Classifying Smart Personal Assistants: An Empirical Cluster Analysis

The digital age has yielded systems that increasingly reduce the complexity of our everyday lives. As such, smart personal assistants such as Amazon’s Alexa or Apple’s Siri combine the comfort of intuitive natural language interaction with the utility of personalized and situation-dependent information and service provision. However, research on SPAs is becoming increasingly complex and opaque. To reduce complexity, this paper introduces a classification system for SPAs. Based on a systematic literature review, a cluster analysis reveals five SPA archetypes: Adaptive Voice (Vision) Assistants, Chatbot Assistants, Embodied Virtual Assistants, Passive Pervasive Assistants, and Natural Conversation Assistants

Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies