Reviewerly/exHarmony huggingface dataset:
Warwick Image Forensics Dataset for Device Fingerprinting in Multimedia Forensics

Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multi-frame photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community.
Are we Estimating or Guesstimating Translation Quality?

Recent advances in pre-trained multilingual language models lead to state-of-the-art results on the task of quality estimation (QE) for machine translation.A carefully engineered ensemble of such models won the QE shared task at WMT19.Our in-depth analysis, however, shows that the success of using pre-trained language models for QE is overestimated due to three issues we observed in current QE datasets: (i) The distributions of quality scores are imbalanced and skewed towards good quality scores; (ii) QE models can perform well on these datasets while looking at only source or translated sentences; (iii) They contain statistical artifacts that correlate well with human-annotated QE labels.Our findings suggest that although QE models might capture fluency of translated sentences and complexity of source sentences, they cannot model adequacy of translations effectively.
MSFSR: A Multi-Stage Face Super-Resolution with Accurate Facial Representation via Enhanced Facial Boundaries

The majority of Face Super-Resolution (FSR) approaches apply specific facial priors as guidance in super-resolving the given low-resolution (LR) into high-resolution (HR) images. To improve the FSR performance, various kinds of facial representations were explored in the past decades. Nevertheless, there remains a challenge in estimating high-quality facial representations for LR images. To address this problem, we propose novel facial representation - enhanced facial boundaries. By semantically connecting the facial landmark points, enhanced facial boundaries retain rich semantic information and are robust to different spatial resolution scales. Based on the enhanced facial boundaries, we design a novel Multi-Stage FSR (MS-FSR) approach, which applies the multi-stage strategy to recover high-quality face images progressively. The enhanced facial boundaries and the coarse-to-fine supervision facilitate the facial boundaries estimation process in producing high quality facial representation. The one-time projection of the FSR task is decomposed into multiple simpler sub-processes. In these ways, the MSFSR estimates a more robust facial representation and achieves better performance. Experimental results indicate the superiority of our approach to the state-of-the-art approaches in both qualitative and quantitative measurements.
Examining the State-of-the-Art in News Timeline Summarization

Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved.This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task.In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the stateof-the-art on all tested benchmarks.For a more robust evaluation, we also present a new TLS dataset, which is larger and spans longer time periods than previous datasets.
Temporal continuity of visual attention for future gaze prediction in immersive virtual reality

Eye tracking technology is receiving increased attention in the field of virtual reality. Specifically, future gaze prediction is crucial in pre-computation for many applications such as gaze-contingent rendering, advertisement placement, and content-based design. To explore future gaze prediction, it is necessary to analyze the temporal continuity of visual attention in immersive virtual reality. In this paper, the concept of temporal continuity of visual attention is presented. Subsequently, an autocorrelation function method is proposed to evaluate the temporal continuity. Thereafter, the temporal continuity is analyzed in both free-viewing and task-oriented conditions. Specifically, in free-viewing conditions, the analysis of a free-viewing gaze dataset indicates that the temporal continuity performs well only within a short time interval. A task-oriented game scene condition was created and conducted to collect users' gaze data. An analysis of the collected gaze data finds the temporal continuity has a similar performance with that of the free-viewing conditions. Temporal continuity can be applied to future gaze prediction and if it is good, users' current gaze positions can be directly utilized to predict their gaze positions in the future. The current gaze's future prediction performances are further evaluated in both free-viewing and task-oriented conditions and discover that the current gaze can be efficiently applied to the task of short-term future gaze prediction. The task of long-term gaze prediction still remains to be explored.
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

Neural architecture search (NAS) has become an important approach to automatically find effective architectures. To cover all possible good architectures, we need to search in an extremely large search space with billions of candidate architectures. More critically, given a large search space, we may face a very challenging issue of space explosion. However, due to the limitation of computational resources, we can only sample a very small proportion of the architectures, which provides insufficient information for the training. As a result, existing methods may often produce suboptimal architectures. To alleviate this issue, we propose a curriculum search method that starts from a small search space and gradually incorporates the learned knowledge to guide the search in a large space. With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method significantly improves the search efficiency and finds better architectures than existing NAS methods. Extensive experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of the proposed method.
Label-Noise Robust Domain Adaptation.

Domain adaptation aims to correct the classifiers when faced with distribution shift between source (training) and target (test) domains. State-of-the-art domain adaptation methods make use of deep networks to extract domain-invariant representations. However, existing methods assume that all the instances in the source domain are correctly labeled; while in reality, it is unsurprising that we may obtain a source domain with noisy labels. In this paper, we are the first to comprehensively investigate how label noise could adversely affect existing domain adaptation methods in various scenarios. Further, we theoretically prove that there exists a method that can essentially reduce the side-effect of noisy source labels in domain adaptation. Specifically, focusing on the generalized target shift scenario, where both label distribution PY and the class-conditional distribution PX|Y can change, we discover that the denoising Conditional Invariant Component (DCIC) framework can provably ensures (1) extracting invariant representations given examples with noisy labels in the source domain and unlabeled examples in the target domain and (2) estimating the label distribution in the target domain with no bias. Experimental results on both synthetic and real-world data verify the effectiveness of the proposed method.
Context-Aware Generation-Based Net For Multi-Label Visual Emotion Recognition

Visual Emotion Recognition has attracted more and more research attention in recent years. Existing approaches mainly depend on facial expression or analyze the whole image between positive and negative. Actually, people can recognize multiple emotions from one image based on global and 10-cal information. In this paper, we propose a Context-Aware Generation-Based Net (CAGBN), a novel architecture that makes full use of global and local information of the image by considering both the whole image and details of the target person. Inspired by psychological studies that when viewing a person in his situation, we tend to give judgments gradually rather than assign all labels at the same time, CAGBN transforms the multi-label classification problem into a sequence generation task for better recognition. Extensive experimental results on the emotion recognition dataset demonstrate the superiority and rationality of CAGBN.
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

Dynamic medical images are often limited in its application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the volume samples in the dynamic sequence by interpolating the volumes between the acquired samples. However, these methods are limited to either 2D images and/or are unable to support large but periodic variations in the functional motion between the image volume samples. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: the first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from a pair of image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures. We also introduce an adaptive multi-scale architecture to capture the volumetric large anatomy motions. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation method that has been extended to support volumetric images. Code is available at [1].
Learning Human Objectives by Evaluating Hypothetical Behavior

We seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function. We start with a generative model of initial states and a forward dynamics model trained on off-policy data. Our method uses these models to synthesize hypothetical behaviors, asks the user to label the behaviors with rewards, and trains a neural network to predict the rewards. The key idea is to actively synthesize the hypothetical behaviors from scratch by maximizing tractable proxies for the value of information, without interacting with the environment. We call this method reward query synthesis via trajectory optimization (ReQueST). We evaluate ReQueST with simulated users on a state-based 2D navigation task and the image-based Car Racing video game. The results show that ReQueST significantly outperforms prior methods in learning reward models that transfer to new environments with different initial state distributions. Moreover, ReQueST safely trains the reward model to detect unsafe states, and corrects reward hacking before deploying the agent.
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT

Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively.We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately.For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees.Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts.Our code is available at https://github.com/freesunshine0316/lab-zp-joint.
Retrieval-Polished Response Generation for Chatbot

Chatbot communication, in which a robot communicates with a human being in natural language in an open domain, has achieved significant progress. However, it still suffers from problems such as a lack of diversity and contextual relevance. In this paper, we propose a retrieval-polished (RP) model for response generation that polishes a draft response based on a retrieved prototype. In particular, we first adopt a prototype selector to retrieve a contextually similar prototype. Then, a generation-based polisher is designed to obtain a polished response. Finally, we introduce a polished response filter to choose whether the final reply should be the retrieved response or the polished response. Extensive experiments on a dialog corpus show that our method outperforms retrieval-based and generation-based chatbots with respect to fluency, contextual relevance, and response diversity. Specifically, our model achieves substantial improvement compared with several strong baselines.
Quantum Computer Systems: Research for Noisy Intermediate-Scale Quantum Computers

This book targets computer scientists and engineers who are familiar with concepts in classical computer systems but are curious to learn the general architecture of quantum computing systems. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.
MultiMix: A Robust Data Augmentation Strategy for Cross-Lingual NLP.

Transfer learning has yielded state-of-the-art results in many supervisednatural language processing tasks. However, annotated data for every targettask in every target language is rare, especially for low-resource languages.In this work, we propose MultiMix, a novel data augmentation method forsemi-supervised learning in zero-shot transfer learning scenarios. Inparticular, MultiMix targets to solve cross-lingual adaptation problems from asource (language) distribution to an unknown target (language) distributionassuming it has no training labels in the target language task. In its heart,MultiMix performs simultaneous self-training with data augmentation andunsupervised sample selection. To show its effectiveness, we have performedextensive experiments on zero-shot transfers for cross-lingual named entityrecognition (XNER) and natural language inference (XNLI). Our experiments showsizeable improvements in both tasks outperforming the baselines by a goodmargin.
Hybrid Quantum Annealing Heuristic Method for Solving Job Shop Scheduling Problem

Scheduling problems have attracted the attention of researchers and practitioners for several decades. The quality of different methods developed to solve these problems on classical computers have been collected and compared in various benchmark repositories. Recently, quantum annealing has appeared as promising approach to solve some scheduling problems. The goal of this paper is to check experimentally if and how this approach can be applied for solving a well-known benchmark of the classical Job Shop Scheduling Problem. We present the existing capabilities provided by the D-Wave 2000Q quantum annealing system in the light of this benchmark. We have tested the quantum annealing system features experimentally, and proposed a new heuristic method as a proof-of-concept. In our approach we decompose the considered scheduling problem into a set of smaller optimization problems which fit better into a limited quantum hardware capacity. We have tuned experimentally various parameters of limited fully-connected graphs of qubits available in the quantum annealing system for the heuristic. We also indicate how new improvements in the upcoming D-Wave quantum processor might potentially impact the performance of our approach.
Combination of Hiding and Encryption for Data Security

