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Computer Game Innovations
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The aim of the described system is to provide an online solution that profiles customers of a call centre. As an auxiliary module it might enhance functionality of modern call centre systems by active voice analysis. Integrated with existing databases, our system allows for analysis of constant and temporal caller
characteristics during a call — respectively identity, age, gender,
emotional state, speech rate and an acoustic background. The specifically developed tool both shortens call time and enhances the amount of information gathered, and consequently reduces cost and workload of call centre responders.
This book covers both the theory and practice of game engine software development, bringing together complete coverage of a wide range of topics. The concepts and techniques described are the actual ones used by real game studios like Electronic Arts and Naughty Dog. The examples are often grounded in specific technologies, but the discussion extends way beyond any particular engine or API. The references and citations make it a great jumping off point for those who wish to dig deeper into any particular aspect of the game development process. Intended as the text for a college level series in game programming, this book can also be used by amateur software engineers, hobbyists, self-taught game programmers, and existing members of the game industry. Junior game engineers can use it to solidify their understanding of game technology and engine architecture. Even senior engineers who specialize in one particular field of game development can benefit from the bigger picture presented in these pages.
This paper improves recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We, first, analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within the inverted file and are efficiently exploited for all images, even in the case of very large datasets. Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy.
You're being asked to quantify your usability improvements with statistics. But even with a background in statistics, you are hesitant to statistically analyze their data, as they are often unsure which statistical tests to use and have trouble defending the use of small test sample sizes. The book is about providing a practical guide on how to solve common quantitative problems arising in usability testing with statistics. It addresses common questions you face every day such as: Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on the 1st attempt? How long will it take users to purchase products on the website? This book shows you which test to use, and how provide a foundation for both the statistical theory and best practices in applying them. The authors draw on decades of statistical literature from Human Factors, Industrial Engineering and Psychology, as well as their own published research to provide the best solutions. They provide both concrete solutions (excel formula, links to their own web-calculators) along with an engaging discussion about the statistical reasons for why the tests work, and how to effectively communicate the results. *Provides practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practices *Show practitioners which test to use, why they work, best practices in application, along with easy-to-use excel formulas and web-calculators for analyzing data *Recommends ways for practitioners to communicate results to stakeholders in plain English. © 2012 Jeff Sauro and James R. Lewis Published by Elsevier Inc. All rights reserved.
In this paper a new particle swarm optimization algorithm called RPSO for solving high dimensional optimization problems is proposed and analyzed both in terms of their efficiency, the ability to avoid local optima and resistance to the problem of premature convergence. In RPSO, a repair procedure was introduced the aim of which was to determine new, better velocities for some particles, when their current velocities are inefficient. New velocities are the functions of previous and current velocities. The new algorithm was tested with a set of benchmark functions and the results were compared with those obtained through the standard PSO (SPSO) and IPSO. Simulation results show that new RPSO is faster and more effective than the standard PSO and IPSO.
