OpenRaiser/Intern-Atlas huggingface dataset:
A stabilized total-Lagrangian SPH method for large deformation and failure in geomaterials
Venue: arXiv cs.CE 2019
Year: 2019

Conventional smoothed particle hydrodynamics based on Eulerian kernels (CESPH) is widely-used in large deformation analysis in geomaterials. Despite being popular, it suffers from tensile instability and rank-deficiency; thus, it needs several numerical treatments to be stable. In this work, we present a stabilized total-Lagrangian SPH method (TLSPH), which is inherently free of tensile instability. A stiffness-based hourglass control algorithm is employed to cure the hourglass mode caused by rank-deficiency. Periodic update of reference configuration is used in simulations to allow TLSPH to model large deformation and post-failure flow in geomaterials. Several numerical examples are presented to show the performance of the stabilized TLSPH method. The comparison between TLSPH and CESPH are discussed. The influences of hourglass control and configuration update are also discussed and shown in the numerical examples. It is found that the presented stabilized TLSPH is robust and can model large deformation and plastic flows in geomaterials. Particularly, the stabilized TLSPH delivers accurate and smooth stress results.
Improving Semantic Segmentation via Dilated Affinity
Venue: arXiv cs.CV 2019
Year: 2019

Introducing explicit constraints on the structural predictions has been an effective way to improve the performance of semantic segmentation models. Existing methods are mainly based on insufficient hand-crafted rules that only partially capture the image structure, and some methods can also suffer from the efficiency issue. As a result, most of the state-of-the-art fully convolutional networks did not adopt these techniques. In this work, we propose a simple, fast yet effective method that exploits structural information through direct supervision with minor additional expense. To be specific, our method explicitly requires the network to predict semantic segmentation as well as dilated affinity, which is a sparse version of pair-wise pixel affinity. The capability of telling the relationships between pixels are directly built into the model and enhance the quality of segmentation in two stages. 1) Joint training with dilated affinity can provide robust feature representations and thus lead to finer segmentation results. 2) The extra output of affinity information can be further utilized to refine the original segmentation with a fast propagation process. Consistent improvements are observed on various benchmark datasets when applying our framework to the existing state-of-the-art model. Codes will be released soon.
Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection
Venue: arXiv cs.CL 2019
Year: 2019

The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment limited labeled rumor source tweets. This work is based on rumor spreading patterns revealed by recent rumor studies and semantic relatedness between labeled and unlabeled data. A state-of-the-art neural language model (NLM) and large credibility-focused Twitter corpora are employed to learn context-sensitive representations of rumor tweets. Six different real-world events based on three publicly available rumor datasets are employed in our experiments to provide a comparative evaluation of the effectiveness of the method. The results show that our method can expand the size of an existing rumor data set nearly by 200% and corresponding social context (i.e., conversational threads) by 100% with reasonable quality. Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs). With augmented data, the performance of rumor detection can be improved by 12.1% in terms of F-score. Our experiments also indicate that augmented training data can help to generalize rumor detection models on unseen rumors.
Minimisation of Event Structures
Venue: arXiv cs.LO 2019
Year: 2019

Event structures are fundamental models in concurrency theory, providing a representation of events in computation and of their relations, notably concurrency, conflict and causality. In this paper we present a theory of minimisation for event structures. Working in a class of event structures that generalises many stable event structure models in the literature (e.g., prime, asymmetric, flow and bundle event structures), we study a notion of behaviour-preserving quotient, referred to as a folding, taking (hereditary) history preserving bisimilarity as a reference behavioural equivalence. We show that for any event structure a folding producing a uniquely determined minimal quotient always exists. We observe that each event structure can be seen as the folding of a prime event structure, and that all foldings between general event structures arise from foldings of (suitably defined) corresponding prime event structures. This gives a special relevance to foldings in the class of prime event structures, which are studied in detail. We identify folding conditions for prime and asymmetric event structures, and show that also prime event structures always admit a unique minimal quotient (while this is not the case for various other event structure models).
Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings
Venue: arXiv cs.CV 2019
Year: 2019

