In this paper, we propose a novel fully convolutional two-stream fusion network (FCTSFN) for interactive image segmentation. The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution. The TSLFN includes two distinct deep streams followed by a fusion network. The intuition is that, since user interactions are more direct information on foreground/background than the image itself, the two-stream structure of the TSLFN reduces the number of layers between the pure user interaction features and the network output, allowing the user interactions to have a more direct impact on the segmentation result. The MSRN fuses the features from different layers of TSLFN with different scales, in order to seek the local to global information on the foreground to refine the segmentation result at full resolution. We conduct comprehensive experiments on four benchmark datasets. The results show that the proposed network achieves competitive performance compared to current state-of-the-art interactive image segmentation methods 1 .

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Purpose – The purpose of this paper is to identify individual variables with an impact on knowledge sharing and explore the under-discussed construct of employees’ ignorance. This can enhance the knowledge-sharing process and facilitate the development of greater intellectual capital. Design/methodology/approach – Eighty-four dependent variables affecting knowledge sharing are analyzed and classified into 11 categories. In addition, the direct effect of employees’ ignorance on knowledge sharing is introduced and empirically investigated in a case study of a multinational organization operating within the aerospace and defense industry. Findings – The findings suggest that employees’ ignorance may negatively affect their intention to share knowledge, thus leading to poor decision-making and communication in organizations. Employees’ ignorance could also limit the organizational ability to repel external threats, implement innovation and manage future risks. Originality/value – A classification scheme based on different categories of employees’ ignorance is developed, providing tailor-made recommendations for practitioners facing different types of ill-informed organizational scenarios. Further, the need to shift the emphasis away from the management of knowledge to the management of ignorance is also an important contribution of this paper.

The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as the stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K -nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K -neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature.

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