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)

Special Section: Moving Forward in Animal Research Ethics Guest Editorial Reassessing Animal Research Ethics

Animal research has long been a source of biomedical aspirations and moral concern. Examples of both hope and concern are abundant today. In recent months, as is common practice, monkeys have served as test subjects in promising preclinical trials for an Ebola vaccine or treatment 1 , 2 , 3 and in controversial maternal deprivation studies. 4 The unresolved tension between the noble aspirations of animal research and the ethical controversies it often generates motivates the present issue of the Cambridge Quarterly of Healthcare Ethics. As editors of this special section, we hope that these original and timely articles will push the professional discussion of animal research ethics in a positive direction that will benefi t research scientists and others interested in moral problems in animal research. We also look forward to a day when animal research will genuinely meet both appropriate scientifi c and appropriate ethical criteria—criteria that themselves can be improved by critical scrutiny. Animal research—that is, the use of live animals as experimental subjects in biomedical and behavioral fi elds of learning—has been deeply entrenched for well over half a century. One signal development was the enactment in the late 1930s of federal product safety legislation in the United States and other nations that required animal testing of food, drugs, and medical devices prior to use by human subjects or consumers. 5 Another development was the publication of codes of research ethics that called for animal research prior to human research. The Nuremberg Code, published by an American military tribunal in 1947–48 after scrutiny of Nazi medical atrocities, stated that experiments involving the use of human subjects should be " based on the results of animal experimentation. " 6 The Declaration of Helsinki, fi rst published in 1964, reaffi rmed this assumption and added, rather imprecisely, that " the welfare of animals used for research must be respected. " 7 Against the background of such statements, the institutionalization and widespread acceptance of animal research in the twentieth century rested on two basic assumptions, one factual and one moral. The factual assumption was that animal research is suffi ciently reliable as a basis for predicting the effects of drugs, products, and other materials on human beings that animal trials can be expected to yield signifi cant scientifi c conclusions and medical benefi ts to humanity

A Revised Publication Model for ECML PKDD

ECML PKDD is the main European conference on machine learning and data mining. Since its foundation it implemented the publication model common in computer science: there was one conference deadline; conference submissions were reviewed by a program committee; papers were accepted with a low acceptance rate. Proceedings were published in several Springer Lecture Notes in Artificial (LNAI) volumes, while selected papers were invited to special issues of the Machine Learning and Data Mining and Knowledge Discovery journals. In recent years, this model has however come under stress. Problems include: reviews are of highly variable quality; the purpose of bringing the community together is lost; reviewing workloads are high; the information content of conferences and journals decreases; there is confusion among scientists in interdisciplinary contexts. In this paper, we present a new publication model, which will be adopted for the ECML PKDD 2013 conference, and aims to solve some of the problems of the traditional model. The key feature of this model is the creation of a journal track, which is open to submissions all year long and allows for revision cycles.Comment: 13 page

Is violence increasing or decreasing?: a new methodology to measure repeat attacks making visible the significance of gender and domestic relations

The fall in the rate of violent crime has stopped. This is a finding of an investigation using the Crime Survey for England and Wales, 1994–2014, and an improved methodology to include the experiences of high-frequency victims. The cap on the number of crimes included has been removed. We prevent overall volatility from rising by using three-year moving averages and regression techniques that take account of all the data points rather than point to point analysis. The difference between our findings and official statistics is driven by violent crime committed against women and by domestic perpetrators. The timing of the turning point in the trajectory of violent crime corresponds with the economic crisis in 2008/09

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

Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm

The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing. However, solving the nuclear norm based relaxed convex problem usually leads to a suboptimal solution of the original rank minimization problem. In this paper, we propose to perform a family of nonconvex surrogates of $L_0$-norm on the singular values of a matrix to approximate the rank function. This leads to a nonconvex nonsmooth minimization problem. Then we propose to solve the problem by Iteratively Reweighted Nuclear Norm (IRNN) algorithm. IRNN iteratively solves a Weighted Singular Value Thresholding (WSVT) problem, which has a closed form solution due to the special properties of the nonconvex surrogate functions. We also extend IRNN to solve the nonconvex problem with two or more blocks of variables. In theory, we prove that IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthesized data and real images demonstrate that IRNN enhances the low-rank matrix recovery compared with state-of-the-art convex algorithms