Latest Articles from JUCS - Journal of Universal Computer Science Latest 22 Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Fri, 29 Mar 2024 13:32:30 +0200 Pensoft FeedCreator https://lib.jucs.org/i/logo.jpg Latest Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Efficiently Finding Cyclical Patterns on Twitter Considering the Inherent Spatio-temporal Attributes of Data https://lib.jucs.org/article/112523/ JUCS - Journal of Universal Computer Science 29(11): 1404-1421

DOI: 10.3897/jucs.112523

Authors: Claudio Gutiérrez-Soto, Patricio Galdames, Daniel Navea

Abstract: Social networks such as Twitter provide thousands of terabytes per day, which can be exploited to find relevant information. This relevant information is used to promote marketing strategies, analyze current political issues, and track market trends, to name a few examples. One instance of relevant information is finding cyclic behavior patterns (i.e., patterns that frequently repeat themselves over time) in the population. Because trending topics on Twitter change rapidly, efficient algorithms are required, especially when considering location and time (i.e., the specific location and time) during broadcasts. This article presents an efficient algorithm based on association rules to find cyclical patterns on Twitter, considering the inherent spatio-temporal attributes of data. Using a Hash Table enhances the efficiency of this algorithm, called HashCycle. Notably, HashCycle does not use minimum support and can detect patterns in a single run over a sequence. The processing times of HashCycle were compared to the Apriori (which is a well-known and widely used on diverse platforms) and Projection-based Partial Periodic Patterns (PPA) algorithms (which is one of the most efficient algorithms in terms of processing times). Empirical results from two spatio-temporal databases (a synthetic data set and one based on Twitter) show that HashCycle has more efficient processing times than two state-of-the-art algorithms: Apriori and PPA.

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Research Article Tue, 28 Nov 2023 16:00:09 +0200
Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics https://lib.jucs.org/article/22604/ JUCS - Journal of Universal Computer Science 25(4): 390-417

DOI: 10.3217/jucs-025-04-0390

Authors: Matias Micheletto, Rodrigo Santos, Javier Orozco

Abstract: In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.

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Research Article Sun, 28 Apr 2019 00:00:00 +0300
Adaptive Sharing Scheme Based Sub-Swarm Multi-Objective PSO https://lib.jucs.org/article/23372/ JUCS - Journal of Universal Computer Science 23(7): 673-691

DOI: 10.3217/jucs-023-07-0673

Authors: Yanxia Sun, Zenghui Wang

Abstract: To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms particles dynamically search their corresponding areas which are around some points of the Pareto front set in the objective space, and the chosen points of the Pareto front set are determined based on the adaptive sharing scheme. The second sub-swarm particles search the rest objective space, and they are away from the Pareto front set, which can promote the global search ability of the method. Moreover, the core points of the first sub-swarms are dynamically determined by this new adaptive sharing scheme. Some Simulations are used to test the proposed method, and the results show that the proposed method can achieve better optimization performance comparing with some existing methods.

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Research Article Fri, 28 Jul 2017 00:00:00 +0300
Stochastic Computing with Spiking Neural P Systems https://lib.jucs.org/article/23362/ JUCS - Journal of Universal Computer Science 23(7): 589-602

DOI: 10.3217/jucs-023-07-0589

Authors: Ming Wong, Mou Ling Dennis Wong

Abstract: This paper presents a new computational framework to address the challenges in deeply scaled technologies by implementing stochastic computing (SC) using the Spiking Neural P (SN P) Systems. SC is well known for its high fault tolerance and its ability to compute complex mathematical operations using minimal amount of resources. However, one of the key issues for SC is data correlation. This computation can be abstracted and elegantly modeled by using SN P systems where the stochastic bit-stream can be generated through the neurons spiking. Furthermore, since SN P systems are not affected by data correlations, this effectively mitigate the accuracy issue in the ordinary SC circuitry. A new stochastic scaled addition realized using SN P systems is reported at the end of this paper. Though the work is still at the early stage of investigation, we believe this study will provide insights to future IC design development.

