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Submission + - It's Not Developers Slowing Things Down, It's the Process (sprint.ly)

Submitted by Anonymous Coward
An anonymous reader writes: Software engineers understand the pace of writing code, but frequently managers don't. One line of code might take 1 minute, and another line of code might take 1 day. But generally, everything averages out, and hitting your goals are more a function of properly setting your goals than of coding quickly or slowly. Sprint.ly, a company than analyzes productivity, has published some data to back this up. The amount of time actually developing a feature was a small and relatively consistent portion of its lifetime as a work ticket. The massively variable part of the process is when "stakeholders are figuring out specs and prioritizing work." The top disrupting influences (as experienced devs will recognize) are unclear and changing requirements. Another big cause of slowdowns is interrupting development work on one task to work on a second one. The article encourages managers to let devs contribute to the process and say "No" if the specs are too vague. Is there anything you'd add to this list?

Submission + - Machine-Learning Algorithm Ranks the World's Most Notable Authors

Submitted by HughPickens.com
HughPickens.com writes: Every year the works of thousands of authors enter the public domain, but only a small percentage of these end up being widely available. So how do organizations such as Project Gutenberg choose which works to focus on? Allen Riddell has developed an algorithm that automatically generates an independent ranking of notable authors for any given year. It is then a simple task to pick the works to focus on or to spot notable omissions from the past. Riddell’s approach is to look at what kind of public domain content the world has focused on in the past and then use this as a guide to find content that people are likely to focus on in the future.

Riddell’s algorithm begins with the Wikipedia entries of all authors in the English language edition (PDF)—more than a million of them. His algorithm extracts information such as the article length, article age, estimated views per day, time elapsed since last revision, and so on. This produces a “public domain ranking” of all the authors that appear on Wikipedia. For example, the author Virginia Woolf has a ranking of 1,081 out of 1,011,304 while the Italian painter Giuseppe Amisani, who died in the same year as Woolf, has a ranking of 580,363. So Riddell’s new ranking clearly suggests that organizations like Project Guttenberg should focus more on digitizing Woolf’s work than Amisani’s. Of the individuals who died in 1965 and whose work will enter the public domain next January in many parts of the world, the new algorithm picks out TS Eliot as the most highly ranked individual. Others highly ranked include Somerset Maugham, Winston Churchill, and Malcolm X.

Comment Re:spittake (Score 1) 74

by Z00L00K (#48396605) Attached to: Machine Learning Used To Predict Military Suicides

If it's possible to find persons at risk and counter their behavior it's good. Most interesting are the high-skilled persons that joins late. It's not easy to put a diagnosis straight on each case, and if someone is depressed the cause may vary, ranging from bipolar disorder to post traumatic stress reaction and adrenaline junkies.

Sudden relief from stress to a mode of passivity can be highly problematic for some persons.

Submission + - Dealing with VOIP Fraud/Phising Scams 1

Submitted by Anonymous Coward
An anonymous reader writes: I run the IT department for a medium-sized online retailer, and we own a set of marketing toll-free numbers that route to our VOIP system for sales. Yesterday we began receiving dozens and now hundreds of calls from non-customers claiming that we're calling out from our system and offering them $1 million in prizes and asking for their checking account details (a classic phishing scheme).

After verifying that our own system wasn't compromised, we realized that someone was spoofing the Caller ID of our company on a local phone number, and then they were forwarding call-backs to their number to one of our 1-800 numbers.

We contacted the registered provider of the scammer's phone number, Level3, but they haven't been able to resolve the issue yet and have left the number active (apparently one of their sub-carriers owns it). At this point, the malicious party is auto-dialing half of the phone book in the DC metro area and it's causing harm to our business reputation.

Disabling our inbound 800 number isn't really possible due to the legitimate marketing traffic. Does Slashdot have any suggestions?

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