134,097 research outputs found
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Meaning Management: A Framework for Leadership Ontology
Leadership is a multifaceted and complex subject of research and demands a sound ontological stance that guides studies for the development of more integrative leadership theories. In this paper, I propose the leadership ontology PVA (perception formation – value creation – achievement realization) and associate it with the two existing leadership ontologies: TRIPOD (leader – member – shared goals) and DAC (direction – alignment – commitment). The leadership ontology PVA, based on a new theory called “meaning management,” consists of three circularly supporting functions: cognitive function to form perception, creative function to generate value, and communicative function to realize higher levels of achievement. The PVA is an epistemology-laden ontology since the meaning management theory allows one to make propositions that explicitly link its three functions with the leadership outcomes: perception, value, and achievement. Moreover, the PVA leadership ontology transcends and includes both the conventional TRIPOD ontology and the DAC ontology
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A classification of emerging and traditional grid systems
The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings.
However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area
Scenarios for the development of smart grids in the UK: synthesis report
‘Smart grid’ is a catch-all term for the smart options that could transform the ways society produces, delivers and consumes energy, and potentially the way we conceive of these services. Delivering energy more intelligently will be fundamental to decarbonising the UK electricity system at least possible cost, while maintaining security and reliability of supply.
Smarter energy delivery is expected to allow the integration of more low carbon technologies and to be much more cost effective than traditional methods, as well as contributing to economic growth by opening up new business and innovation opportunities. Innovating new options for energy system management could lead to cost savings of up to £10bn, even if low carbon technologies do not emerge. This saving will be much higher if UK renewable energy targets are achieved.
Building on extensive expert feedback and input, this report describes four smart grid scenarios which consider how the UK’s electricity system might develop to 2050. The scenarios outline how political decisions, as well as those made in regulation, finance, technology, consumer and social behaviour, market design or response, might affect the decisions of other actors and limit or allow the availability of future options. The project aims to explore the degree of uncertainty around the current direction of the electricity system and the complex interactions of a whole host of factors that may lead to any one of a wide range of outcomes. Our addition to this discussion will help decision makers to understand the implications of possible actions and better plan for the future, whilst recognising that it may take any one of a number of forms
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
We present DRLViz, a visual analytics interface to interpret the internal
memory of an agent (e.g. a robot) trained using deep reinforcement learning.
This memory is composed of large temporal vectors updated when the agent moves
in an environment and is not trivial to understand due to the number of
dimensions, dependencies to past vectors, spatial/temporal correlations, and
co-correlation between dimensions. It is often referred to as a black box as
only inputs (images) and outputs (actions) are intelligible for humans. Using
DRLViz, experts are assisted to interpret decisions using memory reduction
interactions, and to investigate the role of parts of the memory when errors
have been made (e.g. wrong direction). We report on DRLViz applied in the
context of video games simulators (ViZDoom) for a navigation scenario with item
gathering tasks. We also report on experts evaluation using DRLViz, and
applicability of DRLViz to other scenarios and navigation problems beyond
simulation games, as well as its contribution to black box models
interpretability and explainability in the field of visual analytics
Determining how information technology is changing the role of leadership in virtual organization
Includes bibliographical references
Identifying research priorities for the competitiveness of arable crops
EU agriculture and arable crops in particular are suffering from competitiveness deficits compared to other producers in the world economy. One potential strategy to cope with competitiveness challenges is to focus on research and technological innovation. The objective of this paper is to present the results of the project EUROCROP (Agricultural research for improving arable crop competitiveness – EUROCROP - http://www.eurocrop.cetiom.fr/), aimed at the identification of research priorities for arable crop competitiveness. The project adopts a definition of competitiveness based on a combination of economic competitiveness and social/environmental sustainability. Furthermore, the project utilises both a crop chain and a horizontal issue perspective, and develops research priorities through the interaction of the scientific level (expert group approach) and the stakeholder level (scenario analysis). The main result of the project is the elaboration of approximately eighty research topics. Among these, the main areas for research identified are A: Risk management and adaptation of arable farming; B: Innovation in cropping systems for high environmental and economic performances; C: Limiting the impact of arable crop cropping systems on green-house gas emissions; D: Better understanding of public concern about arable crop production and products and communication with global and local societies. The project confirms that a number of well established research topics retain their importance (e.g. yield improvement, plant protection). However, they require cautious coordination with an increasingly complex system of short term priorities.Arable crops, crop chain, competitiveness, research priorities, foresight, Agricultural and Food Policy,
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