基于语义的智能信息处理技术的研究

智能信息处理是模拟人或者自然界其他生物处理各种信息的行为,是当前信息处理技术向多样化、集成化与智能化方向的发展,具有很高的理论研究和应用价值.经过多年的研究和探索,智能信息处理技术已经取得较大的发展,但在实际应用中还存在许多问题需要解决和完善.分析了基于概念图的语义研究在智能信息处理中的应用,并设计与实现了一个基于概念图的智能答疑系统CGQAS,通过实验结果验证了基于概念图的智能信息处理的可行性和有效性.     

Artificial intelligence and other approaches to speech understanding: reflections on methodology

This paper characterizes the methodology of Artificial Intelligence (AI) by looking at research in speech understanding, a field where AI approaches contrast starkly with the alternatives, particularly engineering approaches. Four values of AI stand out as influential: ambitious goals, introspective plausibility, computational elegance, and wide significance. The paper also discusses the utility and larger significance of these values.     

The ECHORD project proposals analysis – Research profiles,collaboration patterns and research topic trends

This paper investigates the research profiles, collaboration patterns and research topic trends which can be identified in the proposals submitted to the ECHORD (European Clearing House for Open Robotics Development) FP7 project. On a country level, clusters were identified and characterized by patterns of proposal production per inhabitant, score and international cooperation. Belgium and Sweden constitute a cluster characterized by high proposal production, with very high scores and extensive international collaboration. Belgium also excels from another cluster analysis, being as the only country where 100% of proposals involve industry–academia cooperation and obtain scores above 10. Other findings show that single partner proposals have significantly lower quality than multi-partner proposals but, on the other hand, the number of countries involved shows no influence on the quality of the proposals. Despite the high number of industrial participants present on the proposals, it is observed that they play secondary roles in the proposals, with a very low number projects leaded by companies. Also, it is observed that partnerships between research institutions (non-universities) are the most successful. Concerning topics of the proposals, the technology human–robot interface and the product vision robot for small-scale manufacturing are the most significant. Finally, the paper shows clusters of institutions extracted from the giant network of relations obtained from the ECHORD set of proposals.     

Affective Experience Measurement in Outdoor Environment via Machine Visual Cognition

Outdoor environments with quality landscapes can benefit people’s physical and mental health. Real-time assessment on individuals’ environmental affective experience can improve the scientism in measuring the quality of outdoor environments. Existing measurement methods are often insufficient for the cases of a larger site area or sample size. The machine visual cognition of Artificial Intelligence can realize the recognition of facial expressions and the changes in video images, which supports high-precision and long-cycle measurements on individuals’ affective experience in outdoor environments. Taking an urban community square as the study site, this research simultaneously collects participants’ facial data from video images and their electrodermal activity data, wherein Convolutional Neural Network algorithm model is trained with a deep learning algorithm, i.e. codec–SVM optimized model, whose reliability is tested through an additional experiment. The research reveals that: 1) The accuracy rate of the main and additional experiments in measuring individuals’ affective experience is 82.01% and 65.08%, respectively; 2) The additional experiment verifies the application potential of the codec–SVM optimized model; And 3) the model works more effective for outdoor scenarios with varying usage behaviors and open views. Therefore, machine visual cognition can be used for emotion measurement in a larger site area or sample size and contributes to the effectiveness of landscape optimization efforts, especially as an instrumental tool to study the affective experience of the ones who have communication or reading disability. The findings also demonstrate the model’s great potential in building Smart Cities with refined public services.     

Theoretical and empirical approaches to quantitatively valuing research

Abstract

This article provides historical narratives describing approaches to studying, managing, and quantitatively valuing research; methods used by industry, particularly the pharmaceutical industry and approaches taken by economists, including government economists using the Leontief Input–Output framework. The article documents the persistent belief that research expenditures generate future economic value along with the equally persistent frustrations of attempting to measure such value, particularly for basic research. The article then discusses the results of applying the Leontief method to Association of University Technology Manager (AUTM) data. Strengths and weaknesses of these approaches are noted. Additional studies and calls for data capture are suggested and the potential benefits of such efforts are described.     

Innovation performance of the US American and European electricity supply industry

Using a production function approach based on Cobb–Douglas, this analysis relates R&D efforts of 32 electric utilities on both sides of the Atlantic to their performance in terms of labour productivity. We find that higher R&D levels generally have a positive impact on revenues. However, only in the sub-sample of 16 electricity suppliers in Europe this effect is significant. Knowledge spill-over effects can be estimated for the US American sub-sample, since US utilities have bundled their R&D efforts in a centralized research institution and have to report that data. Our analysis reveals, though, that collaborative research efforts do not lead to positive spill-overs at the assumption of a time delay of one year.     

Should emoji replace emotion words in questionnaire-based food-related consumer research?

Emotion word questionnaires remain dominant in food-related emotion research despite acknowledged limitations. Emoji questionnaires can overcome some of these limitations but are not yet regarded as a strong alternative to emotion word questionnaires. We believe they have the potential to earn this right and use this invited opinion piece to explain why. Following a brief introduction about emoji, advantages of emoji questionnaires are summarised: – enhanced ecological validity, familiarity and cross-culturally shared meanings. We then address concerns that hinder uptake of emoji questionnaires, including emoji meanings/interpretations, appropriateness for older consumers and ability to represent emotional arousal (activation to deactivation). Finally, we offer suggestions for how to accelerate the uptake of emoji questionnaires in sensory and consumer science, which include: i) more applied studies using emoji questionnaires, ii) establishing emoji meanings in the context of eating of drinking, iii) adopting open-ended question formats, iv) comparing emoji and emotion word questionnaires, and v) establishing guidelines for emoji questionnaires. An important spill-over benefit of research to develop emoji questionnaires is that it exposes the lack of similar research for emotion word questionnaires. We encourage colleagues in the field to engage in the task of conducting needed methodological research on emoji and emotion words alike.