Labour Market Information for Educational Investments

A well-trained workforce is generally seen as an important precondition for economic growth. But decisions about investments in education and training must be taken under uncertainty, because the benefits will only be reaped in the long term. To predict what these future benefits may be, it is necessary to have some insight into how the labour market functions with respect to education and training. There are various theories, in the literature, which outline a picture of the role played by education and training in the labour market. This paper begins with a sketch of the various policy approaches to the match between the education system and the labour market and an explanation of the importance of labour market information for policy choices. Five labour market theories in which workers'' educational backgrounds is an important factor will be described. Then, on the basis of these theories, we infer what labour market information could be significant in educational decisions. Some basic principles for the preparation of labour market forecasts are identified, and a structure which could be used in making forecasts is outlined. The paper concludes with a plea for a European approach.education, training and the labour market;

Machine-Assisted Map Editing

Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD

Learning spatiotemporal patterns for monitoring smart cities and infrastructure

Recent advances in the Internet of Things (IoT) have changed the way we interact with the world. The ability to monitor and manage objects in the physical world electronically makes it possible to bring data-driven decision making to new realms of city infrastructure and management. Large volumes of spatiotemporal data have been collected from pervasive sensors in both indoor and outdoor environments, and this data reveals dynamic patterns in cities, infrastructure, and public property. In light of the need for new approaches to analysing such data, in this thesis, we propose present relevant data mining techniques and machine learning approaches to extract knowledge from spatiotemporal data to solve real-world problems. Many challenges and problems are under-addressed in smart cities and infrastructure monitoring systems such as indoor person identification, evaluation of city regions segmentation with parking events, fine collection from cars in violations, parking occupancy prediction and airport aircraft path map reconstruction. All the above problems are associated with both spatial and temporal information and the accurate pattern recognition of these spatiotemporal data are essential for determining problem solutions. Therefore, how to incorporate spatiotemporal data mining techniques, artificial intelligence approaches and expert knowledge in each specific domain is a common challenge. In the indoor person identification area, identifying the person accessing a secured room without vision-based or device-based systems is very challenging. In particular, to distinguish time-series patterns on high-dimensional wireless signal channels caused by different activities and people, requires novel time-series data mining approaches. To solve this important problem, we established a device-free system and proposed a two-step solution to identify a person who has accessed a secure area such as an office. Establishing smart parking systems in cities is a key component of smart cities and infrastructure construction. Many sub-problems such as parking space arrangements, fine collection and parking occupancy prediction are urgent and important for city managers. Arranging parking spaces based on historical data can improve the utilisation rate of parking spaces. To arrange parking spaces based on collected spatiotemporal data requires reasonable region segmentation approaches. Moreover, evaluating parking space grouping results needs to consider the correlation between the spatial and temporal domains since these are heterogeneous. Therefore, we have designed a spatiotemporal data clustering evaluation approach, which exploits the correlation between the spatial domain and the temporal domain. It can evaluate the segmentation results of parking spaces in cities using historical data and similar clustering results that group data consisting of both spatial and temporal domains. For fine collection problem, using the sensor instrumentation installed in parking spaces to detect cars in violation and issue infringement notices in a short time-window to catch these cars in time is significantly difficult. This is because most cars in violation leave within a short period and multiple cars are in violation at the same time. Parking officers need to choose the best route to collect fines from these drivers in the shortest time. Therefore, we proposed a new optimisation problem called the Travelling Officer Problem and a general probability-based model. We succeeded in integrating temporal information and the traditional optimisation algorithm. This model can suggest to parking officers an optimised path that maximise the probability to catch the cars in violation in time. To solve this problem in real-time, we incorporated the model with deep learning methods. We proposed a theoretical approach to solving the traditional orienteering problem with deep learning networks. This approach could improve the efficiency of similar urban computing problems as well. For parking occupancy prediction, a key problem in parking space management is with providing a car parking availability prediction service that can inform car drivers of vacant parking lots before they start their journeys using prediction approaches. We proposed a deep learning-based model to solve this parking occupancy prediction problem using spatiotemporal data analysis techniques. This model can be generalised to other spatiotemporal data prediction problems also. In the airport aircraft management area, grouping similar spatiotemporal data is widely used in the real world. Determining key features and combining similar data are two key problems in this area. We presented a new framework to group similar spatiotemporal data and construct a road graph with GPS data. We evaluated our framework experimentally using a state-of-the-art test-bed technique and found that it could effectively and efficiently construct and update airport aircraft route map. In conclusion, the studies in this thesis aimed to discover intrinsic and dynamic patterns from spatiotemporal data and proposed corresponding solutions for real-world smart cities and infrastructures monitoring problems via spatiotemporal pattern analysis and machine learning approaches. We hope this research will inspire the research community to develop more robust and effective approaches to solve existing problems in this area in the future

