https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Computers are beginning to acquire the ability to express and recognize affect, and may soon be given the ability to " have emotions. " The essential role of emotion in both human cognition and perception, as demonstrated by recent neurological studies, indicates that affective computers should not only provide better performance in assisting humans, but also might enhance computers' abilities to make decisions. This paper presents and discusses key issues in " affective computing, " computing that relates to, arises from, or influences emotions. Models are suggested for computer recognition of human emotion, and new applications are presented for computer-assisted learning, perceptual information retrieval, arts and entertainment, and human health and interaction. Affective computing, coupled with new wear-able computers, will also provide the ability to gather new data necessary for advances in emotion and cog-nition theory. Nothing in life is to be feared. It is only to be understood. – Marie Curie Emotions have a stigma in science; they are believed to be inherently non-scientific. Scientific principles are derived from rational thought, logical arguments, testable hypotheses, and repeatable experiments. There is room alongside science for " non-interfering " emotions such as those involved in curiosity, frustration, and the pleasure of discovery. In fact, much scientific research has been prompted by fear. Nonetheless, the role of emotions is marginalized at best. Why bring " emotion " or " affect " into any of the deliberate tools of science? Moreover, shouldn't it be completely avoided when considering properties to design into computers? After all, computers control significant parts of our lives – the phone system, the stock market, nuclear power plants, jet landings, and more. Who wants a computer to be able to " feel angry " at them? To feel contempt for any living thing? In this essay I will submit for discussion a set of ideas on what I call " affective computing, " computing that relates to, arises from, or influences emotions. This will need some further clarification which I shall attempt below. I should say up front that I am not proposing the pursuit of computerized cingulotomies 1 or even into the business of building " emotional computers ". 1 The making of small wounds in the ridge of the limbic system known as the cingulate gyrus, a surgical procedure to aid severely depressed patients. Nor will I propose answers to the difficult and intriguing questions , " …

Affective Computing

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Management innovation

House of Quality

Quality function deployment: A literature review

Customer-driven product development through quality function deployment in the u.s. and japan

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief

We survey several computational procedures for the partially observed Markov decision process (POMDP) that have been developed since the Monahan survey was published in 1982. The POMDP generalizes the standard, completely observed Markov decision process by permitting the possibility that state observations may be noise-corrupted and/or costly. Several computational procedures presented are convergence accelerating variants of, or approximations to, the Smallwood-Sondik algorithm. Finite-memory suboptimal design results are reported, and new research directions involving heuristic search are discussed.

/pdf/a-survey-of-solution-techniques-for-the-partially-observed-11z5qknr2g.pdf

A Survey of solution techniques for the partially observed Markov decision process

In the product design and development process, quality function deployment (QFD) provides a comprehensive, systematic approach to support the design of new products intended to meet or exceed customer expectations. The authors use multiattribute value theory to support new product design and hence to augment a design team's experience and judgment. We introduce the concept of a target set, the set of all value score vectors that are at least as preferred as the value score vectors of any of the given design alternatives. Assuming mutually preferential independence, we characterize the target set and indicate how it can be used to support selecting targets: 1) for the level of customer satisfaction to be attained by the new product, for each customer requirement and 2) for design requirements, presumably based in part on information from engineering competitive assessments. We then use the target set concept to augment the product planning phase of QFD. The concepts are illustrated and evaluated through the retrospective application of the methodology to an actual surgical product (universal converter). Although the analysis was performed retrospectively, without knowledge of the market success of the product and its competitors, the results were consistent with product acceptance and provided valuable insights to the lead engineer.

Chelsea C. White

Papers

Customer-driven product development through quality function deployment in the u.s. and japan

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief

A Survey of solution techniques for the partially observed Markov decision process

Application of multiattribute decision analysis to quality function deployment for target setting

Question selection for multi-attribute decision-aiding