Reviewerly/exHarmony huggingface dataset:
Unbabel’s Submission to the WMT2019 APE Shared Task: BERT-Based Encoder-Decoder for Automatic Post-Editing

This paper describes Unbabel's submission to the WMT2019 APE Shared Task for the English-German language pair.Following the recent rise of large, powerful, pretrained models, we adapt the BERT pretrained model to perform Automatic Post-Editing in an encoder-decoder framework.Analogously to dual-encoder architectures we develop a BERT-based encoder-decoder (BED) model in which a single pretrained BERT encoder receives both the source src and machine translation mt strings.Furthermore, we explore a conservativeness factor to constrain the APE system to perform fewer edits.As the official results show, when trained on a weighted combination of in-domain and artificial training data, our BED system with the conservativeness penalty improves significantly the translations of a strong Neural Machine Translation (NMT) system by -0.78 and +1.23 in terms of TER and BLEU, respectively.Finally, our submission achieves a new state-of-the-art, exaequo, in English-German APE of NMT.
User Experience Guidelines for Designing HMD Extended Reality Applications

With the rise of Extended Reality (XR) technologies, such as head mounted displays (HMD) for Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR), designers are presented with many unique challenges and opportunities when creating applications. Publications can be found from research and industry that offer insights and ideas surrounding user experience (UX) for XR applications. However, these publications often vary in format and content. Based on a thorough analysis of 68 different resources from research, industry, and 2D design, we present a set of eleven UX guidelines for designing XR applications. Our work serves as a reference to the literature for understanding what others have tried and discovered and provides an integrated set of guidelines. Furthermore, our guidelines offer guidance to a software developer to aid in the design of XR applications for HMD devices.
A Bayesian Approach for Sequence Tagging with Crowds

Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data.However, annotators are often unreliable and current aggregation methods cannot capture common types of span annotation errors.To address this, we propose a Bayesian method for aggregating sequence tags that reduces errors by modelling sequential dependencies between the annotations as well as the ground-truth labels.By taking a Bayesian approach, we account for uncertainty in the model due to both annotator errors and the lack of data for modelling annotators who complete few tasks.We evaluate our model on crowdsourced data for named entity recognition, information extraction and argument mining, showing that our sequential model outperforms the previous state of the art.We also find that our approach can reduce crowdsourcing costs through more effective active learning, as it better captures uncertainty in the sequence labels when there are few annotations.
SLA-Based Sharing Economy Service with Smart Contract for Resource Integrity in the Internet of Things

Recently, technology startups have leveraged the potential of blockchain-based technologies to govern institutions or interpersonal trust by enforcing signed treaties among different individuals in a decentralized environment. However, it is going to be hard enough convincing that the blockchain technology could completely replace the trust among trading partners in the sharing economy as sharing services always operate in a highly dynamic environment. With the rapid expanding of the rental market, the sharing economy faces more and more severe challenges in the form of regulatory uncertainty and concerns about abuses. This paper proposes an enhanced decentralized sharing economy service using the service level agreement (SLA), which documents the services the provider will furnish and defines the service standards the provider is obligated to meet. The SLA specifications are defined as the smart contract, which facilitates multi-user collaboration and automates the process with no involvement of the third party. To demonstrate the usability of the proposed solution in the sharing economy, a notebook sharing case study is implemented using the Hyperledger Fabric. The functionalities of the smart contract are tested using the Hyperledger Composer. Moreover, the efficiency of the designed approach is demonstrated through a series of experimental tests using different performance metrics.
Cumulative Adaptation for BLSTM Acoustic Models

This paper addresses the robust speech recognition problem as an adaptation task. Specifically, we investigate the cumulative application of adaptation methods. A bidirectional Long Short-Term Memory (BLSTM) based neural network, capable of learning temporal relationships and translation invariant representations, is used for robust acoustic modelling. Further, i-vectors were used as an input to the neural network to perform instantaneous speaker and environment adaptation, providing 8\% relative improvement in word error rate on the NIST Hub5 2000 evaluation test set. By enhancing the first-pass i-vector based adaptation with a second-pass adaptation using speaker and environment dependent transformations within the network, a further relative improvement of 5\% in word error rate was achieved. We have reevaluated the features used to estimate i-vectors and their normalization to achieve the best performance in a modern large scale automatic speech recognition system.
Cognitive Machine Learning

Cognitive machine learning refers to the combination of machine learning and brain cognitive mechanism, specifically, combining machine learning with mind model CAM.Three research directions are proposed in this paper, that is, emergency of learning, complementary learning system and evolution of learning.
A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation

Most neural machine translation systems are built upon subword units extracted by methods such as Byte-Pair Encoding (BPE) or wordpiece. However, the choice of number of merge operations is generally made by following existing recipes. In this paper, we conduct a systematic exploration on different numbers of BPE merge operations to understand how it interacts with the model architecture, the strategy to build vocabularies and the language pair. Our exploration could provide guidance for selecting proper BPE configurations in the future. Most prominently: we show that for LSTM-based architectures, it is necessary to experiment with a wide range of different BPE operations as there is no typical optimal BPE configuration, whereas for Transformer architectures, smaller BPE size tends to be a typically optimal choice. We urge the community to make prudent choices with subword merge operations, as our experiments indicate that a sub-optimal BPE configuration alone could easily reduce the system performance by 3-4 BLEU points.
A Filter for SAR Image Despeckling Using Pre-Trained Convolutional Neural Network Model

Despeckling is a longstanding topic in synthetic aperture radar (SAR) images. Recently, many convolutional neural network (CNN) based methods have been proposed and shown state-of-the-art performance for SAR despeckling problem. However, these CNN based methods always need many training data or can only deal with specific noise level. To solve these problems, we directly embed an efficient CNN pre-trained model for additive white Gaussian noise (AWGN) with Multi-channel Logarithm with Gaussian denoising (MuLoG) algorithm to deal with the multiplicative noise in SAR images. This flexible pre-trained CNN model takes the noise level as input, thus only a single pre-trained model is needed to deal with different noise levels. We also use a detector to find the homogeneous region automatically to estimate the noise level of image as input. Embedded with MuLoG, our proposed filter can despeckle not only single channel but also multi-channel SAR images. Finally, both simulated and real (Pol)SAR images were tested in experiments, and the results show that the proposed method has better and more robust performance than others.
Learning on the Edge: Investigating Boundary Filters in CNNs

Abstract Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as , or padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of boundary filters. At testing-time, we use these filters which have learned to extrapolate features at the boundary in an optimal way for the specific task. Our extensive evaluation, over a wide range of architectural changes (variations of layers, feature channels, or both), shows how the filters result in improved boundary handling. Furthermore, we investigate the efficacy of variations of such boundary filters with respect to convergence speed and accuracy. Finally, we demonstrate an improvement of 5–20% across the board of typical CNN applications (colorization, de-Bayering, optical flow, disparity estimation, and super-resolution). Supplementary material and code can be downloaded from the project page: http://geometry.cs.ucl.ac.uk/projects/2019/investigating-edge/ .
Prediction of blood glucose concentration for type 1 diabetes based on echo state networks embedded with incremental learning

Valid prediction of blood glucose concentration can help people to manage diabetes mellitus, alert hypoglycemia/hyperglycemia, exploit artificial pancreas, and plan a treatment program. Along the development of continuous glucose monitoring system (CGMS), the massive historical data require a new modeling framework based on a data-driven perspective. Studies indicate that the glucose time series (i.e., CGMS readings) involve chaotic properties; therefore, echo state networks (ESN) and its improved variants are proposed to establish subject-specific prediction models owing to their superiority in processing chaotic systems. This study mainly has two innovations: (1) a novel combination of incremental learning and ESN is developed to obtain a suitable network structure through partial optimization of parameters; (2) a feedback ESN is proposed to excavate the relationship of different predictions. These methods are assessed on ten patients with diabetes mellitus. Experimental results substantiate that the proposed methods achieve superior prediction performance in terms of four evaluation metrics compared with three conventional methods.
Are augmented reality headsets in surgery a dead end?

In performing the surgical procedure, the surgeon must always mentally merge a huge amount of patient-specific information about the pathology and the surrounding healthy tissues. This whole proces...
TAP: A static analysis model for PHP vulnerabilities based on token and deep learning technology

With the widespread usage of Web applications, the security issues of source code are increasing. The exposed vulnerabilities seriously endanger the interests of service providers and customers. There are some models for solving this problem. However, most of them rely on complex graphs generated from source code or regex patterns based on expert experience. In this paper, TAP, which is based on token mechanism and deep learning technology, was proposed as an analysis model to discover the vulnerabilities of PHP: Hypertext Preprocessor (PHP) Web programs conveniently and easily. Based on the token mechanism of PHP language, a custom tokenizer was designed, and it unifies tokens, supports some features of PHP and optimizes the parsing. Besides, the tokenizer also implements parameter iteration to achieve data flow analysis. On the Software Assurance Reference Dataset(SARD) and SQLI-LABS dataset, we trained the deep learning model of TAP by combining the word2vec model with Long Short-Term Memory (LSTM) network algorithm. According to the experiment on the dataset of CWE-89, TAP not only achieves the 0.9941 Area Under the Curve(AUC), which is better than other models, but also achieves the highest accuracy: 0.9787. Further, compared with RIPS, TAP shows much better in multiclass classification with 0.8319 Kappa and 0.0840 hamming distance.
Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention

To enjoy more social network services, users nowadays are usually involved in multiple online sites at the same time. Aligned social networks provide more information to alleviate the problem of data insufficiency. In this paper, we target on the collective link prediction problem and aim to predict both the intra-network social links as well as the inter-network anchor links across multiple aligned social networks. It is not an easy task, and the major challenges involve the network characteristic difference problem and different directivity properties of the social and anchor links to be predicted. To address the problem, we propose an application oriented network embedding framework, Hierarchical Graph Attention based Network Embedding (HGANE), for collective link prediction over directed aligned networks. Very different from the conventional general network embedding models, HGANE effectively incorporates the collective link prediction task objectives into consideration. It learns the representations of nodes by aggregating information from both the intra-network neighbors (connected by social links) and inter-network partners (connected by anchor links). What's more, we introduce a hierarchical graph attention mechanism for the intra-network neighbors and inter-network partners respectively, which resolves the network characteristic differences and the link directivity challenges effectively. Extensive experiments have been conducted on real-world aligned networks datasets to demonstrate that our model outperformed the state-of-the-art baseline methods in addressing the collective link prediction problem by a large margin.
Agent, Gatekeeper, Drug Dealer

Online content creators have to manage their relations with opaque, proprietary algorithms that platforms employ to rank, filter, and recommend content. How do content creators make sense of these algorithms and what does that teach us about the roles that algorithms play in the social world? We take the case of YouTube because of its widespread use and the spaces for collective sense-making and mutual aid that content creators (YouTubers) have built within the last decade. We engaged with YouTubers in one-on-one interviews, performed content analysis on YouTube videos that discuss the algorithm, and conducted a wiki survey on YouTuber online groups. This triangulation of methodologies afforded us a rich understanding of content creators' understandings, priorities, and wishes as they relate to the algorithm. We found that YouTubers assign human characteristics to the algorithm to explain its behavior; what we have termed algorithmic personas. We identify three main algorithmic personas on YouTube: Agent, Gatekeeper, and Drug Dealer. We propose algorithmic personas as a conceptual framework that describes the new roles that algorithmic systems take on in the social world. As we face new challenges around the ethics and politics of algorithmic platforms such as YouTube, algorithmic personas describe roles that are familiar and can help develop our understanding of algorithmic power relations and accountability mechanisms.
Private Hypothesis Selection

We provide a differentially private algorithm for hypothesis selection. Given samples from an unknown probability distribution $P$ and a set of $m$ probability distributions $\mathcal{H}$, the goal is to output, in a $\varepsilon$-differentially private manner, a distribution from $\mathcal{H}$ whose total variation distance to $P$ is comparable to that of the best such distribution (which we denote by $\alpha$). The sample complexity of our basic algorithm is $O\left(\frac{\log m}{\alpha^2} + \frac{\log m}{\alpha \varepsilon}\right)$, representing a minimal cost for privacy when compared to the non-private algorithm. We also can handle infinite hypothesis classes $\mathcal{H}$ by relaxing to $(\varepsilon,\delta)$-differential privacy. We apply our hypothesis selection algorithm to give learning algorithms for a number of natural distribution classes, including Gaussians, product distributions, sums of independent random variables, piecewise polynomials, and mixture classes. Our hypothesis selection procedure allows us to generically convert a cover for a class to a learning algorithm, complementing known learning lower bounds which are in terms of the size of the packing number of the class. As the covering and packing numbers are often closely related, for constant $\alpha$, our algorithms achieve the optimal sample complexity for many classes of interest. Finally, we describe an application to private distribution-free PAC learning.
Adaptive algorithm for quantum circuit simulation

Efficient simulation of quantum computers is essential for the development and validation of near-term quantum devices and the research on quantum algorithms. Up to date, two main approaches to simulation have been use, based on either full-state or single-amplitude evaluation. We propose an algorithm that efficiently interpolates between these two possibilities. Our approach elucidates the connection between quantum circuit simulation and partial evaluation of expressions in tensor algebra.
Systematically localizable operators for quantum simulations of quantum field theories

Correlations and measures of entanglement in ground state wavefunctions of relativistic quantum field theories are spatially localized over length scales set by the mass of the lightest particle. We utilize this localization to design digital quantum circuits for preparing the ground states of lattice scalar quantum field theories. Controlled rotations that are exponentially localized in their position-space extent are found to provide exponentially convergent wavefunction fidelity. These angles scale with the correlation between sites and the classical two-point correlation function, as opposed to the more localized mutual information or the hyper-localized negativity. We anticipate that further investigations will uncover quantum circuit designs with controlled rotations dictated by the measures of entanglement. This work is expected to impact quantum simulations of systems of importance to nuclear physics, high-energy physics, and basic energy sciences research.
Real-Time Multi-person Motion Capture from Multi-view Video and IMUs

Abstract A real-time motion capture system is presented which uses input from multiple standard video cameras and inertial measurement units (IMUs). The system is able to track multiple people simultaneously and requires no optical markers, specialized infra-red cameras or foreground/background segmentation, making it applicable to general indoor and outdoor scenarios with dynamic backgrounds and lighting. To overcome limitations of prior video or IMU-only approaches, we propose to use flexible combinations of multiple-view, calibrated video and IMU input along with a pose prior in an online optimization-based framework, which allows the full 6-DoF motion to be recovered including axial rotation of limbs and drift-free global position. A method for sorting and assigning raw input 2D keypoint detections into corresponding subjects is presented which facilitates multi-person tracking and rejection of any bystanders in the scene. The approach is evaluated on data from several indoor and outdoor capture environments with one or more subjects and the trade-off between input sparsity and tracking performance is discussed. State-of-the-art pose estimation performance is obtained on the Total Capture (mutli-view video and IMU) and Human 3.6M (multi-view video) datasets. Finally, a live demonstrator for the approach is presented showing real-time capture, solving and character animation using a light-weight, commodity hardware setup.
Zero-shot Text Classification With Generative Language Models

This work investigates the use of natural language to enable zero-shot model adaptation to new tasks. We use text and metadata from social commenting platforms as a source for a simple pretraining task. We then provide the language model with natural language descriptions of classification tasks as input and train it to generate the correct answer in natural language via a language modeling objective. This allows the model to generalize to new classification tasks without the need for multiple multitask classification heads. We show the zero-shot performance of these generative language models, trained with weak supervision, on six benchmark text classification datasets from the torchtext library. Despite no access to training data, we achieve up to a 45% absolute improvement in classification accuracy over random or majority class baselines. These results show that natural language can serve as simple and powerful descriptors for task adaptation. We believe this points the way to new metalearning strategies for text problems.
Improving employees’ intellectual capacity for cybersecurity through evidence-based malware training

Purpose An organization’s ability to successfully manage intellectual capital is determined by the actions of its employees to prevent or minimize information security incidents. To prevent more data breaches to intellectual capital, organizations must provide regular cybersecurity awareness training for all personnel. The purpose of this paper is to investigate the effect of different evidence-based cybersecurity training methods on employees’ cybersecurity risk perception and self-reported behavior. Design/methodology/approach The study participants were randomly assigned into four groups (i.e. malware report, malware videos, both malware report and malware videos and no interventions) to assess the effects of cybersecurity training on their perceptions of vulnerability, severity, self-efficacy, security intention as well as their self-reported cybersecurity behaviors. Findings The results show that evidence-based malware report is a relatively better training method in affecting employees’ intentions of engaging in recommended cybersecurity behaviors comparing with the other training methods used in this study. A closer analysis suggests whether the training method contains self-relevant information could make a difference to the training effects. Originality/value This paper reports an in-depth investigation on how different evidence-based cybersecurity training methods impact employees’ perceptions of susceptibility, severity, self-efficacy, security intention as well as on their self-reported cybersecurity behaviors.
Regional Tree Regularization for Interpretability in Deep Neural Networks

The lack of interpretability remains a barrier to adopting deep neural networks across many safety-critical domains. Tree regularization was recently proposed to encourage a deep neural network's decisions to resemble those of a globally compact, axis-aligned decision tree. However, it is often unreasonable to expect a single tree to predict well across all possible inputs. In practice, doing so could lead to neither interpretable nor performant optima. To address this issue, we propose regional tree regularization – a method that encourages a deep model to be well-approximated by several separate decision trees specific to predefined regions of the input space. Across many datasets, including two healthcare applications, we show our approach delivers simpler explanations than other regularization schemes without compromising accuracy. Specifically, our regional regularizer finds many more “desirable” optima compared to global analogues.