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Today usually every student owns a reasonably powerful mobile device that allows to be integrated in scenarios. One of the drawbacks of the fast evolution of reasonably powerful devices, is the
heterogeneity of that these kind of devices us ually bring with them. This paper provides an overview how rich mobile learning scenarios can be implemented platform independent on the basis of HTML5 and JavaScript. The paper presents a mobile learning application based on the principles of Situated Lea
rning entirely developed in HTML5. The paper also presents the results of tests performed with the application which were aimed at finding out the difference in performance users perceived when compared with the native desktop version of the
application and the added value that mobility introduces in learning activities.
This paper presents some ideas of how to use Web Services
for the implementation of innovative collaborative technologies. A major goal here is the idea to build re-usable collaborative software components to foster knowledge exchange and learning. This paper describes two examples of how we used Web Services to achieve this goal. The first example we will describe implements a digital notice board with large, public displays. Here, we used web service to provide flexible data access. Web services provide the possibility to use our infrastructure with different programming languages and devices. The second example we will present is an application that enables students to construct and
model experiments descriptions using a control plant-growth system, the biotube, remotely via Web Services.
Women are still underrepresented at the highest management levels. The think-manager-think-male phenomenon suggests that leadership is associated with male rather than female attributes. Although styling has been shown to influence the evaluation of women's leadership abilities, the relevant specific features have been left remarkably unaddressed. In a 2 × 2 × 2 × 2 (skirt/pants, with/without jewelry, loose hair/braid, with/without makeup) between-subjects design, 354 participants evaluated a woman in a photograph. Women with makeup, pants, or with jewelry were rated as more competent than women without makeup, with skirts, or without jewelry. A combination of loose hair and no makeup was perceived as warmest, and women with loose hair were more likely to be hired than those with braids. In sum, even subtle changes in styling have a strong impact on how women's leadership abilities are evaluated.
The continuous evolution of learning technologies combined with the changes within ubiquitous learning environments in which they operate result in dynamic and complex requirements that are challenging to meet. The fact that these systems evolve over time makes it difficult to adapt to the constant changing requirements. Existing approaches in the field of Technology Enhanced Learning (TEL) are generally not addressing those issues and they fail to adapt to those dynamic situations. In this chapter, we investigate the notion of an adaptive and adaptable architecture as a possible solution to address these challenges. We conduct a literature survey upon the state of the art and state of practice in this area. The outcomes of those efforts result in an initial model of a Domain-specific architecture to tackle the issues of adaptability and adaptiveness. To illustrate these ideas, we provide a number of scenarios where this architecture can be applied or is already applied. Our proposed approach serves as a foundation for addressing future ubiquitous learning applications where new technologies constantly emerge and new requirements evolve.
This chapter describes our research efforts related to the design of mobile learning (m-learning) applications in cloud-computing (CC) environments. Many cloud-based services can be used/integrated in m-learning scenarios, hence, there is a rich source of applications that could easily be applied to design and deploy those within the context of cloud-based services. Here, we present two cloud-based approaches—a flexible framework for an easy generation and deployment of mobile learning applications for teachers, and a flexible contextualization service to support personalized learning environment for mobile learners. The framework provides a flexible approach that supports teachers in designing mobile applications and automatically deploys those in order to allow teachers to create their own m-learning activities supported by mobile devices. The contextualization service is proposed to improve the content delivery of learning objects (LOs). This service allows adapting the learning content and the mobile user interface (UI) to the current context of the user. Together, this leads to a powerful and flexible framework for the provisioning of potentially ad hoc mobile learning scenarios. We provide a description of the design and implementation of two proposed cloud-based approaches together with scenario examples. Furthermore, we discuss the benefits of using flexible and contextualized cloud applications in mobile learning scenarios. Hereby, we contribute to this growing field of research by exploring new ways for designing and using flexible and contextualized cloud-based applications that support m-learning.
Anonymity-preserving Methods for Client-side Filtering in Position-based Collaboration Approaches
(2017)
In recent times, a lot of attacks against central server infrastructures have been recognized. Those infrastructures have seen attacks ranging from attacks against Internt of Things (IoT) infrastructures, via attacks against public infrastructure to attacks against cryptocurrency exchanges and blockchain based infrastructures themselves, e.g., the already almost legendary Decentralized Autonomous Organization (DAO) hack. Measured by press coverage, attacks against cryptocurrency exchanges and infrastructures seem to be among the most prominently reported attacks, probably due to the large amount of money that is stolen during those attacks and the great (but obviously still quite risky) potential (and financial involvement) of the blockchain technology. Naturally, attacks like the ones we have seen recently in crease the notion of uncertainty of blockchain technologies among the people,mreflected in lower values of cryptocurrencies in general. Obviously, this demands for an overall increase of security of cryptocurrency based technologies. Therefore, this paper provides an architectural approach, based on a proxy,to increase security of publicly available nodes of a blockchain based technology. Furthermore, it provides a first evaluation of the approach based on the results of an extensive community test of a new cryptocurrency.
