ChucK re-factors the idea of a computer music language into three orthogonal basis components: unit generator connections that are data-flow only, globally consistent ”first-class” time control, and sample-synchronous concurrency. The syntax, semantic, and usage have been discussed in previous works. The focus and contributions of this paper are (1) to examine the philosophies and decisions in the language design (2) to describe ChucK’s implementation and runtime model, and (3) to outline potential applications enabled by this framework. We present an experiment in designing a computer music language ”from scratch” and show how things work. We hope these ideas may provides an interesting reference for future computer music systems.
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Over the last several years, computing systems based on adaptive learning with fine-grained parallel architectures have moved from obscurity to front-page prominence. These systems derive some of their novel architecture from ideas gleaned from biology, hence the name “neural network”. Although many of the ideas behind this field are not new, improved computing hardware, better understanding of learning algorithms, and limitations of traditional approaches have combined to renew interest in neural nets.
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The combination of evolving ICT standards that increase the interoperability between applications and the ever-increasing need for a more seamless access to and exchange of information is a major driver in the OECD ICT strategy. This paper describes the role and use of web services in the context of recent developments at the OECD to improve the accessibility of statistical information. It follows up on a paper presented at the 2002 ISIS meeting about “Improving Access to Statistical Information at OECD in Response to Users’ Requirements” and describes a technology framework – called “dot.STAT” – that has been devised for the implementation of applications that enable easy access to certain OECD reference data. The concepts outlined in this paper have been developed in close collaboration with the Statistics Directorate, specialised OECD-internal groups and task forces 2, as well as members of the SDMX Consortium3.
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Many software developers are attracted to the idea of aspect-oriented programming (AOP) but unsure about how to begin using the technology. They recognize the concept of crosscutting concerns, and know that they have had problems with the implementation of such concerns in the past. But there are many questions about how to adopt AOP into the development process.
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Early AI research emphasized the optimization of search algorithms. This approach made a lot of sense because many AI tasks can be solved by effectively by defining state spaces and using search algorithms to define and explore search trees in this state space. Search programs were frequently made tractable by using heuristics to limit areas of search in these search trees. This use of heuristics converts intractable problems to solvable problems by compromising the quality of solutions; this trade off of less computational complexity for less than optimal solutions has become a standard design pattern for AI programming. We will see in this chapter that we trade off memory for faster computation time and better results; often, by storing extra data we can make search time faster, and make future searches in the same search space even more efficient.
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Most of the current methodologies for building ontologies rely on specialized knowledge engineers. This is in contrast to real-world settings, where the need for maintenance of domain specific ontologies emerges in the daily work of users. But in order to allow for participatory ontology engineering, we need to have a more realistic conceptual model of how ontologies develop in the real world. We introduce the ontology maturing processes which is based on the insight that ontology engineering is a collaborative informal learning process and for which we analyze characteristic evolution steps and triggers that have users engage in ontology engineering within their everyday work processes.
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Prosuming is one of the magic activities of the Web 2.0. The idea that media con- sumers can become media producers has been validated with all its pros and cons in systems like YouTube and Flickr.com. There is a re-newed discussion about expert and amateur cultures or even cults [3]. Especially, making movies was thought to be an area where only teams of highly specialized experts equipped with a lot of money can produce high quality movies with a world-wide audience. But next to Hollywood other production centers emerged like ”Bollywood” in India or ”Nollywood” in Nigeria with local practices. YouTube has proven that amateurishly produced content can fascinate people on the Internet.
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Everybody who wants to install a web server database but does not know which software is necessary and how it is installed should benefit from reading this text. This text provides all information necessary to get a SQL database for a web server going; it does not go into any detail of CGI programming, nor does it explain the SQL database language. Excellent books are available on both topics, and it is the intention of this text to provide a working platform based on which a user can then study CGI programming and SQL. For getting a small scale SQL system running (not the notorious example of a major airline booking system, or space mission management database) it will be sufficient to have the software described in this text and the documentation accompanying it. The user manual of msql (a database introduced in this text) provides sufficient information on SQL for building your own database.
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