From missions to systems : generating transparently distributable programs for sensor-oriented systems

Early Wireless Sensor Networks aimed simply to collect as much data as possible for as long as possible. While this remains true in selected cases, the majority of future sensor network applications will demand much more intelligent use of their resources as networks increase in scale and support multiple applications and users. Specifically, we argue that a computational model is needed in which the ways that data flows through networks, and the ways in which decisions are made based on that data, is transparently distributable and relocatable as requirements evolve. In this paper we present an approach to achieving this using high-level mission specifications from which we can automatically derive transparently distributable programs.Postprin

epyc : computational experiment management in Python

epyc is a Python module for designing, executing, storing, and analysing the results of large sets of (possibly long-running) computational experiments, as are often found when writing simulations of complex networks and other domains. It allows the same experimental code to be run on single machines, multicore machines, and computational clusters without modification, and automatically manages the execution of an experiment for different parameter values and for multiple repetitions.Publisher PDFPeer reviewe

Trajectories of point particles in cosmology and the Zel'dovich approximation

Using a Green's function approach, we compare the trajectories of classical Hamiltonian point particles in an expanding space-time to the effectively inertial trajectories in the Zel'dovich approximation. It is shown that the effective gravitational potential accelerating the particles relative to the Zel'dovich trajectories vanishes exactly initially as a consequence of the continuity equation, and acts only during a short, early period. The Green's function approach suggests an iterative scheme for improving the Zel'dovich trajectories, which can be analytically solved. We construct these trajectories explicitly and show how they interpolate between the Zel'dovich and the exact trajectories. The effective gravitational potential acting on the improved trajectories is substantially smaller at late times than the potential acting on the exact trajectories. The results may be useful for Lagrangian perturbation theory and for numerical simulations.Comment: 7 pages, 3 figure

Open Badges : a best-practice framework

The widespread adoption of online education is severely challenged by issues of verifiability, reliability, security and credibility. Open Badges exist to address these challenges, but there is no consensus as to what constitutes best practices regarding the implementation of an Open Badge system within an educational context. In this paper we survey the current landscape of Open Badges from educational and technological perspectives. We analyze a broad set of openly-reported pilot projects and case studies, and derive a comprehensive best practice framework that tries to capture the requirements for successful implementation within educational institutions. We conclude by identifying some significant gaps in the technology and identify some possible future research directions.Postprin

Self-stabilising target counting in wireless sensor networks using Euler integration

Target counting is an established challenge for sensor networks: given a set of sensors that can count (but not identify) targets, how many targets are there? The problem is complicated because of the need to disambiguate duplicate observations of the same target by different sensors. A number of approaches have been proposed in the literature, and in this paper we take an existing technique based on Euler integration and develop a fully-distributed, self-stabilising solution. We derive our algorithm within the field calculus from the centralised presentation of the underlying integration technique, and analyse the precision of the counting through simulation of several network configurations.Postprin

XLearn : learning activity labels across heterogeneous datasets

Sensor-driven systems often need to map sensed data into meaningfully labelled activities to classify the phenomena being observed. A motivating and challenging example comes from human activity recognition in which smart home and other datasets are used to classify human activities to support applications such as ambient assisted living, health monitoring, and behavioural intervention. Building a robust and meaningful classifier needs annotated ground truth, labelled with what activities are actually being observed—and acquiring high-quality, detailed, continuous annotations remains a challenging, time-consuming, and error-prone task, despite considerable attention in the literature. In this article, we use knowledge-driven ensemble learning to develop a technique that can combine classifiers built from individually labelled datasets, even when the labels are sparse and heterogeneous. The technique both relieves individual users of the burden of annotation and allows activities to be learned individually and then transferred to a general classifier. We evaluate our approach using four third-party, real-world smart home datasets and show that it enhances activity recognition accuracies even when given only a very small amount of training data.PostprintPeer reviewe

ContrasGAN : unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning

Human Activity Recognition (HAR) makes it possible to drive applications directly from embedded and wearable sensors. Machine learning, and especially deep learning, has made significant progress in learning sensor features from raw sensing signals with high recognition accuracy. However, most techniques need to be trained on a large labelled dataset, which is often difficult to acquire. In this paper, we present ContrasGAN, an unsupervised domain adaptation technique that addresses this labelling challenge by transferring an activity model from one labelled domain to other unlabelled domains. ContrasGAN uses bi-directional generative adversarial networks for heterogeneous feature transfer and contrastive learning to capture distinctive features between classes. We evaluate ContrasGAN on three commonly-used HAR datasets under conditions of cross-body, cross-user, and cross-sensor transfer learning. Experimental results show a superior performance of ContrasGAN on all these tasks over a number of state-of-the-art techniques, with relatively low computational cost.PostprintPeer reviewe

Degree correlations in graphs with clique clustering

Funding: This work was partially supported by the UK Engineering and Physical Sciences Research Council under grant number EP/N007565/1 (Science of Sensor Systems Software).Correlations among the degrees of nodes in random graphs often occur when clustering is present. In this paper we define a joint-degree correlation function for nodes in the giant component of clustered configuration model networks which are comprised of higher-order subgraphs. We use this model to investigate, in detail, the organisation among nearest-neighbour subgraphs for random graphs as a function of subgraph topology as well as clustering. We find an expression for the average joint degree of a neighbour in the giant component at the critical point for these networks. Finally, we introduce a novel edge-disjoint clique decomposition algorithm and investigate the correlations between the subgraphs of empirical networks.PostprintPeer reviewe

Two-pathogen model with competition on clustered networks

Networks provide a mathematically rich framework to represent social contacts sufficient for the transmission of disease. Social networks are often highly clustered and fail to be locally tree-like. In this paper, we study the effects of clustering on the spread of sequential strains of a pathogen using the generating function formulation under a complete cross-immunity coupling, deriving conditions for the threshold of coexistence of the second strain. We show that clustering reduces the coexistence threshold of the second strain and its outbreak size in Poisson networks, whilst exhibiting the opposite effects on uniform-degree models. We conclude that clustering within a population must increase the ability of the second wave of an epidemic to spread over a network. We apply our model to the study of multilayer clustered networks and observe the fracturing of the residual graph at two distinct transmissibilities.Publisher PDFPeer reviewe

Prof. dr. Mihovil Gračanin, 1901—1981

Human activity recognition (HAR) systems will be increasingly deployed in real-world environments and for long periods of time. This significantly challenges current approaches to HAR, which have to account for changes in activity routines, the evolution of situations, and sensing technologies. Driven by these challenges, in this paper, we argue the need to move beyond learning to lifelong machine learning—with the ability to incrementally and continuously adapt to changes in the environment being learned. We introduce a conceptual framework for lifelong machine learning to structure various relevant proposals in the area and identify some key research challenges that remain.PostprintPeer reviewe