pinecone/core-2020-05-10-deduplication huggingface dataset:
On the dual post correspondence problem

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38771-5_16The Dual Post Correspondence Problem asks whether, for a given word α, there exists a pair of distinct morphisms σ, τ, one of which needs to be non-periodic, such that σ(α) = τ(α) is satisfied. This problem is important for the research on equality sets, which are a vital concept in the theory of computation, as it helps to identify words that are in trivial equality sets only. Little is known about the Dual PCP for words α over larger than binary alphabets. In the present paper, we address this question in a way that simplifies the usual method, which means that we can reduce the intricacy of the word equations involved in dealing with the Dual PCP. Our approach yields large sets of words for which there exists a solution to the Dual PCP as well as examples of words over arbitrary alphabets for which such a solution does not exist
Operator precedence ω-languages

Omega-languages are becoming more and more relevant nowadays when most applications are "ever-running”. Recent literature, mainly under the motivation of widening the application of model checking techniques, extended the analysis of these languages from the simple regular ones to various classes of languages with "visible structure", i.e., languages whose syntax structure is immediately visible in their strings, such as visibly pushdown languages (VPLs).\ud
Floyd’s operator precedence languages (FLs), instead, where defined with the original motivation of supporting deterministic parsing. These seemingly unrelated classes of languages exhibit interesting relations: precisely FLs, which strictly include VPLs, enjoy all relevant closure properties. FLs have also been characterized in terms of a suitable automata family and in terms of a logic notation.\ud
In this paper we introduce Floyd Omega-languages (FOmegaLs); we investigate their expressiveness depending on various acceptance criteria and their closure properties. Whereas some properties are natural extensions of those holding for regular languages, others required novel investigation techniques.\ud
A simple application-oriented example shows the considerable gain in expressiveness and verifiability offered by FOLs w.r.t. previous classes
A computationally and cognitively plausible model of supervised and unsupervised learning

Author version made available in accordance with the publisher's policy. "The final publication is available at link.springer.com”The issue of chance correction has been discussed for many decades in the context of\ud
statistics, psychology and machine learning, with multiple measures being shown to\ud
have desirable properties, including various definitions of Kappa or Correlation, and\ud
the psychologically validated ΔP measures. In this paper, we discuss the relationships\ud
between these measures, showing that they form part of a single family of measures,\ud
and that using an appropriate measure can positively impact learning
GRIMACE: GeneRIc MetAmodel for domain Component modElling.

Component Based Software Engineering (CBSE) is a popular and widely adopted software engineering paradigm that has proven his usefulness and success to increase reusability and efficiency in various application domains. In this paper, we propose a common metamodel to support CBSE requirements taking into account the specificities of each domain. The resulting modeling framework serves primarily to capture the basic concepts of concerns related to component systems development based on the clear separation between the development process, interactions and the domain knowledge
Training Interpreters using Virtual Worlds

With the rise in population migration there has been an increased need for professional interpreters who can bridge language barriers and operate in a variety of fields such as business, legal, social and medical. Interpreters require specialized training to cope with the idiosyncrasies of each eld and their potential clients need to be aware of professional parlance. We present `Project IVY'. In IVY, users can make a selection from over 30 interpreter training scenarios situated in the 3D virtual world. Users then interpret the oral interaction of two avatar actors. In addition to creating di erent 3D scenarios, we have developed an asset management system for the oral les and permit users (mentors of the training interpreters) to easily upload and customize the 3D environment and observe which scenario is being used by a student. In this article we present the design and development of the IVY Virtual Environment and the asset management system. Finally we make discussion over our plans for further development
Spacings - An Example for Universality in Random Matrix Theory

Universality of local eigenvalue statistics is one of the most striking
phenomena of Random Matrix Theory, that also accounts for a lot of the
attention that the field has attracted over the past 15 years. In this paper we
focus on the empirical spacing distribution and its Kolmogorov distance from
the universal limit. We describe new results, some analytical, some numerical,
that are contained in [27]. A large part of the paper is devoted to explain
basic definitions and facts of Random Matrix Theory, culminating in a sketch of
the proof of a weak version of convergence for the empirical spacing
distribution $\sigma_N$.Comment: reference [4] update
A corpus-based approach for the induction of ontology lexica

