OpenRaiser/Intern-Atlas huggingface dataset:
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing
Venue: arXiv cs.CV 2019
Year: 2019

Face Anti-Spoofing (FAS) is significant for the security of face recognition systems. Convolutional Neural Networks (CNNs) have been introduced to the field of the FAS and have achieved competitive performance. However, CNN-based methods are vulnerable to the adversarial attack. Attackers could generate adversarial-spoofing examples to circumvent a CNN-based face liveness detector. Studies about the transferability of the adversarial attack reveal that utilizing handcrafted feature-based methods could improve security in a system-level. Therefore, handcrafted feature-based methods are worth our exploration. In this paper, we introduce the deep forest, which is proposed as an alternative towards CNNs by Zhou et al., in the problem of the FAS. To the best of our knowledge, this is the first attempt at exploiting the deep forest in the problem of FAS. Moreover, we propose to re-devise the representation constructing by using LBP descriptors rather than the Grained-Scanning Mechanism in the original scheme. Our method achieves competitive results. On the benchmark database IDIAP REPLAY-ATTACK, 0\% Equal Error Rate (EER) is achieved. This work provides a competitive option in a fusing scheme for improving system-level security and offers important ideas to those who want to explore methods besides CNNs.
Semantic-aware Image Deblurring
Venue: arXiv cs.CV 2019
Year: 2019

Image deblurring has achieved exciting progress in recent years. However, traditional methods fail to deblur severely blurred images, where semantic contents appears ambiguously. In this paper, we conduct image deblurring guided by the semantic contents inferred from image captioning. Specially, we propose a novel Structured-Spatial Semantic Embedding model for image deblurring (termed S3E-Deblur), which introduces a novel Structured-Spatial Semantic tree model (S3-tree) to bridge two basic tasks in computer vision: image deblurring (ImD) and image captioning (ImC). In particular, S3-tree captures and represents the semantic contents in structured spatial features in ImC, and then embeds the spatial features of the tree nodes into GAN based ImD. Co-training on S3-tree, ImC, and ImD is conducted to optimize the overall model in a multi-task end-to-end manner. Extensive experiments on severely blurred MSCOCO and GoPro datasets demonstrate the significant superiority of S3E-Deblur compared to the state-of-the-arts on both ImD and ImC tasks.
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
Venue: arXiv cs.IT 2019
Year: 2019

Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The mainstream edge learning approach, federated learning, has been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for updating a global AI model. Since each stochastic gradient is typically high-dimensional (with millions to billions of coefficients), communication overhead becomes a bottleneck for edge learning. To address this issue, we propose in this work a novel framework of hierarchical stochastic gradient quantization and study its effect on the learning performance. First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively. Subsequently, the quantized normalized block gradients are scaled and cascaded to yield the quantized normalized stochastic gradient using a so-called hinge vector designed under the criterion of minimum distortion. The hinge vector is also efficiently compressed using another low-dimensional Grassmannian quantizer. The other feature of the framework is a bit-allocation scheme for reducing the quantization error. The scheme determines the resolutions of the low-dimensional quantizers in the proposed framework. The framework is proved to guarantee model convergency by analyzing the convergence rate as a function of the quantization bits. Furthermore, by simulation, our design is shown to substantially reduce the communication overhead compared with the state-of-the-art signSGD scheme, while both achieve similar learning accuracies.
A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification
Venue: arXiv cs.CV 2019
Year: 2019

In video surveillance, person re-identification is the task of searching person images in non-overlapping cameras. Though supervised methods for person re-identification have attained impressive performance, obtaining large scale cross-view labeled training data is very expensive. However, unlabelled data is available in abundance. In this paper, we propose a semi-supervised metric learning approach that can utilize information in unlabelled data with the help of a few labelled training samples. We also address the small sample size problem that inherently occurs due to the few labeled training data. Our method learns a discriminative space where within class samples collapse to singular points, achieving the least within class variance, and then use a maximum margin criterion over a high dimensional kernel space to maximally separate the distinct class samples. A maximum margin criterion with two levels of high dimensional mappings to kernel space is used to obtain better cross-view discrimination of the identities. Cross-view affinity learning with reciprocal nearest neighbor constraints is used to mine new pseudo-classes from the unlabelled data and update the distance metric iteratively. We attain state-of-the-art performance on four challenging datasets with a large margin.
Novel Applications of Factored Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

