pinecone/core-2020-05-10-deduplication huggingface dataset:
Reducing Multidimensional Data

Our aim is to elaborate a multidimensional database reduction process which will specify aggregated schema applicable over a period of time as well as retains useful data for decision support. Firstly, we describe a multi-dimensional database schema composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. Each reduced state is defined through reduction operators. Secondly, we describe our experi-ments and discuss their results. Evaluating our solution implies executing different requests in various contexts: unreduced single fact table, unreduced re-lational star schema, reduced star schema or reduced snowflake schema. We show that queries are more efficiently calculated within a reduced star schema
Using Closed n-set Patterns for Spatio-Temporal Classification

Today,huge volumes of sensor data are collected from many different sources. One of the most crucial data mining task considering this data is the ability to predict and classify data to anticipate trends or failures and take adequate steps. While the initial data might be of limited interest itself, the use of additional information, e.g., latent attributes, spatio-temporal details, etc., can add significant values and interestingness. In this paper we present a classification approach, called Closed n-set Spatio-Temporal Classification (CnSC), which is based on the use of latent attributes, pattern mining, and classification model construction. As the amount of patterns generated is huge, we employ a scalable NoSQL-based graph database for efficient storage and retrieval. The classification model for a specific context is constructed by aggre- gating the most similar patterns. By considering hierarchies in the latent attributes, we define pattern and context similarity scores, and use these for ranking and aggregation. Pattern-based classification shows compet- itive results compared with other prediction strategie
Semi-supervised learning to support the exploration of association rules

In the last years, many approaches for post-processing association rules have been proposed. The automatics are simple to use, but they don’t consider users’ subjectivity. Unlike, the approaches that consider subjectivity need an explicit description of the users’ knowledge and/or interests, requiring a considerable time from the user. Looking at the problem from another perspective, post-processing can be seen as a classification task, in which the user labels some rules as interesting [I] or not interesting [NI], for example, in order to propagate these labels to the other unlabeled rules. This work presents a framework for post-processing association rules that uses semi-supervised learning in which: (a) the user is constantly directed to the [I] patterns of the domain, minimizing his exploration effort by reducing the exploration space, since his knowledge and/or interests are iteratively propagated; (b) the users’ subjectivity is considered without using any formalism, making the task simpler.CAPESFAPESP (2013/12392-0)16th International Conference on Data Warehousing and Knowledge Discovery (DaWaK). \ud
Munich, Germany. 2-4 September 2014
Parameter-Free Extended Edit Distance

The edit distance is the most famous distance to compute the similarity between two strings of characters. The main drawback of the edit distance is that it is based on local procedures which reflect only a local view of similarity. To remedy this problem we presented in a previous work the extended edit distance, which adds a global view of similarity between two strings. However, the extended edit distance includes a parameter whose computation requires a long training time. In this paper we present a new extension of the edit distance which is parameter-free. We compare the performance of the new extension to that of the extended edit distance and we show how they both perform very similarly
An ontology engineering approach to gamify collaborative learning scenarios

The design of collaborative learning (CL) scenarios that increase both students’ learning and motivation is a challenge that the CSCL community has been addressing in the past few years. On one hand, CSCL design (i.e. scripts) has been shown to be effective to support meaningful interactions and better learning. On the other hand, scripted collaboration often does not motivate students to participate in the CL process, which makes more difficult the use of group activities over time. To deal with the problem of motivation, researchers and educators are now looking at gamification techniques to engage students. Gamification is an interesting concept that deals with the introduction and use of game design elements in a proper way to satisfy individual motivational needs. The use of gamification in educational settings is a complex task that requires, from instructional designers, knowledge about game elements (such as leaderboards and point systems), game design (e.g. how to combine game elements) and their impact on motivation and learning. Today, to the best of our knowledge, there are no approaches for the formal systematization of the instructional design knowledge about gamification and its application in CL scenarios. Thus, to address this issue, we have applied ontological engineering techniques to develop an Ontology called OntoGaCLeS. In this paper, we present the main concepts and ontological structure used to represent gamified CL scenarios. In this ontology, we formalize the representation of gamification concepts and explain how they affect motivation in the context of collaborative learning. Particularly, we will focus on the definition of player roles and gameplay strategies. Furthermore, to show the utility of our approach, we illustrate how to use our ontology to define a personalized gamification model that is used to gamify a CL scenario based on motivational needs and individual traits of learners in a group.CNPqCAPES20th International Conference on Collaboration and Technology (CRIWG).\ud
Santiago, Chile. 7-10 September 2014
Crowd-based mining of reusable process model patterns