Integrating Social Networking Sites in Day-to-Day Learning Scenarios - A Facebook Based Approach
(2012)
In recent years, the number of users in social networking sites regularly increased. Especially younger people spend a tremendous time on social networking sites like Facebook, YouTube, Flickr, Google+ and many more. Since obviously this is the place on the World-Wide-Web where our students spent their spare time, we integrated social networking sites in our day-to-day learning scenarios. This is on the one hand to start working with our students where they feel comfortable, and on the other hand to allow to foster the communication among our students about the topic of the lectures.
Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems. In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.
Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple. In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.
In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS 1 , we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.
Practical application of object detection systems, in research or industry, favors highly optimized black box solutions. We show how such a highly optimized system can be further augmented in terms of its reliability with only a minimal increase of computation times, i.e. preserving realtime boundaries. Our solution leaves the initial (HOG-based) detector unchanged and introduces novel concepts of non-linear metrics and fusion of ROIs. In this context we also introduce a novel way of combining feature vectors for mean-shift grouping. We evaluate our approach on a standarized image database with a HOG detector, which is representative for practical applications. Our results show that the amount of false-positive detections can be reduced by a factor of 4 with a negligable complexity increase. Although introduced and applied to a HOG-based system, our approach can easily be adapted for different detectors.
The Bitcoin whitepaper states that security of the system is guaranteed as long as honest miners control more than half of the current total computational power. The whitepaper assumes a static difficulty, thus it is equally hard to solve a cryptographic proof-of-work puzzle for any given moment of the system history. However, the real Bitcoin network is using an adaptive difficulty adjustment mechanism. In this paper we introduce and analyze a new kind of attack on a mining difficulty retargeting function used in Bitcoin. A malicious miner is increasing his mining profits from the attack, named coin-hopping attack, and, as a side effect, an average delay between blocks is increasing. We propose an alternative difficulty adjustment algorithm in order to reduce an incentive to perform coin-hopping, and also to improve stability of inter-block delays. Finally, we evaluate the presented approach and show that the novel algorithm performs better than the original algorithm of Bitcoin.
Web based security applications have become increasingly important in the past years. Especially in times of blockchain based crypto currencies, user authentication is a critical aspect for the overall security, integrity and acceptance of such systems. While blockchain technologies provide a decentralized approach, the client side still largely relies on centralized security approaches. Those centralized approaches are easier to implement, but at the same time bear the risk of usual security flaws. Therefore, this paper presents a decentralized approach for increasing the security by adding a decentralized two-factor authentication mechanism to the execution of
operations.
Detection of influential actors in social media plays an important role for increasing the quality and efficiency of work and services in many fields such as education, marketing, etc. This work aims to introduce a new approach for the characterization of influential actors in online social media, such as Twitter. We present on a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.
Industry 4.0 is known as the fourth industrial revolution which refers to the integration of technologies that make the factories interoperable by seamlessly connecting machines, employees and sensors for communication. In Industry 4.0, one of the key features is the use of new technologies to recognize the current context. Thus, the employees are supported with contextual information for speeding up decision-making during various processes related to planning, production, maintenance, etc. As a contribution to this area, the work described here aims to introduce a cyber-physical system (CPS) approach to provide context-based and intelligent support to employees in heavy industries using new technologies, especially in the field of mobile devices. In this work, mobile device sensors and image processing techniques are used to recognize the context which requires specific support. In addition, new scenarios and associated processes are developed to support the employees on the basis of new, flexible, adaptive and mobile technologies.