In this work, we explore the usefulness of target factors in neural machine translation (NMT) beyond their original purpose of predicting word lemmas and their inflections, as proposed by Garcìa-Martìnez et al., 2016. For this, we introduce three novel applications of the factored output architecture: In the first one, we use a factor to explicitly predict the word case separately from the target word itself. This allows for information to be shared between different casing variants of a word. In a second task, we use a factor to predict when two consecutive subwords have to be joined, eliminating the need for target subword joining markers. The third task is the prediction of special tokens of the operation sequence NMT model (OSNMT) of Stahlberg et al., 2018. Automatic evaluation on English-to-German and English-to-Turkish tasks showed that integration of such auxiliary prediction tasks into NMT is at least as good as the standard NMT approach. For the OSNMT, we observed a significant improvement in BLEU over the baseline OSNMT implementation due to a reduced output sequence length that resulted from the introduction of the target factors.
The Impacts of Dimensionality, Diffusion, and Directedness on Intrinsic Universality in the abstract Tile Assembly Model
Venue: arXiv cs.CG 2019
Year: 2019

We present a series of results related to mathematical models of self-assembling tiles and the impacts that three diverse properties have on their dynamics. We expand upon a series of prior results which showed that (1) the abstract Tile Assembly Model (aTAM) is intrinsically universal (IU) [FOCS 2012], and (2) the class of directed aTAM systems is not IU [FOCS 2016]. IU for a model (or class of systems within a model) means that there is a universal tile set which can be used to simulate an arbitrary system within that model (or class). Furthermore, the simulation must not only produce the same resultant structures, it must also maintain the full dynamics of the systems being simulated modulo only a scale factor. While the FOCS 2012 result showed the standard, two-dimensional (2D) aTAM is IU, here we show this is also the case for the 3D version. Conversely, the FOCS 2016 result showed the class of aTAM systems which are directed (a.k.a. deterministic, or confluent) is not IU, implying that nondeterminism is fundamentally required for such simulations. Here, however, we show that in 3D the class of directed aTAM systems is actually IU, i.e. there is a universal directed simulator for them. We then consider the influence of more rigid notions of dimensionality. Namely, we introduce the Planar aTAM, where tiles are not only restricted to binding in the plane, but also to traveling in the plane, and prove that the Planar aTAM is not IU, and that the class of directed systems within the Planar aTAM also is not IU. Finally, analogous to the Planar aTAM, we introduce the Spatial aTAM, its 3D counterpart, and prove that it is IU. To prove our positive results we have not only designed, but also implemented what we believe to be the first IU tile set ever implemented and simulated in any tile assembly model. We've made it and a simulator which can demonstrate it freely available.
BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we introduce the first and largest Hindi text corpus, named BHAAV, which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader. The corpus consists of 20,304 sentences collected from 230 different short stories spanning across 18 genres such as Inspirational and Mystery. Each sentence has been annotated into one of the five emotion categories - anger, joy, suspense, sad, and neutral, by three native Hindi speakers with at least ten years of formal education in Hindi. We also discuss challenges in the annotation of low resource languages such as Hindi, and discuss the scope of the proposed corpus along with its possible uses. We also provide a detailed analysis of the dataset and train strong baseline classifiers reporting their performances.
Metric Dimension
Venue: arXiv cs.DM 2019
Year: 2019

In this manuscript, we provide a concise review of the concept of metric dimension for both deterministic as well as random graphs. Algorithms to approximate this quantity, as well as potential applications, are also reviewed. This work has been partially funded by the NSF IIS grant 1836914.
DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation
Venue: arXiv cs.IT 2019
Year: 2019

Faced with the massive connection, sporadic transmission, and small-sized data packets in future cellular communication, a grant-free non-orthogonal random access (NORA) system is considered in this paper, which could reduce the access delay and support more devices. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In this algorithm, the message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the weights could alleviate the convergence problem of the MP-BSBL algorithm. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations.
Disciplinary Variations in Altmetric Coverage of Scholarly Articles
Venue: arXiv cs.DL 2019
Year: 2019

