OpenRaiser/Intern-Atlas huggingface dataset:
Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019
Venue: arXiv cs.CL 2019
Year: 2019

Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation tasks (NLG). However, generation of long descriptive summaries conditioned on structured data remains an open challenge. Likewise, MT that goes beyond sentence-level context is still an open issue (e.g., document-level MT or MT with metadata). To address these challenges, we propose to leverage data from both tasks and do transfer learning between MT, NLG, and MT with source-side metadata (MT+NLG). First, we train document-based MT systems with large amounts of parallel data. Then, we adapt these models to pure NLG and MT+NLG tasks by fine-tuning with smaller amounts of domain-specific data. This end-to-end NLG approach, without data selection and planning, outperforms the previous state of the art on the Rotowire NLG task. We participated to the "Document Generation and Translation" task at WNGT 2019, and ranked first in all tracks.
Debian Package usage profiler for Debian based Systems
Venue: arXiv cs.PF 2019
Year: 2019

The embedded devices of today due to their CPU, RAM capabilities can run various Linux distributions but in most cases they are different from general purpose distributions as they are usually lighter and specific to the needs of that particular system. In this project, we share the problems associated in adopting a fully heavy-weight Debian based system like Ubuntu in embedded/automotive platforms and provide solutions to optimize them to identify unused/redundant content in the system. This helps developer to reduce the hefty general purpose distribution to an application specific distribution. The solution involves collecting usage data in the system in a non-invasive manner (to avoid any drop in performance) to suggest users the redundant, unused parts of the system that can be safely removed without impacting the system functionality.
Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction
Venue: arXiv cs.SE 2019
Year: 2019

Blockchain and blockchain-based decentralized applications are attracting increasing attentions recently. In public blockchain systems, users usually connect to third-party peers or run a peer to join the P2P blockchain network. However, connecting to unreliable blockchain peers will make users waste resources and even lose millions of dollars of cryptocurrencies. In order to select the reliable blockchain peers, it is urgently needed to evaluate and predict the reliability of them. Faced with this problem, we propose H-BRP, Hybrid Blockchain Reliability Prediction model to extract the blockchain reliability factors then make personalized prediction for each user. Large-scale real-world experiments are conducted on 100 blockchain requesters and 200 blockchain peers. The implement and dataset of 2,000,000 test cases are released. The experimental results show that the proposed model obtains better accuracy than other approaches.
Which Factors Impact Engagement on News Articles on Facebook?
Venue: arXiv cs.CY 2019
Year: 2019

Social media is increasingly being used as a news-platform. To reach their intended audience, newspapers need for their articles to be well ranked by Facebook's news-feed algorithm. The number of likes, shares and other reactions determine the lead scoring criteria. This paper will try to assess how the reaction volume is impacted by the following criteria: (1) Delay between event and post release; (2) Time of the day the post is published; and (3) Post format: video, photo or text. To isolate the effect of the publication time and post format on a post, we need to control for the news-event and the publishing newspaper. For that end, a news-aggregator is designed and implemented, to group together posts that relate to the same news-event. This tool gave some spin-off results, allowing the ability to map newspapers by similarity and to detect some topic omissions.
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems. Recent works got us closer to those goals, addressing non-stationarity of the environment from a single agent's perspective by utilizing a deep net critic which depends on all observations and actions. The critic input concatenates agent observations and actions in a user-specified order. However, since deep nets aren't permutation invariant, a permuted input changes the critic output despite the environment remaining identical. To avoid this inefficiency, we propose a 'permutation invariant critic' (PIC), which yields identical output irrespective of the agent permutation. This consistent representation enables our model to scale to 30 times more agents and to achieve improvements of test episode reward between 15% to 50% on the challenging multi-agent particle environment (MPE).
Privacy Protection in Distributed Fingerprint-based Authentication
Venue: arXiv cs.CR 2019
Year: 2019

Biometric authentication is getting increasingly popular due to the convenience of using unique individual traits, such as fingerprints, palm veins, irises. Especially fingerprints are widely used nowadays due to the availability and low cost of fingerprint scanners. To avoid identity theft or impersonation, fingerprint data is typically stored locally, e.g., in a trusted hardware module, in a single device that is used for user enrollment and authentication. Local storage, however, limits the ability to implement distributed applications, in which users can enroll their fingerprint once and use it to access multiple physical locations and mobile applications afterwards. In this paper, we present a distributed authentication system that stores fingerprint data in a server or cloud infrastructure in a privacy-preserving way. Multiple devices can be connected and perform user enrollment or verification. To secure the privacy and integrity of sensitive data, we employ a cryptographic construct called fuzzy vault. We highlight challenges in implementing fuzzy vault-based authentication, for which we propose and compare alternative solutions. We conduct a security analysis of our biometric cryptosystem, and as a proof of concept, we build an authentication system for access control using resource-constrained devices (Raspberry Pis) connected to fingerprint scanners and the Microsoft Azure cloud environment. Furthermore, we evaluate the fingerprint matching algorithm against the well-known FVC2006 database and show that it can achieve comparable accuracy to widely-used matching techniques that are not designed for privacy, while remaining efficient with an authentication time of few seconds.
Robust contrastive learning and nonlinear ICA in the presence of outliers
Venue: arXiv cs.LG 2019
Year: 2019