While there are many large knowledge bases (e.g. Freebase, Yago, DBpedia) as well as linked data sets available on the web, they typically lack lexical information stating how the properties and classes are realized lexically. If at all, typically only one label is attached to these properties, thus lacking any deeper syntactic information, e.g. about syntactic arguments and how these map to the semantic arguments of the property as well as about possible lexical variants or paraphrases. While there are lexicon models such as \emph{lemon} allowing to define a lexicon for a given ontology, the cost involved in creating and maintaining such lexica is substantial, requiring a high manual effort. Towards lowering this effort, in this paper we present a semi-automatic approach that exploits a corpus to find occurrences in which a given property is expressed, and generalizing over these occurrences by extracting dependency paths that can be used as a basis to create lemon lexicon entries. We evaluate the resulting automatically generated lexica with respect to DBpedia as dataset and Wikipedia as corresponding corpus, both in an automatic mode, by comparing to a manually created lexicon, and in a semi-automatic mode in which a lexicon engineer inspected the results of the corpus-based approach, adding them to the existing lexicon if appropriate.
BWW ontology as a lens on IS design theory: Extending the design science research roadmap

The Design Science Research Roadmap (DSR-Roadmap) [1] aims to give detailed methodological guidance to novice researchers in Information Systems (IS) DSR. Focus group evaluation, one phase of the overall study, of the evolving DSR-Roadmap revealed that a key difficulty faced by both novice and expert researchers in DSR, is abstracting design theory from design. This paper explores the extension of the DSR-Roadmap by employing IS deep structure ontology (BWW [2-4]) as a lens on IS design to firstly yield generalisable design theory, specifically 'IS Design Theory' (ISDT) elements [5]. Consideration is next given to the value of BWW in the application of the design theory by practitioners. Results of mapping BWW constructs to ISDT elements suggest that the BWW is promising as a common language between design researchers and practitioners, facilitating both design theory and design implementatio
Can 'Soft' Organisational Problems be solved by 'Hard' Process Reference Models?

Process Reference Models (PRM) and their associated Assessment Models (PAM) are best known for their application to well-defined input-process-output work-flows in the Systems and Software Engineering domains. Model-based process improvement (MBPI) is now well-established as a discipline within that domain. Arguably though, MBPI can be applied successfully to multiple domains. The question has been to find a way. This paper discusses a mature Process Reference Model and Assessment Model for the leadership of complex virtual teams, developed in accordance with the recognized standards (ISO/IEC 15504 [8] and ISO/IEC 24774 [9]), yet which is applied to difficult 'soft' organisational problems. Earlier work on this topic focused on how to develop a PRM in soft, organisational contexts [1]. This paper focuses on the derived Process Assessment Model which has had a three-level Capability Dimension added to the existing Performance Dimension, and with associated work-products identified. It reports on preliminary trials at Griffith University.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Exploring the Impact of IT Service Management Process Improvement Initiatives: A Case Study Approach

IT companies worldwide have started to improve their service management processes based on best practice frameworks, such as IT Infrastructure Library (ITIL). However, many of these companies face difficulties in demonstrating the positive outcomes of IT service management (ITSM) process improvement. This has led us to investigate the research problem: What positive impacts have resulted from IT service management process improvement? The main contributions of this paper are 1) to identify the ITSM process improvement outcomes in two IT service provider organizations and 2) provide advice as lessons learnt.Full Tex
Critical Design Decisions in the Development of the Standard for Process Assessment

The development of an International Standard for Process Assessment com-menced in 1993.  Over the past 20 years, the standard suite has moved through three formal releases, and multiple drafts; during this time, several key design issues have been addressed, and in many cases reconsidered.  This paper identi-fies key issues in the design of the Standard, and discusses decisions taken and their impact on the Standard, and on the theory and practice of process assess-ment.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation

Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multi-domain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that cross-domain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it. © 2013 Springer-Verlag
Modeling emotions with social tags

