Reviewerly/exHarmony huggingface dataset:
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 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.
A Multiview Approach to Learning Articulated Motion Models

In order for robots to operate effectively in homes and workplaces, they must be able to manipulate the articulated objects common within environments built for and by humans. Kinematic models provide a concise representation of these objects that enable deliberate, generalizable manipulation policies. However, existing approaches to learning these models rely upon visual observations of an object’s motion, and are subject to the effects of occlusions and feature sparsity. Natural language descriptions provide a flexible and efficient means by which humans can provide complementary information in a weakly supervised manner suitable for a variety of different interactions (e.g., demonstrations and remote manipulation). In this paper, we present a multimodal learning framework that incorporates both vision and language information acquired in situ to estimate the structure and parameters that define kinematic models of articulated objects. The visual signal takes the form of an RGB-D image stream that opportunistically captures object motion in an unprepared scene. Accompanying natural language descriptions of the motion constitute the linguistic signal. We model linguistic information using a probabilistic graphical model that grounds natural language descriptions to their referent kinematic motion. By exploiting the complementary nature of the vision and language observations, our method infers correct kinematic models for various multiple-part objects on which the previous state-of-the-art, visual-only system fails. We evaluate our multimodal learning framework on a dataset comprised of a variety of household objects, and demonstrate a $$23\%$$ improvement in model accuracy over the vision-only baseline.
Cycle-SfM: Joint self-supervised learning of depth and camera motion from monocular image sequences

Understanding 3D scene geometry is a fundamental research topic in computer vision, including various subproblems, such as depth prediction, visual odometry, optical flow, etc. With the advent of artificial intelligence methods like deep learning, many approaches have emerged to deal with such problems in an end-to-end manner. These pipelines take the 3D understanding task as a nonlinear optimization problem, with the purpose of minimizing the cost function of the whole framework. Here, we present a self-supervised framework for jointly learning the monocular depth and camera’s ego-motion from unlabeled, unstructured, and monocular video sequences. We propose a forward-backward consistency constraint on view reconstruction to capture temporal relations across adjacent frames, whose purpose is to explore and make full use of the bidirectional projection information. A simple and practicable improvement on the design of cost function is proposed to enhance the estimated accuracy. Due to the fact that our improvement is a lightweight and general module, it can be integrated into any self-supervised architectures seamlessly, and more accurate results can be obtained. The evaluation on the KITTI dataset demonstrates that our approach is highly efficient and performs better than the existing works in pose estimation, while the results in depth estimation perform comparably with the existing ones.
BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES

Defenses against adversarial attacks can be classified into certified and non-certified. Certifiable defenses make networks robust within a certain $\ell_p$-bounded radius, so that it is impossible for the adversary to make adversarial examples in the certificate bound. We present an attack that maintains the imperceptibility property of adversarial examples while being outside of the certified radius. Furthermore, the proposed Shadow Attack can fool certifiably robust networks by producing an imperceptible adversarial example that gets misclassified and produces a strong ``spoofed'' certificate.
Application of Adversarial Examples in Communication Modulation Classification

Cognitive radio technology is an important branch in the field of wireless communication, and automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is a major part of cognitive radio technology. Adversarial examples can reduce the performance of a deep learning-based AMC system with a hardly perceptible perturbation, which can prevent the signal being intercepted by non-cooperative deep learning-based signal receiver. In this paper, we test the attack efficiency of two kinds of adversarial examples in dataset RML2016.04C, and discuss the reason why adversarial examples can attack the deep learning-based AMC successfully, verifying the underfitting of networks of AMC is ubiquitous. Modulations which are similar in time domain and frequency domain, such as {8PSK, QPSK}, {QAM16, QAM64}, {AM-DSB, WBFM}, are easily vulnerable to adversarial examples.
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting.

