough, or maybe Zeno's Paradox is in syndication — -"open and public trials" that still protect "classified information."
In the words of a Center for Constitutional Rights attorney who has been trying, in vain, to contact one of the detainees now allowed the "right" to request representation, for over a year, the Defense Department is trying to "put some gloss," and create the aura of legitimacy, but this is mostly lip service, and a flawed facade in light of kangaroo military tribunals, and the Pentagon's insistence that counsel get "necessary security clearance," as well as submit to a complete background check. (WaPo)
Not to put too fine a point on it, but one can't help but wonder if some in this administration, including the current commander-in-chief , were coerced to submit to a background check, if they would still be running our military. The illusory right to counsel might implode on impact were details of what was said, and what transpired, during "classified" interrogations of these detainees to mysteriously surface. How much did they have to give up, or "confess" to in order to obtain the "right" to be represented. More importantly, how far have we deviated from accepted precepts of international law that it is now considered a victory to "allow" not representation, per se, but a request for representation.
This is not an argument in favor of one like Khalid Sheikh Mohammed, who is among this group of "high value" terror suspects. Indeed, no one can defend the indefensible. That said, the indefensible is still entitled to a defense; at least, it was under the judiciial system this country has had in place for the past 200 plus years before the onset of the current constitutional revisionists who have consistently shown that justice goes to the highest bidder.
But, the larger question is why should anybody care about a bunch of illegal aliens being held without charge, indefinitely, and being subjected to "alternate interrogation techniques," by our government, which are so controversial that they are deemed classified, or secret. Along with the Bush jihad on terror comes the so-called Patriot Act which neutralizes the Fourth Amendment, as the Padilla case demonstrates, such that citizens and noncitizens alike get harassed equally, leaving us all vulnerable to being dubbed "terrorists," and held without charge indefinitely.
When those we capture and hold as our nation's enemy are compelled to complete a "Legal Representation Request" form in order to seek redress from a designation that has never been satisfactorily explained, the entire infrastructure of our judicial system is at stake. And, when a district judge in Portland, U.S. District Judge Ann Aiken, joins the ranks of other judges, throughout the country, in suggesting that the Patriot Act contains provisions that are unconstitutional, and that the latest revision to FISA law now enables the executive branch to "conduct surveillance and searches of Americans without satisfying the probable cause requirement of the Fourth Amendment," (AP) take heed — you are no safer in your home, from illegal search and seizure, than any of those currently held at Guantanamo Bay, or in any secret CIA prison around the world.
What's more, those searching your person, car, and home are no more obliged to tell you why they're searching you, charge you, or provide legal proof that they have reason to secretly enter your home, and go through your things as was done to Portland attorney Brandon Mayfield who sued the federal government after being wrongly accused of having been involved in the Madrid train bombings.
Justice has to count for something, or the study of jurisprudence is nothing more than the random collection of artifacts in the name of irrelevancy. And, in the words of a U.S. district judge in Oregon: "For over 200 years, this Nation has adhered to the rule of law — with unparalleled success. A shift to a Nation based on extra-constitutional authority is prohibited, as well as ill-advised." Yes, and a shift to a nation that expunges key sections of military reports which allege abuse of Iraqi citizens, and prisoners, as well as one that requires the completion of forms in order to request a right that has been guaranteed for generations is one that is unacceptable, and must be denounced by all who pledge allegiance to liberty and justice for all.
In the next fourteen months before Election Day, it is up to us to insist that each and every presidential candidate state their position on what they will do about extraordinary rendition, secret CIA terror cells, "alternative interrogation techniques," what is, and is not,classifiable by government, and how they will restore the Bill of Rights, and the integrity of the U.S. Constitution.
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Comments (1)
Someone who reads the whole story said:
amazing the references you choose to use This is at least the second article by this author referencing the student who was tasered. Having watched the video - its been shown hundreds of times, it appear to me the student was tasered for resisting arrest and becoming combative with officers who were asked to remove him. He was told his time was up, his microphone had been shut off. The officers asked him to leave then attempted to lead him from the area and he resisted. To live in a society, we have to live by rules. He chose to break the rules and suffered the consequences. I personally hope all possible charges are filed against him. He was clearly in the wrong. Who was he hurting? All the other audience members who had their own questions to ask and wanted their fair turn at the microphone. He was NOT special and does NOT get to make the rules. So I have to wonder why the author keeps referencing that incident? Does Jayne Lyn Stahl believe she is special and exempt from the rules the rest of us abide by? |
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Aston Villa Football Club
- Nickname:The Villains
- Founded:1874
- Manager:Paul Lambert
- Ground:Villa Park
- Capacity:42584
- Last Game: 28-Feb-2015 15:00
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O'Neill happy with Villa's season
11 May 2010 - 13:25:05
Aston Villa manager Martin O'Neill has called the Villa Park outfit a 'remarkable club' after slamming continuing talk of him moving on in the summer.
O'Neill guided his side to sixth place in the Premier League this season, with Villa also reaching the Carling Cup final and the semi-finals of the FA Cup.
The Ulsterman told the Birmingham Mail: "I heard some fella on the radio saying that Tottenham Hotspur and Manchester City are better clubs than Aston Villa.
"I accept the fact that Manchester City in the last couple of years have spent an awful lot of money and obviously become big players and as a consequence become a big club.
"It's something that we maybe hadn't foreseen and Tottenham have done remarkably well and always been a really fine club. They've won the double and won the championship.
"But Aston Villa is a remarkable club, honestly, and there's not much more I can add to that really."
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