IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

Editorial : living labs and user innovation (December 2015)

Welcome to the January 2016 issue of the Technology Innovation Management Review – the second of two issues on the theme of Living Labs and User Innovation. It is my pleasure welcome back our guest editors for December and January: Seppo Leminen (Laurea University of Applied Sciences and Aalto University, Finland), Dimitri Schuurman (iMinds and Ghent University, Belgium), Mika Westerlund (Carleton University, Canada), and Eelko Huizingh (University of Groningen, Netherlands)

Thoughts from the IAAER’s 12th World Congress of Accounting Educators and Researchers

No abstract available

SPsimSeq : semi-parametric simulation of bulk and single-cell RNA-sequencing data

SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects

Designing transformative spaces for sustainability in social-ecological systems

Transformations toward sustainability have recently gained traction, triggered in part by a growing recognition of the dramatic socio-cultural, political, economic, and technological changes required to move societies toward more desirable futures in the Anthropocene. However, there is a dearth of literature that emphasizes the crucial aspects of sustainability transformations in the diverse contexts of the Global South. Contributors to this Special Feature aim to address this gap by weaving together a series of case studies that together form an important navigational tool on the “how to” as well as the “what” and the “where to” of sustainability transformations across diverse challenges, sectors, and geographies. They propose the term “transformative space” as a “safe-enough” collaborative process whereby actors invested in sustainability transformations can experiment with new mental models, ideas, and practices that can help shift social-ecological systems onto more desirable pathways. The authors also highlight the challenges posed to researchers as they become “transformative space-makers,” navigating the power dynamics inherent in these processes. Because researchers and practitioners alike are challenged to provide answers to complex and often ambiguous or incomplete questions around sustainability, the ideas, reflections and learning gathered in this Special Feature provide some guidance on new ways of engaging with the world

Tourism supply chain & strategic partnerships for managing the complexity in tourism industry

The paper aims to investigate the possible relationship between Tourism Supply Chain and Strategic Partnership, read as a way to reduce and better manage the complexity in Tourism Industry. This last has been analysed under multi-disciplinary approaches (economic, sociological, psychological, anthropological and geographic) to better understand its main components. A synthesis of origin of Tourism Supply Chain term was provided. VRIO framework and PEST analysis was used with the aim to better understand the strategic decision of integration the chain with a single or multiple rings. Starting from this, a theoretical framework from a holistic analysis is provided

Adaptation and learning over networks for nonlinear system modeling

In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies or other factors. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.Comment: To be published as a chapter in `Adaptive Learning Methods for Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C. Principe (2018