Simulations of lateral mixing in cross-channel flow

Cross flow phenomena between connected sub-channels are studied by means of numerical simulations based on lattice-Boltzmann discretization. The cross (that is lateral) transfer is largely due to macroscopic instabilities developing at two shear layers. The characteristic size and advection velocity of the instabilities favorably compare with experimental results from the literature on a geometrically similar system. The strength of the cross flow strongly depends on the Reynolds number, with cross flow developing only for Reynolds numbers (based on macroscopic flow quantities) larger than 1360. Mass transfer between the sub-channels has been assessed by adding a passive scalar to the flow and solving its transport equation. As a result of the intimate connection of cross flow and lateral mass transfer, also the mass transfer coefficient is a pronounced function of Re.     

Detection of process anomalies using an improved statistical learning framework

Maintenance technologies have been progressed from a time-based to a condition-based manner. The fundamental idea of condition-based maintenance (CBM) is built on the real-time diagnosis of impending failures and/or the prognosis of residual lifetime of equipment by monitoring health conditions using various sensors. The success of CBM, therefore, hinges on the capability to develop accurate diagnosis/prognosis models. Even though there may be an unlimited number of methods to implement models, the models can normally be classified into two categories in terms of their origins: using physical principles or historical observations. We have focused on the latter method (sometimes referred as the empirical model based on statistical learning) because of some practical benefits such as context-free applicability, configuration flexibility, and customization adaptability. While several pilot-scale systems using empirical models have been applied to work sites in Korea, it should be noted that these do not seem to be generally competitive against conventional physical models. As a result of investigating the bottlenecks of previous attempts, we have recognized the need for a novel strategy for grouping correlated variables such that an empirical model can accept not only statistical correlation but also some extent of physical knowledge of a system. Detailed examples of problems are as follows: (1) missing of important signals in a group caused by the lack of observations, (2) problems of signals with the time delay, and (3) problems of optimal kernel bandwidth. This paper presents an improved statistical learning framework including the proposed strategy and case studies illustrating the performance of the method.     

Robust speaker recognition in cross-channel condition based on Gaussian mixture model

One of the most difficult challenges for speaker recognition is dealing with channel variability. In this paper, several new cross-channel compensation techniques are introduced for a Gaussian mixture model—universal background model (GMM-UBM) speaker verification system. These new techniques include wideband noise reduction, echo cancellation, a simplified feature-domain latent factor analysis (LFA) and data-driven score normalization. A novel dynamic Gaussian selection algorithm is developed to reduce the feature compensation time by more than 60% without any performance loss. The performance of different techniques across varying channel train/test conditions are presented and discussed, finding that speech enhancement, which used to be neglected for telephone speech, is essential for cross-channel tasks, and the channel compensation techniques developed for telephone channel speech also perform effectively. The per microphone performance analysis further shows that speech enhancement can boost the effects of other techniques greatly, especially on channels with larger signal-to-noise ratio (SNR) variance. All results are presented on NIST SRE 2006 and 2008 data, showing a promising performance gain compared to the baseline. The developed system is also compared with other state-of-the-art speaker verification systems. The result shows that the developed system can obtain comparable or even better performance but consumes much less CPU time, making it more suitable for practical use.     

An empirical study of the cross-channel effects between web and mobile shopping channels

With the increasing popularity of mobile applications, increasingly more e-commerce websites are providing mobile shopping services that enable their consumers to access their products and services through an additional mobile channel. The question arises here as to whether the new channel brings new sales or merely shifts consumers from the web to the mobile channel. Using the 2½-year transaction data obtained from an e-commerce company that expanded its web service onto a mobile platform, we investigated the impact of the newly introduced mobile channel on the sales of the incumbent web channel, and whether it could stimulate new consumption from consumers. Our empirical results indicate that after the adoption of the mobile channel, the purchases on the web channel were slightly cannibalized; however, the consumers’ purchases increased overall, suggesting that the positive synergy effect of the new channel overrode the negative cannibalization effect. Our investigation contributes to multichannel e-commerce literature by empirically testing the cross-channel effects of a new mobile channel and also providing insights for e-retailers interested in introducing a new mobile channel.     

Online keyword based advertising: Impact of ad impressions on own-channel and cross-channel click-through rates

Keyword-based ads are becoming the dominant form of advertising online as they enable customization and tailoring of messages relevant to potential consumers. Two prominent channels within this sphere are the search channel and the content channel. We empirically examine the interaction between these two channels. Our results indicate significant cannibalization across the two channels as well as significant diminishing returns to impressions within each channel. This suggests that under certain conditions both channels may need to be used to optimize returns to advertising both for advertisers and service providers such as Google. Our game theoretic analysis which builds upon our empirical findings reveals that for intermediate budget values it is optimal to use both channels whereas for very low (very high) budget values it is optimal to use only the content (search) channel. Further as budget increases the advertiser should offer more for ads displayed on the search channel to optimally incentivize the service provider.     