To enable a robotic assistant to autonomously reach for and transport objects while avoiding obstacles we have generalized the attractor dynamics approach established for vehicles to trajectory formation in robot arms. This approach is able to deal with the time-varying environments that occur when a human operator moves in a shared workspace. Stable fixed points (attractors) for the heading direction of the end-effector shift during movement and are being tracked by the system. This enables the attractor dynamics approach to avoid the spurious states that hamper potential field methods. Separating planning and control computationally, the approach is also simpler to implement. The stability properties of the movement plan make it possible to deal with fluctuating and imprecise sensory information. We implement this approach on a seven degree of freedom anthropomorphic arm reaching for objects on a working surface. We use an exact solution of the inverse kinematics, which enables us to steer the spatial position of the elbow clear of obstacles. The straight-line trajectories of the end-effector that emerge as long as the arm is far from obstacles make the movement goals of the robotic assistant predictable for the human operator, improving man-machine interaction
For any kind of assistant systems, the ability to interact with the human operator and taking into account his or her assumptions and expectations, is the basis for a reasonable behavior. As a consequence the human behavior have to be studied in order to generate driver models that are learned from human driving data. In this work we focus on the improvement of the immersion in driving simulation environment by developing and implementing a cheap and efficient method for head tracking. We also explain why head tracking feedback is crucial for the quality of collected behavioural data, especially for simulators with close screen distances.
In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.
Die transurethrale Resektion der Prostata (TUR-P) ist der Gold Standard für die endoskopisch-chirurgische Behandlung der Benignen Prostatahyperplasie (BPH). Unbekannt ist jedoch, ob bei der bipolaren TUR-P die Möglichkeit von elektrothermischen Verletzung von benachbar-
tem nichtopertiertem Gewebe besteht. Grund hierfür könnten höhere Ausgangsleistungen der Chirurgie-Generatoren sein. Deshalb werden experimentell gemessene Werte der räumlichen Verlustleistungsdichte um ein bipolares Resektoskop als Quellterm für die Berechnung der Temperaturänderung im operationsnahen Gewebe verwendet. Die Temperaturberechnung basiert auf der numerischen Lösung der Wärmeleitungsgleichung nach Penne. Für die gewählten Randbedingungen und Eingabeparameter sind keine signifikanten Temperaturerhöhungen im Berechnungsgebiet am proximalen Ende des Resektoskop-Schaftes festzustellen. Um ein umfassendes Verständnis zu gewinnen sollen weiterführende Untersuchungen mit einer Variation der Randbedingungen und Eingabeparameter durchgeführt werden.
The term “Cloud Computing” does not primarily specify new types of core technologies but rather addresses features to do with integration, interoperability and accessibility. Although not new, virtualization and automation are core features that characterize Cloud Computing. In this paper, we intend to explore the possibility of integrating cloud services with educational scenarios without re-defining neither the technology nor the usage scenarios from scratch. Our suggestion is based on certain solutions that have already been implemented and tested for specific cases.
It is common to have a large noise and/or a strong interference around the frequency band of a Power Line Communication (PLC) system due to the fact that the PLC channel is not designed for communication. If there are no efficient operations at the receiver to suppress this out-of-band noise and interference to some extent, the Signal-to-Noise Ratio (SNR) will decrease and system will suffer performance loss consequently. Normally, the effort of Analog Front End (AFE) on the suppression of out-of-band interference is finite and it is uneconomic to change the AFE structure to make a performance improvement. Therefore, an appropriate structure of Digital Front End (DFE) at the receiver is necessary to reduce the impact of out-of-band noise and interference furthermore. In this paper, three different kinds of DFE structure at the receiver are introduced: classic DFE, time domain Nyquist windowing and Equivalent Complex Baseband (ECB) approach. The performance of these DFE structures is compared, not only from the aspect of out-of-band suppression, but also from the system overhead they need.
In recent years, hardware for the production and consumption of virtual reality content has reached level of prices that make it affordable to everyone. Accordingly schools and universities are showing increased interest in implementations of virtual reality technologies for supporting their innovative educational activities. Hence, this paper presents a flexible architecture for supporting the development of virtual reality learning scenarios conveniently deployed for educational purposes. We also suggest an example of such
educational scenario for medical purposes deployable with the suggested architecture. In addition, we developed and used a questionnaire answered by 17 medical students in order to derive additional requirements for refining such scenarios. Then, we present these efforts while aiming at deployments usable also for additional domains. Finally, we summarize and mention aspects we will address
in our coming efforts while deploying such activities.
In this paper we describe an architecture for behavioral organization based on dynamical systems. This architecture
enables the generation of complex behavioral sequences, which is demonstrated using the example of approaching and
passing a door. The behavioral sequence is generated by activating and deactivating the elementary behaviors dependent
on sensory information and internal logical conditions. The architecture is demonstrated on a mobile KOALA robot and
in simulation as well.
