The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2021 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

This work proposes an approach for solving the linear regression problem by maximizing the dependence between prediction values and the response variable. The proposed algorithm uses the Hilbert-Schmidt independence criterion as a generic measure of dependence and can be used to maximize both nonlinear and linear dependencies. The algorithm is important in applications such as continuous analysis of affective speech, where linear dependence, or correlation, is commonly set as the measure of goodness of fit. The applicability of the proposed algorithm is verified using two synthetic, one affective speech, and one affective bodily posture datasets. Experimental results show that the proposed algorithm outperforms support vector regression (SVR) in 84% (264/314) of studied cases, and is noticeably faster than SVR, as an order of 25, on average.

Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt independence criterion (HSIC) has been proposed that learns the dictionary and the corresponding sparse coefficients in a space where the dependency between the data and the corresponding labels is maximized. In this paper, two multiview dictionary learning techniques are proposed based on this HSIC-based SDL. While one of these two techniques learns one dictionary and the corresponding coefficients in the space of fused features in all views, the other learns one dictionary in each view and subsequently fuses the sparse coefficients in the spaces of learned dictionaries. The effectiveness of the proposed multiview learning techniques in using the complementary information of single views is demonstrated in the application of speech emotion recognition (SER). The fully-continuous sub-challenge (FCSC) of the AVEC 2012 dataset is used in two different views: baseline and spectral energy distribution (SED) feature sets. Four dimensional affects, i.e., arousal, expectation, power, and valence are predicted using the proposed multiview methods as the continuous response variables. The results are compared with the single views, AVEC 2012 baseline system, and also other supervised and unsupervised multiview learning approaches in the literature. Using correlation coefficient as the performance measure in predicting the continuous dimensional affects, it is shown that the proposed approach achieves the highest performance among the rivals. The relative performance of the two proposed multiview techniques and their relationship are also discussed. Particularly, it is shown that by providing an additional constraint on the dictionary of one of these approaches, it becomes the same as the other.

Various regression models are used to predict the continuous emotional contents of social signals. The common trend to train those models is by minimizing a sense of prediction error or maximizing the likelihood of the training data. According to those optimization criteria, among two models, the one which results in a lower prediction error, or higher likelihood, should be favored. However, that might not be the case, since to compare the prediction quality of different models, the correlation coefficient of their prediction with the actual values is prevalently used. Hence, given the fact that a lower prediction error does not imply a higher correlation coefficient, we might need to reconsider the optimization criteria that we undertake in order to learn the regression coefficients, in order to synchronize it with the hypothesis testing criteria. Motivated by this reasoning, in this work we suggest to maximize a sense of correlation for learning regression coefficients. Two senses of correlation, namely Pearson's correlation coefficient and Hilbert-Schmidt independence criterion, are seen for this purpose. We have chosen the continuous audio/visual emotion challenge 2012 as the framework of our experiments. The numerical results of this study show that compared to support vector regression, the suggested learning algorithms offer higher correlation coefficient and lower prediction error.

Given the fact that the length of the feature vector that is being used for the paralinguistic recognition of speech has exceeded some thousands, the importance of a sparse representation of a model becomes notable. The importance of a sparse representation is mainly due to the more interpretability, higher generalization capability, and numerically more efficiency of such a model. In this work, as an endeavor to search for a sparse representation of speech features used for paralinguistic speech modeling, we make use of the elastic net. As for the benchmark, we use the frameworks of the second audio/visual emotion challenge and the Interspeech 2012 speaker trait challenge. Also proposed in this work is the use of part-of-speech tags as syntactic features of speech for emotional speech recognition. Results of this work show that despite the relatively small number of features that is used for the modeling tasks, generalization capability of the suggested models is comparable to those of other models that use thousands of features and more elaborate learning algorithms.