Tools, building blocks, and rewards : traditional media organizations learn to engage with productive audiences

This paper explores how traditional media organizations (such as magazines, music, film, books, and newspapers) develop routines for coping with an increasingly productive audience. While previous studies have reported on how such organizations have been affected by digital technologies, this study makes a contribution to this literature by being one of the first to show how organizational routines for engaging with an increasingly productive audience actually emerge and diffuse between industries. The paper explores to what extent routines employed by two traditional media organizations have been brought in from other organizational settings, specifically from so-called ‘software platform operators’. Data on routines for engaging with productive audiences have been collected from two information-rich cases in the music and the magazine industries, and from eight high-profile software platform operators. The paper concludes that the routines employed by the two traditional media organizations and by the software platform operators are based on the same set of principles: Provide the audience with (a) tools that allow them to easily generate cultural content; (b) building blocks which facilitate their creative activities; and (c) recognition and rewards based on both rationality and emotion.

FaceXpress : an integrated software suite for facial emotion stimulus manipulation and facial measurement

The characterisation of facial expression through landmark-based analysis methods such as FACEM (Pilowsky & Katsikitis, 1994) has a variety of uses in psychiatric and psychological research. In these systems, important structural relationships are extracted from images of facial expressions by the analysis of a pre-defined set of feature points. These relationship measures may then be used, for instance, to assess the degree of variability and similarity between different facial expressions of emotion. FaceXpress is a multimedia software suite that provides a generalised workbench for landmark-based facial emotion analysis and stimulus manipulation. It is a flexible tool that is designed to be specialised at runtime by the user. While FaceXpress has been used to implement the FACEM process, it can also be configured to support any other similar, arbitrary system for quantifying human facial emotion. FaceXpress also implements an integrated set of image processing tools and specialised tools for facial expression stimulus production including facial morphing routines and the generation of expression-representative line drawings from photographs.

A low-cost attack on branch-based software watermarking schemes

In 2005, Ginger Myles and Hongxia Jin proposed a software watermarking scheme based on converting jump instructions or unconditional branch statements (UBSs) by calls to a fingerprint branch function (FBF) that computes the correct target address of the UBS as a function of the generated fingerprint and integrity check. If the program is tampered with, the fingerprint and integrity checks change and the target address will not be computed correctly. In this paper, we present an attack based on tracking stack pointer modifications to break the scheme and provide implementation details. The key element of the attack is to remove the fingerprint and integrity check generating code from the program after disassociating the target address from the fingerprint and integrity value. Using the debugging tools that give vast control to the attacker to track stack pointer operations, we perform both subtractive and watermark replacement attacks. The major steps in the attack are automated resulting in a fast and low-cost attack.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.