Ponències/Comunicacions de congressos
http://hdl.handle.net/2117/3974
Wed, 10 Feb 2016 01:26:55 GMT2016-02-10T01:26:55ZReasoning about policy behavior in logic-based trust management systems: Some complexity results and an operational framework
http://hdl.handle.net/2117/82625
Reasoning about policy behavior in logic-based trust management systems: Some complexity results and an operational framework
Pasarella Sánchez, Ana Edelmira; Lobo, Jorge
In this paper we show that the logical framework proposed by Becker et al. [1] to reason about security policy behavior in a trust management context can be captured by an operational framework that is based on the language proposed by Miller in 1989 to deal with scoping and/or modules in logic programming. The framework of Becker et al. uses propositional Horn clauses to represent both policies and credentials, implications in clauses are interpreted in counterfactual logic, a Hilbert-style proof system is defined and a system based on SAT is used to prove whether properties about credentials, permissions
and policies are valid, i.e. true under all possible policies. Our
contributions in this paper are three. First, we show that this kind
of validation can rely on an operational semantics (derivability
relation) of a language very similar to Miller’s language, which is
very close to derivability in logic programs. Second, we are able
to establish that, as in propositional logic, validity of formulas
is a co-NP-complete problem. And third, we present a provably
correct implementation of a goal-oriented algorithm for validity.
Fri, 05 Feb 2016 12:38:45 GMThttp://hdl.handle.net/2117/826252016-02-05T12:38:45ZPasarella Sánchez, Ana EdelmiraLobo, JorgeIn this paper we show that the logical framework proposed by Becker et al. [1] to reason about security policy behavior in a trust management context can be captured by an operational framework that is based on the language proposed by Miller in 1989 to deal with scoping and/or modules in logic programming. The framework of Becker et al. uses propositional Horn clauses to represent both policies and credentials, implications in clauses are interpreted in counterfactual logic, a Hilbert-style proof system is defined and a system based on SAT is used to prove whether properties about credentials, permissions
and policies are valid, i.e. true under all possible policies. Our
contributions in this paper are three. First, we show that this kind
of validation can rely on an operational semantics (derivability
relation) of a language very similar to Miller’s language, which is
very close to derivability in logic programs. Second, we are able
to establish that, as in propositional logic, validity of formulas
is a co-NP-complete problem. And third, we present a provably
correct implementation of a goal-oriented algorithm for validity.A cost-benefit analysis of continuous assessment
http://hdl.handle.net/2117/82536
A cost-benefit analysis of continuous assessment
Duch Brown, Amalia; Gabarró Vallès, Joaquim; Petit Silvestre, Jordi; Blesa Aguilera, Maria Josep; Serna Iglesias, María José
The first course on programming is fundamental in the Facultat d’Informàtica de Barcelona. After a major redesign of the Programming-1 course in 2006 to give it a more practical flavor, an increasing number of measures have been undertaken over the years to try to increase its pass rate while maintaining a fixed quality level. These measures, that can be roughly summarized as an important increase in assessment, imply an increase in the workload of both students and instructors that does not always correspond to the increase of pass rate they provide. In this paper, and within the context of this course, we analyze quantitatively the amount of work required from faculty to implement the series of measures and we conclude that, within this course, continuous assessment is expensive and has reached its limit.
Thu, 04 Feb 2016 09:49:32 GMThttp://hdl.handle.net/2117/825362016-02-04T09:49:32ZDuch Brown, AmaliaGabarró Vallès, JoaquimPetit Silvestre, JordiBlesa Aguilera, Maria JosepSerna Iglesias, María JoséThe first course on programming is fundamental in the Facultat d’Informàtica de Barcelona. After a major redesign of the Programming-1 course in 2006 to give it a more practical flavor, an increasing number of measures have been undertaken over the years to try to increase its pass rate while maintaining a fixed quality level. These measures, that can be roughly summarized as an important increase in assessment, imply an increase in the workload of both students and instructors that does not always correspond to the increase of pass rate they provide. In this paper, and within the context of this course, we analyze quantitatively the amount of work required from faculty to implement the series of measures and we conclude that, within this course, continuous assessment is expensive and has reached its limit.A hierarchical perspective to fuzzy inductive reasoning: an attempt to obtain more understandable fuzzy inductive reasoning rules
http://hdl.handle.net/2117/82363
A hierarchical perspective to fuzzy inductive reasoning: an attempt to obtain more understandable fuzzy inductive reasoning rules
Bagherpour, Solmaz; Múgica Álvarez, Francisco; Nebot Castells, M. Àngela
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques, involvement of human experts is crucial. In this paper we are proposing a Hierarchical perspective to Fuzzy Inductive Reasoning (FIR) method as a classifier, in order to provide more insights for experts to the predictive model offered by FIR. Also, This method puts a hierarchical constrain on FIR's generalization which might be useful in finding and predicting exceptional cases of data that don't follow the general rule offered by the model.
