Reviewerly/exHarmony huggingface dataset:
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully supervised methods. These results demonstrate the promise of self-supervision for improving robustness and uncertainty estimation and establish these tasks as new axes of evaluation for future self-supervised learning research.
t|ket⟩: a retargetable compiler for NISQ devices

Abstract We present t | ket ⟩, a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t | ket ⟩ is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has been extensively benchmarked and outperforms most competitors in terms of circuit optimisation and qubit routing.
Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.
DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks

Deep Neural Networks (DNNs) are vulnerable to Neural Trojan (NT) attacks where the adversary injects malicious behaviors during DNN training. This type of ‘backdoor’ attack is activated when the input is stamped with the trigger pattern specified by the attacker, resulting in an incorrect prediction of the model. Due to the wide application of DNNs in various critical fields, it is indispensable to inspect whether the pre-trained DNN has been trojaned before employing a model. Our goal in this paper is to address the security concern on unknown DNN to NT attacks and ensure safe model deployment. We propose DeepInspect, the first black-box Trojan detection solution with minimal prior knowledge of the model. DeepInspect learns the probability distribution of potential triggers from the queried model using a conditional generative model, thus retrieves the footprint of backdoor insertion. In addition to NT detection, we show that DeepInspect’s trigger generator enables effective Trojan mitigation by model patching. We corroborate the effectiveness, efficiency, and scalability of DeepInspect against the state-of-the-art NT attacks across various benchmarks. Extensive experiments show that DeepInspect offers superior detection performance and lower runtime overhead than the prior work.
A Practical Privacy-Preserving Data Aggregation (3PDA) Scheme for Smart Grid

The real-time electricity consumption data can be used in value-added service such as big data analysis, meanwhile the single user's privacy needs to be protected.How to balance the data utility and the privacy preservation is a vital issue, where the privacy-preserving data aggregation could be a feasible solution.Most of the existing data aggregation schemes rely on a trusted third party (TTP).However, this assumption will have negative impact on reliability, because the system can be easily knocked down by the denial of service attack.In this paper, a practical privacypreserving data aggregation scheme is proposed without TTP, in which the users with some extent trust construct a virtual aggregation area to mask the single user's data, and meanwhile, the aggregation result almost has no effect for the data utility in large scale applications.The computation cost and communication overhead are reduced in order to promote the practicability.Moreover, the security analysis and the performance evaluation show that the proposed scheme is robust and efficient.
Dynamic Hypergraph Neural Networks

In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not directly represented in the inherent structure. To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). Considering initially constructed hypergraph is probably not a suitable representation for data, the DHG module dynamically updates hypergraph structure on each layer. Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure. The HGC module includes two phases: vertex convolution and hyperedge convolution, which are designed to aggregate feature among vertices and hyperedges, respectively. We have evaluated our method on standard datasets, the Cora citation network and Microblog dataset. Our method outperforms state-of-the-art methods. More experiments are conducted to demonstrate the effectiveness and robustness of our method to diverse data distributions.
Generative adversarial network: An overview of theory and applications

In recent times, image segmentation has been involving everywhere including disease diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one of the vital works and it is relatively complicated than other vision undertakings as it needs low-level spatial data. Especially, Deep Learning has impacted the field of segmentation incredibly and gave us today different successful models. The deep learning associated Generated Adversarial Networks (GAN) has presenting remarkable outcomes on image segmentation. In this study, the authors have presented a systematic review analysis on recent publications of GAN models and their applications. Three libraries such as Embase (Scopus), WoS, and PubMed have been considered for searching the relevant papers available in this area. Search outcomes have identified 2084 documents, after two-phase screening 52 potential records are included for final review. The following applications of GAN have been emerged: 3D object generation, medicine, pandemics, image processing, face detection, texture transfer, and traffic controlling. Before 2016, research in this field was limited and thereafter its practical usage came into existence worldwide. The present study also envisions the challenges associated with GAN and paves the path for future research in this realm.
Meta-Learning with Implicit Gradients

A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning. In this formulation, meta-parameters are learned in the outer loop, while task-specific models are learned in the inner-loop, by using only a small amount of data from the current task. A key challenge in scaling these approaches is the need to differentiate through the inner loop learning process, which can impose considerable computational and memory burdens. By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer. This effectively decouples the meta-gradient computation from the choice of inner loop optimizer. As a result, our approach is agnostic to the choice of inner loop optimizer and can gracefully handle many gradient steps without vanishing gradients or memory constraints. Theoretically, we prove that implicit MAML can compute accurate meta-gradients with a memory footprint that is, up to small constant factors, no more than that which is required to compute a single inner loop gradient and at no overall increase in the total computational cost. Experimentally, we show that these benefits of implicit MAML translate into empirical gains on few-shot image recognition benchmarks.
Efficient path planning for UAV formation via comprehensively improved particle swarm optimization

