ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
http://hdl.handle.net/2117/3092
2018-02-23T10:59:43ZSymbolic model generation for graph properties
http://hdl.handle.net/2117/114397
Symbolic model generation for graph properties
Schneider, Sven; Lambers, Leen; Orejas Valdés, Fernando
Graphs are ubiquitous in Computer Science. For this reason, in many areas, it is very important to have the means to express and reason about graph properties. In particular, we want to be able to check automatically if a given graph property is satisfiable. Actually, in most application scenarios it is desirable to be able to explore graphs satisfying the graph property if they exist or even to get a complete and compact overview of the graphs satisfying the graph property.
We show that the tableau-based reasoning method for graph properties as introduced by Lambers and Orejas paves the way for a symbolic model generation algorithm for graph properties. Graph properties are formulated in a dedicated logic making use of graphs and graph morphisms, which is equivalent to first-order logic on graphs as introduced by Courcelle. Our parallelizable algorithm gradually generates a finite set of so-called symbolic models, where each symbolic model describes a set of finite graphs (i.e., finite models) satisfying the graph property. The set of symbolic models jointly describes all finite models for the graph property (complete) and does not describe any finite graph violating the graph property (sound). Moreover, no symbolic model is already covered by another one (compact). Finally, the algorithm is able to generate from each symbolic model a minimal finite model immediately and allows for an exploration of further finite models. The algorithm is implemented in the new tool AutoGraph.
2018-02-23T09:50:06ZSchneider, SvenLambers, LeenOrejas Valdés, FernandoGraphs are ubiquitous in Computer Science. For this reason, in many areas, it is very important to have the means to express and reason about graph properties. In particular, we want to be able to check automatically if a given graph property is satisfiable. Actually, in most application scenarios it is desirable to be able to explore graphs satisfying the graph property if they exist or even to get a complete and compact overview of the graphs satisfying the graph property.
We show that the tableau-based reasoning method for graph properties as introduced by Lambers and Orejas paves the way for a symbolic model generation algorithm for graph properties. Graph properties are formulated in a dedicated logic making use of graphs and graph morphisms, which is equivalent to first-order logic on graphs as introduced by Courcelle. Our parallelizable algorithm gradually generates a finite set of so-called symbolic models, where each symbolic model describes a set of finite graphs (i.e., finite models) satisfying the graph property. The set of symbolic models jointly describes all finite models for the graph property (complete) and does not describe any finite graph violating the graph property (sound). Moreover, no symbolic model is already covered by another one (compact). Finally, the algorithm is able to generate from each symbolic model a minimal finite model immediately and allows for an exploration of further finite models. The algorithm is implemented in the new tool AutoGraph.The parallel complexity of positive linear programming
http://hdl.handle.net/2117/114140
The parallel complexity of positive linear programming
Trevisan, Luca; Xhafa Xhafa, Fatos
In this paper we study the parallel complexity of Positive Linear Programming (PLP), i.e. the special case of Linear Programming in packing/covering form where the input constraint matrix and constraint vector consist entirely of positive entries. We show that the problem of exactly solving PLP is P-complete.
2018-02-15T11:36:16ZTrevisan, LucaXhafa Xhafa, FatosIn this paper we study the parallel complexity of Positive Linear Programming (PLP), i.e. the special case of Linear Programming in packing/covering form where the input constraint matrix and constraint vector consist entirely of positive entries. We show that the problem of exactly solving PLP is P-complete.Uncertainty in basic short-term macroeconomic models with angel-daemon games
http://hdl.handle.net/2117/114068
Uncertainty in basic short-term macroeconomic models with angel-daemon games
Gabarró Vallès, Joaquim; Serna Iglesias, María José
We propose the use of an angel-daemon framework to perform an uncertainty analysis of short-term macroeconomic models. The angel-daemon framework defines a strategic game where two agents, the angel and the daemon, act selfishly. These games are defined over an uncertainty profile which presents a short and macroscopic description of a perturbed situation. The Nash equilibria on these games provide stable strategies in perturbed situations, giving a natural estimation of uncertainty. We apply the framework to the uncertainty analysis of linear versions of the IS-LM and the IS-MP models.
2018-02-12T18:31:46ZGabarró Vallès, JoaquimSerna Iglesias, María JoséWe propose the use of an angel-daemon framework to perform an uncertainty analysis of short-term macroeconomic models. The angel-daemon framework defines a strategic game where two agents, the angel and the daemon, act selfishly. These games are defined over an uncertainty profile which presents a short and macroscopic description of a perturbed situation. The Nash equilibria on these games provide stable strategies in perturbed situations, giving a natural estimation of uncertainty. We apply the framework to the uncertainty analysis of linear versions of the IS-LM and the IS-MP models.Construct, merge, solve and adapt versus large neighborhood search for solving the multi-dimensional Knapsack problem: Which one works better when?
http://hdl.handle.net/2117/114023
Construct, merge, solve and adapt versus large neighborhood search for solving the multi-dimensional Knapsack problem: Which one works better when?
Lizárraga Olivas, Evelia; Blesa Aguilera, Maria Josep; Blum, Christian
Both, Construct, Merge, Solve and Adapt (CMSA) and Large Neighborhood Search (LNS), are hybrid algorithms that are based on iteratively solving sub-instances of the original problem instances, if possible, to optimality. This is done by reducing the search space of the tackled problem instance in algorithm-specific ways which differ from one technique to the other. In this paper we provide first experimental evidence for the intuition that, conditioned by the way in which the search space is reduced, LNS should generally work better than CMSA in the context of problems in which solutions are rather large, and the opposite is the case for problems in which solutions are rather small. The size of a solution is hereby measured by the number of components of which the solution is composed, in comparison to the total number of solution components. Experiments are conducted in the context of the multi-dimensional knapsack problem.
2018-02-12T09:29:45ZLizárraga Olivas, EveliaBlesa Aguilera, Maria JosepBlum, ChristianBoth, Construct, Merge, Solve and Adapt (CMSA) and Large Neighborhood Search (LNS), are hybrid algorithms that are based on iteratively solving sub-instances of the original problem instances, if possible, to optimality. This is done by reducing the search space of the tackled problem instance in algorithm-specific ways which differ from one technique to the other. In this paper we provide first experimental evidence for the intuition that, conditioned by the way in which the search space is reduced, LNS should generally work better than CMSA in the context of problems in which solutions are rather large, and the opposite is the case for problems in which solutions are rather small. The size of a solution is hereby measured by the number of components of which the solution is composed, in comparison to the total number of solution components. Experiments are conducted in the context of the multi-dimensional knapsack problem.A hybrid evolutionary algorithm based on solution merging for the longest arc-preserving common subsequence problem
http://hdl.handle.net/2117/114020
A hybrid evolutionary algorithm based on solution merging for the longest arc-preserving common subsequence problem
Blum, Christian; Blesa Aguilera, Maria Josep
The longest arc-preserving common subsequence problem is an NP-hard combinatorial optimization problem from the field of computational biology. This problem finds applications, in particular, in the comparison of art-annotated ribonucleic acid (RNA) sequences. In this work we propose a simple, hybrid evolutionary algorithm to tackle this problem. The most important feature of this algorithm concerns a crossover operator based on solution merging. In solution merging, two or more solutions to the problem are merged, and an exact technique is used to find the best solution within this union. It is experimentally shown that the proposed algorithm outperforms a heuristic from the literature.
2018-02-12T08:33:14ZBlum, ChristianBlesa Aguilera, Maria JosepThe longest arc-preserving common subsequence problem is an NP-hard combinatorial optimization problem from the field of computational biology. This problem finds applications, in particular, in the comparison of art-annotated ribonucleic acid (RNA) sequences. In this work we propose a simple, hybrid evolutionary algorithm to tackle this problem. The most important feature of this algorithm concerns a crossover operator based on solution merging. In solution merging, two or more solutions to the problem are merged, and an exact technique is used to find the best solution within this union. It is experimentally shown that the proposed algorithm outperforms a heuristic from the literature.An angel-daemon approach to assess the uncertainty in the power of a collectivity to act
http://hdl.handle.net/2117/114018
An angel-daemon approach to assess the uncertainty in the power of a collectivity to act
Fragnito, Giulia; Gabarró Vallès, Joaquim; Serna Iglesias, María José
We propose the use of the angel-daemon framework to assess the Coleman's power of a collectivity to act under uncertainty in weighted voting games.
In this framework uncertainty profiles describe the potential changes in the weights of a weighted game and fixes the spread of the weights' change. For each uncertainty profile a strategic angel-daemon game can be considered. This game has two selfish players, the angel and the daemon, the angel selects its action as to maximize the effect on the measure under consideration while daemon acts oppositely.
Players angel and daemon give a balance between the best and the worst. The angel-daemon games associated to the Coleman's power are zero-sum games and therefore the expected utilities of all the Nash equilibria
are the same. In this way we can asses the Coleman's power under uncertainty. Besides introducing the framework for this particular setting we analyse basic properties and make some computational complexity considerations. We provide several examples based in the evolution of the voting rules of the EU Council of Ministers.
The final publication is available at link.springer.com
2018-02-12T08:02:40ZFragnito, GiuliaGabarró Vallès, JoaquimSerna Iglesias, María JoséWe propose the use of the angel-daemon framework to assess the Coleman's power of a collectivity to act under uncertainty in weighted voting games.
In this framework uncertainty profiles describe the potential changes in the weights of a weighted game and fixes the spread of the weights' change. For each uncertainty profile a strategic angel-daemon game can be considered. This game has two selfish players, the angel and the daemon, the angel selects its action as to maximize the effect on the measure under consideration while daemon acts oppositely.
Players angel and daemon give a balance between the best and the worst. The angel-daemon games associated to the Coleman's power are zero-sum games and therefore the expected utilities of all the Nash equilibria
are the same. In this way we can asses the Coleman's power under uncertainty. Besides introducing the framework for this particular setting we analyse basic properties and make some computational complexity considerations. We provide several examples based in the evolution of the voting rules of the EU Council of Ministers.Economía aplicada al estudio de la evolución de un curso
http://hdl.handle.net/2117/113931
Economía aplicada al estudio de la evolución de un curso
Blesa Aguilera, Maria Josep; Duch Brown, Amalia; Gabarró Vallès, Joaquim; Petit Silvestre, Jordi; Serna Iglesias, María José
In this paper we propose a method to quantitatively analyze the effectiveness of the different measures taken to improve a course’s evaluation process. Our proposal uses simple tools from
economics and the social sciences. First, we analyze the effect of these measures on the different categories of grades obtained by the students in relation to the increment of faculty’s workload by
means of a marginal cost-benefit approach. In second place, we analyze inequality using different types of data disaggregation in several subpopulations related to the students’ grades. Finally, we
study the evolution of statistical indicators such as mean and satisfaction.
This method is applied to the study of the CS1 at the Facultat d’Informàtica de Barcelona of the Universitat Politècnica de Catalunya along the first 5 years after the introduction of the new degree in Computer Engineering. The proposed methodology aims to introduce new techniques that allow an objective analysis of the impact of educational measures in any university course.; En este trabajo se propone un método para analizar cuantitativamente la efectividad de las diferentes medidas adoptadas para mejorar el proceso de evaluación continua de una asignatura. Nuestra propuesta utiliza herramientas simples provenientes de la economía y de las ciencias sociales. Primero se analiza el efecto de dichas medidas sobre las categorías de calificaciones obtenidas por los estudiantes en relación al incremento de carga docente del profesorado. En el estudio de la evolución de las calificaciones y el trabajo de los profesores a lo largo del tiempo se utiliza una aproximación coste-beneficio marginal. En segundo lugar se realiza un análisis de desigualdad utilizando distintos tipos de desagregación de datos y en varias subpoblaciones relacionadas con las calificaciones de los estudiantes. Finalmente se analiza la evolución de indicadores estadísticos como la media y la satisfacción. Dicho método se aplica al estudio de la asignatura de Programación-1 de la Facultat d’Informàtica de Barcelona de la Universitat Politècnica de Catalunya en los primeros 5 cursos de implantación del Grado en Ingeniería Informática. La metodología propuesta pretende introducir nuevas técnicas que permitan un análisis objetivo del impacto de medidas docentes en cualquier asignatura universitaria.
2018-02-08T07:53:54ZBlesa Aguilera, Maria JosepDuch Brown, AmaliaGabarró Vallès, JoaquimPetit Silvestre, JordiSerna Iglesias, María JoséIn this paper we propose a method to quantitatively analyze the effectiveness of the different measures taken to improve a course’s evaluation process. Our proposal uses simple tools from
economics and the social sciences. First, we analyze the effect of these measures on the different categories of grades obtained by the students in relation to the increment of faculty’s workload by
means of a marginal cost-benefit approach. In second place, we analyze inequality using different types of data disaggregation in several subpopulations related to the students’ grades. Finally, we
study the evolution of statistical indicators such as mean and satisfaction.
This method is applied to the study of the CS1 at the Facultat d’Informàtica de Barcelona of the Universitat Politècnica de Catalunya along the first 5 years after the introduction of the new degree in Computer Engineering. The proposed methodology aims to introduce new techniques that allow an objective analysis of the impact of educational measures in any university course.
