Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices. The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic - specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their “native” logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device. IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance. Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions. Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an “acceptable” average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were compared on a simple problem where the AAF target was changed during evolution, revealing that dynamically tracking the AAF target can yield a higher success rate in meeting the AAF. The combination of IDGP and FI offers a novel approach for achieving online learning with GP on highly resource-constrained embedded systems. Furthermore, it simultaneously considers the acceptable average performance of the system which may change during the operational lifetime. This approach could be applied to swarm and cooperative robot systems, WSN motes or IoT devices allowing them to cooperatively learn and adapt their logic locally to meet dynamic performance requirements

Synergistic cytotoxicity of irinotecan and cisplatin in dual-drug targeted polymeric nanoparticles

Aim: Two unexplored aspects for irinotecan and cisplatin (I&C) combination chemotherapy are: actively targeting both drugs to a specific diseased cell type, and delivering both drugs on the same vehicle to ensure their synchronized entry into the cell at a well-defined ratio. In this work, the authors report the use of targeted polymeric nanoparticles (NPs) to coencapsulate and deliver I&C to cancer cells expressing the prostate-specific membrane antigen. Materials & methods: Targeted NPs were prepared in a single step by mixing four different precursors inside microfluidic devices. Results: I&C were encapsulated in 55-nm NPs and showed an eightfold increase in internalization by prostate-specific membrane antigen-expressing LNCaP cells compared with nontargeted NPs. NPs coencapsulating both drugs exhibited strong synergism in LNCaP cells with a combination index of 0.2. Conclusion: The strategy of coencapsulating both I&C in a single NP targeted to a specific cell type could potentially be used to treat different types of cancer.Prostate Cancer Foundation (Nanotherapeutics Award)MIT-Harvard Center of Cancer Nanotechnology Excellence (U54-CA151884)National Science Foundation (U.S.). Graduate Research Fellowship ProgramAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle

BUMPER v1.0: a Bayesian user-friendly model for palaeo-environmental reconstruction

We describe the Bayesian user-friendly model for palaeo-environmental reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring ~2 s to build a 100-taxon model from a 100-site training set on a standard personal computer. We apply the model’s probabilistic framework to generate thousands of artificial training sets under ideal assumptions.We then use these to demonstrate the sensitivity of reconstructions to the characteristics of the training set, considering assemblage richness, taxon tolerances, and the number of training sites. We find that a useful guideline for the size of a training set is to provide, on average, at least 10 samples of each taxon. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. An identically configured model is used in each application, the only change being the input files that provide the training-set environment and taxon-count data. The performance of BUMPER is shown to be comparable with weighted average partial least squares (WAPLS) in each case. Additional artificial datasets are constructed with similar characteristics to the real data, and these are used to explore the reasons for the differing performances of the different training sets

Evolving embodied intelligence from materials to machines

International audienceNatural lifeforms specialise to their environmental niches across many levels; from low-level features such as DNA and proteins, through to higher-level artefacts including eyes, limbs, and overarching body plans. We propose Multi-Level Evolution (MLE), a bottom-up automatic process that designs robots across multiple levels and niches them to tasks and environmental conditions. MLE concurrently explores constituent molecular and material 'building blocks', as well as their possible assemblies into specialised morphological and sensorimotor configurations. MLE provides a route to fully harness a recent explosion in available candidate materials and ongoing advances in rapid manufacturing processes. We outline a feasible MLE architecture that realises this vision, highlight the main roadblocks and how they may be overcome, and show robotic applications to which MLE is particularly suited. By forming a research agenda to stimulate discussion between researchers in related fields, we hope to inspire the pursuit of multi-level robotic design all the way from material to machin

Energy-efficient Localization for Virtual Fencing

International audienceThis poster addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. It focuses on combining GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. The focus is on an outdoor location monitoring application for tracking cattle using smart collars that contain wireless sensor nodes and GPS modules. We use empirically-derived models to explore duty cycling strategies for maintaining position uncertainty within specified bounds. Specifically we explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off. Results show that GPS combined with radio-contact logging is effective in extending node lifetime while meeting application-specific positioning criteria

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 5 - Phase 2 Implementation of the Concept Demonstrator

This report describes the second phase of the implementation of the Concept Demonstrator experimental test-bed system containing sensors and processing hardware distributed throughout the structure, which uses multi-agent algorithms to characterize impacts and determine a suitable response to these impacts. This report expands and adds to the report of the first phase implementation. The current status of the system hardware is that all 192 physical cells (32 on each of the 6 hexagonal prism faces) have been constructed, although only four of these presently contain data-acquisition sub-modules to allow them to acquire sensor data. Impact detection.. location and severity have been successfully demonstrated. The software modules for simulating cells and controlling the test-bed are fully operational. although additional functionality will be added over time. The visualization workstation displays additional diagnostic information about the array of cells (both real and simulated) and additional damage information. Local agent algorithms have been developed that demonstrate emergent behavior of the complex multi-agent system, through the formation of impact damage boundaries and impact networks. The system has been shown to operate well for multiple impacts. and to demonstrate robust reconfiguration in the presence of damage to numbers of cells

Lectura de contexto y abordaje psicosocial desde los enfoques narrativos. La Dorada.

El presente documento es una reflexión y análisis de los diferentes contextos de violencia que han sido planteados para el desarrollo del trabajo final, nos invita a la reflexión de los diferentes impactos generados por la violencia en cualquier entorno, en este caso específicamente el conflicto armado, y sus múltiples consecuencias a nivel psicosocial. La violencia es un flagelo que ha venido afectando nuestro país desde años atrás, la realidad constante de nuestro país, un país afectado por el conflicto interno desde varios años atrás, donde hay miles de víctimas, que vivieron y conviven actualmente dentro del mismo, el acompañamiento psicosocial a las víctimas es un elemento clave para la restitución y restablecimiento, y reintegración social a las diferentes víctimas del conflicto, el papel de los profesionales como psicólogos y trabajadores sociales, entre otros, son de gran importancia y preparación a nivel educativo y pedagógico, se debe verificar los conocimientos y capacidades que tienen los profesionales para abordar las diferentes eventualidades, la mayor empatía y humanidad hacia los afectados del conflicto, hay que sentir realmente y entender realmente los contextos bajo los cuales han sido victimizadas cada una de estas personas.This document is a reflection and analysis of the different contexts of violence that have been raised for the development of the final work, it invites us to reflect on the different impacts generated by violence in any environment, in this case specifically the armed conflict, and its multiple consequences at the psychosocial level. Violence is a scourge that has been affecting our country for years, the constant reality of our country, a country affected by internal conflict since several years ago, where there are thousands of victims, who lived and currently live within it, the psychosocial accompaniment to victims is a key element for the restitution and restoration, and social reintegration to the different victims of the conflict, the role of professionals as psychologists and social workers, among others, are of great importance and educational and pedagogical preparation , we must verify the knowledge and skills that professionals have to deal with the different eventualities, the greater empathy and humanity towards those affected by the conflict, we really have to truly feel and understand the contexts under which each one of these people has been victimized