The basic constructive logic for a weak sense of consistency

In this paper, consistency is understood as the absence of the negation of a theorem, and not, in general, as the absence of any contradiction. We define the basic constructive logic B_Kc1 adequate to this sense of consistency in the ternary relational semantics without a set of designated points. Then we show how to define a series of logics extending B_Kc1 within the spectrum delimited by contractionless minimal intuitionistic logic. All logics defined in the paper are paraconsistent logics.     

Completing open logic programs by constructive induction

We consider part of the problem of schema-biased inductive synthesis of recursive logic programs from incomplete specifications, such as clausal evidence (for instance, but not necessarily, ground positive and negative examples). After synthesizing the base clause and introducing recursive call(s) to the recursive clause, it remains to combine the overall result from the partial results obtained through recursion, so as to complete the recursive clause. Evidence for this combination relation can be abduced from the initially given evidence for the top-level relation. A program for this combination relation can be anything, from a single clause performing a unification (such as for lastElem) to multiple guarded clauses performing unifications (such as for filtering programs) to recursive programs (such as for naive reverse). Existing methods cannot induce guarded clause programs for this combination relation from the abduced evidence. Some existing methods cannot even detect that the combination program itself may have to be recursive and thus they then do not recursively invoke themselves the overall recursive program synthesizer. We introduce our Program Completion Method as a suitable extension and generalization of the existing methods. ©1999 John Wiley & Sons, Inc.     

Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms

Linguistic rules in natural language are useful and consistent with human way of thinking. They are very important in multi-criteria decision making due to their interpretability. In this paper, our discussions concentrate on extracting linguistic rules from data sets. In the end, we firstly analyze how to extract complex linguistic data summaries based on fuzzy logic. Then, we formalize linguistic rules based on complex linguistic data summaries, in which, the degree of confidence of linguistic rules from a data set can be explained by linguistic quantifiers and its linguistic truth from the fuzzy logical point of view. In order to obtain a linguistic rule with a higher degree of linguistic truth, a genetic algorithm is used to optimize the number and parameters of membership functions of linguistic values. Computational results show that the proposed method is an alternative method for extracting linguistic rules with linguistic truth from data sets.     

A bridge between constructive logic and computer programming

Some logic notions have their analogies among programming concepts and vice versa. But people often try to understand these analogies in too straightforward a manner. A collection of analogies arising between constructive logics and programming is summarized and illustrated here. Some examples of complexities usually not taken into account are shown.     

构造性覆盖下不完整数据修正填充方法

不完整数据处理是数据挖掘、机器学习等领域中的重要问题,缺失值填充是处理不完整数据的主流方法。当前已有的缺失值填充方法大多运用统计学和机器学习领域的相关技术来分析原始数据中的剩余信息,从而得到较为合理的值来替代缺失部分。缺失值填充大致可以分为单一填充和多重填充,这些填充方法在不同的场景下有着各自的优势。但是,很少有方法能进一步考虑样本空间分布中的邻域信息,并以此对缺失值的填充结果进行修正。鉴于此,本文提出了一种可广泛应用于诸多现有填充方法的框架用以提升现有方法的填充效果,该框架由预填充、空间邻域信息挖掘和修正填充三部分构成。本文对7种填充方法在8个UCI数据集上进行了实验,实验结果验证了本文所提框架的有效性和鲁棒性。     

Extracting motion data from video using optical flow with physically-based constraints

Motion analysis of video data is a powerful tool for studying dynamic behavior and determining sources of failures. In the case of failure analysis, the available video may be of poor quality, such as from surveillance cameras. It is also likely to have been taken from a bad angle, with poor lighting, and occlusions may be present. To address such cases, this paper presents an optical flow-based tracking algorithm incorporating physically-based constraints to extract motion data from video. The technique can accurately track a significant number of data points with a high degree of automation and efficiency. Many traditional methods of video data extraction from poor-quality video have proven tedious and time-consuming due to extensive user-input requirements. With this in mind, the proposed optical flow-based algorithm functions with a minimal degree of user involvement. Points identified at the outset of a video sequence, and within a small subset of frames spaced throughout, can be automatically tracked even when they become occluded or undergo translational, rotational, or deformational motion. The proposed algorithm improves upon previous optical flow-based tracking algorithms by providing greater flexibility and robustness. Example results are presented that show the method tracking machines with flexible components, Segway personal transporters, and athletes pole vaulting.     

Extracting reduced logic programs from artificial neural networks

Artificial neural networks can be trained to perform excellently in many application areas. Whilst they can learn from raw data to solve sophisticated recognition and analysis problems, the acquired knowledge remains hidden within the network architecture and is not readily accessible for analysis or further use: Trained networks are black boxes. Recent research efforts therefore investigate the possibility to extract symbolic knowledge from trained networks, in order to analyze, validate, and reuse the structural insights gained implicitly during the training process. In this paper, we will study how knowledge in form of propositional logic programs can be obtained in such a way that the programs are as simple as possible—where simple is being understood in some clearly defined and meaningful way.     

Comparative semantics for flow of control in logic programming without logic

We study semantic issues concerning control flow notions in logic programming languages by exploring a two-stage approach. The first considers solely uninterpreted (or schematic) elementary actions, rather than operations such as unification, substitution generation, or refutation. Accordingly, logic is absent at this first stage. We provide a comparative survey of the semantics of a variety of control flow notions in (uninterpreted) logic programming languages including notions such as don't know versus don't care nondeterminism, the cut operator, and/or parallel logic programming, and the commit operator. In all cases considered, we develop operational and denotational models, and prove their equivalence. A central tool both in the definitions and in the equivalence proofs is Banach's theorem on (the uniqueness of) fixed points of contracting functions on complete metric spaces. The second stage of the approach proceeds by interpreting the elementary actions, first as arbitrary state transformations, and next by suitably instantiating the sets of states and of state transformations (and by articulating the way in which a logic program determines a set of recursive procedure declarations). The paper concentrates on the first stage. For the second stage, only a few hints are included. Furthermore, references to papers which supply details for the languages PROLOG and CONCURRENT PROLOG are provided.     

Extracting a statistical data matrix from electronic patient records

This paper describes the processing and transformation of medical data from a clinical database to a statistical data matrix. Precise extraction and linking tools must be available for the desired data to be processed for statistical purposes. We show that flexible mechanisms are required for the different types of users, such as physicians and statisticians. In our retrieval tools we use logical queries based on operands and operators. The paper describes the method and appliance of the operators with which the desired matrix is created through a process of selection and linking. Examples with a Kaplan-Meier function and time-dependent covariables demonstrate how our model is useful for different user groups.     

Data positioning as an instrument of constructive data compression

A fundamentally new algorithm for data compression is considered. A theoretical basis is provided for the formation, modification, and conversion of data positions aimed at their reversible compression. The specifics are described of a method development based on structure encryption, i.e., reformatting the architecture of the compressed data alphabet based on the positionality and conditionality properties of the compression level and the structural numbering of the reformatted alphabet??s architecture in a positional multilevel space. We substantiate the principle of redundancy reduction due to the following: the simultaneous modification of the positions inhibiting the appearance of noninteger values of new positions and constraints on the digit capacity of the elements in positioning arrays, the identification of the carriers of stabilizing elements in the digital space, and the reduction of the positions with code combinations in the course of the array processing. A particular case of a data conversion protocol is presented. Problems are solved of combining the principles of data reversibility and supercompression both in the modes of single and multiple use of one and the same encryption scenario. The cost of the positioning processes and their influence on the efficiency of the processes of the data encoding and decoding have been determined.     

Extracting 3D vortices in turbulent fluid flow

This paper presents a computational framework to extract salient patterns, called vortex structures, from 3D turbulent fluid flows. These structures can be characterized as regions of dominating rotational motion in the velocity fields and intensity concentrations in the corresponding vorticity fields. A pointwise linear representation is employed to approximate the kinematics of the flow field, and the fluid motion is classified according to motion analysis or topological patterns. The regions of vortex structures are identified as those dominated by rotational motion or those of focus-type singularity. The 2D vortices, as a special case of 3D vortices, are detected by searching for regions of vorticity concentrations     

抽取XML文档到关系数据库

XML在关系数据库中的存储问题是XML研究领域中的一个重要问题。在总结多种映射方法的基础上,提出了一种方法将多个相似的XML文档进行解析,根据映射关系,生成各自的关系模式,并分析归纳出一个集成的关系模式,然后创建一个关系数据库,并在映射关系的基础上提取并存储XML文档数据到关系数据库。此方法以较为简洁的结构保存了XML文档的数据信息,其最大的特点就是不用考虑文档的模式信息（DTD,XML Schema）。并通过一个具体的实验结果来说明这种方法的有效性。     

Twitter data laid almost bare: An insightful exploratory analyser

In today’s world, social networks and online communities continuously generate tons of data that reflect users’ habits, personal interests, opinions and emotions. However, little profit can be gained from such huge raw data collections unless we are able to translate them into useful knowledge. Microblogs like Twitter have recently attracted a great body of research works to mine useful insights about users interests and preferences in different geographical areas and time periods. Indeed, the rather heterogeneous dimensions characterizing Twitter data, such as space, time and text content, impose innovative methods in the data mining discovery process.This paper presents TCharM, a data analytics methodology based on cluster analysis and association rule discovery to gain interesting knowledge from large collections of Twitter data. TCharM explores tweet collections along the three dimensions characterizing tweets (i.e., text content, posting time and place) to support context-aware topic trend analysis. To discover groups of tweets with a good cohesion on the three tweet features, TCharM exploits a novel distance measure (TASTE) which allows driving the clustering task by considering in one step the three tweet features. Association rule analysis is then exploited to concisely describe the cluster content with a set of understandable and significant patterns which reveal underlying correlations among frequent topics, tweeting times and places. TCharM can provide useful information to understand the evolution of people’s involvement in different topics, across geographical areas and over time. TCharM find applications in various domains by providing a valuable support in decision making to domain experts. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in discovering cohesive clusters and actionable knowledge from Twitter data.