ppxscal/embedding_network huggingface dataset:
Approximative Methods for Monotone Systems of Min-Max-Polynomial Equations
Authors: Javier Esparza, Thomas Gawlitza, Stefan Kiefer, Helmut Seidl
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

A monotone system of min-max-polynomial equations (min-max-MSPE) over the variables X1,...,Xn has for every i exactly one equation of the form X i = fi(X 1,...,Xn) where each fi(X1,...,X n) is an expression built up from polynomials with non-negative coefficients, minimum- and maximum-operators. The question of computing least solutions of min-max-MSPEs arises naturally in the analysis of recursive stochastic games [5,6,14]. Min-max-MSPEs generalize MSPEs for which convergence speed results of Newton's method are established in [11,3]. We present the first methods for approximatively computing least solutions of min-max-MSPEs which converge at least linearly. Whereas the first one converges faster, a single step of the second method is cheaper. Furthermore, we compute &epsilon;-optimal positional strategies for the player who wants to maximize the outcome in a recursive stochastic game.
Abstraction and Modelling -- A Complementary Partnership
Authors: Jeff Kramer
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Why is it that some software engineers are able to produce clear, elegant designs and programs, while others cannot? Is it purely a matter of intelligence? One hypothesis is that the answer lies in abstraction: the ability to exhibit abstraction skills and perform abstract thinking and reasoning. Abstraction is a cognitive means by which engineers, mathematicians and others deal with complexity. It covers both aspects of removing detail as well as the identification of generalisations or common features, and has been identified as a crucial skill for software engineering professionals. Is it possible to improve the skills and abilities of those less able through further education and training? Are there any means by which we can measure the abstraction skills of an individual? In this talk, we explore these questions, and argue that abstraction and modelling are complementary partners: that abstraction is the key skill for modelling and that modelling provides a sound means for practising and improving abstraction skills.
Agent-Based Approach to Solving the Resource Constrained Project Scheduling Problem
Authors: Piotr Jedrzejowicz, Ewa Ratajczak-Ropel
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

JABAT is a middleware supporting the construction of the dedicated A-Team architecture that can be used for solving variety of computationally hard optimization problems. The paper includes a general overview of the JABAT followed by a description and evaluation of the architecture designed by the authors with a view to solving RCPSP and MRCPSP instances. To construct the proposed system a number of agents, each representing a different optimization algorithm including local search, tabu search, as well as several specialized heuristics have been used. The system has been evaluated experimentally through solving a set of benchmark instances of the RCPSP and MRCPSP.
A Phylogenetic Approach to Genetic Map Refinement
Authors: Denis Bertrand, Mathieu Blanchette, Nadia El-Mabrouk
Venue: Proceedings of the international workshop on Comparative Genomics (2008)

Following various genetic mapping techniques conducted on different segregating populations, one or more genetic maps are obtained for a given species. However, recombination analyses and other methods for gene mapping often fail to resolve the ordering of some pairs of neighboring markers, thereby leading to sets of markers ambiguously mapped to the same position. Each individual map is thus a partial order defined on the set of markers, and can be represented as a Directed Acyclic Graph (DAG). In this paper, given a phylogenetic tree with a set of DAGs labeling each leaf (species), the goal is to infer, at each leaf, a single combined DAG that is as resolved as possible, considering the complementary information provided by individual maps, and the phylogenetic information provided by the species tree. After combining the individual maps of a leaf into a single DAG, we order incomparable markers by using two successive heuristics for minimizing two distances on the species tree: the breakpoint distance, and the Kemeny distance. We apply our algorithms to the plant species represented in the Gramene database, and we evaluate the simplified maps we obtained.
Application of the Cross Entropy Method to the Credit Risk Assessment in an Early Warning System
Authors: Hong Zhou, Jing Wang, Yue Qiu
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Traditional Monte Carlo method usually takes a long time to simulate rare event, while importance sampling techniques can effectively reduce the simulation time and improve simulation efficiency. In this paper, an importance sampling method – cross entropy is presented to deal with credit risk assessment problems for commercial banks. The failure event of repaying loans is treated as rare event due to the relatively low probability, and the failure probability of repaying loans is taken as the criterion to measure the level of credit risk. Numerical experiments have shown that the cross entropy method has a strong capability to identify the credit risk and it is a good tool for credit risk early warning system.
Validation and Storage of Polyhedra through Constrained Delaunay Tetrahedralization
Authors: Edward Verbree, Hang Si
Venue: Proceedings of the 5th international conference on Geographic Information Science (2008)