This paper studies the interplay between kinematics (position and velocity) and appearance cues for establishing correspondences in multi-target pedestrian tracking. We investigate tracking-by-detection approaches based on a deep learning detector, joint integrated probabilistic data association (JIPDA), and appearance-based tracking of deep correspondence embeddings. We first addressed the fixed-camera setup by fine-tuning a convolutional detector for accurate pedestrian detection and combining it with kinematic-only JIPDA. The resulting submission ranked first on the 3DMOT2015 benchmark. However, in sequences with a moving camera and unknown ego-motion, we achieved the best results by replacing kinematic cues with global nearest neighbor tracking of deep correspondence embeddings. We trained the embeddings by fine-tuning features from the second block of ResNet-18 using angular loss extended by a margin term. We note that integrating deep correspondence embeddings directly in JIPDA did not bring significant improvement. It appears that geometry of deep correspondence embeddings for soft data association needs further investigation in order to obtain the best from both worlds.
SGD momentum optimizer with step estimation by online parabola model
Venue: arXiv cs.LG 2019
Year: 2019

In stochastic gradient descent, especially for neural network training, there are currently dominating first order methods: not modeling local distance to minimum. This information required for optimal step size is provided by second order methods, however, they have many difficulties, starting with full Hessian having square of dimension number of coefficients. This article proposes a minimal step from successful first order momentum method toward second order: online parabola modelling in just a single direction: normalized $\hat{v}$ from momentum method. It is done by estimating linear trend of gradients $\vec{g}=\nabla F(\vecθ)$ in $\hat{v}$ direction: such that $g(\vecθ_\bot+θ\hat{v})\approx λ(θ-p)$ for $θ= \vecθ\cdot \hat{v}$, $g= \vec{g}\cdot \hat{v}$, $\vecθ_\bot=\vecθ-θ\hat{v}$. Using linear regression, $λ$, $p$ are MSE estimated by just updating four averages (of $g$, $θ$, $gθ$, $θ^2$) in the considered direction. Exponential moving averages allow here for inexpensive online estimation, weakening contribution of the old gradients. Controlling sign of curvature $λ$, we can repel from saddles in contrast to attraction in standard Newton method. In the remaining directions: not considered in second order model, we can simultaneously perform e.g. gradient descent. There is also discussed its learning rate approximation as $μ=σ_θ/ σ_g$, allowing e.g. for adaptive SGD - with learning rate separately optimized (2nd order) for each parameter.
Concentration of the matrix-valued minimum mean-square error in optimal Bayesian inference
Venue: arXiv cs.IT 2019
Year: 2019

We consider Bayesian inference of signals with vector-valued entries. Extending concentration techniques from the mathematical physics of spin glasses, we show that the matrix-valued minimum mean-square error concentrates when the size of the problem increases. Such results are often crucial for proving single-letter formulas for the mutual information when they exist. Our proof is valid in the optimal Bayesian inference setting, meaning that it relies on the assumption that the model and all its hyper-parameters are known. Examples of inference and learning problems covered by our results are spiked matrix and tensor models, the committee machine neural network with few hidden neurons in the teacher-student scenario, or multi-layers generalized linear models.
DeepRace: Finding Data Race Bugs via Deep Learning
Venue: arXiv cs.DC 2019
Year: 2019

With the proliferation of multi-core hardware, parallel programs have become ubiquitous. These programs have their own type of bugs known as concurrency bugs and among them, data race bugs have been mostly in the focus of researchers over the past decades. In fact, detecting data races is a very challenging and important task. There have been several research paths in this area with many sophisticated tools designed and utilized that focus on detecting data race at the file level. In this paper, we propose DeepRace, a novel approach toward detecting data races in the source code. We build a deep neural network model to find data races instead of creating a data race detector manually. Our model uses a one-layer convolutional neural network (CNN) with different window size to find data races method. Then we adopt the class activation map function with global average pooling to extract the weights of the last convolutional layer and backpropagate it with the input source code to extract the line of codes with a data race. Thus, the DeepRace model can detect the data race bugs on a file and line of code level. In addition, we noticed that DeepRace successfully detects several buggy lines of code at different locations of the file. We tested the model with OpenMP and POSIX source code datasets which consist of more than 5000 and 8000 source code files respectively. We were able to successfully classify buggy source code files and achieve accuracies ranging from 81% and 86%. We also measured the performance of detecting and visualizing the data race at the line of code levels and our model achieved promising results. We only had a small number of false positives and false, ranging from 1 to 10. Furthermore, we used the intersection of union to measure the accuracy of the buggy lines of code, our model achieved promising results of 66 percent.
Helen: Maliciously Secure Coopetitive Learning for Linear Models
Venue: arXiv cs.CR 2019
Year: 2019

