Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

Detecting Traffic Problems with ILP

Expert systems for decision support have recently been successfully introduced in road transport management. In this paper, we apply three state-of-the art ILP systems to learn how to detect traffic problems.

ILP Experiments in Detecting Traffic Problems

The paper describes experiments in automated acquisition of knowledge in traffic problem detection. Preliminary results show that ILP can be used to successfully learn to detect traffic problems.

Detecting common scientific workflow fragments using templates and execution provenance

Provenance plays a major role when understanding and reusing the methods applied in a scientic experiment, as it provides a record of inputs, the processes carried out and the use and generation of intermediate and nal results. In the specic case of in-silico scientic experiments, a large variety of scientic workflow systems (e.g., Wings, Taverna, Galaxy, Vistrails) have been created to support scientists. All of these systems produce some sort of provenance about the executions of the workflows that encode scientic experiments. However, provenance is normally recorded at a very low level of detail, which complicates the understanding of what happened during execution. In this paper we propose an approach to automatically obtain abstractions from low-level provenance data by finding common workflow fragments on workflow execution provenance and relating them to templates. We have tested our approach with a dataset of workflows published by the Wings workflow system. Our results show that by using these kinds of abstractions we can highlight the most common abstract methods used in the executions of a repository, relating different runs and workflow templates with each other.

Detecting periodicity with horizontal visibility graphs

The horizontal visibility algorithm was recently introduced as a mapping between time series and networks. The challenge lies in characterizing the structure of time series (and the processes that generated those series) using the powerful tools of graph theory. Recent works have shown that the visibility graphs inherit several degrees of correlations from their associated series, and therefore such graph theoretical characterization is in principle possible. However, both the mathematical grounding of this promising theory and its applications are in its infancy. Following this line, here we address the question of detecting hidden periodicity in series polluted with a certain amount of noise. We first put forward some generic properties of horizontal visibility graphs which allow us to define a (graph theoretical) noise reduction filter. Accordingly, we evaluate its performance for the task of calculating the period of noisy periodic signals, and compare our results with standard time domain (autocorrelation) methods. Finally, potentials, limitations and applications are discussed.

Detecting unknown attacks in wireless sensor networks that contain mobile nodes

As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

Detecting acronyms from capital letter sequences in Spanish

This paper presents an automatic strategy to decide how to pronounce a Capital Letter Sequence (CLS) in a Text to Speech system (TTS). If CLS is well known by the TTS, it can be expanded in several words. But when the CLS is unknown, the system has two alternatives: spelling it (abbreviation) or pronouncing it as a new word (acronym). In Spanish, there is a high relationship between letters and phonemes. Because of this, when a CLS is similar to other words in Spanish, there is a high tendency to pronounce it as a standard word. This paper proposes an automatic method for detecting acronyms. Additionaly, this paper analyses the discrimination capability of some features, and several strategies for combining them in order to obtain the best classifier. For the best classifier, the classification error is 8.45%. About the feature analysis, the best features have been the Letter Sequence Perplexity and the Average N-gram order.

Detecting Good Practices and Pitfalls when Publishing Vocabularies on the Web

The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies bringing their semantic to the data being published. These ontologies should be evaluated at different stages, both during their development and their publication. As important as correctly modelling the intended part of the world to be captured in an ontology, is publishing, sharing and facilitating the (re)use of the obtained model. In this paper, 11 evaluation characteristics, with respect to publish, share and facilitate the reuse, are proposed. In particular, 6 good practices and 5 pitfalls are presented, together with their associated detection methods. In addition, a grid-based rating system is generated. Both contributions, the set of evaluation characteristics and the grid system, could be useful for ontologists in order to reuse existing LD vocabularies or to check the one being built.

