On a Service-Oriented Approach for an Engineering Knowledge Desktop

Increasingly, manufacturing companies are shifting their focus from selling products to providing services. As a result, when designing new products, engineers must increasingly consider the life cycle costs in addition to any design requirements. To identify possible areas of concern, designers are required to consult existing maintenance information from identical products. However, in a large engineering company, the amount of information available is significant and in wide range of formats. This paper presents a prototype knowledge desktop suitable for the design engineer. The Engineering Knowledge Desktop analyses and suggests relevant information from ontologically marked-up heterogeneous web resources. It is designed using a Service-Oriented Architecture, with an ontology to mediate between Web Services. It has been delivered to the user community for evaluation

Limits of Kansei – Kansei unlimited

This article discusses momentary limitations of the Kansei Engineering methods. There are for example the focus on the evaluation of colour and form factors, as well as the highly time consuming creation of the questionnaires. To overcome these limits we firstly suggest the integration of word lists from related research fields, like sociology and cognitive psychology on product emotions in the Kansei questionnaires. Thereafter we present a study on the wide range of Kansei attributes treated in an industrial setting. Concept words used by designers are being collected through word maps and categorized into attributes. In a third step we introduce a user-product interaction schema in which the Kansei attributes from the study are positioned. This schema unfolds potential expansion points for future applications of Kansei engineering beyond its current limits

Towards guidelines for building a business case and gathering evidence of software reference architectures in industry

Background: Software reference architectures are becoming widely adopted by organizations that need to support the design and maintenance of software applications of a shared domain. For organizations that plan to adopt this architecture-centric approach, it becomes fundamental to know the return on investment and to understand how software reference architectures are designed, maintained, and used. Unfortunately, there is little evidence-based support to help organizations with these challenges. Methods: We have conducted action research in an industry-academia collaboration between the GESSI research group and everis, a multinational IT consulting firm based in Spain. Results: The results from such collaboration are being packaged in order to create guidelines that could be used in similar contexts as the one of everis. The main result of this paper is the construction of empirically-grounded guidelines that support organizations to decide on the adoption of software reference architectures and to gather evidence to improve RA-related practices. Conclusions: The created guidelines could be used by other organizations outside of our industry-academia collaboration. With this goal in mind, we describe the guidelines in detail for their use.Peer ReviewedPostprint (published version

Commercial-off-the-shelf simulation package interoperability: Issues and futures

Commercial-Off-The-Shelf Simulation Packages (CSPs) are widely used in industry to simulate discrete-event models. Interoperability of CSPs requires the use of distributed simulation techniques. Literature presents us with many examples of achieving CSP interoperability using bespoke solutions. However, for the wider adoption of CSP-based distributed simulation it is essential that, first and foremost, a standard for CSP interoperability be created, and secondly, these standards are adhered to by the CSP vendors. This advanced tutorial is on an emerging standard relating to CSP interoperability. It gives an overview of this standard and presents case studies that implement some of the proposed standards. Furthermore, interoperability is discussed in relation to large and complex models developed using CSPs that require large amount of computing resources. It is hoped that this tutorial will inform the simulation community of the issues associated with CSP interoperability, the importance of these standards and its future

Tangible user interfaces : past, present and future directions

In the last two decades, Tangible User Interfaces (TUIs) have emerged as a new interface type that interlinks the digital and physical worlds. Drawing upon users' knowledge and skills of interaction with the real non-digital world, TUIs show a potential to enhance the way in which people interact with and leverage digital information. However, TUI research is still in its infancy and extensive research is required in or- der to fully understand the implications of tangible user interfaces, to develop technologies that further bridge the digital and the physical, and to guide TUI design with empirical knowledge. This paper examines the existing body of work on Tangible User In- terfaces. We start by sketching the history of tangible user interfaces, examining the intellectual origins of this field. We then present TUIs in a broader context, survey application domains, and review frame- works and taxonomies. We also discuss conceptual foundations of TUIs including perspectives from cognitive sciences, phycology, and philoso- phy. Methods and technologies for designing, building, and evaluating TUIs are also addressed. Finally, we discuss the strengths and limita- tions of TUIs and chart directions for future research

Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained with Maximum Likelihood Estimation (MLE). When context information (called factor) is involved, it is difficult to analyze when and how each individual factor would affect the final recommendation performance. For this purpose, we take a new perspective and introduce adversarial learning to sequential recommendation. In this paper, we present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Specifically, our proposed MFGAN has two kinds of modules: a Transformer-based generator taking user behavior sequences as input to recommend the possible next items, and multiple factor-specific discriminators to evaluate the generated sub-sequence from the perspectives of different factors. To learn the parameters, we adopt the classic policy gradient method, and utilize the reward signal of discriminators for guiding the learning of the generator. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods, in terms of effectiveness and interpretability

Designing and Deploying Online Field Experiments

Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis. Few tools exist that support these needs. We thus introduce a language for online field experiments called PlanOut. PlanOut separates experimental design from application code, allowing the experimenter to concisely describe experimental designs, whether common "A/B tests" and factorial designs, or more complex designs involving conditional logic or multiple experimental units. These latter designs are often useful for understanding causal mechanisms involved in user behaviors. We demonstrate how experiments from the literature can be implemented in PlanOut, and describe two large field experiments conducted on Facebook with PlanOut. For common scenarios in which experiments are run iteratively and in parallel, we introduce a namespaced management system that encourages sound experimental practice.Comment: Proceedings of the 23rd international conference on World wide web, 283-29