ppxscal/embedding_network huggingface dataset:
Fuzzy multi-objective optimization modeling with mathematica
Authors: Andre A. Keller
Venue: Proceedings of the 10th WSEAS international conference on Fuzzy systems (2009)

In the real situations, decision makers are often faced to a plurality of objectives and constraints in a world of imprecise data about the preferences of agents, the local constraints and the global environment. In a fuzzy environment, fuzzy linear programming (FLP) and fuzzy goal programming (FGP) problems introduce fuzzy objective functions and constraints, fuzzy parameter and variable sets. Mathematical operators are used to aggregate the fuzzy objective functions and constraints. The optimal solution corresponds to the maximum degree of the membership function in the decision set. The resolution of the multi-objective FLP consists in reducing the vector optimization of objective functions to a single objective. Weighted goal programming problems consider the relative importance of objectives. This contribution presents essential techniques with numerical applications to simple economic problems. The computations are carried out using the software MATHEMATICA® 7.0.1 and the sub-package Fuzzy Logic 2, whose selected primitives are proposed.
Bisimulation Minimisation of Weighted Automata on Unranked Trees
Authors: Johanna Högberg, Andreas Maletti, Heiko Vogler
Venue: Fundamenta Informaticae (2009)

Several models of automata are available that operate unranked trees. Two well-known examples are the stepwise unranked tree automaton (suta) and the parallel unranked tree automaton (puta). By adding a weight, taken from some semiring, to every transition we generalise these two qualitative automata models to quantitative models, thereby obtaining weighted stepwise unranked tree automata (wsuta) and weighted parallel unranked tree automata (wputa); the qualitative automata models are reobtained by choosing the BOOLEAN semiring. The weighted versions have applications in natural language processing, XML-based data management and quantitative information retrieval. We address the minimisation problem of wsuta and wputa by using (forward and backward) bisimulations and we prove the following results: (1) for every wsuta an equivalent forward (resp. backward) bisimulation minimal wsuta can be computed in time O(mn) where n is the number of states and m is the number of transitions of the given wsuta; (2) the same result is proved for wputa instead of wsuta; (3) if the semiring is additive cancellative or the BOOLEAN semiring, then the bound can be improved to O(mlog n) for both wsuta and wputa; (4) for every deterministic puta we can compute a minimal equivalent deterministic puta in time O(mlog n); (5) the automata models wsuta, wputa, and weighted unranked tree automaton have the same computational power.
Landscape as a continuum: an examination of the urban landscape structures and dynamics of Indianapolis City, 1991-2000, by using satellite images
Authors: Qihao Weng, Dengsheng Lu
Venue: International Journal of Remote Sensing (2009)

The majority of the vast literature on remote sensing of urban landscapes has adopted a 'hard classification' approach, in which each image pixel is assigned a single land use and land cover category. Owing to the nature of urban landscapes, the confusion between land use and land cover definitions and the constraints of widely applied medium spatial resolution satellite images, high classification accuracy has been difficult to achieve with the conventional 'hard' classifiers. The prevalence of the mixed pixel problem in urban landscapes indicates a crucial need for an alternative approach to urban analyses. Identification, description and quantification, rather than classification, may provide a better understanding of the compositions and processes of heterogeneous landscapes such as urban areas. This study applied the Vegetation-Impervious Surface-Soil (V-I-S) model for characterizing urban landscapes and analysing their dynamics in Indianapolis, USA, between 1991 and 2000. To extract these landscape components from three dates of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images in 1991 1995 and 2000, we used the technique of linear spectral mixture analysis (LSMA). These components were further classified into urban thematic classes, and used for analysis of the landscape patterns and dynamics. The results indicate that LSMA provides a suitable technique for detecting and mapping urban materials and V-I-S component surfaces in repetitive and consistent ways, and for solving the spectral mixing of medium spatial resolution satellite imagery. The reconciliation between the V-I-S model with LSMA for Landsat imagery allowed this continuum landscape model to be an alternative, effective approach to characterizing and quantifying the spatial and temporal changes of the urban landscape compositions in Indianapolis from 1991 to 2000. It is suggested that the model developed in this study offers a more realistic and robust representation of the true nature of urban landscapes, as compared with the conventional method based on 'hard classification' of satellite imagery. The general applicability of this continuum model, especially its spectral, spatial and temporal variability, is discussed.
A semiparametric generative model for efficient structured-output supervised learning
Authors: Fabrizio Costa, Andrea Passerini, Marco Lippi, Paolo Frasconi
Venue: Annals of Mathematics and Artificial Intelligence (2008)

