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Robust Indoor Localization Using Optimal Fusion Filter For Sensors And Map Layout Information
(2014)
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.
Humans exhibit flexible and robust behavior in achieving their goals. We make suitable substitutions for objects, actions, or tools to get the job done. When opportunities that would allow us to reach our goals with less effort arise, we often take advantage of them. Robots are not nearly as robust in handling such situations. Enabling a domestic service robot to find ways to get a job done by making substitutions is the goal of our work. In this paper, we highlight the challenges faced in our approach to combine Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. We present open questions in modeling the necessary knowledge, creating planning problems, and enabling the system to handle cases where plan generation fails due to missing/unavailable objects.
Updating a shared data structure in a parallel program is usually done with some sort of high-level synchronization operation to ensure correctness and consistency. The realization of such high-level synchronization operations is done with appropriate low-level atomic synchronization instructions that the target processor architecture provides. These instructions are costly and often limited in their scalability on larger multi-core / multi-processor systems. In this paper, a technique is discussed that replaces atomic updates of a shared data structure with ordinary and cheaper read/write operations. The necessary conditions are specified that must be fulfilled to ensure overall correctness of the program despite missing synchronization. The advantage of this technique is the reduction of access costs as well as more scalability due to elided atomic operations. But on the other side, possibly more work has to be done caused by missing synchronization. Therefore, additional work is traded against costly atomic operations. A practical application is shown with level-synchronous parallel Breadth-First Search on an undirected graph where two vertex frontiers are accessed in parallel. This application scenario is also used for an evaluation of the technique. Tests were done on four different large parallel systems with up to 64-way parallelism. It will be shown that for the graph application examined the amount of additional work caused by missing synchronization is neglectible and the performance is almost always better than the approach with atomic operations.
Level-Synchronous Parallel Breadth-First Search Algorithms For Multicore and Multiprocessor Systems
(2014)
Breadth-First Search (BFS) is a graph traversal technique used in many applications as a building block, e.g.,~to systematically explore a search space. For modern multicore processors and as application graphs get larger, well-performing parallel algorithms are favourable. In this paper, we systematically evaluate an important class of parallel BFS algorithms and discuss programming optimization techniques for their implementation. We concentrate our discussion on level-synchronous algorithms for larger multicore and multiprocessor systems. In our results, we show that for small core counts many of these algorithms show rather similar behaviour. But, for large core counts and large graphs, there are considerable differences in performance and scalability influenced by several factors. This paper gives advice, which algorithm should be used under which circumstances.
Hybrid system models exploit the modelling abstraction that fast state transitions take place instantaneously so that they encompass discrete events and the continuous time behaviour for the while of a system mode. If a system is in a certain mode, e.g. two rigid bodies stick together, then residuals of analytical redundancy relations (ARRs) within certain small bounds indicate that the system is healthy. An unobserved mode change, however, invalidates the current model for the dynamic behaviour. As a result, ARR residuals may exceed current thresholds indicating faults in system components that have not happened. The paper shows that ARR residuals derived from a bond graph cannot only serve as fault indicators but may also be used for bond graph model-based system mode identification. ARR residuals are numerically computed in an off-line simulation by coupling a bond graph of the faulty system to a non-faulty system bond graph through residual sinks. In real-time simulation, the faulty system model is to be replaced by measurements from the real system. As parameter values are uncertain, it is important to determine adaptive ARR thresholds that, given uncertain parameters, allow to decide whether the dynamic behaviour in a current system mode is the one of the healthy system so that false alarms or overlooking of true faults can be avoided. The paper shows how incremental bond graphs can be used to determine adaptive mode-dependent ARR thresholds for switched linear time-invariant systems with uncertain parameters in order to support robust fault detection. Bond graph-based hybrid system mode identification as well as the determination of adaptive fault thresholds is illustrated by application to a power electronic system easy to survey. Some simulation results have been analytically validated.
The RoCKIn@Work Challenge
(2014)
The perceived direction of “up” is determined by gravity, visual information, and an internal estimate of body orientation (Mittelstaedt, 1983; Dyde et al., 2006). Is the gravity level found on other worlds sufficient to maintain gravity’s contribution to this perception? Difficulties in stability reported anecdotally by astronauts on the lunar surface (NASA 1972) suggest that the moon’s gravity may not be, despite this value being far above the threshold for detecting linear acceleration. Knowing how much gravity is needed to provide a reliable orientation cue is required for training and preparing astronauts for future missions to the moon, mars and beyond.
