Current state of Learning Management Systems’ log data-based learning analytics

The application of learning analytics techniques to log data from Learning Management Systems (LMS) has raised increasing interest in the past years. Advances in this field include the selection of adequate indicators and development of research frameworks. However, log data-based analysis of courses still poses some obstacles and challenges for researchers and practitioners in order to effectively improve and optimize learning processes. This paper highlights the challenges, and presents approaches that can help complement log data-based learning analytics. These approaches may be especially effective in collaborative settings, and include analysis of information flows, social interactions, and content analysis. This conceptual work aims to promote the debate surrounding the need for comprehensive and comparable studies and frameworks, and to foster advances in log data-based learning analytics

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Developments in nanoparticles for use in biosensors to assess food safety and quality

The following will provide an overview on how advances in nanoparticle technology have contributed towards developing biosensors to screen for safety and quality markers associated with foods. The novel properties of nanoparticles will be described and how such characteristics have been exploited in sensor design will be provided. All the biosensor formats were initially developed for the health care sector to meet the demand for point-of-care diagnostics. As a consequence, research has been directed towards miniaturization thereby reducing the sample volume to nanolitres. However, the needs of the food sector are very different which may ultimately limit commercial application of nanoparticle based nanosensors. © 2014 Elsevier Ltd

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward