Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.     

Reliability analysis and optimal version-updating for open source software

Context

Although reliability is a major concern of most open source projects, research on this problem has attracted attention only recently. In addition, the optimal version-dating for open source software considering its special properties is not yet discussed.

Objective

In this paper, the reliability analysis and optimal version-updating for open source software are studied.

Method

A modified non-homogeneous Poisson process model is developed for open source software reliability modeling and analysis. Based on this model, optimal version-updating for open source software is investigated as well. In the decision process, the rapid release strategy and the level of reliability are the two most important factors. However, they are essentially contradicting with each other. In order to consider these two conflicting factors simultaneously, a new decision model based on multi-attribute utility theory is proposed.

Results

Our models are tested on the real world data sets from two famous open source projects: Apache and GNOME. It is found that traditional software reliability models provide overestimations of the reliability of open source software. In addition, the proposed decision model can help management to make a rational decision on the optimal version-updating for open source software.

Conclusion

Empirical results reveal that the proposed model for open source software reliability can describe the failure process more accurately. Furthermore, it can be seen that the proposed decision model can assist management to appropriately determine the optimal version-update time for open source software.     

A dynamic attribute-based data filtering and recovery scheme for web information processing

Web data being transmitted over a network channel on the Internet with excessive amount of data causes data processing problems, which include selectively choosing useful information to be retained for various data applications. In this paper, we present an approach for filtering less-informative attribute data from a source Website. A scheme for filtering attributes, instead of tuples (records), from a Website becomes imperative, since filtering a complete tuple would lead to filtering some informative, as well as less-informative, attribute data in the tuple. Since filtered data at the source Website may be of interest to the user at the destination Website, we design a data recovery approach that maintains the minimal amount of information for data recovery purpose while imposing minimal overhead for data recovery at the source Website. Our data filtering and recovery approach (1) handles a wide range of Web data in different application domains (such as weather, stock exchanges, Internet traffic, etc.), (2) is dynamic in nature, since each filtering scheme adjusts the amount of data to be filtered as needed, and (3) is adaptive, which is appealing in an ever-changing Internet environment.     

Adapting application execution in CMPs using helper threads

In parallel to the changes in both the architecture domain–the move toward chip multiprocessors (CMPs)–and the application domain–the move toward increasingly data-intensive workloads–issues such as performance, energy efficiency and CPU availability are becoming increasingly critical. The CPU availability can change dynamically due to several reasons such as thermal overload, increase in transient errors, or operating system scheduling. An important question in this context is how to adapt, in a CMP, the execution of a given application to CPU availability change at runtime. Our paper studies this problem, targeting the energy-delay product (EDP) as the main metric to optimize. We first discuss that, in adapting the application execution to the varying CPU availability, one needs to consider the number of CPUs to use, the number of application threads to accommodate and the voltage/frequency levels to employ (if the CMP has this capability). We then propose to use helper threads to adapt the application execution to CPU availability change in general with the goal of minimizing the EDP. The helper thread runs parallel to the application execution threads and tries to determine the ideal number of CPUs, threads and voltage/frequency levels to employ at any given point in execution. We illustrate this idea using four applications (Fast Fourier Transform, MultiGrid, LU decomposition and Conjugate Gradient) under different execution scenarios. The results collected through our experiments are very promising and indicate that significant EDP reductions are possible using helper threads. For example, we achieved up to 66.3%, 83.3%, 91.2%, and 94.2% savings in EDP when adjusting all the parameters properly in applications FFT, MG, LU, and CG, respectively. We also discuss how our approach can be extended to address multi-programmed workloads.     