Many organizations wish to collaboratively train machine learning models on their combined datasets for a common benefit (e.g., better medical research, or fraud detection). However, they often cannot share their plaintext datasets due to privacy concerns and/or business competition. In this paper, we design and build Helen, a system that allows multiple parties to train a linear model without revealing their data, a setting we call coopetitive learning. Compared to prior secure training systems, Helen protects against a much stronger adversary who is malicious and can compromise m-1 out of m parties. Our evaluation shows that Helen can achieve up to five orders of magnitude of performance improvement when compared to training using an existing state-of-the-art secure multi-party computation framework.
Cooperative UAVs Gas Monitoring using Distributed Consensus
Venue: arXiv cs.RO 2019
Year: 2019

This paper addresses the problem of target detection and localisation in a limited area using multiple coordinated agents. The swarm of Unmanned Aerial Vehicles (UAVs) determines the position of the dispersion of stack effluents to a gas plume in a certain production area as fast as possible, that makes the problem challenging to model and solve, because of the time variability of the target. Three different exploration algorithms are designed and compared. Besides the exploration strategies, the paper reports a solution for quick convergence towards the actual stack position once detected by one member of the team. Both the navigation and localisation algorithms are fully distributed and based on the consensus theory. Simulations on realistic case studies are reported.
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Venue: arXiv cs.HC 2019
Year: 2019

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks. As the deployment of artificial intelligence technologies becomes ubiquitous, it is unsurprising that adversaries have begun developing methods to manipulate machine learning models to their advantage. While the visual analytics community has developed methods for opening the black box of machine learning models, little work has focused on helping the user understand their model vulnerabilities in the context of adversarial attacks. In this paper, we present a visual analytics framework for explaining and exploring model vulnerabilities to adversarial attacks. Our framework employs a multi-faceted visualization scheme designed to support the analysis of data poisoning attacks from the perspective of models, data instances, features, and local structures. We demonstrate our framework through two case studies on binary classifiers and illustrate model vulnerabilities with respect to varying attack strategies.
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning
Venue: arXiv cs.CV 2019
Year: 2019

We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled gound truth, our goal is to train an animal detector that can be re-used for repeated acquisitions, e.g. in follow-up years. Domain shifts between datasets typically prevent such a direct model application. We thus propose to bridge this gap using AL and introduce a new criterion called Transfer Sampling (TS). TS uses Optimal Transport to find corresponding regions between the source and the target datasets in the space of CNN activations. The CNN scores in the source dataset are used to rank the samples according to their likelihood of being animals, and this ranking is transferred to the target dataset. Unlike conventional AL criteria that exploit model uncertainty, TS focuses on very confident samples, thus allowing a quick retrieval of true positives in the target dataset, where positives are typically extremely rare and difficult to find by visual inspection. We extend TS with a new window cropping strategy that further accelerates sample retrieval. Our experiments show that with both strategies combined, less than half a percent of oracle-provided labels are enough to find almost 80% of the animals in challenging sets of UAV images, beating all baselines by a margin.
CLaRO: a Data-driven CNL for Specifying Competency Questions
Venue: arXiv cs.AI 2019
Year: 2019

Competency Questions (CQs) for an ontology and similar artefacts aim to provide insights into the contents of an ontology and to demarcate its scope. The absence of a controlled natural language, tooling and automation to support the authoring of CQs has hampered their effective use in ontology development and evaluation. The few question templates that exists are based on informal analyses of a small number of CQs and have limited coverage of question types and sentence constructions. We aim to fill this gap by proposing a template-based CNL to author CQs, called CLaRO. For its design, we exploited a new dataset of 234 CQs that had been processed automatically into 106 patterns, which we analysed and used to design a template-based CNL, with an additional CNL model and XML serialisation. The CNL was evaluated with a subset of questions from the original dataset and with two sets of newly sourced CQs. The coverage of CLaRO, with its 93 main templates and 41 linguistic variants, is about 90% for unseen questions. CLaRO has the potential to facilitate streamlining formalising ontology content requirements and, given that about one third of the competency questions in the test sets turned out to be invalid questions, assist in writing good questions.
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search
Venue: arXiv cs.IR 2019
Year: 2019

