Indirect reciprocity enables cooperation between strangers when (i) individuals can be recognized and (ii) reputations can be remembered. Here, we show that neither condition is necessary. If reputation is embodied in a recognizable token issued by an arbiter, donors can base their decisions on token possession rather than memory. By allowing for multiple tokens, such an externalized reputational system promotes more cooperation than existing models of indirect reciprocity. This advantage arises from greater robustness to noise and comes despite the absence of individual identification or shared memory. Our results identify a previously overlooked class of mechanisms-object-based reputation-within evolutionary models of cooperation. Since such tokens already possess the structure required for later transfer, they may offer a conceptual bridge between reputation systems and token-based exchange or money.

Border Gateway Protocol (BGP) anomalies, such as hijacking, is currently growing in trend due to limited detection capabilities. BGP hijacking maliciously reroutes Internet traffic, causing Denial of Service (DoS) to major Internet Service Providers (ISPs) or redirection attacks to Internet users. While it has been shown that BGP anomalies can be detected using machine learning (ML) methods, the features used to train these ML models are not comprehensive. This is because node level features, such as the number of BGP announcements, average Autonomous System (AS) path length and average edit distance do not consider the structure or relationships present in the network graph. In this paper, an approach to extract information from BGP updates to build a network graph is proposed. Then, centrality information is used as features to model the graphical structure of the network to build an early detection tool for BGP anomalies using ML. The proposed method has been validated on real world data from the CenturyLink outage and shows promising results for anomaly detection (as early as one hour before the event was reported) in both individual and a defined group of networks. Furthermore, the anomaly source can be determined using the proposed method.

For many inverse problems, the data on which the solution is based is acquired sequentially. We present an approach to the solution of such inverse problems where a sensor can be directed (or otherwise reconfigured on the fly) to acquire a particular measurement. An example problem is magnetic resonance image reconstruction. We use an estimate of mutual information derived from an empirical conditional distribution provided by a generative model to guide our measurement acquisition given measurements acquired so far. The conditionally generated data is a set of samples which are representative of the plausible solutions that satisfy the acquired measurements. We present experiments on toy and real world data sets. We focus on image data but we demonstrate that the method is applicable to a broader class of problems. We also show how a learned model such as a deep neural network can be leveraged to allow generalisation to unseen data. Our informed adaptive sensing method outperforms random sampling, variance based sampling, sparsity based methods, and compressed sensing.

The Reynolds number, Re, is an important quantity for describing a turbulent flow. It tells us about the bandwidth over which energy can cascade from large scales to smaller ones, prior to the onset of dissipation. However, calculating it for nearly collisionless plasmas like the solar wind is challenging. Previous studies have used formulations of an "effective" Reynolds number, expressing Re as a function of the correlation scale and either the Taylor scale or a proxy for the dissipation scale. We find that the Taylor scale definition of the Reynolds number has a sizable prefactor of approximately 27, which has not been employed in previous works. Drawing from 18 years of data from the Wind spacecraft at 1 au, we calculate the magnetic Taylor scale directly and use both the ion inertial length and the magnetic spectrum break scale as approximations for the dissipation scale, yielding three distinct Re estimates for each 12 hr interval. Average values of Re range between 116,000 and 3,406,000 within the general distribution of past work. We also find considerable disagreement between the methods, with linear associations of between 0.38 and 0.72. Although the Taylor scale method is arguably more physically motivated, due to its dependence on the energy cascade rate, more theoretical work is needed in order to identify the most appropriate way of calculating effective Reynolds numbers for kinetic plasmas. As a summary of our observational analysis, we make available a data product of 28 years of 1 au solar wind and magnetospheric plasma measurements from Wind.