Nonlinear independent component analysis (ICA) is a general framework for unsupervised representation learning, and aimed at recovering the latent variables in data. Recent practical methods perform nonlinear ICA by solving a series of classification problems based on logistic regression. However, it is well-known that logistic regression is vulnerable to outliers, and thus the performance can be strongly weakened by outliers. In this paper, we first theoretically analyze nonlinear ICA models in the presence of outliers. Our analysis implies that estimation in nonlinear ICA can be seriously hampered when outliers exist on the tails of the (noncontaminated) target density, which happens in a typical case of contamination by outliers. We develop two robust nonlinear ICA methods based on the γ-divergence, which is a robust alternative to the KL-divergence in logistic regression. The proposed methods are shown to have desired robustness properties in the context of nonlinear ICA. We also experimentally demonstrate that the proposed methods are very robust and outperform existing methods in the presence of outliers. Finally, the proposed method is applied to ICA-based causal discovery and shown to find a plausible causal relationship on fMRI data.
A Robust Data-Driven Approach for Dialogue State Tracking of Unseen Slot Values
Venue: arXiv cs.CL 2019
Year: 2019

A Dialogue State Tracker is a key component in dialogue systems which estimates the beliefs of possible user goals at each dialogue turn. Deep learning approaches using recurrent neural networks have shown state-of-the-art performance for the task of dialogue state tracking. Generally, these approaches assume a predefined candidate list and struggle to predict any new dialogue state values that are not seen during training. This makes extending the candidate list for a slot without model retaining infeasible and also has limitations in modelling for low resource domains where training data for slot values are expensive. In this paper, we propose a novel dialogue state tracker based on copying mechanism that can effectively track such unseen slot values without compromising performance on slot values seen during training. The proposed model is also flexible in extending the candidate list without requiring any retraining or change in the model. We evaluate the proposed model on various benchmark datasets (DSTC2, DSTC3 and WoZ2.0) and show that our approach, outperform other end-to-end data-driven approaches in tracking unseen slot values and also provides significant advantages in modelling for DST.
An Implementation of Homotopy Type Theory in Isabelle/Pure
Venue: arXiv cs.LO 2019
Year: 2019

In this Masters thesis we present an implementation of a fragment of "book HoTT" as an object logic for the interactive proof assistant Isabelle. We also give a mathematical description of the underlying theory of the Isabelle/Pure logical framework, and discuss various issues and design decisions that arise when attempting to encode intensional dependent type theory with universes inside a simple type-theoretic logical foundation.
Variations of Genetic Algorithms
Venue: arXiv cs.NE 2019
Year: 2019

The goal of this project is to develop the Genetic Algorithms (GA) for solving the Schaffer F6 function in fewer than 4000 function evaluations on a total of 30 runs. Four types of Genetic Algorithms (GA) are presented - Generational GA (GGA), Steady-State (mu+1)-GA (SSGA), Steady-Generational (mu,mu)-GA (SGGA), and (mu+mu)-GA.
Predicting Weather Uncertainty with Deep Convnets
Venue: arXiv cs.LG 2019
Year: 2019

Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be used on a small set of numerical weather simulations to estimate the spread of a weather forecast, significantly reducing computational cost. To train the system, we both modify the 3D U-Net architecture and explore models that incorporate temporal data. Our models serve as a starting point to improve uncertainty quantification in current real-time weather forecasting systems, which is vital for predicting extreme events.
FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweight networks that prefer to utilize shallow structure, FDDWNet makes an effort to design more deeper network architecture, while maintains faster inference speed and higher segmentation accuracy. Our network uses factorized dilated depth-wise separable convolutions (FDDWC) to learn feature representations from different scale receptive fields with fewer model parameters. Additionally, FDDWNet has multiple branches of skipped connections to gather context cues from intermediate convolution layers. The experiments show that FDDWNet only has 0.8M model size, while achieves 60 FPS running speed on a single RTX 2080Ti GPU with a 1024x512 input image. The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.
Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media
Venue: arXiv cs.CL 2019
Year: 2019

