OpenRaiser/Intern-Atlas huggingface dataset:
Illuminating a Blind Spot in Digitalization -- Software Development in Sweden's Private and Public Sector
Venue: arXiv cs.CY 2020
Year: 2020

As Netscape co-founder Marc Andreessen famously remarked in 2011, software is eating the world - becoming a pervasive invisible critical infrastructure. Data on the distribution of software use and development in society is scarce, but we compile results from two novel surveys to provide a fuller picture of the role software plays in the public and private sectors in Sweden, respectively. Three out of ten Swedish firms, across industry sectors, develop software in-house. The corresponding figure for Sweden's government agencies is four out of ten, i.e., the public sector should not be underestimated. The digitalization of society will continue, thus the demand for software developers will further increase. Many private firms report that the limited supply of software developers in Sweden is directly affecting their expansion plans. Based on our findings, we outline directions that need additional research to allow evidence-informed policy-making. We argue that such work should ideally be conducted by academic researchers and national statistics agencies in collaboration.
Long-Term Cloth-Changing Person Re-identification
Venue: arXiv cs.CV 2020
Year: 2020

Person re-identification (Re-ID) aims to match a target person across camera views at different locations and times. Existing Re-ID studies focus on the short-term cloth-consistent setting, under which a person re-appears in different camera views with the same outfit. A discriminative feature representation learned by existing deep Re-ID models is thus dominated by the visual appearance of clothing. In this work, we focus on a much more difficult yet practical setting where person matching is conducted over long-duration, e.g., over days and months and therefore inevitably under the new challenge of changing clothes. This problem, termed Long-Term Cloth-Changing (LTCC) Re-ID is much understudied due to the lack of large scale datasets. The first contribution of this work is a new LTCC dataset containing people captured over a long period of time with frequent clothing changes. As a second contribution, we propose a novel Re-ID method specifically designed to address the cloth-changing challenge. Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable. We, therefore, introduce a shape embedding module as well as a cloth-elimination shape-distillation module aiming to eliminate the now unreliable clothing appearance features and focus on the body shape information. Extensive experiments show that superior performance is achieved by the proposed model on the new LTCC dataset. The code and dataset will be available at https://naiq.github.io/LTCC_Perosn_ReID.html.
SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis
Venue: arXiv cs.CV 2020
Year: 2020

Multi-view stereopsis (MVS) tries to recover the 3D model from 2D images. As the observations become sparser, the significant 3D information loss makes the MVS problem more challenging. Instead of only focusing on densely sampled conditions, we investigate sparse-MVS with large baseline angles since the sparser sensation is more practical and more cost-efficient. By investigating various observation sparsities, we show that the classical depth-fusion pipeline becomes powerless for the case with a larger baseline angle that worsens the photo-consistency check. As another line of the solution, we present SurfaceNet+, a volumetric method to handle the 'incompleteness' and the 'inaccuracy' problems induced by a very sparse MVS setup. Specifically, the former problem is handled by a novel volume-wise view selection approach. It owns superiority in selecting valid views while discarding invalid occluded views by considering the geometric prior. Furthermore, the latter problem is handled via a multi-scale strategy that consequently refines the recovered geometry around the region with the repeating pattern. The experiments demonstrate the tremendous performance gap between SurfaceNet+ and state-of-the-art methods in terms of precision and recall. Under the extreme sparse-MVS settings in two datasets, where existing methods can only return very few points, SurfaceNet+ still works as well as in the dense MVS setting. The benchmark and the implementation are publicly available at https://github.com/mjiUST/SurfaceNet-plus.
Cubical Ripser: Software for computing persistent homology of image and volume data
Venue: arXiv cs.CV 2020
Year: 2020

We introduce Cubical Ripser for computing persistent homology of image and volume data (more precisely, weighted cubical complexes). To our best knowledge, Cubical Ripser is currently the fastest and the most memory-efficient program for computing persistent homology of weighted cubical complexes. We demonstrate our software with an example of image analysis in which persistent homology and convolutional neural networks are successfully combined. Our open-source implementation is available online.
CERT: Contrastive Self-supervised Learning for Language Understanding
Venue: arXiv cs.CL 2020
Year: 2020