Mutualistic interactions among members of different species are common, seemingly stable, and thus apparently enduring. This is at odds with standard mathematical models based solely on between-species interactions, which show mutualisms to be inherently unstable. Models incorporating parameters for punishment and reward strategies demonstrate that the range of conditions over which stability is observed can be extended; however, the role of community-level dynamics impacted by within-species interactions remains relatively unexplored. Here we develop a general and readily applicable approach for analysing a broad range of mutualisms. By incorporating within-species interactions, we show that mutualisms can be stably maintained across diverse environmental conditions without introducing changes to between-species interaction parameters. Further, a balance of within- and between-species interactions is sufficient to allow the persistence of mutualisms encountering ecological perturbations. Our simple and robust framework resonates with emerging empirical data highlighting the role of community-level interactions and population dynamics in maintaining mutualisms.

Helping strangers at a cost to oneself is a hallmark of many human interactions, but difficult to justify from the viewpoint of natural selection, particularly in anonymous one-shot interactions. Reputational scoring can provide the necessary motivation via "indirect reciprocity," but maintaining reliable scores requires close oversight to prevent cheating. We show that in the absence of such supervision, it is possible that scores might be managed by mutual consent between the agents themselves instead of by third parties. The space of possible strategies for such "consented" score changes is very large but, using a simple cooperation game, we search it, asking what kinds of agreement can i) invade a population from rare and ii) resist invasion once common. We prove mathematically and demonstrate computationally that score mediation by mutual consent does enable cooperation without oversight. Moreover, the most invasive and stable strategies belong to one family and ground the concept of value by incrementing one score at the cost of the other, thus closely resembling the token exchange that underlies money in everyday human transactions. The most successful strategy has the flavor of money except that agents without money can generate new score if they meet. This strategy is evolutionarily stable, and has higher fitness, but is not physically realizable in a decentralized way; when conservation of score is enforced more money -like strategies dominate. The equilibrium distribution of scores under any of this family of strategies is geometric, meaning that agents with score 0 are inherent to money-like strategies.

The challenge of anomaly detection is to obtain an accurate understanding of expected behaviour which is intensified when the data are distributed heterogeneously. Transmitting raw data to a central site incurs high communication overhead and raises privacy issues. The concept of Edge AI allows computation to be performed at the edge site allowing for quick decision making in mission critical scenarios such as self-driving cars. A model is learnt locally and its parameters are transmitted and aggregated. However, existing methods of aggregation do not account for variance and heterogeneous distribution of data. They also do not consider edge constraints such as limited computational, memory and communication capabilities of edge devices. In this work, a fully Bayesian approach is employed by means of a Bayesian Random Vector Functional Link AutoEncoder being incorporated with Expectation Propagation for distributed training. Our anomaly detection system operates without any transmission of raw data, is robust under inhomogeneous network densities and under uneven and biased data distributions. It allows for asynchronous updates to converge in a few iterations and is a relatively simple neural network addressing edge constraints without compromising on performance as compared to existing more complex models.

Humans invest in fantastic stories—mythologies. Recent evolutionary theories suggest that cultural selection may favour moralising stories that motivate prosocial behaviours. A key challenge is to explain the emergence of mythologies that lack explicit moral exemplars or directives. Here, we resolve this puzzle with an evolutionary model in which arbitrary mythologies transform a collection of egoistic individuals into a cooperative. We show how these otherwise puzzling amoral, nonsensical, and fictional narratives act as exquisitely functional coordination devices and facilitate the emergence of trust and cooperativeness in both large and small populations. Especially, in small populations, reflecting earlier hunter-gatherers communities, relative to our contemporary community sizes, the model is robust to the cognitive costs in adopting fictions.