Social media currently provide a window on our lives, making it possible to learn how people from different places, with different backgrounds, ages, and genders use language. In this work we exploit a newly-created Arabic dataset with ground truth age and gender labels to learn these attributes both individually and in a multi-task setting at the sentence level. Our models are based on variations of deep bidirectional neural networks. More specifically, we build models with gated recurrent units and bidirectional encoder representations from transformers (BERT). We show the utility of multi-task learning (MTL) on the two tasks and identify task-specific attention as a superior choice in this context. We also find that a single-task BERT model outperform our best MTL models on the two tasks. We report tweet-level accuracy of 51.43% for the age task (three-way) and 65.30% on the gender task (binary), both of which outperforms our baselines with a large margin. Our models are language-agnostic, and so can be applied to other languages.
DeepBlindness: Fast Blindness Map Estimation and Blindness Type Classification for Outdoor Scene from Single Color Image
Venue: arXiv cs.CV 2019
Year: 2019

Outdoor vision robotic systems and autonomous cars suffer from many image-quality issues, particularly haze, defocus blur, and motion blur, which we will define generically as "blindness issues". These blindness issues may seriously affect the performance of robotic systems and could lead to unsafe decisions being made. However, existing solutions either focus on one type of blindness only or lack the ability to estimate the degree of blindness accurately. Besides, heavy computation is needed so that these solutions cannot run in real-time on practical systems. In this paper, we provide a method which could simultaneously detect the type of blindness and provide a blindness map indicating to what degree the vision is limited on a pixel-by-pixel basis. Both the blindness type and the estimate of per-pixel blindness are essential for tasks like deblur, dehaze, or the fail-safe functioning of robotic systems. We demonstrate the effectiveness of our approach on the KITTI and CUHK datasets where experiments show that our method outperforms other state-of-the-art approaches, achieving speeds of about 130 frames per second (fps).
Furniture Free Mapping using 3D Lidars
Venue: arXiv cs.RO 2019
Year: 2019

Mobile robots depend on maps for localization, planning, and other applications. In indoor scenarios, there is often lots of clutter present, such as chairs, tables, other furniture, or plants. While mapping this clutter is important for certain applications, for example navigation, maps that represent just the immobile parts of the environment, i.e. walls, are needed for other applications, like room segmentation or long-term localization. In literature, approaches can be found that use a complete point cloud to remove the furniture in the room and generate a furniture free map. In contrast, we propose a Simultaneous Localization And Mapping (SLAM)-based mobile laser scanning solution. The robot uses an orthogonal pair of Lidars. The horizontal scanner aims to estimate the robot position, whereas the vertical scanner generates the furniture free map. There are three steps in our method: point cloud rearrangement, wall plane detection and semantic labeling. In the experiment, we evaluate the efficiency of removing furniture in a typical indoor environment. We get $99.60\%$ precision in keeping the wall in the 3D result, which shows that our algorithm can remove most of the furniture in the environment. Furthermore, we introduce the application of 2D furniture free mapping for room segmentation.
Visual Relationship Detection with Relative Location Mining
Venue: arXiv cs.CV 2019
Year: 2019

Visual relationship detection, as a challenging task used to find and distinguish the interactions between object pairs in one image, has received much attention recently. In this work, we propose a novel visual relationship detection framework by deeply mining and utilizing relative location of object-pair in every stage of the procedure. In both the stages, relative location information of each object-pair is abstracted and encoded as auxiliary feature to improve the distinguishing capability of object-pairs proposing and predicate recognition, respectively; Moreover, one Gated Graph Neural Network(GGNN) is introduced to mine and measure the relevance of predicates using relative location. With the location-based GGNN, those non-exclusive predicates with similar spatial position can be clustered firstly and then be smoothed with close classification scores, thus the accuracy of top $n$ recall can be increased further. Experiments on two widely used datasets VRD and VG show that, with the deeply mining and exploiting of relative location information, our proposed model significantly outperforms the current state-of-the-art.
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
Venue: arXiv cs.CL 2019
Year: 2019