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

3D visualization of bioerosion in archaeological bone

Palaeoradiology is increasingly being used in archaeological and forensic sciences as a minimally invasive alternative to traditional histological methods for investigating bone microanatomy and its destruction by diagenetic processes. To better understand ancient mortuary practices, taphonomic studies using microCT scanning methods are gaining an ever more important role. Recently it was demonstrated that 2D virtual sections obtained by microCT scanning of intact samples are comparable to physical sections for the rating and diagnosis of bioerosion in archaeological bone. Importantly, volume image data obtained from tomographic methods also allow the rendering and analysis of 3D models. Building on these methods we provide (1) detailed descriptions of bioerosion in 3D volume renderings, virtual sections, and traditional micrographs, and (2) accessible techniques for the visualization of bioerosion in skeletal samples. The dataset is based on twenty-eight cortical bone samples, including twenty femora (of which five are cremated), two ribs, two parietals, one mandibular ramus, one humerus, and two faunal long bones from five archaeological sites in Lower Austria dating from the Early Neolithic to the Late Iron Age. Notably, we reduce the need for time-consuming image segmentation by sequentially applying two noise-reducing, edge-preserving filters, and using an image-display transfer function that visualizes bioerosion, as well as Haversian and Volkmann canal structure and density in 3D. In doing so we are also able to visualize in 3D the invasion of canals by microbiota, which has previously only been reported in 2D sections. Unlike conventional thin sections, the 3D volume images shown here are easy to create and interpret, even for archaeologists inexperienced in histology, and readily facilitate the illustration and communication of microtaphonomic effects

Context-aware personalization environment for mobile computing

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaCurrently, we live in a world where the amount of on-line information vastly outstrips any individual’s capability to survey it. Filtering that information in order to obtain only useful and interesting information is a solution to this problem. The mobile computing area proposes to integrate computation in users’ daily activities in an unobtrusive way, in order to guarantee an improvement in their experience and quality of life. Furthermore, it is crucial to develop smaller and more intelligent devices to achieve this area’s goals, such as mobility and energy savings. This computing area reinforces the necessity to filter information towards personalization due to its humancentred paradigm. In order to attend to this personalization necessity, it is desired to have a solution that is able to learn the users preferences and needs, resulting in the generation of profiles that represent each style of interaction between a user and an application’s resources(e.g. buttons and menus). Those profiles can be obtained by using machine learning algorithms that use data derived from the user interaction with the application, combined with context data and explicit user preferences. This work proposes an environment with a generic context-aware personalization model and a machine learning module. It is provided the possibility to personalize an application, based on user profiles obtained from data, collected from implicit and explicit user interaction. Using a provided personalization API (Application Programming Interface) and other configuration modules, the environment was tested on LEY (Less energy Empowers You), a persuasive mobile-based serious game to help people understand domestic energy usage

An investigation into automated processes for generating focus maps

The use of geographic information for mobile applications such as wayfinding has increased rapidly, enabling users to view information on their current position in relation to the neighbouring environment. This is due to the ubiquity of small devices like mobile phones, coupled with location finding devices utilising global positioning system. However, such applications are still not attractive to users because of the difficulties in viewing and identifying the details of the immediate surroundings that help users to follow directions along a route. This results from a lack of presentation techniques to highlight the salient features (such as landmarks) among other unique features. Another problem is that since such applications do not provide any eye-catching distinction between information about the region of interest along the route and the background information, users are not tempted to focus and engage with wayfinding applications. Although several approaches have previously been attempted to solve these deficiencies by developing focus maps, such applications still need to be improved in order to provide users with a visually appealing presentation of information to assist them in wayfinding. The primary goal of this research is to investigate the processes involved in generating a visual representation that allows key features in an area of interest to stand out from the background in focus maps for wayfinding users. In order to achieve this, the automated processes in four key areas - spatial data structuring, spatial data enrichment, automatic map generalization and spatial data mining - have been thoroughly investigated by testing existing algorithms and tools. Having identified the gaps that need to be filled in these processes, the research has developed new algorithms and tools in each area through thorough testing and validation. Thus, a new triangulation data structure is developed to retrieve the adjacency relationship between polygon features required for data enrichment and automatic map generalization. Further, a new hierarchical clustering algorithm is developed to group polygon features under data enrichment required in the automatic generalization process. In addition, two generalization algorithms for polygon merging are developed for generating a generalized background for focus maps, and finally a decision tree algorithm - C4.5 - is customised for deriving salient features, including the development of a new framework to validate derived landmark saliency in order to improve the representation of focus maps