Recently, real-time affect-awareness has been applied in several commercial systems, such as dialogue systems and computer games. Real-time recognition of affective states, however, requires the application of costly feature extraction methods and/or labor-intensive annotation of large datasets, especially in the case of Asian languages where large annotated datasets are seldom available. To improve recognition accuracy, we propose the use of cognitive context in the form of “emotion-sensitive” intentions. Intentions are often represented through dialogue acts and, as an emotion-sensitive model of dialogue acts, a tagset of interpersonal-relations-directing interpersonal acts (the IA model) is proposed. The model's adequacy is assessed using a sentiment classification task in comparison with two well-known dialogue act models, the SWBD-DAMSL and the DIT++. For the assessment, five Japanese in-game dialogues were annotated with labels of sentiments and the tags of all three dialogue act models which were used to enhance a baseline sentiment classifier system. The adequacy of the IA tagset is demonstrated by a 9% improvement to the baseline sentiment classifier's recognition accuracy, outperforming the other two models by more than 5%.

We introduce a novel way of proving information flow properties of a program based on its self-composition. Similarly to the universal information flow type system of Hunt and Sands, our analysis explicitly computes the dependencies of variables in the final state on variables in the initial state. Accordingly, the analysis result is independent of specific information flow lattices, and allows to derive information flow w.r.t. any of these. While our analysis runs in polynomial time, we prove that it never loses precision against the type system of Hunt and Sands, and may gain extra precision by taking similarities between different branches of conditionals into account. Also, we indicate how it can be smoothly generalized to an interprocedural analysis.

Information flow properties of programs can be formalized as hyperproperties specifying the relation of multiple executions. In this paper, we therefore introduce a framework for proving 2-hypersafety properties by means of abstract interpretation. The main idea is to apply abstract interpretation on the self-compositions of the control flow graphs of programs. As a result, our method is inherently capable of analyzing relational properties of even dissimilar programs.