We present a semiparametric generative model for supervised learning with structured outputs. The main algorithmic idea is to replace the parameters of an underlying generative model (such as a stochastic grammars) with input-dependent predictions obtained by (kernel) logistic regression. This method avoids the computational burden associated with the comparison between target and predicted structure during the training phase, but requires as an additional input a vector of sufficient statistics for each training example. The resulting training algorithm is asymptotically more efficient than structured output SVM as the size of the output structure grows. At the same time, by computing parameters of a joint distribution as a function of the full input structure, typical expressiveness limitations of related conditional models (such as maximum entropy Markov models) can be potentially avoided. Empirical results on artificial and real data (in the domains of natural language parsing and RNA secondary structure prediction) show that the method works well in practice and scales up with the size of the output structures.
Plenary lecture I: computer modelling of granular flow
Authors: Zdzislaw Wieckowski
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

Processes of granular flow are still hard to model although many well-developed computational tools are available. When, for instance, the finite element method formulated in purely Lagrangian format is used, the large distortions of the analysed granular material and the element mesh in the sequel lead to numerical inaccuracies or even make the analysis impossible. To overcome these numerical difficulties, the material point method is utilised, which is a variant of the finite element method formulated in an arbitrary Lagrangian-Eulerian description of motion. The motion of material (Lagrangian) points is traced with respect to a computational (Eulerian) mesh which is not subject to distortions. As the material points are defined independently of the computational mesh, the method is also classified as a point-based (mesh-free) method. Examples of granular flow during the processes of filling and discharging silos will be presented including the doming phenomenon which can appear in the case of slightly cohesive material. The flow of the granular material on a revolving pan and some dynamic problems of geomechanics with large strains will also be presented. The granular material is described by the use of the elastic-viscoplastic constitutive model with the Drucker-Prager yield condition.
Open Constraints in a Boundable World
Authors: Michael J. Maher
Venue: Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2009)

Open forms of global constraints allow the addition of new variables to an argument during the execution of a constraint program. Such forms are needed for difficult constraint programming problems where problem construction and problem solving are interleaved. We introduce a new model of open global constraint where the length of the sequence of variables can be constrained but there is no a priori restriction on the variables that might be added. In general, propagation that is sound for a global constraint can be unsound when the constraint is open. We identify properties of constraints that simplify the design of algorithms for propagation by identifying when no propagation can be done, and use them to design propagation algorithms for several open global constraints.
Visual perception of ambiguous figures: synchronization based neural models
Authors: Roman Borisyuk, David Chik, Yakov Kazanovich
Venue: Biological Cybernetics (2009)

We develop and study two neural network models of perceptual alternations. Both models have a star-like architecture of connections with a central element connected to a set of peripheral elements. A particular perception is simulated in terms of partial synchronization between the central element and some sub-group of peripheral elements. The first model is constructed from phase oscillators and the mechanism of perceptual alternations is based on chaotic intermittency under fixed parameter values. Similar to experimental evidence, the distribution of times between perceptual alternations is represented by the gamma distribution. The second model is built of spiking neurons of the Hodgkin&#x2013;Huxley type. The mechanism of perceptual alternations is based on plasticity of inhibitory synapses which increases the inhibition from the central unit to the neural assembly representing the current percept. As a result another perception is formed. Simulations show that the second model is in good agreement with behavioural data on switching times between percepts of ambiguous figures and with experimental results on binocular rivalry of two and four percepts.
Proper Noun Recognition and Classification Using Weighted Finite State Transducers
Authors: Jörg Didakowski, Marko Drotschmann
Venue: Proceeding of the 2009 conference on Finite-State Methods and Natural Language Processing: Post-proceedings of the 7th International Workshop FSMNLP 2008 (2009)

This paper presents a new approach to proper noun recognition and classification in which the knowledge of ambiguities within morphological analyses is used exhaustively in the analysis. Here a proper noun recognizer/classifier is defined by proper noun context patterns on the one hand and by a filter that takes the ambiguity information into account on the other hand. Furthermore, techniques like a lemma based coreference resolution or the softening of the closed world assumption made by the morphology are presented which improve the analysis. The approach is implemented by weighted finite state transducers and tested within the analysis system SynCoP via a hand-written grammar.
Context and resource awareness in opportunistic network data dissemination
Authors: Chiara Boldrini, Marco Conti, Andrea Passarella
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