In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many Convolutional Neural Networks (CNN) are designed for tackling this task. However, currently released datasets are so small-scale that they can not meet the needs of the supervised CNN-based algorithms. To remedy this problem, we construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (0~20,033). Besides, a benchmark website is developed for impartially evaluating the different methods, which allows researchers to submit the results of the test set. Based on the proposed dataset, we further describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data. What's more, the benchmark is deployed at \url{this https URL}, and the dataset/code/models/results are available at \url{this https URL}.
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP

Catastrophic forgetting — whereby a model trained on one task is fine-tuned on a second, and in doing so, suffers a “catastrophic” drop in performance over the first task — is a hurdle in the development of better transfer learning techniques. Despite impressive progress in reducing catastrophic forgetting, we have limited understanding of how different architectures and hyper-parameters affect forgetting in a network. With this study, we aim to understand factors which cause forgetting during sequential training. Our primary finding is that CNNs forget less than LSTMs. We show that max-pooling is the underlying operation which helps CNNs alleviate forgetting compared to LSTMs. We also found that curriculum learning, placing a hard task towards the end of task sequence, reduces forgetting. We analysed the effect of fine-tuning contextual embeddings on catastrophic forgetting and found that using embeddings as feature extractor is preferable to fine-tuning in continual learning setup.
A Privacy Preservation Based Scheme for Task Assignment in Internet of Things

With the rapid increasing demands of social services, mobile crowd sensing (MCS) is getting growing attraction with the wide applicability of mobile applications. This effective paradigm depends on sensing services from crowd contributors for sensing data collection and sharing. However, on one hand, the sensing data is tampered by malicious users without the reputation evaluation. On the other hand, the privacy information is exposed when mobile users submit sensing data to others. Moreover, the efficiency of task assignment is low without incentives. Therefore, we propose a privacy preservation based scheme for task assignment in internet of things (IoT) to improve the performance of crowd sensing. Firstly, a novel trusted framework is developed to judge mobile user's reputation value. In the proposed framework, the reputation evaluation scheme is established based on the social trust, recommendation credibility, and history evaluation. Secondly, the location-privacy preservation algorithm (LPA) based differential privacy method is designed to protect mobile user's location privacy and select the credible users to complete sensing tasks. Thirdly, to incentivize mobile users, we model the transaction process between mobile requesters and mobile responders as the first-order sealed price auction game. After that, the task assignment based bidding-preferred (TAB) algorithm is proposed to assign tasks to mobile users. Finally, simulation results demonstrate that the proposed scheme can effectively improve the number of finished tasks and bring more utilities to mobile users compared with the conventional methods.
An Internet of Things Fog-Assisted Sleep-Deprivation Prediction Framework for Spinal Cord Injury Patients

Sleep deprivation is one of the most underdiagnosed ailments in patients with spinal cord injuries, causing anxiety and leading to physical and mental imbalance. To address this issue, we propose a fog-assisted smart monitoring framework to detect patients' health and environmental conditions in real time.
High-speed three-dimensional absolute shape measurement with three projected binary patterns

Reducing the number of structured patterns for three-dimensional (3-D) reconstruction is of great importance for high-speed 3-D shape measurement. We present a method that reconstructs absolute 3-D shape using three projected binary patterns: one direct current (DC), one low-frequency, and one high-frequency fringe pattern. The procedures are (1) take the difference between the sinusoidal fringe patterns and the DC pattern; (2) apply Hilbert transform to the difference images to generate two phase maps; (3) employ the geometric-constraint-based phase unwrapping method to unwrap the low-frequency phase map; (4) unwrap the high-frequency phase map using the unwrapped low-frequency phase map; and (5) reconstruct 3-D shape. We developed a prototype system that can capture two-dimensional images at 6000 Hz, achieving 2000 Hz 3-D shape measurement speed.
Simultaneous Localization and Parameter Estimation for Single Particle Tracking via Sigma Points based EM

Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop an algorithm for jointly estimating both particle trajectory and motion model parameters from the data. Our approach uses Expectation Maximization (EM) combined with an Unscented Kalman filter (UKF) and an Unscented Rauch-Tung-Striebel smoother (URTSS), allowing us to use an accurate, nonlinear model of the observations acquired by the camera. Due to the shot noise characteristics of the photon generation process, this model uses a Poisson distribution to capture the measurement noise inherent in imaging. In order to apply a UKF, we first must transform the measurements into a model with additive Gaussian noise. We consider two approaches, one based on variance stabilizing transformations (where we compare the Anscombe and Freeman-Tukey transforms) and one on a Gaussian approximation to the Poisson distribution. Through simulations, we demonstrate efficacy of the approach and explore the differences among these measurement transformations.
Alexandria: A Proof-of-Concept Implementation and Evaluation of Generalised Data Deduplication

