Analysing and visualising data sets of cybercrime investigations using structured occurrence nets

Ph. D. Thesis.Structured Occurrence Nets (SONs) are a Petri net based formalism for portraying the behaviour of complex evolving systems. As a concept, SONs are derived from Occurrence Nets (ONs). SONs provide a powerful framework for evolving system analysis and are supported by the existing SONCraft toolset. On the other hand, modelling of cybercrime investigations has become of interest in recent years, and large-scale criminal investigations have been considered as complex evolving systems. Right now, they present a significant challenge for police investigators and analysts. The current thesis contributes to addressing this challenge in two different ways: (i) by presenting an algorithm and an implemented tool that visualise data sets using maximal concurrency; and (ii) by detecting DNS tunnelling through a novel SON-based technique and tool. Moreover, the theoretical contribution of this thesis focuses on model extensions and abstraction; in particular, it introduces a new class of SONs based on multi-coloured tokens

The Symmetry Method for Coloured Petri Nets

This booklet is the author's PhD-dissertation

Process windows

We describe a method for formally representing the behaviour of complex processes by process windows. Each window covers a part of the system behaviour, i.e. a part of the underlying transition system, and is easier to understand and analyse than the complete transition system. Process windows can overlap and have shared states and transitions so that the complete system behaviour is the union of window behaviours. We demonstrate the advantage of such representations when dealing with complex system behaviours, and discuss potential applications in circuit design and process mining. As a motivational example we consider the problem of covering transition systems by marked graphs, or more generally choicefree Petri nets. The obtained windows correspond to choice-free behavioural scenarios of the system, wherein one window can take over, or wake up, after another window has become inactive. The corresponding wake-up conditions and wake-up markings can be derived automatically.Peer ReviewedPostprint (author's final draft

Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks

Most state-of-the-art approaches for Facial Action Unit (AU) detection rely upon evaluating facial expressions from static frames, encoding a snapshot of heightened facial activity. In real-world interactions, however, facial expressions are usually more subtle and evolve in a temporal manner requiring AU detection models to learn spatial as well as temporal information. In this paper, we focus on both spatial and spatio-temporal features encoding the temporal evolution of facial AU activation. For this purpose, we propose the Action Unit Lifecycle-Aware Capsule Network (AULA-Caps) that performs AU detection using both frame and sequence-level features. While at the frame-level the capsule layers of AULA-Caps learn spatial feature primitives to determine AU activations, at the sequence-level, it learns temporal dependencies between contiguous frames by focusing on relevant spatio-temporal segments in the sequence. The learnt feature capsules are routed together such that the model learns to selectively focus more on spatial or spatio-temporal information depending upon the AU lifecycle. The proposed model is evaluated on the commonly used BP4D and GFT benchmark datasets obtaining state-of-the-art results on both the datasets.Comment: Updated Figure 6 and the Acknowledgements. Corrected typos. 11 pages, 6 figures, 3 table

Interaction nets: programming language design and implementation

This paper presents a compiler for interaction nets, which, just like term rewriting systems, are user-definable rewrite systems which offer the ability to specify and program. In the same way that the lambda-calculus is the foundation for functional programming, or horn clauses are the foundation for logic programming, we give in this paper an overview of a substantial software system that is currently under development to support interaction based computation, and in particular the compilation of interaction nets

DeepPermNet: Visual Permutation Learning

We present a principled approach to uncover the structure of visual data by solving a novel deep learning task coined visual permutation learning. The goal of this task is to find the permutation that recovers the structure of data from shuffled versions of it. In the case of natural images, this task boils down to recovering the original image from patches shuffled by an unknown permutation matrix. Unfortunately, permutation matrices are discrete, thereby posing difficulties for gradient-based methods. To this end, we resort to a continuous approximation of these matrices using doubly-stochastic matrices which we generate from standard CNN predictions using Sinkhorn iterations. Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet, an end-to-end CNN model for this task. The utility of DeepPermNet is demonstrated on two challenging computer vision problems, namely, (i) relative attributes learning and (ii) self-supervised representation learning. Our results show state-of-the-art performance on the Public Figures and OSR benchmarks for (i) and on the classification and segmentation tasks on the PASCAL VOC dataset for (ii).Comment: Accepted in IEEE International Conference on Computer Vision and Pattern Recognition CVPR 201

Learning to Read by Spelling: Towards Unsupervised Text Recognition

This work presents a method for visual text recognition without using any paired supervisory data. We formulate the text recognition task as one of aligning the conditional distribution of strings predicted from given text images, with lexically valid strings sampled from target corpora. This enables fully automated, and unsupervised learning from just line-level text-images, and unpaired text-string samples, obviating the need for large aligned datasets. We present detailed analysis for various aspects of the proposed method, namely - (1) impact of the length of training sequences on convergence, (2) relation between character frequencies and the order in which they are learnt, (3) generalisation ability of our recognition network to inputs of arbitrary lengths, and (4) impact of varying the text corpus on recognition accuracy. Finally, we demonstrate excellent text recognition accuracy on both synthetically generated text images, and scanned images of real printed books, using no labelled training examples

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.Comment: ECCV 201

Coloured Petri Nets - a Pragmatic Formal Method for Designing and Analysing Distributed Systems

The thesis consists of six individual papers, where the present paper contains the mandatory overview, while the remaining five papers are found separately from the overview. The five papers can roughly be divided into three areas of research, namely case studies, education, and extensions to the CPN method.The primary purpose of the PhD thesis is to study the pragmatics, practical aspects, and intuition of CP-nets viewed as a formal method for describing and reasoning about concurrent systems. The perspective of pragmatics is our leitmotif, but at the same time in the context of CP-nets it is a kind of hypothesis of this thesis. This overview paper summarises the research conducted as an investigation of the hypothesis in the three areas of case studies, education, and extensions.The provoking claim of pragmatics should not be underestimated. In the present overview of the thesis, the CPN method is compared with a representative selection of formal methods. The graphics and simplicity of semantics, yet generality and expressiveness of the language constructs, essentially makes CP-nets a viable and attractive alternative to other formal methods. Similar graphical formal methods, such as SDL and Statecharts, typically have significantly more complicated semantics, or are domain-specific languages.research conducted in this thesis, opens a new complex of problems. Firstly, to get wider acceptance of CP-nets in industry, it is important to identify fruitful areas for the effective introduction of the CPN method. Secondly, it would be useful to identify a few extensions to the CPN method inspired by specific domains for easier adaption in industry. Thirdly, which analysis methods do future systems make use of