Algorithms for Affective Sensing — Results show that the system achieves a six-emotion decision-level correct classification rate of 80% for an acted dataset with clean speech. This PhD thesis is research into algorithm for determining emotion from speech samples, which does so more accurately than humans in a controlled test. (via New Scientist)
The Mystery Machine (A Paper a Day) — rundown of Facebook’s Mystery Machine, which can measure end-to-end performance from the initiation of a page load in a Web browser, all the way through the server-side infrastructure, and back out to the point where the page has finished rendering. Doing this requires a causal model of the relationships between components (happens-before). How do you get that? And especially, how do you get that if you can’t assume a uniform environment for instrumentation?
Smartphone Energy Consumption (Pete Warden) — I love new ways of looking at familiar things. Looking at code and features through the lens of power consumption is another such lens. (I remember Craig from Craigslist talking at OSCON about using power as the denominator in your data center, changing how I saw the Web). The article is full of surprising numbers and fascinating factoids. Active cell radio might use 800 mW. Bluetooth might use 100 mW. Accelerometer is 21 mW. Gyroscope is 130 mW. Microphone is 101 mW. GPS is 176 mW. Using the camera in ‘viewfinder’ mode, focusing and looking at a picture preview, might use 1,000 mW. Actually recording video might take another 200 to 1,000 mW on top of that.
The Infinite Hows (John Allspaw) — when finding ways to improve systems to prevent errors, the process of diagnosis should be focused on the systems and less on the people. (aka “human error” is the result of a preceding systems error.) (aka “design for failure.”)