The astronomy domain provides rich opportunities for learning about natural phenomena. It can involve and motivate a variety of mathematical and physical knowledge and skills. However it is difficult to connect astronomic observations to modelling and calculation tools and to embed them into educational scenarios. It is particularly this challenge which is focused in this paper. Concretely, we build on an existing collaborative modelling framework (Cool Modes) and extend it with specific representations to support learning activities in astronomy. A first field test has been conducted with these extensions.
Der Einsatz von virtuellen Servern im LDS NRW erfolgte bisher unter dem Blickwinkel der Konsolidierung von einfachen und sehr einfachen Systemen, die keine dedizierte Serversystemtechnik benötigten.
Mittlerweile bietet VMware Funktionalitäten, die neben dem Konsolidierungsgedanken hoch interessante Möglichkeiten für vielfältigste, individuelle Kundenanforderungen bieten. Dies reicht von flexiblen, preiswerten und einfachen
Systemen bis hin zu Serverplattformen mit hohen Ansprüchen an Performance und Verfügbarkeit.
This contribution demonstrates the efficient embedding of a single depth-camera into the automotive environment making mid-air gesture interaction for mobile applications viable in such a scenario. In this setting a new human-machine interface is implemented to give an idea of future improvements in automation processes in industrial applications. Our system is based on a data-driven approach by learning hand poses as well as gestures from a large database in order to apply them on mobile devices. We register any movement in a nearby driver area and crop data efficiently with the means of PCA transforming it into so-called feature vectors which present the input for our multi-layer perceptrons (MLPs). After MLP classification, the interpretation of user input is sent via WiFi to a tablet PC mounted into the car interior visualizing an infotainment system which the user is able to interact with. We demonstrate that by this setup hand gestures as well as hand poses are easily and efficiently interpretable insofar as that they become an intuitive and supplementary means of interaction for automotive HMI in mobile scenarios realizable in real-time.
In recent years, the number of reasonable powerful mobile devices increased. In 2011, the number of smartphones(e.g.)increased to more than 300 million units. A lot of research has already been conducted with respect of mobile devices acting as Cloud Service consumers, but
still not much effort is put on mobile devices in the role of Cloud Service providers. Therefore, this paper presents an approach that allows to utilize mobile devices like smart phones or tablets as Cloud Service providers. In order to make this a reasonable approach, some of the occurring problems are discussed and it is shown how the presented architecture is able to overcome these problems. Last
but not least, this paper
describes some performance
tests of the chosen implementa
tion for mobile Web Services.
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
We extend the attractor dynamics approach to generate goal-directed movement of a redundant, anthropomorphic arm while avoiding dynamic obstacles and respecting joint limits. To make the robot's movements human-like, we generate approximately straight-line trajectories by using two heading direction angles of the tool-point quite analogously to how movement is represented in the primate central nervous system. Two additional angles control the tool's spatial orientation so that it follows the tool-point's collision-free path. A fifth equation governs the redundancy angle, which controls the elevation of the elbow so as to avoid obstacles and respect joint limits. These variables make it possible to generate movement while sitting in an attractor (or, in the language of the potential field approach, in a minimum). We demonstrate the approach on an assistant robot, which interacts with human users in a shared workspace
The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.
Generating collision free reaching movements for redundant manipulators using dynamical systems
(2010)
For autonomous robots to manipulate objects in unknown environments, they must be able to move their arms without colliding with nearby objects, other agents or humans. The simultaneous avoidance of multiple obstacles in real time by all link segments of a manipulator is still a hard task both in practice and in theory. We present a systematic scheme for the generation of collision free movements for redundant manipulators in scenes with arbitrarily many obstacles. Based on the dynamical systems approach to robotics, constraints are formulated as contributions to a dynamical system that erect attractors for targets and repellors for obstacles. These contributions are formulated in terms of variables relevant to each constraint and then transformed into vector fields over the manipulator joint velocity vector as an embedding space in which all constraints are simultaneously observed. We demonstrate the feasibility of the approach by implementing it on a real anthropomorphic 8-degrees-of-freedom redundant manipulator. In addition, performance is characterized by detecting failures in a systematic simulation experiment in randomized scenes with varying numbers of obstacles.
Generating flexible collision-free reaching move-
ments is a standard task for autonomous articulated robots that
is critical especially when such systems interact with humans in
a service robotics setting. Current solutions are still challenging
to put into practice. Here we generalize an approach
first
used to plan end-effector movement that is based on attractor
dynamical systems. We show, how different contributions to
the motion planning dynamics can be formulated in constraint-
specific reference frames and then transformed into the frame
of the joint velocity vector. We implement this system on an
8 DoF redundant manipulator and show its feasibility in a
simulation. A systematic experiment with randomly generated
obstacle scenes characterizes the performance of the system.
