A simplistic approach to keyhole plan recognition

When applying plan recognition to Human - Computer Interaction, one must cope with users exhibiting a large amount of reactive behaviour: users that change tasks, or change strategies for achieving tasks. Most current approaches to keyhole plan recognition do not address this problem. We describe an application domain for plan recognition, where users exhibit reactive rather than plan-based behaviour, and where existing approaches to plan recognition do not perform well. In order to enable plan recognition in this domain, we have developed an extremely simplistic mechanism for keyhole plan recognition, "intention guessing". The algorithm is based on descriptions of observable behaviour, and is able to recognize certain instances of plan failures, suboptimal plans and erroneous actions. At run-time, the algorithm only keeps track of a limited number of the most recent actions, which makes the algorithm "forgetful". This property makes the algorithm suitable for domains where users frequently change strategies

The Swedish Parallel Sounding Method State of the Art

In the sixties the Swedish Hydrographic Department developed a new method for hydrographic surveys. Instead of a single ship, a formation of up to nine vessels, navigating along parallel lines, was used for the echo sounding work. The formation consists of a mother vessel, positioned from the shore stations, and the satellite boats, positioned from the mother vessel. During the years the method has been refined in various aspects and is today a powerful instrument with an efficiency remarkably superior to that achieved in earlier days. The contributions to the increase in efficiency derive from such sources as equipment, like vessels, positioning systems and echo sounders, from computer and computer programs, and from new methods for calibration, surveying, evaluation of collected data, etc

Sea Surveying a Probability Model

The ambition to optimize all hydrographic work has today become very important. This is the logical outcome of the increased demand for closely spaced lines of soundings and the high cost of all operational work. This probability model is presented in order to make planning easier and analysing of the surveying operations more accurate. To the best of my knowledge such a model has never been constructed. By introducing some of the variable parameters in hydrographic surveying into the model a probabilistic analysis is achieved

Making sense of risk - An analysis of framings in media of the chemical risks of textiles, toys and paint

Chemical risks of consumer goods are a rather new concern in society. These risks are usually based on sythetic chemicals and man-made, often imperceptible and potentially devastating. These risks are reliant on knowledge claims and experiences as basis for risk judgments since, for example, chemical properties, threshold values and exposure are part of defining the risk. But risks are also defined and managed in society by different processes, including for example labelling, legislation and collective understandings of risks, that can jointly be called risk governance. One such process of particular interest when it comes to chemical risks of consumer goods is the role of the general public – as citizens, consumers or citizen-consumers. This thesis, in line with that uses theories of the risk society and the citizen-consumer. Chemical risks of consumer goods need to be visualised and brought on the agenda to become concerns in society. Media is one of the most important arenas for the public debate of risks since they are difficult to experience first-hand. However, media coverage about risks does not reflect “reality” but is constructed by effects inside and outside of the media organisation. Media coverage thus functions as framings, that in this thesis are defined as organizing ideas that suggests what to think about and how to think about it. This thesis explores framings in media of chemical risks of consumer goods. This thesis uses Swedish print media articles from the mid-1990s to 2009 as its empirical material when analysing framings of the chemical risks of consumer goods for three cases – textiles, toys and paint. The framings are divided according to a priori analytical categories of substantive (knowledge claims and experiences (scientific or non-scientific) as basis for risk judgments), procedural (processes in society that contribute to the cause, effect and solution of risk) and the citizen-consumer (how the general public is viewed in relation to risk issues) where the empirical material forms in vivo categories within these three main categories. The aim of the analysis is to understand and problematise the co-construction of chemical risks of consumer goods as concerns in society, by analyzing how framings support or contradict, and exclude or highlight different aspects of risk, and the implication of this. The conclusions are that substantive, procedural and citizen-consumer framings show dissimilar pattern for the three cases, acting as an explanation to why in some cases the chemical or the product group, in some cases the context of risk, and in some cases the governance of risk, are in focus in risk issues. The main reason behind this is to which extent the risk is viewed as managed, often by being part of risk governance, which sets conditions for how the risks are framed. However, this thesis probelmatises the governance of risk and suggest that chemical risks of consumer goods are difficult to manage. And that efforts made to create safe are rather making the limits to “safe” explicit due to the discrepancy between the measures taken to come to terms with risk and the expressions of the risk. Citizen-consumers can act as governance mechanism in themselves, suggesting that certain risks are cemented in the market place. But for some risks there is also a tendency to frame everyday practices, rather than consumption, as the link between the individual and the political. Finally, it is more tentatively concluded that, on the basis of the empirical material, the three product groups cannot be viewed as one late modern risk but are spread along a gliding scale, and that only the case of textiles fullfill all criteria of late moderrn risks – risk contestation, lack of governance, globalisation, individualisation and reflexivity. This thesis thus advances the understandings of how different aspects work together in framing risk as an issue of societal concern and what implications that has for society’s governance of risk. But it is also clear that this thesis is limited in scope and further studies are recommended for different product groups and for other audiences. In particular is there a need to study why only some risks are part of the public debate, suggestedly through analysing how different actors contribute to the co-construction of risk into societal concerns

