An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Performance Following: Real-Time Prediction of Musical Sequences Without a Score

(c)2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Double Parton Scattering, Multi-Parton Interactions, underlying event and identified hadrons: summary of recent results

Recent results related to Double-Parton Scattering (DPS) and Multi-Parton Interactions (MPI) from the LHC experiments (ALICE, ATLAS, CMS and LHCb) are reviewed and discussed together with a brief overview of relevant literature. The robust evidence collected for DPS in different channels at LHC energies is complemented by an increasing understanding of our description of MPI in high energy collisions and the corresponding modelling of the underlying event (UE) in hadronic interactions. Potential new results expected during Run 2 at the LHC are also anticipated. The relation and the interplay between the relevant observables for DPS, MPI and UE analyses are discussed presenting recent attempts to bring together their description in a single Monte Carlo tune. Identified hadron spectra at the LHC have been now measured by all collaborations and results are reviewed with an emphasis on strangeness production and baryon/meson ratio. The data collected during Run 1 at the LHC with different collision systems (pp, p-Pb, Pb-Pb) show that several particle production features appear to be more correlated with the event multiplicity than the collision system itself.Comment: 7 pages, no figures, LHCP2014 conference proceeding

Identifying Unmaintained Projects in GitHub

Background: Open source software has an increasing importance in modern software development. However, there is also a growing concern on the sustainability of such projects, which are usually managed by a small number of developers, frequently working as volunteers. Aims: In this paper, we propose an approach to identify GitHub projects that are not actively maintained. Our goal is to alert users about the risks of using these projects and possibly motivate other developers to assume the maintenance of the projects. Method: We train machine learning models to identify unmaintained or sparsely maintained projects, based on a set of features about project activity (commits, forks, issues, etc). We empirically validate the model with the best performance with the principal developers of 129 GitHub projects. Results: The proposed machine learning approach has a precision of 80%, based on the feedback of real open source developers; and a recall of 96%. We also show that our approach can be used to assess the risks of projects becoming unmaintained. Conclusions: The model proposed in this paper can be used by open source users and developers to identify GitHub projects that are not actively maintained anymore.Comment: Accepted at 12th International Symposium on Empirical Software Engineering and Measurement (ESEM), 10 pages, 201

Strange Quark Matter Theory

Theoretical approaches to strange and other types of quark matter accounted for at the SQM2003 Meeting are reviewed. They range from simple statistical models through perturbative QCD supported mico-dynamical simulations til lattice gauge theory and astrophysics results. Finally some ideas for future research in this field are outlined.Comment: Theory summary talk given at Strange Quark Matter 2003 Conference, March 12-17, 2003, Atlantic Beach, NC, USA. (LateX 19 pages, 15 postscript figures.

Instructor perspectives on iteration during upper-division optics lab activities

Although developing proficiency with modeling is a nationally endorsed learning outcome for upper-division undergraduate physics lab courses, no corresponding research-based assessments exist. Our longterm goal is to develop assessments of students' modeling ability that are relevant across multiple upper-division lab contexts. To this end, we interviewed 19 instructors from 16 institutions about optics lab activities that incorporate photodiodes. Interviews focused on how those activities were designed to engage students in some aspects of modeling. We find that, according to many interviewees, iteration is an important aspect of modeling. In addition, interviewees described four distinct types of iteration: revising apparatuses, revising models, revising data-taking procedures, and repeating data collection using existing apparatuses and procedures. We provide examples of each type of iteration, and discuss implications for the development of future modeling assessments.Comment: 4 pages, 1 figure; under revie