Walking Through Cantor's Paradise and Escher's Garden: Epistemological Reflections on the Mathematical Infinite (I)

Infinity is not an easy concept. A number of difficulties that people cope with when dealing with problems related to infinity include its abstract nature: understanding of infinity as a never-ending process, understanding infinity as a set of an infinite number of elements, and understanding some well-known paradoxes. Infinity can be understood in a number of ways, some of which are incompatible, and can involve value judgments or assumptions that are neither explicit nor desired. In its definition, we distinguish several aspects, teleological, artistic (Escher 2000 Escher, M. C. Estampas y dibujos. Colonia: Ed. Taschen, 2000. (In Spanish). [Google Scholar]), some essential, some potential, and others actual. Cantor's work on set theory is linked to infinity and has implications for belief in God.     

概念漂移数据流半监督分类综述

在开放环境下,数据流具有数据高速生成、数据量无限和概念漂移等特性.在数据流分类任务中,利用人工标注产生大量训练数据的方式昂贵且不切实际.包含少量有标记样本和大量无标记样本且还带概念漂移的数据流给机器学习带来了极大挑战.然而,现有研究主要关注有监督的数据流分类,针对带概念漂移的数据流的半监督分类的研究尚未引起足够的重视....     

Opinion Mining and Information Fusion: A survey

Interest in Opinion Mining has been growing steadily in the last years, mainly because of its great number of applications and the scientific challenge it poses. Accordingly, the resources and techniques to help tackle the problem are many, and most of the latest work fuses them at some stage of the process. However, this combination is usually executed without following any defined guidelines and overlooking the possibility of replicating and improving it, hence the need for a deeper understanding of the fusion process becomes apparent. Information Fusion is the field charged with researching efficient methods for transforming information from different sources into a single coherent representation, and therefore can be used to guide fusion processes in Opinion Mining. In this paper we present a survey on Information Fusion applied to Opinion Mining. We first define Opinion Mining and describe its most fundamental aspects, later explain Information Fusion and finally review several Opinion Mining studies that rely at some point on the fusion of information.     

Model approach to grammatical evolution: theory and case study

Many deficiencies with grammatical evolution (GE) such as inconvenience in solution derivations, modularity analysis, and semantic computing can partly be explained from the angle of genotypic representations. In this paper, we deepen some of our previous work in visualizing concept relationships, individual structures and total evolutionary process, contributing new ideas, perspectives, and methods in these aspects; reveal the principle hidden in early work so that to develop a practical methodology; provide formal proofs for issues of concern which will be helpful for understanding of mathematical essence of issues, establishing of an unified formal framework as well as practical implementation; exploit genotypic modularity like modular discovery systematically which for the lack of supporting mechanism, if not impossible, is done poorly in many existing systems, and finally demonstrate the possible gains through semantic analysis and modular reuse. As shown in this work, the search space and the number of nodes in the parser tree are reduced using concepts from building blocks, and concepts such as the codon-to-grammar mapping and the integer modulo arithmetic used in most existing GE can be abnegated.     

Implementing associations: UML 2.0 to Java 5

A significant current software engineering problem is the conceptual mismatch between the abstract concept of an association as found in modelling languages such as UML and the lower level expressive facilities available in object-oriented languages such as Java. This paper introduces some code generation patterns that aid the production of Java based implementations from UML models. The work is motivated by a project to construct model driven development tools in support of the construction of embedded systems. This involves the specification and implementation of a number of meta-models (or models of languages). Many current UML oriented tools provide code generation facilities, in particular the generation of object-oriented code from class diagrams. However, many of the more complex aspects of class diagrams, such as qualified associations are not supported. In addition, several concepts introduced in UML version 2.0 are also not supported.The aim of the work presented in this paper is to develop a number of code generation patterns that allow us to support the automatic generation of Java code from UML class diagrams that support these new and complex association concepts. These patterns significantly improve the code generation abilities of UML tools, providing a useful automation facility that bridges the gap between the concept of an association and lower level object-oriented programming languages.     

Keys to engineering a workable contract

A major stumbling block to designing a good contract is a legal concept called meeting of the minds. The basis for this concept is that two parties cannot agree unless they understand what they are contracting for. The process of drawing up a written contract is intended to assist in this understanding. However, much room remains for error when negotiating something as technical as software development. The paper discusses some legal and policy aspects of information technology use and development     

Optimal Node Visitation in Stochastic Digraphs

The optimal node visitation (ONV) problem addressed in this paper concerns the visitation of a subset of nodes in a stochastic graph a specified number of times, while minimizing the expected visits to another node in this graph. The presented results first provide a formulation of the ONV problem as a stochastic shortest path problem, and subsequently they develop a suboptimal policy that is computationally tractable and asymptotically optimal. In particular, it is established that the ratio of the expected performance of this policy to the expected performance of an optimal policy converges to one, as the underlying visitation requirements are scaled uniformly to infinity. Furthermore, it is shown that under some stronger assumptions, the divergence of the performance of this policy from the performance of the optimal policy remains uniformly bounded by a constant, as the visitation requirements are scaled to infinity. Finally, it is shown that, for certain problem structures, the considered policy admits a closed-form characterization of its performance, which subsequently enables its optimized paremeterization and its efficient integration into adaptive control schemes of even higher efficiency.      

Ontology-based information content computation

The information content (IC) of a concept provides an estimation of its degree of generality/concreteness, a dimension which enables a better understanding of concept’s semantics. As a result, IC has been successfully applied to the automatic assessment of the semantic similarity between concepts. In the past, IC has been estimated as the probability of appearance of concepts in corpora. However, the applicability and scalability of this method are hampered due to corpora dependency and data sparseness. More recently, some authors proposed IC-based measures using taxonomical features extracted from an ontology for a particular concept, obtaining promising results. In this paper, we analyse these ontology-based approaches for IC computation and propose several improvements aimed to better capture the semantic evidence modelled in the ontology for the particular concept. Our approach has been evaluated and compared with related works (both corpora and ontology-based ones) when applied to the task of semantic similarity estimation. Results obtained for a widely used benchmark show that our method enables similarity estimations which are better correlated with human judgements than related works.     

Backtracking and random constraint satisfaction

The average running time used by backtracking on random constraint satisfaction problems is studied. This time is polynomial when the ratio of constraints to variables is large, and it is exponential when the ratio is small. When the number of variables goes to infinity, whether the average time is exponential or polynomial depends on the number of variables per constraint, the number of values per variable, and the probability that a random setting of variables satisfies a constraint. A method for computing the curve that separates polynomial from exponential time and several methods for approximating the curve are given. The version of backtracking studied finds all solutions to a problem, so the running time is exponential when the number of solutions per problem is exponential. For small values of the probability, the curve that separates exponential and polynomial average running time coincides with the curve that separates an exponential average number of solutions from a polynomial number. For larger probabilities the two curves diverge. Random problems similar to those that arise in understanding line drawings with shadows require a time that is mildly exponential when they are solved by simple backtracking. Slightly more sophisticated algorithms (such as constraint propagation combined with backtracking) should be able to solve these rapidly. This revised version was published online in June 2006 with corrections to the Cover Date.     

Invited Paper: UHP‐lamp systems for projection applications

Abstract— Projection systems have found widespread use in conference rooms and other professional applications during the last decade and are now entering the home‐TV market with considerable pace. Projectors as small as about one liter are nowadays able to deliver a screen flux of several thousand lumens and are, with a system efficacy of more than 10 lm/W, the most‐efficient display system realized today. Because such highly efficient projectors employ microdisplays as light valves, short‐arc lamps are a key component in realizing these properties. The introduction of the UHP‐lamp system by Philips in 1995 can be identified as one of the key enablers for the commercial success of projection systems. The ultra‐high‐performance (UHP) lamp concept features outstanding arc luminance, a well‐suited spectrum, long life, and excellent flux maintenance. For the first time, it combines a very‐high‐pressure mercury‐discharge lamp having an extremely short and stable arc length with a regenerative chemical cycle that keeps the discharge walls free from blackening, leading to lifetimes of over 10,000 hours. In this review, the most important aspects of the UHP concept that enabled its success in the projection market are described, followed by a discussion of some recent additions to the UHP‐product portfolio.     