Closed, watertight, 3D geometries are represented by polyhedra. Current data models define these polyhedra basically as a set of polygons, leaving the test on intersecting polygons or open gaps to external validation rules. If this testing is not performed well, or not at all, non-valid polyhedra could be stored in geo-databases. This paper proposes the utilization of the Constrained Delaunay Tetrahedralization (CDT) for the validation (i.e. check on self-intersecting and closeness) of polyhedra on the one hand, and the efficient storage of valid polyhedra on the other hand. The paper stresses on the decomposition of a polyhedron through a CDT and the possibility to store and compose the polyhedron through the vertices of the CDT, a bitmap that indicates which faces of the Delaunay Tetrahedralization (DT) links to a CDT-face, and a list of non-recovered CDT-faces.
A Hybrid Intelligent System for Robot Ego Motion Estimation with a 3D Camera
Authors: Ivan Villaverde, Manuel Graña
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

A Hybrid Intelligent System (HIS) for self-localization working on the readings of innovative 3D cameras is presented in this paper. The system includes a preprocessing step for cleaning the 3D camera readings. The HIS consist of two main modules. First the Self-Organizing Map (SOM) is used to provide models of the preprocessed 3D readings of the camera. The 2D grid of the SOM units is assumed as a surface modeling the 3D data obtained from each snapshot of the 3D camera. The second module is an Evolution Strategy, which is used to perform the estimation of the motion of the robot between frames. The fitness function of the Evolution Strategy (ES) is given by the distance computed as the matching of the SOM unit grids.
Mining Web Data for Image Semantic Annotation
Authors: Roberto Basili, Riccardo Petitti, Dario Saracino
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

In this paper, an unsupervised image classification technique combining features from different media levels is proposed. In particular geometrical models of visual features are here integrated with textual descriptions derived through Information Extraction processes from Web pages. While the higher expressivity of the combined individual descriptions increases the complexity of the adopted clustering algorithms, methods for dimensionality reduction (i.e. LSA) are applied effectively. The evaluation on an image classification task confirms that the proposed Web mining model outperforms other methods acting on the individual levels for cost-effective annotation.
Analysis of a Trust and Reputation Model Applied to a Computational Grid Using Software Agents
Authors: Robson de Oliveira Albuquerque, Fernanda Fontes Cohen, Jovelina Lima Teixeira Mota, Rafael Timóteo de Sousa Júnior
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

Trust is an aspect that is treated carefully in Grid environments for its open and decentralized nature. This paper extends the discussion in terms of the behavior of nodes members of a computational Grid which applies trust concepts as decision criteria for jobs delegation using multi-agent systems. Also is presented an analysis of trust issues between computational Grid nodes in the moment of task delegation. A trust model was implemented on a multi-agents system simulating a Grid environment in order to make agents interactions and analyze trust situations. The results of the implementation consider when personal experience of the agents dismisses the reputation information and how dishonest opinions influence trust calculation.
A Relaxation Approach to Dynamic Sensor Selection in Large-Scale Wireless Networks
Authors: James E. Weimer, Bruno Sinopoli, Bruce H. Krogh
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

Wireless sensor networks (WSNs) require more complex sensor selection strategies than other distributed networks to perform optimal state estimation. In addition to constraints associated with distributed state estimation, wireless sensor networks have limitations on bandwidth, energy consumption, and transmission range. This paper introduces and empirically evaluates a dynamic sensor selection strategy. A discrete-time Kalman filter is used for state estimation. At each time step, a subset of sensors is selected to gather data on the following time step because of power and bandwidth constraints that prohibit using all of the sensors. A standard criterion for selecting this subset of sensors is to maximize the information to be gained by minimizing a function of the next-step error covariance matrix. We propose a relaxation of this non-convex combinatorial optimization problem and demonstrate its applicability to large-scale sensor networks. The proposed dynamic sensor selection strategy is compared empirically to other dynamic and static sensor selection strategies with respect to state estimation performance of a convection-dispersion field arising from the problem of surface-based monitoring of CO$_2$ sequestration sites.
Detecting Motion in the Environment with a Moving Quadruped Robot
Authors: Peggy Fidelman, Thayne Coffman, Risto Miikkulainen
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

