Computational modeling of the emotions evoked by art in humans is a challenging problem because of the subjective and nuanced nature of art and affective signals. In this paper, we consider the above-mentioned problem of understanding emotions evoked in viewers by artwork using both text and visual modalities. Specifically, we analyze images and the accompanying text captions from the viewers expressing emotions as a multimodal classification task. Our results show that single-stream multimodal transformer-based models like MMBT and VisualBERT perform better compared to both image-only models and dual-stream multimodal models having separate pathways for text and image modalities. We also observe improvements in performance for extreme positive and negative emotion classes, when a single-stream model like MMBT is compared with a text-only transformer model like BERT.

Keywords: Affective Computing, Multimodal classification, Emotion Recognition

Emotion recognition from text is a challenging task due to diverse emotion taxonomies, lack of reliable labeled data in different domains, and highly subjective annotation standards. Few-shot and zero-shot techniques can generalize across unseen emotions by projecting the documents and emotion labels onto a shared embedding space. In this work, we explore the task of few-shot emotion recognition by transferring the knowledge gained from supervision on the GoEmotions Reddit dataset to the SemEval tweets corpus, using different emotion representation methods. The results show that knowledge transfer using external knowledge bases and fine-tuned encoders perform comparably as supervised baselines, requiring minimal supervision from the task dataset.

Keywords: Emotion Recognition, Few Shot classification, Unsupervised methods

Robust resource demand prediction is crucial for efficient allocation of resources to service requests in a distributed service delivery system. There are two problems in resource demand prediction: firstly to estimate the volume of service requests that come in at different time points and at different geo-locations, secondly to estimate the resource demand given the estimated volume of service requests. While a lot of literature exists to address the first problem, in this work, we have proposed a data-driven statistical method for robust resource demand prediction to address the second problem. The method automates the identification of various system operational characteristics and contributing factors that influence the system behavior to generate an adaptive low variance resource demand prediction model. Factors can be either continuous or categorical in nature. The method assumes that each service request resolution involves multiple tasks. Each task is composed of multiple activities. Each task belongs to a task type, based on the type of the resource it requires to resolve that task. Our method supports configurable tasks per service request, and configurable activities per task. The demand prediction model produces an aggregated resource demand required to resolve all the activities under a task by activity sequence modeling; and aggregated resource demand by resource type, required to resolve all the activities under a service request by task sequence modeling.

Keywords: factor analysis, hierarchical Bayesian model, robust estimation, Distributed service delivery system

Object recognition is one of the challenging tasks in computer vision and the problem becomes increasingly difficult when the image categories are visually correlated among themselves i.e. they are visually similar and only fine differences exist among the categories. This paper has a two-fold objective which involves organization of the image categories in a hierarchical tree like structure using self tuning spectral clustering for exploiting the correlations among them. The organization phase is followed by a node specific large margin nearest neighbor classification scheme, where a Mahalnobis distance metric is learnt for each non-leaf node. Further a procedure for hyperparameters selection has been discussed w.r.t two strategies i.e. grid search and Bayesian optimization. The proposed algorithm's effectiveness is tested on selected classes of the popular Imagenet dataset.

Keywords: large margin nearest neighbor classification, hierarchical organization, visually correlated categories, self tuning spectral clustering, object recognition

The domain of analog filter design revolves around the selection of proper values of the circuit components from a possible set of values manufactured keeping in mind the associated cost overhead. Normal design procedures result in a set of values for the discrete components that do not match with the preferred set of values. This results in the selection of approximated values that cause error in the associated design process. An optimal solution to the design problem would include selection of the best possible set of components from the numerous possible combinations. The search procedure for such an optimal solution necessitates the usage of Evolutionary Computation (EC) as a potential tool for determining the best possible set of circuit components. Recently algorithms based on Swarm Intelligence (SI) have gained prominence due to the underlying focus on collective intelligent behavior. In this paper a novel hybrid variant of a swarm-based metaheuristics called Artificial Bee Colony (ABC) algorithm is proposed and shall be referred to as CRbABC_Dt (Collective Resource-based ABC with Decentralized tasking) and it incorporates the idea of decentralization of attraction from super-fit members along with neighborhood information and wider exploration of search space. Two separate filter design instances have been tested using CRbABC_Dt algorithm and the results obtained are compared with several competitive state-of-the-art optimizing algorithms. All the components considered in the design are selected from standard series and the resulting deviation from the idealized design procedure has been investigated. Additional empirical experimentation has also been included based on the benchmarking problems proposed for the CEC 2013 Special Session & Competition on Real-Parameter Single Objective Optimization.

Keywords: Global optimization, Analog filter design, Passive circuit component, Information sharing, Swarm intelligence, Artificial bee colony algorithm