Opportunistic networks are challenging mobile ad hoc networks characterised by frequent disconnections and partitioning. In this paper we focus on data dissemination services, i.e. cases in which data should be disseminated in the network without a priori knowledge about the set of intended destinations. We propose a general autonomic data dissemination framework that exploits information about the users’ context and social behaviour, to decide how to replicate and replace data on nodes’ buffers. Furthermore, our data dissemination scheme explicitly takes into account resource constraints, by jointly considering the expected utility of data replication and the associated costs. The results we present show that our solution is able to improve data availability, provide fairness among nodes, and reduce the network load, with respect to reference proposals available in the literature.
Compiling the π-calculus into a Multithreaded Typed Assembly Language
Authors: Tiago Cogumbreiro, Francisco Martins, Vasco T. Vasconcelos
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

We extend a previous work on a multithreaded typed assembly language (MIL) targeted at shared memory multiprocessors, and describe the design of a type-preserving compiler from the @p-calculus into MIL. The language enforces a policy on lock usage through a typing system that also ensures race-freedom for typable programs, while allowing for typing various important concurrency patterns. Our translation to MIL generates code that is then linked to a library supporting a generic unbounded buffer monitor, variant of Hoare's bounded buffer monitor, entirely written in MIL. Such a monitor shields client code (the @p-calculus compiler in particular) from the hazardous task of direct lock manipulation, while allowing for the representation of @p-calculus channels. The compiler produces type correct MIL programs from type correct source code, generating low-contention cooperative multithreaded programs.
Empirical Evaluation of Wireless Underground-to-Underground Communication in Wireless Underground Sensor Networks
Authors: Agnelo R. Silva, Mehmet C. Vuran
Venue: Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems (2009)

Many applications for irrigation management and environment monitoring exploit buried sensors wired-connected to the soil surface for information retrieval. Wireless Underground Sensor Networks (WUSNs) is an emerging area of research that promises to provide communication capabilities to these sensors. To accomplish this, a reliable wireless underground communication channel is necessary, allowing the direct communication between the buried sensors without the help of an aboveground device. However, the significantly high attenuation caused by soil is the main challenge for the feasibility of WUSNs. Recent theoretical results highlight the potential of smaller attenuation rates with the use of smaller radio frequencies. In this work, experimental measurements are presented at the frequency of 433MHz, which show a good agreement with the theoretical studies. We observe that (a) a decrease of the frequency of the wireless signal implies a smaller soil attenuation rate, (b) the wireless underground communication channel presents a high level of temporal stability, and (c) the volumetric water content (VWC) of the soil is the most important factor to adversely affect the communication. The results show the potential feasibility of the WUSNs with the use of powerful RF transceivers at smaller frequencies (e.g., 300-500MHz band). We also propose a classification for wireless underground communication, defining and showing the differences between Subsoil and Topsoil WUSNs. To the best of our knowledge, this is the first work that reports experiment results for underground to underground communication using commodity sensor motes.
An analysis of phase separation processes for stochastic and nonlocal extensions of the classical phase-field model
Authors: Thomas Wanner
Venue:  (2008)

Phase separation in composite materials, or materials that are made up of two or more different components, is a process in which the material separates over time into regions of predominantly one component or the other. Because these composite materials are often developed to be used under certain conditions, significant changes of this nature in their material composition can dramatically affect their performance, thus it is important to understand the dynamic behavior of these changes. One mathematical model of phase separation is the classical phase field model, which is a system of nonlinear evolution equations that describes nonisothermal phase separation. Instead of a composite material, the phase field model describes pure materials which can assume two different phases; for example, a solid and a liquid phase. In this dissertation, I analyze the behavior of solutions of the phase field model, along with two extensions to the classical model. One extension is the addition of stochastic terms to the model, thus incorporating random behavior into the equations to address fluctuations in temperature, structural imperfections of the material, or other unknown effects. The other extension is the addition of a nonlocal operator to account for long range interactions. The behavior of all three variations of the phase field model is studied through numerical simulations, and the results compared to describe the differences between the models. In addition, I present a proof showing that the numerical method converges to the true solution of the phase field model.
An improved box-counting method for image fractal dimension estimation
Authors: Jian Li, Qian Du, Caixin Sun
Venue: Pattern Recognition (2009)