The popular social media platforms are now making it possible for scholarly articles to be shared rapidly in different forms, which in turn can significantly improve the visibility and reach of articles. Many authors are now utilizing the social media platforms to disseminate their scholarly articles (often as pre- or post- prints) beyond the paywalls of journals. It is however not very well established if the level of social media coverage and attention of scholarly articles is same across all research disciplines or there exist discipline-wise variations. This paper aims to explore the disciplinary variations in coverage and altmetric attention by analyzing a significantly large amount of data from Web of Science and Altmetric.com. Results obtained show interesting patterns. Medical Sciences and Biology are found to account for more than 50% of all instances in Altmetrics. In terms of coverage, disciplines like Biology, Medical Science and Multidisciplinary Sciences have more than 60% of their articles covered in Altmetrics, whereas disciplines like Engineering, Mathematics and Material Science have less than 25% of their articles covered in Altmetrics. The coverage percentages further vary across different altmetric platforms, with Twitter and Mendeley having much higher overall coverage than Facebook and News. Disciplinary variations in coverage are also found in different altmetric platforms, with variations as large as 7.5% for Engineering discipline to 55.7% for Multidisciplinary in Twitter. The paper also looks into the possible role of source of publication in altmetric coverage level of articles. Interestingly, some journals are found to have a higher altmetric coverage in comparison to the average altmetric coverage level of that discipline.
Out-of-distribution Detection in Classifiers via Generation
Venue: arXiv cs.LG 2019
Year: 2019

By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples. For their real-world deployments, detecting out-of-distribution (OOD) samples is essential. Assuming OOD to be outside the closed boundary of in-distribution, typical neural classifiers do not contain the knowledge of this boundary for OOD detection during inference. There have been recent approaches to instill this knowledge in classifiers by explicitly training the classifier with OOD samples close to the in-distribution boundary. However, these generated samples fail to cover the entire in-distribution boundary effectively, thereby resulting in a sub-optimal OOD detector. In this paper, we analyze the feasibility of such approaches by investigating the complexity of producing such "effective" OOD samples. We also propose a novel algorithm to generate such samples using a manifold learning network (e.g., variational autoencoder) and then train an n+1 classifier for OOD detection, where the $n+1^{th}$ class represents the OOD samples. We compare our approach against several recent classifier-based OOD detectors on MNIST and Fashion-MNIST datasets. Overall the proposed approach consistently performs better than the others.
Membership Model Inversion Attacks for Deep Networks
Venue: arXiv cs.LG 2019
Year: 2019

With the increasing adoption of AI, inherent security and privacy vulnerabilities formachine learning systems are being discovered. One such vulnerability makes itpossible for an adversary to obtain private information about the types of instancesused to train the targeted machine learning model. This so-called model inversionattack is based on sequential leveraging of classification scores towards obtaininghigh confidence representations for various classes. However, for deep networks,such procedures usually lead to unrecognizable representations that are uselessfor the adversary. In this paper, we introduce a more realistic definition of modelinversion, where the adversary is aware of the general purpose of the attackedmodel (for instance, whether it is an OCR system or a facial recognition system),and the goal is to find realistic class representations within the corresponding lower-dimensional manifold (of, respectively, general symbols or general faces). To thatend, we leverage properties of generative adversarial networks for constructinga connected lower-dimensional manifold, and demonstrate the efficiency of ourmodel inversion attack that is carried out within that manifold.
Minimum Cuts in Surface Graphs
Venue: arXiv cs.DS 2019
Year: 2019

We describe algorithms to efficiently compute minimum $(s,t)$-cuts and global minimum cuts of undirected surface-embedded graphs. Given an edge-weighted undirected graph $G$ with $n$ vertices embedded on an orientable surface of genus $g$, our algorithms can solve either problem in $g^{O(g)} n \log \log n$ or $2^{O(g)} n \log n$ time, whichever is better. When $g$ is a constant, our $g^{O(g)} n \log \log n$ time algorithms match the best running times known for computing minimum cuts in planar graphs. Our algorithms for minimum cuts rely on reductions to the problem of finding a minimum-weight subgraph in a given $\mathbb{Z}_2$-homology class, and we give efficient algorithms for this latter problem as well. If $G$ is embedded on a surface with $b$ boundary components, these algorithms run in $(g + b)^{O(g + b)} n \log \log n$ and $2^{O(g + b)} n \log n$ time. We also prove that finding a minimum-weight subgraph homologous to a single input cycle is NP-hard, showing it is likely impossible to improve upon the exponential dependencies on $g$ for this latter problem.
Unconstrained Road Marking Recognition with Generative Adversarial Networks
Venue: arXiv cs.CV 2019
Year: 2019

Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often over-dependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates high-quality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a class-conditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.
On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention
Venue: arXiv cs.CV 2019
Year: 2019

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods still fail to recognize texts in arbitrary shapes, such as heavily curved or rotated texts, which are abundant in daily life (e.g. restaurant signs, product labels, company logos, etc). This paper introduces a novel architecture to recognizing texts of arbitrary shapes, named Self-Attention Text Recognition Network (SATRN), which is inspired by the Transformer. SATRN utilizes the self-attention mechanism to describe two-dimensional (2D) spatial dependencies of characters in a scene text image. Exploiting the full-graph propagation of self-attention, SATRN can recognize texts with arbitrary arrangements and large inter-character spacing. As a result, SATRN outperforms existing STR models by a large margin of 5.7 pp on average in "irregular text" benchmarks. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e.g. rotated and multi-line text). We will open-source the code.
Representing All Stable Matchings by Walking a Maximal Chain
Venue: arXiv cs.GT 2019
Year: 2019

The seminal book of Gusfield and Irving [GI89] provides a compact and algorithmically useful way to represent the collection of stable matches corresponding to a given set of preferences. In this paper, we reinterpret the main results of [GI89], giving a new proof of the characterization which is able to bypass a lot of the "theory building" of the original works. We also provide a streamlined and efficient way to compute this representation. Our proofs and algorithms emphasize the connection to well-known properties of the deferred acceptance algorithm.
Simulation Reproducibility of a Chaotic Circuit
Venue: arXiv cs.OH 2019
Year: 2019

An evergreen scientific feature is the ability for scientific works to be reproduced. This feature allows researchers to understand, enhance, or even question works that have been developed by other scientists. In control theory the importance of modeling and simulation of systems is widely recognized. Despite this recognition, less attention is paid to the effects of finite precision of computers on the simulation reproducibility of nonlinear dynamic systems. In this work, a case study of reproducibility is presented in the simulation of a chaotic Jerk circuit, using the software LtSpice. In order to do so, we performed simulations of the circuit in the same version of the software on different computers, in order to collect the data and compare them with experimental results. The comparison was made with the NRMSE (Normalized Root Mean Square Error), in order to identify the computer with the highest prediction horizon. Tests performed in 4 different configurations showed the difficulties of simulation reproducibility in LtSpice. The methodology developed was efficient in identifying the computer with better performance, which allows applying it to other cases in the literature.
Design, Modelling and Validation of a Novel Extra Slender Continuum Robot for In-situ Inspection and Repair in Aeroengine
Venue: arXiv cs.RO 2019
Year: 2019

In-situ aeroengine maintenance works are highly beneficial as it can significantly reduce the current maintenance cycle which is extensive and costly due to the disassembly requirement of engines from aircrafts. However, navigating in/out via inspection ports and performing multi-axis movements with end-effectors in constrained environments (e.g. combustion chamber) are fairly challenging. A novel extra-slender (diameter-to-length ratio <0.02) dual-stage continuum robot (16 degree-of-freedom) is proposed to navigate in/out confined environments and perform required configuration shapes for further repair operations. Firstly, the robot design presents several innovative mechatronic solutions: (i) dual-stage tendon-driven structure with bevelled disks to perform required shapes and to provide selective stiffness for carrying high payloads; (ii) various rigid-compliant combined joints to enable different flexibility and stiffness in each stage; (iii) three commanding cables for each 2-DoF section to minimise the number of actuators with precise actuations. Secondly, a segment-scaled piecewise-constant-curvature-theory based kinematic model and a Kirchhoff-elastic-rod-theory based static model are established by considering the applied forces/moments (friction, actuation, gravity and external load), where the friction coefficient is modelled as a function of bending angle. Finally, experiments were carried out to validate the proposed static modelling and to evaluate the robot capabilities of performing the predefined shape and stiffness.