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A trace monitor observes the sequence of actions in a software system, and when it detects that this sequence matches a given pattern, it executes some extra code of its own. Trace monitors are often specified declaratively using patterns based on regular expressions, context free grammars or logical formulae, and then the trace monitor implementation is generated from the specification. Trace monitors are particularly useful for runtime verification, and many variations have been proposed. Despite this intense interest, there have been hardly any systems that implement the idea in its full generality, because it is hard to generate e#cient code from a purely declarative statement of the pattern. This paper identifies and addresses the challenges faced in generating e#cient trace monitors from declarative pattern-based specifications

Pencil A Platform-Neutral Compute Intermediate Language for DSL Compilers

International audienceProgramming accelerators such as GPUs with low-level APIs and languages like OpenCL and CUDA is difficult, error prone, and not performance-portable. Automatic parallelization and domain specific languages (DSLs) have been proposed to hide this complexity and to regain some performance portability. In this presentation, I will present PENCIL (Platform-Neutral Compute Intermediate Language) and present some details about how it is compiled. PENCIL is a rigorously defined subset of GNU C99 with specific programming rules and few extensions. Adherence to this subset and the use of these extensions enable compilers to exploit parallelism and to better optimize code when targeting accelerators. We intend PENCIL both as a portable language to facilitate accelerator programming, and as an intermediate language for DSL compilers. We validate the potential of PENCIL on a state-of-the-art polyhedral compiler, extending the applicability of the compiler to dynamic, data-dependent control flow and non-affine array accesses

Datalog as a pointcut language

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Facial emotion recognition using deep residual networks in real-world environments

Automatic affect recognition using visual cues is an important task towards a complete interaction between humans and machines. Applications can be found in tutoring systems and human computer interaction. A critical step towards that direction is facial feature extraction. In this paper, we propose a facial feature extractor model trained on an in-the-wild and massively collected video dataset provided by the RealEyes company. The dataset consists of a million labelled frames and 2,616 thousand subjects. As temporal information is important to the emotion recognition domain, we utilise LSTM cells to capture the temporal dynamics in the data. To show the favourable properties of our pre-trained model on modelling facial affect, we use the RECOLA database, and compare with the current state-of-the-art approach. Our model provides the best results in terms of concordance correlation coefficient