Type-driven automated program transformations and cost modelling for optimising streaming programs on FPGAs

In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain

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

Be vicarious: the challenge for project management in the service economy

Purpose. The paper aims to answer to the following questions: which are the critical dynamic capabilities to survive in the rubber landscape of service economy? Does it exist in service economy a dynamic capabilities provider? Methodology. The paper combines the literature review on dynamic capability perspective and that on vicariance to the Project Management professional services. Findings. Firstly, the paper identifies vicariance as an intriguing dynamic capability, crucial to survive in the rubber landscape of service economy. Secondly, the paper sheds light on Project Management (PM) as a vicarious that provides vicariance. Practical implications. For each critical organizational dimension, the paper identifies the links among the service economy challenges and the vicariance typology required to the project manager to face those challenge. Originality/value.The approach to conceive the PM as a vicarious that provides vicariance is original and leads to new insights on the professional services management. In fact, on one hand, dynamic capabilities cannot easily be bought through a market transaction; on the other hand, they must be built. This building can be achieved internally, by the organization itself (i.e. hierarchy), or through a partnership (i.e. hybrid form among hierarchy and market). PM professional services enrich organizations with additional information variety according to a hybrid (i.e. non- market) coordination model