pinecone/core-2020-05-10-deduplication huggingface dataset:
Automatic rule generation based on genetic programming for event correlation

The widespread adoption of autonomous intrusion detection technology is overwhelming current frameworks for network security management. Modern intrusion detection systems (IDSs) and intelligent agents are the most mentioned in literature and news, although other risks such as broad attacks (e.g. very widely spread in a distributed fashion like botnets), and their consequences on incident response management cannot be overlooked. Event correlation becomes then essential. Basically, security event correlation pulls together detection, prevention and reaction tasks by means of consolidating huge amounts of event data. Providing adaptation to unknown distributed attacks is a major requirement as well as their automatic identification. This positioning paper poses an optimization challenge in the design of such correlation engine and a number of directions for research. We present a novel approach for automatic generation of security event correlation rules based on Genetic Programming which has been already used at sensor level
Multiple Factorizations of Bivariate Linear Partial Differential
  Operators

We study the case when a bivariate Linear Partial Differential Operator
(LPDO) of orders three or four has several different factorizations.
  We prove that a third-order bivariate LPDO has a first-order left and right
factors such that their symbols are co-prime if and only if the operator has a
factorization into three factors, the left one of which is exactly the initial
left factor and the right one is exactly the initial right factor. We show that
the condition that the symbols of the initial left and right factors are
co-prime is essential, and that the analogous statement "as it is" is not true
for LPDOs of order four.
  Then we consider completely reducible LPDOs, which are defined as an
intersection of principal ideals. Such operators may also be required to have
several different factorizations. Considering all possible cases, we ruled out
some of them from the consideration due to the first result of the paper. The
explicit formulae for the sufficient conditions for the complete reducibility
of an LPDO were found also
CDs have fingerprints too

We introduce a new technique for extracting unique fingerprints from identical CDs. The proposed technique takes advantage of manufacturing variability found in the length of the CD lands and pits. Although the variability measured is on the order of 20 nm, the technique does not require the use of microscopes or any advanced equipment. Instead, we show that the electrical signal produced by the photodetector inside the CD reader is sufficient to measure the desired variability. We investigate the new technique by analyzing data collected from 100 identical CDs and show how to extract a unique fingerprint for each CD. Furthermore, we introduce a technique for utilizing fuzzy extractors over the Lee metric without much change to the standard code offset construction. Finally, we identify specific parameters and a code construction to realize the proposed fuzzy extractor and convert the derived fingerprints into 128-bit cryptographic keys. © 2009 Springer
A community-based platform for machine learning experimentation

We demonstrate the practical uses of a community-based platform for the sharing and in-depth investigation of the thousands of machine learning experiments executed every day. It is aimed at researchers and practitioners of data mining techniques, and is publicly available at http://expdb.cs.kuleuven.be. The system offers standards and API’s for sharing experimental results, extensive  querying capabilities of the gathered results and allows easy integration in existing data mining toolboxes. We believe such a system may speed up scientific discovery and enhance the scientific rigor of machine learning research.status: publishe
Mining graph evolution rules

US patent "Network Graph Evolution Rule Generation" filed by Yahoo!
(The four authors listed as inventors)
acceptance rate = 25%In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that describe the evolution of large networks over time, at a local level. Given a sequence of snapshots of an evolving graph, we aim at discovering rules describing the local changes occurring in it. Adopting a definition of support based on minimum image we study the problem of extracting patterns whose frequency is larger than a minimum support threshold. Then, similar to the classical association rules framework, we derive graph-evolution rules from frequent patterns that satisfy a given minimum confidence constraint. We discuss merits and limits of alternative definitions of support and confidence, justifying the chosen framework. To evaluate our approach we devise GERM (Graph Evolution Rule Miner), an algorithm to mine all graph-evolution rules whose support and confidence are greater than given thresholds. The algorithm is applied to analyze four large real-world networks (i.e., two social networks, and two co-authorship networks from bibliographic data), using different time granularities. Our extensive experimentation confirms the feasibility and utility of the presented approach. It further shows that different kinds of networks exhibit different evolution rules, suggesting the usage of these local patterns to globally discriminate different kind of networks.status: publishe
Computing equilibrium points of genetic regulatory networks

