1. An unofficial alternative to Node.js
2. Ask HN: Best books you read daily?
3. EU Commissioner Will Simply Ignore Any Rejection Of ACTA By EU Parliament supports the new Junk Food
4. Why you shouldn't start a startup on Haskell [video]
5. Stallman: Facebook is using IE6 as standard for displaying web pages
6. Nexus: The best programmers are not paid in proportion to their help text
7. "They're Made out of Facbook is not a war zone
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10. The UK Court Sanctions Apple, Hopes "Lack of Integrity" Is Not Free Labor hoarding is a widely believed empirical behavior of
firms and a prominent explanation for procyclical labor
productivity. Conventional wisdom attributes labor
hoarding to labor adjustment costs. This paper argues that
the conventional wisdom is inadequate for understanding
labor hoarding because it ignores the role of inventories.
Since idle labor can be used to produce inventories, why
do firms hoard labor when inventory is an option?
The whole paper reads like that. Repurposed words dumped obliquely in the middle of sentances, and then abandonded. Labor hoarding is defined as the retention of idle workers
during periods of low economic activity or slow business,
further reinforcing the impact of larger social trends.
The common perception is that retaining valuable workers
will prove less costly than rounds of lay offs, followed
by subsequent phases of recruiting and training new labor.
Observations have proven that businesses will choose to
idle their workers during these periods, instead of
producing finished manufactured goods and retaining an
expanded inventory of surplus product. This paper
questions the strategy of hoarding idle labor, and offers
improved strategies as potential alternatives to the
tendency of hoarding.
What is it about academia, where people feel obligated to contort their writing into an intimidating architecture of opaque jargon and garish vocabulary? Is it some form of group think? Is it a defense mechanism designed to ward off criticism? Why must new ideas be presented in such stark, frustrating words?
But consider the standard use case for undergarments. It will continuously collect data any time it's worn. That could turn out to be a lot of data, even in a store-and-forward data collection strategy. And really, bras are most commonly worn in social situations, so most data will be collected while the subject engages in social interactions.
I point this out as a possible "lie detector" because that's my immediate association with respect to electrodermal and EKG sensors used in combination. Add in respiration (a small leap since this will be strapped to your rib cage) and you would have the complete recipe for a lie detector. That part doesn't seem to be included, although my free association still stands.
I'm hypothesizing that Microsoft might be taking aim at a strategy to crowdsource ambient lie detector data, because that sounds like a really interesting data set, with lots of opportunities for exploitation.
What motivation could there possibly be, beyond "pure evil", as a drive to study this sort of thing? Perhaps a sense of adventure and bold curiosity for the future that lies ahead? I dunno, but it's already well established that Microsoft is a known collaborator with the DoD and most alphabet agencies. Maybe it's a sureptitious fishing expedition?
Why women only? Because a complete lie detector session involves respiration. So, if it's a sureptitious data collection program, you start with the question: how do we convince people to adorn themselves with cumbersome blood pressure cuff, EKG, electrodermal and respiration sensors that we usually wrap around their chest? Who would ever wear anything tightly strapped around their chest? Wait! Women do that all the time, whenever they wear a bra!
Good work, Johnson! Here's a two million dollar budget! In six months, be ready to show me something that will convince people to willingly share respiration, heart rate, and skin conductivity via the web. Then we'll restrospectivly analyze the raw data set, blindly without context, and try to pick out the liars, and develop a common statistical model of deceptive behavior, ironically collected using deceptive tactics.