Building Networks of Practice

{Excerpt} Extensive media coverage of applications such as FaceBook, MySpace, and LinkedIn suggests that networks are a new phenomenon. They are not: the first network was born the day people decided to create organizational structures to serve common interests—that is, at the dawn of mankind. However, the last 10–20 years have witnessed rapid intensification and evolution of networking activities, driven of course by information and communication technologies as well as globalization. These make it possible for individuals to exchange data, information, and knowledge; work collaboratively; and share their views much more quickly and widely than ever before. Thus, less and less of an organization’s knowledge resides within its formal boundaries or communities of practice. Knowledge cannot be separated from the networks that create, use, and transform it. In parallel, networks now play significant roles in how individuals, groups, organizations, and related systems operate. They will be even more important tomorrow. Since we can no longer assume that closely knit groups are the building blocks of human activity—or treat these as discrete units of analysis—we need to recognize and interface with less-bounded organizations, from non-local communities to links among websites. We should make certain that knowledge harvested in the external environment is integrated with what exists within, especially in dynamic fields where innovation stems from inter-organizational knowledge sharing and learning. Therefore, the structure and composition of nodes and ties, and how these affect norms and determine usefulness, must become key concerns. This makes the study of networks of practice a prime interest for both researchers and practitioners

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Networks and Networking in the Cameroon Highlands: An Occasional Paper on Capacity Builders' Experience

This report looks at the impacts of civil society networking trainings in Cameroon Highland