pinecone/core-2020-05-10-deduplication huggingface dataset:
Language Detection and Tracking in Multilingual Documents Using Weak Estimators

Published version of an article from the book: Structural, Syntactic, and Statistical Pattern Recognition 
. The original publication is available at Spingerlink. http://dx.doi.org/DOI: 10.1007/978-3-642-14980-1_59This paper deals with the extremely complicated problem of language detection and tracking in real-life electronic (for example, in Word-of-Mouth (WoM)) applications, where various segments of the text are written in different languages. The difficulties in solving the problem are many-fold. First of all, the analyst has no knowledge of when one language stops and when the next starts. Further, the features which one uses for any one language (for example, the n-grams) will not be valid to recognize another. Finally, and most importantly, in most real-life applications, such as in WoM, the fragments of text available before the switching, are so small that it renders any meaningful classification using traditional estimation methods almost meaningless. Earlier, the authors of [10] had recommended that for a variety of problems, the use of strong estimators (i.e., estimators that converge with probability 1) is sub-optimal. In this vein, we propose to solve the current problem using novel estimators that are pertinent for non-stationary environments. The classification results which involve as many as 8 languages demonstrates that our proposed methodology is both powerful and efficient
A structured learning approach to attributed graph embedding

In this paper, we describe the use of concepts from structural and statistical pattern recognition for recovering a mapping which can be viewed as an operator on the graph attribute-set. This mapping can be used to embed graphs into spaces where tasks such as categorisation and relational matching can be effected. We depart from concepts in graph theory to introduce mappings as operators over graph spaces. This treatment leads to the recovery of a mapping based upon the graph attributes which is related to the edge-space of the graphs under study. As a result, this mapping is a linear operator over the attribute set which is associated with the graph topology. Here, we employ an optimisation approach whose cost function is related to the target function used in discrete Markov Random Field approaches. Thus, the proposed method provides a link between concepts in graph theory, statistical inference and linear operators. We illustrate the utility of the recovered embedding for shape matching and categorisation on MPEG7 CE-Shape-1 dataset. We also compare our results to those yielded by alternatives.Full Tex
Substructure Topology Preserving Simplification of Tetrahedral Meshes

International audienceInterdisciplinary efforts in modeling and simulating phenomena have led to complex multi-physics models involving different physical properties and materials in the same system. Within a 3d domain, substructures of lower dimensions appear at the interface between different materials. Correspondingly, an unstructured tetrahedral mesh used for such a simulation includes 2d and 1d substructures embedded in the vertices, edges and faces of the mesh. The simplification of such tetrahedral meshes must preserve (1) the geometry and the topology of the 3d domain, (2) the simulated data and (3) the geometry and topology of the embedded substructures. Although intensive research has been conducted on the first two goals, the third objective has received little attention. This paper focuses on the preservation of the topology of 1d and 2d substructures embedded in an unstructured tetrahedral mesh, during edge collapse simplification. We define these substructures as simplicial sub-complexes of the mesh, which is modeled as an extended simplicial complex. We derive a robust algorithm, based on combinatorial topology results, in order to determine if an edge can be collapsed without changing the topology of both the mesh and all embedded substructures. Based on this algorithm we have developed a system for simplifying scientific datasets defined on irregular tetrahedral meshes with substructures. The implementation of our system is discussed in detail. We demonstrate the power of our system with real world scientific datasets from electromagnetism simulations
The algebra of adjacency patterns: Rees matrix semigroups with reversion

We establish a surprisingly close relationship between universal Horn classes
of directed graphs and varieties generated by so-called adjacency semigroups
which are Rees matrix semigroups over the trivial group with the unary
operation of reversion. In particular, the lattice of subvarieties of the
variety generated by adjacency semigroups that are regular unary semigroups is
essentially the same as the lattice of universal Horn classes of reflexive
directed graphs. A number of examples follow, including a limit variety of
regular unary semigroups and finite unary semigroups with NP-hard variety
membership problems.Comment: 30 pages, 9 figure
Improving trading saystems using the RSI financial indicator and neural networks.

Proceedings of: 11th International Workshop on Knowledge Management and Acquisition for
Smart Systems and Services (PKAW 2010), 20 August-3 September 2010, Daegu (Korea)Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.This work is supported by the Spanish Ministry of Industry,
Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148),
SONAR2 (TSI-020100-2008-665 and GO2 (TSI-020400-2009-127). Furthermore,
this work is supported by the General Council of Superior Technological Education of
Mexico (DGEST). Additionally, this work is sponsored by the National Council of
Science and Technology (CONACYT) and the Public Education Secretary (SEP)
through PROMEP.Publicad
Specifying Reusable Components

Reusable software components need expressive specifications. This paper
outlines a rigorous foundation to model-based contracts, a method to equip
classes with strong contracts that support accurate design, implementation, and
formal verification of reusable components. Model-based contracts
conservatively extend the classic Design by Contract with a notion of model,
which underpins the precise definitions of such concepts as abstract
equivalence and specification completeness. Experiments applying model-based
contracts to libraries of data structures suggest that the method enables
accurate specification of practical software
High Throughput Data-Compression for Cloud Storage

