pinecone/core-2020-05-10-deduplication huggingface dataset:
Time-Table Disjunctive Reasoning for the Cumulative Constraint

Scheduling has been a successful domain of application for constraint programming since its beginnings. The cumulative constraint – which enforces the usage of a limited resource by several tasks – is one of the core components that are surely responsible of this success. Unfortunately, ensuring bound-consistency for the cumulative constraint is already NP-Hard. Therefore, several relaxations were proposed to reduce domains in polynomial time such as Time-Tabling, Edge-Finding, Energetic Reasoning, and Not-First-Not-Last. Recently, Vilim introduced the Time-Table Edge-Finding reasoning which strengthens Edge-Finding by considering the time-table of the resource. We pursue the idea of exploiting the time-table to detect disjunctive pairs of tasks dynamically during the search. This new type of filtering – which we call time-table disjunctive reasoning – is not dominated by existing filtering rules. We propose a simple algorithm that implements this filtering rule with a (n2) time complexity (where n is the number of tasks) without relying on complex data structures. Our results on well known benchmarks highlight that using this new algorithm can substantially improve the solving process for some instances and only adds a marginally low computation overhead for the other ones
The Smart Table Constraint

Table Constraints are very useful for modeling combinatorial problems in Constraint Programming (CP). They are a universal mechanism for representing constraints, but unfortunately the size of their tables can grow exponentially with their arities. In this paper, we propose to authorize entries in tables to contain simple arithmetic constraints, replacing classical tuples of values by so-called smart tuples. Smart table constraints can thus be viewed as logical combinations of those simple arithmetic constraints. This new form of tuples allows us to encode compactly many constraints, including a dozen of well-known global constraints. We show that, under a very reasonable assumption about the acyclicity of smart tuples, a Generalized Arc Consistency algorithm of low time complexity can be devised. Our experimental results demonstrate that the smart table constraint is a highly promising general purpose tool for CP
Constraint-based sequence mining using constraint programming

The goal of constraint-based sequence mining is to find sequences
of symbols that are included in a large number of input sequences
and that satisfy some constraints specified by the user. Many
constraints have been proposed in the literature, but a general
framework is still missing. We investigate the use of constraint
programming as general framework for this task.  We first
identify four categories of constraints that are applicable to
sequence mining. We then propose two constraint programming
formulations. The first formulation introduces a new global
constraint called exists-embedding.

This formulation is the most efficient but does not support one
type of constraint. To support such constraints, we develop a
second formulation that is more general but incurs more
overhead. Both formulations can use the projected database
technique used in specialised algorithms.  Experiments
demonstrate the flexibility towards constraint-based settings and
compare the approach to existing methods.status: publishe
Constraint-based local search for Golomb rulers

This paper presents a constraint-based local search algorithm to find an optimal Golomb ruler of a specified order. While the state-of-the-art search algorithms for Golomb rulers hybridise a range of sophisticated techniques, our algorithm relies on simple tabu meta-heuristics and constraint-driven variable selection heuristics. Given a reasonable time limit, our algorithm effectively finds 16-mark optimal rulers with success rate 60 % and 17-mark rulers with 6 % near-optimality.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Understanding the Potential of Propagators

Propagation is at the very core of Constraint Programming (CP): it can provide significant performance boosts as long as the search space reduction is not outweighed by the cost for running the propagators. A lot of research effort in the CP community is directed toward improving this trade-off, which for a given type of filtering amounts to reducing the computation cost. This is done chiefly by 1) devising more efficient algorithms or by 2) using on-line control policies to limit the propagator activations. In both cases, obtaining improvements is a long and demanding process with uncertain outcome. We propose a method to assess the potential gain of both approaches before actually starting the endeavor, providing the community with a tool to best direct the research efforts. Our approach is based on instrumenting the constraint solver to collect statistics, and we rely on replaying search trees to obtain more realistic assessments. The overall approach is easy to setup and is showcased on the Energetic Reasoning (ER) and the Revisited Cardinality Reasoning for BinPacking (RCRB) propagators
Mining Uncertain Sequential Patterns in Iterative MapReduce

This paper proposes a sequential pattern mining (SPM) algorithm in large scale uncertain databases. Uncertain sequence databases are widely used to model inaccurate or imprecise timestamped data in many real applications, where traditional SPM algorithms are inapplicable because of data uncertainty and scalability. In this paper, we develop an efficient approach to manage data uncertainty in SPM and design an iterative MapReduce framework to execute the uncertain SPM algorithm in parallel. We conduct extensive experiments in both synthetic and real uncertain datasets. And the experimental results prove that our algorithm is efficient and scalable
Towards Efficient Sequential Pattern Mining in Temporal Uncertain Databases