Proteomic examination of Leishmania chagasi plasma membrane proteins: Contrast between avirulent and virulent (metacyclic) parasite forms

Purpose: About two million new cases of leishmaniasis with 50 000 associated deaths occur worldwide each year. Promastigotes of the causative Leishmania spp. develop from the procyclic stage to the highly virulent metacyclic stage within the sand fly vector. We hypothesized that proteins important for promastigote virulence might be uniquely represented in the plasma membrane of metacyclic, but not procyclic, promastigotes. Experimental design: Procyclic (logarithmic) promastigotes and purified metacyclic promastigotes from stationary phase cultures of Leishmania chagasi were used to prepare membrane preparations either by surface biotinylation‐streptavidin affinity separation or by octyl glucoside detergent extraction. Results: These membrane fractions were enriched over 130‐ and 250‐fold, respectively, as estimated by Western blotting for the plasma membrane's major surface protease. Hundreds or dozens of proteins were identified by LC‐MS/MS in the surface biotinylation or detergent extraction, respectively. Confocal microscopy suggested the difference between the lists was due to the fact that proteins localized both on the surface membrane and within the flagellar pocket were accessible to surface biotinylation, whereas only proteins on the membrane were obtained by detergent extraction. Using detergent extraction, we found different proteins were present in membranes of the procyclic stage compared to metacyclic stage promastigotes. Several dozen were stage specific. Conclusions and clinical relevance: These data provide a foundation for identifying virulence factors in the plasma membranes of Leishmania spp. promastigotes during metacyclogenesis.     

Leader’s dilemma game: An experimental design for cyber insider threat research

One of the problems with insider threat research is the lack of a complete 360° view of an insider threat dataset due to inadequate experimental design. This has prevented us from modeling a computational system to protect against insider threat situations. This paper provides a contemporary methodological approach for using online games to simulate insider betrayal for predictive behavioral research. The Leader’s Dilemma Game simulates an insider betrayal scenario for analyzing organizational trust relationships, providing an opportunity to examine the trustworthiness of focal individuals, as measured by humans as sensors engaging in computer-mediated communication. This experimental design provides a window into trustworthiness attribution that can generate a rigorous and relevant behavioral dataset, and contributes to building a cyber laboratory that advances future insider threat study.     

Development of a web-based insulin decision aid for the elderly: usability barriers and guidelines

Universal Access in the Information Society - In recent years, researchers have attempted to shift patient decision aids (PDAs) from paper-based to web-based to increase its accessibility. Insulin...     

Optimization of Geoscience Laser Altimeter System waveform metrics to support vegetation measurements

The Geoscience Laser Altimeter System (GLAS) has collected over 250 million measurements of vegetation height over forests globally. Accurate vegetation heights can be determined using waveform metrics that include vertical extent and extent of the waveform's trailing and leading edges. All three indices are highly dependent upon the signal strength, background noise and signal-to-noise ratio of the waveform, as the background noise contribution to the waveforms has to be removed before their calculation. Over the last six years, GLAS has collected data during thirteen observation periods using illumination from three different lasers. The power levels of these lasers have changed over time, resulting in variable signal power and noise characteristics. Atmospheric conditions vary continuously, also influencing signal power and noise.To minimize these effects, we optimized a noise coefficient which could be constant or vary according to observation period or noise metric. This parameter is used with the mean and standard deviation of the background noise to determine a noise level threshold that is removed from each waveform. An optimization analysis was used with a global dataset of waveforms that are near-coincident with waveforms from other observation periods; the goal of the optimization was to minimize the difference in vertical extent between spatially overlapping GLAS observations. Optimizations based on absolute difference in height led to situations in which the total extent was minimized as well; further optimizations reduced a normalized difference in height extent. The simplest optimizations were based on a constant value to be applied to all observations; noise coefficients of 2.7, 3.2, 3.4 and 4.0 were determined for datasets consisting of global forests, global vegetation, forest in the legal Amazon basin and boreal forests respectively. Optimizations based on the power level or the signal-to-noise ratio of waveforms best minimized differences in waveform extent, decreasing the percent root mean squared height difference by 25-54% over the constant value approach. Further development of methods to ensure temporal consistency of waveform indices will be necessary to support long-term satellite lidar missions and will result in more accurate and precise estimates of canopy height.     

Customer Retention via Data Mining

``Customer Retention' is an increasingly pressing issue intoday's ever-competitive commercial arena. This is especially relevantand important for sales and services related industries. Motivated by areal-world problem faced by a large company, we proposed a solution thatintegrates various techniques of data mining, such as featureselection via induction, deviation analysis, and mining multipleconcept-level association rules to form an intuitive and novel approachto gauging customer loyalty and predicting their likelihood ofdefection. Immediate action triggered by these ``early-warnings'resulting from data mining is often the key to eventual customerretention.