Supporting UK Repositories; a Cohesive Strategy

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

OpenAIRE compatibility for DSpace repositories

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Sustainable Business models for Open Access Services

Panel at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Getting Started with Islandora

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Visual Arts Data Skills for Researchers (VADS4R)

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

“Between publication culture and funder mandates - What is the future for disciplinary repositories?”

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Integrating Student Learning into the Repository Ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Building the ‘Digital’ Library: Routes to Managing our Institutional Digital Learning, Teaching and Research Assets Together

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Packaging Content for Aggregated and Federated Repositories through APTrust

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

New business models for open research

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

GIS in Digital Repositories

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

The ETH Zurich DOI Desk: DOIs as standard persistent identifier and best practice in Swiss Open Access repositories

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Developing Repositories for Collecting 21st Century Ephemera

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

International Partnering strategy for e-learning provider in emerging economies: Evidence from East Africa learning institutions

International partnership has received growing interest in the literature during the past decades due to globalization, increased technological approaches and rapid changes in competitive environments. The study specifically determines the support provided by international partners on promotion of e-learning in East Africa, assess the motives of partner selection criteria, the determinants of selecting partners, partner models and partner competence of e-learning provider. The study also evaluates obstacles of e-learning partnering strategy in East Africa learning institutions. The research adopts a descriptive survey design. Target population involved East Africa learning institutions with a list of potential institutions generated from the Ministry of Higher Education database. Through a targeted reduction of the initial database, consisting of all learning institutions, both public and private, the study created a target sample base of 200 learning institutions. Structured questionnaires scheduled were used to collect primary data. Study findings showed the approach way East African communities in selecting their e-learning partners depend on international reputation of partners, partner with ability to negotiate with foreign governments, partner with international and local experiences, nationality of foreign partner and partners with local market knowledge.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.