SLOWPIXELS: Slow-design for reflective retrieval of personal photos

Department of Creative Design EngineeringWe introduced the design and findings in our user study of SLOWPIXELS, a photo printer which aims to stimulate reflection & memory recall through the re-materialization of digital photos. Photos were serendipitously retrieved dependent upon position of two sliders: When did I take the photo (photo date slider) & Where did I take the photo (distance from home slider). SLOWPIXELS used its owner???s Instagram account metadata. We tested SLOWPIXELS to 20 participants for 11 days to investigate how the Slow-design could support reflective retrieval of personal photos. Findings revealed that SLOWPIXELS encourage users to reflect their past by unexpected pictures and promote their social bond. We also found that SLOWPIXELS has a design possibility in the public space for event-like situation. For example, in caf?? or student community centre. These findings suggested several contributions, such as designing for revisiting serendipitous memories on the past, empowering social communication, and new way of retrieval of photography while exploring the slow-design agenda within the Design Research community.clos

Finding branch-decompositions of matroids, hypergraphs, and more

Given $n$ subspaces of a finite-dimensional vector space over a fixed finite field $\mathcal F$, we wish to find a "branch-decomposition" of these subspaces of width at most $k$, that is a subcubic tree $T$ with $n$ leaves mapped bijectively to the subspaces such that for every edge $e$ of $T$, the sum of subspaces associated with leaves in one component of $T-e$ and the sum of subspaces associated with leaves in the other component have the intersection of dimension at most $k$. This problem includes the problems of computing branch-width of $\mathcal F$-represented matroids, rank-width of graphs, branch-width of hypergraphs, and carving-width of graphs. We present a fixed-parameter algorithm to construct such a branch-decomposition of width at most $k$, if it exists, for input subspaces of a finite-dimensional vector space over $\mathcal F$. Our algorithm is analogous to the algorithm of Bodlaender and Kloks (1996) on tree-width of graphs. To extend their framework to branch-decompositions of vector spaces, we developed highly generic tools for branch-decompositions on vector spaces. The only known previous fixed-parameter algorithm for branch-width of $\mathcal F$-represented matroids was due to Hlin\v{e}n\'y and Oum (2008) that runs in time $O(n^3)$ where $n$ is the number of elements of the input $\mathcal F$-represented matroid. But their method is highly indirect. Their algorithm uses the non-trivial fact by Geelen et al. (2003) that the number of forbidden minors is finite and uses the algorithm of Hlin\v{e}n\'y (2005) on checking monadic second-order formulas on $\mathcal F$-represented matroids of small branch-width. Our result does not depend on such a fact and is completely self-contained, and yet matches their asymptotic running time for each fixed $k$.Comment: 73 pages, 10 figure

Introduction to the R package TDA

We present a short tutorial and introduction to using the R package TDA, which provides some tools for Topological Data Analysis. In particular, it includes implementations of functions that, given some data, provide topological information about the underlying space, such as the distance function, the distance to a measure, the kNN density estimator, the kernel density estimator, and the kernel distance. The salient topological features of the sublevel sets (or superlevel sets) of these functions can be quantified with persistent homology. We provide an R interface for the efficient algorithms of the C++ libraries GUDHI, Dionysus and PHAT, including a function for the persistent homology of the Rips filtration, and one for the persistent homology of sublevel sets (or superlevel sets) of arbitrary functions evaluated over a grid of points. The significance of the features in the resulting persistence diagrams can be analyzed with functions that implement recently developed statistical methods. The R package TDA also includes the implementation of an algorithm for density clustering, which allows us to identify the spatial organization of the probability mass associated to a density function and visualize it by means of a dendrogram, the cluster tree

LEARNING ANALYTICS IN POST-SECONDARY BUSINESS EDUCATION: A SCOPING REVIEW OF REVIEWS PROTOCOL

Learning Analytics is a growing discipline as educational institutions aim to exploit data and data analytics for several reasons, especially in higher education. Unfortunately, there is a lack of consensus on how learning analytics should be defined and what subjects fall under the purview of learning analytics. The blurred boundaries of what learning analytics encompasses have given rise to multiple studies and systematic reviews that have been published without any consistent agreement to develop the field in a particular direction. Consequently, we are outlining a protocol for a scoping review to map and summarize existing scoping reviews that have been published regarding learning analytics. More specifically, the scoping review of reviews will focus on learning analytics in business education as a use case when it involves machine learning to inform educational interventions. This scoping review will hopefully be the first step in unifying learning analytics for all stakeholders to further develop it into a field of study where it can benefit everyone relying on learning analytics