Generalized pattern extraction from concept lattices

In this paper, we show how the existence of taxonomies on objects and/or attributes can be used in Formal Concept Analysis to help discover generalized concepts. To that end, we analyze three generalization cases ( ?, ?, and α) and present different scenarios of a simultaneous generalization on both objects and attributes. We also discuss the cardinality of the generalized pattern set against the number of simple patterns produced from the initial data set.     

Application of the L-fuzzy concept analysis in the morphological image and signal processing

In this work we are going to set up a new relationship between the L-fuzzy Concept Analysis and the Fuzzy Mathematical Morphology. Specifically we prove that the problem of finding fuzzy images or signals that remain invariant under a fuzzy morphological opening or under a fuzzy morphological closing, is equal to the problem of finding the L-fuzzy concepts of some L-fuzzy context. Moreover, since the Formal Concept Analysis and the Mathematical Morphology are the particular cases of the fuzzy ones, the showed result has also an interpretation for binary images or signals.     

A semantic approach to concept lattice-based information retrieval

The volume of available information is growing, especially on the web, and in parallel the questions of the users are changing and becoming harder to satisfy. Thus there is a need for organizing the available information in a meaningful way in order to guide and improve document indexing for information retrieval applications taking into account more complex data such as semantic relations. In this paper we show that Formal Concept Analysis (FCA) and concept lattices provide a suitable and powerful support for such a task. Accordingly, we use FCA to compute a concept lattice, which is considered both a semantic index to organize documents and a search space to model terms. We introduce the notions of cousin concepts and classification-based reasoning for navigating the concept lattice and retrieve relevant information based on the content of concepts. Finally, we detail a real-world experiment and show that the present approach has very good capabilities for semantic indexing and document retrieval.     

Entity-relationship diagrams which are in BCNF

In Ref. 8, we introduced a simplifying assumption about entity-relationship diagrams (ERDs), called regularity, and showed that regular ERDs have several desirable properties. One such property is that every relation schema in the ERD's canonical relational scheme can be put into Third Normal Form. We left open there the more basic question: under what conditions would the original relation schemas actually be in Boyce-Codd Normal Form (BCNF)? Since the visible semantics of ERDs determine naturally their associated functional dependencies (fd's), it is important to know when an ERD, as designed, already has this strongest normal form given purely in terms of fd's. We show here a sufficient diagrammatic condition (loop-free) under which a regular ERD will have databases enjoying the benefits of BCNF.     

Normalization and lossless join decomposition of similarity‐based fuzzy relational databases

Fuzzy relational database models generalize the classical relational database model by allowing uncertain and imprecise information to be represented and manipulated. In this article, we introduce fuzzy extensions of the normal forms for the similarity‐based fuzzy relational database model. Within this framework of fuzzy data representation, similarity, conformance of tuples, the concept of fuzzy functional dependencies, and partial fuzzy functional dependencies are utilized to define the fuzzy key notion, transitive closures, and the fuzzy normal forms. Algorithms for dependency preserving and lossless join decompositions of fuzzy relations are also given. We include examples to show how normalization, dependency preserving, and lossless join decomposition based on the fuzzy functional dependencies of fuzzy relation are done and applied to some real‐life applications. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 885–917, 2004.     

A Middleware-Based Simulation and Control Framework for Mobile Service Robots

A mobile service robot is a complex distributed system integrating various technologies and having large heterogeneity. In order to facilitate component development and system integration of the mobile service robots, a middleware-based simulation and control framework for system integration and application development, as well as the robotic functional component (RFC) framework with a simplified structure and an efficient transmission scheme, is proposed for mobile service robot systems. Designed to implement a distributed modular mechanism for a mobile service robot, the middleware-based framework for simulation and control is comprised of four layers: low-level abstraction layer, communication layer, high-level abstraction layer, and application layer. Common Object Request Broker Architecture (CORBA) and Robot Technology Middleware (RTM) are employed as middleware for the development of RFCs and for system integration. Communication between the components and the graphical programming tool is done by the communication layer (CORBA ORB). The conducted experiments validated the proposed framework in terms of ideal performance of reusability, interoperability, and extensibility, as well as indicated that the proposed RFC framework is simplified and easy enough to perform well in data transmission, which will reduce the costs and the threshold of robot development.     

Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

The lithium-ion battery cycle life prediction with particle filter (PF) depends on the physical or empirical model. However, in observation equation based on model, the adaptability and accuracy for individual battery under different operating conditions are not fully considered. Therefore, a novel fusion prognostic framework is proposed, in which the data-driven time series prediction model is adopted as observation equation, and combined to PF algorithm for lithium-ion battery cycle life prediction. Firstly, the nonlinear degradation feature of the lithium-ion battery capacity degradation is analyzed, and then, the nonlinear accelerated degradation factor is extracted to improve prediction ability of linear AR model. So an optimized nonlinear degradation autoregressive (ND–AR) time series model for remaining useful life (RUL) estimation of lithium-ion batteries is introduced. Then, the ND–AR model is used to realize multi-step prediction of the battery capacity degradation states. Finally, to improve the uncertainty representation ability of the standard PF algorithm, the regularized particle filter is applied to design a fusion RUL estimation framework of lithium-ion battery. Experimental results with the lithium-ion battery test data from NASA and CALCE (The Center for Advanced Life Cycle Engineering, the University of Maryland) show that the proposed fusion prognostic approach can effectively predict the battery RUL with more accurate forecasting result and uncertainty representation of probability density distribution (pdf).     

JackHare: a framework for SQL to NoSQL translation using MapReduce

As data exploration has increased rapidly in recent years, the datastore and data processing are getting more and more attention in extracting important information. To find a scalable solution to process the large-scale data is a critical issue in either the relational database system or the emerging NoSQL database. With the inherent scalability and fault tolerance of Hadoop, MapReduce is attractive to process the massive data in parallel. Most of previous researches focus on developing the SQL or SQL-like queries translator with the Hadoop distributed file system. However, it could be difficult to update data frequently in such file system. Therefore, we need a flexible datastore as HBase not only to place the data over a scale-out storage system, but also to manipulate the changeable data in a transparent way. However, the HBase interface is not friendly enough for most users. A GUI composed of SQL client application and database connection to HBase will ease the learning curve. In this paper, we propose the JackHare framework with SQL query compiler, JDBC driver and a systematical method using MapReduce framework for processing the unstructured data in HBase. After importing the JDBC driver to a SQL client GUI, we can exploit the HBase as the underlying datastore to execute the ANSI-SQL queries. Experimental results show that our approaches can perform well with efficiency and scalability.     

Towards improved parallelism through order reduction of accessing data in nD matrices

This paper encompasses the presentation of an enhanced approach with the capacity to reduce the time complexity of accessing nodes in m-dimensional matrices from \(O(n^m)\) to \(O(n\log n)\) . The accomplishment of this process is attained by the serialization of nD (nD) matrices to single-dimensional arrays followed by the access of nodes accordingly. Linear representation of nD matrix data structure induces a superior parallelism of matrix calculations over dense, parallel core micro-architecture computers, including NVIDIA GPGPU Supercomputing and Intel Xeon Phi processing boards. This approach is feasibly implemented as the core of matrix data representation in Math software such as Matlab, Mathematica and Maple, in IDEs for more optimized code generation and in Parallel Computing Libraries such as CUBLAS and Magma.     

How to invent a Prolog machine

In this paper we study the compilation of Prolog by making visible hidden operations (especially unification), and then optimizing them using well-known partial evaluation techniques. Inspection of straightforward partially evaluated unification algorithms gives an idea how to design special abstract machine instructions which later form the target language of our compilation. We handle typical compiler problems like representation of terms explicitly. This work gives a logical reconstruction of abstract Prolog machine code, and represents an approach of constructing a correct compiler from Prolog to such a code. As an example, we are explaining the unification principles of Warren’s New Prolog Engine within our framework.     

Sparse representation of precision matrices used in GMMs

The paper presents a novel precision matrix modeling technique for Gaussian Mixture Models (GMMs), which is based on the concept of sparse representation. Representation coefficients of each precision matrix (inverse covariance), as well as an accompanying overcomplete matrix dictionary, are learned by minimizing an appropriate functional, the first component of which corresponds to the sum of Kullback-Leibler (KL) divergences between the initial and the target GMM, and the second represents the sparse regularizer of the coefficients. Compared to the existing, alternative approaches for approximate GMM modeling, like popular subspace-based representation methods, the proposed model results in notably better trade-off between the representation error and the computational (memory) complexity. This is achieved under assumption that the training data in the recognition system utilizing GMM have an inherent sparseness property, which enables application of the proposed model and approximate representation using only one dictionary and a significantly smaller number of coefficients. Proposed model is experimentally compared with the Subspace Precision and Mean (SPAM) model, a state of the art instance of subspace-based representation models, using both the data from a real Automatic Speech Recognition (ASR) system, and specially designed sets of artificially created/synthetic data.     

