In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs.bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants of a recurrent neural network that models the temporal structure of video sequences of students solving math problems. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in mean F -score of 50 % over the state-of-the-art method on this new dataset.

In this paper, we focus on visual complexity, an image attribute that humans can subjectively evaluate based on the level of details in the image. We explore unsupervised information extraction from intermediate convolutional layers of deep neural networks to measure visual complexity. We derive an activation energy metric that combines convolutional layer activations to quantify visual complexity. To show the effectiveness of our proposed metric for various applications, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven diverse image categories (e.g., advertisement and interior design). We demonstrate high correlations of our deep neural network-based measure of visual complexity with human-curated ground-truth (GT) scores on various widely used network architectures, e.g., VGG16, ResNet-v2-152, and EfficientNet, and in networks trained on two classification tasks (object and scene classification). This result reveals that intermediate convolutional layers of deep neural networks carry information about the complexity of images that is meaningful to people. Furthermore, we show that our method of measuring visual complexity outperforms traditional methods on Savoias and two other state-of-the-art benchmark datasets. Moreover, we perform extensive analysis on the performance difference between our unsupervised method and a supervised method trained on the feature map, and show that by supervision, we can improve the prediction. Finally, we demonstrate that, within the context of a category, visually more complex images are also more memorable to human observers.

The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.

Advanced analytics is a booming area in the data management industry and a hot research topic. Almost all toolkits that implement machine learning (ML) algorithms assume that the input is a single table, but most relational datasets are not stored as single tables due to normalization. Thus, analysts often join tables to obtain a denormalized table. Also, analysts typically ignore any functional dependencies among features because ML toolkits do not support them. In both cases, time is wasted in learning over data with redundancy. We demonstrate Santoku , a toolkit to help analysts improve the performance of ML over normalized data. Santoku applies the idea of factorized learning and automatically decides whether to denormalize or push ML computations through joins. Santoku also exploits database dependencies to provide automatic insights that could help analysts with exploratory feature selection. It is usable as a library in R, which is a popular environment for advanced analytics. We demonstrate the benefits of Santoku in improving ML performance and helping analysts with feature selection.