Especially challenging confi
gurations of obstacles are discussed
to illustrate how the method solves these cases
This paper deals with the question how to integrate smart devices in Java appli-
cations. It will outline how different smart devices can be used to enrich learning
environments, we will point to some of the problems one has to face while dealing
with smart devices, a differentiation of smart devices will be done and we will give an
overview about existing Java Virtual Machines available for different smart devices.
Furthermore we will tackle the question of the communication between different smart
devices and also between different kinds of smart devices. An outlook to the future
work will also be given at the end of this work
Integrating Orientation Constraints into the Attractor Dynamics Approach for Autonomous Manipulation
(2010)
For the rod shape measurement of hot rolled round steel bars (rods) the high frequency eddy current method is especially well suited as it requires no contact point and is not limited to below the Curie temperature. Defects of the rod's shape can be detected by measuring the impedance spectrum of the RLC-oscillator. In the first laboratory setup an Agilent impedance analyser was used for initial tests. Nevertheless, this setup cannot be applied in a steel plant due to the difficult environmental conditions. Hence, a vector network analyser for passive impedance measurement that is applicable in these surroundings was developed.
In this paper we describe our efforts to foster educational interoperability in scenarios using mobile and wireless technologies to support hands-on scientific experimentation and learning. A special focus is given to the idea that innovative uses of mobile and wireless technologies enhance the learners' scientific experience. Specific contributions include the creation of new applications to support interoperability between different mobile devices, thus to provide "glue" between different learning situations. We describe a number of educational scenarios as well as the technologies and the architectural principles behind them.
We present an architecture based on the Dynamic Field Theory for the problem of scene representation. At the core of this architecture are three-dimensional neural fields linking feature to spatial information. These three-dimensional fields are coupled to lower-dimensional fields that provide both a close link to the sensory surface and a close link to motor behavior. We highlight the updating mechanism of this architecture, both when a single object is selected and followed by the robot's head in smooth pursuit and in multi-item tracking when several items move simultaneously
The WWW is the killerapp of the internet. In recent years an enormously increasing number of Web Applications, as a means of human-to-computer interaction, showed up, that allows a visitor of a certain website to interact with the website. Additionally the approach of Web Services was introduced in order to allow computer-to-computer Interaction on the basis of standardized protocols. This paper shows how the gap between Web Applications and Web Services can be closed by making Web Applications available to computer-to-computer interaction by a systematic approach.
In recent years the diversity and the ownership of mobile devices steadily increased while the prices for this kind of devices decreased to a level that allows many students to own reasonably powerful devices. As mobile devices are also being used in learning scenarios, the challenge of today is the integration of multiple heterogeneous devices into existing and upcoming learning scenarios. This paper describes an architecture that allows easy integration of various kinds of mobile and non-mobile devices. The presented architecture will be exemplified by a group discussion scenario in a heterogeneous learning environment. The paper concludes with the description of a pilot study using the described system.
This paper presents an approach towards a mobile learning environment, which is flexible in terms of supported scenarios, supported devices and input channels. The approach makes use of existing and commonly used channels like SMS, Twitter or Face book to increase acceptance and ease-of-use of mobile devices in learning scenarios. Envisaged application scenarios are described along with technical details for their realization.
The first robots are currently appearing on the consumer market. Initially they are targeted at rather simple applications such as entertainment and home convenience. For more complex areas, these robots will need to collaborate and interactively communicate with their human users, which requires appropriate man-machine interaction technologies and considerable cognitive abilities on the robot's side. Consumer acceptance will strongly depend on the integrated system. Thus, system integration and evaluation of the integrated system is becoming increasingly important. This paper describes our approach to construct a robotic assistance system. We present experience with an integrated technology demonstration and exposure of the integrated system to the public.
In diesem Artikel stellen wir ein System zur Synchronisierung beliebiger, in Java geschriebener, Anwendungen vor. Nach einer kurzen Diskussion der Vor- und Nachteile von replizierter Datenhaltung – wie sie in unserem System verwendet wird – werden wir am Beispiel einer komplexen Diskussions- und Modellierungsumgebung
zeigen, wie man mit unserer Architektur Anwendungen partiell koppeln kann und somit flexible Kooperationsmöglichkeiten technisch unterstützen kann.
