pinecone/core-2020-05-10-deduplication huggingface dataset:
Inequalities of Babuška-Aziz and Friedrichs-Velte for differential forms

International audienceFor sufficiently smooth bounded plane domains, the equivalence between the inequalities of Babuška –Aziz for right inverses of the divergence and of Friedrichs on conjugate harmonic functions was shown by Horgan and Payne in 1983 [7]. In a previous paper [4] we proved that this equivalence, and the equality between the associated constants, is true without any regularity condition on the domain. In three dimensions, Velte [9] studied a generalization of the notion of conjugate harmonic functions and corresponding generalizations of Friedrich's inequality, and he showed for sufficiently smooth simply-connected domains the equivalence with inf-sup conditions for the divergence and for the curl. For this equivalence, Zsuppán [10] observed that our proof can be adapted, proving the equality between the corresponding constants without regularity assumptions on the domain. Here we formulate a generalization of the Friedrichs inequality for conjugate harmonic differential forms on bounded open sets in any dimension that contains the situations studied by Horgan–Payne and Velte as special cases. We also formulate the corresponding inf-sup conditions or Babuška –Aziz inequalities and prove their equivalence with the Friedrichs inequalities, including equality between the corresponding constants. No a-priori conditions on the regularity of the open set nor on its topology are assumed
Concept Discovery and Argument Bundles in the Experience Web

In this paper we focus on a particular interesting web user-generated content: people¿s experiences. We extend our previous work on aspect extraction and sentiment analysis and propose a novel approach to create a vocabulary of basic level concepts with the appropriate granularity to characterize a set of products. This concept vocabulary is created by analyzing the usage of the aspects over a set of reviews, and allows us to find those features with a clear positive and negative polarity to create the bundles of arguments. The argument bundles allow us to define a concept-wise satisfaction degree of a user query over a set of bundles using the notion of fuzzy implication, allowing the reuse experiences of other people to the needs a specific user. © Springer International Publishing AG 2016.This research has been partially supported by NASAID (CSIC Intramural 201550E022).Peer Reviewe
A Search Based Approach for Stress-Testing Integrated Circuits

In order to reduce software complexity and be power efficient, hardware platforms are increasingly incorporating functionality that was traditionally administered at a software-level (such as cache management). This functionality is often complex, incorporating multiple processors along with a multitude of design parameters. Such devices can only be reliably tested at a ‘system’ level, which presents various testing challenges; behaviour is often non-deterministic (from a software perspective), and finding suitable test sets to ‘stress’ the system adequately is often an inefficient, manual activity that yields fixed test sets that can rarely be reused. In this paper we investigate this problem with respect to ARM’s Cache Coherent Interconnect (CCI) Unit. We present an automated search-based testing approach that combines a parameterised test-generation framework with the hill-climbing heuristic to find test sets that maximally ‘stress’ the CCI by producing much larger numbers of data stall cycles than the corresponding manual test sets.Peer-reviewedPost-printInternational Symposium on Search Based Software Engineering (SSBSE'16) Raleigh, NC, US
Verification of Temporal Properties of Neuronal Archetypes Modeled as Synchronous Reactive Systems

International audienceThere exists many ways to connect two, three or more neu-rons together to form different graphs. We call archetypes only the graphs whose properties can be associated with specific classes of biologically relevant structures and behaviors. These archetypes are supposed to be the basis of typical instances of neuronal information processing. To model different representative archetypes and express their temporal properties, we use a synchronous programming language dedicated to reactive systems (Lustre). The properties are then automatically validated thanks to several model checkers supporting data types. The respective results are compared and depend on their underlying abstraction methods
Relationship between the Reprogramming Determinants of Boolean Networks
  and their Interaction Graph

In this paper, we address the formal characterization of targets triggering
cellular trans-differentiation in the scope of Boolean networks with
asynchronous dynamics. Given two fixed points of a Boolean network, we are
interested in all the combinations of mutations which allow to switch from one
fixed point to the other, either possibly, or inevitably. In the case of
existential reachability, we prove that the set of nodes to (permanently) flip
are only and necessarily in certain connected components of the interaction
graph. In the case of inevitable reachability, we provide an algorithm to
identify a subset of possible solutions.Comment: Accepted at HSB 201
A semi-supervised large margin algorithm for white matter hyperintensity segmentation

Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation
On NIS-Apriori Based Data Mining in SQL

We have proposed a framework of Rough Non-deterministic Information Analysis (RNIA) for tables with non-deterministic information, and applied RNIA to analyzing tables with uncertainty. We have also developed the RNIA software tool in Prolog and getRNIA in Python, in addition to these two tools we newly consider the RNIA software tool in SQL for handling large size data sets. This paper reports the current state of the prototype named NIS-Apriori in SQL, which will afford us more convenient environment for data analysis.Proc. Int\u27l. Conf. on IJCRS, October 7-11, 2016, Santiago, Chil
Conditions for compatibility of components: The case of masters and slaves

We consider systems composed of reactive components that collaborate through synchronised execution of common actions. These multi-component systems are formally represented as team automata, a model that allows a wide spectrum of synchronisation policies to combine components into higher-level systems. We investigate the\ud
correct-by-construction engineering of such systems of systems from the point of view of correct communications between the components (no message loss or deadlocks due to indefinite waiting). This leads to a proposal for a generic definition of compatibility of components relative to the adopted synchronisation policy. This definition appears\ud
to be particularly appropriate for so-called master-slave synchronisations by which input actions (for `slaves') are driven\ud
by output actions (from `masters').Peer ReviewedPostprint (author's final draft
A Logic for the Statistical Model Checking of Dynamic Software Architectures

International audienceDynamic software architectures emerge when addressing important features of contemporary systems, which often operate in dynamic environments subjected to change. Such systems are designed to be reconfigured over time while maintaining important properties, e.g., availability, correctness, etc. Verifying that reconfiguration operations make the system to meet the desired properties remains a major challenge. First, the verification process itself becomes often difficult when using exhaustive formal methods (such as model checking) due to the potentially infinite state space. Second, it is necessary to express the properties to be verified using some notation able to cope with the dynamic nature of these systems. Aiming at tackling these issues, we introduce DynBLTL, a new logic tailored to express both structural and behavioral properties in dynamic software architectures. Furthermore, we propose using statistical model checking (SMC) to support an efficient analysis of these properties by evaluating the probability of meeting them through a number of simulations. In this paper, we describe the main features of DynBLTL and how it was implemented as a plug-in for PLASMA, a statistical model checker
SelfBACK: Activity recognition for self-management of low back pain.

Low back pain (LBP) is the most significant contributor to years lived with disability in Europe and results in significant financial cost to European economies. Guidelines for the management of LBP have self-management at their cornerstone, where patients are advised against bed rest, and to remain active. In this paper, we introduce SelfBACK, a decision support system used by the patients themselves to improve and reinforce self-management of LBP. SelfBACK uses activity recognition from wearable sensors in order to automatically determine the type and level of activity of a user.  This is used by the system to automatically determine how well users adhere to prescribed physical activity guidelines. Important parameters of an activity recognition system include windowing, feature extraction and classification. The choices of these parameters for the SelfBACK system are supported by empirical comparative analysis which are presented in this paper.  In addition, two approaches are presented for detecting step counts for ambulation activities (e.g. walking and running) which help to determine activity intensity. Evaluation shows the SelfBACK system is able to distinguish between five common daily activities with 0.9 macro-averaged F1 and detect step counts with 6.4 and 5.6 root mean squared error for walking and running respectively.European Commission Horizon 2020 (grant No 689043)
Harnessing background knowledge for e-learning recommendation.

The growing availability of good quality, learning-focused content on the
Web makes it an excellent source of resources for e-learning systems. However,
learners can find it hard to retrieve material well-aligned with their learning goals
because of the difficulty in assembling effective keyword searches due to both an inherent
lack of domain knowledge, and the unfamiliar vocabulary often employed by
domain experts. We take a step towards bridging this semantic gap by introducing a
novel method that automatically creates custom background knowledge in the form
of a set of rich concepts related to the selected learning domain. Further, we develop
a hybrid approach that allows the background knowledge to influence retrieval in
the recommendation of new learning materials by leveraging the vocabulary associated
with our discovered concepts in the representation process. We evaluate the
effectiveness of our approach on a dataset of Machine Learning and Data Mining
papers and show it to outperform the benchmark methods. This paper has won the Donald Michie Memorial Award for Best Technical Paper at AI-2016
An Investigation on Online Versus Batch Learning in Predicting User Behaviour

An investigation on how to produce a fast and accurate prediction of user behaviour on the Web is conducted. First, the problem of predicting user behaviour as a classification task is formulated and then the main problems of such real-time predictions are specified: the accuracy and time complexity of the prediction. Second, a method for comparison of online and batch (offline) algorithms used for user behaviour prediction is proposed. Last, the performance of these algorithms using the data from a popular question and answer platform, Stack Overflow, is empirically explored. It is demonstrated that a simple online learning algorithm outperforms state-of-the-art batch algorithms and performs as well as a deep learning algorithm, Deep Belief Networks. The proposed method for comparison of online and offline algorithms as well as the provided experimental evidence can be used for choosing a machine learning set-up for predicting user behaviour on the Web in scenarios where the accuracy and the time performance are of main concern
Extraction of Knowledge Rules for the Retrieval of Mesoscale Oceanic Structures in Ocean Satellite Images

The processing of ocean satellite images has as goal the detection of phenomena related with ocean dynamics. In this context, Mesoscale Oceanic Structures (MOS) play an essential role. In this chapter we will present the tool developed in our group in order to extract knowledge rules for the retrieval of MOS in ocean satellite images. We will describe the implementation of the tool: the workflow associated with the tool, the user interface, the class structure, and the database of the tool. Additionally, the experimental results obtained with the tool in terms of fuzzy knowledge rules as well as labeled structures with these rules are shown. These results have been obtained with the tool analyzing chlorophyll and temperature images of the Canary Islands and North West African coast captured by the SeaWiFS and MODIS-Aqua sensors
Modeling Contagion of Behavior in Friendship Networks as Coordination Games

It has been shown that humans are heavily influenced by peers when it comes to choice of behavior, norms or opinions. In order to better understand and help to predict society’s behavior, it is therefore desirable to design social simulations that incorporate representations of those network aspects. We address this topic, by investigating the performance of a coordination game mechanism in representing the diffusion of behavior for distinct data sets and diverse behaviors in children and adolescent social networks. We introduce a set of quality measurements in order to assess the adequacy of our simulations and find evidence that a coordination game environment could underlie some of the diffusion processes, while other processes may not be modeled coherently as a coordination game
A Simple-to-Use BDI Architecture for Agent-Based Modeling and Simulation

With the increase of computing power and the development of user-friendly multi-agent simulation frameworks, social simulations have become increasingly realistic. However, most agent architectures in these simulations use simple reactive models. Cognitive architectures face two main obstacles: their complexity for the field-expert modeler, and their computational cost. In this paper, we propose a new cognitive agent architecture based on the Belief-Desire-Intention paradigm integrated into the GAMA modeling platform. Based on the GAML modeling language, this architecture was designed to be simple-to-use for modelers, flexible enough to manage complex behaviors, and with low computational cost. This architecture is illustrated with a simulation of the evolution of land-use in the Mekong Delta
Computing, Design, Art: Reflections on an Innovative Moment in History

The chapter is concerned with the role of art and design in the history and philosophy of computing, and the role of computing in models of design and art. It offers insights arising from research into a period in the 1960s and 70s, particularly in the UK, when computing became more available to artists and designers, focusing on Bruce Archer (1922-2005) and John Lansdown (1929-1999) in London. It suggests that models of computing interacted with conceptualisations of art, design and creative activities in important ways
Encoding temporal features of skilled movements-what, whether and how?

In order to reliably produce intelligible speech or fluently play a melody on a piano, learning the precise timing of muscle activations is essential. Surprisingly, the fundamental question of how memories of complex temporal dynamics of movement are stored across the brain is still unresolved. This review outlines the constraints that determine whether and how the timing of skilled movements is represented in the central nervous system and introduces different computational and neural mechanisms that can be harnessed for temporal encoding. It concludes by proposing a schematic model of how these different mechanisms may complement and interact with each other in fast feedback loops to achieve skilled motor timing
Forecasting store foot traffic using facial recognition, time series and support vector machines

In this paper, we explore data collected in a pilot project that used a digital camera and facial recognition to detect foot traffic to a sports store. Using a time series approach, we model daily incoming store traffic under three classes (all faces, female, male) and compare six forecasting approaches, including Holt-Winters (HW), a Support Vector Machine (SVM) and a HW-SVM hybrid that includes other data features (e.g., weather conditions). Several experiments were held, under a robust rolling windows scheme that considers up to one week ahead predictions and two metrics (predictive error and estimated store benefit). Overall, competitive results were achieved by the SVM (all faces), HW (female) and HW-SVM (male) methods, which can potentially lead to valuable gains (e.g., enhancing store marketing or human resource management).This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013
Using non-invasive wearables for detecting emotions with intelligent agents

This paper proposes the use of intelligent wristbands for the automatic
detection of emotional states in order to develop an application which
allows to extract, analyze, represent and manage the social emotion of a group
of entities. Nowadays, the detection of the joined emotion of an heterogeneous
group of people is still an open issue. Most of the existing approaches are centered
in the emotion detection and management of a single entity. Concretely,
the application tries to detect how music can influence in a positive or negative
way over individuals’ emotional states. The main goal of the proposed
system is to play music that encourages the increase of happiness of the overall
patrons.This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R
and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon. This work is
supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a
Ciência e Tecnologia within the projects UID/CEC/00319/2013 and Post-Doc scholarship
SFRH/BPD/102696/2014 (A. Cost
A Logic for the Compliance Budget

Security breaches often arise as a result of users? failure to comply with security policies. Such failures to comply may simply be innocent mistakes. However, there is evidence that, in some circumstances, users choose not to comply because they perceive that the security benefit of compliance is outweighed by the cost that is the impact of compliance on their abilities to complete their operational tasks. That is, they perceive security compliance as hindering their work. The ?compliance budget? is a concept in information security that describes how the users of an organization?s systems determine the extent to which they comply with the specified security policy. The purpose of this paper is to initiate a qualitative logical analysis of, and so provide reasoning tools for, this important concept in security economics for which quantitative analysis is difficult to establish. We set up a simple temporal logic of preferences, with a semantics given in terms of histories and sets of preferences, and explain how to use it to model and reason about the compliance budget. The key ingredients are preference update, to account for behavioural change in response to policy change, and an ability to handle uncertainty, to account for the lack of quantitative measures
Bond Graph Modeling and Simulation of a Vibration Absorber System in Helicopters

n the last 20 years, computer science has considerably progressed and there has been a resurgence of interest in bond graphs. The evolution of bond graph software has allowed for the full exploitation of its graphical aspects and for its simulation directly from the modeling environment without the need for the modeler to derive the associated dynamic equations. However, within this last decade, few simulations of complex multibody systems modeled with bond graphs have been conducted directly from a graphic software platform.

In this context, the objective of this chapter is to show how bond graphs can be used to model and simulate a complex mechatronic system with bond graph simulation software. The multibody system studied in this chapter is a helicopter’s vibration absorber suspension. The structural and modular approach allowed by bond graphs permits a systematic modeling of mechatronic multibody systems. The model is then built as an assembly of components or modules (rigid bodies and compliant kinematic joints) by following the structure of the actual system. This approach was carried out with the use of the parasitic elements method. The bond graph model of the suspension has been verified with another multibody tool for three different excitations (pumping, roll, and yaw).

The first part of this chapter will be dedicated to giving the reader an overview of the modeling of multibody systems with bond graphs. BG models of the rigid body and all the basic kinematic joints will be presented. The main existing methods (zero-causal paths ZCPs, Lagrange multipliers, and singular perturbation) for modeling and carrying out simulations of multibody systems will be recalled.

The second part of this chapter will present a vector bond graph (also called multibond graph) model of a helicopter’s antivibratory system and the associated simulations. This system is a specific suspension of a helicopter, which filters the vibration coming from the rotor to the fuselage. It is a complex multibody system with four closed kinematic chains (CKC). The dynamic equations of such a CKC system are differential-algebraic equation systems (DAE) that are often difficult to treat and which require specific solving methods.

The intention of writing this chapter was to give to bond graph practitioners a detailed and comprehensive method so as to model and conduct simulations of complex multibody systems directly from a bond graph modeling interface.Chaire "dynamique des systèmes complexes" - Fondation d'entreprises EAD
Personalization framework for adaptive robotic feeding assistance

The final publication is available at link.springer.comThe deployment of robots at home must involve robots with pre-defined skills and the capability of\ud
personalizing their behavior by non-expert users. A framework to tackle this personalization is presented and applied\ud
to an automatic feeding task. The personalization involves the caregiver providing several examples of feeding using\ud
Learning-by- Demostration, and a ProMP formalism to compute an overall trajectory and the variance along the path.\ud
Experiments show the validity of the approach in generating different feeding motions to adapt to user’s preferences,\ud
automatically extracting the relevant task parameters. The importance of the nature of the demonstrations is also\ud
assessed, and two training strategies are compared. © Springer International Publishing AG 2016.Peer ReviewedPostprint (author's final draft