Bayesian inference for quantifying Listeria monocytogenes prevalence and concentration in minced pork meat from presence/absence microbiological testing

The purpose of this work was to estimate the prevalence and concentration of Listeria monocytogenes in minced pork meat by the application of a Bayesian modeling approach. Samples (n?=?100) collected from local markets were tested for L.?monocytogenes using in parallel the PALCAM, ALOA and RAPID'L.mono selective media. Presence of the pathogen was confirmed through biochemical and molecular tests. Independent experiments (n?=?10) for validation purposes were performed. No L.?monocytogenes was enumerated by direct-plating (<10?CFU/g), though the pathogen was detected in 22% of the samples. Sensitivity and specificity varied depending on the culture method. L.?monocytogenes concentration was estimated at 14-17?CFU/kg. Validation showed good agreement between observed and predicted prevalence (error?=?-2.17%). The use of at least two culture media in parallel enhanced the efficiency of L.?monocytogenes detection. Bayesian modeling may reduce the time needed to draw conclusions regarding L.?monocytogenes presence and the uncertainty of the results obtained.     

Entropy maximised queueing networks with blocking and multiple job classes

The method of entropy maximisation (MEM) is applied in a state space partitioning mode for the approximation of the joint stationary queue length distribution of an M/M/1/N queue with finite capacity, N( > 1), multiple and distinct classes of jobs, R( > 1), under a complete buffer sharing scheme and mixed service disciplines drawn from the first-come-first-served (FCFS), last-come-first-served with (LCFS-PR) or without (LCFS-NPR) preemption and processor sharing (PS) rules. The marginal and aggregate maximum entropy (ME) queue length distributions and the associated blocking probabilities per class are also determined. These ME results in conjunction with the first moments of the effective flows are used, as building blocks, in order to establish a new product-form approximation for arbitrary exponential open queueing networks with multiple classes of jobs under repetitive-service (RS) blocking with random destination (RD). It is verified that the ME approximation reduces to the exact truncated solution of open multi-class reversible queueing networks. Numerical experiments demonstrate a good accuracy level of ME statistics in relation to simulation. Moreover, recent extentions of MEM for arbitrary GE-type queueing networks with RS-RD blocking and multiple classes of jobs are presented.     

CloudsStorm: A framework for seamlessly programming and controlling virtual infrastructure functions during the DevOps lifecycle of cloud applications

The infrastructure-as-a-service (IaaS) model of cloud computing provides virtual infrastructure functions (VIFs), which allow application developers to flexibly provision suitable virtual machines' (VM) types and locations, and even configure the network connection for each VM. Because of the pay-as-you-go business model, IaaS provides an elastic way to operate applications on demand. However, in current cloud applications DevOps (software development and operations) lifecycle, the VM provisioning steps mainly rely on manually leveraging these VIFs. Moreover, these functions cannot be programmatically embedded into the application logic to control the infrastructure at runtime. Especially, the vendor lock-in issue, which different clouds provide different VIFs, also enlarges this gap between the cloud infrastructure management and application operation. To mitigate this gap, we designed and implemented a framework, CloudsStorm, which enables developers to easily leverage VIFs of different clouds and program them into their cloud applications. To be specific, CloudsStorm empowers applications with infrastructure programmability at design-level, infrastructure-level, and application-level. CloudsStorm also provides two infrastructure controlling modes, ie, active and passive mode, for applications at runtime. Besides, case studies about operating task-based and big data applications on clouds show that the monetary cost is significantly reduced through the seamless and on-demand infrastructure management provided by CloudsStorm. Finally, the scaling and recovery operation evaluations of CloudsStorm are performed to show its controlling performance. Compared with other tools, ie, “jcloud” and “cloudinit.d”, the scaling and provisioning performance evaluations demonstrate that CloudsStorm can achieve at least 10% efficiency improvement in our experiment settings.     

On the consistency of cardinal direction constraints

We present a formal model for qualitative spatial reasoning with cardinal directions utilizing a co-ordinate system. Then, we study the problem of checking the consistency of a set of cardinal direction constraints. We introduce the first algorithm for this problem, prove its correctness and analyze its computational complexity. Utilizing the above algorithm, we prove that the consistency checking of a set of basic (i.e., non-disjunctive) cardinal direction constraints can be performed in O(n⁵) time. We also show that the consistency checking of a set of unrestricted (i.e., disjunctive and non-disjunctive) cardinal direction constraints is NP-complete. Finally, we briefly discuss an extension to the basic model and outline an algorithm for the consistency checking problem of this extension.     

Composing cardinal direction relations

We study the recent proposal of Goyal and Egenhofer who presented a model for qualitative spatial reasoning about cardinal directions. Our approach is formal and complements the presentation of Goyal and Egenhofer. We focus our efforts on the composition operator for two cardinal direction relations. We consider two interpretations of the composition operator: consistency-based and existential composition. We point out that the only published method to compute the consistency-based composition does not always work correctly. Then, we consider progressively more expressive classes of cardinal direction relations and give consistency-based composition algorithms for these classes. Our theoretical framework allows us to prove formally that our algorithms are correct. When we consider existential composition, we demonstrate that the binary relation resulting from the composition of two cardinal direction relations cannot be expressed using the relations defined by Goyal and Egenhofer. Finally, we discuss some extensions to the basic model and consider the composition problem for these extensions.     

Outlier detection by example

Outlier detection is a useful technique in such areas as fraud detection, financial analysis and health monitoring. Many recent approaches detect outliers according to reasonable, pre-defined concepts of an outlier (e.g., distance-based, density-based, etc.). However, the definition of an outlier differs between users or even datasets. This paper presents a solution to this problem by including input from the users. Our OBE (Outlier By Example) system is the first that allows users to provide examples of outliers in low-dimensional datasets. By incorporating a small number of such examples, OBE can successfully develop an algorithm by which to identify further outliers based on their outlierness. Several algorithmic challenges and engineering decisions must be addressed in building such a system. We describe the key design decisions and algorithms in this paper. In order to interact with users having different degrees of domain knowledge, we develop two detection schemes: OBE-Fraction and OBE-RF. Our experiments on both real and synthetic datasets demonstrate that OBE can discover values that a user would consider outliers.     

Exploring hybrid parallel systems for probabilistic record linkage

The Journal of Supercomputing - Record linkage is a technique widely used to gather data stored in disparate data sources that presumably pertain to the same real world entity. This integration can...