Learning through life

Web 2.0 und soziale Netzwerke gaben erste Impulse für neue Formen der Online-Lehre, welche die umfassende Vernetzung von Objekten und Nutzern im Internet nachhaltig einsetzen. Die Vielfältigkeit der unterschiedlichen Systeme erschwert aber deren ganzheitliche Nutzung in einem umfassenden Lernszenario, das den Anforderungen der modernen Informationsgesellschaft genügt. In diesem Beitrag wird eine auf dem Konnektivismus basierende Plattform für die Online-Lehre namens “Wiki-Learnia” präsentiert, welche alle wesentlichen Abschnitte des lebenslangen Lernens abbildet. Unter Einsatz zeitgemäßer Technologien werden nicht nur Nutzer untereinander verbunden, sondern auch Nutzer mit dedizierten Inhalten sowie ggf. zugehörigen Autoren und/oder Tutoren verknüpft. Für ersteres werden verschiedene Kommunikations-Werkzeuge des Web 2.0 (soziale Netzwerke, Chats, Foren etc.) eingesetzt. Letzteres fußt auf dem sogenannten “Learning-Hub”-Ansatz, welcher mit Hilfe von Web-3.0-Mechanismen insbesondere durch eine semantische Metasuchmaschine instrumentiert wird. Zum Aufzeigen der praktischen Relevanz des Ansatzes wird das mediengestützte Juniorstudium der Universität Rostock vorgestellt, ein Projekt, das Schüler der Abiturstufe aufs Studium vorbereitet. Anhand der speziellen Anforderungen dieses Vorhabens werden der enorme Funktionsumfang und die große Flexibilität von Wiki-Learnia demonstriert.

From Concrete to Abstract: Teaching for Transfer of Learning when Using Manipulatives

One of the most important uses of manipulatives in a classroom is to aid a learner to make connection from tangible concrete object to its abstraction. In this paper we discuss how teacher educators can foster deeper understanding of how manipulatives facilitate student learning of math concepts by emphasizing the connection between concrete objects and math symbolization with, preservice elementary teachers, the future implementers of knowledge. We provide an example and a model, with specific steps of how teacher educators can effectively demonstrate connections between concrete objects and abstract math concepts.

Multisensor data fusion for accurate modelling of mobile objects

In the last decade, multi-sensor data fusion has become a broadly demanded discipline to achieve advanced solutions that can be applied in many real world situations, either civil or military. In Defence,accurate detection of all target objects is fundamental to maintaining situational awareness, to locating threats in the battlefield and to identifying and protecting strategically own forces. Civil applications, such as traffic monitoring, have similar requirements in terms of object detection and reliable identification of incidents in order to ensure safety of road users. Thanks to the appropriate data fusion technique, we can give these systems the power to exploit automatically all relevant information from multiple sources to face for instance mission needs or assess daily supervision operations. This paper focuses on its application to active vehicle monitoring in a particular area of high density traffic, and how it is redirecting the research activities being carried out in the computer vision, signal processing and machine learning fields for improving the effectiveness of detection and tracking in ground surveillance scenarios in general. Specifically, our system proposes fusion of data at a feature level which is extracted from a video camera and a laser scanner. In addition, a stochastic-based tracking which introduces some particle filters into the model to deal with uncertainty due to occlusions and improve the previous detection output is presented in this paper. It has been shown that this computer vision tracker contributes to detect objects even under poor visual information. Finally, in the same way that humans are able to analyze both temporal and spatial relations among items in the scene to associate them a meaning, once the targets objects have been correctly detected and tracked, it is desired that machines can provide a trustworthy description of what is happening in the scene under surveillance. Accomplishing so ambitious task requires a machine learning-based hierarchic architecture able to extract and analyse behaviours at different abstraction levels. A real experimental testbed has been implemented for the evaluation of the proposed modular system. Such scenario is a closed circuit where real traffic situations can be simulated. First results have shown the strength of the proposed system.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

Simulating Nursing: Rhetoric, Materiality, and Disciplinary Learning

Thesis (Ph.D.)--University of Washington, 2016-06

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Applying machine learning to catalogue matching in astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study, we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the H I Parkes All Sky Survey (HIPASS) and SuperCOSMOS optical survey. Previous work had matched 44 per cent (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12 per cent correct). Applying this model to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72 per cent matched.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

User-centred ontology learning for knowledge management

Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.

