Improved facial expression recognition via uni-hyperplane classification

Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.

Controlled flexibility and lifecycle management of business processes through extensibility

Vendors provide reference process models as consolidated, off-the-shelf solutions to capture best practices in a given industry domain. Customers can then adapt these models to suit their specific requirements. Traditional process flexibility approaches facilitate this operation, but do not fully address it as they do not sufficiently take controlled change guided by vendors' reference models into account. This tension between the customer's freedom of adapting reference models, and the ability to incorporate with relatively low effort vendor-initiated reference model changes, thus needs to be carefully balanced. This paper introduces process extensibility as a new paradigm for customizing reference processes and managing their evolution over time. Process extensibility mandates a clear recognition of the different responsibilities and interests of reference model vendors and consumers, and is concerned with keeping the effort of customer-side reference model adaptations low while allowing sufficient room for model change.

What value do users derive from social network applications?

Research on social networking sites like Facebook is emerging but sparse. This exploratory study investigates the value users derive from self-described ‘cool’ Facebook applications, and explores the features that either encourage or discourage users to recommend applications to their friends. The concepts of value and cool are explored in a social networking context. Our qualitative data reveals consumers derive a combination of functional value along with either social or emotional value from the applications. Female Facebook users indicate self-expression as important motivators, while males tend to use Facebook applications to socially compete. Three broad categories emerged for application features; symmetrical features can both encourage or discourage recommendation, polar features where different levels of the same feature encourage or discourage, and uni-directional features only encourage or discourage but not both. Recommending or not recommending an application tends to be the result of a combination of features and context, rather than one feature in isolation.