Network data constantly evolves with new network applications and protocols. There is a need for robust techniques to detect anomalous behaviour. Offline models trained with static data lose validity when new variants of traffic emerge. They require retraining but the need for ground truth and lengthy training times make this task challenging. Meanwhile, online models which detect outliers in streaming data are susceptible to the curse of dimensionality and natural variability. Today’s anomalies may be tomorrow’s new traffic and existing methods do not provide a way to differentiate between them. We propose a framework that makes the most of both approaches: an offline deep learning model extracts features of normal traffic and provides a bias for an online outlier detection model to select data for training. The online model retains its previously learnt knowledge and retrains itself with new data. Online thresholds are updated in a drifting manner and the Mann-Whitney U test is incorporated to prevent inaccurate updates. We perform analysis on the scores, develop heuristics to detect new traffic and evaluate using three deep learning models and four outlier detection methods on the UNSW-NB15 and CTU-13 datasets. The framework improves upon any individual offline or online models in isolation.

Time series data sets often have missing or corrupted entries, which need to be handled in subsequent data analysis. For example, in the context of space physics, calibration issues, satellite telemetry issues, and unexpected events can make parts of a time series unusable. This causes problems for understanding the dynamics of the heliosphere and space weather environment. Various approaches exist to tackle this problem, including mean/median imputation, linear interpolation, and autoregressive modeling. Here, we study the utility of artificial neural networks (ANNs) to predict statistics of sparse time series. Our focus is not on time series prediction but on gleaning the best possible information about the statistical behavior of the system. As an example application, we focus on the structure functions of turbulent time series measured in the solar wind. Using a data set with artificial gaps, a neural network is trained to predict second‐order structure functions and then tested on an unseen data set to quantify its performance. A small feedforward ANN, with only 20 hidden neurons, can predict the large‐scale fluctuation amplitudes better than mean imputation or linear interpolation when the percentage of missing data is high. Although they perform worse than the other methods when it comes to capturing both the shape and fluctuation amplitude together, their performance is better in a statistical sense for large fractions of missing data. Caveats regarding their utility, the optimization procedure, and potential future improvements are discussed.

Strangers routinely cooperate and exchange goods without any knowledge of one another in one-off encounters without recourse to a third party, an interaction that is fundamental to most human societies. However, this act of reciprocal exchange entails the risk of the other agent defecting with both goods. We examine the choreography for safe exchange between strangers, and identify the minimum requirement, which is a shared hold, either of an object, or the other party; we show that competing agents will settle on exchange as a local optimum in the space of payoffs. Truly safe exchanges are rarely seen in practice, even though unsafe exchange could mean that risk-averse agents might avoid such interactions. We show that an 'implicit' hold, whereby an actor believes that they could establish a hold if the other agent looked to be defecting, is sufficient to enable the simple swaps that are the hallmark of human interactions and presumably provide an acceptable trade-off between risk and convenience. We explicitly consider the particular case of purchasing, where money is one of the goods.

Particle filtering provides an approximate representation of a tracked posterior density which converges asymptotically to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational complexity. This complexity can be implemented by operating the particle filter in parallel architectures. However, the resampling step in the particle filter requires a high level of synchronization and extensive information interchange between the particles, which impedes the use of parallel hardware systems. This paper establishes a new perspective for understanding particle filtering — that particle filtering can be achieved by adopting the principles of information exchange within a network, the nodes of which are now the particles in the particle filter. We propose to connect particles via a minimally connected network and resample each locally. This strategy facilitates full information exchange among the particles, but with each particle communicating with only a small fixed set of other particles, thus leading to minimal communication overhead. The key benefit is that this approach facilitates the use of many particles for accurate posterior approximation and tracking accuracy.

International audience Identifying, contacting and engaging missing shareholders constitutes an enormous challenge for Māori incorporations, iwi and hapū across Aotearoa New Zealand. Without accurate data or tools to har-monise existing fragmented or conflicting data sources, issues around land succession, opportunities for economic development, and maintenance of whānau relationships are all negatively impacted. This unique three-way research collaboration between Victoria University of Wellington (VUW), Parininihi ki Waitotara Incorporation (PKW), and University of Auckland funded by the National Science Challenge | Science for Technological Innovation catalyses innovation through new digital humanities-inflected data science modelling and analytics with the kaupapa of reconnecting missing Māori shareholders for a prosperous economic, cultural, and socially revitalised future. This paper provides an overview of VUW's culturally-embedded social network approach to the project, discusses the challenges of working within an indigenous worldview, and emphasises the importance of decolonising digital humanities.

