Refine
Year of publication
- 2016 (34) (remove)
Document Type
- Conference Proceeding (13)
- Article (9)
- Part of a Book (3)
- Part of Periodical (3)
- Contribution to a Periodical (2)
- Book (1)
- Doctoral Thesis (1)
- Report (1)
- Research Data (1)
Is part of the Bibliography
- no (34)
Keywords
- Automation (1)
- Fachhochschule (1)
- Gewebediagnostik (1)
- Hochschule Ruhr West (1)
- Human-machine Interaction (1)
- Induktive Messsysteme (1)
- Mülheim an der Ruhr (1)
- Open educational resources (1)
- Risk Management (1)
- Safety-critical Systems (1)
This article presents a omparative study of the barriers to open e-learning in public administrations in Luxembourg, Germany, Montenegro and Ireland. It discusses the current state of open e-learning of public administration employees at the local government level and derives the barriers to such learning. This paper's main contribution is its presentation of an empirical set of barriers in the four European countries. The results allow informed assumptions about which barriers will arise in the forthcoming use of open-source e-learning technology, particularly open educational resources as means of learning. Furthermore, this study offers a contextualised barrier framework that allows the systematic capture and comparison of challenges for future studies in the field. Other practical contributions include providing advice about open e-learning programmes, systematising lessons learned and addressing managerial implications.
Technologie die beflügelt
(2016)
Die spezifischen Herausforderungen des Fachgebiets bedürfen jedoch auch weiterhin einer Diskussion und der Entwicklung neuer Methoden und Ansätze zur Gestaltung von Informationssystemen. Diese sollen dieses Jahr adressiert werden. Generell fokussieren wir eher auf die Effekte von Technologien auf realweltliche Praktiken, als auf die isolierte Technologie. Auch der auf diesen Beiträgen basierende Workshop legt aktuelle Entwicklungen und Fragestellungen offen und gibt neue Impulse für das Forschungsgebiet. Der Workshop wird dabei zweigeteilt gestaltet: Innerhalb des ersten Teils wird den Vortragenden die Möglichkeit gegeben die eigenen Forschungsarbeiten zu präsentieren. Dabei sind sowohl designorientierte, praxisbasierte Analysen und Studien, als auch entwickelte und evaluierte Prototypen neuer Technologien von Interesse. Es wird den Vortragenden die Möglichkeit gegeben die eigenen Forschungsarbeiten teilweise in einem eher frühen Stadium in kompakter Form zu präsentieren und anschließend in Hinblick auf deren Weiterentwicklung diskutieren.
We present a publicly available benchmark database for the problem of hand posture recognition from noisy depth data and fused RGB-D data obtained from low-cost time-of-flight (ToF) sensors. The database is the most extensive database of this kind containing over a million data samples (point clouds) recorded from 35 different individuals for ten different static hand postures. This captures a great amount of variance, due to person-related factors, but also scaling, translation and rotation are explicitly represented. Benchmark results achieved with a standard classification algorithm are computed by cross-validation both over samples and persons, the latter implying training on all persons but one and testing on the remaining one. An important result using this database is that cross-validation performance over samples (which is the standard procedure in machine learning) is systematically higher than cross-validation performance over persons, which is to our mind the true application-relevant measure of generalization performance.
Given the success of convolutional neural networks (CNNs) during recent years in numerous object recognition tasks, it seems logical to further extend their applicability to the treatment of three-dimensional data such as point clouds provided by depth sensors. To this end, we present an approach exploiting the CNN’s ability of automated feature generation and combine it with a novel 3D feature computation technique, preserving local information contained in the data. Experiments are conducted on a large data set of 600.000 samples of hand postures obtained via ToF (time-of-flight) sensors from 20 different persons, after an extensive parameter search in order to optimize network structure. Generalization performance, measured by a leave-one-person-out scheme, exceeds that of any other method presented for this specific task, bringing the error for some persons down to 1.5 %.
In catastrophic events, the potential of help has grown through new technologies. Voluntary help has many forms. Within this paper different categories of voluntary help are suggested. Those categories are based on properties like organizational structures, helping process, kind of prosocial behavior and many more. A focus is clearly on the organizational structure and motivational aspects of helper groups. Examples are given for each category. The categorization’s aim is to give a brief overview of possible properties a group of system users could have.
We present a light-weight real-time applicable 3D-gesture recognition system on mobile devices for improved Human-Machine Interaction. We utilize time-of-flight data coming from a single sensor and implement the whole gesture recognition pipeline on two different devices outlining the potential of integrating these sensors onto mobile devices. The main components are responsible for cropping the data to the essentials, calculation of meaningful features, training and classifying via neural networks and realizing a GUI on the device. With our system we achieve recognition rates of up to 98% on a 10-gesture set with frame rates reaching 20Hz, more than sufficient for any real-time applications.
This contribution presents a novel approach of utilizing Time-of-Flight (ToF) technology for mid-air hand gesture recognition on mobile devices. ToF sensors are capable of providing depth data at high frame rates independent of illumination making any kind of application possible for in- and outdoor situations. This comes at the cost of precision regarding depth measurements and comparatively low lateral resolution. We present a novel feature generation technique based on a rasterization of the point clouds which
realizes fixed-sized input making Deep Learning approaches applicable using Convolutional Neural Networks. In order to increase precision we introduce several methods to reduce noise and normalize the input to overcome difficulties in scaling. Backed by a large-scale database of about half
a million data samples taken from different individuals our
contribution shows how hand gesture recognition is realiz-
able on commodity tablets in real-time at frame rates of up to 17Hz. A leave-one out cross-validation experiment
demonstrates the feasibility of our approach with classification errors as low as 1,5% achieved persons unknown to the model.
Technical Report
(2016)
This internal report discusses the theoretical and practical aspects of the cluster management framework SimpleHydra, which was developed in order to allow researchers the quick setup of classical small to mid-scale computation clusters while being as lightweight and platform independent as possible. We motivate crucial design choices with a theoretical analysis in the aspect of time and space complexity, furthermore we give a comprehensive introduction regarding the frameworks usage (which includes examples and detailed description of fundamental concepts as well as data structures). In addition to that we illustrate application scenarios with complete source code examples. Furthermore we hope that this document proves valuable not only as a development report but also as a practical manual for SimpleHydra.