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Research Article Fri, 28 Jul 2017 00:00:00 +0300
Solving a Large Real-world Bus Driver Scheduling Problem with a Multi-assignment based Heuristic Algorithm https://lib.jucs.org/article/23215/ JUCS - Journal of Universal Computer Science 23(5): 479-504

DOI: 10.3217/jucs-023-05-0479

Authors: Ademir Constantino, Candido Ferreira Xavier De Mendonca Neto, Silvio De Araujo, Dario Landa-Silva, Rogério Calvi, Allainclair Flausino dos Santos

Abstract: The bus driver scheduling problem (BDSP) under study consists in finding a set of duties that covers the bus schedule from a Brazilian public transportation bus company with the objective of minimizing the total cost. A deterministic 2-phase heuristic algorithm is proposed using multiple assignment problems that arise from a model based on a weighted multipartite graph. In the first phase, the algorithm constructs an initial feasible solution by solving a number of assignment problems. In the second phase, the algorithm attempts to improve the solution by two different procedures. One procedure takes the whole set of duties and divides them in a set of partial duties which are recombined. The other procedure seeks to improve single long duties by eliminating the overtime time and inserting it into another duty. Computational tests are performed using large-scale real-world data with more than 2,300 tasks and random instances extracted from real data. Three different objective functions are analyzed. The overall results indicate that the proposed approach is competitive to solve large BDSP.

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Research Article Sun, 28 May 2017 00:00:00 +0300
All-Pairs Shortest Paths Algorithm for Regular 2D Mesh Topologies https://lib.jucs.org/article/23669/ JUCS - Journal of Universal Computer Science 22(11): 1437-1455

DOI: 10.3217/jucs-022-11-1437

Authors: Vladimir Ciric, Aleksandar Cvetkovic, Ivan Milentijevic, Oliver Vojinovic

Abstract: Motivated by the large number of vertices that future technologies will put in the front of path-search algorithms, and inspired by highly regular 2D mesh structures that exist in the domain applications, in this paper we propose a new allpairs shortest paths algorithm, for any given regular 2D mesh topology, with complexity Ο(|V|2), where |V| is the number of vertices in the graph. The proposed algorithm can achieve better runtime than other known algorithms at the cost of narrowing the scope of the graphs that it can process to the graphs with regular 2D topology. The algorithm is developed into formalism by algebraic transformations in tropical algebra of the well-known Floyd-Warshall's algorithm. First we prove the equivalency of the Floyd-Warshall's algorithm and its tropical algebraic representation, and put the transformations of the algorithm into the algebraic domain. Secondly, having in mind the structure of the target class of graphs, we transform the original algorithm in the algebraic domain and develop a simple, low-complexity iterative algorithm for all-pairs shortest paths calculation. Decreasing of computational complexity can contribute to better exploitation of the algorithm in the wide range of applications from hardware design in new emerging technologies to big data problems in information technologies.

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Research Article Tue, 1 Nov 2016 00:00:00 +0200
Heuristic Algorithms for Manufacturing and Replacement Strategies of the Production System https://lib.jucs.org/article/23110/ JUCS - Journal of Universal Computer Science 21(4): 503-525

DOI: 10.3217/jucs-021-04-0503

Authors: Robert Bucki, Bronislav Chramcov, Petr Suchánek

Abstract: The paper highlights the problem of minimizing economic costs of making orders in the automated manufacturing system which consists of work centres arranged in a series. Each of them is equipped with tools which carry out defined manufacturing operations. Tools are replaced with new ones only when no manufacturing operation can be performed any more in order to minimize the residual pass. The equations of state of the production line are presented and heuristic control strategies are discussed in detail. The criterion is to minimize the number of replacement procedures which results in maximizing the use of tools in work centres. To prove the correctness of the presented approach the paper is supported with an extended simulation study based on implementing available combinations of either manufacturing or replacement strategies taking into account various configurations which come into being in the real manufacturing environment. The simulation results form the basis for the detailed analysis to meet the requirements of the applicable decision-making procedures.

