pinecone/core-2020-05-10-deduplication huggingface dataset:
A tableau algorithm for paraconsistent and nonmonotonic reasoning in description logic-based system

This paper proposes a paraconsistent and nonmonotonic extension of  description logic by planting a nonmonotonic mechanism called  /emph{minimal inconsistency} in paradoxical description logics,  which is a paraconsistent version of description logics. A  precedence relation between two paradoxical models of knowledge  bases is firstly introduced to obtain minimally paradoxical models  by filtering those models which contain more inconsistencies than  others. A new entailment relationship between a KB and an axiom  characterized by minimal paradoxical models is applied to  characterize the semantics of a paraconsistent and nonmonotonic  description logic. An important advantage of our adaptation is  simultaneously overtaking proverbial shortcomings of existing two  kinds extensions of description logics: the weak inference power of  paraconsistent description logics and the incapacity of nonmonotonic  description logics in handling inconsistencies. Moreover, our  paraconsistent and nonmonotonic extension not only preserves the  syntax of description logic but also maintains the decidability of  basic reasoning problems in description logics. Finally, we develop  a sound and complete tableau algorithm for instance checking with  the minimally paradoxical semantics.Full Tex
Aggregate distance based clustering using Fibonacci Series -FIBCLUS

This paper proposes an innovative instance similarity based evaluation metric that reduces the search map for clustering to be performed. An aggregate global score is calculated for each instance using the novel idea of Fibonacci series. The use of Fibonacci numbers is able to separate the instances effectively and, in hence, the intra-cluster similarity is increased and the inter-cluster similarity is decreased during clustering. The proposed FIBCLUS algorithm is able to handle datasets with numerical, categorical and a mix of both types of attributes. Results obtained with FIBCLUS are compared with the results of existing algorithms such as k-means, x-means expected maximization and hierarchical algorithms that are widely used to cluster numeric, categorical and mix data types. Empirical analysis shows that FIBCLUS is able to produce better clustering solutions in terms of entropy, purity and F-score in comparison to the above described existing algorithms
Toward a scalable and collaborative network monitoring overlay

This paper presents ongoing work toward the definition of a new network monitoring model which resorts to  a cooperative interaction among measurement entities to monitor the quality of network services.  Exploring (i) the definition of representative measurement points to form a network monitoring overlay; (ii)  the removal of measurement redundancy through composition of metrics; and (iii) a simple active measurement methodology, the proposed model aims to contribute to a scalable, robust and reliable end-to-end monitoring. Besides the model proposal, a JAVA prototype was implemented to test the conceptual model and its design goals
Mining Unclassified Traffic Using Automatic Clustering Techniques

In this paper we present a fully unsupervised algorithm to identify classes of traffic inside an aggregate. The algorithm leverages on the K-means clustering algorithm, augmented with a mechanism to automatically determine the number of traffic clusters. The signatures used for clustering are statistical representations of the application layer protocols. The proposed technique is extensively tested considering UDP traffic traces collected from operative networks. Performance tests show that it can clusterize the traffic in few tens of pure clusters, achieving an accuracy above 95%. Results are promising and suggest that the proposed approach might effectively be used for automatic traffic monitoring, e.g., to identify the birth of new applications and protocols, or the presence of anomalous or unexpected traffi
Validating privacy requirements in large survey rating data

Recent study shows that supposedly anonymous movie rating records are de-identified by using a little auxiliary information. In this chapter, we study a problem of protecting privacy of individuals in large public survey rating data. Such rating data usually contains both ratings of sensitive and non-sensitive issues, and the ratings of sensitive issues belong to personal privacy. Even when survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. To amend this, in this chapter, we propose a novel (k;e; l)-anonymity model to protect privacy in large survey rating data, in which each survey record is required to be 'similar' with at least k−1 others based on the non-sensitive ratings, where the similarity is controlled by e, and the standard deviation of sensitive ratings is at least l. We study an interesting yet non-trivial satisfaction problem of the proposed model, which is to decide whether a survey rating data set satisfies the privacy requirements given by the user. For this problem, we investigate its inherent properties theoretically, and devise a novel slice\ud
technique to solve it. We discuss the idea of how to anonymize data by using the result of satisfaction problem. Finally, we conduct extensive experiments on two real-life data sets, and the results show that the slicing technique is fast and scalable with data size and much more efficient in terms of execution time and space overhead than the heuristic pairwise method
A guided search non-dominated sorting genetic algorithm for the multi-objective university course timetabling problem

