This paper proposes a novel active contour model called weighted kernel mapping (WKM) model along with an extended watershed transformation (EWT) method for the level set image segmentation, which is a hybrid model based on the global and local intensity information. The proposed EWT method simulates a general spring on a hill with a fountain process and a rainfall process, which can be considered as an image pre-processing step for improving the image intensity homogeneity and providing the weighted information to the level set function. The WKM model involves two new energy functionals which are used to segment the image in the low dimensional observation space and the higher dimensional feature space respectively. The energy functional in the low dimensional space is used to demonstrate that the proposed WKM model is right in theory. The energy functional in the higher dimensional space obtains the region parameters through the weighted kernel function by utilising mean shift technique. Since the region parameters can better represent the values of the evolving regions due to the better image homogeneity, the proposed method can more accurately segment various types of images. Meanwhile, by adding the weighted information, the level set elements can be updated faster and the image segmentation can be achieved with fewer iterations. Experimental results on synthetic, medical and natural images show that the proposed method can increase the accuracy of image segmentation and reduce the iterations of level set evolution for image segmentation.
Despite the fact that task-oriented conversation systems have received much attention from the dialogue research community, only a handful of them have been studied in a real-world manufacturing context using industrial robots. One stumbling block is the lack of a domain-specific discourse corpus for training these systems. Another difficulty is that earlier attempts to integrate natural language interfaces (such as chatbots) into the industrial sector have primarily focused on task completion rates. When designing a dialogue system for social robots, the user experience is prioritized above industrial robots. We provide the Industrial Robots Domain Wizard-of-Oz dataset (IRWoZ) to overcome these challenges, a fully-labeled discourse dataset covering four robotics domains. It delivers simulated discussions between shop floor workers and industrial robots, with over 401 dialogues, to promote language-assisted Human-Robot Interaction (HRI) in industrial settings. Small talk concepts and human-to-human conversation strategies are provided to support humanlike answer generation and give a more natural and adaptable dialogue environment to increase user experience and engagement. Finally, we propose and evaluate an end-to-end Task-oriented Dialogue for Industrial Robots (ToD4IR) using two types of pre-trained backbone models: GPT-2 and GPT-Neo, on the IRWoZ dataset. ToD4IR's performance in a real manufacturing context was validated through a series of trials. Our experiments demonstrate that ToD4IR outperforms three downstream task-oriented dialogue tasks, i.e., dialogue state tracking, dialogue act generation, and response generation, on the IRWoZ dataset. Our source code of ToD4IR and the IRWoZ dataset is accessible at https://github.com/lcroy/ToD4IR for reproducible research.
Rumors regarding food, medicine, epidemic diseases, and public emergencies greatly impact consumers’ purchase intention, disrupt market demand, affect enterprises’ operating strategies, and eventually increase the risk of market chaos. Governments must play an active role with limited resources under the situation of rumor spreading and demand disruption to maintain stable and sustainable market development. To identify the optimal evolutionary stable strategy (ESS) of both small and large enterprises when facing rumors, this paper investigates the following two choices of enterprises: reasonable and unreasonable pricing. The results reveal that government supervision priority should be set based on the rumor severity, collusion in markup and the endogeneity of the enterprises. From an exogenous perspective, rumor spreading induces enterprises to overcharge, and government supervision has the opposite effect. However, the demand disruption ratio is proven to motivate enterprises to implement reasonable pricing. The profit and loss ratio and homoplasy are two endogenous factors affecting enterprise decisions. Small enterprises are more likely to take advantage of public panic and overcharge, while large enterprises are inclined to choose reasonable pricing in consideration of their corporate image. In addition, the evidence indicates that the ESS of large firms has a stronger impact on small firms.
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