&lt;pre&gt;One of the techniques used in information security is the concealment technique, where the information to be hidden within another information medium to be saved in the process of messaging between two sides without detection. In this paper, an algorithm was proposed to conceal and encrypt data using several means.in order to ensure its preservation from detection and hackers. Wavelet transformer was used to change the shape of a wave of information (one and two-dimensional data) and its different mathematical formulas. Two sets of data were used, the first group used in a hidden process. The second group was considered as a means of both embedding and encryption. The data in the second group is reduced to the extent of sufficient for the modulation process, by extracting its high-value properties and then removing them from the mother's information wave. The process of encrypting of the two sets of data comes together using an exponential function. The result is undetectable information signals. Algorithms were built to hide and encrypt one and two-dimensional data. High-security signals and images were obtained. Decryption algorithms were built to return encrypted data to their original forms, and getting the replica data.&lt;/pre&gt;&lt;p&gt; &lt;/p&gt;
OpusFilter: A Configurable Parallel Corpus Filtering Toolbox

This paper introduces OpusFilter, a flexible and modular toolbox for filtering parallel corpora.It implements a number of components based on heuristic filters, language identification libraries, character-based language models, and word alignment tools, and it can easily be extended with custom filters.Bitext segments can be ranked according to their quality or domain match using single features or a logistic regression model that can be trained without manually labeled training data.We demonstrate the effectiveness of OpusFilter on the example of a Finnish-English news translation task based on noisy web-crawled training data.Applying our tool leads to improved translation quality while significantly reducing the size of the training data, also clearly outperforming an alternative ranking given in the crawled data set.Furthermore, we show the ability of OpusFilter to perform data selection for domain adaptation.
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University

AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020.For the offline task, we create both cascaded and end-to-end speech translation systems, paying attention to careful data selection and weighting.In the cascaded approach, we combine high-quality hybrid automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT).Our endto-end direct speech translation systems benefit from pretraining of adapted encoder and decoder components, as well as synthetic data and fine-tuning and thus are able to compete with cascaded systems in terms of MT quality.For simultaneous translation, we utilize a novel architecture that makes dynamic decisions, learned from parallel data, to determine when to continue feeding on input or generate output words.Experiments with speech and text input show that even at low latency this architecture leads to superior translation results.
Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity

In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain. In low-resource setting of clinical STS, these large models tend to be impractical and prone to overfitting. Building on BERT, we study the impact of a number of model design choices, namely different fine-tuning and pooling strategies. We observe that the impact of domain-specific fine-tuning on clinical STS is much less than that in the general domain, likely due to the concept richness of the domain. Based on this, we propose two data augmentation techniques. Experimental results on N2C2-STS 1 demonstrate substantial improvements, validating the utility of the proposed methods.
Formal verification of neural agents in non-deterministic environments

Abstract We introduce a model for agent-environment systems where the agents are implemented via feed-forward ReLU neural networks and the environment is non-deterministic. We study the verification problem of such systems against CTL properties. We show that verifying these systems against reachability properties is undecidable. We introduce a bounded fragment of CTL, show its usefulness in identifying shallow bugs in the system, and prove that the verification problem against specifications in bounded CTL is in co NExpTime and PSpace -hard. We introduce sequential and parallel algorithms for MILP-based verification of agent-environment systems, present an implementation, and report the experimental results obtained against a variant of the VerticalCAS use-case and the frozen lake scenario.
Span-Based LCFRS-2 Parsing

The earliest models for discontinuous constituency parsers used mildly context-sensitive grammars, but the fashion has changed in recent years to grammar-less transition-based parsers that use strong neural probabilistic models to greedily predict transitions.We argue that grammar-based approaches still have something to contribute on top of what is offered by transition-based parsers.Concretely, by using a grammar formalism to restrict the space of possible trees we can use dynamic programming parsing algorithms for exact search for the most probable tree.Previous chart-based parsers for discontinuous formalisms used probabilistically weak generative models.We instead use a span-based discriminative neural model that preserves the dynamic programming properties of the chart parsers.Our parser does not use an explicit grammar, but it does use explicit grammar formalism constraints: we generate only trees that are within the LCFRS-2 formalism.These properties allow us to construct a new parsing algorithm that runs in lower worst-case time complexity of O(l n 4 +n 6 ), where n is the sentence length and l is the number of unique nonterminal labels.This parser is efficient in practice, provides best results among chart-based parsers, and is competitive with the best transition based parsers.We also show that the main bottleneck for further improvement in performance is in the restriction of fan-out to degree 2. We show that well-nestedness is helpful in speeding up parsing, but lowers accuracy.