Imagery in multi-modal object learning

Spatial objects may not only be perceived visually but also by touch. We report recent experiments investigating to what extent prior object knowledge acquired in either the haptic or visual sensory modality transfers to a subsequent visual learning task. Results indicate that even mental object representations learnt in one sensory modality may attain a multi-modal quality. These findings seem incompatible with picture-based reasoning schemas but leave open the possibility of modality-specific reasoning mechanisms.

Diversifying into technical clothing manufacture as entrepreneurial learning:a situated learning theory perspective

Purpose - The purpose of this paper is to demonstrate analytically how entrepreneurial action as learning relating to diversifying into technical clothing - i.e. a high-value manufacturing sector - can take place. This is particularly relevant to recent discussion and debate in academic and policy-making circles concerning the survival of the clothing manufacture industry in developed industrialised countries. Design/methodology/approach - Using situated learning theory (SLT) as the major analytical lens, this case study examines an episode of entrepreneurial action relating to diversification into a high-value manufacturing sector. It is considered on instrumentality grounds, revealing wider tendencies in the management of knowledge and capabilities requisite for effective entrepreneurial action of this kind. Findings - Boundary events, brokers, boundary objects, membership structures and inclusive participation that addresses power asymmetries are found to be crucial organisational design elements, enabling the development of inter- and intracommunal capacities. These together constitute a dynamic learning capability, which underpins entrepreneurial action, such as diversification into high-value manufacturing sectors. Originality/value - Through a refinement of SLT in the context of entrepreneurial action, the paper contributes to an advancement of a substantive theory of managing technological knowledge and capabilities for effective diversification into high-value manufacturing sectors. Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.

Learning to be different:heterogeneity and efficiency in distributed smart camera networks

In this paper we study the self-organising behaviour of smart camera networks which use market-based handover of object tracking responsibilities to achieve an efficient allocation of objects to cameras. Specifically, we compare previously known homogeneous configurations, when all cameras use the same marketing strategy, with heterogeneous configurations, when each camera makes use of its own, possibly different marketing strategy. Our first contribution is to establish that such heterogeneity of marketing strategies can lead to system wide outcomes which are Pareto superior when compared to those possible in homogeneous configurations. However, since the particular configuration required to lead to Pareto efficiency in a given scenario will not be known in advance, our second contribution is to show how online learning of marketing strategies at the individual camera level can lead to high performing heterogeneous configurations from the system point of view, extending the Pareto front when compared to the homogeneous case. Our third contribution is to show that in many cases, the dynamic behaviour resulting from online learning leads to global outcomes which extend the Pareto front even when compared to static heterogeneous configurations. Our evaluation considers results obtained from an open source simulation package as well as data from a network of real cameras. © 2013 IEEE.

Cluster Analysis of Some Clases of Objects by Application of the Test Recognition Algorithms

The paper treats the task for cluster analysis of a given assembly of objects on the basis of the information contained in the description table of these objects. Various methods of cluster analysis are briefly considered. Heuristic method and rules for classification of the given assembly of objects are presented for the cases when their division into classes and the number of classes is not known. The algorithm is checked by a test example and two program products (PP) – learning systems and software for company management. Analysis of the results is presented.

Learning by Doing in the Context of Modern Information and Media Literacy of Students in “Library and Information Management” in ULSIT Using the Digital Collection “Bulgarian Revival”

The purpose of this article is to evaluate the effectiveness of learning by doing as a practical tool for managing the training of students in "Library Management" at the ULSIT, Sofia, Bulgaria, by using the creation of project 'Data Base “Bulgarian Revival Towns” (CD), financed by Bulgarian Ministry of Education, Youth and Science (1/D002/144/13.10.2011) headed by Prof. DSc Ivanka Yankova, which aims to create new information resource for the towns which will serve the needs of scientific researches. By participating in generating the an array in the database through searching, selection and digitization of documents from these period, at the same time students get an opportunity to expand their skills to work effectively in a team, finding the interdisciplinary, a causal connection between the studied items, objects and subjects and foremost – practical experience in the field of digitization, information behavior, strategies for information search, etc. This method achieves good results for the accumulation of sustainable knowledge and it generates motivation to work in the field of library and information professions.