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The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed.
We explore the potential of stereoscopic 3D (S3D) vision in offering distinct gameplay using an S3D-specific game called Deepress3D. Our game utilizes established S3D design principles for optimizing GUI design, visual comfort and game mechanics which rely on depth perception in time-pressured spatial conflicts. The game collects detailed S3D player metrics and allows players to choose between different, evenly matched strategies. We conducted a between subjects study comparing S3D and monoscopic versions of Deepress3D that examined player behavior and performance and measured user-reported data on presence, simulator sickness, and game experience.
The work being described in this paper is the result of a cooperation project between the Institute of Visual Computing at the Bonn-Rhein-Sieg University of Applied Sciences, Germany and the Laboratory of Biomedical Engineering at the Federal University of Uberlândia, Brazil. The aim of the project is the development of a virtual environment based training simulator which enables for better and faster learning the control of upper limb prostheses. The focus of the paper is the description of the technical setup since learning tutorials still need to be developed as well as a comprehensive evaluation still needs to be carried out.
This article describes an approach to rapidly prototype the parameters of a Java application run on the IBM J9 Virtual Machine in order to improve its performance. It works by analyzing VM output and searching for behavioral patterns. These patterns are matched against a list of known patterns for which rules exist that specify how to adapt the VM to a given application. Adapting the application is done by adding parameters and changing existing ones. The process is fully automated and carried out by a toolkit. The toolkit iteratively cycles through multiple possible parameter sets, benchmarks them and proposes the best alternative to the user. The user can, without any prior knowledge about the Java application or the VM improve the performance of the deployed application and quickly cycle through a multitude of different settings to benchmark them. When tested with the representative benchmarks, improvements of up to 150% were achieved.
A cost-efficient alternative to outside-in tracking systems for pointing interaction with large displays is to equip the pointing device with a camera, whose images are matched to display content. This work presents the Dynamic Marker Camera Tracking (DMCT) framework for display-based camera tracking. It accounts for typical display characteristics and uses dynamic on-screen markers overlaid to the display content that follow the camera. An example marker implementation and a tracking recovery method are presented. DMCT can measure pointing locations with sub-millimeter precision in large tracking volumes and computes 6-DoF camera poses for 3D interaction. 60 Hz update rate and 24 ms latency were achieved. DMCT's main limitation is the visible marker interfering with display content. In pointing effciency, the prototype is comparable to an OptiTrack system.
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining is often only interested in one of the two types e.g. in natural language text when looking at text mining. Regardless of which type is being investigated, any techniques used have to deal with noise caused by fragments of the other type i.e. methods interested in natural language have to deal with technical fragments and vice versa. This paper proposes an approach to classify unstructured data, e.g. development documents, into natural language text and technical information using a mixture of text heuristics and agglomerative hierarchical clustering. The approach was evaluated using 225 manually annotated text passages from developer emails and issue tracker data. Using white space tokenization as a basis, the overall precision of the approach is 0.84 and the recall is 0.85.
After an introduction, we discuss the conflicts that occurred in a highly experimental course setting, in which we implemented a student-centered course in urban higher education with a constructivist, blended-learning design. We analyse to which extent the cultural country profiles from our Learning Culture Survey suffice to prevent intercultural conflicts in education and provide support for the design of respective interventions.
This work describes extensions to the well-known Distributed Coordination Function (DCF) model to account for IEEE802.11n point-to-point links. The developed extensions cover adaptions to the throughput and delay estimation for this type of link as well peculiarities of hardware and implementations within the Linux Kernel. Instead of using simulations, the approach was extensively verified on real-world deployments at various link distances. Additionally, trials were conducted to optimize the CWmin values and the number of retries to maximize throughput and minimize delay. The results of this work can be used to estimate the properties of long-distance 802.11 links beforehand, allowing the network to be planned more accurately.