Computing equilibrium points of genetic regulatory networks is a problem of primary importance for numerous investigations in these systems. This paper addresses this problem for differential equation models, with the regulation function expressed in a general form which includes both SUM form and PROD form for saturation functions of any type. Specifically, a recursive algorithm is proposed, which provides at each recursion a region guaranteed to contain all equilibrium points. This region progressively shrinks, and asymptotically converges to the sought set of equilibrium points. Moreover, the proposed algorithm can also allow one to delimit and find limit cycles. Some numerical examples are reported to illustrate and validate the proposed algorithm, including examples where standard mathematical tools fail to compute the sought equilibrium points. © 2009 Springer Berlin Heidelberg.postprin
Rule-based modelling and model perturbations

International audienceRule-based modelling has already proved successful for taming the combinatorial complexity, typical of cellular signalling, caused by the combination of physical protein-protein interactions and modifications that may generate astronomical numbers of unique molecular species. However, traditional rule-based approaches, based on an unstructured space of agents and rules, remain susceptible to other combinatorial explosions such as those caused by mutated agents, that share most but not all rules with their wild-type counterparts, and drugs, which must be distinguished from physiological ligands. We present here a syntactic extension of Kappa, an established rule-based modelling platform, that enables the expression of a structured space of agents and rules that simultaneously allows us to comfortably express mutated agents, splice variants, families of related proteins and drug interventions. This also enables a mode of model construction where, starting from our current consensus model, we attempt to reproduce \emph{in numero} the mutational and more generally perturbational analyses that were used in the process of inferring those pathways---and in the light of this, we may need to upgrade our consensus model
Building Real-World Ad-Hoc Networks to Support Mobile Collaborative Applications: Lessons Learned

Mobile collaboration is required in several work scenarios, i.e.\ud
education, healthcare, business and disaster relief. The features and capabilities\ud
of the communication infrastructure used by mobile collaborative applications\ud
will influence the type of coordination and collaboration that can be supported\ud
in real work scenarios. Developers of these applications are typically unaware\ud
of the constraints the communication infrastructure imposes on the\ud
collaborative system. Therefore, this paper presents an experimental study of\ud
how ad-hoc networks can effectively support mobile collaborative work. The\ud
article analyzes several networking issues and it determines how they influence\ud
the collaborative work. The paper also presents the lessons learned and it\ud
provides recommendations to deal with the networking issues intrinsic to adhoc\ud
networks.Postprint (published version
A Simple Distributed Conflict-Driven Answer Set Solver

We propose an approach to distributed Answer Set Solving based on  Message Passing. Our approach aims at taking advantage of modern ASP solvers  rather than proposing a genuine yet involved parallel ASP solver. To this end, we  rely upon a simple master-worker architecture in which each worker amounts to  an off-the-shelf ASP solver augmented with a separate communication module  being only lightly connected to the actual solver. The overall communication is  driven by the workers' communication modules, which asynchronously exchange  messages with the master. We have implemented our approach and report upon  an empirical study demonstrating its computational impact.Full Tex
The second answer set programming competition

This paper reports on the Second Answer Set Programming System Competition. The competitions in areas of Satisfiability checking, Pseudo-Boolean constraint solving and Quantified Boolean Formula evaluation have proven to be a strong driving force for a community to develop better performing systems. Following this experience, the Answer Set Programming competition series was set up in 2007, and 
ran as part of the International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR). This second competition, held in conjunction with LPNMR 2009, differed from the first one in two important ways. First, while the original competition was restricted to systems designed for the answer set programming language, the sequel was open to systems designed for other modeling languages, as well. Consequently, among the contestants of the second competition were a CLP(FD) team and three model generation systems for (extensions of) classical logic. Second, this latest competition covered not only satisfiability problems but also optimization ones. We present and discuss the set-up and the results of the competition.status: publishe
Modeling Adaptation Effects in fMRI Analysis

The standard general linear model (GLM) for rapid event-related fMRI design protocols typically ignores reduction in hemodynamic responses in successive stimuli in a train due to incomplete recovery from the preceding stimuli. To capture this adaptation effect, we incorporate a region-specific adaptation model into GLM. The model quantifies the rate of adaptation across brain regions, which is of interest in neuroscience. Empirical evaluation of the proposed model demonstrates its potential to improve detection sensitivity. In the fMRI experiments using visual and auditory stimuli, we observed that the adaptation effect is significantly stronger in the visual area than in the auditory area, suggesting that we must account for this effect to avoid bias in fMRI detection.available in PMC 2013 June 30National Institutes of Health (U.S.) (NIH R01 NS048279)National Institutes of Health (U.S.) (NIH R01 EB006385)National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIH NIBIB NAMIC U54-EB005149)National Center for Research Resources (U.S.) (NIH NCRR P41-RR14075)National Science Foundation (U.S.) (NSF CAREER Award 0642971)Sigrid Jusélius FoundationAcademy of FinlandFinnish Cultural FoundationUnited States. Public Health Service (PHS training grant DA022759-03
Joint Segmentation of Image Ensembles via Latent Atlases

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method by segmenting 50 brain MR volumes. Segmentation accuracy for cortical and subcortical structures approaches the quality of state-of-the-art atlas-based segmentation results, suggesting that the latent atlas method is a reasonable alternative when existing atlases are not compatible with the data to be processed.12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part INational Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41-RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NCRR mBIRN U24-RR021382)National Science Foundation (U.S.) (CAREER Grant 0642971
Towards interactive planning of coil embolization in brain aneurysms

The original publication is available at www.springerlink.comInternational audienceMany vascular pathologies can now be treated in a minimally invasive way thanks to interventional radiology. Instead of open surgery, it allows to reach the lesion of the arteries with therapeutic devices through a catheter. As a particular case, intracranial aneurysms are treated by ﬁlling the localized widening of the artery with a set of coils to prevent a rupture due to the weakened arterial wall. Considering the location of the lesion, close to the brain, and its very small size, the procedure requires a combination of careful planning and excellent technical skills. An interactive and reliable simulation, adapted to the patient anatomy, would be an interesting tool for helping the interventional neuroradiologist plan and rehearse a coil embolization procedure. This paper describes an original method to perform interactive simulations of coil embolization and proposes a clinical metric to quantitatively measure how the ﬁrst coil ﬁlls the aneurysm. The simulation relies on an accurate reconstruction of the aneurysm anatomy and a real-time model of the coil for which sliding and friction contacts are taken into account. Simulation results are compared to real embolization procedure and exhibit good adequacy
Asymmetric Image-Template Registration

A natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. In this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates.Authors Manuscript received: 2010 May 4. 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part INAMIC (NIH NIBIB NAMIC U54-EB005149)NAC (NIH NCRR NAC P41- RR13218)mBIRN (NIH NCRR mBIRN U24-RR021382)NIH NINDS (R01-NS051826 Grant)National Science Foundation (U.S.) (CAREER Grant 0642971)NIBIB (R01 EB001550)NIBIB (R01EB006758)NCRR (R01 RR16594-01A1)NCRR (P41-RR14075)NINDS (R01 NS052585-01)Singapore. Agency for Science, Technology and Researc
Supervised Nonparametric Image Parcellation

Segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. Summarizing the data alleviates the computational burden at the expense of possibly losing valuable information on inter-subject variability. This paper presents a novel framework for Supervised Nonparametric Image Parcellation (SNIP). SNIP models the intensity and label images as samples of a joint distribution estimated from the training data in a non-parametric fashion. By capitalizing on recently developed fast and robust pairwise image alignment tools, SNIP employs the entire training data to segment a new image via Expectation Maximization. The use of multiple registrations increases robustness to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with manual labels for the white matter, cortex and subcortical structures. SNIP yields better segmentation than state-of-the-art algorithms in multiple regions of interest.Author Manuscript 2010 August 25. 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part IINAMIC (NIHNIBIBNAMICU54-EB005149)NAC (NIHNCRRNACP41-RR13218)mBIRN (NIHNCRRmBIRNU24-RR021382)NIH NINDS (Grant R01-NS051826)National Science Foundation (U.S.) (CAREER Grant 0642971)NCRR (P41-RR14075)NCRR (R01 RR16594-01A1)NIBIB (R01 EB001550)NIBIB (R01EB006758)NINDS (R01 NS052585-01)Mind Research InstituteEllison Medical FoundationSingapore. Agency for Science, Technology and Researc
Setting priors and enforcing constraints on matches for nonlinear registration of meshes.

International audienceWe show that a simple probabilistic modelling of the registration problem for surfaces allows to solve it by using standard clustering techniques. In this framework, point-to-point correspondences are hypothesized between the two free-form surfaces, and we show how to specify priors and to enforce global constraints on these matches with only minor changes to the optimisation algorithm. The purpose of these two modifications is to increase its capture range and to obtain more realistic geometrical transformations between the surfaces. We conclude with some validation experiments and results on synthetic and real data
Graph-based pancreatic islet segmentation for early type 2 diabetes mellitus on histopathological tissue

It is estimated that in 2010 more than 220 million people will be affected by type 2 diabetes mellitus (T2DM). Early evidence indicates that specific markers for alpha and beta cells in pancreatic islets of Langerhans can be used for early T2DM diagnosis. Currently, the analysis of such histological tissues is manually performed by trained pathologists using a light microscope. To objectify classification results and to reduce the processing time of histological tissues, an automated computational pathology framework for segmentation of pancreatic islets from histopathological fluorescence images is proposed. Due to high variability in the staining intensities for alpha and beta cells, classical medical imaging approaches fail in this scenario. \ud
The main contribution of this paper consists of a novel graph-based segmentation approach based on cell nuclei detection with randomized tree ensembles. The algorithm is trained via a cross validation scheme on a ground truth set of islet images manually segmented by 4 expert pathologists. Test errors obtained from the cross validation procedure demonstrate that the graph-based computational pathology analysis proposed is performing competitively to the expert pathologists while outperforming a baseline morphological approach
Statistical instance-based ensemble pruning for multi-class problems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04274-4_10Proceedings of 19th International Conference, Limassol, Cyprus, September 14-17, 2009.Recent research has shown that the provisional count of votes of an ensemble of classifiers can be used to estimate the probability that the final ensemble prediction coincides with the current majority class. For a given instance, querying can be stopped when this probability is above a specified threshold. This instance-based ensemble pruning procedure can be efficiently implemented if these probabilities are pre-computed and stored in a lookup table. However, the size of the table and the cost of computing the probabilities grow very rapidly with the number of classes of the problem. In this article we introduce a number of computational optimizations that can be used to make the construction of the lookup table feasible. As a result, the application of instance-based ensemble pruning is extended to multi-class problems. Experiments in several UCI multi-class problems show that instance-based pruning speeds-up classification by a factor between 2 and 10 without any significant variation in the prediction accuracy of the ensemble
Fuzzy cluster validation using the partition negentropy criterion

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04277-5_24Proceedings of the 19th International Conference, Limassol, Cyprus, September 14-17, 2009We introduce the Partition Negentropy Criterion (PNC) for cluster validation. It is a cluster validity index that rewards the average normality of the clusters, measured by means of the negentropy, and penalizes the overlap, measured by the partition entropy. The PNC is aimed at finding well separated clusters whose shape is approximately Gaussian. We use the new index to validate fuzzy partitions in a set of synthetic clustering problems, and compare the results to those obtained by the AIC, BIC and ICL criteria. The partitions are obtained by fitting a Gaussian Mixture Model to the data using the EM algorithm. We show that, when the real clusters are normally distributed, all the criteria are able to correctly assess the number of components, with AIC and BIC
allowing a higher cluster overlap. However, when the real cluster distributions are not Gaussian (i.e. the distribution assumed by the mixture model) the PNC outperforms the other indices, being able to correctly
evaluate the number of clusters while the other criteria (specially AIC and BIC) tend to overestimate it.This work has been partially supported with funds from
MEC BFU2006-07902/BFI, CAM S-SEM-0255-2006 and CAM/UAM project CCG08-UAM/TIC-442
Noiseless Independent Factor Analysis with mixing constraints in a semi-supervised framework. Application to railway device fault diagnosis.

International audienceIn Independent Factor Analysis (IFA), latent components (or sources) are recovered from only their linear observed mixtures. Both the mixing process and the source densities (that are assumed to be gener- ated according to mixtures of Gaussians) are learned from observed data. This paper investigates the possibility of estimating the IFA model in its noiseless setting when two kinds of prior information are incorporated: constraints on the mixing process and partial knowledge on the cluster membership of some examples. Semi-supervised or partially supervised learning frameworks can thus be handled. These two proposals have been initially motivated by a real-world application that concerns fault diag- nosis of a railway device. Results from this application are provided to demonstrate the ability of our approach to enhance estimation accuracy and remove indeterminacy commonly encountered in unsupervised IFA such as source permutations