Interdisciplinary knowledge‐based implicit emotion recognition

Abstract

Detecting different emotions is a key step toward intelligent recommendation and affective computing, which plays an increasingly important role in guiding decisions. There have been a large number of previous work of machine learning in this topic. However, most existing methods neglect the help of long-term accumulated and precious interdisciplinary knowledge, which cover both computer science and psychology. In this work, we distill part of the psychological knowledge as following: (i) The emotions with the same polarity co-occur frequently, while emotions with contrary polarity co-exist hardly; (ii) The relations between emotions are variable and complicated; (iii) The number of emotions that can be aroused in the same moment is limited. In this paper, we propose two novel modules to execute above prior knowledge, by adding external constraints to our proposed model Double Attention Convolutional Neural Network. In the first module, the emotion relationships are captured by an automatically extracted emotion template. In the second module, uncertainty punishment is applied to limit the too frequent emotions. Our proposed model, which employs interdisciplinary knowledge, performs potentially and get state-of-the-art result when compared with models without above constraints.

Influence Mechanism of Structural Characteristics of Interdisciplinary Knowledge Network on College Students’ Innovation Ability

“…One significant development trend of science since the 20th century is its integration with technology and mutual penetration of science, technology, and society, i.e., the transformation from single disciplinary knowledge production mode into interdisciplinary interactive mode [1][2][3][4][5]. Interdisciplinary research has been advanced rapidly and its prosperity also reflects the basic situation for the development of science and technology in modern times, such as complicated scientific structure, organic scientific activity and softened ideal objectives [6][7][8][9][10][11][12][13]. As the optimal mode oriented to practical research topic, interdisciplinary scientific research method has become an important impetus of promoting technological innovation [14][15][16][17].…”

Unmanned surface vessel obstacle avoidance with prior knowledge‐based reward shaping

“…However, the use of reinforcement learning to solve the problem of USV autonomous obstacle avoidance has the challenge of lack of proper reward function prevents USV from achieving obstacle avoidance. In the field of text processing, the combination of neural networks and prior knowledge has achieved many successes 9,10 . Therefore, we can design a series of reward functions for USV obstacle avoidance based on reward shaping of prior knowledge that satisfies USV's navigation, acceleration, collision avoidance, and other behaviors.…”

Geometric relation based point clouds classification and segmentation

“…Generally, the grid-based methods are inevitable to loss 3D spatial information during transformation or voxelization, while most point-based methods ignore the geometric relations in point clouds. Our method further reasons about geometric relations in point clouds, as prior knowledge, 25,26 which is crucial to improve the classification and segmentation performance.…”