In this contribution we present a novel approach to transform data from time-of-flight (ToF) sensors to be interpretable by Convolutional Neural Networks (CNNs). As ToF data tends to be overly noisy depending on various factors such as illumination, reflection coefficient and distance, the need for a robust algorithmic approach becomes evident. By spanning a three-dimensional grid of fixed size around each point cloud we are able to transform three-dimensional input to become processable by CNNs. This simple and effective neighborhood-preserving methodology demonstrates that CNNs are indeed able to extract the relevant information and learn a set of filters, enabling them to differentiate a complex set of ten different gestures obtained from 20 different individuals and containing 600.000 samples overall. Our 20-fold cross-validation shows the generalization performance of the network, achieving an accuracy of up to 98.5% on validation sets comprising 20.000 data samples. The real-time applicability of our system is demonstrated via an interactive validation on an infotainment system running with up to 40fps on an iPad in the vehicle interior.
Background:
Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.
Methods:
The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time.
Results:
We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.
Conclusions:
Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.
Background:
Influential actors detection in social media such as twitter or Facebook can play a major role in gathering opinions on particular topics, improving the market
-
ing efficiency, predicting the trends, etc.
Proposed methods:
This work aims to extend our formally defined
T
measure to
present a new measure aiming to recognize the actor’s influence by the strength of
attracting new important actors into a networked community. Therefore, we propose a
model of the actor’s influence based on the attractiveness of the actor in relation to the
number of other attractors with whom he/she has established connections over time.
Results and conclusions:
Using an empirically collected social network for the
underlying graph, we have applied the above-mentioned measure of influence in
order to determine optimal seeds in a simulation of influence maximization. We study
our extended measure in the context of information diffusion because this measure is
based on a model of actors who attract others to be active members in a community.
This corresponds to the idea of the IC simulation model which is used to identify the
most important spreaders in a set of actors.
Keywords: Actor influence, Social media networks, Twitter, IC model, Information
diffusion, Independent cascade model, T measure
Temporal stabilization of discrete movement in variable environments: An attractor dynamics approach
(2009)
The ability to generate discrete movement with distinct and stable time courses is important for interaction scenarios both between different robots and with human partners, for catching and interception tasks, and for timed action sequences. In dynamic environments, where trajectories are evolving online, this is not a trivial task. The dynamical systems approach to robotics provides a framework for robust incorporation of fluctuating sensor information, but control of movement time is usually restricted to rhythmic motion and realized through stable limit cycles. The present work uses a Hopf oscillator to produce discrete motion and formulates an online adaptation rule to stabilize total movement time against a wide range of disturbances. This is integrated into a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion as an example. The approach is demonstrated on a Khepera mobile unit in order to show its reliability even when depending on low-level sensor information.
Recognition of emotions from multimodal cues is of basic interest for the design of many adaptive interfaces in human-machine interaction (HMI) in general and human-robot interaction (HRI) in particular. It provides a means to incorporate non-verbal feedback in the course of interaction. Humans express their emotional and affective state rather unconsciously exploiting their different natural communication modalities such as body language, facial expression and prosodic intonation. In order to achieve applicability in realistic HRI settings, we develop person-independent affective models. In this paper, we present a study on multimodal recognition of emotions from such auditive and visual cues for interaction interfaces. We recognize six classes of basic emotions plus the neutral one of talking persons. The focus hereby lies on the simultaneous online visual and accoustic analysis of speaking faces. A probabilistic decision level fusion scheme based on Bayesian networks is applied to draw benefit of the complementary information from both – the acoustic and the visual – cues. We compare the performance of our state of the art recognition systems for separate modalities to the improved results after applying our fusion scheme on both DaFEx database and a real-life data that captured directly from robot. We furthermore discuss the results with regard to the theoretical background and future applications.
To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems arose in the past years. Fully or partly autonomously guided vehicles, particularly for road traffic, pose high demands on the development of reliable algorithms. Principal problems are caused by having a moving observer in predominantly natural environments. At the Institut fur Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a solution for a driver assistance system. We concentrate on the aspects of video-based scene analysis and organization of behavior.
Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation.
Schlussbericht ; Laufzeit des Vorhabens/Berichtszeitraum: 01.10.2005-30.11.2009
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Förderkennzeichen BMBF 01IME01K [richtig] - 01IMEO1K [falsch]. - Verbund-Nr. 01042153. - Engl. Berichtsbl. u.d.T.: Face recognition on mobile robot plattforms. - Literaturverz. Bl. 32
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Relevance & Research Question: Smartphones have become an integrated part in everyday life facilitating communication, information access, entertainment and organization anytime and anywhere. However, the omnipresence of such devices can evoke psychological dependencies and the need of being always connected resulting in discomfort when the smartphone is not accessible. While few studies have found heightened anxiety during smartphone absence (e.g. Cheever, Rosen, Carrier, & Chavez, 2014), such research is scarce. Therefore, we aimed at expanding existing research asking whether the mere imagination of smartphone absence suffices to trigger anxiety and affect user’s context evaluations.
