Industry 4.0 production comprises complicated highly automated processes. However, human activities are also a crucial component of these processes, e.g., for machine main- tenance. Task assignment of human resources in this domain is challenging, as many factors have to be taken into account to ensure effective and efficient activity execution and satisfy special conditions (like worker safety). To overcome the limita- tions of current Business Process Management (BPM) Systems regarding activity resource assignment, this contribution provides a BPM-integrated approach that applies fuzzy sets for activity assignment. Our findings suggest that this approach can be easily applied to complex production scenarios, while providing efficient performance even with a large number of concurrent activity assignment requests. Additionally, our evaluation shows its potential for improved work distribution which can lead to cost savings in Industry 4.0 production processes.
Human emotion prediction is an important aspect of conversational interactions in social robotics. Conversational interactions involve a combination of dialogs, facial expression, speech modulation, pose analysis, head gestures, and hand gestures in varying lighting conditions and noisy environment involving multi-party interaction. Head motions during conversational gestures, multi-agent conversations and varying lighting conditions cause occlusion of the facial feature-points. Popular Convolution Neural Network (CNN) based predictions of facial expressions degrade significantly due to occluded feature-points during extreme head-movements during conversational gestures and multi-agent interaction in realworld scenarios. In this research, facial symmetry is exploited to reduce the loss of discriminatory feature-point information during conversational head rotations. CNN-based model is augmented with a new rotation invariant symmetry-based geometric modeling. The proposed geometric model corresponds to Facial Action Units (FAU) for facial expressions. Experimental data show hybrid model comprising a CNN-based model and the proposed geometric model outperforms the CNNbased model by 8%-20%, depending upon the type of facialexpression, beyond partial head rotations. Keywords-Artificial Intelligence; conversation; emotion analysis; facial expression analysis; facial occlusion; facial symmetry; head movement; multimedia.
The present paper aims to control selective information to understand the main mechanism of information processing in multi-layered neural networks. We propose two types of selective information, namely, individual and collective selective information, or simply, individual and collective information. The individual information represents to what degree a neuron is connected specifically to another one, and it should be increased as much as possible. Then, we try to use this abundant information as impartially as possible, reducing the specificity of collective neurons and reducing collective information. By controlling the ratio of individual and collective information, we can realize a number of different types of states to be interpreted, leading to the interpretation of the inference mechanism. The method was applied to the bankruptcy data set. In the experiments, we successfully increased individual information and decreased collective information. By examining partially compressed weights, we could see how neural networks, by controlling the selective information, can process information content in multi-layered neural networks. This examination of information flow can lead us to understand the main inference mechanism of neural networks. Keywords—individual, collective, information, selectivity, partial compression, interpretation, generalization