International audienceAs data volumes processed by large-scale distributed data-intensive applications grow at high-speed, an increasing I/O pressure is put on the underlying storage service, which is responsible for data management. One particularly difficult challenge, that the storage service has to deal with, is to sustain a high I/O throughput in spite of heavy access concurrency to massive data. In order to do so, massively parallel data transfers need to be performed, which invariably lead to a high bandwidth utilization. With the emergence of cloud computing, data intensive applications become attractive for a wide public that does not have the resources to maintain expensive large scale distributed infrastructures to run such applications. In this context, minimizing the storage space and bandwidth utilization is highly relevant, as these resources are paid for according to the consumption. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on I/O throughput when under heavy access concurrency. Our solution builds on BlobSeer, a highly parallel distributed data management service specifically designed to enable reading, writing and appending huge data sequences that are fragmented and distributed at a large scale. We demonstrate the benefits of our approach by performing extensive experimentations on the Grid'5000 testbed
Feature-based Composition of Software Architectures

Acceptance rate: 19/75 (25%)International audienceIn Software Product Lines variability refers to the definition and utilization of differences between several products. Feature Diagrams (FD) are a well-known approach to express variability, and can be used to automate the derivation process. Nevertheless, this may be highly complex due to possible interactions between selected features and the artifacts realizing them. Deriving concrete products typically involves the composition of such inter-dependent software artifacts. This paper presents a feature-based composition approach to automatically derive a product architecture from a given feature configuration. The proposed approach relies on the combination of Model-Driven Engineering (MDE) and Aspect-Oriented Modeling (AOM) techniques. We introduce a metamodel to reify each feature as a high-level aspect model. Product derivation is achieved by weaving the set of aspect models corresponding to a particular feature configuration. The weaving strategy is derived from an in-depth cross-analysis of both the feature interactions and the aspect model dependencies
An ADL-Approach to Specifying and Analyzing Centralized-Mode Architectural Connection

A rigorous paradigm coordinating components is important in the design stage of large-scale software engineering. In this paper we propose a new Architecture Description Language, called ACDL, to represent the centralized-mode architectural connection in which all components are linked by a single connector. Following one usual approach to architectural description, in which component types and components are distinguished, and connectors integrate behaviors of components by specifying their coordination protocols, ACDL describes connectors in such a way that connectors are insensitive to the numbers of attached same-type components. Based on ACDL, we develop analytic techniques to facilitate the system checking of temporal properties of an architecture. In particular, our method shows to what extent one can add, delete and replace components without making the whole system lose desired temporal properties, and improves the system checking in several ways, for example enhancing the use of previous checking results to deal with new checking problems
A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg
Proximal business intelligence on the semantic web

This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to\ud
improve specific information access and transcoding but not on how the information\ud
can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology\ud
language and then re-used to provide the invisibility of pervasive access; uncovering\ud
more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and  fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO
A Privacy-Preserving Architecture for the Semantic Web Based on Tag Suppression

We propose an architecture that preserves user privacy in the semantic Web via tag suppression. In tag suppression, users may wish to tag some resources and refrain from tagging some others in order to hinder privacy attackers in their efforts to profile users’ interests. Following this strategy, our architecture helps users decide which tags should be suppressed. We describe the implementation details of the proposed architecture and provide further insight into the modeling of profiles. In addition, we present a mathematical formulation of the optimal tradeoff between privacy and tag suppression rate.Peer ReviewedPostprint (published version
New Developments in Quantum Algorithms

In this survey, we describe two recent developments in quantum algorithms.
  The first new development is a quantum algorithm for evaluating a Boolean
formula consisting of AND and OR gates of size N in time O(\sqrt{N}). This
provides quantum speedups for any problem that can be expressed via Boolean
formulas. This result can be also extended to span problems, a generalization
of Boolean formulas. This provides an optimal quantum algorithm for any Boolean
function in the black-box query model.
  The second new development is a quantum algorithm for solving systems of
linear equations. In contrast with traditional algorithms that run in time
O(N^{2.37...}) where N is the size of the system, the quantum algorithm runs in
time O(\log^c N). It outputs a quantum state describing the solution of the
system.Comment: 11 pages, 1 figure, to appear as an invited survey talk at MFCS'201
Robust computations with dynamical systems

International audienceIn this paper we discuss the computational power of Lipschitz dynamical systems which are robust to infinitesimal perturbations. Whereas the study in [1] was done only for not-so-natural systems from a classical mathematical point of view (discontinuous differential equation systems, discontinuous piecewise affine maps, or perturbed Turing machines), we prove that the results presented there can be generalized to Lipschitz and computable dynamical systems. In other words, we prove that the perturbed reachability problem (i.e. the reachability problem for systems which are subjected to infinitesimal perturbations) is co-recursively enumerable for this kind of systems. Using this result we show that if robustness to infinitesimal perturbations is also required, the reachability problem becomes decidable. This result can be interpreted in the following manner: undecidability of verification doesn't hold for Lipschitz, computable and robust systems. We also show that the perturbed reachability problem is co-r.e. complete even for C ∞ -systems
On Factor Universality in Symbolic Spaces