Mon, 01 Feb 2016 15:25:10 GMThttp://hdl.handle.net/2117/823632016-02-01T15:25:10ZBagherpour, SolmazMúgica Álvarez, FranciscoNebot Castells, M. ÀngelaGeneralizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques, involvement of human experts is crucial. In this paper we are proposing a Hierarchical perspective to Fuzzy Inductive Reasoning (FIR) method as a classifier, in order to provide more insights for experts to the predictive model offered by FIR. Also, This method puts a hierarchical constrain on FIR's generalization which might be useful in finding and predicting exceptional cases of data that don't follow the general rule offered by the model.A fuzzy inductive approach for rule-based modelling of high level structures in algorithmic composition systems
http://hdl.handle.net/2117/82353
A fuzzy inductive approach for rule-based modelling of high level structures in algorithmic composition systems
Múgica Álvarez, Francisco; Paz Ortiz, Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique
Algorithmic composition systems are now widely understood. However, its capacity for producing outputs consistently showing high level structures is still a field of research. In the present work, the Fuzzy Inductive Reasoning (FIR) methodology and an extension of it, the Linguistic rules in FIR (LR-FIR) are the main tools chosen for modeling such features. FIR/LR-FIR operates over the produced outputs of an algorithmic composition system, and through qualitative user evaluation is able to extract rules using configurations of low level characteristics that models high level features. Subsequently, the rules are used for the exploration of all possible outputs of an algorithmic system finding a subset of outputs showing the desired property. Finally extracted rules are evaluated and discussed in the context of musical knowledge.
Mon, 01 Feb 2016 14:55:22 GMThttp://hdl.handle.net/2117/823532016-02-01T14:55:22ZMúgica Álvarez, FranciscoPaz Ortiz, IvánNebot Castells, M. ÀngelaRomero Merino, EnriqueAlgorithmic composition systems are now widely understood. However, its capacity for producing outputs consistently showing high level structures is still a field of research. In the present work, the Fuzzy Inductive Reasoning (FIR) methodology and an extension of it, the Linguistic rules in FIR (LR-FIR) are the main tools chosen for modeling such features. FIR/LR-FIR operates over the produced outputs of an algorithmic composition system, and through qualitative user evaluation is able to extract rules using configurations of low level characteristics that models high level features. Subsequently, the rules are used for the exploration of all possible outputs of an algorithmic system finding a subset of outputs showing the desired property. Finally extracted rules are evaluated and discussed in the context of musical knowledge.Approximating the expressive power of logics in finite models
http://hdl.handle.net/2117/82062
Approximating the expressive power of logics in finite models
Arratia Quesada, Argimiro Alejandro; Ortiz, Carlos E.
We present a probability logic (essentially a first order language extended with quantifiers that count the fraction of elements in a model that satisfy a first order formula) which, on the one hand, captures uniform circuit classes such as AC0 and TC0 over arithmetic models, namely, finite structures with linear order and arithmetic relations, and, on the other hand, their semantics, with respect to our arithmetic models, can be closely approximated by giving interpretations of their formulas on finite structures where all relations (including the order) are restricted to be “modular” (i.e. to act subject to an integer modulo). In order to give a precise measure of the proximity between satisfaction of a formula in an arithmetic model and satisfaction of the same formula in the “approximate” model, we define the approximate formulas and work on a notion of approximate truth. We also indicate how to enhance the expressive power of our probability logic in order to capture polynomial time decidable queries.
There are various motivations for this work. As of today, there is not known logical description of any computational complexity class below NP which does not requires a built–in linear order. Also, it is widely recognized that many model theoretic techniques for showing definability in logics on finite structures become almost useless when order is present. Hence, if we want to obtain significant lower bound results in computational complexity via the logical description we ought to find ways of by-passing the ordering restriction. With this work we take steps towards understanding how well can we approximate, without a true order, the expressive power of logics that capture complexity classes on ordered structures.
Tue, 26 Jan 2016 13:41:06 GMThttp://hdl.handle.net/2117/820622016-01-26T13:41:06ZArratia Quesada, Argimiro AlejandroOrtiz, Carlos E.We present a probability logic (essentially a first order language extended with quantifiers that count the fraction of elements in a model that satisfy a first order formula) which, on the one hand, captures uniform circuit classes such as AC0 and TC0 over arithmetic models, namely, finite structures with linear order and arithmetic relations, and, on the other hand, their semantics, with respect to our arithmetic models, can be closely approximated by giving interpretations of their formulas on finite structures where all relations (including the order) are restricted to be “modular” (i.e. to act subject to an integer modulo). In order to give a precise measure of the proximity between satisfaction of a formula in an arithmetic model and satisfaction of the same formula in the “approximate” model, we define the approximate formulas and work on a notion of approximate truth. We also indicate how to enhance the expressive power of our probability logic in order to capture polynomial time decidable queries.
There are various motivations for this work. As of today, there is not known logical description of any computational complexity class below NP which does not requires a built–in linear order. Also, it is widely recognized that many model theoretic techniques for showing definability in logics on finite structures become almost useless when order is present. Hence, if we want to obtain significant lower bound results in computational complexity via the logical description we ought to find ways of by-passing the ordering restriction. With this work we take steps towards understanding how well can we approximate, without a true order, the expressive power of logics that capture complexity classes on ordered structures.A flexible fuzzy inductive reasoning approach for load modelling able to cope with missing data
http://hdl.handle.net/2117/82054
A flexible fuzzy inductive reasoning approach for load modelling able to cope with missing data
Jurado Gómez, Sergio; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco
Load forecasting in buildings and homes has been in recent years a task of increasing importance. New services and functionalities can be offered in the home environment due to this predictions, for instance, the detection of potential demand response programs and peaks that may increase the energy bill in a dynamic tariff framework. Almost real-time predictions are key for these services but missing values can dramatically affect the performance of the energy forecasting or distort the prediction significantly. Fuzzy Inductive Reasoning has been proven to model load consumptions with high accuracy compared to other typical AI and statistical techniques. Nevertheless, it has several limitations when missing data is presented in the training data of the model and during prediction. In this paper, we present an improved version of Fuzzy Inductive Reasoning, called Flexible FIR Prediction that can cope with missing information in the input pattern as well as, in situations where patterns are not found in the behaviour matrix. The new technique has been tested with real data from one building of the Universitat Politècnica de Catalunya (UPC) and the results show that Flexible FIR Prediction is able to generate good predictions with low errors (less than 15%) although missing data is present in the training and online prediction phases.
Tue, 26 Jan 2016 12:48:06 GMThttp://hdl.handle.net/2117/820542016-01-26T12:48:06ZJurado Gómez, SergioNebot Castells, M. ÀngelaMúgica Álvarez, FranciscoLoad forecasting in buildings and homes has been in recent years a task of increasing importance. New services and functionalities can be offered in the home environment due to this predictions, for instance, the detection of potential demand response programs and peaks that may increase the energy bill in a dynamic tariff framework. Almost real-time predictions are key for these services but missing values can dramatically affect the performance of the energy forecasting or distort the prediction significantly. Fuzzy Inductive Reasoning has been proven to model load consumptions with high accuracy compared to other typical AI and statistical techniques. Nevertheless, it has several limitations when missing data is presented in the training data of the model and during prediction. In this paper, we present an improved version of Fuzzy Inductive Reasoning, called Flexible FIR Prediction that can cope with missing information in the input pattern as well as, in situations where patterns are not found in the behaviour matrix. The new technique has been tested with real data from one building of the Universitat Politècnica de Catalunya (UPC) and the results show that Flexible FIR Prediction is able to generate good predictions with low errors (less than 15%) although missing data is present in the training and online prediction phases.The robustness of periodic orchestrations in uncertain evolving environments
http://hdl.handle.net/2117/82017
The robustness of periodic orchestrations in uncertain evolving environments
Castro Rabal, Jorge; Gabarró Vallès, Joaquim; Serna Iglesias, María José; Stewart, Alan
A framework for assessing the robustness of long-duration repetitive orchestrations in uncertain evolving environments is proposed. The model assumes that service-based evaluation environments are stable over short time-frames only; over longer periods service-based environments evolve as demand fluctuates and contention for shared resources varies.
The behaviour of a short-duration orchestration E in a stable environment is assessed by an uncertainty profile U and a corresponding zero-sum angel-daemon game Gamma (U).
Here the angel-daemon approach is extended to assess evolving environments by means of a subfamily of stochastic games. These games are called strategy oblivious because their transition probabilities are strategy independent. It is shown that the value of a strategy oblivious stochastic game is well defined and that it can be computed by solving a linear system. Finally, the proposed stochastic framework is used to assess the evolution of the Gabrmn IT system.
Tue, 26 Jan 2016 08:45:30 GMThttp://hdl.handle.net/2117/820172016-01-26T08:45:30ZCastro Rabal, JorgeGabarró Vallès, JoaquimSerna Iglesias, María JoséStewart, AlanA framework for assessing the robustness of long-duration repetitive orchestrations in uncertain evolving environments is proposed. The model assumes that service-based evaluation environments are stable over short time-frames only; over longer periods service-based environments evolve as demand fluctuates and contention for shared resources varies.
The behaviour of a short-duration orchestration E in a stable environment is assessed by an uncertainty profile U and a corresponding zero-sum angel-daemon game Gamma (U).
Here the angel-daemon approach is extended to assess evolving environments by means of a subfamily of stochastic games. These games are called strategy oblivious because their transition probabilities are strategy independent. It is shown that the value of a strategy oblivious stochastic game is well defined and that it can be computed by solving a linear system. Finally, the proposed stochastic framework is used to assess the evolution of the Gabrmn IT system.Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing
http://hdl.handle.net/2117/81250
Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing
Kaytoue, Mehdi; Codocedo, Victor; Aleksey, Buzmakov; Baixeries i Juvillà, Jaume
This article aims at presenting recent advances in Formal Concept Analysis (2010-2015), especially when the question is dealing with complex data (numbers, graphs, sequences, etc.) in domains such as databases (functional dependencies), data-mining (local pattern discovery), information retrieval and information fusion. As these advances
are mainly published in artificial intelligence and FCA dedicated venues, a dissemination towards data mining and machine learning is worthwhile.
Mon, 11 Jan 2016 18:47:36 GMThttp://hdl.handle.net/2117/812502016-01-11T18:47:36ZKaytoue, MehdiCodocedo, VictorAleksey, BuzmakovBaixeries i Juvillà, JaumeThis article aims at presenting recent advances in Formal Concept Analysis (2010-2015), especially when the question is dealing with complex data (numbers, graphs, sequences, etc.) in domains such as databases (functional dependencies), data-mining (local pattern discovery), information retrieval and information fusion. As these advances
are mainly published in artificial intelligence and FCA dedicated venues, a dissemination towards data mining and machine learning is worthwhile.Brain activity changes induced by open and closed eyes during low-g maneuvers
http://hdl.handle.net/2117/80863
Brain activity changes induced by open and closed eyes during low-g maneuvers
Dubert, D.; Ruiz, Xavier; Gavaldà, Josefina; Pérez Poch, Antoni
The present work reports and discuses the changes in brain
bioelectrical signals induced on normal subjects by open and
closed eyes during their first parabolic flight in a small
aerobatic plane. A parabolic flight maneuver is characterized by
gravity changes from 1g to ~3g (first hypergravity phase, P1) to
~0.05g (hypogravity phase, P2) to ~ 2g (second hypergravity
phase, P3) to 1g (inflight phase, B1). EEG signals have been
obtained using a 14 channels EMOTIV EPOC device. Digital
preprocessing techniques have been applied in order to properly
clean all the experimental signals. Standardized Low Resolution
Brain Electromagnetic Tomography (sLORETA) allowed
obtaining intracranial activity. Statistical analysis of this
intracranial activity was performed by using analysis of
variance techniques (ANOVA). If ANOVA results were
significant, post-hoc analyses were carried out. The results
clearly show a decreasing of the intracranial activity during the
hypogravity phase with open and closed eyes. Concerning mean
values, significant differences have been detected between the
hypogravity, P2, and both hypergravity phases, P1 and P3.
Significant differences have also been detected for open eyes, by
lobes. To check if intracranial activity presents significant
differences along the phases, Even Related Potential, ERP,
analyses were carried out. For both open and closed eyes tests,
sLORETA images show statistical significant differences in the
Brodmann areas 18 (Left Occipital Lobe) and 39 (Right
Temporal Lobe) between B1-P2, respectively.
Thu, 17 Dec 2015 12:22:53 GMThttp://hdl.handle.net/2117/808632015-12-17T12:22:53ZDubert, D.Ruiz, XavierGavaldà, JosefinaPérez Poch, AntoniThe present work reports and discuses the changes in brain
bioelectrical signals induced on normal subjects by open and
closed eyes during their first parabolic flight in a small
aerobatic plane. A parabolic flight maneuver is characterized by
gravity changes from 1g to ~3g (first hypergravity phase, P1) to
~0.05g (hypogravity phase, P2) to ~ 2g (second hypergravity
phase, P3) to 1g (inflight phase, B1). EEG signals have been
obtained using a 14 channels EMOTIV EPOC device. Digital
preprocessing techniques have been applied in order to properly
clean all the experimental signals. Standardized Low Resolution
Brain Electromagnetic Tomography (sLORETA) allowed
obtaining intracranial activity. Statistical analysis of this
intracranial activity was performed by using analysis of
variance techniques (ANOVA). If ANOVA results were
significant, post-hoc analyses were carried out. The results
clearly show a decreasing of the intracranial activity during the
hypogravity phase with open and closed eyes. Concerning mean
values, significant differences have been detected between the
hypogravity, P2, and both hypergravity phases, P1 and P3.
Significant differences have also been detected for open eyes, by
lobes. To check if intracranial activity presents significant
differences along the phases, Even Related Potential, ERP,
analyses were carried out. For both open and closed eyes tests,
sLORETA images show statistical significant differences in the
Brodmann areas 18 (Left Occipital Lobe) and 39 (Right
Temporal Lobe) between B1-P2, respectively.AVALUANT LA COMPETÈNCIA EN SOSTENIBILITAT
http://hdl.handle.net/2117/79499
AVALUANT LA COMPETÈNCIA EN SOSTENIBILITAT
Hernández Gómez, M. Angeles; Segalàs Coral, Jorge
El treball de recerca que es presenta respon a la necessitat d'avaluar la competència en sostenibilitat dins de l'àrea de l'Enginyeria i l'Arquitectura. Primerament es presenta el procediment d'avaluació de les competències en general. A continuació la competència en sostenibilitat i compromís social (CSCS) es analitzada juntament amb el conjunt de competències més específiques. Posteriorment s'analitza el concepte de resultat d'aprenentatge i la relació amb la CSCS. Finalment es presenta la classificació dels resultats d'aprenentatge utilitzant les taxonomies de Bloom i de Krathwohl. Aquesta classificació és el
primer pas per definir una metodologia d'avaluació de la Competència
Thu, 19 Nov 2015 17:40:37 GMThttp://hdl.handle.net/2117/794992015-11-19T17:40:37ZHernández Gómez, M. AngelesSegalàs Coral, JorgeEl treball de recerca que es presenta respon a la necessitat d'avaluar la competència en sostenibilitat dins de l'àrea de l'Enginyeria i l'Arquitectura. Primerament es presenta el procediment d'avaluació de les competències en general. A continuació la competència en sostenibilitat i compromís social (CSCS) es analitzada juntament amb el conjunt de competències més específiques. Posteriorment s'analitza el concepte de resultat d'aprenentatge i la relació amb la CSCS. Finalment es presenta la classificació dels resultats d'aprenentatge utilitzant les taxonomies de Bloom i de Krathwohl. Aquesta classificació és el
primer pas per definir una metodologia d'avaluació de la Competència