For a robot in a dynamic environment, the ability to detect motion is crucial. Motion often indicates areas of the robot's surroundings that are changing, contain another agent, or are otherwise worthy of attention. Although legs are arguably the most versatile means of locomotion for a robot, and thus the best suited to an unknown or changing domain, existing methods for motion detection either require that the robot have wheels or that its walking be extremely slow and tightly constrained. This paper presents a method for detecting motion from a quadruped robot walking at its top speed. The method is based on a neural network that learns to predict optic flow caused by its walk, thus allowing environment motion to be detected as anomalies in the flow. The system is demonstrated to be capable of detecting motion in the robot's surroundings, forming a foundation for intelligently directed behavior in complex, changing environments.
Population-Based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization
Authors: Min-Rong Chen, Yong-Zai Lu, Genke Yang
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

Recently, a local-search heuristic algorithm called Extremal Optimization (EO) has been successfully applied in some combinatorial optimization problems. However, there are only limited papers studying on the mechanism of EO applied to the numerical optimization problems so far. This paper presents the studies on the applications of EO to numerical constrained optimization problems with a set of popular benchmark problems. To enhance and improve the search performance and efficiency of EO, we developed a novel EO strategy with population based search. The newly developed EO algorithm is named population-based EO (PEO). Additionally, we adopted the adaptive Lévy mutation, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Compared with three state-of-the-art stochastic search methods with six popular benchmark problems, it has been shown that our approach is a good choice to deal with the numerical constrained optimization problems.
Semantic Foundations and Inference of Non-null Annotations
Authors: Laurent Hubert, Thomas Jensen, David Pichardie
Venue: Proceedings of the 10th IFIP WG 6.1 international conference on Formal Methods for Open Object-Based Distributed Systems (2008)

This paper proposes a semantics-based automatic null pointer analysis for inferring non-null annotations of fields in object-oriented programs. The analysis is formulated for a minimalistic OO language and is expressed as a constraint-based abstract interpretation of the program which for each field of a class infers whether the field is definitely non-null or possibly null after object initialization. The analysis is proved correct with respect to an operational semantics of the minimalistic OO language. This correctness proof has been machine checked using the Coq proof assistant. We also prove the analysis complete with respect to the non-null type system proposed by Fähndrich and Leino, in the sense that for every typable program the analysis is able to prove the absence of null dereferences without any hand-written annotations. Experiments with a prototype implementation of the analysis show that the inference is feasible for large programs.
Route Learning Strategies in a Virtual Cluttered Environment
Authors: Rebecca Hurlebaus, Kai Basten, Hanspeter A. Mallot, Jan M. Wiener
Venue: Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space (2008)

Here we present an experiment investigating human route learning behavior. Specific interest concerned the learning strategies as well as the underlying spatial knowledge. In the experiment naive participants were asked to learn a path between two locations in a complex, cluttered virtual environment that featured local and global landmark information. Participants were trained for several days until they solved the wayfinding task fastly and efficiently. The analysis of individual navigation behavior demontrates strong interindividual differences suggesting different route learning strategies: while some participants were very conservative in their route choices, always selecting the same route, other participants showed a high variability in their route choices. In the subsequent test phase we systematically varied the availability of local and global landmark information to gain first insights into the spatial knowledge underlying these different behavior. Participants showing high variability in route choices strongly depended on global landmark information. Moreover, participants who were conservative in their route choices were able to reproduce the basic form of the learned routes even without any local landmark information, suggesting that their route memory contained metric information. The results of this study suggest two alternative strategies for solving route learning and wayfinding tasks that are reflected in the spatial knowledge aquired during learning.
Grid-Enabled Workflow Management System Based On BPEL
Authors: Ru-Yue Ma, Yong-Wei Wu, Xiang-Xu Meng, Shi-Jun Liu, Li Pan
Venue: International Journal of High Performance Computing Applications (2008)