Automated program understanding by concept recognition

Program understanding can be seen as the process of understanding abstract concepts in the program code. Thus, automated recognition of abstract concepts may greatly assist the human understanding process. This paper describes an approach to automated concept recognition and its implementation. In the approach, we use a concept model and a library of concept recognition rules to describe what the concepts are and how to recognize them from lower-level concepts. Programming language knowledge as well as domain knowledge are used to aid the recognition of abstract concepts.     

A study of factors that contribute to online review helpfulness

Helpfulness of online reviews is a multi-faceted concept that can be driven by several types of factors. This study was designed to extend existing research on online review helpfulness by looking at not just the quantitative factors (such as word count), but also qualitative aspects of reviewers (including reviewer experience, reviewer impact, reviewer cumulative helpfulness). This integrated view uncovers some insights that were not available before. Our findings suggest that word count has a threshold in its effects on review helpfulness. Beyond this threshold, its effect diminishes significantly or becomes near non-existent. Reviewer experience and their impact were not statistically significant predictors of helpfulness, but past helpfulness records tended to predict future helpfulness ratings. Review framing was also a strong predictor of helpfulness. As a result, characteristics of reviewers and review messages have a varying degree of impact on review helpfulness. Theoretical and practical implications are discussed.     

Nondeterministic flowchart programs with recursive procedures: semantics and correctness I

In this paper, we study some aspects of the semantics of nondeterministic flowchart programs with recursive procedures. In the first part of this work we provide the operational semantics of programs using the concept of an execution tree. We propose a new definition of the semantics of a non-deterministic recursive program as a mapping from the input domain to the set of execution trees determined by the program. Using this new concept, we prove that every nondeterministic flowchart program with recursive procedures can be unfolded into a semantically equivalent infinite pure flowchart (without procedures). This result is applied in the second part of this work to prove the soundness of an inductive assertion method which is also complete with a finite number of assertions (contrary to De Bakker and Meertens's method [11]).     

High-Level Views of Distributed Executions: Convex Abstract Events

Due to the complexity of distributed applications, understanding their behaviour is a challenging task. To remedy this problem, graphical visualizations of distributed executions in the form of process-time diagrams are frequently employed. However, such process-time diagrams do not scale for long-running and complex distributed applications. To reduce the display complexity, abstract graphical views of an execution are frequently suggested. One commonly used abstraction is to group primitive events into abstract events. This paper discusses some of the problems encountered when analyzing executions at abstract levels and introduces the concept of convex abstract events. Such abstract events can be used to reason about executions at higher levels, facilitating program development, debugging, and maintenance. We discuss some fundamental aspects, such as the precedence relation and its efficient detection, for abstract events. The paper also presents a graphical representation for convex abstract events which can easily be included in process-time diagrams. Poet, our visualization tool, was enhanced with a facility to display abstract events. Using a non-trivial distributed application, examples of the resulting abstract execution visualizations are discussed.     

Structure at infinity revisited

The structure at infinity of an ordinary differential control system is a finite sequence of increasing integers ending with the differential output rank of the system, namely the number of outputs that can be given as arbitrary functions of time when the inputs are unknown. Its definition and construction, originally done for linear systems, has been extended to affine nonlinear systems and used in order to study dynamic decoupling or model matching. It essentially relies on a state representation. The purpose of this paper is to make a critical examination of this concept and to modify it in order to avoid the state representation. At the same time, we extend it to nonlinear partial differential control systems by exhibiting a link with formal integrability, a highly important concept in the formal theory of systems of partial differential equations that cannot be handled by means of a transfer matrix approach. Many explicit examples will illustrate the main results and the possibility to use computer algebra techniques will be pointed out.     

Shape understanding system: Visual intelligence tests

Understanding is based on a large number of highly varied abilities called intelligence that can be measured. In this paper understanding abilities of the shape understanding system (SUS) are tested based on the methods used in intelligence tests. These tests are formulated as tasks given to the system and performance is compared with the human performance of these tasks. The main novelty of the presented method is that the process of understanding is related to the visual concept represented as a symbolic name of the possible class of shapes. The visual concept is one of the ingredients of the concept of the visual object (the phantom concept) that makes it possible to perform different tasks that are characteristic for visual understanding. The presented results are part of the research aimed at developing the shape understanding method that would be able to perform complex visual tasks connected with visual thinking. The shape understanding method is implemented as the shape understanding system. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 799–826, 2005.     

Learning classification rules from data

We present ELEM2, a machine learning system that induces classification rules from a set of data based on a heuristic search over a hypothesis space. ELEM2 is distinguished from other rule induction systems in three aspects. First, it uses a new heuristtic function to guide the heuristic search. The function reflects the degree of relevance of an attribute-value pair to a target concept and leads to selection of the most relevant pairs for formulating rules. Second, ELEM2 handles inconsistent training examples by defining an unlearnable region of a concept based on the probability distribution of that concept in the training data. The unlearnable region is used as a stopping criterion for the concept learning process, which resolves conflicts without removing inconsistent examples. Third, ELEM2 employs a new rule quality measure in its post-pruning process to prevent rules from overfitting the data. The rule quality formula measures the extent to which a rule can discriminate between the positive and negative examples of a class. We describe features of ELEM2, its rule induction algorithm and its classification procedure. We report experimental results that compare ELEM2 with C4.5 and CN2 on a number of datasets.     

The Challenges of Using Design Thinking in Industry – Experiences from Five Large Firms

Design Thinking (DT) is advocated as a user‐centred approach to innovation, based on the way designers think and work. Despite being increasingly promoted as an approach to innovation, there is still little evidence of successful impact. Rather, indications suggest that firms find implementation challenging. The purpose of this paper is to analyse challenges of using DT in light of literature on innovation barriers, in order to discuss whether there is something unique about DT as a concept that makes it particularly challenging to use. The paper is based on an interview study of five large firms that all have at least five years of experience of using DT. The analysis shows that several of the perceived challenges can be linked to known barriers to innovation. However, other challenges have not previously been described in the innovation literature, and the paper suggests that there are some unique aspects of DT that makes it particularly challenging for firms to integrate it in innovation work. These are related to its core themes: user focus, problem framing, experimentation, visualization and diversity. Additionally, the paper contributes with an empirically based categorization of challenges that managers will find useful if they want to implement design thinking in their organizations.     

A tutorial – game theory-based extended H infinity filtering approach to nonlinear problems in signal processing

In this paper, we provide a tutorial for the applications of “game-theory-based extended H infinity filtering (EHIF)” approach to various problems in disciplines of signal processing. The algorithm of this filtering approach is similar to that of the extended Kalman filtering (EKF). Since its invention, the Kalman filtering approach has been successfully and widely employed for many problems in scientific and engineering fields, e.g. target tracking, satellite systems, control, communications, etc. Therefore, the H infinity filtering approach also can be applied to all these problems. One big difference of EHIF from the EKF approach is that we apply it with unknown noise statistics of the state and measurement. In this tutorial, we introduce this non-well-known approach in spite of its practical usefulness, by providing the step by step algorithm with example problems of a number of signal processing disciplines. We also show that EHIF can outperform other approaches including the EKF that need to know the noise statistics in their applications, in some scenarios. By the contribution of this tutorial, we look forward to easy, and disseminative applications of EHIF to problems where, particularly, the EKF or particle filter could have been applied if noise statistics were known.