Automatic generation of optimized flyable path is a key technology and challenge for autonomous unmanned aerial vehicle (UAV) formation system. Aiming to improve the rapidity and optimality of automatic path planner, this paper presents a three dimensional path planning algorithm for UAV formation based on comprehensively improved particle swarm optimization (PSO). In the proposed method, a chaos-based Logistic map is firstly adopted to improve the particle initial distribution. Then, the common used constant acceleration coefficients and maximum velocity are designed to adaptive linear-varying ones, which adjusts to the optimization process and meanwhile improves solution optimality. Besides, a mutation strategy that undesired particles are replaced by those desired ones is also proposed and the algorithm convergence speed is accelerated. Theoretically, the comprehensively improved PSO not only speeds up the convergence but also improves the solution optimality. Finally, Monte-Carlo simulation for UAV formation under terrain and threat constraints are carried out and the results illustrate the rapidity and optimality of the proposed method.
Seeing the Forest and the Trees: A Meta-Analysis of the Antecedents to Information Security Policy Compliance

A rich stream of research has identified numerous antecedents to employee compliance (and noncompliance) with information security policies. However, the number of competing theoretical perspectives and inconsistencies in the reported findings have hampe
Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis

Magnetic resonance (MR) imaging is a widely used medical imaging protocol that can be configured to provide different contrasts between the tissues in human body. By setting different scanning parameters, each MR imaging modality reflects the unique visual characteristic of scanned body part, benefiting the subsequent analysis from multiple perspectives. To utilize the complementary information from multiple imaging modalities, cross-modality MR image synthesis has aroused increasing research interest recently. However, most existing methods only focus on minimizing pixel/voxel-wise intensity difference but ignore the textural details of image content structure, which affects the quality of synthesized images. In this paper, we propose edge-aware generative adversarial networks (Ea-GANs) for cross-modality MR image synthesis. Specifically, we integrate edge information, which reflects the textural structure of image content and depicts the boundaries of different objects in images, to reduce this gap. Corresponding to different learning strategies, two frameworks are proposed, i.e., a generator-induced Ea-GAN (gEa-GAN) and a discriminator-induced Ea-GAN (dEa-GAN). The gEa-GAN incorporates the edge information via its generator, while the dEa-GAN further does this from both the generator and the discriminator so that the edge similarity is also adversarially learned. In addition, the proposed Ea-GANs are 3D-based and utilize hierarchical features to capture contextual information. The experimental results demonstrate that the proposed Ea-GANs, especially the dEa-GAN, outperform multiple state-of-the-art methods for cross-modality MR image synthesis in both qualitative and quantitative measures. Moreover, the dEa-GAN also shows excellent generality to generic image synthesis tasks on benchmark datasets about facades, maps, and cityscapes.
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

In this paper, we present the SemEval-2019 Task 3 -EmoContext: Contextual Emotion Detection in Text.Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem.For instance, as humans, on reading "Why don't you ever text me!" we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines.However, the context of dialogue can prove helpful in detection of the emotion.In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes -Happy, Sad, Angry and Others.To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps.A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants.A total of 311 teams made submissions to this task.The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score.Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.
Scene Text Detection with Supervised Pyramid Context Network

Scene text detection methods based on deep learning have achieved remarkable results over the past years. However, due to the high diversity and complexity of natural scenes, previous state-of-the-art text detection methods may still produce a considerable amount of false positives, when applied to images captured in real-world environments. To tackle this issue, mainly inspired by Mask R-CNN, we propose in this paper an effective model for scene text detection, which is based on Feature Pyramid Network (FPN) and instance segmentation. We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.Benefited from the guidance of semantic information and sharing FPN, SPCNET obtains significantly enhanced performance while introducing marginal extra computation. Experiments on standard datasets demonstrate that our SPCNET clearly outperforms start-of-the-art methods. Specifically, it achieves an F-measure of 92.1% on ICDAR2013, 87.2% on ICDAR2015, 74.1% on ICDAR2017 MLT and 82.9% on
The metaverse in the hospitality and tourism industry: An overview of current trends and future research directions