Detection of anomalies in compiled computer program files inspired by immune mechanisms using a template method

Journal of Computer Virology and Hacking Techniques - An intrusion detection system inspired by the human immune system is described: a custom artificial immune system that monitors a local area...     

Passive analysis of TCP anomalies

In this paper, we focus on passive measurements of TCP traffic. We propose a heuristic technique to classify TCP anomalies, i.e., segments that have a sequence number different from the expected one, such as out-of-sequence and duplicate segments. Since TCP is a closed-loop protocol that infers network conditions from packet losses and reacts accordingly, the possibility of carefully distinguishing the causes of anomalies in TCP traffic is very appealing and may be instrumental to understand TCP behavior in real environments. We apply the proposed heuristic to traffic traces collected at both network edges and backbone links. By comparing results obtained from traces collected over several years, we observe some phenomena such as the impact of the introduction of TCP SACK which reduces the unnecessary retransmissions, the large percentage of network reordering, etc. By further studying the statistical properties of TCP anomalies, we find that, while their aggregate exhibits long-range dependence, anomalies suffered by individual long-lived flows are on the contrary uncorrelated. Interestingly, no dependence on the actual link load is observed.     

Detecting and discriminating behavioural anomalies

This paper aims to address the problem of anomaly detection and discrimination in complex behaviours, where anomalies are subtle and difficult to detect owing to the complex temporal dynamics and correlations among multiple objects’ behaviours. Specifically, we decompose a complex behaviour pattern according to its temporal characteristics or spatial-temporal visual contexts. The decomposed behaviour is then modelled using a cascade of Dynamic Bayesian Networks (CasDBNs). In contrast to existing standalone models, the proposed behaviour decomposition and cascade modelling offers distinct advantage in simplicity for complex behaviour modelling. Importantly, the decomposition and cascade structure map naturally to the structure of complex behaviour, allowing for a more effective detection of subtle anomalies in surveillance videos. Comparative experiments using both indoor and outdoor data are carried out to demonstrate that, in addition to the novel capability of discriminating different types of anomalies, the proposed framework outperforms existing methods in detecting durational anomalies in complex behaviours and subtle anomalies that are difficult to detect when objects are viewed in isolation.     

Empirical analysis of safety-critical anomalies during operations

Analysis of anomalies that occur during operations is an important means of improving the quality of current and future software. Although the benefits of anomaly analysis of operational software are widely recognized, there has been relatively little research on anomaly analysis of safety-critical systems. In particular, patterns of software anomaly data for operational, safety-critical systems are not well understood. We present the results of a pilot study using orthogonal defect classification (ODC) to analyze nearly two hundred such anomalies on seven spacecraft systems. These data show several unexpected classification patterns such as the causal role of difficulties accessing or delivering data, of hardware degradation, and of rare events. The anomalies often revealed latent software requirements that were essential for robust, correct operation of the system. The anomalies also caused changes to documentation and to operational procedures to prevent the same anomalous situations from recurring. Feedback from operational anomaly reports helped measure the accuracy of assumptions about operational profiles, identified unexpected dependencies among embedded software and their systems and environment, and indicated needed improvements to the software, the development process, and the operational procedures. The results indicate that, for long-lived, critical systems, analysis of the most severe anomalies can be a useful mechanism both for maintaining safer, deployed systems and for building safer, similar systems in the future.     

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.     

Performance anomalies in boundary data structures

A Δ-shaped data structure that is more compact and efficient under certain conditions than existing ones is proposed. The method has excellent storage efficiency (6E, with E being the total number of edges) and provides improved access time in a virtual memory environment. The discussion covers previous work, virtual memory and databases, determining record access costs, constant-time data schemes, multiple entities, and implementation methods     

Analyzing business process anomalies using autoencoders

Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business intelligence, classic anomaly detection is not very frequently researched. In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process. Our method does not rely on any prior knowledge about the process and can be trained on a noisy dataset already containing the anomalies. We demonstrate its effectiveness by evaluating it on 700 different datasets and testing its performance against three state-of-the-art anomaly detection methods. This paper is an extension of our previous work from 2016 (Nolle et al. in Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders. In: International conference on discovery science, Springer, pp 442–456, 2016). Compared to the original publication we have further refined the approach in terms of performance and conducted an elaborate evaluation on more sophisticated datasets including real-life event logs from the Business Process Intelligence Challenges of 2012 and 2017. In our experiments our approach reached an \(F_1\) score of 0.87, whereas the best unaltered state-of-the-art approach reached an \(F_1\) score of 0.72. Furthermore, our approach can be used to analyze the detected anomalies in terms of which event within one execution of the process causes the anomaly.     