Internet Service Providers need to deploy and maintain many wireless sites in isolated or inaccessible terrain to provide Internet connectivity to rural communities. Addressing failures at such sites can be very expensive, both in identifying the fault, and also in the repair or rectification. Data monitoring can be useful, to spot anomalies and predict a fault (and possibly pre-empt it altogether), or to locate and isolate it quickly once it causes an issue for the network. There might be hundreds of variables to be monitored in principle, but only a few of significance for detecting faults. Here, in a case study involving a Wireless Internet Service Provider (WISP) in a rural area, we first illustrate a bottom-up approach to the identification of variables likely to be of use in an automatic anomaly detector. For the purpose of this study, the detector consists of an autoencoder neural network with weights optimized by machine learning (ML). We then show how the cause of an anomaly can be derived from indirect measurements, and use the model to learn relationships between certain variables.

With the advancement in technology, normal network traffic is becoming more heterogeneous. In this scenario, the problem of detecting anomalies is intensified. In the literature, offline methods see more data and can be optimised to achieve lower false positive rates. However, they cannot readily adapt to changing network conditions or capture concept-drift. This necessitates an incremental online learning model. On the other hand, online training is easily affected by noise. In this paper, we propose a hybrid Online Offline system in which the Offline model retains general characteristics of network traffic while the Online model continuously learns. The Offline model acts as a bias for the Online model to select new data to learn from. The Online model retains its knowledge and adapts to the changing ground truth. They are put to work together to detect anomalies. We implement this idea with an Online Support Vector Machine (SVM) which retains its support vectors and shifts its decision boundary guided by an Offline Radius Nearest Neighbor (Rad-NN). The method is evaluated on the NSL-KDD 2009 dataset. This relatively simple model achieves over 95% accuracy on known anomalies and over 60% detection rate on most of the unknown anomalies.

Humans and many animals can selectively sample necessary part of the visual scene to carry out daily activities like foraging and finding prey or mates. Selective attention allows them to efficiently use the limited resources of the brain by deploying sensory apparatus to collect data believed to be pertinent to the organisms current situation. Robots operating in dynamic environments are similarly exposed to a wide variety of stimuli, which they must process with limited sensory and computational resources. Computational saliency models inspired by biological studies have previously been used in robotic applications, but these had limited capacity to deal with dynamic environments and have no capacity to reason about uncertainty when planning their sensor placement strategy. This paper generalises the traditional model of saliency by using a Kalman filter estimator to describe an agent’s understanding of the world. The resulting modelling of uncertainty allows the agents to adopt a richer set of strategies to deploy sensory apparatus than is possible with the winner-take-all mechanism of the traditional saliency model. This paper demonstrates the use of three utility functions that are used to encapsulate the perceptual state that is valued by the agent. Each utility function thereby produces a distinct sensory deployment behaviour.

Different approaches to detecting objects have used either perceptionally nonuniform or human perception based colour spaces to mimics human vision in machines. In this paper, we have compared nonuniform RGB derived opponencies with the HSV colour space. The main motivation about this particular comparison is to improve the quality of saliency detection in challenging situations such as lighting change. Here, Precision-Recall curves are used to compare the colour spaces using bottom-up pyramidal and top-down non-pyramidal saliency models. Our study concludes that if we combine the Saturation-Value or Hue-Value channels then this improves the detection of salient objects and gives a higher Precision-Recall curve. We have also shown in the paper that the RGB colour space gives a low precision score as it detects the object using the colour information present in an image.