In this work methods are described, which are used for an individual adaption of a dialog system. Anyway, an automatic real-time capable visual user attention estimation for a face to face human machine interaction is described. Furthermore, an emotion estimation is presented, which combines a visual and an acoustic method. Both, the attention estimation and the visual emotion estimation based on Active Appearance Models (AAMs). Certainly, for the attention estimation Multilayer Perceptrons (MLPs) are used to map the Active Appearance Parameters (AAM-Parameters) onto the current head pose. Afterwards, the chronology of the head poses is classified as attention or inattention. In the visual emotion estimation the AAM-Parameter will be classified by a Support-Vector-Machine (SVM). The acoustic emotion estimation also use a SVM to classifies emotion related audio signal features into the 5 basis emotions (neutral, happy, sad, anger, surprise). Afterward, a Bayes network is used to combine the results of the visual and the acoustic estimation in the decision level. The visual attention estimation as well as the emotion estimation will be used in service robotic to allow a more natural and human like dialog. Furthermore, the human head pose is very efficient interpreted as head nodding or shaking by the use of adaptive statistical moments. Especially, the head movement of many demented people are restricted, so they often only use their eyes to look around. For that reason, this work examine a simple gaze estimation with the help of an ordinary webcam. Moreover, a full body user re-identification method is described, which allows an individual state estimation of several people for hight dynamic situations. In this work an appearance based method is described, which allows a fast people re-identification over a short time span to allow the usage of individual parameter.
Global software development changes the requirements in terms of soft competency and increases the complexity of social interaction by including intercultural aspects. While soft competency is often seen as crucial for the success of global software development projects, the concrete competence requirements remain unknown. Internationalization competency represents one of the first attempts to structure and describe the soft competence requirements for global software developers. Based on the diversity of tasks, competence requirements will differ among the various phases of software development. By conducting a survey on the importance of internationalization competences for the different phases of global software development, we identified differences in terms of competence importance and requirements in the phases. “Adaptability” (of one's working style) and “Cultural Awareness” were the main differences. “Cultural Awareness” distinguishes requirements engineering and software design from testing and implementation while “Adaptability” distinguishes implementation and software design from requirements engineering and testing.
The goal of this paper is to define relevant barriers to the exchange of Open Educational Resources in local public administrations. Building upon a cultural model, eleven experts were interviewed and asked to evaluate several factors, such as openness in discourse, learning at the workplace, and superior support, among others. The result is a set of socio-cultural factors that shape the use of Open Educational Resources in public administrations. Significant factors are, in this respect, the independent choice of learning resources, the spirit of the platform, the range of available formats and access to technologies. Practitioners use these factors to elaborate on the readiness of public administrations towards the use of open e-Learning systems. To academic debates on culture in e-Learning, the results provide an alternative model that is contextualized to meet the demands of public sector contexts. Overall, the paper contributes to the lack of research about open e-Learning systems in the public sector, as well as regarding culture in the management of learning and knowledge exchange.
The open education movement has witnessed ups and downs from initial interest in transparency and openness, followed by a lack of reuse of open educational resources (OER) and the massive boost of interest in massive open online courses (MOOCs). This article addresses educators' online behaviors and perceptions regarding participation in collaborative development of OER in online settings. Using a data-driven approach to study educators' perceptions, this article presents multiple considerations for collaborative OER development and validates a new model explaining educators' intention to participate in collaborative action. The findings reveal the contradictory nature of emotional ownership of knowledge: a critical enabling factor for commitment and a barrier to knowledge exchange in an open and transparent manner. The findings also show how outcome expectations regarding increase in reputation and status in the network do not influence the intention to share knowledge. Further interviews with idea-sharing platform users enable us to explain the favorable settings to resolve the dilemma of emotional ownership. The study contributes not only to further development of the open education movement but also to theory development of educators’ collaborative behaviors online.
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.
Immer mehr ältere Menschen leben von ihren Angehörigen getrennt und können über Kommunikationsmedien wie Telefon und Skype nur eingeschränkt gemeinsame Erlebnisse erzeugen. In diesem Paper wird die technische Umsetzung eines Konzeptes vorgestellt, das es Familienmitgliedern ermöglicht über das Internet gemeinsam „Mensch-ärgere-dich-nicht“ zu spielen. Durch Videotelefonie und eine besondere Anordnung der Hardware werden die Spieler trotz räumlicher Trennung virtuell an einen Tisch gebracht und dadurch ein gemeinsames Erlebnis erzeugt. Die Clientanwendung wird dabei als plattformunabhängiger Webservice und die Videotelefonie mittels verschiedener Standards und Server realisiert.
