Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL

A Novel Design Science Approach for Integrating Chinese User-Generated Content in Non-Chinese Market Intelligence

Market research has long relied on reactive means of data gathering, such as questionnaires or focus groups. With the wide-spread use of social media, millions of comments about customer opinions and feedback regarding products and brands are available. However, before using this ‘wisdom of the crowd’ as a source for marketing research, several challenges have to be tackled: the sheer volume of posts, their unstructured format, and the dozens of different languages used on the internet. All of them make automated usage of this data challenging. In this paper, we draw on dashboard design principles and follow a design science research approach to develop a framework for search, integration, and analysis of cross-language user-generated content. With ‘MarketMiner’, we implement the framework in the automotive industry by analyzing Chinese auto forums. The results are promising in that MarketMiner can dramatically improve utilization of foreign-language social media content for market intelligence purposes

Towards Semantic e-Science for Traditional Chinese Medicine

<p>Abstract</p> <p>Background</p> <p>Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science.</p> <p>Results</p> <p>We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research.</p> <p>Conclusion</p> <p>Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline.</p

Exploring Knowledge Transfer and Knowledge Building at Offshore Technical Support Centers

This is an exploratory investigation into knowledge transfer and knowledge building processes observed at offshore Technical Support Centers (TSCs) in China. Utilizing a multiple case study approach, the study examines how knowledge was transferred from the US-based support center to the China-based offshore support center, and how individuals and the organization built and expanded knowledge in a dynamic changing business context. The field cases were three Technical Support Centers in China. Three models were developed from the qualitative analysis of the field data to explain how knowledge is transferred and built in offshore TSCs. The knowledge transfer type adoption model identifies the relationships amongst the levels of knowledge (novice, advanced beginner, competency, and proficiency), the types of knowledge and the knowledge transfer approaches (structured transfer stages, unstructured copy, unstructured adaptation, and unstructured fusion). The basic individual tacit knowledge building model shows that tacit knowledge is acquired and built through two continuous knowledge building loops, an explicit learning loop and an implicit learning loop. The organizational knowledge building model demonstrates the interaction amongst knowledge flow, absorptive capacity, knowledge stock and knowledge intermediary in offshore knowledge transfer and building within the three levels (individual, group and organization levels) of the SECI spiral (socialization, externalization, combination and internalization). The three models provide new insights into the knowledge transfer process for different levels of knowledge acquisition, individual tacit knowledge building processes and organizational knowledge building processes in an offshore outsourcing business context. By applying these models to appropriate field situations, both practitioners and academics may be able to gain a deeper understanding of knowledge transfer approaches, be able to better guide new employees’ expertise and confidence building through controlled and monitored experiential learning process, and be able to improve understanding of how knowledge is built and evolves within organizations

Technology Integration around the Geographic Information: A State of the Art

One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented