pinecone/core-2020-05-10-deduplication huggingface dataset:
Some sufficient conditions on an arbitrary class of stochastic processes for the existence of a predictor.

International audienceWe consider the problem of sequence prediction in a probabilistic setting. Let there be given a class C of stochastic processes (probability measures on the set of one-way infinite sequences). We are interested in the question of what are the conditions on C under which there exists a predictor (also a stochastic process) for which the predicted probabilities converge to the correct ones if any of the processes in C is chosen to generate the data. We find some sufficient conditions on C under which such a predictor exists. Some of the conditions are asymptotic in nature, while others are based on the local (truncated to first observations) behaviour of the processes. The conditions lead to constructions of the predictors. In some cases we also obtain rates of convergence that are optimal up to an additive logarithmic term
Supermartingales in Prediction with Expert Advice

This paper compares two methods of prediction with expert advice, the Aggregating Algorithm and the Defensive Forecasting, in two different settings. The first setting is traditional, with a countable number of experts and a finite number of outcomes. Surprisingly, these two methods of fundamentally different origin lead to identical procedures. In the second setting the experts can give advice conditional on the learner’s future decision. Both methods can be used in the new setting and give the same performance guarantees as in the traditional setting. However, whereas defensive forecasting can be applied directly, the AA requires substantial modifications
Using multiplicity automata to identify transducer relations from membership and equivalence queries

Multiplicity Automata are devices that implement functions from a string space to a field. Usually the real number’s field is used. From a learning point of view there exists some algorithms that are able to identify any multiplicity automaton from membership and equivalence queries. In this work we realize that those algorithms can also be used if the algebraic structure of a field is relaxed to a divisive ring structure, that is, the commutativity of the product operation is dropped. Moreover, we define an algebraic structure, which is an extension of the string monoid, that allows the identification of any transduction that can be realized by finite state machines without empty-transitions.Partially supported by the Pascal Network of Excellence
Characterizing relations between architectural views

It is commonly agreed that an architectural description (AD) consists of multiple views. Each view describes the architecture from the perspective of particular stakeholder concerns. Although views are constructed separately, they are related as they describe the same system. 
A thorough study of the literature reveals that research on relations between views is fragmented and that a comprehensive study is hampered by an absence of common terminology. This has become apparent in the discussion on inter-view relational concepts in the revision of IEEE 1471 as ISO/IEC 42010 (Systems and Software Engineering — Architectural Description). 
This paper puts forward a framework that employs a consistent terminology to characterize relations between views. The framework sheds light on the usage, scope and mechanisms for relations, and is illustrated using several representative approaches from the literature. We conclude with a reﬂection on whether the revision of ISO 42010 aligns with our ﬁndings.status: publishe
Clustering Dynamic Web Usage Data

Most classification methods are based on the assumption that data conforms to
a stationary distribution. The machine learning domain currently suffers from a
lack of classification techniques that are able to detect the occurrence of a
change in the underlying data distribution. Ignoring possible changes in the
underlying concept, also known as concept drift, may degrade the performance of
the classification model. Often these changes make the model inconsistent and
regular updatings become necessary. Taking the temporal dimension into account
during the analysis of Web usage data is a necessity, since the way a site is
visited may indeed evolve due to modifications in the structure and content of
the site, or even due to changes in the behavior of certain user groups. One
solution to this problem, proposed in this article, is to update models using
summaries obtained by means of an evolutionary approach based on an intelligent
clustering approach. We carry out various clustering strategies that are
applied on time sub-periods. To validate our approach we apply two external
evaluation criteria which compare different partitions from the same data set.
Our experiments show that the proposed approach is efficient to detect the
occurrence of changes
Hybrid behavioural-based multi-objective space trajectory optimization

In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combinedwith the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design
Different goals in multiscale simulations and how to reach them

In this paper we sum up our works on multiscale programs, mainly simulations.
We first start with describing what multiscaling is about, how it helps
perceiving signal from a background noise in a ?ow of data for example, for a
direct perception by a user or for a further use by another program. We then
give three examples of multiscale techniques we used in the past, maintaining a
summary, using an environmental marker introducing an history in the data and
finally using a knowledge on the behavior of the different scales to really
handle them at the same time
Invariant Manifolds of Complex Systems

