Visualized Cognitive Knowledge Map Integration for P2P Networks

In the knowledge management field, knowledge map created under the client-server architecture has been widely used to direct the knowledge sharing process. Peer-to-peer (P2P) architecture has been practicable for file sharing, distributed computing, instant messaging, etc. by virtue of the increases of Internet bandwidth and personal computer capability. P2P architecture attracts researchers and practitioners to study knowledge sharing issues in its autonomy and self-organization characters. It calls for academic efforts to design knowledge management supporting system as that for client-server architecture may not be applicable. This study proposes a visualized cognitive knowledge map integration system to facilitate knowledge management on P2P networks. By using the SOM (Self-Organized Map)-like model, called Egocentric SOM (ESOM), the prototyping system can merge the external knowledge under a focal peer’s knowledge structure and present the cognitive knowledge map visually. In evaluating the proposed integration method, this study allocates abstracts of industrial research reports from an industrial technology research institute according to their corresponding author peers and generates individual cognitive knowledge maps. The results from the evaluation experiments reveal that ESOM is capable to retain individual peer’s knowledge structure while articulating with that of other peers in the cognitive knowledge network

DERIVING TECHNOLOGY ROADMAPS WITH TECH MINING TECHNIQUES

Technology monitoring has been a knowledge intensive and time-consuming task for IT managers or domain experts. Tech mining techniques can be used to mitigate these efforts. This paper proposes a technology monitoring framework based on tech mining techniques to facilitate the derivative of information and communication technology (ICT) roadmaps. With this framework, a tech mining engine is able to allocate the most relevant documents which describe a category of technologies. Domain experts were participated in a scan meeting to verify the generated roadmaps based on the selected cluster of documents. The draft roadmaps can be further articulated with domain experts\u27 judgment for technology forecasting and assessment

Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects

We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and structurally. Beautifying a sketch is challenging because of its highly abstract and heavily diverse drawing manner. Existing methods are usually confined to the distribution of their limited training samples and thus cannot beautify freely drawn sketches with rich variations. To address this challenge, we adopt a divide-and-combine strategy. Specifically, we first parse an input sketch into semantic components, beautify individual components by a learned part beautification module based on part-level implicit manifolds, and then reassemble the beautified components through a structure beautification module. With this strategy, our method can go beyond the training samples and handle novel freehand sketches. We demonstrate the effectiveness of our system with extensive experiments and a perceptive study.Comment: 13 figure