Clubs of Clubs : A Networks Approach to the Logic of Membership in Intergovernmental Organizations

Political scientists are paying increasing attention to the effect that shared membership in intergovernmental organizations (IGOs) has in international politics. A number of studies have examined the role that shared membership in IGOs has on dependent variables such as conflict, trade, interest convergence and the diffusion of human rights norms. More recently, scholars have turned their attention to explaining the variation that exists in the extent to which states join IGOs in the first place. In this paper we advance this literature by adopting a network theoretic perspective of IGO membership. Rather than considering the IGO network as simply a collection of ties between states, we consider the ways in which the IGO network can be conceptualized as a number of distinct communities that consist of states and IGOs. We posit that accounting for membership in these communities allows IR scholars adopt a more nuanced understanding of the causes and effects of IGO membership. Our argument is that, depending on the logic of IGO joining, we would expect these clubs of clubs or IGO communities to be defined on differing grounds. In the empirical part of the paper we use the network analytic tool of modularity maximization to detect the IGO communities in the global network for the period 1950-2000. We describe how the IGO communities have developed over time and test the extent to which factors such as development, geography, regime type, alliance ties, language, religion and colonial ties explain the IGO community structure

Observations and modeling of H_2 fluorescence with partial frequency redistribution in giant planet atmospheres

Partial frequency redistribution (PRD), describing the formation of the line profile, has negligible observational effects for optical depths smaller than ~10^3, at the resolving power of most current instruments. However, when the spectral resolution is sufficiently high, PRD modeling becomes essential in interpreting the line shapes and determining the total line fluxes. We demonstrate the effects of PRD on the H_2 line profiles observed at high spectral resolution by the Far-Ultraviolet Spectroscopic Explorer (FUSE) in the atmospheres of Jupiter and Saturn. In these spectra, the asymmetric shapes of the lines in the Lyman (v"- 6) progression pumped by the solar Ly-beta are explained by coherent scattering of the photons in the line wings. We introduce a simple computational approximation to mitigate the numerical difficulties of radiative transfer with PRD, and show that it reproduces the exact radiative transfer solution to better than 10%. The lines predicted by our radiative transfer model with PRD, including the H_2 density and temperature distribution as a function of height in the atmosphere, are in agreement with the line profiles observed by FUSE. We discuss the observational consequences of PRD, and show that this computational method also allows us to include PRD in modeling the continuum pumped H_2 fluorescence, treating about 4000 lines simultaneously.Comment: 17 pages, accepted for publication in Ap

Jacobian ensembles improve robustness trade-offs to adversarial attacks

Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integration into many systems also brings with it the vulnerability to test time attacks in the form of Universal Adversarial Perturbations (UAPs). UAPs are a class of perturbations that when applied to any input causes model misclassification. Although there is an ongoing effort to defend models against these adversarial attacks, it is often difficult to reconcile the trade-offs in model accuracy and robustness to adversarial attacks. Jacobian regularization has been shown to improve the robustness of models against UAPs, whilst model ensembles have been widely adopted to improve both predictive performance and model robustness. In this work, we propose a novel approach, Jacobian Ensembles – a combination of Jacobian regularization and model ensembles to significantly increase the robustness against UAPs whilst maintaining or improving model accuracy. Our results show that Jacobian Ensembles achieves previously unseen levels of accuracy and robustness, greatly improving over previous methods that tend to skew towards only either accuracy or robustness

Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection

Recent statistics show that in 2015 more than 140 millions new malware samples have been found. Among these, a large portion is due to ransomware, the class of malware whose specific goal is to render the victim's system unusable, in particular by encrypting important files, and then ask the user to pay a ransom to revert the damage. Several ransomware include sophisticated packing techniques, and are hence difficult to statically analyse. We present EldeRan, a machine learning approach for dynamically analysing and classifying ransomware. EldeRan monitors a set of actions performed by applications in their first phases of installation checking for characteristics signs of ransomware. Our tests over a dataset of 582 ransomware belonging to 11 families, and with 942 goodware applications, show that EldeRan achieves an area under the ROC curve of 0.995. Furthermore, EldeRan works without requiring that an entire ransomware family is available beforehand. These results suggest that dynamic analysis can support ransomware detection, since ransomware samples exhibit a set of characteristic features at run-time that are common across families, and that helps the early detection of new variants. We also outline some limitations of dynamic analysis for ransomware and propose possible solutions

Decision-making in policy governed human-autonomous systems teams

Policies govern choices in the behavior of systems. They are applied to human behavior as well as to the behavior of autonomous systems but are defined differently in each case. Generally humans have the ability to interpret the intent behind the policies, to bring about their desired effects, even occasionally violating them when the need arises. In contrast, policies for automated systems fully define the prescribed behavior without ambiguity, conflicts or omissions. The increasing use of AI techniques and machine learning in autonomous systems such as drones promises to blur these boundaries and allows us to conceive in a similar way more flexible policies for the spectrum of human-autonomous systems collaborations. In coalition environments this spectrum extends across the boundaries of authority in pursuit of a common coalition goal and covers collaborations between human and autonomous systems alike. In social sciences, social exchange theory has been applied successfully to explain human behavior in a variety of contexts. It provides a framework linking the expected rewards, costs, satisfaction and commitment to explain and anticipate the choices that individuals make when confronted with various options. We discuss here how it can be used within coalition environments to explain joint decision making and to help formulate policies re-framing the concepts where appropriate. Social exchange theory is particularly attractive within this context as it provides a theory with “measurable” components that can be readily integrated in machine reasoning processes

Sharing data through confidential clouds: an architectural perspective

© 2015 IEEE.Cloud and mobile are two major computing paradigms that are rapidly converging. However, these models still lack a way to manage the dissemination and control of personal and business-related data. To this end, we propose a framework to control the sharing, dissemination and usage of data based on mutually agreed Data Sharing Agreements (DSAs). These agreements are enforced uniformly, and end-to-end, both on Cloud and mobile platforms, and may reflect legal, contractual or user-defined preferences. We introduce an abstraction layer that makes available the enforcement functionality across different types of nodes whilst hiding the distribution of components and platform specifics. We also discuss a set of different types of nodes that may run such a layer