The study of factoring relations between subshifts or cellular automata is
central in symbolic dynamics. Besides, a notion of intrinsic universality for
cellular automata based on an operation of rescaling is receiving more and more
attention in the literature. In this paper, we propose to study the factoring
relation up to rescalings, and ask for the existence of universal objects for
that simulation relation. In classical simulations of a system S by a system T,
the simulation takes place on a specific subset of configurations of T
depending on S (this is the case for intrinsic universality). Our setting,
however, asks for every configurations of T to have a meaningful interpretation
in S. Despite this strong requirement, we show that there exists a cellular
automaton able to simulate any other in a large class containing arbitrarily
complex ones. We also consider the case of subshifts and, using arguments from
recursion theory, we give negative results about the existence of universal
objects in some classes
Iterated Regret Minimization in Game Graphs

Iterated regret minimization has been introduced recently by J.Y. Halpern and
R. Pass in classical strategic games. For many games of interest, this new
solution concept provides solutions that are judged more reasonable than
solutions offered by traditional game concepts -- such as Nash equilibrium --.
Although computing iterated regret on explicit matrix game is conceptually and
computationally easy, nothing is known about computing the iterated regret on
games whose matrices are defined implicitly using game tree, game DAG or, more
generally game graphs. In this paper, we investigate iterated regret
minimization for infinite duration two-player quantitative non-zero sum games
played on graphs.
  We consider reachability objectives that are not necessarily antagonist.
Edges are weighted by integers -- one for each player --, and the payoffs are
defined by the sum of the weights along the paths. Depending on the class of
graphs, we give either polynomial or pseudo-polynomial time algorithms to
compute a strategy that minimizes the regret for a fixed player. We finally
give algorithms to compute the strategies of the two players that minimize the
iterated regret for trees, and for graphs with strictly positive weights only.Comment: 19 pages. Bug in introductive example fixed
On Functionality of Visibly Pushdown Transducers

Visibly pushdown transducers form a subclass of pushdown transducers that
(strictly) extends finite state transducers with a stack. Like visibly pushdown
automata, the input symbols determine the stack operations. In this paper, we
prove that functionality is decidable in PSpace for visibly pushdown
transducers. The proof is done via a pumping argument: if a word with two
outputs has a sufficiently large nesting depth, there exists a nested word with
two outputs whose nesting depth is strictly smaller. The proof uses technics of
word combinatorics. As a consequence of decidability of functionality, we also
show that equivalence of functional visibly pushdown transducers is
Exptime-Complete.Comment: 20 page
The Complexity of Finding Reset Words in Finite Automata

We study several problems related to finding reset words in deterministic
finite automata. In particular, we establish that the problem of deciding
whether a shortest reset word has length k is complete for the complexity class
DP. This result answers a question posed by Volkov. For the search problems of
finding a shortest reset word and the length of a shortest reset word, we
establish membership in the complexity classes FP^NP and FP^NP[log],
respectively. Moreover, we show that both these problems are hard for
FP^NP[log]. Finally, we observe that computing a reset word of a given length
is FNP-complete.Comment: 16 pages, revised versio
Does Treewidth Help in Modal Satisfiability?

Many tractable algorithms for solving the Constraint Satisfaction Problem
(CSP) have been developed using the notion of the treewidth of some graph
derived from the input CSP instance. In particular, the incidence graph of the
CSP instance is one such graph. We introduce the notion of an incidence graph
for modal logic formulae in a certain normal form. We investigate the
parameterized complexity of modal satisfiability with the modal depth of the
formula and the treewidth of the incidence graph as parameters. For various
combinations of Euclidean, reflexive, symmetric and transitive models, we show
either that modal satisfiability is FPT, or that it is W[1]-hard. In
particular, modal satisfiability in general models is FPT, while it is
W[1]-hard in transitive models. As might be expected, modal satisfiability in
transitive and Euclidean models is FPT.Comment: Full version of the paper appearing in MFCS 2010. Change from v1:
  improved section 5 to avoid exponential blow-up in formula siz
Enumeration of the Monomials of a Polynomial and Related Complexity
  Classes

We study the problem of generating monomials of a polynomial in the context
of enumeration complexity. In this setting, the complexity measure is the delay
between two solutions and the total time. We present two new algorithms for
restricted classes of polynomials, which have a good delay and the same global
running time as the classical ones. Moreover they are simple to describe, use
little evaluation points and one of them is parallelizable. We introduce three
new complexity classes, TotalPP, IncPP and DelayPP, which are probabilistic
counterparts of the most common classes for enumeration problems, hoping that
randomization will be a tool as strong for enumeration as it is for decision.
Our interpolation algorithms proves that a lot of interesting problems are in
these classes like the enumeration of the spanning hypertrees of a 3-uniform
hypergraph.
  Finally we give a method to interpolate a degree 2 polynomials with an
acceptable (incremental) delay. We also prove that finding a specified monomial
in a degree 2 polynomial is hard unless RP = NP. It suggests that there is no
algorithm with a delay as good (polynomial) as the one we achieve for
multilinear polynomials