Detecting false testimonies in reputation systems using self-organizing maps

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

Detecting changes in the basin of attraction of a dynamical system: Application to the postural restoring system

A method that provides athree-dimensional representation ofthe basin ofattraction of a dynamical system from experimen tal data was applied tothe problem ofdynamic balance restoration. The method isbased onthe density ofthe data onthe phase space ofthe system under study and makes use ofmodeling and numerical curve fittingtools.For the dynamical system ofbalance restora tion,the shape and the size of the basin of attraction depend on the dynamics of the postural restoring mechanisms and contain important information regarding the biomechanical,as well as the neuromuscular condition of the individual. The aim ofthis work was toexamine the ability ofthe method todetect, through the observed changes inthe shape and/or the size ofthe calculated basins of attraction, (a)the inherent differences between different systems (in the current application, postural restoring systems of different individuals)and (b)induced chan ges in the same system (thepostural restoring system of an individual).The results ofthe study confirm the validity of the method and furthermore justify its robustness.

Piezoelectric sensors system for impact detecting

This work describes an acoustic system that allows the automatic detection and location of mechanical impacts on metallic based structures, which is suitable in robotics and industrial applications. The system is based on the time delays of propagation of the acoustic waves along the metallic based structure and it determines the instant and the position when and were the impact has been produced by piezoelectric sensors and an electronic-computerized system. We have obtained that for distance impact of 40 cm and 50 cm the time delay is 2 s and 72 s respectively.

Application of Improved Direct Calibration for Hyperspectral image processing: Detecting peanut traces in wheat flour.

NIR Hyperspectral imaging (1000-2500 nm) combined with IDC allowed the detection of peanut traces down to adulteration percentages 0.01% Contrary to PLSR, IDC does not require a calibration set, but uses both expert and experimental information and suitable for quantification of an interest compound in complex matrices. The obtained results shows the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA

DAEDALUS at RepLab 2014: Detecting RepTrak reputation dimensions on tweets

This paper describes our participation at the RepLab 2014 reputation dimensions scenario. Our idea was to evaluate the best combination strategy of a machine learning classifier with a rule-based algorithm based on logical expressions of terms. Results show that our baseline experiment using just Naive Bayes Multinomial with a term vector model representation of the tweet text is ranked second among runs from all participants in terms of accuracy.

Detecting and characterising ramp events in wind power time series

In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

Effectiveness for detecting faults within and outside the scope of testing techniques: an independent replication

The verification and validation activity plays a fundamental role in improving software quality. Determining which the most effective techniques for carrying out this activity are has been an aspiration of experimental software engineering researchers for years. This paper reports a controlled experiment evaluating the effectiveness of two unit testing techniques (the functional testing technique known as equivalence partitioning (EP) and the control-flow structural testing technique known as branch testing (BT)). This experiment is a literal replication of Juristo et al. (2013). Both experiments serve the purpose of determining whether the effectiveness of BT and EP varies depending on whether or not the faults are visible for the technique (InScope or OutScope, respectively). We have used the materials, design and procedures of the original experiment, but in order to adapt the experiment to the context we have: (1) reduced the number of studied techniques from 3 to 2; (2) assigned subjects to experimental groups by means of stratified randomization to balance the influence of programming experience; (3) localized the experimental materials and (4) adapted the training duration. We ran the replication at the Escuela Polite?cnica del Eje?rcito Sede Latacunga (ESPEL) as part of a software verification & validation course. The experimental subjects were 23 master?s degree students. EP is more effective than BT at detecting InScope faults. The session/program and group variables are found to have significant effects. BT is more effective than EP at detecting OutScope faults. The session/program and group variables have no effect in this case. The results of the replication and the original experiment are similar with respect to testing techniques. There are some inconsistencies with respect to the group factor. They can be explained by small sample effects. The results for the session/program factor are inconsistent for InScope faults. We believe that these differences are due to a combination of the fatigue effect and a technique x program interaction. Although we were able to reproduce the main effects, the changes to the design of the original experiment make it impossible to identify the causes of the discrepancies for sure. We believe that further replications closely resembling the original experiment should be conducted to improve our understanding of the phenomena under study.