Visualizing and fuzzy filtering for discovering temporal trajectories of association rules

We propose a user-centric rule filtering method that allows to identify association rules that exhibit a certain user-specified temporal behavior with respect to rule evaluation measures. The method can considerably reduce the number of association rules that have to be assessed manually after a rule induction. This is especially necessary if the rule set contains many rules as it is the case for the task of finding rare patterns inside the data. For the proposed method, we will reuse former work on the visualization of association rules [M. Steinbrecher, R. Kruse, Visualization of possibilistic potentials, in: Foundations of Fuzzy Logic and Soft Computing, in: Lecture Notes in Comput. Sci., vol. 4529, Springer-Verlag, Berlin/Heidelberg, 2007, pp. 295–303] and use an extension of it to motivate and assess the presented filtering technique. We put the focus on rules that are induced from a data set that contains a temporal variable and build our approach on the requirement that temporally ordered sets of association rules are available, i.e., one set for every time frame. To illustrate this, we propose an ad-hoc learning method along the way. The actual rule filtering is accomplished by means of fuzzy concepts. These concepts use linguistic variables to partition rule-related domains of interest, such as the confidence change rate. The original rule sets are then matched against these user concepts and result in only those rules that match the respective concepts to a predefined extent. We provide empirical evidence by applying the proposed methods to hand-crafted as well as real-world data sets and critically discuss the current state and further prospects.     

Influence of a chip scale package on the frequency response of a MEMS microphone

This paper describes the influence of a chip scale MEMS package (CSMP) on the acoustic behaviour of a silicon microphone. The influence was calculated using an electro-mechanical–acoustical equivalent circuit. Standard packaging of microphones using die bonding and wire bonding leads to a large front volume which acts as a Helmholtz resonator. This can dramatically influence the frequency response of the microphone system by adding a second resonance. In the worst case this second resonance is in the acoustic frequency range, thus degrading its performance in an unacceptable way. In case of the CSMP only a small front volume is generated between the substrate and the flip-chipped microphone chip. Thus the resonance step-up is very small compared to standard packages. Furthermore the frequency response can be flattened by optimizing the geometry of the small sound holes in the substrate. By choosing an appropriate geometry of these sound holes the package can act as a low pass filter where the cut-off frequency can be placed to the desired value of the acoustic spectrum.     

Study on the replication quality of micro-structures in the injection molding process with dynamical tool tempering systems

The injection molding of micro-structures is a promising mass-production method for a broad range of materials. However, the replication quality of these structures depends significantly on the heat flow during the filling stage. In this paper, the filling and heat transfer of v-groove and random structures below 5 μm is investigated with the help of an AFM (atomic force microscope) and thermo couples. A numerical model is developed to predict the filling of surface structures during the filling and packing stage. The model implies the use of simple fully developed flow models taking the power-law material model into account. This permits investigation into which ways several processing parameters affect the polymer flow in the surface structures. The mold wall temperature, which has significant effects on the polymer flow, is varied by using a variothermal mold temperature control system to validate the model proposed.     

Automated GUI performance testing

A significant body of prior work has devised approaches for automating the functional testing of interactive applications. However, little work exists for automatically testing their performance. Performance testing imposes additional requirements upon GUI test automation tools: the tools have to be able to replay complex interactive sessions, and they have to avoid perturbing the application’s performance. We study the feasibility of using five Java GUI capture and replay tools for GUI performance test automation. Besides confirming the severity of the previously known GUI element identification problem, we also describe a related problem, the temporal synchronization problem, which is of increasing importance for GUI applications that use timer-driven activity. We find that most of the tools we study have severe limitations when used for recording and replaying realistic sessions of real-world Java applications and that all of them suffer from the temporal synchronization problem. However, we find that the most reliable tool, Pounder, causes only limited perturbation and thus can be used to automate performance testing. Based on an investigation of Pounder’s approach, we further improve its robustness and reduce its perturbation. Finally, we demonstrate in a set of case studies that the conclusions about perceptible performance drawn from manual tests still hold when using automated tests driven by Pounder. Besides the significance of our findings to GUI performance testing, the results are also relevant to capture and replay-based functional GUI test automation approaches.     

Adaptive Finite Element Simulation of the Time-dependent Simplified <Emphasis Type="Italic">P</Emphasis><Subscript><Emphasis Type="Italic">N</Emphasis></Subscript> Equations

The steady-state simplified P _N approximation to the radiative transfer equation has been successfully applied to many problems involving radiation. Recently, time-dependent simplified P _N equations have been derived by an asymptotic analysis similar to the asymptotic derivation of the steady-state SP _N equations (Frank et al. in J. Comput. Phys. 226:2289–2305, 2007). In this paper, we present computational results for the time-dependent SP _N equations in two dimensions, obtained by using an adaptive finite element approach. Several numerical comparisons with other existing models are shown.     

Statistical relational learning of trust

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.     

Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H gate and R&N gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H gate and R&N gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified.     

Distributed top-<Emphasis Type="Italic">k</Emphasis> aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.     

Robust Cardiac Function Assessment in 4D PC‐MRI Data of the Aorta and Pulmonary Artery

Four‐dimensional phase‐contrast magnetic resonance imaging (4D PC‐MRI) allows the non‐invasive acquisition of time‐resolved, 3D blood flow information. Stroke volumes (SVs) and regurgitation fractions (RFs) are two of the main measures to assess the cardiac function and severity of valvular pathologies. The flow rates in forward and backward direction through a plane above the aortic or pulmonary valve are required for their quantification. Unfortunately, the calculations are highly sensitive towards the plane's angulation since orthogonally passing flow is considered. This often leads to physiologically implausible results. In this work, a robust quantification method is introduced to overcome this problem. Collaborating radiologists and cardiologists were carefully observed while estimating SVs and RFs in various healthy volunteer and patient 4D PC‐MRI data sets with conventional quantification methods, that is, using a single plane above the valve that is freely movable along the centerline. By default it is aligned perpendicular to the vessel's centerline, but free angulation (rotation) is possible. This facilitated the automation of their approach which, in turn, allows to derive statistical information about the plane angulation sensitivity. Moreover, the experts expect a continuous decrease of the blood flow volume along the vessel course. Conventional methods are often unable to produce this behaviour. Thus, we present a procedure to fit a monotonous function that ensures such physiologically plausible results. In addition, this technique was adapted for the usage in branching vessels such as the pulmonary artery. The performed informal evaluation shows the capability of our method to support diagnosis; a parameter evaluation confirms the robustness. Vortex flow was identified as one of the main causes for quantification uncertainties.