The amount of data generated worldwide is expected to grow from 33 to 175 ZB by 2025 in part driven by the growth of Internet of Things (IoT) and cyber-physical systems (CPS). To cope with this enormous amount of data, new cloud storage techniques must be developed. Generalised Data Deduplication (GDD) is a new paradigm for reducing the cost of storage by systematically identifying near identical data chunks, storing their common component once, and a compact representation of the deviation to the original chunk for each chunk. This paper presents a system architecture for GDD and a proof-of-concept implementation. We evaluated the compression gain of Generalised Data Deduplication using three data sets of varying size and content and compared to the performance of the EXT4 and ZFS file systems, where the latter employs classic deduplication. We show that Generalised Data Deduplication provide up to 16.75% compression gain compared to both EXT4 and ZFS with data sets with less than 5 GB of data.
Quantum Inspire: QuTech’s platform for co-development and collaboration in quantum computing

The mission of QuTech is to bring quantum technology to industry and society by translating fundamental scientific research into applied research. To this end we are developing Quantum Inspire (QI), a full-stack quantum computer prototype for future co-development and collaborative R&amp;D in quantum computing. A prerelease of this prototype system is already offering the public cloud-based access to QuTech technologies such as a programmable quantum computer simulator (with up to 31 qubits) and tutorials and user background knowledge on quantum information science (www.quantum-inspire.com). Access to a programmable CMOS-compatible Silicon spin qubit-based quantum processor will be provided in the next deployment phase. The first generation of QI’s quantum processors consists of a double quantum dot hosted in an in-house grown SiGe/<sup>28</sup>Si/SiGe heterostructure, and defined with a single layer of Al gates. Here we give an overview of important aspects of the QI full-stack. We illustrate QI’s modular system architecture and we will touch on parts of the manufacturing and electrical characterization of its first generation two spin qubit quantum processor unit. We close with a section on QI’s qubit calibration framework. The definition of a single qubit Pauli X gate is chosen as concrete example of the matching of an experiment to a component of the circuit model for quantum computation.
Parallel Batch-Dynamic <i>k</i>-Clique Counting

In this paper, we study new batch-dynamic algorithms for the k-clique counting problem, which are dynamic algorithms where the updates are batches of edge insertions and deletions. We study this problem in the parallel setting, where the goal is to obtain algorithms with low (polylogarithmic) depth. Our first result is a new parallel batch-dynamic triangle counting algorithm with amortized work and O(log∗ (Δ + m)) depth with high probability, and O(Δ + m) space for a batch of Δ edge insertions or deletions. Our second result is an algebraic algorithm based on parallel fast matrix multiplication. Assuming that a parallel fast matrix multiplication algorithm exists with parallel matrix multiplication constant Ωp, the same algorithm solves dynamic k-clique counting with amortized work and depth with high probability, and space. Using a recently developed parallel k-clique counting algorithm, we also obtain a simple batch-dynamic algorithm for k-clique counting on graphs with arboricity α running in expected work and O(logk–2 n) depth with high probability, and O(m + Δ) space. Finally, we present a multicore CPU implementation of our parallel batch-dynamic triangle counting algorithm. On a 72-core machine with two-way hyper-threading, our implementation achieves 36.54-74.73x parallel speedup, and in certain cases achieves significant speedups over existing parallel algorithms for the problem, which are not theoretically-efficient.
KubeSphere: An Approach to Multi-Tenant Fair Scheduling for Kubernetes Clusters