Fractal dimension (FD) is a useful feature for texture segmentation, shape classification, and graphic analysis in many fields. The box-counting approach is one of the frequently used techniques to estimate the FD of an image. This paper presents an efficient box-counting-based method for the improvement of FD estimation accuracy. A new model is proposed to assign the smallest number of boxes to cover the entire image surface at each selected scale as required, thereby yielding more accurate estimates. The experiments using synthesized fractional Brownian motion images, real texture images, and remote sensing images demonstrate this new method can outperform the well-known differential boxing-counting (DBC) method.
Refactorings detection using hierarchical clustering
Authors: Istvan Gergely Czibula, Gabriela Czibula
Venue: Proceedings of the 2nd conference on European computing conference (2008)

Refactoring is a process that helps to maintain the internal software quality, during the whole software lifecycle. This paper aims at introducing a new hierarchical clustering algorithm that can be used for improving software systems design, by identifying the appropriate refactorings. The algorithm is named HARD (Hierarchical Clustering Algorithm for Refactorings Determination) and uses a newly introduced measure that estimates the "quality" of a software system. Clustering is used in order to recondition the class structure of a software system. The proposed approach can be useful for assisting software engineers in their daily works of refactoring software systems. We evaluate our approach using the open source case study JHotDraw and a real software system, providing a comparison with previous approaches.
Predicting faults using the complexity of code changes
Authors: Ahmed E. Hassan
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

Predicting the incidence of faults in code has been commonly associated with measuring complexity. In this paper, we propose complexity metrics that are based on the code change process instead of on the code. We conjecture that a complex code change process negatively affects its product, i.e., the software system. We validate our hypothesis empirically through a case study using data derived from the change history for six large open source projects. Our case study shows that our change complexity metrics are better predictors of fault potential in comparison to other well-known historical predictors of faults, i.e., prior modifications and prior faults.
CINDI: a digital library for academics
Authors: Rui Chen, Alirio Perez, Krishma Dutta, Bipin C. Desai
Venue: Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering (2009)

In this paper, we present Concordia INdexing and DIscovery (CINDI) system, a digital library system for academics. The CINDI system distinguishes itself from other existing digital libraries by its extended functionalities, which provide the academic communities a unified platform for exploring, browsing, uploading and annotating academic and scientific documents under various topics. This paper briefly introduces the nine components of the CINDI system and presents their integration process. The emphasis of this paper is placed on two salient features of the CINDI system: Automatic Semantic Header Generator and Virtual Question Answering System.
Contributions to the design of an ultra acoustic system used in active applications of ultrasound
Authors: Gheorghe Amza, Zoia Apostolescu, Gabriel Dan Tasca
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

This paper deals with ultra acoustic systems, the elements of calculus and the steps needed to be taken in order to design the piezoceramic elements.
Brief paper: Fast conflict detection using probability flow
Authors: Corné E. van Daalen, Thomas Jones
Venue: Automatica (Journal of IFAC) (2009)

Probabilistic conflict detection has recently attracted much attention from researchers due to its importance in safe automated transport systems; however, current methods struggle to accurately calculate in real-time the probability of conflict for complex vehicle maneuvers in cluttered environments. We present a formulation for the general probabilistic conflict detection problem using the flow rate of conflict probability at the boundary of a conflict zone. For Gaussian distributed vehicle states, we then derive a tight upper bound for the probability of conflict over a time period, which can be calculated in real-time using adaptive numerical integration techniques. We present two examples to illustrate the performance of this method: the first example shows that this method is very efficient for simple environments and the second example shows that this method can calculate the conflict probability upper bound in real-time even for complex vehicle maneuvers in cluttered environments.
Mining frequent itemsets in data streams using the weighted sliding window model
Authors: Pauray S. M. Tsai
Venue: Expert Systems with Applications: An International Journal (2009)

In recent years, data stream mining has become an important research topic. With the emergence of new applications, the data we process are not again static, but the continuous dynamic data stream. Examples include network traffic analysis, Web click stream mining, network intrusion detection, and on-line transaction analysis. In this paper, we propose a new framework for data stream mining, called the weighted sliding window model. The proposed model allows the user to specify the number of windows for mining, the size of a window, and the weight for each window. Thus users can specify a higher weight to a more significant data section, which will make the mining result closer to user's requirements. Based on the weighted sliding window model, we propose a single pass algorithm, called WSW, to efficiently discover all the frequent itemsets from data streams. By analyzing data characteristics, an improved algorithm, called WSW-Imp, is developed to further reduce the time of deciding whether a candidate itemset is frequent or not. Empirical results show that WSW-Imp outperforms WSW under the weighted sliding window model.