Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

No abstract

No abstract

Abstract:Harbor surveillance is a critical and challenging part of maritime security procedures. Building a surveil-lance picture to support decision makers in detection of potential threats requires the integration of data and information coming from heterogeneous sources. Context plays a key role in achieving this task by providing expectations, constraints and additional information for inference about the items of interest. This paper proposes a fusion system for context-based situation and threat assessment with application to harbor surveillance. The architecture of the system is organized in two levels. The lowest level uses an ontological model to formally represent input data and to classify harbor objects and basic situations by deductive reasoning according to the harbor regulations. The higher level applies Belief-based Argumen-tation to evaluate the threat posed by suspicious vessels. The functioning of the system is illustrated with several examples that reproduce common harbor scenarios. IntroductionMaritime security is an area of strategic importance for the international community. As stated in [1], ''a terrorist incident against a marine transportation system would have a disaster impact on global shipping, international trade, and the world economy in addition to the strategic military value of many ports and waterways''. For that reason, one of the principal goals of strengthening maritime security is to ''increase maritime domain awareness'' by building a ''surveillance picture as complete as possible to assess the threats and vulnerabilities in the maritime realm''. In particular, harbor surveillance is a critical part of maritime security procedures because of its multiple objectives: recognition of terrorist threats, prevention of maritime and ecological accidents, detection of illegal immigration, fishing and drug trafficking, and so forth. However, it is nowadays mostly developed by human operators [2], who have to evaluate an overwhelming amount of information. This makes it very difficult to keep track of the event stream with the required level of attention due to distraction, fatigue and oversight. In addition, their decisions may be strongly affected by sensor data imprecision and subjective judgment. make informed decisions. According to the JDL data fusion model, the latter task belongs to the domain of Situation Assessment, defined as the estimation of ''sets of relationships among entities and their implications for the states of the related entities '' [4]. In this domain, it requires understanding the intrinsic information provided by coastal sensors in the context determined by extrinsic factors, like harbor environment, operational regulations, traffic data and intelligence reports.Recently, the increasing interest in higher-level information fusion has led to several proposals for context management -see for example the special sessions on context-based information fusion celebrated in the International Conferences on Information Fusion since 2007. Detection and characteri...

Simple versus complex fistulas is the most determinant factor for healing. In IBD fistulas, ulcerative colitis shows better prognosis than Crohn's disease. For complex fistulas, a temporary diverting stoma seems necessary.

Multiple minima of water cluster hypersurfaces are explored to find thermodynamic properties by means of the corresponding partition functions of their canonical distributions. The combination of semiempirical quantum chemical procedures for calculating the cluster energies in local minima of supermolecules and the statistical thermodynamics approach for both the evaluation of macroscopic association energies and the possible reduction by average of absolute errors intrinsic to the parametrized Hamiltonian are shown to provide an appropriate model for comparison between experimental and theoretical results. The method can explicitly take into account environmental effects due to intermolecular interaction. Water trimer and tetramer association energies of −10.9 and −14.1 kJ/mol obtained from virial coefficient calculations compare very well to the values of −10.5 and −16.4 kJ/mol, respectively, found for the theoretical association energies in this paper.

This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In it we propose a global stopping criterion, which we have named MGBM. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. The MDR indicator, which is also introduced, is a special-purpose solution designed for the purpose of stopping a multi-objective optimization.In this paper we describe the criterion from a theoretical perspective and examine its performance on a number of test problems. We also compare this method with similar approaches to the issue. The results of these experiments suggest that MGBM is a good starting point for research in this direction.

Smart mobile devices have fostered new learning scenarios that demand sophisticated interfaces. Multimodal conversational agents have became a strong alternative to develop human-machine interfaces that provide a more engaging and human-like relationship between students and the system. The main developers of operating systems for such devices have provided application programming interfaces for developers to implement their own applications, including different solutions for developing graphical interfaces, sensor control and voice interaction. Despite the usefulness of such resources, there are no strategies defined for coupling the multimodal interface with the possibilities that these devices offer to enhance mobile educative apps with intelligent communicative capabilities and adaptation to the user needs. In this paper, we present a practical m-learning application that integrates features of Android application programming interfaces on a modular architecture that emphasizes interaction management and context-awareness to foster user-adaptively, robustness and maintainability.