In a multi-tenant environment, users' resource demands must be understood by cluster administrators to efficiently and fairly share cluster resources without hindering performance. Kubernetes is a container orchestration system that enables users to share cluster resources, such as CPU, memory, and disk, for the execution of their tasks. Kubernetes provides a monolithic scheduler to make scheduling decisions for all users in a multi-tenant shared cluster. Kube-batch enables Kubernetes to make a scheduling decision based on a multi-resource fairness policy called Dominant Resource Fairness (DRF). DRF has been proven to be a successful mechanism for fine-grained resource allocation. However, it does not incorporate other fairness aspects of a shared cluster. Our fairness metrics take into account the use of DRF along with a task's resource demand and average waiting time. We have developed a policy-driven meta-scheduler, KubeSphere, for a Kubernetes cluster where tasks for individual users can be scheduled based on each user's overall resource demands and current resource consumption. Our experimental results show how the dominant share of a task along with the overall resource demand can improve fairness in a multi-tenant cluster.
OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision

Omnidirectional and 360{\deg} images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the environment from only an image. However, the distortion of these images requires the development of specific algorithms for their treatment and interpretation. Moreover, a high number of images is essential for the correct training of computer vision algorithms based on learning. In this paper, we present a tool for generating datasets of omnidirectional images with semantic and depth information. These images are synthesized from a set of captures that are acquired in a realistic virtual environment for Unreal Engine 4 through an interface plugin. We gather a variety of well-known projection models such as equirectangular and cylindrical panoramas, different fish-eye lenses, catadioptric systems, and empiric models. Furthermore, we include in our tool photorealistic non-central-projection systems as non-central panoramas and non-central catadioptric systems. As far as we know, this is the first reported tool for generating photorealistic non-central images in the literature. Moreover, since the omnidirectional images are made virtually, we provide pixel-wise information about semantics and depth as well as perfect knowledge of the calibration parameters of the cameras. This allows the creation of ground-truth information with pixel precision for training learning algorithms and testing 3D vision approaches. To validate the proposed tool, different computer vision algorithms are tested as line extractions from dioptric and catadioptric central images, 3D Layout recovery and SLAM using equirectangular panoramas, and 3D reconstruction from non-central panoramas.
Humanizing the Chatbot with Semantics based Natural Language Generation

This paper introduces approach made for improving the efficiency of the chatbot or artificial conversational entity used in various commercial and banking sector. Humanizing is to improving the response generation ability of the chatbot. In this work, an attempt has been made to generate more natural response for a question asked to an artificial conversational entity by using various Natural Language Processing (NLP) and Natural Language Generation (NLG) techniques. Paraphrase generation plays a main role by generating semantically similar response for a query making it more natural.
Modeling the transmittance of anisotropic diffuse radiation towards estimating energy losses in solar panel coverings

The performance assessment of photovoltaic arrays involves estimating the energy that is lost due to different sources, such as optical losses in the protective coverings. These transparent coatings do not transmit all incoming solar radiation due to partial reflections and absorptions that intensify with the angle of solar incidence. In the case of beam radiation, which consists of parallel rays with a deterministic incidence, the transmittance can be assessed with well-known analytical models. Alternately, more complex diffuse radiation consists of scattered rays with stochastic and anisotropic distributions. Consequently, the transmittance of diffuse radiation is commonly ignored or oversimplified with limited models. This work presents a new set of general models for estimating the transmittance of anisotropic diffuse radiation. Transmittance was determined from surface integrals models solved over the geometrical regions corresponding to the diffuse radiation components, i.e., sky isotropic, circumsolar, horizon brightening, and albedo. This approach was validated against stochastic rays simulations, which converged with RMSE below 0.05% transmittance. The surface integrals were then solved for the relevant discretized input ranges, and regression models were fitted to capture the resulting behavior. With high flexibility, the inputs of these regression models allow for specifying the covering characteristics and the sun and panel angular positions. The proposed models allowed us to incorporate the estimation of transmittance losses to a broader energy harvesting analysis. In a case study, the effect on annual energy was calculated comparing four transmittance modeling approaches with three different covering specifications, three panel inclinations, three sky models, and in two locations. The range of variation due to the covering specifications can result in energy losses of 16.33% compared to using a covering with the highest transmittance. The findings and methods presented in this work have implications in areas ranging from the modeling of energy harvesting to the design and development of solar panels and protective transparent coatings.