Perception is one of the most important cognitive capabilities of an entity since it determines how an entity perceives its environment. The presented work focuses on providing cost efficient but realistic perceptual processes for intelligent virtual agents (IVAs) or NPCs with the goal of providing a sound information basis for the entities' decision making processes. In addition, an agent-central perception process should rovide a common interface for developers to retrieve data from the IVAs' environment. The overall process is evaluated by applying it to a scenario demonstrating its benefits. The evaluation indicates, that such a realistically simulated perception process provides a powerful instrument to enhance the (perceived) realism of an IVA's simulated behavior.
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.
Improving data acquisition techniques and rising computational power keep producing more and larger data sets that need to be analyzed. These data sets usually do not fit into a GPU's memory. To interactively visualize such data with direct volume rendering, sophisticated techniques for problem domain decomposition, memory management and rendering have to be used. The volume renderer Volt is used to show how CUDA is efficiently utilised to manage the volume data and a GPU's memory with the aim of low opacity volume renderings of large volumes at interactive frame rates.
Current computer architectures are multi-threaded and make use of multiple CPU cores. Most garbage collections policies for the Java Virtual Machine include a stop-the-world phase, which means that all threads are suspended. A considerable portion of the execution time of Java programs is spent in these stop-the-world garbage collections. To improve this behavior, a thread-local allocation and garbage collection that only affects single threads, has been proposed. Unfortunately, only objects that are not accessible by other threads ("do not escape") are eligible for this kind of allocation. It is therefore necessary to reliably predict the escaping of objects. The work presented in this paper analyzes the escaping of objects based on the line of code (program counter – PC) the object was allocated at. The results show that on average 60-80% of the objects do not escape and can therefore be locally allocated.
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.
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.
In the field of domestic service robots, recovery from faults is crucial to promote user acceptance. In this context we focus in particular on some specific faults, which arise from the interaction of a robot with its real world environment. Even a well-modelled robot may fail to perform its tasks successfully due to unexpected situations, which occur while interacting. These situations occur as deviations of properties of the objects (manipulated by the robot) from their expected values. Hence, they are experienced by the robot as external faults.
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.
Unexpected Situations in Service Robot Environment: Classification and Reasoning Using Naive Physics
(2014)
Breadth-First Search is a graph traversal technique used in many applications as a building block, e.g., to systematically explore a search space or to determine single source shortest paths in unweighted graphs. For modern multicore processors and as application graphs get larger, well-performing parallel algorithms are favorable. In this paper, we systematically evaluate an important class of parallel algorithms for this problem and discuss programming optimization techniques for their implementation on parallel systems with shared memory. We concentrate our discussion on level-synchronous algorithms for larger multicore and multiprocessor systems. In our results, we show that for small core counts many of these algorithms show rather similar performance behavior. But, for large core counts and large graphs, there are considerable differences in performance and scalability influenced by several factors, including graph topology. This paper gives advice, which algorithm should be used under which circumstances.
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.
Dysregulation of IL12 Signaling As a Novel Cause of an Autoimmune Lymphoproliferative like Syndrome
(2014)
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.
Application systems are often advertised with features, and features are used heavily for requirements man- agement. However, often software manufacturers only have incomplete information about the features of their software. The information is distributed over different sources, such as requirements documents, issue trackers, user manuals, and code. In this paper, we research the occurrence of feature information in open source software engineering data. We report on a case study with three open source systems. We analyze what information about features can be found in issue trackers and user documentation. Furthermore, we study the abstraction levels on which the features are described, how feature information is related, and we discuss the possibility to discover such information semi-automatically. To mirror the diversity of software development contexts, we choose open source systems, which are quite different, e.g., in the rigor of issue tracker usage. The results differ accordingly. One main result is that the user documentation did not provide more accurate information than the issue tracker compared to a provided feature list. The results also give hints on how the management of feature relevant information can be supported.
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.
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.
The RoCKIn@Home Challenge
(2014)