Data-driven agriculture for rural smallholdings

Spatial information science has a critical role to play in meeting the major challenges facing society in the coming decades, including feeding a population of 10 billion by 2050, addressing environmental degradation, and acting on climate change. Agriculture and agri-food value-chains, dependent on spatial information, are also central. Due to agriculture\u27s dual role as not only a producer of food, fibre and fuel, but also as a major land, water and energy consumer, agriculture is at the centre of both the food-water-energy-environment nexus and resource security debates. The recent confluence of a number of advances in data analytics, cloud computing, remote sensing, computer vision, robotic and drone platforms, and IoT sensors and networks have lead to a significant reduction in the cost of acquiring and processing data for decision support in the agricultural sector. When combined with cost-effective automation through development of swarm farming technologies, the technology has the potential to decouple productivity and cost efficiency from economies of size, reducing the need to increase farm size to remain economically viable. We argue that these pressures and opportunities are driving agricultural value-chains towards high-resolution data-driven decision-making, where even decisions made by small rural landowners can be data-driven. We survey recent innovations in data, especially focusing on sensor, spatial and data mining technologies with a view to their agricultural application; discuss economic feasibility for small farmers; and identify some technical challenges that need to be solved to reap the benefits. Flexibly composable information resources, coupled with sophisticated data sharing technologies, and machine learning with transparently embedded spatial and aspatial methods are all required

IIoT Data Ness: From Streaming to Added Value

In the emerging Industry 4.0 paradigm, the internet of things has been an innovation driver, allowing for environment visibility and control through sensor data analysis. However the data is of such volume and velocity that data quality cannot be assured by conventional architectures. It has been argued that the quality and observability of data are key to a project’s success, allowing users to interact with data more effectively and rapidly. In order for a project to become successful in this context, it is of imperative importance to incorporate data quality mechanisms in order to extract the most value out of data. If this goal is achieved one can expect enormous advantages that could lead to financial and innovation gains for the industry. To cope with this reality, this work presents a data mesh oriented methodology based on the state-of-the-art data management tools that exist to design a solution which leverages data quality in the Industrial Internet of Things (IIoT) space, through data contextualization. In order to achieve this goal, practices such as FAIR data principles and data observability concepts were incorporated into the solution. The result of this work allowed for the creation of an architecture that focuses on data and metadata management to elevate data context, ownership and quality.O conceito de Internet of Things (IoT) é um dos principais fatores de sucesso para a nova Indústria 4.0. Através de análise de dados sobre os valores que os sensores coletam no seu ambiente, é possível a construção uma plataforma capaz de identificar condições de sucesso e eventuais problemas antes que estes ocorram, resultando em ganho monetário relevante para as empresas. No entanto, este caso de uso não é de fácil implementação, devido à elevada quantidade e velocidade de dados proveniente de um ambiente de IIoT (Industrial Internet of Things)

Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) 2019 Annual Report

Prepared for: Dr. Brian Bingham, CRUSER DirectorThe Naval Postgraduate School (NPS) Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) provides a collaborative environment and community of interest for the advancement of unmanned systems (UxS) education and research endeavors across the Navy (USN), Marine Corps (USMC) and Department of Defense (DoD). CRUSER is a Secretary of the Navy (SECNAV) initiative to build an inclusive community of interest on the application of unmanned systems (UxS) in military and naval operations. This 2019 annual report summarizes CRUSER activities in its eighth year of operations and highlights future plans.Deputy Undersecretary of the Navy PPOIOffice of Naval Research (ONR)Approved for public release; distribution is unlimited

A generic approach to the evolution of interaction in ubiquitous systems

This dissertation addresses the challenge of the configuration of modern (ubiquitous, context-sensitive, mobile et al.) interactive systems where it is difficult or impossible to predict (i) the resources available for evolution, (ii) the criteria for judging the success of the evolution, and (iii) the degree to which human judgements must be involved in the evaluation process used to determine the configuration. In this thesis a conceptual model of interactive system configuration over time (known as interaction evolution) is presented which relies upon the follow steps; (i) identification of opportunities for change in a system, (ii) reflection on the available configuration alternatives, (iii) decision-making and (iv) implementation, and finally iteration of the process. This conceptual model underpins the development of a dynamic evolution environment based on a notion of configuration evaluation functions (hereafter referred to as evaluation functions) that provides greater flexibility than current solutions and, when supported by appropriate tools, can provide a richer set of evaluation techniques and features that are difficult or impossible to implement in current systems. Specifically this approach has support for changes to the approach, style or mode of use used for configuration - these features may result in more effective systems, less effort involved to configure them and a greater degree of control may be offered to the user. The contributions of this work include; (i) establishing the the need for configuration evolution through a literature review and a motivating case study experiment, (ii) development of a conceptual process model supporting interaction evolution, (iii) development of a model based on the notion of evaluation functions which is shown to support a wide range of interaction configuration approaches, (iv) a characterisation of the configuration evaluation space, followed by (v) an implementation of these ideas used in (vi) a series of longitudinal technology probes and investigations into the approaches

Continuity in categorization and theoretical implications

sorry for the silly error. hopefully this'll do the trick. --rickTraditional theories of cognition assume that motor action is executed in an all-or-none fashion, and has little importance for understanding cognitive representation and processing. A series of experiments and simulations presented here challenges this assumption. A relatively higher-order cognitive process, categorization, is shown to have graded effects that are reflected in manual motor output, measured through streaming x-y coordinates from mouse trajectories. Two simulations show that these effects are likely generated from a system in which cognition and action interact fluidly. Finally, theoretical implications of these experiments are drawn out. Symbolic dynamics is introduced, a potential means for reconciling both traditional and continuous accounts of cognition. A broad philosophical discussion follows, in which an integrative and pluralistic approach to cognition is proposed and briefly discussed