Background Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by executive function (EF) dynamics disturbances. Notwithstanding, current advances in translational neuroscience, no ADHD objective, clinically useful, diagnostic marker is available to date. Objectives Using a customized definition of EF and a new clinical paradigm, we performed a prospective diagnostic accuracy trial to assess the diagnostic value of several fractal measures from the thinking processes or inferences in a cohort of ADHD children and typically developing controls. Method We included children from age five to twelve diagnosed with a reference standard based on case history, physical and neurological examination, Conners 3 rd Edition, and DSM-V™. The index test consisted of a computer-based inference task with a set of eight different instances of the “Battleships” game to be solved. A consecutive series of 18 cases and 18 controls (n = 36) recruited at the primary paediatrics service from the Nelson Marlborough Health in New Zealand underwent the reference standard and the index test. Several fractal measures were obtained from the inference task to produce supervised classification models. Results Notably, the summarized logistic regression’s predicted probabilities from the eight games played by each children yielded a 100% classification accuracy, sensitivity and specificity in both a training and an independent testing/validating cohort. Conclusions From a translational vantage point the expeditious method and the robust results make this technique a promising candidate to develop a screening, diagnostic and monitoring system for ADHD, and may serve to assess other EF disturbances.

The solution to many science and engineering problems includes identifying the minimum or maximum of an unknown continuous function whose evaluation inflicts non-negligible costs in terms of resources such as money, time, human attention or computational processing. In such a case, the choice of new points to evaluate is critical. A successful approach has been to choose these points by considering a distribution over plausible surfaces, conditioned on all previous points and their evaluations. In this sequential bi-step strategy, also known as Bayesian Optimization, first a prior is defined over possible functions and updated to a posterior in the light of available observations. Then using this posterior, namely the surrogate model, an infill criterion is formed and utilized to find the next location to sample from. By far the most common prior distribution and infill criterion are Gaussian Process and Expected Improvement, respectively. The popularity of Gaussian Processes in Bayesian optimization is partially due to their ability to represent the posterior in closed form. Nevertheless, the Gaussian Process is afflicted with several shortcomings that directly affect its performance. For example, inference scales poorly with the amount of data, numerical stability degrades with the number of data points, and strong assumptions about the observation model are required, which might not be consistent with reality. These drawbacks encourage us to seek better alternatives. This thesis studies the application of Neural Networks to enhance Bayesian Optimization. It proposes several Bayesian optimization methods that use neural networks either as their surrogates or in the infill criterion. This thesis introduces a novel Bayesian Optimization method in which Bayesian Neural Networks are used as a surrogate. This has reduced the computational complexity of inference in surrogate from cubic (on the number of observation) in GP to linear. Different variations of Bayesian Neural Networks (BNN) are put into practice and inferred using a Monte Carlo sampling. The results show that Monte Carlo Bayesian Neural Network surrogate could performed better than, or at least comparably to the Gaussian Process-based Bayesian optimization methods on a set of benchmark problems. This work develops a fast Bayesian Optimization method with an efficient surrogate building process. This new Bayesian Optimization algorithm utilizes Bayesian Random-Vector Functional Link Networks as surrogate. In this family of models the inference is only performed on a small subset of the entire model parameters and the rest are randomly drawn from a prior. The proposed methods are tested on a set of benchmark continuous functions and hyperparameter optimization problems and the results show the proposed methods are competitive with state-of-the-art Bayesian Optimization methods. This study proposes a novel Neural network-based infill criterion. In this method locations to sample from are found by minimizing the joint conditional likelihood of the new point and parameters of a neural network. The results show that in Bayesian Optimization methods with Bayesian Neural Network surrogates, this new infill criterion outperforms the expected improvement. Finally, this thesis presents order-preserving generative models and uses it in a variational Bayesian context to infer Implicit Variational Bayesian Neural Network (IVBNN) surrogates for a new Bayesian Optimization. This new inference mechanism is more efficient and scalable than Monte Carlo sampling. The results show that IVBNN could outperform Monte Carlo BNN in Bayesian optimization of hyperparameters of machine learning models.