Jupyter Notebooks – a publishing format for reproducible computational workflows

It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents

Ethical Use of Machine Learning in Higher Education Admission

A machine learning model called GRADE was used for PhD admission at UT Austin from the year 2013-2020. The model was trained a small set of past admission decisions which are already bias and was used immediately without further tuning or human validation. The model will score all applicants and the decision is made without further human assessment for applicants with the highest and lowest score. Only 362/588 full human reviews are conducted with a few people admitted and the majority of the rest being rejected by algorithm.https://digitalcommons.hamilton.edu/posters/1017/thumbnail.jp

Mining oral history collections using music information retrieval methods

Recent work at the Sussex Humanities Lab, a digital humanities research program at the University of Sussex, has sought to address an identified gap in the provision and use of audio feature analysis for spoken word collections. Traditionally, oral history methodologies and practices have placed emphasis on working with transcribed textual surrogates, rather than the digital audio files created during the interview process. This provides a pragmatic access to the basic semantic content, but obviates access to other potentially meaningful aural information; our work addresses the potential for methods to explore this extra-semantic information, by working with the audio directly. Audio analysis tools, such as those developed within the established field of Music Information Retrieval (MIR), provide this opportunity. This paper describes the application of audio analysis techniques and methods to spoken word collections. We demonstrate an approach using freely available audio and data analysis tools, which have been explored and evaluated in two workshops. We hope to inspire new forms of content analysis which complement semantic analysis with investigation into the more nuanced properties carried in audio signals

RootJS: Node.js Bindings for ROOT 6

We present rootJS, an interface making it possible to seamlessly integrate ROOT 6 into applications written for Node.js, the JavaScript runtime platform increasingly commonly used to create high-performance Web applications. ROOT features can be called both directly from Node.js code and by JIT-compiling C++ macros. All rootJS methods are invoked asynchronously and support callback functions, allowing non-blocking operation of Node.js applications using them. Last but not least, our bindings have been designed to platform-independent and should therefore work on all systems supporting both ROOT 6 and Node.js. Thanks to rootJS it is now possible to create ROOT-aware Web applications taking full advantage of the high performance and extensive capabilities of Node.js. Examples include platforms for the quality assurance of acquired, reconstructed or simulated data, book-keeping and e-log systems, and even Web browser-based data visualisation and analysis.Comment: 7 pages, 1 figure. To appear in the Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016