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Research Article Wed, 1 Apr 2015 00:00:00 +0300
Solving Economic Dispatch Problems with Valve-point Effects using Particle Swarm Optimization https://lib.jucs.org/article/23798/ JUCS - Journal of Universal Computer Science 18(13): 1842-1852

DOI: 10.3217/jucs-018-13-1842

Authors: Kusum Deep, Jagdish Bansal

Abstract: Particle Swarm Optimization (PSO) is a swarm intelligence optimization method inspired from birds' flocking or fish schooling. Many improved versions of PSO are reported in literature, including some by the authors. Original as well as improved versions of PSO have proven their applicability to various fields like science, engineering and industries. Economic dispatch (ED) problem is one of the fundamental issues in power system operations. This problem turns out to be a non linear continuous optimization problem. In this paper, economic dispatch problem is solved using original PSO and two of its improved variants, namely, Laplace Crossover PSO (LXPSO) and Quadratic Approximation PSO (qPSO), in order to find better results than reported in the literature. Results are also compared with the earlier published results.

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Research Article Sun, 1 Jul 2012 00:00:00 +0300
A New Short-term Power Load Forecasting Model Based on Chaotic Time Series and SVM https://lib.jucs.org/article/29516/ JUCS - Journal of Universal Computer Science 15(13): 2726-2745

DOI: 10.3217/jucs-015-13-2726

Authors: Dongxiao Niu, Yongli Wang, Chunming Duan, Mian Xing

Abstract: This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. The time series matrix has also been established according to the theory of phase-space reconstruction. The Lyapunov exponents, one important component of chaotic time series, are used to determine time delay and embedding dimension, the decisive parameters for SVM. Then support vector machines algorithm is used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions are selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm is used to compare with the results of SVM. Findings show that the model is effective and highly accurate in the forecasting of short-term power load. It means that the model combined with SVM and chaotic time series learning system have more advantage than other models.

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Research Article Wed, 1 Jul 2009 00:00:00 +0300
Mining Dynamic Databases using Probability-Based Incremental Association Rule Discovery Algorithm https://lib.jucs.org/article/29489/ JUCS - Journal of Universal Computer Science 15(12): 2409-2428

DOI: 10.3217/jucs-015-12-2409

Authors: Ratchadaporn Amornchewin, Worapoj Kreesuradej

Abstract: In dynamic databases, new transactions are appended as time advances. This paper is concerned with applying an incremental association rule mining to extract interesting information from a dynamic database. An incremental association rule discovery can create an intelligent environment such that new information or knowledge such as changing customer preferences or new seasonal trends can be discovered in a dynamic environment. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has better performance than that of previous work.

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Research Article Sun, 28 Jun 2009 00:00:00 +0300
Application of Intelligent Strategies for Cooperative Manufacturing Planning https://lib.jucs.org/article/29452/ JUCS - Journal of Universal Computer Science 15(9): 1907-1923

DOI: 10.3217/jucs-015-09-1907

Authors: Weidong Li, Liang Gao, Xinyu Li

Abstract: Manufacturing planning is crucial for the quality and efficiency of product development. Process planning and scheduling are the most important and challenging tasks in manufacturing planning. These two processes are usually arranged in a sequential way. Recently, a significant trend is to make the processes to work more concurrently and cooperatively to achieve a globally optimal result. In this paper, several intelligent strategies have been developed to build up Cooperative Process Planning and Scheduling (CPPS). Three Game Theory-based strategies, i.e., Pareto strategy, Nash strategy and Stackelberg strategy, have been introduced to analyze the cooperative integration of the two processes in a systematic way. To address the multiple constraints in CPPS, a fuzzy logic-based Analytical Hierarchical Process (AHP) technique has been applied. Modern heuristic algorithms, including Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithms (GAs), have been developed and applied to CPPS to identify optimal or near-optimal solutions from the vast search space efficiently. Experiments have been conducted and results show the objectives of the research have been achieved.

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Research Article Fri, 1 May 2009 00:00:00 +0300
Authorization Algorithms for Permission-Role Assignments https://lib.jucs.org/article/29440/ JUCS - Journal of Universal Computer Science 15(9): 1782-1796

DOI: 10.3217/jucs-015-09-1782

Authors: Lili Sun, Hua Wang, Jianming Yong

Abstract: Permission-role assignments (PRA) is one important process in Role-based access control (RBAC) which has been proven to be a flexible and useful access model for information sharing in distributed collaborative environments. However, problems may arise during the procedures of PRA. Conflicting permissions may assign to one role, and as a result, the role with the permissions can derive unexpected access capabilities. This paper aims to analyze the problems during the procedures of permission-role assignments in distributed collaborative environments and to develop authorization allocation algorithms to address the problems within permission-role assignments. The algorithms are extended to the case of PRA with the mobility of permission-role relationship. Finally, comparisons with other related work are discussed to demonstrate the effective work of the paper.

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Research Article Fri, 1 May 2009 00:00:00 +0300
PDE-PEDA: A New Pareto-Based Multi-objective Optimization Algorithm https://lib.jucs.org/article/29335/ JUCS - Journal of Universal Computer Science 15(4): 722-741

DOI: 10.3217/jucs-015-04-0722

Authors: Xuesong Wang, Minglin Hao, Yuhu Cheng, Ruhai Lei

Abstract: Differential evolution (DE) algorithm puts emphasis particularly on imitating the microscopic behavior of individuals, while estimation of distribution algorithm (EDA) tries to estimate the probabilistic distribution of the entire population. DE and EDA can be extended to multi-objective optimization problems by using a Pareto-based approach, called Pareto DE (PDE) and Pareto EDA (PEDA) respectively. In this study, we describe a novel combination of PDE and PEDA (PDE-PEDA) for multi-objective optimization problems by taking advantage of the global searching ability of PEDA and the local optimizing ability of PDE, which can, effectively, maintain the balance between exploration and exploitation. The basic idea is that the offspring population of PDE-PEDA is composed of two parts, one part of the trial solution generated originates from PDE and the other part is sampled in the search space from the constructed probabilistic distribution model of PEDA. A scaling factor Pr used to balance contributions of PDE and PEDA can be adjusted in an on-line manner using a simulated annealing method. At an early evolutionary stage, a larger Pr should be adopted to ensure PEDA is used more frequently, whereas at later stage, a smaller Pr should be adopted to ensure that offspring is generated more often using PDE. The hybrid algorithm is evaluated on a set of benchmark problems and the experimental results show that PDE-PEDA outperforms the NSGA-II and PDE algorithms.

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Research Article Sat, 28 Feb 2009 00:00:00 +0200
Co-evolution for Communication: An EHW Approach https://lib.jucs.org/article/28852/ JUCS - Journal of Universal Computer Science 13(9): 1300-1308

DOI: 10.3217/jucs-013-09-1300

Authors: Yasser Damavandi, Karim Mohammadi

Abstract: Evolvable Hardware (EHW) is a new concept that aims the application of evolutionary algorithms to hardware design. EHW can adapt itself to unknown environment based on features of the reconfigurable hardware. This paper presents outlines of the idea of using some EHW agents in a distributed system. These agents need to set up a self-organized communication to achieve the predesigned goal. The experiment that is demonstrated during the presentation, is to distribute a serial adder into two EHW parts, where good results has been shown in a co-evolutionary process.

