This literature review highlights the tension between cohesiveness and incoherence in operationalizing categories of educational technology professionals. Literature on learning technologists, educational technologists, e-learning technologists, information and communications technology coordinators and information technology coordinators was analyzed through a multilevel model of comparative e...

In this article, we propose a structure of threaded learning conversations taking place in virtual learning communities. This new structure aims at facilitating the emergence of learning conversations in these learning communities. These threaded conversations take place in a forum-type tool that uses a tree data structure composed of threads to organize group communication. We identify two sit...

this paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinalcysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3d spectral-domain optical coherencetomography (sd-oct) images. in particular, we focus on the spectralis sd-oct (heidelberg engineering, heidelberg, germany) system, and...

Listeners detected a small amount of interaural incoherence in reproducible noises with narrow bandwidths and a center frequency of 500 Hz. The durations of the noise stimuli were 100, 50, or 25 ms, and every one of the noises had the same value of interaural coherence, namely 0.992. When the nominal noise bandwidth was 14 Hz, the ability to detect incoherence was found to depend strongly on th...

We study Dictionary Learning (aka sparse coding). By geometrically interpreting an exact formulation of Dictionary Learning, we identify related problems and draw formal relationships among them. Dictionary Learning is viewed as the minimum generating set of a subspace arrangement. This formulation leads to a new family of dictionary learning algorithms. When the data are sufficiently general a...

Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns pedestrian motion. Most existing algorithms use offline learning approaches learn such motion behaviors. However, these cannot take advantage streams data that available after training has concluded, typically not generalizable they have seen before. To solv...

Dictionary learning is a branch of signal processing and machine learning that aims at finding a frame (called dictionary) in which some training data admits a sparse representation. The sparser the representation, the better the dictionary. The resulting dictionary is in general a dense matrix, and its manipulation can be computationally costly both at the learning stage and later in the usage...

We introduce a coe cient update procedure into existing batch and online dictionary learning algorithms. We rst propose an algorithm which is a coe cient updated version of the Method of Optimal Directions (MOD) dictionary learning algorithm (DLA). The MOD algorithm with coe cient updates presents a computationally expensive dictionary learning iteration with high convergence rate. Secondly, we...

Sparsity and Nullity: Paradigm for Analysis Dictionary Learning Report Title Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and have also recently emerged with increasing research interest. Another interesting related problem based on linear equality constraint, namely the sparse null space problem (SNS), f...