Designing RBF Networks Using the Agent-Based Population Learning Algorithm

Radial Basis Function Neural Networks (RBFNs) are nowadays quite popular due to their ability to discover and approximate complex nonlinear dependencies within the data under analysis. Performance of the RBF network depends on numerous factors related to its initialization and training. The paper proposes an approach to the radial basis function networks design, where initial parameters of the network, output weights and parameters of the transfer function are set using the proposed agent-based population learning algorithm (PLA). The algorithm is validated experimentally. Advantages and main features of the PLA-based RBF designs are discussed basing on results of the computational experiment.     

TST——An Algorithm for Global Microcode Compaction with Timing Constraints

Existing global microcode compaction approaches have all assumed a target architecture that has microoperation conflicts and data dependencies as the two fundamental compaction constraints.However,new practical micromachine features demand that the timing constraint be introduced into the traditional compaction model to guarantee compaction correctness.This paper starts by an analysis on the nature of timing constraints,then modifies the rules for microoperation motions,presents an algorithm,TST,based on Trace Scheduling,for global compaction under timing constraints,and finally shows results of experiments.     

Migration,Location and Provision of Support to Older Parents: The Case of Romania

Low fertility and rapid out-migration in Romania are consequential for the migrants that confront challenges of providing support to ageing parents. Systematic data allowing examination of intergenerational support are difficult to find for Eastern Europe, a region undergoing demographic and socio-economic transition. Using recently collected data from Romania this study models monetary and instrumental support from an adult child to an older parent as a function of location of residence and additional covariates that assume Romanian families operate following an integrative family framework wherein support obligations are considered to be shared across a family network and support probabilities depend upon characteristics of the provider and the older parent. Multilevel multinomial models with random intercepts indicate international migrants are likely to give money; within Romania migrants and those living in the same locality as parents are unlikely to give money but likely to provide instrumental support. But, specific probabilities vary depending having sibling and where siblings live. Support is more likely provided to rural parents and to parents with functional limitations. Results elucidate the degree to which and why support is being provided within a rapidly ageing environment.     

Extending existing dependency theory to temporal databases

Normal forms play a central role in the design of relational databases. Several normal forms for temporal relational databases have been proposed. These definitions are particular to specific temporal data models, which are numerous and incompatible. The paper attempts to rectify this situation. We define a consistent framework of temporal equivalents of the important conventional database design concepts: functional dependencies, primary keys, and third and Boyce-Codd normal forms. This framework is enabled by making a clear distinction between the logical concept of a temporal relation and its physical representation. As a result, the role played by temporal normal forms during temporal database design closely parallels that of normal forms during conventional database design. These new normal forms apply equally well to all temporal data models that have timeslice operators, including those employing tuple timestamping, backlogs, and attribute value timestamping. As a basis for our research, we conduct a thorough examination of existing proposals for temporal dependencies, keys, and normal forms. To demonstrate the generality of our approach, we outline how normal forms and dependency theory can also be applied to spatial and spatiotemporal databases     

Inductive logic programming

A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears. The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles from both Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship between Algorithmic Complexity theory and Probably-Approximately-Correct (PAC) Learning. In terms of logic we provide a unifying framework for Muggleton and Buntine’s Inverse Resolution (IR) and Plotkin’s Relative Least General Generalisation (RLGG) by rederiving RLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates, previously discussed only within the context of IR.     

Hermes: a simple and efficient algorithm for building the AOC-poset of a binary relation

Given a relation ?? ? ?? × ?? on a set ?? of objects and a set ?? of attributes, the AOC-poset (Attribute/Object Concept poset), is the partial order defined on the “introducers” of objects and attributes in the corresponding concept lattice. In this paper, we present Hermes, a simple and efficient algorithm for building an AOC-poset which runs in O(m i n{n m, n ^α }), where n is the number of objects plus the number of attributes, m is the size of the relation, and n ^α is the time required to perform matrix multiplication (currently α = 2.376). Finally, we compare the runtime of Hermes with the runtime of other algorithms computing the AOC-poset: Ares, Ceres and Pluton. We characterize the cases where each algorithm is the more relevant.     

Exploring aspects of cell intelligence with artificial reaction networks

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence.     

FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING

Formal Concept Analysis (FCA), in which data is represented as a formal context, offers a framework for Association Rules Mining (ARM) by handling functional dependencies in the data. However, with the size of the formal context, the number of rules grows exponentially. In this article, we apply Fuzzy K-Means clustering on the data set to reduce the formal context and FCA on the reduced data set for mining association rules. With experiments on two real-world healthcare data sets, we offer the evidence for performance of FKM-based FCA in mining association rules.