Uncertain sequence databases are widely used to model data with inaccurate or imprecise timestamps in many real world applications. In this paper, we use uniform distributions to model uncertain timestamps and adopt possible world semantics to interpret temporal uncertain database. We design an incremental approach to manage temporal uncertainty efficiently, which is integrated into the classic pattern-growth SPM algorithm to mine uncertain sequential patterns. Extensive experiments prove that our algorithm performs well in both efficiency and scalability
Graph Based Relational Features for Collective Classification

Statistical Relational Learning (SRL) methods have shown that classification
accuracy can be improved by integrating relations between samples. Techniques
such as iterative classification or relaxation labeling achieve this by
propagating information between related samples during the inference process.
When only a few samples are labeled and connections between samples are sparse,
collective inference methods have shown large improvements over standard
feature-based ML methods. However, in contrast to feature based ML, collective
inference methods require complex inference procedures and often depend on the
strong assumption of label consistency among related samples. In this paper, we
introduce new relational features for standard ML methods by extracting
information from direct and indirect relations. We show empirically on three
standard benchmark datasets that our relational features yield results
comparable to collective inference methods. Finally we show that our proposal
outperforms these methods when additional information is available.Comment: Pacific-Asia Conference on Knowledge Discovery and Data Minin
CPT+: Decreasing the time/space complexity of the Compact Prediction Tree

Accepted as "REGULAR PAPER WITH LONG PRESENTATION" 27 of 405 submisisons accepted in this category.Predicting next items of sequences of symbols has many applications in a wide range of domains. Several sequence prediction models have been proposed such as DG, All-k-order markov and PPM. Recently, a model named Compact Prediction Tree (CPT) has been proposed. It relies on a tree structure and a more complex prediction algorithm to offer considerably more accurate predictions than many state-of-the-art prediction models. However, an important limitation of CPT is its high time and space complexity. In this article, we address this issue by proposing three novel strategies to reduce CPT’s size and prediction time, and increase its accuracy. Experimental results on seven real life datasets show that the resulting model (CPT+) is up to 98 times more compact and 4.5 times faster than CPT, and has the best overall accuracy when compared to six state-of-the-art models from the literature: All-K-order Markov, CPT, DG, Lz78, PPM and TDAG.Supported by a NSERC grant from the Government of Canada.Peer-reviewedPost-printThe 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Ho Chi Minh Cit
Coupling multiple views of relations for recommendation

© Springer International Publishing Switzerland 2015. Learning user/item relation is a key issue in recommender system, and existing methods mostly measure the user/item relation from one particular aspect, e.g., historical ratings, etc. However, the relations between users/items could be influenced by multifaceted factors, so any single type of measure could get only a partial view of them. Thus it is more advisable to integrate measures from different aspects to estimate the underlying user/item relation. Furthermore, the estimation of underlying user/item relation should be optimal for current task. To this end, we propose a novel model to couple multiple relations measured on different aspects, and determine the optimal user/item relations via learning the optimal way of integrating these relation measures. Specifically, matrix factorization model is extended in this paper by considering the relations between latent factors of different users/items. Experiments are conducted and our method shows good performance and outperforms other baseline methods
Activity-partner recommendation

LNCS v. 9077 entitled: Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015 ... Proceedings, Part 1In many activities, such as watching movies or having dinner, people prefer to find partners before participation. Therefore, when recommending activity items (e.g., movie tickets) to users, it makes sense to also recommend suitable activity partners. This way, (i) the users save time for finding activity partners, (ii) the effectiveness of the item recommendation is increased (users may prefer activity items more if they can find suitable activity partners), (iii) recommender systems become more interesting and enkindle users' social enthusiasm. In this paper, we identify the usefulness of suggesting activity partners together with items in recommender systems. In addition, we propose and compare several methods for activity-partner recommendation. Our study includes experiments that test the practical value of activity-partner recommendation and evaluate the effectiveness of all suggested methods as well as some alternative strategies.postprin
Rank matrix factorisation

We introduce the problem of rank matrix factorisation (RMF). That is, we consider the decomposition of a rank matrix, in which each row is a (partial or complete) ranking of all columns. Rank matrices naturally appear in many applications of interest, such as sports competitions. Summarising such a rank matrix by two smaller matrices, in which one contains partial rankings that can be interpreted as local patterns, is therefore an important problem. 

