FusionSense: Emotion Classification using Feature Fusion of Multimodal Data and Deep learning in a Brain-inspired Spiking Neural Network

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.Peer reviewe

Investigation of the stresses in a continuous two-span highway bridge

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Civil and Sanitary Engineering, 1929.MICROFICHE COPY AVAILABLE IN BARKER ENGINEERING LIBRARY.by Clarence C.T. Loo.B.S

RTSDF: Generating Signed Distance Fields in Real Time for Soft Shadow Rendering

Signed Distance Fields (SDFs) for surface representation are commonly generated offline and subsequently loaded into interactive applications like games. Since they are not updated every frame, they only provide a rigid surface representation. While there are methods to generate them quickly on GPU, the efficiency of these approaches is limited at high resolutions. This paper showcases a novel technique that combines jump flooding and ray tracing to generate approximate SDFs in real-time for soft shadow approximation, achieving prominent shadow penumbras while maintaining interactive frame rates

Information And Communication Technology As A Pedagogical Tool In Teacher Preparation And Higher Education

Under the current trend of globalization and economic dynamics, the accountability of our educational systems is being seriously tested. In response to the demands of the future, the Ministry of Education (MOE) in Singapore has wisely proposed several initiatives to promote the integration of Information and Communication Technology (ICT) in education, and to increase the competitiveness of the workforce by emphasizing inquiry-based learning, higher order thinking, and problem solving (i.e., Thinking Schools Learning Nation, Students Effective Engagement and Development). This study asserts that these two goals, rather than being mutually exclusive, are highly related. Research has shown that integrating technology in teaching and learning can have positive influences on higher order thinking, students motivation, inquiry-based learning, attitudes, achievement, and peer interactions in the classrooms (Bennett, 2001; Schofield, 1995)