Proceedings of 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38844-6_30We present an emotion model based on social tags, which is built upon an automatically generated lexicon that describes emotions by means of synonym and antonym terms. Using this model we develop a number of methods that transform social tag-based item profiles into emotion-oriented item profiles. We show that the model’s representation of a number of basic emotions is in accordance with the well known psychological circumplex model of affect, and we report results from a user study that show a high precision of our methods to infer the emotions evoked by items in the movie and music domains.This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542)
Mining semantic data, user generated contents, and contextual information for cross-domain recommendation

Proceedings of 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38844-6_42Cross-domain recommender systems suggest items in a target domain by exploiting user preferences and/or domain knowledge available in a source domain. In this thesis we aim to develop a framework for cross-domain recommendation capable of mining heterogeneous sources of information such as semantically annotated data, user generated contents, and contextual signals. For this purpose, we investigate a number of approaches to extract, process, and integrate knowledge for linking distinct domains, and various models that exploit such knowledge for making effective recommendations across domains
NMF-Based Spectral Analysis for Acoustic Event Classification Tasks

Proceedings of: 6th International Conference The Non-Linear Speech Processing (NOLISP 2013). Mons, Belgium, June 19-21, 2013.In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC). First, we study the spectral contents of different acoustic events by applying Non-Negative Matrix Factorization (NMF) on their spectral magnitude and compare them with the structure of speech spectra. Second, from the findings of this study, we propose a new parameterization for AEC, which is an extension of the conventional Mel Frequency Cepstrum Coefficients (MFCC) and is based on the high pass filtering of acoustic event spectra. Also, the influence of different frequency scales on the classification rate of the whole system is studied. The evaluation of the proposed features for AEC shows that relative error reductions about 12% at segment level and about 11% at target event level with respect to the conventional MFCC are achieved.This work has been partially supported by the Spanish Government grants TSI-020110-2009-103, IPT-120000-2010-24 and TEC2011-26807. Financial support from the Fundaci´on Carolina and Universidad Católica San Pablo, Arequipa.Publicad
A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent
  Based Simulations

This article proposes a methodology to model and simulate complex systems,
based on IRM4MLS, a generic agent-based meta-model able to deal with
multi-level systems. This methodology permits the engineering of dynamic
multi-level agent-based models, to represent complex systems over several
scales and domains of interest. Its goal is to simulate a phenomenon using
dynamically the lightest representation to save computer resources without loss
of information. This methodology is based on two mechanisms: (1) the activation
or deactivation of agents representing different domain parts of the same
phenomenon and (2) the aggregation or disaggregation of agents representing the
same phenomenon at different scales.Comment: Presented at 3th International Workshop on Multi-Agent Based
  Simulation, Valencia, Spain, 5th June 201
Optimizing data collection for object tracking in wireless sensor
  networks

In this paper some modifications are proposed to optimize an algorithm of
object tracking in wireless sensor network (WSN). The task under consideration
is to control movement of a mobile sink, which has to reach a target in the
shortest possible time. Utilization of the WSN resources is optimized by
transferring only selected data readings (target locations) to the mobile sink.
Simulations were performed to evaluate the proposed modifications against
state-of-the-art methods. The obtained results show that the presented tracking
algorithm allows for substantial reduction of data collection costs with no
significant increase in the amount of time that it takes to catch the target.Comment: 10 pages, 6 figure
Practical Aspects of Log File Analysis for E-Commerce

The paper concerns Web server log file analysis to discover knowledge useful for online retailers. Data for one month of the online bookstore operation was analyzed with respect to the probability of making a purchase by e-customers. Key states and characteristics of user sessions were distinguished and their relations to the session state connected with purchase confirmation were analyzed. Results allow identification of factors increasing the probability of making a purchase in a given Web store and thus, determination of user sessions which are more valuable in terms of e-business profitability. Such results may be then applied in practice, e.g. in a method for personalized or prioritized service in the Web server system.
Sparse Projections of Medical Images onto Manifolds

Manifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold. Commonly used approaches such as the Nyström extension and kernel ridge regression require using all training points. We propose an interpolation function that only depends on a small subset of the input training data. Consequently, in the testing phase each new point only needs to be compared against a small number of input training data in order to project the point onto the low-dimensional space. We interpret our method as an out-of-sample extension that approximates kernel ridge regression. Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications. Tuning this error bound controls the sparsity of the resulting interpolation function. We illustrate our method in two clinical applications that require fast mapping of input images onto a low-dimensional space.National Alliance for Medical Image Computing (U.S.) (grant NIH NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (grant NIH NCRR NAC P41-RR13218)National Institutes of Health (U.S.) (grant NIH NIBIB NAC P41-EB-015902
Cohort-level brain mapping: learning cognitive atoms to single out specialized regions

International audienceFunctional Magnetic Resonance Imaging (fMRI) studies map the human brain by testing the response of groups of individuals to carefully-crafted and contrasted tasks in order to delineate specialized brain regions and networks. The number of functional networks extracted is limited by the number of subject-level contrasts and does not grow with the cohort. Here, we introduce a new group-level brain mapping strategy to differentiate many regions reflecting the variety of brain network configurations observed in the population. Based on the principle of functional segregation, our approach singles out functionally-specialized brain regions by learning group-level functional profiles on which the response of brain regions can be represented sparsely. We use a dictionary-learning formulation that can be solved efficiently with on-line algorithms, scaling to arbitrary large datasets. Importantly, we model inter-subject correspondence as structure imposed in the estimated functional profiles, integrating a structure-inducing regularization with no additional computational cost. On a large multi-subject study, our approach extracts a large number of brain networks with meaningful functional profiles
Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography

International audienceParticle filtering has recently been introduced to perform probabilistic tractography in conjunction with DTI and Q-Ball models to estimate the diffusion information. Particle filters are particularly well adapted to the tractography problem as they offer a way to approximate a probability distribution over all paths originated from a specified voxel, given the diffusion information. In practice however, they often fail at consistently capturing the multi-modality of the target distribution. For brain white matter tractography, this means that multiple fiber pathways are unlikely to be tracked over extended volumes. We propose to remedy this issue by formulating the filtering distribution as an adaptive M-component non-parametric mixture model. Such a formulation preserves all the properties of a classical particle filter while improving multi-modality capture. We apply this multi-modal particle filter to both DTI and Q-Ball models and propose to estimate dynamically the number of modes of the filtering distribution. We show on synthetic and real data how this algorithm outperforms the previous versions proposed in the literature
Partial Test Oracle in Model Transformation Testing

International audienceWriting test oracles for model transformations is a difficult task. First, oracles must deal with models which are complex data. Second, the tester cannot always predict the expected value of all the properties of the output model produced by a transformation. In this paper, we propose an approach to create efficient oracles for validating part of the produced output model. In this approach we presume that output models can be divided into two parts, a predictable part and a non-predictable one. After identifying the latter, we use it to create a filter. Before providing a (partial) verdict, the oracle compares actual output model with the expected output model, returning a difference model, and uses the filter to discard the differences related to the unpredictable part. The approach infers the unpredictable part from the model transformation specification, or from older output models, in the case of regression testing. The approach is supported by a tool to build such partial oracles. We run several experiments writing partial oracles to validate output models returned by two model transformations. We validate our proposal comparing the effectiveness and complexity of partial oracles with oracles based on full model comparisons and contracts
Streaming model transformations: Scenarios, challenges and initial solutions

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38883-5_1Proceedings of 6th International Conference, ICMT 2013, Budapest, Hungary, June 18-19, 2013Several styles of model transformations are well-known and widely used, such as batch, live, incremental and lazy transformations. While they permit tackling advanced scenarios, some applications deal with models that are only available as a possibly infinite stream of elements. Hence, in streaming transformations, source model elements are continuously produced by some process, or very large models are fragmented and fed into the transformation engine. This poses a series of issues that cannot be tackled using current transformation engines. In this paper we motivate the applicability of this kind of transformations, explore the elements involved, and review several strategies to deal with them. We also propose a concrete approach, built on top of the Eclectic transformation tool.This work was funded by the Spanish Ministry of Economy and Competitivity (project “Go Lite” TIN2011-24139) and the R&D programme of the Madrid Region (project “e-Madrid” S2009/TIC-1650