Process mining is a domain where computers undoubtedly outperform humans. It is a mathematically complex and computationally demanding problem, and event logs are at too low a level of abstraction to be intelligible in large scale to humans. We demonstrate that if instead the data to mine from are models (not logs), datasets are small (in the order of dozens rather than thousands or millions), and the knowledge to be discovered is complex (reusable model patterns), humans outperform computers. We design, implement, run, and test a crowd-based pattern mining approach and demonstrate its viability compared to automated mining. We specifically mine mashup model patterns (we use them to provide interactive recommendations inside a mashup tool) and explain the analogies with mining business process models. The problem is relevant in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices or organizational conventions, from which modelers can learn and benefit when designing own models. © 2014 Springer International Publishing Switzerland
A recommender system for process discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft
Beyond tasks and gateways: discovering BPMN models with subprocesses, boundary events and activity markers

Existing techniques for automated discovery of process models from event logs largely focus on extracting flat process models. In other words, they fail to exploit the notion of subprocess, as well as structured error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of BPMN models containing subprocesses, interrupting and non-interrupting boundary events, and loop and multi-instance markers. The technique analyzes dependencies between data attributes associated with events, in order to identify subprocesses and to extract their associated logs. Parent process and subprocess models are then discovered separately using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. A validation with one synthetic and two real-life logs shows that process models derived using the proposed technique are more accurate and less complex than those derived with flat process model discovery techniques
Attacking Randomized Exponentiations Using Unsupervised Learning

International audienceCountermeasures to defeat most of side-channel attacks onexponentiations are based on randomization of processed data. The ex-ponent and the message blinding are particular techniques to thwartsimple, collisions, differential and correlation analyses. Attacks based ona single (trace) execution of exponentiations, like horizontal correlationanalysis and profiled template attacks, have shown to be efficient againstmost of popular countermeasures. In this paper we show how an unsuper-vised learning can explore the remaining leakages caused by conditionalcontrol tests and memory addressing in a RNS-based implementation ofthe RSA. The device under attack is protected with the exponent blind-ing and the leak resistant arithmetic. The developed attack combinesthe leakage of several samples over the segments of the exponentiationin order to recover the entire exponent. We demonstrate how to find thepoints of interest using trace pre-processing and clustering algorithms.This attack can recover the exponent using a single trace
Verifying Modal Workflow Specifications Using Constraint Solving

International audienceNowadaysworkflowsareextensivelyusedbycompaniestoimproveorganizationalefficiencyandproductivity.Thispaperfocusesontheverificationofmodalworkflowspecificationsusingconstraintsolvingasacomputationaltool.ItsmaincontributionconsistsindevelopinganinnovativeformalframeworkbasedonconstraintsystemstomodelexecutionsofworkflowPetrinetsandtheirstructuralproperties,aswellastoverifytheirmodalspecifications.Finally,animplementationandpromisingexperimentalresultsconstituteapracticalcontribution
Managing LTL properties in Event-B refinement

Refinement in Event-B supports the development of systems via proof based step-wise refinement of events. This refinement approach ensures safety properties are preserved, but additional reasoning is required in order to establish liveness and fairness properties. In this paper we present results which allow a closer integration of two formal methods, Event-B and linear temporal logic. In particular we show how a class of temporal logic properties can carry through a refinement chain of machines. Refinement steps can include introduction of new events, event renaming and event splitting. We also identify a general liveness property that holds for the events of the initial system of a refinement chain. The approach will aid developers in enabling them to verify linear temporal logic properties at early stages of a development, knowing they will be preserved at later stages. We illustrate the results via a simple case study
A Probabilistic Framework for Security Scenarios with Dependent Actions

International audienceThis work addresses the growing need of performing meaningful probabilistic analysis of security. We propose a framework that integrates the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. This allows us to perform probabilistic evaluation of attack–defense scenarios involving dependent actions. To improve the efficiency of our computations, we make use of inference algorithms from Bayesian networks and encoding techniques from constraint reasoning. We discuss the algebraic theory underlying our framework and point out several generalizations which are possible thanks to the use of semiring theory
Parallelization of Kinetic Theory Simulations

Numerical studies of shock waves in large scale systems via kinetic
simulations with millions of particles are too computationally demanding to be
processed in serial. In this work we focus on optimizing the parallel
performance of a kinetic Monte Carlo code for astrophysical simulations such as
core-collapse supernovae. Our goal is to attain a flexible program that scales
well with the architecture of modern supercomputers. This approach requires a
hybrid model of programming that combines a message passing interface (MPI)
with a multithreading model (OpenMP) in C++. We report on our approach to
implement the hybrid design into the kinetic code and show first results which
demonstrate a significant gain in performance when many processors are applied.Comment: 10 pages, 3 figures, conference proceeding
Controlled Natural Language Generation from a Multilingual
  FrameNet-based Grammar

This paper presents a currently bilingual but potentially multilingual
FrameNet-based grammar library implemented in Grammatical Framework. The
contribution of this paper is two-fold. First, it offers a methodological
approach to automatically generate the grammar based on semantico-syntactic
valence patterns extracted from FrameNet-annotated corpora. Second, it provides
a proof of concept for two use cases illustrating how the acquired multilingual
grammar can be exploited in different CNL applications in the domains of arts
and tourism
Explaining violation traces with finite state natural language generation models

An essential element of any verification technique is that of identifying and communicating to the user, system behaviour which leads to a deviation\ud
\ud
from the expected behaviour. Such behaviours are typically made available as long\ud
traces of system actions which would benefit from a natural language explanation\ud
of the trace and especially in the context of business logic level specifications. In\ud
this paper we present a natural language generation model which can be used to\ud
explain such traces. A key idea is that the explanation language is a CNL that is,\ud
\ud
formally speaking, regular language susceptible transformations that can be expressed with finite state machinery. At the same time it admits various forms of\ud
\ud
abstraction and simplification which contribute to the naturalness of explanations\ud
that are communicated to the user.peer-reviewe
Linguistic Analysis of Requirements of a Space Project and Their Conformity with the Recommendations Proposed by a Controlled Natural Language

International audienceWe propose a linguistic analysis of requirements written in French for a project carried out by the French National Space Agency (CNES). The aim is to determine to what extent they conform to some of the rules laid down in INCOSE, a recent guide for writing requirements, with a focus on the notion of sentence " comprehensibility ". Although CNES engineers are not obliged to follow any Controlled Natural Language, we believe that language regularities are likely to emerge from this task, mainly due to the writers' experience. As a first step, we use natural language processing tools to identify sentences that do not comply with INCOSE rules. We further review these sentences to understand why the recommendations cannot (or should not) always be applied when specifying large-scale projects, and how they could be improved. This paper presents a corpus linguistics approach applied to the melioration of requirements writing. We propose a linguistic diagnosis of the way requirements are written in a space project by comparing these requirements with a guide for writing specifications (a controlled natural language). Initial results obtained from this analysis suggest that guides for writing specifications are not fully adapted to the real writing process: they are sometimes too constraining, and sometimes insufficiently so. In the medium term, the aim is to propose another guide based on the spontaneous regularities observed in requirements. The paper comprises two parts. In the first one (see section 2), we present the context of our study and the tool-assisted method used for making the diagnosis. In the second one (see section 3), we describe and discuss our preliminary results
Pervasive and intelligent decision support in Intensive Medicine – the complete picture

Series : Lecture notes in computer science (LNCS), vol. 8649In the Intensive Care Units (ICU) it is notorious the high number of
data sources available. This situation brings more complexity to the way of how
a professional makes a decision based on information provided by those data
sources. Normally, the decisions are based on empirical knowledge and
common sense. Often, they don’t make use of the information provided by the
ICU data sources, due to the difficulty in understanding them. To overcome
these constraints an integrated and pervasive system called INTCare has been
deployed. This paper is focused in presenting the system architecture and the
knowledge obtained by each one of the decision modules: Patient Vital Signs,
Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is
able to make hourly predictions in terms of organ failure and outcome. High
values of sensitivity where reached, e.g. 97.95% for the cardiovascular system,  
99.77% for the outcome. In addition, the system is prepared for tracking
patients’ critical events and for evaluating medical scores automatically and in
real-time
Nonlinear Fokker-Planck equation: stability, distance and corresponding
  extremal problem in the spatially inhomogeneous case