This paper reconsiders common benchmarking approaches to nearest neighbor search. It is shown that the concept of local intrinsic dimensionality (LID) allows to choose query sets of a wide range of difficulty for real-world datasets. Moreover, the effect of different LID distributions on the running time performance of implementations is empirically studied. To this end, different visualization concepts are introduced that allow to get a more fine-grained overview of the inner workings of nearest neighbor search principles. The paper closes with remarks about the diversity of datasets commonly used for nearest neighbor search benchmarking. It is shown that such real-world datasets are not diverse: results on a single dataset predict results on all other datasets well.
Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation
Venue: arXiv cs.CV 2019
Year: 2019

Enhancing visual qualities for underexposed images is an extensively concerned task that plays important roles in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we in this work develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to solve the underexposed image correction task. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. Additionally, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What's more, we employ our method to single image haze removal whose experimental results further demonstrate its superiorities.
Online Firefighting on Grids
Venue: arXiv cs.DM 2019
Year: 2019

The Firefighter Problem (FP) is a graph problem originally introduced in 1995 to model the spread of a fire in a graph, which has attracted considerable attention in the literature. The goal is to devise a strategy to employ a given sequence of firefighters on strategic points in the graph in order to contain efficiently the fire (which spreads from each unprotected vertex to all of it neighbours on successive time steps). Recently, an online version of FP---where the number of firefighters available at each turn are revealed in real-time--- has been introduced in the literature and studied on trees. In this paper, we consider the online containment of fire on square grids. In particular, we provide a set of sufficient conditions that allow to solve the online version of the firefighting problem on infinite square grids, illustrating the corresponding fire containment strategies.
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods
Venue: arXiv cs.LG 2019
Year: 2019

Epidemiologists use a variety of statistical algorithms for the early detection of outbreaks. The practical usefulness of such methods highly depends on the trade-off between the detection rate of outbreaks and the chances of raising a false alarm. Recent research has shown that the use of machine learning for the fusion of multiple statistical algorithms improves outbreak detection. Instead of relying only on the binary output (alarm or no alarm) of the statistical algorithms, we propose to make use of their p-values for training a fusion classifier. In addition, we also show that adding additional features and adapting the labeling of an epidemic period may further improve performance. For comparison and evaluation, a new measure is introduced which captures the performance of an outbreak detection method with respect to a low rate of false alarms more precisely than previous works. Our results on synthetic data show that it is challenging to improve the performance with a trainable fusion method based on machine learning. In particular, the use of a fusion classifier that is only based on binary outputs of the statistical surveillance methods can make the overall performance worse than directly using the underlying algorithms. However, the use of p-values and additional information for the learning is promising, enabling to identify more valuable patterns to detect outbreaks.
Computational Human Dynamics
Venue: arXiv cs.SI 2019
Year: 2019

This thesis summarises my scientific contributions in the domain of network science, human dynamics and computational social science. These contributions are associated to computer science, physics, statistics, and applied mathematics. The goal of this thesis is twofold, on one hand to write a concise summary of my most interesting scientific contributions, and on the other hand to provide an up-to-date view and perspective about my field. I start my dissertation with an introduction to position the reader on the landscape of my field and to put in perspective my contributions. In the second chapter I concentrate on my works on bursty human dynamics, addressing heterogeneous temporal characters of human actions and interactions. Next, I discuss my contributions to the field of temporal networks and give a synthesises of my works on various methods of the representation, characterisation, and modelling of time-varying structures. Finally, I discuss my works on the data-driven observations and modelling of collective social phenomena. There, I summarise studies on the static observations of emergent patterns of socioeconomic inequalities and their correlations with social-communication networks, and with linguistic patterns. I also discuss dynamic observations and modelling of social contagion processes.