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The Mixed-Membership Stochastic Blockmodel (MMSB) is proposed as one of the state-of-the-art Bayesian relational methods suitable for learning the complex hidden structure underlying the network data. However, the current formulation of MMSB suffers from the following two issues: (1), the prior information (e.g. entities' community structural information) can not be well embedded in the modelling; (2), community evolution can not be well described in the literature. Therefore, we propose a non-parametric fragmentation coagulation based Mixed Membership Stochastic Blockmodel (fcMMSB). Our model performs entity-based clustering to capture the community information for entities and linkage-based clustering to derive the group information for links simultaneously. Besides, the proposed model infers the network structure and models community evolution, manifested by appearances and disappearances of communities, using the discrete fragmentation coagulation process (DFCP). By integrating the community structure with the group compatibility matrix we derive a generalized version of MMSB. An efficient Gibbs sampling scheme with Polya Gamma (PG) approach is implemented for posterior inference. We validate our model on synthetic and real world data.

Automatic detection of animals that have strayed into human inhabited areas has important security and road safety applications. This paper attempts to solve this problem using deep learning techniques from a variety of computer vision fields including object detection, tracking, segmentation and edge detection. Several interesting insights are elicited into transfer learning while adapting models trained on benchmark datasets for real world deployment. Empirical evidence is presented to demonstrate the inability of detectors to generalize from training images of animals in their natural habitats to deployment scenarios of man-made environments. A solution is also proposed using semi-automated synthetic data generation for domain specific training. Code and data used in the experiments are made available to facilitate further work in this domain.

The outcome economy is a relatively new economic and business paradigm that promotes focusing on the effects that the use of provided products and services create for customers in their markets, rather than focusing on these products or services themselves from the providers’ perspective. This paradigm has been embraced in various fields of business but has not yet been fully integrated with the concept of smart industry. To fill this gap, in this vision paper we provide a framework that does make this integration, showing the full structure of customer outcome management in smart manufacturing, from both business and digital technology perspectives. In applying this structure, a feedback loop is created that spans the markets of provider and customer and supports data-driven product evolution, manufacturing, and delivery. We propose a business reference framework that can be used as a blueprint for designing practical scenarios. We show how integrated digital support for such a scenario can be realized using a well-structured combination of technologies from the fields of the internet of things, business intelligence and federated learning, blockchain, and business process management. We illustrate all of this with a visionary case study inspired by industrial practice in the automotive domain. In doing so, we provide both an academic basis for the integration of several currently dispersed research fields that need to be integrated to further smart manufacturing towards outcome management and a practical basis for the well-structured design and implementation of customer outcome management business cases in smart manufacturing.

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