(How) Does Data-based Music Discovery Work?

This paper analyses a new type of business operations that mediate the production and consumption of music. Online environment has largely abolished constraints on the variety of music that can be economically distributed, but, at the same time, it reveals another problem. How do people learn what music items do they want to listen to? In the music industry, the product space consists of thousands of artists, songs and albums, and is expanding rapidly. More effective forms of music discovery could therefore create considerable new value by allowing people to listen to music that better matches their taste. We analyse data from Last.fm music discovery service that deploys a collaborative filtering recommender system and social media features to aid music discovery. The analysis finds evidence that the new form of music discovery is valuable to consumers, yet it is relatively less important than an opportunity to listen to music for free. The findings lead us to discuss how the nature of analytical problem and product space, consumer taste, and social media features shape the potential value of created by big data

A comparative evaluation of algorithms for discovering translational patterns in Baroque keyboard works

We consider the problem of intra-opus pattern discovery, that is, the task of discovering patterns of a specified type within a piece of music. A music analyst undertook this task for works by Domenico Scarlattti and Johann Sebastian Bach, forming a benchmark of 'target' patterns. The performance of two existing algorithms and one of our own creation, called SIACT, is evaluated by comparison with this benchmark. SIACT out-performs the existing algorithms with regard to recall and, more often than not, precision. It is demonstrated that in all but the most carefully selected excerpts of music, the two existing algorithms can be affected by what is termed the 'problem of isolated membership'. Central to the relative success of SIACT is our intention that it should address this particular problem. The paper contrasts string-based and geometric approaches to pattern discovery, with an introduction to the latter. Suggestions for future work are given

Generating Preview Tables for Entity Graphs

Users are tapping into massive, heterogeneous entity graphs for many applications. It is challenging to select entity graphs for a particular need, given abundant datasets from many sources and the oftentimes scarce information for them. We propose methods to produce preview tables for compact presentation of important entity types and relationships in entity graphs. The preview tables assist users in attaining a quick and rough preview of the data. They can be shown in a limited display space for a user to browse and explore, before she decides to spend time and resources to fetch and investigate the complete dataset. We formulate several optimization problems that look for previews with the highest scores according to intuitive goodness measures, under various constraints on preview size and distance between preview tables. The optimization problem under distance constraint is NP-hard. We design a dynamic-programming algorithm and an Apriori-style algorithm for finding optimal previews. Results from experiments, comparison with related work and user studies demonstrated the scoring measures' accuracy and the discovery algorithms' efficiency.Comment: This is the camera-ready version of a SIGMOD16 paper. There might be tiny differences in layout, spacing and linebreaking, compared with the version in the SIGMOD16 proceedings, since we must submit TeX files and use arXiv to compile the file

Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user's musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.Comment: Author pre-print version, 20 pages, 6 figures, 4 table

The SATIN component system - a metamodel for engineering adaptable mobile systems

Mobile computing devices, such as personal digital assistants and mobile phones, are becoming increasingly popular, smaller, and more capable. We argue that mobile systems should be able to adapt to changing requirements and execution environments. Adaptation requires the ability-to reconfigure the deployed code base on a mobile device. Such reconfiguration is considerably simplified if mobile applications are component-oriented rather than monolithic blocks of code. We present the SATIN (system adaptation targeting integrated networks) component metamodel, a lightweight local component metamodel that offers the flexible use of logical mobility primitives to reconfigure the software system by dynamically transferring code. The metamodel is implemented in the SATIN middleware system, a component-based mobile computing middleware that uses the mobility primitives defined in the metamodel to reconfigure both itself and applications that it hosts. We demonstrate the suitability of SATIN in terms of lightweightedness, flexibility, and reusability for the creation of adaptable mobile systems by using it to implement, port, and evaluate a number of existing and new applications, including an active network platform developed for satellite communication at the European space agency. These applications exhibit different aspects of adaptation and demonstrate the flexibility of the approach and the advantages gaine

Tranquillity, Guided Visualisation and Personal Discovery for Disengaged ‘Dispirited’ Pupils

Swindon Youth Empowerment Project (SYEP) is currently working in six schools in urban disadvantaged areas in Swindon. The project encourages young people with disaffected and challenging behaviour to reflect on their own behaviour, relationships and potential. The particular innovation of SYEP includes guided personal reflection using visualisation, words and music in an ambient environment without distractions (called “the Tranquillity Zone”), followed by focused activities to stimulate personal discovery (called “the Discovery Zone”). The current phase is to train Learning Mentors in schools in the Excellence in Cities initiative in Swindon to run sessions for pupils at risk in their schools, and assess the impact of these programmes. The project team calls these “dispirited pupils” as they have never learnt to reflect on their self-worth and potential. The main education staff involved are two trainers from the Swindon Youth Empowerment Project (SYEP), who are working in partnership with the Excellence in Cities initiative (EiC) to train 9 Learning Mentors. These Learning Mentors organizationally are part of the EiC, and are employed to guide and support challenging pupils (mainly from secondary schools), so that these pupils become more engaged and motivated with their learning and improve their behaviour. The Swindon Youth Empowerment Project team has trained the Learning Mentors in the Tranquillity Zone and Discovery Zone programmes, which are designed to stimulate pupils in a non-authoritarian way to reflect on their attitudes, reactions, relationships and actions, to consider the consequences of these, and devise alternative life strategies. This is described as reflection on and development of their “higher nature” in ways designed to have a positive effect on relationships and self-esteem. The Tranquillity Zone is guided with text and music in an ambient environment and is linked with the Discovery Zone, which inspires young people to move to their higher nature through personal discovery and activities to develop and articulate their understanding and thinking. The project seeks to influence behaviour by addressing the root causes of personal insecurities and open up new possibilities. Within the 18 project elements of personal, moral, social and emotional learning, the organization is non-authoritarian and aims to illuminate staff, pupils and parents with a positive outlook, which helps them to rise above their problems. The project is developing and expanding, and has involved me as researcher as a dynamic part of that developmental process. Feedback from the Excellence in Cities government initiative has been enthusiastic, recognizing it as an innovative new strategy to refocus and re-energize disaffected young people both in primary and secondary schools. The Learning Mentors who operate the project in schools regard it as most effective and have enthusiastic views on their training. Pupils who have been through the project express strong views that it is been personally effective to them and even “turned them round” from failure to success. All concerned have the highest opinion of the effectiveness of this project in terms of increasing the personal confidence of disaffected young people and giving them a sense of direction, agency and aspiration. The relationship between the project team and these young people is crucial to its success, and the process of building capacity through training is beginning. As the project is not funded by mainstream educational funding, it is totally reliant for its survival on marginal funding bids which are currently restricting expansion. For this it needs to follow up the pupils who have benefited from the programme, and their parents – and to create long term evaluation procedures

Explaining clusters with inductive logic programming and linked data

Knowledge Discovery consists in discovering hidden regularities in large amounts of data using data mining techniques. The obtained patterns require an interpretation that is usually achieved using some background knowledge given by experts from several domains. On the other hand, the rise of Linked Data has increased the number of connected cross-disciplinary knowledge, in the form of RDF datasets, classes and relationships. Here we show how Linked Data can be used in an Inductive Logic Programming process, where they provide background knowledge for finding hypotheses regarding the unrevealed connections between items of a cluster. By using an example with clusters of books, we show how different Linked Data sources can be used to automatically generate rules giving an underlying explanation to such clusters