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Research Article Fri, 28 Sep 2007 00:00:00 +0300
An Interval Constraint Branching Scheme for Lattice Domains https://lib.jucs.org/article/28695/ JUCS - Journal of Universal Computer Science 12(11): 1466-1499

DOI: 10.3217/jucs-012-11-1466

Authors: Antonio J. Fernández Leiva, Patricia Hill

Abstract: Abstract This paper presents a branching schema for the solving of a wide range of interval constraint satisfaction problems defined on any domain of computation, finite or infinite, provided the domain forms a lattice. After a formal definition of the branching schema, useful and interesting properties, satisfied by all instances of the schema, are presented. Examples are then used to illustrate how a range of operational behaviors can be modelled by means of different schema instantiations. It is shown how the operational procedures of many constraint systems (including cooperative systems) can be viewed as instances of this branching schema. Basic directives to adapt this schema to solving constraint optimization problems are also provided.

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Research Article Tue, 28 Nov 2006 00:00:00 +0200
Eliminating Redundant Join-Set Computations in Static Single Assignment https://lib.jucs.org/article/28647/ JUCS - Journal of Universal Computer Science 12(8): 1007-1019

DOI: 10.3217/jucs-012-08-1007

Authors: Angela French, Jose Amaral

Abstract: The seminal algorithm developed by Ron Cytron, Jeanne Ferrante and colleagues in 1989 for the placement of φ-nodes in a control flow graph is still widely used in commercial compilers. Placing φ-nodes is necessary when converting a program representation to Static Single Assignment (SSA) form. This paper shows that if a variable x is defined in a set of basic blocks A(x), then the iterated join set of A(x) can be decomposed into the computation of the iterated join set of a disjoint collection of subsets of A(x). We use this result to show that some join set computations are redundant. We measured the number of redundant computations in the Open Research Compiler (ORC) in a selection of SPEC 2000 benchmarks.

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Research Article Mon, 28 Aug 2006 00:00:00 +0300
An O(√n) Distributed Mutual Exclusion Algorithm Using Queue Migration https://lib.jucs.org/article/28572/ JUCS - Journal of Universal Computer Science 12(2): 140-159

DOI: 10.3217/jucs-012-02-0140

Authors: Pranay Chaudhuri, Thomas Edward

Abstract: In this paper a distributed algorithm is proposed that realises mutual exclusion among n nodes in a computer network. There is no common or global memory shared by the nodes and there is no global controller. The nodes of the network communicate among themselves by exchanging messages only. The proposed algorithm is based on queue migration and achieves a message complexity of O(√n) per mutual exclusion invocation. Under heavy load, the number of required messages approaches 2 per mutual exclusion.

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Research Article Tue, 28 Feb 2006 00:00:00 +0200
Multiple Explanations Driven Naïve Bayes Classifier https://lib.jucs.org/article/28571/ JUCS - Journal of Universal Computer Science 12(2): 127-139

DOI: 10.3217/jucs-012-02-0127

Authors: Ahmad Almonayyes

Abstract: Exploratory data analysis over foreign language text presents virtually untapped opportunity. This work incorporates Naïve Bayes classifier with Case-Based Reasoning in order to classify and analyze Arabic texts related to fanaticism. The Arabic vocabularies are converted to equivalent English words using conceptual hierarchy structure. The understanding process operates at two phases. At the first phase, a discrimination network of multiple questions is used to retrieve explanatory knowledge structures each of which gives an interpretation of a text according to a particular aspect of fanaticism. Explanation structures organize past documents of fanatic content. Similar documents are retrieved to generate additional valuable information about the new document. In the second phase, the document classification process based on Naïve Bayes is used to classify documents into their fanatic class. The results show that the classification accuracy is improved by incorporating the explanation patterns with the Naïve Bayes classifier.