En este trabajo se propone un método para analizar cuantitativamente la efectividad de las diferentes medidas adoptadas para mejorar el proceso de evaluación continua de una asignatura. Nuestra propuesta utiliza herramientas simples provenientes de la economía y de las ciencias sociales. Primero se analiza el efecto de dichas medidas sobre las categorías de calificaciones obtenidas por los estudiantes en relación al incremento de carga docente del profesorado. En el estudio de la evolución de las calificaciones y el trabajo de los profesores a lo largo del tiempo se utiliza una aproximación coste-beneficio marginal. En segundo lugar se realiza un análisis de desigualdad utilizando distintos tipos de desagregación de datos y en varias subpoblaciones relacionadas con las calificaciones de los estudiantes. Finalmente se analiza la evolución de indicadores estadísticos como la media y la satisfacción. Dicho método se aplica al estudio de la asignatura de Programación-1 de la Facultat d’Informàtica de Barcelona de la Universitat Politècnica de Catalunya en los primeros 5 cursos de implantación del Grado en Ingeniería Informática. La metodología propuesta pretende introducir nuevas técnicas que permitan un análisis objetivo del impacto de medidas docentes en cualquier asignatura universitaria.Hybrid techniques based on solving reduced problem instances for a longest common subsequence problem
http://hdl.handle.net/2117/113930
Hybrid techniques based on solving reduced problem instances for a longest common subsequence problem
Blum, Christian; Blesa Aguilera, Maria Josep
Finding the longest common subsequence of a given set of input strings is a relevant problem arising in various practical settings. One of these problems is the so-called longest arc-preserving common subsequence problem. This NP-hard combinatorial optimization problem was introduced for the comparison of arc-annotated ribonucleic acid (RNA) sequences. In this work we present an integer linear programming (ILP) formulation of the problem. As even in the context of rather small problem instances the application of a general purpose ILP solver is not viable due to the size of the model, we study alternative ways based on model reduction in order to take profit from this ILP model. First, we present a heuristic way for reducing the model, with the subsequent application of an ILP solver. Second, we propose the application of an iterative hybrid algorithm that makes use of an ILP solver for generating high quality solutions at each iteration. Experimental results concerning artificial and real problem instances show that the proposed techniques outperform an available technique from the literature.
2018-02-08T07:26:47ZBlum, ChristianBlesa Aguilera, Maria JosepFinding the longest common subsequence of a given set of input strings is a relevant problem arising in various practical settings. One of these problems is the so-called longest arc-preserving common subsequence problem. This NP-hard combinatorial optimization problem was introduced for the comparison of arc-annotated ribonucleic acid (RNA) sequences. In this work we present an integer linear programming (ILP) formulation of the problem. As even in the context of rather small problem instances the application of a general purpose ILP solver is not viable due to the size of the model, we study alternative ways based on model reduction in order to take profit from this ILP model. First, we present a heuristic way for reducing the model, with the subsequent application of an ILP solver. Second, we propose the application of an iterative hybrid algorithm that makes use of an ILP solver for generating high quality solutions at each iteration. Experimental results concerning artificial and real problem instances show that the proposed techniques outperform an available technique from the literature.A datalog framework for modeling relationship-based access control policies
http://hdl.handle.net/2117/113608
A datalog framework for modeling relationship-based access control policies
Pasarella Sánchez, Ana Edelmira; Lobo, Jorge
Relationships like friendship to limit access to resources have been part of social network applications since their beginnings. Describing access control policies in terms of relationships is not particular to social networks and it arises naturally in many situations. Hence, we have recently seen several proposals formalizing different Relationship-based Access Control (ReBAC) models. In this paper, we introduce a class of Datalog programs suitable for modeling ReBAC and argue that this class of programs, that we called ReBAC Datalog policies, provides a very general framework to specify and implement ReBAC policies. To support our claim, we first formalize the merging of two recent proposals for modeling ReBAC, one based on hybrid logic and the other one based on path regular expressions. We present extensions to handle negative authorizations and temporal policies. We describe mechanism for policy analysis, and then discuss the feasibility of using Datalog-based systems as implementations.