The aim of this work is to establish the existence of invariant manifolds in
complex systems. Considering trajectory curves integral of multiple time scales
dynamical systems of dimension two and three (predator-prey models, neuronal
bursting models) it is shown that there exists in the phase space a curve
(resp. a surface) which is invariant with respect to the flow of such systems.
These invariant manifolds are playing a very important role in the stability of
complex systems in the sense that they are "restoring" the determinism of
trajectory curves.Comment: arXiv admin note: text overlap with arXiv:1408.171
Use of multi-objective evolutionary algorithms in extrusion scale-up

Extrusion scale-up consists in ensuring identical thermo-mechanical environments in machines of different dimensions, but processing the same material. Given a reference extruder with a certain geometry and operating point, the aim is to define the geometry and operating conditions of a target extruder (of a different magnitude), in order to subject the material being processed to the same flow and heat transfer conditions, thus yielding products with the same characteristics. Scale-up is widely used in industry and academia, for example to extrapolate the results obtained from studies performed in laboratorial machines to the production plant. Since existing scale-up rules are very crude, as they consider a single performance measure and produce unsatisfactory results, this work approaches scale-up as a multi-criteria optimization problem, which seeks to define the geometry/operating conditions of the target extruder that minimize the differences between the values of the criteria for the reference and target extruders. Some case studies are discussed in order to validate the concept
Diffusive spin transport

Information to be stored and transported requires physical carriers. The
quantum bit of information (qubit) can for instance be realised as the spin 1/2
degree of freedom of a massive particle like an electron or as the spin 1
polarisation of a massless photon. In this lecture, I first use irreducible
representations of the rotation group to characterise the spin dynamics in a
least redundant manner. Specifically, I describe the decoherence dynamics of an
arbitrary spin S coupled to a randomly fluctuating magnetic field in the
Liouville space formalism. Secondly, I discuss the diffusive dynamics of the
particle's position in space due to the presence of randomly placed impurities.
Combining these two dynamics yields a coherent, unified picture of diffusive
spin transport, as applicable to mesoscopic electronic devices or photons
propagating in cold atomic clouds.Comment: Lecture notes, published in A. Buchleitner, C. Viviescas, and M.
  Tiersch (Eds.), "Entanglement and Decoherence. Foundations and Modern
  Trends", Lecture Notes in Physics 768, Springer, Berlin (2009
A formal soundness proof of region-based memory management for object-oriented paradigm.

Region-based memory management has been proposed as a viable alternative to garbage collection for real-time applications and embedded software. In our previous work we have developed a region type inference algorithm that provides an automatic compile-time region-based memory management for object-oriented paradigm. In this work we present a formal soundness proof of the region type system that is the target of our region inference. More precisely, we prove that the object-oriented programs accepted by our region type system achieve region-based memory management in a safe way. That means, the regions follow a stack-of-regions discipline and regions deallocation never create dangling references in the store and on the program stack. Our contribution is to provide a simple syntactic proof that is based on induction and follows the standard steps of a type safety proof. In contrast the previous safety proofs provided for other region type systems employ quite elaborate techniques
Improving microaggregation for complex record anonymization

Microaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least k original records and replaces the records in each cluster with the centroid of the cluster. Usually, when records are complex, i.e., the number of attributes of the data set is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. In this way, the information loss when collapsing\ud
several values to the centroid of their group is reduced, at the cost of losing the k-anonymity property when at least two attributes of different blocks are known by the intruder.\ud
In this work, we present a new microaggregation method called One dimension microaggregation (Mic1D − κ). This method gathers all the values of the data set into a single sorted vector, independently of the\ud
attribute they belong to. Then, it microaggregates all the mixed values\ud
together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.Peer ReviewedPostprint (published version
A multi-measure nearest neighbor algorithm for time series classification

In this paper, we have evaluated some techniques for the time series classification problem. Many distance measures have been proposed as an alternative to the Euclidean Distance in the Nearest Neighbor Classifier. To verify the assumption that the combination of various similarity measures may produce a more accurate classifier, we have proposed an algorithm to combine several measures based on weights. We have carried out a set of experiments to verify the hypothesis that the new algorithm is better than the classical ones. Our results show an improvement over the well-established Nearest-Neighbor with DTW (Dynamic Time Warping), but in general, they were obtained combining few measures in each problem used in the experimental evaluation
Mixing greedy and evolutive approaches to improve pursuit strategies