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Research Article Tue, 28 Feb 2006 00:00:00 +0200
Pervasive Health Management and Health Management Utilizing Pervasive Technologies : Synergy and Issues https://lib.jucs.org/article/28555/ JUCS - Journal of Universal Computer Science 12(1): 6-14

DOI: 10.3217/jucs-012-01-0006

Authors: Jean Roberts

Abstract: Much development work is ongoing addressing technologies and their application in the health domain, in order to achieve solutions that are non-invasive to every day life and work. As with many previous phases of informatics to support health, currently the developments are in islands and there is considerable untapped potential for synergy. Much research development is happening in other domains and show potential for health reversioning and deployment once proven. This paper explores some of the technological, societal and domain-specific issues surrounding this emerging concept of pervasiveness. It concludes that pervasive support to care is emerging but further work on minimizing risk and marketing the concept to professionals and laypeople is necessary to ensure an effective deployment.

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Research Article Sat, 28 Jan 2006 00:00:00 +0200
MINCE: A Static Global Variable-Ordering Heuristic for SAT Search and BDD Manipulation https://lib.jucs.org/article/28323/ JUCS - Journal of Universal Computer Science 10(12): 1562-1596

DOI: 10.3217/jucs-010-12-1562

Authors: Fadi Aloul, Igor Markov, Karem Sakallah

Abstract: The increasing popularity of SAT and BDD techniques in formal hardware verification and automated synthesis of logic circuits encourages the search for additional speedups. Since typical SAT and BDD algorithms are exponential in the worst-case, the structure of realworld instances is a natural source of improvements. While SAT and BDD techniques are often presented as mutually exclusive alternatives, our work points out that both can be improved via the use of the same structural properties of instances. Our proposed methods are based on efficient problem partitioning and can be easily applied as pre-processing with arbitrary SAT solvers and BDD packages without modifying the source code of SAT/BDD tools. Finding a better variable ordering is a well recognized problem for both SAT solvers and BDD packages. Currently, the best variable-ordering algorithms are dynamic, in the sense that they are invoked many times in the course of the host algorithm that solves SAT or manipulates BDDs. Examples include the DLCS ordering for SAT solvers and variable sifting during BDD manipulations. In this work we propose a universal variable-ordering algorithm MINCE (MIN Cut Etc.) that pre-processes a given Boolean formula in CNF. MINCE is completely independent from target SAT algorithms and in some cases outperforms both the variable state independent decaying sum (VSIDS) decision heuristic for SAT and variable sifting for BDDs. We argue that MINCE tends to capture structural properties of Boolean functions arising from real-world applications. Our contribution is validated on the ISCAS circuits and the DIMACS benchmarks. Empirically, our technique often outperforms existing SAT/BDD techniques by a factor of two or more. Our results motivate the search for better dynamic ordering heuristics and combined static/dynamic techniques.

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Research Article Tue, 28 Dec 2004 00:00:00 +0200
A Practical Extension Mechanism for Decision Procedures: the Case Study of Universal Presburger Arithmetic https://lib.jucs.org/article/27769/ JUCS - Journal of Universal Computer Science 7(2): 124-140

DOI: 10.3217/jucs-007-02-0124

Authors: Alessandro Armando, Silvio Ranise

Abstract: In this paper, we propose a generic mechanism for extending decision procedures by means of a lemma speculation mechanism. This problem is important in order to widen the scope of decision procedures incorporated in state-of-the-art verification systems. Soundness and termination of the extension schema are formally stated and proved. As a case study, we consider extensions of a decision procedure for the quantifier-free fragment of Presburger Arithmetic to significant fragments of non-linear arithmetic.

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Research Article Wed, 28 Feb 2001 00:00:00 +0200
A Method for Proving Theorems in Differential Geometry and Mechanics https://lib.jucs.org/article/27164/ JUCS - Journal of Universal Computer Science 1(9): 658-673

DOI: 10.3217/jucs-001-09-0658

Authors: Dongming Wang

Abstract: A zero decomposition algorithm is presented and used to devise a method for proving theorems automatically in differential geometry and mechanics. The method has been implemented and its practical efficiency is demonstrated by several non-trivial examples including Bertrand s theorem, Schell s theorem and Kepler-Newton s laws.

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Research Article Thu, 28 Sep 1995 00:00:00 +0200