Pretrained language models such as BERT, GPT have shown great effectiveness in language understanding. The auxiliary predictive tasks in existing pretraining approaches are mostly defined on tokens, thus may not be able to capture sentence-level semantics very well. To address this issue, we propose CERT: Contrastive self-supervised Encoder Representations from Transformers, which pretrains language representation models using contrastive self-supervised learning at the sentence level. CERT creates augmentations of original sentences using back-translation. Then it finetunes a pretrained language encoder (e.g., BERT) by predicting whether two augmented sentences originate from the same sentence. CERT is simple to use and can be flexibly plugged into any pretraining-finetuning NLP pipeline. We evaluate CERT on 11 natural language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks. On the averaged score of the 11 tasks, CERT outperforms BERT. The data and code are available at https://github.com/UCSD-AI4H/CERT
How the planned V0 railway line would increase the resilience of the railway network of Hungary against attacks
Venue: arXiv cs.CE 2020
Year: 2020

The spatial distribution of the railway crossings on the river Danube in Hungary is very uneven. There is only one electrified and double-tracked bridge in the country, the Southern Railway Bridge in Budapest. The Újpest bridge in Budapest only provides connection through line 4 which is not electrified and the Baja bridge is not electrified, too, and both of them are single-tracked. The long-planned V0 railway line that is to be cross the Danube approximately halfway between Budapest and Baja would not only help to redistribute the total network flow which currently passes through almost exclusively the Southern bridge but would also provide redundancy for the existing bridges in the case of their disruption. Four of the five proposed V0 path alternatives are analyzed on the basis of these two network properties.
Study of Firecracker MicroVM
Venue: arXiv cs.OS 2020
Year: 2020

Firecracker is a virtualization technology that makes use of Kernel Virtual Machine (KVM). Firecracker belongs to a new virtualization class named the micro-virtual machines (MicroVMs). Using Firecracker, we can launch lightweight MicroVMs in non-virtualized environments in a fraction of a second, at the same time offering the security and workload isolation provided by traditional VMs and also the resource efficiency that comes along with containers \cite{b1}. Firecracker aims to provide a slimmed-down MicroVM, comprised of approximately 50K lines of code in Rust and with a reduced attack surface for guest VMs. This report will examine the internals of Firecracker and understand why Firecracker is the next big thing going forward in virtualization and cloud computing.
Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach
Venue: arXiv cs.SI 2020
Year: 2020

The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users. We analyze 4 million Twitter messages related to the COVID-19 pandemic using a list of 25 hashtags such as "coronavirus," "COVID-19," "quarantine" from March 1 to April 21 in 2020. We use a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected Tweets. Popular unigrams include "virus," "lockdown," and "quarantine." Popular bigrams include "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identify 13 discussion topics and categorize them into five different themes, such as "public health measures to slow the spread of COVID-19," "social stigma associated with COVID-19," "coronavirus news cases and deaths," "COVID-19 in the United States," and "coronavirus cases in the rest of the world". Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public reveals a significant feeling of fear when they discuss the coronavirus new cases and deaths than other topics. The study shows that Twitter data and machine learning approaches can be leveraged for infodemiology study by studying the evolving public discussions and sentiments during the COVID-19. Real-time monitoring and assessment of the Twitter discussion and concerns can be promising for public health emergency responses and planning. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic.
Periodic Strategies II: Generalizations and Extensions
Venue: arXiv cs.GT 2020
Year: 2020

