Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2,000 minutes of audio-visual data of 398 people coming from six cultures, 50 percent female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal, and (dis)liking intensity estimation.

The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valence-arousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior task. For the Challenges, we provide a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one annotated for all these three tasks. In this paper, we describe this Competition, to be held in conjunction with the IEEE Conference on Face and Gesture Recognition, May 2020, in Buenos Aires, Argentina. We present the three Challenges, with the utilized Competition corpora. We outline the evaluation metrics, present both the baseline system and the top-3 performing teams' methodologies per Challenge and finally present their obtained results. More information regarding the Competition, the leaderboard of each Challenge and details for accessing the utilized database, are provided in the Competition site: http://ibug.doc.ic.ac.uk/ resources/fg-2020-competition-affective-behavior-analysis.

Programming accelerators such as GPUs with low-level APIs and languages such as OpenCL and CUDA is difficult, error-prone, and not performance-portable. Automatic parallelization and domain specific languages (DSLs) have been proposed to hide complexity and regain performance portability. We present PENCIL, a rigorously-defined subset of GNU C99-enriched with additional language constructs-that enables compilers to exploit parallelism and produce highly optimized code when targeting accelerators. PENCIL aims to serve both as a portable implementation language for libraries, and as a target language for DSL compilers. We implemented a PENCIL-to-OpenCL backend using a state-of-the-art polyhedral compiler. The polyhedral compiler, extended to handle data-dependent control flow and non-affine array accesses, generates optimized OpenCL code. To demonstrate the potential and performance portability of PENCIL and the PENCIL-to-OpenCL compiler, we consider a number of image processing kernels, a set of benchmarks from the Rodinia and SHOC suites, and DSL embedding scenarios for linear algebra (BLAS) and signal processing radar applications (SpearDE), and present experimental results for four GPU platforms: AMD Radeon HD 5670 and R9 285, NVIDIA GTX 470, and ARM Mali-T604.

Code queries are useful for enforcing coding conventions, navigating a large code base, and for identifying locations to refactor. The program understanding community has long advocated the use of a relational database to facilitate such code queries [3, 9]. While the idea has found some uptake in industry [2, 11], relational queries over code have not yet found widespread use.

In aspect-oriented programming, one can intercept events by writing patterns called pointcuts. The pointcut language of the most popular aspect-oriented programming language, AspectJ, allows the expression of highly complex properties of the static program structure.We present the first rigorous semantics of the AspectJ pointcut language, by translating static patterns into safe ( i.e. range-restricted and stratified) Datalog queries. Safe Datalog is a logic language like Prolog, but it does not have data structures; consequently it has a straightforward least fixpoint semantics and all queries terminate.The translation from pointcuts to safe Datalog consists of a set of simple conditional rewrite rules, implemented using the Stratego system. The resulting queries are themselves executable with the CodeQuest system. We present experiments indicating that direct execution of our semantics is not prohibitively expensive.