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Describes current research work into the design of a 3‐D vision sensor for use in the field of robot navigation and autonomous vehicles. Outlines the development of a stereo vision system which uses fast data processing to extract feature points in the stereo images and a new fast stereo matching algorithm. Gives results of experiments performed using this system and concludes that the applications require fast, self‐adaptive algorithms which can be processed by parallel processors. This was obtained by means of a special configuration and a highly parallelizable stereo vision process based on the declivity feature matched by dynamic programming.

Allan A. S. Rae has been appointed as the new chairman of CIBA‐Geigy (U.K.) Ltd., The Clayton Aniline Company Ltd., CIBA‐Geigy Chemicals Ltd. and Ilford Ltd. Mr Rae succeeds Lord Harvey of Prestbury who retired but continues as a Director of the parent company CIBA‐Geigy AG, Basle, and in the U.K. will act as consultant and adviser to Mr Rae.

The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty function, regularization laws are embedded into the structure of common least square solutions to increase the numerical stability, sparsity, accuracy and robustness of regression weights. Several regularization techniques have been proposed so far which have their own advantages and disadvantages. Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques. However, the proposed numerical and deterministic approaches need certain knowledge of mathematical programming, and also do not guarantee the global optimality of the obtained solution. In this research, the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine (ELM).

To implement the required tools for comparative numerical study, three steps are taken. The considered algorithms contain both classical and swarm and evolutionary approaches. For the classical regularization techniques, Lasso regularization, Tikhonov regularization, cascade Lasso-Tikhonov regularization, and elastic net are considered. For swarm and evolutionary-based regularization, an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered, and its algorithmic structure is modified so that it can efficiently perform the regularized learning. Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme. To test the efficacy of the proposed constraint evolutionary-based regularization technique, a wide range of regression problems are used. Besides, the proposed framework is applied to a real-life identification problem, i.e. identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine, for further assurance on the performance of the proposed scheme.

Through extensive numerical study, it is observed that the proposed scheme can be easily used for regularized machine learning. It is indicated that by defining a proper objective function and considering an appropriate penalty function, near global optimum values of regressors can be easily obtained. The results attest the high potentials of swarm and evolutionary techniques for fast, accurate and robust regularized machine learning.

The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine (OP-ELM). The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system, and also increases the degree of the automation of OP-ELM. Besides, by using different types of metaheuristics, it is demonstrated that the proposed methodology is a general flexible scheme, and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.

In this paper, the authors extend their understanding of the internal dynamics of routines in contexts characterized by increased levels of virtuality. In particular, the authors focus on the role of routine artifacts in the internal dynamics of routines to answer the question: How does extensive reliance on information and communication technologies (ICTs) due to physical distance influence the internal dynamics of the new product development (NPD) routine (i.e. interactions between performative, ostensive and artifacts of routines) enacted by a virtual team?

This paper is based on an 18-month ethnographic study of the NPD routine performed by a virtual team. The authors relied predominantly on qualitative, ethnographic data collection and analysis methods, using semi-structured interviews, non-participant observation, and the collection of archival data and company documents (formal procedures, guidelines, application designs etc). Qualitative research offers a valuable means to investigate dynamic processes in organizations due to its sensitivity to the organizational context and potential to focus on activities as they unfold.

The findings highlight the central role of routine artifacts (ICTs) in the routine dynamics of the NPD routine performed by virtual team. In particular, the authors show that the use of the particular types of ICTs enabled team members to confidently and meaningfully relate to the overall routine activity and coordinate their actions in a context characterized by physical distance and extensive reliance on communication and collaboration technologies.

The paper sheds light into role of routine artifacts in the routine dynamics in a context characterized by a high degree of virtuality. This work contributes to the literature on routine dynamics by theorizing about the processes through which routine artifacts (ICTs) afforded routine participants the ability to act confidently and meaningfully to the present and dynamically coordinate their actions with their fellow routine participants.

The overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.

Material and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.

The paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.

The contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.

The purpose of this study is to advance the understanding of consumer innovativeness during aging. This study explores why older consumers have decreased innovativeness and how awareness of age-related change affects the adoption of innovation.

A survey was conducted on 200 older consumers aged 50 and older to investigate whether awareness of age-related change influences innovativeness.

The results show that awareness of age-related change causes older consumers to have a decreased tendency to adopt novel products. Moreover, the stereotype threat of older consumers is found to play a mediating role. Older individuals who sense they are negatively viewed as older people restrict their innovativeness to avoid situations that would confirm their incompetence to others. Furthermore, the effects of older consumers’ stereotype threat on innovativeness are moderated by self-monitoring. Older consumers who exhibit high self-monitoring cope with stereotype threat by showing increased innovativeness; however, the opposite effect occurs in older consumers with low self-monitors.

The findings deepen the understanding of older adults’ consumption behavior regarding innovative products and show why people are reluctant to adopt innovative products and services because they grow older by identifying the underlying process that hinders customer innovativeness.

Le possibilità di studio e di ricerca offerte dalla scienza dei sistemi si vanno sempre più affermando, quanto più l'analisi delle interconnessioni e interazioni fra varie discipline si dimostra valido strumento nella programmazione e nella operatività delle varie attività umane.

The Nature of Business Policy Business policy — or general management — is concerned with the following six major functions:

The depth and breadth of the market for contingent claims, including exotic options, has expanded dramatically. Regulators have expressed concern regarding the risks of exotics to the financial system, due to the difficulty of hedging these instruments. Recent literature focuses on the difficulties in hedging exotic options, e.g., liquidity risk and other violations of the standard Black‐Scholes model. This article provides insight into hedging problems associated with exotic options: 1) hedging in discrete versus continuous time, 2) transaction costs, 3) stochastic volatility, and 4) non‐constant correlation. The author applies simulation analysis of these problems to a variety of exotics, including Asian options, barrier options, look‐back options, and quanto options.

The purpose of this paper is to study the problem of fashion flat sketches classification and proposed an integrated approach. It aims to propose a fast, reliable method to handle multi-class fashion flat sketches classification problems and lay the foundation for the garment style query in the next step.

The proposed integrated approach adopts wavelet Fourier descriptor (WFD), linear discriminant analysis (LDA) and extreme learning machine (ELM). First, the discrete wavelet and Fourier transform are adopted to extract the shape features of fashion flat sketches. Then, LDA is employed for multi-class classification to reduce dimensionality. Finally, ELM is used as the classifier.

The experimental results show that the classification accuracy of the integrated approach is obtained at about 100 percent. Contrary to the traditional approaches, efficiency and accuracy are the advantages of the present approach.

Fashion concept is conveyed often in the form of the fashion illustration or sketch. This type of sketch is useful to imply the style and overall feel of the design. However, this sketch gives no clue about the parts or sections that make up each garment. For this reason, this paper only studies the classification of flat sketches.

A new shape descriptor named WFD is proposed. The WFD acquires high classification accuracy comparing with Fourier descriptor (FD) and multiscale Fourier descriptor (MFD) without dimensionality reduction and nearly the same classification accuracy comparing with FD while MFD easily causes small sample size problem with dimensionality reduction using LDA. In addition, ELM is first used as the classifier in the textiles field related to the classification problem.