Under the influence: First-year seminars and the librarian teaching role

The discussion will address the benefits and challenges of teaching as a librarian outside of the library.How can a librarian\u27s experiences prepare them to teach a semester-long course? What factors influence the design and delivery of a first-year seminar?What challenges might arise when teaching outside of the library

Neural Shrubs: Using Neural Networks to Improve Decision Trees

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as a poster at the SDSU Data Symposium

Session11: \u3cem\u3eSkip-GCN : A Framework for Hierarchical Graph Representation Learning\u3c/em\u3e

Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph to create the embedding space. We propose a method that takes advantage of both the feature embeddings and topological by an intersection of the two methods. We first represent information across the entire hierarchy of the network by allowing the graph convolutional network to skip neighbors in its convolutions. Then using multilinear algebra, we can capture correlations across the hierarchies to create our node embeddings by representing our convolutions as a tensor. We can follow up the captured node embeddings by a dense layer to perform node classification or link prediction

Multi-Linear Algebraic Eigendecompositions and Their Application in Data Science

Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component Analysis (MPCA) and Mulit-Linear Discriminant Analysis (MLDA)

Temporal Tensor Factorization for Multidimensional Forecasting

In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix factorization (TMF) and low-rank tensor factorization. From this method, we propose an expansion of TMF to two-dimensional data: temporal tensor factorization (TTF). The current research aims to interpolate missing values via low-rank tensor factorization, which produces a latent space of the original multilinear time series. We then can perform forecasting in the latent space. We present experimental results of the proposed method with other state of the art methods on the Jericho-E-Usage energy dataset

Notes from the Field: 10 Short Lessons on One-Shot Instruction

Librarians teach. It might not be what they planned to do when they entered the profession, or it may have been a secret hope all along. Either way, librarians teach, and one teaching scenario remains quintessential: the one-shot library instruction session. In recognition of the centrality of the one-shot, this article shares several authors\u27 notes from the field. The notes provide a range of strategies for developing pedagogically sound one-shot library instruction sessions, grouped loosely into three categories: planning, delivery, and integration. The authors offer these insights in their own words in hopes that other teaching librarians may benefit from their experiences

Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset

Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing. Traditional approaches frequently require hand-crafted speech features and segmentation of speaker turns. In this work, we design end-to-end deep learning methods to recognize each person's affective expression in an audio stream with two speakers, automatically discovering features and time regions relevant to the target speaker's affect. We integrate a local attention mechanism into the end-to-end architecture and compare the performance of three attention implementations -- one mean pooling and two weighted pooling methods. Our results show that the proposed weighted-pooling attention solutions are able to learn to focus on the regions containing target speaker's affective information and successfully extract the individual's valence and arousal intensity. Here we introduce and use a "dyadic affect in multimodal interaction - parent to child" (DAMI-P2C) dataset collected in a study of 34 families, where a parent and a child (3-7 years old) engage in reading storybooks together. In contrast to existing public datasets for affect recognition, each instance for both speakers in the DAMI-P2C dataset is annotated for the perceived affect by three labelers. To encourage more research on the challenging task of multi-speaker affect sensing, we make the annotated DAMI-P2C dataset publicly available, including acoustic features of the dyads' raw audios, affect annotations, and a diverse set of developmental, social, and demographic profiles of each dyad.Comment: Accepted by the 2020 International Conference on Multimodal Interaction (ICMI'20

Pose estimation of spherically correlated images using eigenspace decomposition in conjunction with spectral theory

Department Head: Anthony A. Maciejewski.2009 Summer.Includes bibliographical references (pages 113-122).Eigenspace decomposition represents one computationally efficient approach for dealing with object recognition and pose estimation, as well as other vision-based problems, and has been applied to sets of correlated images for this purpose. The general idea behind eigenspace decomposition is that a large set of highly correlated images can be approximately represented by a much smaller subspace. Unfortunately, determining the dimension of the subspace, as well as computing the subspace itself is computationally prohibitive. To make matters worse, this off-line expense increases drastically as the number of correlated images becomes large (which is the case when doing fully general three-dimensional pose estimation or illumination invariant pose estimation). However, previous work has shown that for data correlated in one-dimension, Fourier analysis can help reduce the computational burden of this off-line expense. The first part of this dissertation extends some of the ideas developed for one-dimensionally correlated image data to data correlated in two- and three-dimensions making fully general three-dimensional pose estimation possible (assuming the object is illuminated from a single distant light source). The first step in this extension is to determine how to capture training images of the object by sampling the two-sphere (S2), and the rotation group (SO(3)) appropriately. Therefore, a thorough analysis of spherical tessellations is performed as applied to the problem of pose estimation. An algorithm is then developed for reducing the off-line computational burden associated with computing the eigenspace by exploiting the spectral information of this spherical data set. The algorithm is based on the fact that, similar to Fourier analysis on the line or circle, spherically correlated functions can be expanded into a finite series using spherical harmonics. It is then shown that the algorithm can be extended to higher dimensions by applying a proper rotation to each of the samples defined on the surface of the sphere. Using this sampling technique, a parameterization of SO(3) is obtained. It is shown that SO(3) correlated functions can be expanded into a finite series by applying a rotation to the set of spherical harmonics and expanding the function using Wigner-D matrices. Experimental results are presented to compare the proposed algorithm to the true eigenspace decomposition, as well as assess the computational savings. The second part of this dissertation deals with the problem of pose estimation when variations in illumination conditions exist. It is shown that the dimensionality of a set of images of an object under a wide range of illumination conditions and fixed pose can be significantly reduced by expanding the image data in a series of spherical harmonics. This expansion results in a reduced dimensional set of \harmonic images". It is shown that the set of harmonic images are capable of recovering a significant amount of information from a set of images captured when both single and multiple illumination sources are present. An algorithm is then developed to estimate the eigenspace of a set of images that contain variation in both illumination and pose. The algorithm is based on projecting the set of harmonic images onto a set of Fourier harmonics by applying Chang's eigenspace decomposition algorithm. Finally, an analysis of eigenspace manifolds is presented when variations in both illumination and pose exist. A technique for illumination invariant pose estimation is developed based on eigenspace partitioning. Experimental results are presented to validate the proposed algorithm in terms of accuracy in estimating the eigenspace, computational savings, and the accuracy of determining the pose of three-dimensional objects under a range of illumination conditions