Scholars have speculated that the metaverse will alter the way in which the hospitality and tourism industry operates. Efforts to understand this new phenomenon in both academia and industry are at a crossroads. In this opinion piece, we first attempt to explain the concept of the metaverse in general and in the context of the hospitality and tourism industry. Next, we propose a conceptual framework for creating metaverse experiences, identifying research gaps, and proposing agenda items with the potential to significantly benefit hospitality and tourism industry players. Finally, we classify future research agendas into three broad categories: staging experiences in the metaverse, understanding possible changes in the consumer behavior, and marketing and operations strategies in the metaverse.学者们推测，metaverse将改变酒店业和旅游业的运作方式. 学术界和工业界对这一新现象的理解正处于十字路口. 在这篇评论文章中，我们首先试图在酒店业和旅游业的背景下解释metaverse的概念. 接下来，我们提出了一个概念框架，用于创建metaverse体验、确定研究差距，并提出有可能使酒店业和旅游业参与者受益的议程项目. 最后，我们将未来的研究议程分为三大类在metaverse中展示体验、了解消费者行为的可能变化，以及metaverse中的营销和运营策略.
Deep Affinity Network for Multiple Object Tracking

Multiple Object Tracking (MOT) plays an important role in solving many fundamental problems in video analysis and computer vision. Most MOT methods employ two steps: Object Detection and Data Association. The first step detects objects of interest in every frame of a video, and the second establishes correspondence between the detected objects in different frames to obtain their tracks. Object detection has made tremendous progress in the last few years due to deep learning. However, data association for tracking still relies on hand crafted constraints such as appearance, motion, spatial proximity, grouping etc. to compute affinities between the objects in different frames. In this paper, we harness the power of deep learning for data association in tracking by jointly modeling object appearances and their affinities between different frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN) learns compact, yet comprehensive features of pre-detected objects at several levels of abstraction, and performs exhaustive pairing permutations of those features in any two frames to infer object affinities. DAN also accounts for multiple objects appearing and disappearing between video frames. We exploit the resulting efficient affinity computations to associate objects in the current frame deep into the previous frames for reliable on-line tracking. Our technique is evaluated on popular multiple object tracking challenges MOT15, MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics demonstrates that our approach is among the best performing techniques on the leader board for these challenges. The open source implementation of our work is available at https://github.com/shijieS/SST.git.
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification

Aspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a specific aspect.One sentence may contain various sentiments for different aspects.Many sophisticated methods such as attention mechanism and Convolutional Neural Networks (CNN) have been widely employed for handling this challenge.Recently, semantic dependency tree implemented by Graph Convolutional Networks (GCN) is introduced to describe the inner connection between aspects and the associated emotion words.But the improvement is limited due to the noise and instability of dependency trees.To this end, we propose a dependency graph enhanced dual-transformer network (named DGEDT) by jointly considering the flat representations learnt from Transformer and graphbased representations learnt from the corresponding dependency graph in an iterative interaction manner.Specifically, a dualtransformer structure is devised in DGEDT to support mutual reinforcement between the flat representation learning and graph-based representation learning.The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa.The results on five datasets demonstrate that the proposed DGEDT outperforms all state-of-the-art alternatives with a large margin.
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Recent works have shown the effectiveness of randomized smoothing as a scalable technique for building neural network-based classifiers that are provably robust to $\ell_2$-norm adversarial perturbations. In this paper, we employ adversarial training to improve the performance of randomized smoothing. We design an adapted attack for smoothed classifiers, and we show how this attack can be used in an adversarial training setting to boost the provable robustness of smoothed classifiers. We demonstrate through extensive experimentation that our method consistently outperforms all existing provably $\ell_2$-robust classifiers by a significant margin on ImageNet and CIFAR-10, establishing the state-of-the-art for provable $\ell_2$-defenses. Moreover, we find that pre-training and semi-supervised learning boost adversarially trained smoothed classifiers even further. Our code and trained models are available at http://github.com/Hadisalman/smoothing-adversarial.
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension

We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. 1 In this task, we adapted and unified 18 distinct question answering datasets into the same format.Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation.Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training, and ensembling.The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT.
Quantum Computer Systems for Scientific Discovery

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by codesigning full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2–10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21–22, 2019 in Alexandria, VA.Received 4 February 2020Accepted 21 October 2020DOI:https://doi.org/10.1103/PRXQuantum.2.017001Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAdiabatic quantum optimizationMachine learningMeasurement-based quantum computingQuantum algorithmsQuantum benchmarkingQuantum computationQuantum controlQuantum gatesQuantum simulationSurface code quantum computingTopological quantum computingQuantum InformationCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical