Today's organizations require techniques for automated transformation of their large data volumes into operational knowledge. This requirement may be addressed by using event recognition systems that detect events/activities of special significance within an organization, given streams of ‘low-level’ information that is very difficult to be utilized by humans. Consider, for example, the recognition of attacks on nodes of a computer network given the Transmission Control Protocol/Internet Protocol messages, the recognition of suspicious trader behaviour given the transactions in a financial market and the recognition of whale songs given a symbolic representation of whale sounds. Various event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention, because, among others, they exhibit a formal, declarative semantics, they have proven to be efficient and scalable and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper, we review representative approaches of logic-based event recognition and discuss open research issues of this field. We illustrate the reviewed approaches with the use of a real-world case study: event recognition for city transport management.

This work provides a general overview of the statistical machine translation (SMT) scientific field, which is a subfield of machine translation (MT). Specifically, this paper focuses on one of the most popular SMT approaches, that is, the phrase-based system.

The phrase-based translation units are typically extracted using statistical criteria, and they are weighted using different models. These models are log-linearly combined in the decoding, which is in charge of choosing the most probable translation. Significant quality improvements have been produced from original phrase-based SMT systems. Among others, the main challenges are reordering, domain adaptation and evaluation.

We show that an orthogonal basis for a finite-dimensional Hilbert space can be equivalently characterised as a commutative †-Frobenius monoid in the category FdHilb, which has finite-dimensional Hilbert spaces as objects and continuous linear maps as morphisms, and tensor product for the monoidal structure. The basis is normalised exactly when the corresponding commutative †-Frobenius monoid is special. Hence, both orthogonal and orthonormal bases are characterised without mentioning vectors, but just in terms of the categorical structure: composition of operations, tensor product and the †-functor. Moreover, this characterisation can be interpreted operationally, since the †-Frobenius structure allows the cloning and deletion of basis vectors. That is, we capture the basis vectors by relying on their ability to be cloned and deleted. Since this ability distinguishes classical data from quantum data, our result has important implications for categorical quantum mechanics.