Middleware for Long-running Applications on Batch Grids. Student Research Symposium poster

Computational grids with multiple batch systems (batch grids) can be powerful infrastructures for executing long-running multicomponent parallel applications. In this paper, we have constructed a middleware framework for executing such long-running applications spanning multiple submissions to the queues on multiple batch systems. We have used our framework for execution of a foremost long-running multi-component application for climate modeling, the Community Climate System Model (CCSM). Our framework coordinates the distribution, execution, migration and restart of the components of CCSM on the multiple queues where the component jobs of the different queues can have different queue waiting and startup times.

A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

Student Engagement in the School: Interpersonal and Inter-Center Differences

Traducido al castellano e inglés

Enhancing the Digital Student Experience postcards

The original ‘Enhancing the digital student experience’ cards have been updated to incorporate the findings from the HE Digital Student consultation and also from the emerging findings from the FE Digital Student project. These ‘Digital Student’ cards are designed to support conversations about students’ digital experience

Student research into the use of technology in higher and further education

Jisc conducted a study into the use of technology in higher and further education to mark the launch of the Jisc Digital Festival.

Summer of Student Innovation 2015

Marketing materials

Digital student further education: FE learners' expectations and experiences of technology - synthesis report

A synthesis report based on the final report of the FE digital student study into FE students' expectations and experiences of the digital environment. The report outlines the key challenges, learning provider solutions, sector solutions and commentary on main findings. Appendix 2 includes a list of project outputs.

Viewpoints for student-staff partnerships handbook

A resource to support setting up, implementing and sustaining student-staff partnerships.

Case study: Transforming the student voice process

Case study on how Pembrokeshire College is using a digital tool and methodology called VocalEyes to support democratic decision-making and improve learner involvement and satisfaction.

Case study: Building a powerful partnership empowering the student community

Case study on two initiatives at Leeds City College to enhance the student digital experience and develop leadership skills, competencies and experience that will prepare student for future study and employment.

Jisc NUS student digital experience benchmarking tool

A benchmarking tool developed by Jisc in collaboration with the National Union of Students (NUS) and the Student Engagement Partnership (TSEP). The tool is a starting point for discussions between staff and students about what is working in the digital learning environment and what they can work on together to improve it.