We start with a global Maxwellian $M_{k}$, which is a stationary solution,
with the constant total density ($\rho(t)\equiv \wt \rho$), of the
Fokker-Planck equation. The notion of distance between the function $M_{k}$ and
an arbitrary solution $f$ (with the same total density $\wt \rho$ at the fixed
moment $t$) of the Fokker-Planck equation is introduced. In this way, we
essentially generalize the important Kullback-Leibler distance, which was
studied before. Using this generalization, we show local stability of the
global Maxwellians in the spatially inhomogeneous case. We compare also the
energy and entropy in the classical and quantum cases.Comment: Minor text corrections are made in the second version. The paper is
  published in "Recent Advances in Inverse Scattering, Schur Analysis and
  Stochastic Processes" Oper. Theory Adv. Appl. 244, 379--394, Birkhauser, 201
Towards End-User Driven Power Saving Control in Android devices

Abstract. During the last decade mobile communications increasingly became part of people's daily routine. Such usage raises new challenges regarding devices' battery lifetime management when using most popular wireless access technologies, such as IEEE 802.11. This paper investigates the energy/delay trade-off of using an end-user driven power saving approach, when compared with the standard IEEE 802.11 power saving algorithms. The assessment was conducted in a real testbed using an Android mobile phone and high-precision energy measurement hardware. The results show clear energy benefits of employing user-driven power saving techniques, when compared with other standard approaches
High Dynamic Range Visual Quality of Experience Measurement: Challenges and Perspectives

International audienceTraditional capture and display devices can only support a limited dynamic range (contrast) and color gamut given the hardware limitations. As a result, the real physical luminance present in a natural scene cannot be captured by these. However, with the recent advancements in the related software and hardware technologies, it is now possible to capture or reproduce higher contrast and luminance ranges. Such scene-referred visual signals are known as High Dynamic Range (HDR) signals. They are visually more appealing because they can represent the dynamic range of the visual stimuli present in the real world. Not surprisingly, the emergence of HDR is seen as an important step towards improving the visual quality of experience (QoE) of the end users. However, HDR comes with its own set of challenges including storage, processing, display and so on. This chapter focuses on some of the issues related to HDR processing from a quality of experience (QoE) viewpoint
A Logical Framework for Systems Biology

International audienceWe propose a novel approach for the formal verification of biological systems based on the use of a modal linear logic. We show how such a logic can be used, with worlds as instants of time, as an unified framework to encode both biological systems and temporal properties of their dynamic behaviour. To illustrate our methodology, we consider a model of the P53/Mdm2 DNA-damage repair mechanism. We prove several properties that are important for such a model to satisfy and serve to illustrate the promise of our approach. We formalize the proofs of these properties in the Coq Proof Assistant, with the help of a Lambda Prolog prover for partial automation of the proofs
Control of a Bioreactor with Quantized Measurements

International audienceWe consider the problem of global stabilization of an unstable bioreactor model (e.g. for anaerobic digestion), when the measurements are discrete and in finite number (``quantized''), with  control of the dilution rate. The model is a differential system with two variables, and the output is the biomass growth. The measurements define regions in the state space, and they can be perfect or uncertain (i.e. without or with overlaps). We show that a quantized control may lead to global stabilization: trajectories have to follow some transitions between the regions, until the final region where they converge toward the reference equilibrium. On the boundary between regions, the solutions are defined as a Filippov differential inclusion
FROctomap: an efficient spatio-temporal environment representation

We present a novel software tool intended for mobile robot mapping in long-term scenarios. The method allows for efficient volumetric representation of dynamic three-dimensional environments over long periods of time. It is based on a combination of a well-established 3D mapping framework called Octomaps and an idea to model environment dynamics by its frequency spectrum. The proposed method allows not only for efficient representation, but also reliable prediction of the future states of dynamic three-dimensional environments. Our spatio-temporal mapping framework is available as an open-source C++ library and a ROS module which allows its easy integration in robotics projects