At a mixed Nash equilibrium, the payoff of a player does not depend on her own action, as long as her opponent sticks to his. In a periodic strategy, a concept developed in a previous paper (arXiv:1307.2035v4), in contrast, the own payoff does not depend on the opponent's action. Here, we generalize this to multi-player simultaneous perfect information strategic form games. We show that also in this class of games, there always exists at least one periodic strategy, and we investigate the mathematical properties of such periodic strategies. In addition, we demonstrate that periodic strategies may exist in games with incomplete information; we shall focus on Bayesian games. Moreover we discuss the differences between the periodic strategies formalism and cooperative game theory. In fact, the periodic strategies are obtained in a purely non-cooperative way, and periodic strategies are as cooperative as the Nash equilibria are. Finally, we incorporate the periodic strategies in an epistemic game theory framework, and discuss several features of this approach.
Applying Evolutionary Metaheuristics for Parameter Estimation of Individual-Based Models
Venue: arXiv cs.NE 2020
Year: 2020

Individual-based models are complex and they have usually an elevated number of input parameters which must be tuned for reproducing the observed population data or the experimental results as accurately as possible. Thus, one of the weakest points of this modelling approach lies on the fact that rarely the modeler has the enough information about the correct values or even the acceptable range for the input parameters. Consequently, several parameter combinations must be tried to find an acceptable set of input factors minimizing the deviations of simulated and the reference dataset. In practice, most of times, it is computationally unfeasible to traverse the complete search space trying all every possible combination to find the best of set of parameters. That is precisely an instance of a combinatorial problem which is suitable for being solved by metaheuristics and evolutionary computation techniques. In this work, we introduce EvoPER, an R package for simplifying the parameter estimation using evolutionary computation methods.
Approximation Schemes for ReLU Regression
Venue: arXiv cs.LG 2020
Year: 2020

We consider the fundamental problem of ReLU regression, where the goal is to output the best fitting ReLU with respect to square loss given access to draws from some unknown distribution. We give the first efficient, constant-factor approximation algorithm for this problem assuming the underlying distribution satisfies some weak concentration and anti-concentration conditions (and includes, for example, all log-concave distributions). This solves the main open problem of Goel et al., who proved hardness results for any exact algorithm for ReLU regression (up to an additive $ε$). Using more sophisticated techniques, we can improve our results and obtain a polynomial-time approximation scheme for any subgaussian distribution. Given the aforementioned hardness results, these guarantees can not be substantially improved. Our main insight is a new characterization of surrogate losses for nonconvex activations. While prior work had established the existence of convex surrogates for monotone activations, we show that properties of the underlying distribution actually induce strong convexity for the loss, allowing us to relate the global minimum to the activation's Chow parameters.
Communicating over the Torn-Paper Channel
Venue: arXiv cs.IT 2020
Year: 2020

We consider the problem of communicating over a channel that randomly "tears" the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\rm Geometric}(p_n)$, we characterize the capacity as $C = e^{-α}$, where $α= \lim_{n\to\infty} p_n \log n$. Our results show that the case of ${\rm Geometric}(p_n)$-length fragments and the case of deterministic length-$(1/p_n)$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.
Skew Gaussian Processes for Classification
Venue: arXiv cs.LG 2020
Year: 2020

Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that the mean and the median coincide, while the mean and median in an asymmetric (skewed) distribution can be different numbers. In this paper, we propose Skew-Gaussian processes (SkewGPs) as a non-parametric prior over functions. A SkewGP extends the multivariate Unified Skew-Normal distribution over finite dimensional vectors to a stochastic processes. The SkewGP class of distributions includes GPs and, therefore, SkewGPs inherit all good properties of GPs and increase their flexibility by allowing asymmetry in the probabilistic model. By exploiting the fact that SkewGP and probit likelihood are conjugate model, we derive closed form expressions for the marginal likelihood and predictive distribution of this new nonparametric classifier. We verify empirically that the proposed SkewGP classifier provides a better performance than a GP classifier based on either Laplace's method or Expectation Propagation.
Evaluating Information Retrieval Systems for Kids
Venue: arXiv cs.IR 2020
Year: 2020