When developing new ICT systems and applications for domestic environments, rich qualitative approaches improve the understanding of the user's integral usage of technology in their daily routines and thereby inform design. This knowledge will often be reached through in-home studies, strong relationships with the users and their involvement in the design and evaluation process. However, whilst this kind of research offers valuable context insights and brings out unexpected findings, it also presents methodological, technical and organizational challenges for the study design and its underlying cooperation processes. In particular, due to heterogeneous users in households in terms of technology affinity, individual needs, age distribution, gender, social constellations, personal role assignment, project expectations, etc. it produces particular demands to collaborate with users in the design process and thereby exposes a range of practical challenges. The full-day workshop wishes to identify these practical challenges, discuss best practice and develop a roadmap for sustainable relationships for design with users.
Realism and plausibility of computer controlled entities in entertainment software have been enhanced by adding both static personalities and dynamic emotions. Here a generic model is introduced that allows findings from real-life personality studies to be transferred to a computational model. Adaptive behavior patterns are enabled by introducing dynamic event-based emotions. The advantages of this model have been validated using a four-way crossroad in a traffic simulation. Driving agents using the introduced model enhanced by dynamics were compared to agents based on static personality profiles and simple rule-based behavior. The results show that adding a dynamic factor to agents improves perceivable plausibility and realism.
Many e-Learning-basedoffers, such as international programs and MOOCs have long since overcome the concept of national education and are designed to attract learners distributed throughout the world. In order to cope with the differences between learners, related offers often include opportunities to support the particular learning styles and learning pace beyond the advantages, which the technology itself naturally provides. Examples arethe individual configuration of the learning platformand display of contents and the provision of stylistically diverse and supplementary learning material. Such measures are relatively easy to implement andonce established, do not generate further expenses. Just, is it appropriate to lay the full responsibility for designing a comfortable (and supportive) learning environment into the hands of the learners and do they get along with such a responsibility? We asked university students from three continents regarding their expectations towards instructor-support and found major differences.
Robots, which are able to carry out their tasks robustly in real world environments, are not only desirable but necessary if we want them to be more welcome for a wider audience. But very often they may fail to execute their actions successfully because of insufficient information about behaviour of objects used in the actions.
In contrast to projection-based systems, large, high resolution multi-display systems offer a high pixel density on a large visualization area. This enables users to step up to the displays and see a small but highly detailed area. If the users move back a few steps they don't perceive details at pixel level but will instead get an overview of the whole visualization. Rendering techniques for design evaluation and review or for visualizing large volume data (e.g. Big Data applications) often use computationally expensive ray-based methods. Due to the number of pixels and the amount of data, these methods often do not achieve interactive frame rates.
A view direction based (VDB) rendering technique renders the user's central field of view in high quality whereas the surrounding is rendered with a level-of-detail approach depending on the distance to the user's central field of view. This approach mimics the physiology of the human eye and conserves the advantage of highly detailed information when standing close to the multi-display system as well as the general overview of the whole scene. In this paper we propose a prototype implementation and evaluation of a focus-based rendering technique based on a hybrid ray tracing/sparse voxel octree rendering approach.
Vor dem Hintergrund knapper Ressourcen, dem zunehmendem Reha-Bedarf und der politischen Diskussion um eine demografische Anpassung der Reha-Budgets gewinnt der Nachweis der Ergebnisqualität medizinischer Reha-Leistungen weiter an zentraler Bedeutung (z. B. Haaf, 2005; Steiner et al., 2009). Die kontinuierliche und klinikvergleichende Überprüfung der Behandlungsergebnisse ist darüber hinaus ein wichtiger Baustein eines funktionierenden Qualitätsmanagements (Schmidt et al., in press). Sie ermöglicht ein "Lernen von den Besten" und führt zu organisatorischen Lernprozessen (Toepler et. al., 2010).
Adapting plans to changes in the environment by finding alternatives and taking advantage of opportunities is a common human behavior. The need for such behavior is often rooted in the uncertainty produced by our incomplete knowledge of the environment. While several existing planning approaches deal with such issues, artificial agents still lack the robustness that humans display in accomplishing their tasks. In this work, we address this brittleness by combining Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. The approach allows a domestic service robot to find ways to get a job done by making substitutions. We show how knowledge is modeled, how the reasoning process is used to create a constrained planning problem, and how the system handles cases where plan generation fails due to missing/unavailable objects. The results of the evaluation for two tasks in a domestic service domain show the viability of the approach in finding and making the appropriate goal transformations.