The pre-training of text encoders normally processes text as a sequence of tokens corresponding to small text units, such as word pieces in English and characters in Chinese. It omits information carried by larger text granularity, and thus the encoders cannot easily adapt to certain combinations of characters. This leads to a loss of important semantic information, which is especially problematic for Chinese because the language does not have explicit word boundaries. In this paper, we propose ZEN, a BERT-based Chinese (Z) text encoder Enhanced by N-gram representations, where different combinations of characters are considered during training. As a result, potential word or phase boundaries are explicitly pre-trained and fine-tuned with the character encoder (BERT). Therefore ZEN incorporates the comprehensive information of both the character sequence and words or phrases it contains. Experimental results illustrated the effectiveness of ZEN on a series of Chinese NLP tasks. We show that ZEN, using less resource than other published encoders, can achieve state-of-the-art performance on most tasks. Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data. The code and pre-trained models of ZEN are available at https://github.com/sinovation/zen.
The Effect of Video Playback Speed on Surgeon Technical Skill Perception
Venue: arXiv cs.HC 2019
Year: 2019

Purpose: Finding effective methods of discriminating surgeon technical skill is a complex problem to solve computationally. Previous research has shown non-expert crowd evaluations of surgical performances are as accurate as the gold standard, expert surgeon review. The aim of this research is to learn whether crowdsourced evaluators give higher ratings of technical skill to video of performances with increased playback speed, the use in discriminating skill levels, and if this increase is related to the evaluator consciously being aware that the video is being manually edited. Methods: A set of ten peg transfer videos (5 novices, 5 experts), were used to evaluate the perceived skill of the performers at each video playback speed used (0.4x-3.6x). Objective metrics used for measuring technical skill were also computed for comparison by manipulating the corresponding kinematic data of each performance. Two videos of an expert and novice performing dry lab laparoscopic trials of peg transfer tasks were used to obtain evaluations at each playback speed (0.2x-3.0x) of perception of whether a video is played at real-time playback speed or not. Results: We found that while both novices and experts had increased perceived technical skill as the video playback was increased, the amount of increase was significantly greater for experts. Each increase in playback speed by 0.4x was associated with, on average, a 0.72-point increase in the GOALS score (95% CI: 0.60-0.84 point increase; p < 0.001) for expert videos and only a 0.24-point increase in the GOALS score (95% CI: 0.13-0.36 point increase; p < 0.001) for novice videos. Conclusion: Due to the differential increase in perceived technical skill due to increased playback speed for experts, the difference between novice and expert skill levels of surgical performances may be more easily discerned by manually increasing the video playback speed.
Robustness and Imperceptibility Enhancement in Watermarked Images by Color Transformation
Venue: arXiv cs.MM 2019
Year: 2019

One of the effective methods for the preservation of copyright ownership of digital media is watermarking. Different watermarking techniques try to set a tradeoff between robustness and transparency of the process. In this research work, we have used color space conversion and frequency transform to achieve high robustness and transparency. Due to the distribution of image information in the RGB domain, we use the YUV color space, which concentrates the visual information in the Y channel. Embedding of the watermark is performed in the DCT coefficients of the specific wavelet subbands. Experimental results show high transparency and robustness of the proposed method.
Ten-year Survival Prediction for Breast Cancer Patients
Venue: arXiv cs.LG 2019
Year: 2019

This report assesses different machine learning approaches to 10-year survival prediction of breast cancer patients.
Enhanced Convolutional Neural Tangent Kernels
Venue: arXiv cs.LG 2019
Year: 2019

Recent research shows that for training with $\ell_2$ loss, convolutional neural networks (CNNs) whose width (number of channels in convolutional layers) goes to infinity correspond to regression with respect to the CNN Gaussian Process kernel (CNN-GP) if only the last layer is trained, and correspond to regression with respect to the Convolutional Neural Tangent Kernel (CNTK) if all layers are trained. An exact algorithm to compute CNTK (Arora et al., 2019) yielded the finding that classification accuracy of CNTK on CIFAR-10 is within 6-7% of that of that of the corresponding CNN architecture (best figure being around 78%) which is interesting performance for a fixed kernel. Here we show how to significantly enhance the performance of these kernels using two ideas. (1) Modifying the kernel using a new operation called Local Average Pooling (LAP) which preserves efficient computability of the kernel and inherits the spirit of standard data augmentation using pixel shifts. Earlier papers were unable to incorporate naive data augmentation because of the quadratic training cost of kernel regression. This idea is inspired by Global Average Pooling (GAP), which we show for CNN-GP and CNTK is equivalent to full translation data augmentation. (2) Representing the input image using a pre-processing technique proposed by Coates et al. (2011), which uses a single convolutional layer composed of random image patches. On CIFAR-10, the resulting kernel, CNN-GP with LAP and horizontal flip data augmentation, achieves 89% accuracy, matching the performance of AlexNet (Krizhevsky et al., 2012). Note that this is the best such result we know of for a classifier that is not a trained neural network. Similar improvements are obtained for Fashion-MNIST.