Copyright @ Springer-Verlag Berlin Heidelberg 2011.The university course timetabling problem is a typical combinatorial optimization problem. This paper tackles the multi-objective university course timetabling problem (MOUCTP) and proposes a guided search non-dominated sorting genetic algorithm to solve the MOUCTP. The proposed algorithm integrates a guided search technique, which uses a memory to store useful information extracted from previous good solutions to guide the generation of new solutions, and two local search schemes to enhance its performance for the MOUCTP. The experimental results based on a set of test problems show that the proposed algorithm is efficient for solving the MOUCTP
Multi-start heuristics for the Two-Echelon Vehicle Routing Problem

In this paper we address the Two-Echelon Vehicle Routing Problem (2E-VRP), an extension of the classical Capacitated VRP, where the delivery from a single depot to the customers is managed by routing and consolidating the freight through intermediate depots called satellites. We present a family of Multi-Start heuristics based on separating the depot-to-satellite transfer and the satellite-to-customer delivery by iteratively solving the two resulting routing subproblems, while adjusting the satellite workloads that link them. The common scheme on which all the heuristics are based consists in, after having found an initial solution, applying a local search phase, followed by a diversification; if the new obtained solutions are feasible, then local search is applied again, otherwise a feasibility search procedure is applied, and if it successful, the local search is applied on the newfound solution. Different diversification strategies and feasibility search rules are proposed. We present computational results on a wide set of instances up to 50 customers and 5 satellites and compare them with results from the literature, showing how the new methods outperform previous existent methods, both in efficiency and accurac
A hybrid dual-population genetic algorithm for the single machine maximum lateness problem

We consider the problem of scheduling a number of jobs, each job having a release time, a processing time and a due date, on a single machine with the objective of minimizing the maximum lateness. We developed a hybrid dual-population genetic algorithm and compared its performance with alternative methods on a new diverse data set. Extensions from a single to a dual population by taking problem specific characteristics into account can be seen as a stimulator to add diversity in the search process, which has a positive influence on the important balance between intensification and diversification. Based on a comprehensive literature study on genetic algorithms in single machine scheduling, a fair comparison of genetic operators was made
Connectedness and Local Search for Bicriteria Knapsack Problems

Best paper award / The final publication is available at www.springerlink.comInternational audienceThis article reports an experimental study on a given structural property of connectedness of optimal solutions for two variants of the bicriteria knapsack problem. A local search algorithm that explores this property is then proposed and its performance is compared against exact algorithms in terms of running time and number of optimal solutions found. The experimental results indicate that this simple local search algorithm is able to find a representative set of optimal solutions in most of the cases, and in much less time than exact approaches
Static analysis techniques for session-oriented calculi

In the Sensoria project, core calculi have been adopted as a linguistic means to model and analyze service-oriented applications. The present chapter reports about the static analysis techniques developed for the Sensoria session-oriented core calculi CaSPiS and CC. In particular, it presents a type system for client progress and control flow analysis in CaSPiS and type systems for conversation fidelity and progress in CC. The chapter gives an overview of the these techniques, summarizes the main results and presents the analysis of a common example taken from the Sensoria financial case-study: the credit request scenario
The Sensoria Approach Applied to the Finance Case Study

This chapter provides an effective implementation of (part of) the Sensoria approach, specifically modelling and formal analysis of service-oriented software based on mathematically founded techniques. The ‘Finance case study’\ud
is used as a test bed for demonstrating the feasibility and effectiveness of the use of the process calculus COWS and some of its related analysis techniques and tools. In particular, we report the results of an application of a temporal logic and its model checker for expressing and checking functional properties of services and a type system for guaranteeing confidentiality properties of services
A formal support to business and architectural design for service-oriented systems

Architectural Design Rewriting (ADR) is an approach for the design of software architectures developed within Sensoria by reconciling graph transformation and process calculi techniques. The key feature that makes ADR a suitable and expressive framework is the algebraic handling of structured graphs, which improves the support for specification, analysis and verification of service-oriented architectures and applications. We show how ADR is used as a formal ground for high-level modelling languages and approaches developed within Sensoria
Statistical distribution of generation-to-success in GP: Application to model accumulated success probability

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-20407-4_14Proceedings of the 14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011.Many different metrics have been defined in Genetic Programming. Depending on the experiment requirements and objectives, a collection of measures are selected in order to achieve an understanding of the algorithm behaviour. One of the most common metrics is the accumulated success probability, which evaluates the probability of an algorithm to achieve a solution in a certain generation. We propose a model of accumulated success probability composed by two parts, a binomial distribution that models the total number of success, and a lognormal approximation to the generation-to-success, that models the variation of the success probability with the generation.This work was partially supported by the MICYT project
ABANT (TIN2010-19872) and Castilla-La Mancha project PEII09- 0266-664
On the multi-mode, multi-skill resource constrained project scheduling problem : a software application

We consider an extension of the Resource-Constrained Project Scheduling Problem (RCPSP) to multi-level (or multi-mode) activities. Each activity must be allocated exactly one unit of each required resource and the resource unit may be used at any of its specified levels. The processing time of an activity is given by the maximum of the durations that would result from a specific allocation of resources. The objective is to find the optimal solution that minimizes the overall project cost which includes a penalty for tardiness beyond the specified delivery date as well as a bonus for early delivery.  We give some of the most important solution details and we report on the preliminary results obtained. The implementation was designed using the C# language
Electromagnetism-like augmented lagrangian algorithm for global optimization

This paper presents an augmented Lagrangian algorithm to solve continuous
constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is solved by a population-based method that uses an electromagnetism-like mechanism to move points towards optimality.
Benchmark problems are solved in a performance evaluation of the proposed
augmented Lagrangian methodology. A comparison with a well-known technique is
also reported
Professionalizing Business Process Management : towards a Body of Knowledge for BPM

Business Process Management (BPM) is rapidly evolving as an established discipline. There are a number of efforts underway to formalize the various aspects of BPM practice; creating a formal Body of Knowledge (BoK) is one such effort. Bodies of knowledge are artifacts that have a proven track record for accelerating the professionalization of various disciplines. In order for this to succeed in BPM, it is vital to involve the broader business process community and derive a BoK that has essential characteristics that addresses the discipline’s needs. We argue for the necessity of a comprehensive BoK for the BPM domain, and present a core list of essential features to consider when developing a BoK based on preliminary empirical evidence. The paper identifies and critiques existing Bodies of Knowledge related to BPM, and firmly calls for an effort to develop a more accurate and sustainable BoK for BPM. An approach for this effort is presented with preliminary outcomes
Supporting context-aware process design : learnings from a design science study

Recent studies have started to explore context-awareness as a driver in the design of adaptable business processes. The emerging challenge of identifying and considering contextual drivers in the environment of a business process\ud
are well understood, however, typical methods and models for business process design do not yet consider this context. In this paper, we describe our work on the design of a method framework and appropriate models to enable a context-aware process design approach. We report on our ongoing work with an Australian insurance provider and describe the design science we employed to develop innovative and useful artifacts as part of a context-aware method framework. We discuss the utility of these artifacts in an application in the claims handling process at the case organization
Gene Expressio Array Exploration Using K-Formal Concept Analysis

Proceeding of: 9th International Conference, ICFCA 2011, Nicosia, Cyprus, May 2-6, 2011.DNA micro-arrays are a mechanism for eliciting gene expression values, the concentration of the transcription products of a set of genes, under different chemical conditions. The phenomena of interest—up-regulation, down-regulation and co-regulation—are hypothesized to stem from the functional relationships among transcription products. In [1,2,3] a generalisation of Formal Concept Analysis was developed with data mining applications in mind, K-Formal Concept Analysis, where incidences take values in certain kinds of semirings, instead of the usual Boolean carrier set. In this paper, we use (Rmin+)- and (Rmax+) to analyse gene expression data for Arabidopsis thaliana. We introduce the mechanism to render the data in the appropriate algebra and profit by the wealth of different Galois Connections available in Generalized Formal Concept Analysis to carry different analysis for up- and down-regulated genes.Spanish Government-Comision Interministerial de Ciencia y Tecnología projects 2008-06382/TEC and 2008-02473/TEC and the regional projects S-505/TIC/0223 (DGUI-CM) and CCG08-UC3M/TIC- 4457 (Comunidad Aut onoma de Madrid - UC3M)
Abstract Concept Lattices

International audienceWe present a view of abstraction based on a structure preserving reduction of the Galois connection between a language of terms and the powerset of a set of instances O. Such a relation is materialized as an extension-intension lattice, namely a concept lattice when L is the powerset of a set P of attributes. We define and characterize an abstraction A as some part of either the language or the powerset of O, defined in such a way that the extension-intension latticial structure is preserved. Such a structure is denoted for short as an abstract lattice. We discuss the extensional abstract lattices obtained by so reducing the powerset of O, together together with the corresponding abstract implications, and discuss alpha lattices as particular abstract lattices. Finally we give formal framework allowing to define a generalized abstract lattice whose language is made of terms mixing abstract and non abstract conjunctions of properties
Border algorithms for computing Hasse diagrams of arbitrary lattices

Lattices are mathematical structures with many applications in computer science; among these, we are interested in fields like data mining, machine learning, or knowledge discovery in databases. One well-established use of lattice theory is in formal concept analysis (FCA), where the concept lattice with its diagram graph allows the visualization and summarization of data in a more concise representation. In the Data Mining community, the same mathematical notions (often under additional “frequency” constraints that bound from below the size of the support set) are studied under the banner of Closed-Set Mining. In these applications, each dataset consists of transactions, also called objects, each of which, besides having received a unique identifier, consists of a set of items or attributes taken from a previously agreed finite set. A concept is a pair formed by a set of transactions —the extent set or support set of the concept— and a set of attributes —the intent set of the concept— defined as the set of all those attributes that are shared by all the transactions present in the extent. Some data analysis processes are based on the family of all intents (the “closures” stemming from the dataset); but others require to determine also their order relation, which is a finite lattice, in the form of a line graph (the Hasse diagram).Peer ReviewedPostprint (published version
Genetic Defect Based March Test Generation for SRAM

The continuos shrinking of semiconductor's nodes makes semiconductor memories increasingly prone to electrical defects tightly related to the internal structure of the memory. Exploring the effect of fabrication defects in future technologies, and identifying new classes of functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper pro- poses a new approach to automate this procedure exploiting a dedicated genetic algorithm
Evolution of Test Programs Exploiting a FSM Processor Model

Microprocessor testing is becoming a challenging task, due to the increasing complexity of modern architectures. Nowadays, most architectures are tackled with a combination of scan chains and Software-Based Self-Test (SBST) methodologies. Among SBST techniques, evolutionary feedback-based ones prove effective in microprocessor testing: their main disadvantage, however, is the considerable time required to generate suitable test programs. A novel evolutionary-based approach, able to appreciably reduce the generation time, is presented. The proposed method exploits a high-level representation of the architecture under test and a dynamically built Finite State Machine (FSM) model to assess fault coverage without resorting to time-expensive simulations on low-level models. Experimental results, performed on an OpenRISC processor, show that the resulting test obtains a nearly complete fault coverage against the targeted fault mode
On Improving the Capacity of Solving Large-scale Wireless Network Design
  Problems by Genetic Algorithms

Over the last decade, wireless networks have experienced an impressive growth
and now play a main role in many telecommunications systems. As a consequence,
scarce radio resources, such as frequencies, became congested and the need for
effective and efficient assignment methods arose. In this work, we present a
Genetic Algorithm for solving large instances of the Power, Frequency and
Modulation Assignment Problem, arising in the design of wireless networks. To
our best knowledge, this is the first Genetic Algorithm that is proposed for
such problem. Compared to previous works, our approach allows a wider
exploration of the set of power solutions, while eliminating sources of
numerical problems. The performance of the algorithm is assessed by tests over
a set of large realistic instances of a Fixed WiMAX Network.Comment: This is the authors' final version of the paper published in Di Chio
  C. et al. (Eds): EvoApplications 2011, LNCS 6625, pp. 11-20, 2011. The final
  publication is available at Springer via
  http://dx.doi.org/10.1007/978-3-642-20520-0_
Using grammatical evolution to parameterise interactive 3D image generation

This paper describes an Interactive Evolutionary system for generating pleasing 3D images using a combination of Grammatical Evolution and Jenn3d, a freely available visualiser of Cayley graphs of finite Coxeter groups. Using interactive GE with some novel enhancements, the parameter space of the Jenn3d image-generating system is navigated by the user, permitting the creation of realistic, unique and award winning images in just a few generations. One of the evolved images has been selected to illustrate the proceedings of the EvoStar conference in 2011
Towards procedural strategy game generation : evolving complementary unit types

The Strategy Game Description Game Language (SGDL) is intended to become a complete description of all aspects of strategy games, including rules, parameters, scenarios, maps, and unit types. One of the main envisioned uses of SGDL, in combination with an evolutionary algorithm and appropriate fitness functions, is to allow the generation of complete new strategy games or variations of old ones. This paper presents a first version of SGDL, capable of describing unit types and their properties, together with plans for how it will be extended to other sub-domains of strategy games. As a proof of the viability of the idea and implementation, an experiment is presented where unit types are evolved so as to generate complementary properties. A fitness function based on Monte Carlo simulation of gameplay is devised to test complementarity.peer-reviewe