A grid-enabled workflow management system provides a set of tools to facilitate building high-level grid application services by orchestrating low-level grid services. BPEL (Business Process Execution Language) is the de facto standard in the Web service community. The purpose of this paper is to design and implement a workflow management system based on BPEL in a grid environment. The approach to integrating BPEL with WSRF (Web Services Resource Framework), is to put forward a simple cluster model of BPEL engines and application component oriented workflow defining respectively to satisfy the specific requirements in a grid environment, such as stateful interactions, dynamic service binding, high throughput and scalability, and friendly workflow defining. Finally, a prototype of a workflow system implemented in ChinaGrid Supporting Platform (CGSP) is also given.
Logical Interpretation: Static Program Analysis Using Theorem Proving
Authors: Ashish Tiwari, Sumit Gulwani
Venue: Proceedings of the 21st international conference on Automated Deduction: Automated Deduction (2007)

This paper presents the foundations for using automated deduction technology in static program analysis. The central principle is the use of logical lattices --- a class of lattices defined on logical formulas in a logical theory --- in an abstract interpretation framework. Abstract interpretation over logical lattices, called logical interpretation, raises new challenges for theorem proving. We present an overview of some of the existing results in the field of logical interpretation and outline some requirements for building expressive and scalable logical interpreters.
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Authors: Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of finding solution trajectories for such problems can be reduced by incorporating limited prior knowledge of the approximative local system dynamics. The presented algorithm builds an adaptive state graph of sample points within the continuous state space. The nodes of the graph are generated by an efficient principled exploration scheme that directs the agent towards promising regions, while maintaining good online performance. Global solution trajectories are formed as combinations of local controllers that connect nodes of the graph, thereby naturally allowing continuous actions and continuous time steps. We demonstrate our approach on various movement planning tasks in continuous domains.
Adaptive Control Using a Grey Box Neural Model: An Experimental Application
Authors: Francisco A. Cubillos, Gonzalo Acuña
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper presents the application of a Grey Box Neural Model (GNM) in adaptive-predictive control of the combustion chamber temperature of a pilot-scale vibrating fluidized dryer. The GNM is based upon a phenomenological model of the process and a neural network that estimates uncertain parameters. The GNM was synthesized considering the energy balance and a radial basis function neural network (RBF) trained on-line to estimate heat losses. This predictive model was then incorporated into a predictive control strategy with one step look-ahead. The proposed system shows excellent results with regard to adaptability, predictability and control when subject to setpoint and disturbances changes.
Test Strategy Generation Using Quantified CSPs
Authors: Martin Sachenbacher, Paul Maier
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

Testing is the process of stimulating a system with inputs in order to reveal hidden parts of the system state. We consider a variant of the constraint-based testing problem that was put forward in the model-based diagnosis literature, and consists of finding input patterns that can discriminate between different, possibly non-deterministic models. We show that this problem can be framed as a game played between two opponents, and naturally lends itself towards a formulation in terms of quantified CSPs. This QCSP-based formulation is a starting point to extend testing to the practically relevant class of systems with limited controllability, where tests consist of stimulation strategies instead of simple input patterns.
Answer Extracting Based on Passage Retrieval in Chinese Question Answering System
Authors: Zhengtao Yu, Lu Han, Cunli Mao, Yunwei Li, Yanxia Qiu, Xiangyan Meng
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Passage retrieval is a base of answer extracting in Chinese question answering system.In order to improve the precision of the passage retrieval which includes answers, this paper analyze first existing passage retrieval methods,and point to their shortcomings, then put forward a new method of passage retrieval, in which factors are considered ,such as frequency of query words and query expansion words in passage, the length of passage, the distribution density and minimum match span of query words and query expansion words in passage ,when calculating passage weight.The new method can realize passage retrieval on the basis of the weight calculation, and obtain optimization answers passage. Final, experiment result of answer extracting based on passage retrieval have shown that the method given has better effect.
On Field Size and Success Probability in Network Coding
Authors: Olav Geil, Ryutaroh Matsumoto, Casper Thomsen
Venue: Proceedings of the 2nd international workshop on Arithmetic of Finite Fields (2008)

Using tools from algebraic geometry and Gröbner basis theory we solve two problems in network coding. First we present a method to determine the smallest field size for which linear network coding is feasible. Second we derive improved estimates on the success probability of random linear network coding. These estimates take into account which monomials occur in the support of the determinant of the product of Edmonds matrices. Therefore we finally investigate which monomials can occur in the determinant of the Edmonds matrix.