Evaluation of information retrieval systems (IRS) is a prominent topic among information retrieval researchers--mainly directed at a general population. Children require unique IRS and by extension different ways to evaluate these systems, but as a large population that use IRS have largely been ignored on the evaluation front. In this position paper, we explore many perspectives that must be considered when evaluating IRS; we specially discuss problems faced by researchers who work with children IRS, including lack of evaluation frameworks, limitations of data, and lack of user judgment understanding.
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling
Venue: arXiv cs.LG 2020
Year: 2020

Wildfire modelling is an attempt to reproduce fire behaviour. Through active fire analysis, it is possible to reproduce a dynamical process, such as wildfires, with limited duration time series data. Recurrent neural networks (RNNs) can model dynamic temporal behaviour due to their ability to remember their internal input. In this paper, we compare the Gated Recurrent Unit (GRU) and the Long Short-Term Memory (LSTM) network. We try to determine whether a wildfire continues to burn and given that it does, we aim to predict which one of the 8 cardinal directions the wildfire will spread in. Overall the GRU performs better for longer time series than the LSTM. We have shown that although we are reasonable at predicting the direction in which the wildfire will spread, we are not able to asses if the wildfire continues to burn due to the lack of auxiliary data.
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation
Venue: arXiv cs.CR 2020
Year: 2020

In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these to generate combinatorial attacks to damage the safety of the system, and to cause economic loss. Results were compared against random attacks, and the performance of the EMO algorithms were evaluated using hypervolume, spread and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using a number of machine learning algorithms. Designed approach was further tested against the developed detection methods. Results demonstrate that EMO algorithms are a promising tool in the identification of the most vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can be used by control and security engineers to design security aware control, and test the effectiveness of security mechanisms, both during design, and later during system operation.
Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
Venue: arXiv cs.CV 2020
Year: 2020

It has witnessed a growing demand for efficient representation learning on point clouds in many 3D computer vision applications. Behind the success story of convolutional neural networks (CNNs) is that the data (e.g., images) are Euclidean structured. However, point clouds are irregular and unordered. Various point neural networks have been developed with isotropic filters or using weighting matrices to overcome the structure inconsistency on point clouds. However, isotropic filters or weighting matrices limit the representation power. In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point using dot-product attention according to a set of evenly distributed kernel points on a sphere's surface and performs shared anisotropic filters. In fact, dot product with kernel points is by analogy with the dot-product with keys in Transformer as widely used in natural language processing (NLP). From this perspective, PAI-Conv can be regarded as the transformer for point clouds, which is physically meaningful and is robust to cooperate with the efficient random point sampling method. Comprehensive experiments on point clouds demonstrate that PAI-Conv produces competitive results in classification and semantic segmentation tasks compared to state-of-the-art methods.
SSM-Net for Plants Disease Identification in Low Data Regime
Venue: arXiv cs.CV 2020
Year: 2020

Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards. Deep learning can offer critical aid in detecting such diseases. However, it is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to address the problem of disease detection in low data regimes. We demonstrated our experiments on two datasets: mini-leaves diseases and sugarcane diseases dataset. We have showcased that the SSM-Net approach can achieve better decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and 94.3% on the sugarcane dataset. The accuracy increased by ~10% and ~5% respectively, compared to the widely used VGG16 transfer learning approach. Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane dataset and 0.91 on the mini-leaves dataset. Our code implementation is available on Github: https://github.com/shruti-jadon/PlantsDiseaseDetection.
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models
Venue: arXiv cs.CL 2020
Year: 2020

Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society. However, due to their inherent opaqueness, some recently raised concerns about using neural models are starting to be taken seriously. In fact, intentional or unintentional behaviors could lead to a dialogue system to generate inappropriate responses. Thus, in this paper, we investigate whether we can learn to craft input sentences that result in a black-box neural dialogue model being manipulated into having its outputs contain target words or match target sentences. We propose a reinforcement learning based model that can generate such desired inputs automatically. Extensive experiments on a popular well-trained state-of-the-art neural dialogue model show that our method can successfully seek out desired inputs that lead to the target outputs in a considerable portion of cases. Consequently, our work reveals the potential of neural dialogue models to be manipulated, which inspires and opens the door towards developing strategies to defend them.