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as—weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases—(i) discovering homogeneous regions, and (ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.     

Applying rule-base anomalies to KADS inference structures

The literature on validation and verification of knowledge-based systems contains a catalogue of anomalies for knowledge-based systems, such as redundant, contradictory or deficient knowledge. Detecting such anomalies is a method for verifying knowledge-based systems. Unfortunately, the traditional formulation of the anomalies in the literature is very specific to a rule-based knowledge representation, which greatly restricts their applicability. In this paper, we show how the traditional anomalies can be reinterpreted in terms of conceptual models (in particular KADS inference structures). For this purpose, we present a formalisation of KADS inference structures which enables us to apply the traditional rule-base anomalies to these inference structures. This greatly improves the usefulness of the anomalies, since they can now be applied to a much wider class of knowledge-based systems. Besides this reformulation and wider applicability of the traditional anomalies, further contributions of this paper are a novel formalisation of KADS inference structures and a number of improvements to the existing formalisation of the traditional anomalies.     

Detection of geothermal anomalies using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA

Surface temperature anomalies associated with geothermal activity at Bradys Hot Springs, Churchill County, Nevada were mapped using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) image data. In order to highlight subsurface contributions of geothermal heat, the ASTER images were processed to minimize temperature variations caused by the diurnal heating effects of the sun. Surface temperature variations caused by changes in albedo were corrected with visible and near-infrared ASTER bands, and a 10-meter-smoothed Digital Elevation Model (DEM) was used to correct for topographic slope effects. Field measurements of ground surface temperatures made over 24-hour periods were used to design a thermal inertia correction incorporating day and night thermal infrared images.In the resulting processed image, background temperature variations were reduced 30-50% without reducing the intensity of geothermal anomalies, thus making it easier to distinguish geothermal activity from ‘false’ anomalies caused by non-thermal springs, topographic effects, and variable rock, soil, and vegetation compositions.     

一种分布式防火墙过滤策略的异常检测模型

分布式防火墙的安全性很大程度上取决于过滤策略正确配置。过滤策略的异常可能导致分布式防火墙系统所保护的网络出现严重的访问漏洞。为了能够自动化地检测分布式防火墙过滤策略存在的异常,对分布式防火墙系统中各过滤节点上的过滤规则之间可能出现的异常进行分类,并建立了一个过滤策略异常检测的模型。该模型能够检测出分布式防火墙过滤规则之间的冗余、冲突、不完整等各种异常,从而保证了分布式防火墙过滤策略的完整性和一致性。     

Fuzzy neural networks for classification and detection of anomalies

A new learning algorithm for the Simpson fuzzy min-max neural network is presented. It overcomes some undesired properties of the Simpson model. Our new algorithm improves the network performance; the classification result does not depend on the presentation order of the patterns in the training set, and at each step, the classification error in the training set cannot increase. The new neural model is particularly useful in classification problems. Tests were executed on three different classification problems: 1) with two-dimensional synthetic data; 2) with realistic data generated by a simulator to find anomalies in the cooling system of a blast furnace; and 3) with real data for industrial diagnosis. The experiments were made following some recent evaluation criteria known in the literature and by using Microsoft Visual C++ development environment on personal computers.     

A neuro-immune model for discriminating and visualizing anomalies

A model that can detect anomalies, even when trained only with normal samples, and can learn from encounters with new anomalies is proposed. The model combines a negative selection algorithm and a self-organizing map (SOM) in an immune inspired architecture. One of the main advantages of the proposed system is that it is able to produce a visual representation of the self/non-self feature space, thanks to the topological two-dimensional map produced by the SOM. Experimental results with anomaly and classification data are presented and discussed.     

Managing space system anomalies using first principles reasoning

This paper discusses the development of a theory of anomaly management that uses fundamental models of a system's structure, behavior, and intended use to support the detection of symptoms, the computation of possible diagnoses, and the generation of resolution actions. A software-based reasoning system based on this theory was implemented and applied to the configuration control of a space system consisting of an on-orbit satellite and a geographically distributed network of ground communication and control stations. This system was found to promote a formal and systematic analysis of possibilities when managing configuration anomalies in the Sapphire space system. This approach can be extended to more complex systems through future optimization of the processing implementation and through incorporation of other innovations such as dynamic and hierarchical modelling