After introducing the general problem, we consider a specific instance called Sparse RMF, in which we enforce the rank profiles to be sparse, i.e., to contain many zeroes. We propose a greedy algorithm for this problem based on integer linear programming. Experiments on both synthetic and real data demonstrate the potential of rank matrix factorisation.status: publishe
Short Text Hashing Improved by Integrating Multi-Granularity Topics and
  Tags

Due to computational and storage efficiencies of compact binary codes,
hashing has been widely used for large-scale similarity search. Unfortunately,
many existing hashing methods based on observed keyword features are not
effective for short texts due to the sparseness and shortness. Recently, some
researchers try to utilize latent topics of certain granularity to preserve
semantic similarity in hash codes beyond keyword matching. However, topics of
certain granularity are not adequate to represent the intrinsic semantic
information. In this paper, we present a novel unified approach for short text
Hashing using Multi-granularity Topics and Tags, dubbed HMTT. In particular, we
propose a selection method to choose the optimal multi-granularity topics
depending on the type of dataset, and design two distinct hashing strategies to
incorporate multi-granularity topics. We also propose a simple and effective
method to exploit tags to enhance the similarity of related texts. We carry out
extensive experiments on one short text dataset as well as on one normal text
dataset. The results demonstrate that our approach is effective and
significantly outperforms baselines on several evaluation metrics.Comment: 12 pages, accepted at CICLing 201
Improving cross language information retrieval using corpus based query suggestion approach

Users seeking information may not find relevant information pertaining to their information need in a specific language. But information may be available in a language different from their own, but users may not know that language. Thus users may experience difficulty in accessing the information present in different languages. Since the retrieval process depends on the translation of the user query, there are many issues in getting the right translation of the user query. For a pair of languages chosen by a user, resources, like incomplete dictionary, inaccurate machine translation system may exist. These resources may be insufficient to map the query terms in one language to its equivalent terms in another language. Also for a given query, there might exist multiple correct translations. The underlying corpus evidence may suggest a clue to select a probable set of translations that could eventually perform a better information retrieval. In this paper, we present a cross language information retrieval approach to effectively retrieve information present in a language other than the language of the user query using the corpus driven query suggestion approach. The idea is to utilize the corpus based evidence of one language to improve the retrieval and re-ranking of news documents in the other language. We use FIRE corpora - Tamil and English news collections in our experiments and illustrate the effectiveness of the proposed cross language information retrieval approach
Information Extraction with Active Learning: A Case Study in Legal Text

International audienceActive learning has been successfully applied to a number of NLP tasks. In this paper, we present a study on Information Extraction for natural language licenses that need to be translated to RDF. The final purpose of our work is to automatically extract from a natural language document specifying a certain license a machine-readable description of the terms of use and reuse identified in such license. This task presents some peculiarities that make it specially interesting to study: highly repetitive text, few annotated or unannotated examples available, and very fine precision needed. In this paper we compare different active learning settings for this particular application. We show that the most straightforward approach to instance selection, uncertainty sampling, does not provide a good performance in this setting, performing even worse than passive learning. Density-based methods are the usual alternative to uncertainty sampling, in contexts with very few labelled instances. We show that we can obtain a similar effect to that of density-based methods using uncertainty sampling, by just reversing the ranking criterion, and choosing the most certain instead of the most uncertain instances
Term network approach for transductive classification

Transductive classification is a useful way to classify texts when just few labeled examples are available. Transductive classification algorithms rely on term frequency to directly classify texts represented in vector space model or to build networks and perform label propagation. Related terms tend to belong to the same class and this information can be used to assign relevance scores of terms for classes and consequently the labels of documents. In this paper we propose the use of term networks to model term relations and perform transductive classification. In order to do so, we propose (i) different ways to generate term networks, (ii) how to assign initial relevance scores for terms, (iii) how to propagate the relevance scores among terms, and (iv) how to use the relevance scores of terms in order to classify documents. We demonstrate that transductive classification based on term networks can surpass the accuracies obtained by transductive classification considering texts represented in other types of networks or vector space model, or even the accuracies obtained by inductive classification. We also demonstrated that we can decrease the size of term networks through feature selection while keeping classification accuracy and decreasing computational complexity.Sao Paulo Research Foundation (FAPESP) (Grants 2011/12823-6, 2011/22749-8, and 2014/08996-0)International Conference on Computational Linguistics and Intelligent Text Processing - CICLing (16. 2015 Cairo
Clique-width of graph classes defined by two forbidden induced subgraphs.