In this paper we discuss how group processes can be influenced by designing specific tools in computer supported collaborative leaning. We present the design of a shared workspace application for co-constructive tasks that is enriched by certain functions that are able to track, analyze and feed back parameters of collaboration to group members. Thereby our interdisciplinary approach is mainly based on an integrative methodology for analyzing collaboration behavior and patterns in an implicit manner combined with explicit surveyed data of group members’ attitudes and its immediate feedback to the groups. In an exploratory study we examined the influence of this feedback function. Although we could only analyze ad-hoc groups in this study, we detected some benefits of our methodology which might enrich real life Learning Communities’ collaboration processes. The data analysis in our study showed advantages of this feedback on processes of a group’s well-being as well as parameters of participation. These results provide a basis for further empirical work on problem solving groups that are supported by means of parallel interaction analysis as well as its re-use as information resource.
This paper describes an educational application that combines handhelds (PDAs) and programmable Lego bricks in a classroom scenario that deals with the problem of letting a robot escape from a maze. It is specific to our setting that the problem can be solved both in the physical world by steering a Lego robot and in a simulated software environment on a PDA or on a PC. This approach enables the students to generate successful sets of rules in the simulation and to test these sets of rules later in physical mazes, or to create new types of mazes as challenges for known rule sets. In this paper we describe the technical setting for this scenario, different pedagogical scenarios and we will report an evaluation with a group of students in a school environment.
Nowadays, teachers and students utilize different ICT devices for conducting innovative and educational activities from anywhere at any time. The enactment of these activities relies on robust communication and computational infrastructures used for supporting technological devices enabling better accessibility to educational resources and pedagogical scaffolds, wherever and whenever necessary. In this paper, we present EDU.Tube: an interactive environment that relies on web and mobile solutions offered to teachers and students for authoring and incorporating educational interactions at specific moments along the time line of occasional YouTube video-clips. The teachers and students could later experience these authored artefacts while interacting from their stationary or mobile devices. We describe our efforts related to the design, deployment and evaluation of an educational activity supported by the EDU.Tube environment. Furthermore, we illustrate the specific teachers’ and students’ efforts practiced along the different phases of this educational activity. The evaluation of this activity and results are presented, followed by a discussion of these findings, as well as some recommendations for future research efforts further elaborating on EDU.Tube’s aspects in relation to learning analytics.
The paper provides a contextualization process to adapt Open Knowledge Resources for the need of public administrations. By help of a matching strategy, culture and context profiles of learners and learning resources are compared. The comparison allows to draw inferences how to contextualize an open knowledge resource for own learning needs. An example is illustrated and future research fields are proposed.
Fahrerassistenzsysteme werden eingesetzt, um dem Fahrer
eines Kraftfahrzeugs Handlungsabläufe abzunehmen. Diese Handlungsabläufe
werden definiert durch eine Aufgabenstellung, die vom Fahrer an das Fahrerassi-
stenzsystem übergeben oder systembedingt gelöst wird. Bei komplexen Fahreras-
sistenzsystemen ist an eine autonome Navigation im Straßenverkehr gedacht. Es
wird ein neues Verfahren vorgestellt, welches eine Bewegungssteuerung eines
autonomen Fahrzeugs durchführen kann. Es werden der Lenkwinkel und die Ge-
schwindigkeit beeinflußt. Für diese Aufgabe wird ein dynamischer Ansatz aus
dem Bereich der neuronalen Felder gewählt. Relevante Attribute für den Fahrt-
verlauf auf unterschiedlichem Abstraktionsniveau können dabei einfach (additiv)
verarbeitet werden.
We describe the general concept, system architecture, hardware, and the behavioral abilities of Cora (Cooperative Robot Assistant, see Fig. 1), an autonomous non mobile robot assistant. Outgoing from our basic assumption that the behavior to perform determines the internal and external structure of the behaving system, we have designed Cora anthropomorphic to allow for humanlike behavioral strategies in solving complex tasks. Although Cora was built as a prototype of a service robot system to assist a human partner in industrial assembly tasks, we will show that Cora’s behavioral abilities are also conferrable in a household environment. After the description of the hardware platform and the basic concepts of our approach, we present some experimental results by means of an assembly task.