Domain knowledge

Résumé

Domain knowledge is knowledge of a specific, specialised discipline or field, in contrast to general (or domain-independent) knowledge. The term is often used in reference to a more general discipline—for example, in describing a software engineer who has general knowledge of computer programming as well as domain knowledge about developing programs for a particular industry. People with domain knowledge are often regarded as specialists or experts in their field. Knowledge capture In software engineering, domain knowledge is knowledge about the environment in which the target system operates, for example, software agents. Domain knowledge usually must be learned from software users in the domain (as domain specialists/experts), rather than from software developers. It may include user workflows, data pipelines, business policies, configurations and constraints and is crucial in the development of a software application. Expert's domain knowledge (frequently informal and ill

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https://en.wikipedia.org/wiki/Domain_knowledge

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Sequestration, Scholarship, Sentinel: The Post-Politics of Peace (and War)

Nikolas Kosmatopoulos

n Lebanon, diverse sociopolitical projects have sought to mend the wounds, repair the cracks, and overhaul the loss of the devastating civil war (1975–90). Experts and technopolitics have featured centrally in almost all of them. In my anthropological research on expertise on peace and crisis in Lebanon, I explore how, in the decades after the war, an abstract ideal of peace gave way to a distinct space occupied by diverse groups of experts. I analyze how a previously political aim was transformed into a professionalized field around which specialized knowledge domains were developed and technopolitical practices deployed. In this essay I briefly explore this new architecture of expert power based on the technopolitics of peace (and war) in the contemporary Middle East.

Cambridge University Press2014

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This thesis proposes to analyse symbolic musical data under a statistical viewpoint, using state-of-the-art machine learning techniques. Our main argument is to show that it is possible to design generative models that are able to predict and to generate music given arbitrary contexts in a genre similar to a training corpus, using a minimal amount of data. For instance, a carefully designed generative model could guess what would be a good accompaniment for a given melody. Conversely, we propose generative models in this thesis that can be sampled to generate realistic melodies given harmonic context. Most computer music research has been devoted so far to the direct modeling of audio data. However, most of the music models today do not consider the musical structure at all. We argue that reliable symbolic music models such a the ones presented in this thesis could dramatically improve the performance of audio algorithms applied in more general contexts. Hence, our main contributions in this thesis are three-fold: We have shown empirically that long term dependencies are present in music data and we provide quantitative measures of such dependencies; We have shown empirically that using domain knowledge allows to capture long term dependencies in music signal better than with standard statistical models for temporal data. We describe many probabilistic models aimed to capture various aspects of symbolic polyphonic music. Such models can be used for music prediction. Moreover, these models can be sampled to generate realistic music sequences; We designed various representations for music that could be used as observations by the proposed probabilistic models.

EPFL2008

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Probabilistic models for music

Jean-François Paiement

École Polytechnique Fédérale de Lausanne2008

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