Psychological distress is a significant and growing issue in society. In particular, depression and anxiety are leading causes of disability that often go undetected or late-diagnosed. Automatic detection, assessment, and analysis of behavioural markers of psychological distress can help improve identification and support prevention and early intervention efforts. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse, which is partly due to the limited available datasets and difficulty in automatically extracting useful body features. To enable our research, we have collected and analyzed a new dataset containing full body videos for interviews and self-reported distress labels. We propose a novel approach to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to correlate with psychological distress. We perform analysis on statistical body gestures and fidgeting features to explore how distress levels affect behaviors. We then propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector Encoding. We demonstrate that our proposed model, combining audio-visual features with detected fidgeting behavioral cues, can successfully predict depression and anxiety in the dataset.

Dynamic analysis can identify improvements to programs that cannot feasibly be identified by static analysis; concurrency improvements are a motivating example. However, mapping these dynamic-analysis-based improvements back to patch-like source-code changes is non-trivial. We describe a system, Scopda, for generating source-code patches for improvements identified by execution-trace-based dynamic analysis. Scopda uses a graph-based static program representation (abstract program graph, APG), containing inter-procedural control flow and local data flow information, to analyse and transform static source-code. We demonstrate Scopda's ability to generate sensible source code patches for Java programs, though it is fundamentally language agnostic.

It is often difficult to analyse why a program executes more slowly than intended. This is particularly true for concurrent programs. We describe and evaluate a system, Rehype, which takes Java programs, performs low-overhead tracing of method calls, analyses the resulting trace-logs to detect inefficient uses of concurrency constructs, and suggests source-code-oriented improvements. Rehype deals with task-based concurrency, specifically a future-based model of tasks. Implementing the suggested improvements on an industrial API server more than doubled request-processing throughput.

Execution tracing has an observer effect: the act of tracing perturbs program behaviour via its overhead, which can in turn affect the accuracy of subsequent dynamic analysis. We investigate this observer effect in the context of concurrent behaviour within JVM-based programs. Concurrent behaviour is especially fragile as task-scheduling ordering can change, which could even lead to deadlock via thread starvation under certain conditions. We analyse three dimensions of overhead, compute volume, memory volume, and uniformity, using a configurable-overhead tracer and a concurrency-performance analyser. We argue that uniformity is a key, and underappreciated, dimension of overhead that can have qualitative effects on program behaviour. Experimental results show that overhead significantly affects real-world concurrent behaviour and subsequent analysis, at times unintuitively.

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the lack of available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We also propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector encoding. We also demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels. To enable this research we introduce a new dataset containing full body videos for short interviews and self-reported distress labels.