If a graph has no induced subgraph isomorphic to any graph in a finite family {H1,…,Hp}, it is said to be (H1,…,Hp)-free. The class of H-free graphs has bounded clique-width if and only if H is an induced subgraph of the 4-vertex path P4. We study the (un)boundedness of the clique-width of graph classes defined by two forbidden induced subgraphs H1 and H2. Prior to our study it was not known whether the number of open cases was finite. We provide a positive answer to this question. To reduce the number of open cases we determine new graph classes of bounded clique-width and new graph classes of unbounded clique-width. For obtaining the latter results we first present a new, generic construction for graph classes of unbounded clique-width. Our results settle the boundedness or unboundedness of the clique-width of the class of (H1,H2)-free graphs\ud
for all pairs (H1,H2), both of which are connected, except two non-equivalent cases, and\ud
for all pairs (H1,H2), at least one of which is not connected, except 11 non-equivalent cases.\ud
We also consider classes characterized by forbidding a finite family of graphs {H1,…,Hp} as subgraphs, minors and topological minors, respectively, and completely determine which of these classes have bounded clique-width. Finally, we show algorithmic consequences of our results for the graph colouring problem restricted to (H1,H2)-free graphs
Contraction blockers for graphs with forbidden induced paths.

We consider the following problem: can a certain graph parameter of some given graph be reduced by at least d for some integer  d via at most k edge contractions for some given integer k? We examine three graph parameters: the chromatic number, clique number and independence number. For each of these graph parameters we show that, when d is part of the input, this problem is polynomial-time solvable on P4-free graphs and NP-complete as well as W[1]-hard, with parameter d, for split graphs. As split graphs form a subclass of P5-free graphs, both results together give a complete complexity classification for Pℓ-free graphs. The W[1]-hardness result implies that it is unlikely that the problem is fixed-parameter tractable for split graphs with parameter  d. But we do show, on the positive side, that the problem is polynomial-time solvable, for each parameter, on split graphs if d is fixed, i.e., not part of the input. We also initiate a study into other subclasses of perfect graphs, namely cobipartite graphs and interval graphs
Complexity Theory for a New Managerial Paradigm: A Research Framework

In this work, we supply a theoretical framework of how organizations\ud
can embed complexity management and sustainable development into their policies\ud
and actions. The proposed framework may lead to a new management paradigm,\ud
attempting to link the main concepts of complexity theory, change management,\ud
knowledge management, sustainable development, and cybernetics. We highlight\ud
how the processes of organizational change have occurred as a result of the move to\ud
adapt to the changes in the various global and international business environments\ud
and how this transformation has led to the shift toward the present innovation\ud
economy. We also point how organizational change needs to deal with sustainability,\ud
so that the change may be consistent with present needs, without compromising\ud
the future
An Introduction to Large Deviations and Equilibrium Statistical
  Mechanics for Turbulent Flows

Two-dimensional turbulent flows, and to some extent, geophysical flows, are
systems with a large number of degrees of freedom, which, albeit fluctuating,
exhibit some degree of organization: coherent structures emerge spontaneously
at large scales. In this short course, we show how the principles of
equilibrium statistical mechanics apply to this problem and predict the
condensation of energy at large scales and allow for computing the resulting
coherent structures. We focus on the structure of the theory using the language
of large deviation theory.Comment: Lecture notes from the "Stochastic Equations for Complex Systems:
  Theory and Applications" summer school organized at the University of Wyoming
  in June 201
Nuclear power: a promising back-up option to promote renewable penetration in the French power system? Topic: Renewable system integration

International audienceThe general 3X20 European directive proposes renewable penetration goals. In France, 27% of the electricity is to be produced by renewable resources by 2020 and this share will be continuously growing up to 2050. Among these resources, some-like wind and solar-are not dispatchable, which triggers challenges to maintain the reliability target level of the power system, both in the short and long term. Wind and solar are expected to contribute to about 12% of the French electricity production in 2020 and according to voluntaristic scenarios they could contribute to over 50% of the total electricity production by 2050. The increase of the non-dispatchable renewable share in the power system modifies the residual demand (which is equal to the demand minus non-dispatchable renewable production) pattern to more power ramping and higher variation amplitudes. To keep the system balanced, backup options are numerous, though scarcely considered exhaustively in power systems modelling. Besides peaking unit production-such as gas turbines-which is usually the preferred option, storage, demand (or supply) curtailment, interconnections and baseload (nuclear for instance) power modulation should also be considered, particularly in such a context of high non-dispatchable renewable penetration. In a French context of significant production of nuclear power, nuclear modulation is investigated as a feasible opportunity to facilitate renewable energy penetration. Available capacity is assessed based on realistic scenarios and through the use of residual load duration curves. The load modulation impact on the nuclear levelized cost of electricity is estimated, and nuclear backup option is compared to gas on economic and environmental terms. Gas backup is more competitive than nuclear in each of the studied scenarios, but including a carbon-tax could change the trends (as low as €20/tonCO2 in some cases). This advocates for such incentives to avoid the effective greenhouse gas release. Nuclear backup would be all the more competitive than power plants used are amortized