HIPs, or Human Interactive Proofs, are challenges meant to be easily solved by humans, while remaining too hard to be economically solved by computers. HIPs are increasingly used to protect services against automatic script attacks. To be effective, a HIP must be difficult enough to discourage script attacks by raising the computation and/or development cost of breaking the HIP to an unprofitable level. At the same time, the HIP must be easy enough to solve in order to not discourage humans from using the service. Early HIP designs have successfully met these criteria [1]. However, the growing sophistication of attackers and correspondingly increasing profit incentives have rendered most of the currently deployed HIPs vulnerable to attack [2,7,12]. Yet, most companies have been reluctant to increase the difficulty of their HIPs for fear of making them too complex or unappealing to humans. The purpose of this study is to find the visual distortions that are most effective at foiling computer attacks without hindering humans. The contribution of this research is that we discovered that 1) automatically generating HIPs by varying particular distortion parameters renders HIPs that are too easy for computer hackers to break, yet humans still have difficulty recognizing them, and 2) it is possible to build segmentation-based HIPs that are extremely difficult and expensive for computers to solve, while remaining relatively easy for humans. ACM Classification H.5.2. [Information interfaces and presentation (HCI)]:User Interfaces − Graphical user interfaces (GUI).

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Some typographers have proposed that typeface familiarity is defined by the amount of time that a reader has been exposed to a typeface design, while other typographers have proposed that familiarity is defined by the commonalities in letter shapes. These two hypotheses were tested by measuring the reading speed and preferences of participants. Participants were tested twice with common and uncommon letter shapes, once before and once after spending 20 minutes reading a story with the font. The results indicate that the exposure period has an effect on the speed of reading, but the uncommon letter shapes did not. Readers did not like the uncommon letter shapes. This has implications for the selection of type and the design of future typefaces.

Over the past few years, the Midwest Independent Transmission System Operator, Inc. (MISO) has transformed the electric utility industry in 13 Midwestern US states through the development and implementation of energy and ancillary services markets. MISO uses mixed-integer programming to determine when each power plant should be on or off. Operations research methods set energy output levels and establish the prices at which energy trades. These new markets increased the efficiency of the existing electric infrastructure (power plants and transmission lines) in the Midwest, improved the reliability of the grid, and reduced the need for future infrastructure investments. These advances enabled the MISO region to realize between $2.1 billion and $3.0 billion in cumulative savings from 2007 through 2010. We expect additional savings of $6.1 billion to $8.1 billion through 2020.

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To enhance typeface legibility we studied how to improve the design of individual letters. Three different fonts were created, each containing several variations of the most frequently misrecognized letters. These variations were tested both with distance and short exposure methodologies. Creating variations within a typeface avoided confounds that occur when letters from different typefaces are compared against each other. The studies found that some variations were more legible than others despite the letters within a font having similar size, weight, and personality. The results showed that narrow letters benefit from being widened, and that x-height characters benefit from using more of the ascending and descending area. These findings can be used to improve the design of future typefaces.

Abstract-The output of image coding and rendering algorithms are presented on a diverse array of display devices. To evaluate these algorithms, image quality metrics should include more information about the spatial and chromatic properties of displays. To understand how to best incorporate such display information, we need a computational and empirical framework to characterize displays. Here we describe a set of principles and an integrated suite of software tools that provide such a framework. The Display Simulation Toolbox (DST) is an integrated suite of software tools that help the user characterize the key properties of display devices and predict the radiance of displayed images. Assuming that pixel emissions are independent, the DST uses the sub-pixel point spread functions, spectral power distributions, and gamma curves to calculate display image radiance. We tested the assumption of pixel independence for two liquid crystal device (LCD) displays and two cathode-ray tube (CRT) displays. For the LCD displays, the independence assumption is reasonably accurate. For the CRT displays it is not. The simulations and measurements agree well for displays that meet the model assumptions and provide information about the nature of the failures for displays that do not meet these assumptions.Index Terms-Display image quality, display linearity, display simulation.