Generalized Skipgrams for Pattern Discovery in Polyphonic Streams

The discovery of patterns using a minimal set of assumptions constitutes a central challenge in the modeling of polyphonic music and complex streams in general. Skipgrams have been found to be a powerful model for capturing semi-local dependencies in sequences of entities when dependencies may not be directly adjacent (see, for instance, the problems of modeling sequences of words or letters in computational linguistics). Since common skipgrams define locality based on indices, they can only be applied to a single stream of non-overlapping entities. This paper proposes a generalized skipgram model that allows arbitrary cost functions (defining locality), efficient filtering, recursive application (skipgrams over skipgrams), and memory efficient streaming. Further, a sampling mechanism is proposed that flexibly controls runtime and output size. These generalizations and optimizations make it possible to employ skipgrams for the discovery of repeated patterns of close, nonsimultaneous events or notes. The extensions to the skipgram model provided here do not only apply to musical notes but to any list of entities that is monotonic with respect to a given cost function

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Mixing Methods: Practical Insights from the Humanities in the Digital Age

The digital transformation is accompanied by two simultaneous processes: digital humanities challenging the humanities, their theories, methodologies and disciplinary identities, and pushing computer science to get involved in new fields. But how can qualitative and quantitative methods be usefully combined in one research project? What are the theoretical and methodological principles across all disciplinary digital approaches? This volume focusses on driving innovation and conceptualising the humanities in the 21st century. Building on the results of 10 research projects, it serves as a useful tool for designing cutting-edge research that goes beyond conventional strategies

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/