The prey-predator pursuit problem is a generic multi-agent testbed referenced many times in literature. Algorithms and conclusions obtained in this domain can be extended and applied to many particular
problems. In first place, greedy algorithms seem to do the job. But when concurrence problems arise, agent communication and coordination is needed to get a reasonable solution. It is quite popular to face these
issues directly with non-supervised learning algorithms to train prey and predators. However, results got by most of these approaches still leave a great margin of improvement which should be exploited.
In this paper we propose to start from a greedy strategy and extend and improve it by adding communication and machine learning. In this proposal, predator agents get a previous movement decision by using a greedy approach. Then, they focus on learning how to coordinate their own pre-decisions with the ones taken by other surrounding agents. Finally, they get a final decission trying to optimize their chase of the prey without colliding between them. For the learning step, a neuroevolution approach is used. The final results show improvements and leave room for open discussion
A study of schedule robustness for job shop with uncertainty

11th Ibero-American Conference on AI, Lisbon, PortugalLa publicación final está disponible en Springer vía  http://dx.doi.org/10.1007/978-3-540-88309-8_4We consider a job shop problem with uncertain processing times modelled as triangular fuzzy numbers and propose a methodology to study solution robustness with respect to different perturbations in the durations. This methodology is applied to obtain experimental results for several problem instances, using a hybrid genetic algorithm that minimises the expected makespan. We conclude that taking into account the uncertainty information provided by fuzzy numbers produces proactive solutions, coping well with posterior changes in processing time
Genetic programming for predicting protein networks

Proceeding of: 11th Ibero-American Conference on AI (IBERAMIA 2008), Lisbon, Portugal, 14-17 Octubre 2008One of the definitely unsolved main problems in molecular biology is the protein-protein functional association prediction problem. Genetic Programming (GP) is applied to this domain. GP evolves an expression, equivalent to a binary classifier, which predicts if a given pair of proteins interacts. We take advantages of GP flexibility, particularly, the possibility of defining new operations. In this paper, the missing values problem benefits from the definition of if-unknown, a new operation which is more appropriate to the domain data semantics. Besides, in order to improve the solution size and the computational time, we use the Tarpeian method which controls the bloat effect of GP. According to the obtained results, we have verified the feasibility of using GP in this domain, and the enhancement in the search efficiency and interpretability of solutions due to the Tarpeian method.Publicad
Termination-insensitive noninterference leaks more than just a bit

Current tools for analysing information flow in programs build upon ideas going back to Denning's work from the 70's. These systems enforce an imperfect notion of information flow which has become known as termination-insensitive noninterference. Under this version of noninterference, information leaks are permitted if they are transmitted purely by the program's termination behaviour (i.e., whether it terminates or not). This imperfection is the price to pay for having a security condition which is relatively liberal (e.g. allowing while-loops whose termination may depend on the value of a secret) and easy to check. But what is the price exactly? We argue that, in the presence of output, the price is higher than the “one bit” often claimed informally in the literature, and effectively such programs can leak all of their secrets. In this paper we develop a definition of termination-insensitive noninterference suitable for reasoning about programs with outputs. We show that the definition generalises “batch-job” style definitions from the literature and that it is indeed satisfied by a Denning-style program analysis with output. Although more than a bit of information can be leaked by programs satisfying this condition, we show that the best an attacker can do is a brute-force attack, which means that the attacker cannot reliably (in a technical sense) learn the secret in polynomial time in the size of the secret. If we further assume that secrets are uniformly distributed, we show that the advantage the attacker gains when guessing the secret after observing a polynomial amount of output is negligible in the size of the secret
The Unlikeability of a Cuddly Toy Interface: An Experimental Study of Preschoolers' Likeability and Usability of a 3D Game Played with a Cuddly Toy Versus a Keyboard

We report on a comparative study of the likeability and usability of a cuddly toy inter-face versus a keyboard interface. We put 35 preschoolers to the test and asked them to play a 3D game via a keyboard and via a cuddly toy interface. Afterwards the usability (user-friendliness) and the likeability (joy-of-use) of both interfaces were assessed. Our results indicate that the cuddly toy interface was neither more likeable nor more usable than the keyboard interface. Based on additional qualitative data, we argument that the causes might be fatigue, a lack of meaningful use of gestures, and the occurrence of 'satisficing'.status: publishe
Analysing chromatographic data using data mining to monitor petroleum content in water