SACMAT'17 Best paper
2018-02-02T10:13:31ZPasarella Sánchez, Ana EdelmiraLobo, JorgeRelationships like friendship to limit access to resources have been part of social network applications since their beginnings. Describing access control policies in terms of relationships is not particular to social networks and it arises naturally in many situations. Hence, we have recently seen several proposals formalizing different Relationship-based Access Control (ReBAC) models. In this paper, we introduce a class of Datalog programs suitable for modeling ReBAC and argue that this class of programs, that we called ReBAC Datalog policies, provides a very general framework to specify and implement ReBAC policies. To support our claim, we first formalize the merging of two recent proposals for modeling ReBAC, one based on hybrid logic and the other one based on path regular expressions. We present extensions to handle negative authorizations and temporal policies. We describe mechanism for policy analysis, and then discuss the feasibility of using Datalog-based systems as implementations.Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment
http://hdl.handle.net/2117/113030
Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment
Ferragina, Paolo; Giancarlo, Raffaele; Greco, Valentina; Manzini, Giovanni; Valiente Feruglio, Gabriel Alejandro
Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. It is currently primarily handled using alignments. However, the alignment methods seem inadequate for post-genomic studies since they do not scale well with data set size and they seem to be confined only to genomic and proteomic sequences. Therefore, alignment-free similarity measures are actively pursued. Among those, USM (Universal Similarity Metric) has gained prominence. It is based on the deep theory of Kolmogorov Complexity and universality is its most novel striking feature. Since it can only be approximated via data compression, USM is a methodology rather than a formula quantifying the similarity of two strings. Three approximations of USM are available, namely UCD (Universal Compression Dissimilarity), NCD (Normalized Compression Dissimilarity) and CD (Compression Dissimilarity). Their applicability and robustness is tested on various data sets yielding a first massive quantitative estimate that the USM methodology and its approximations are of value. Despite the rich theory developed around USM, its experimental assessment has limitations: only a few data compressors have been tested in conjunction with USM and mostly at a qualitative level, no comparison among UCD, NCD and CD is available and no comparison of USM with existing methods, both based on alignments and not, seems to be available.
Results:
We experimentally test the USM methodology by using 25 compressors, all three of its known approximations and six data sets of relevance to Molecular Biology. This offers the first systematic and quantitative experimental assessment of this methodology, that naturally complements the many theoretical and the preliminary experimental results available. Moreover, we compare the USM methodology both with methods based on alignments and not. We may group our experiments into two sets. The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the methodology to discriminate and classify biological sequences and structures. A second set of experiments aims at assessing how well two commonly available classification algorithms, UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and NJ (Neighbor Joining), can use the methodology to perform their task, their performance being evaluated against gold standards and with the use of well known statistical indexes, i.e., the F-measure and the partition distance. Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of USM on biological data. The main ones are reported next.
2018-01-22T10:08:57ZFerragina, PaoloGiancarlo, RaffaeleGreco, ValentinaManzini, GiovanniValiente Feruglio, Gabriel AlejandroSimilarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. It is currently primarily handled using alignments. However, the alignment methods seem inadequate for post-genomic studies since they do not scale well with data set size and they seem to be confined only to genomic and proteomic sequences. Therefore, alignment-free similarity measures are actively pursued. Among those, USM (Universal Similarity Metric) has gained prominence. It is based on the deep theory of Kolmogorov Complexity and universality is its most novel striking feature. Since it can only be approximated via data compression, USM is a methodology rather than a formula quantifying the similarity of two strings. Three approximations of USM are available, namely UCD (Universal Compression Dissimilarity), NCD (Normalized Compression Dissimilarity) and CD (Compression Dissimilarity). Their applicability and robustness is tested on various data sets yielding a first massive quantitative estimate that the USM methodology and its approximations are of value. Despite the rich theory developed around USM, its experimental assessment has limitations: only a few data compressors have been tested in conjunction with USM and mostly at a qualitative level, no comparison among UCD, NCD and CD is available and no comparison of USM with existing methods, both based on alignments and not, seems to be available.
Results:
We experimentally test the USM methodology by using 25 compressors, all three of its known approximations and six data sets of relevance to Molecular Biology. This offers the first systematic and quantitative experimental assessment of this methodology, that naturally complements the many theoretical and the preliminary experimental results available. Moreover, we compare the USM methodology both with methods based on alignments and not. We may group our experiments into two sets. The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the methodology to discriminate and classify biological sequences and structures. A second set of experiments aims at assessing how well two commonly available classification algorithms, UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and NJ (Neighbor Joining), can use the methodology to perform their task, their performance being evaluated against gold standards and with the use of well known statistical indexes, i.e., the F-measure and the partition distance. Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of USM on biological data. The main ones are reported next.