Long-Term Localization for Self-Driving Cars

Long-term localization is hard due to changing conditions, while relative localization within time sequences is much easier. To achieve long-term localization in a sequential setting, such as, for self-driving cars, relative localization should be used to the fullest extent, whenever possible.This thesis presents solutions and insights both for long-term sequential visual localization, and localization using global navigational satellite systems (GNSS), that push us closer to the goal of accurate and reliable localization for self-driving cars. It addresses the question: How to achieve accurate and robust, yet cost-effective long-term localization for self-driving cars?Starting in this question, the thesis explores how existing sensor suites for advanced driver-assistance systems (ADAS) can be used most efficiently, and how landmarks in maps can be recognized and used for localization even after severe changes in appearance. The findings show that:* State-of-the-art ADAS sensors are insufficient to meet the requirements for localization of a self-driving car in less than ideal conditions.GNSS and visual localization are identified as areas to improve.\ua0* Highly accurate relative localization with no convergence delay is possible by using time relative GNSS observations with a single band receiver, and no base stations.\ua0* Sequential semantic localization is identified as a promising focus point for further research based on a benchmark study comparing state-of-the-art visual localization methods in challenging autonomous driving scenarios including day-to-night and seasonal changes.\ua0* A novel sequential semantic localization algorithm improves accuracy while significantly reducing map size compared to traditional methods based on matching of local image features.\ua0* Improvements for semantic segmentation in challenging conditions can be made efficiently by automatically generating pixel correspondences between images from a multitude of conditions and enforcing a consistency constraint during training.\ua0* A segmentation algorithm with automatically defined and more fine-grained classes improves localization performance.\ua0* The performance advantage seen in single image localization for modern local image features, when compared to traditional ones, is all but erased when considering sequential data with odometry, thus, encouraging to focus future research more on sequential localization, rather than pure single image localization

A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation. Enforcing label consistency across the matches makes the final segmentation algorithm robust to seasonal changes. We describe how these 2D-2D matches can be generated with little human interaction by geometrically matching points from 3D models built from images. Two cross-season correspondence datasets are created providing 2D-2D matches across seasonal changes as well as from day to night. The datasets are made publicly available to facilitate further research. We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions.Comment: In Proc. CVPR 201

The Genesis of an Impulsive Coronal Mass Ejection observed at Ultra-High Cadence by AIA on SDO

The study of fast, eruptive events in the low solar corona is one of the science objectives of the Atmospheric Imaging Assembly (AIA) imagers on the recently launched Solar Dynamics Observatory (SDO), which take full disk images in ten wavelengths with arcsecond resolution and 12 sec cadence. We study with AIA the formation of an impulsive coronal mass ejection (CME) which occurred on June 13, 2010 and was associated with an M1.0 class flare. Specifically, we analyze the formation of the CME EUV bubble and its initial dynamics and thermal evolution in the low corona using AIA images in three wavelengths (171, 193 and 211 A). We derive the first ultra-high cadence measurements of the temporal evolution of the CME bubble aspect ratio (=bubble-height/bubble-radius). Our main result is that the CME formation undergoes three phases: it starts with a slow self-similar expansion followed by a fast but short-lived (~ 70 sec) period of strong lateral over-expansion which essentially creates the CME. Then the CME undergoes another phase of self-similar expansion until it exits the AIA field of view. During the studied interval, the CME height-time profile shows a strong, short-lived, acceleration followed by deceleration. The lateral overexpansion phase coincides with the deceleration phase. The impulsive flare heating and CME acceleration are closely coupled. However, the lateral overexpansion of the CME occurs during the declining phase and is therefore not linked to flare reconnection. In addition, the multi-thermal analysis of the bubble does not show significant evidence of temperature change.Comment: 2010 in pres