Chromatography is an important analytical technique that has widespread use in environmental applications. A typical application is the monitoring of water samples to determine if they contain petroleum. These tests are mandated in many countries to enable environmental agencies to determine if tanks used to store petrol are leaking into local water systems.
Chromatographic techniques, typically using gas or liquid chromatography coupled with mass spectrometry, allow an analyst to detect a vast array of compounds—potentially in the order of thousands. Accurate analysis relies heavily on the skills of a limited pool of experienced analysts utilising semi-automatic techniques to analyse these datasets—making the outcomes subjective.
The focus of current laboratory data analysis systems has been on refinements of existing approaches. The work described here represents a paradigm shift achieved through applying data mining techniques to tackle the problem. These techniques are compelling because the efficacy of preprocessing methods, which are essential in this application area, can be objectively evaluated. This paper presents preliminary results using a data mining framework to predict the concentrations of petroleum compounds in water samples. Experiments demonstrate that the framework can be used to produce models of sufficient accuracy—measured in terms of root mean squared error and correlation coefficients—to offer the potential for significantly reducing the time spent by analysts on this task
Passive Testing of Timed Systems

This paper presents a methodology to perform passive testing based on invariants for systems that present temporal restrictions. Invariants represent the most\ud
relevant expected properties of the implementation under test. Intuitively, an invariant expresses the fact that each time the implementation under test performs a given\ud
sequence of actions, then it must exhibit a behavior in a lapse of time reflected in the invariant. In particular, the algorithm presented in this paper are fully implemente
A New Efficient Threshold Ring Signature Scheme Based on Coding Theory

International audience—Ring signatures were introduced by Rivest, Shamir and Tauman in 2001 [32]. These sig-natures allow a signer to anonymously authenticate a message on behalf of a group of his choice. This concept was then extended by Bresson, Stern and Szydlo into -out-of-(threshold) ring signatures in 2002 [9]. We propose in this article a generalization of Stern's code based identification (and signature) scheme [36] to design a practical -out-of-threshold ring signature scheme. The size of the resulting signatures is in () and does not depend on , contrary to most of the existing protocols. Our scheme is existentially unforge-able under a chosen message attack in the random oracle model assuming the hardness of the minimum distance problem, is unconditionally source hiding, has a very short public key and has an overall complexity in (). This protocol is the first efficient code-based ring signature scheme and the first code-based thresh-old ring signature scheme. Moreover it has a better complexity than number-theory based schemes which have a complexity in (). This paper is an extended version of [2] with complete proofs and definitions
Ensemble-trees: Leveraging ensemble power inside decision trees

Decision trees are among the most effective and interpretable classification algorithms while ensembles techniques have been proven to alleviate problems regarding over-fitting and variance. On the other hand, decision trees show a tendency to lack stability given small changes in the data, whereas interpreting an ensemble of trees is challenging to comprehend. We propose the technique of Ensemble-Trees which uses ensembles of rules within the test nodes to reduce over-fitting and variance effects. Validating the technique experimentally, we find that improvements in performance compared to ensembles of pruned trees exist, but also that the technique does less to reduce structural instability than could be expected.status: publishe
A comparison between neural network methods for learning aggregate functions

In various application domains, data can be represented as bags of vectors instead of single vectors. Learning aggregate functions from such bags is a challenging problem. In this paper, a number of simple neural network approaches and a combined approach based on cascade-correlation are examined in order to handle this kind of data. Adapted feedforward networks, recurrent networks and networks with special aggregation units integrated in the network can all be used to construct networks that are capable of learning aggregate function. A combination of these three approaches is possible by using cascade-correlation, creating a method that automatically chooses the best of these options. Results on artificial and multi-instance data sets are reported, allowing a comparison between the different approaches.status: publishe
Active learning for high throughput screening

This paper received the Carl Smith Student Award.An important task in many scientific and engineering disciplines is to set up experiments with the goal of finding the best  instances (substances, compositions, designs) as evaluated on an unknown target function using limited resources. We study this problem using machine learning principles, and introduce the novel task of active k-optimization.
The problem consists of approximating the k best instances with regard to an unknown function and the learner is active, that is, it can present a limited number of instances to an oracle for obtaining the target value. We also develop an algorithm based on Gaussian processes for tackling active k-optimization, and evaluate it on a challenging set of tasks related to structure-activity relationship prediction.status: publishe