Reviewerly/exHarmony huggingface dataset:
What Limits the Simulation of Quantum Computers?

An ultimate goal of quantum computing is to perform calculations beyond the reach of any classical computer. It is therefore imperative that useful quantum computers be very difficult to simulate classically, otherwise classical computers could be used for the applications envisioned for the quantum ones. Perfect quantum computers are unarguably exponentially difficult to simulate: the classical resources required grow exponentially with the number of qubits N or the depth D of the circuit. This difficulty has triggered recent experiments on deep, random circuits that aim to demonstrate that quantum devices may already perform tasks beyond the reach of classical computing. These real quantum computing devices, however, suffer from many sources of decoherence and imprecision which limit the degree of entanglement that can actually be reached to a fraction of its theoretical maximum. They are characterized by an exponentially decaying fidelity F∼(1−ε)ND with an error rate ε per operation as small as ≈1% for current devices with several dozen qubits or even smaller for smaller devices. In this work, we provide new insight on the computing capabilities of real quantum computers by demonstrating that they can be simulated at a tiny fraction of the cost that would be needed for a perfect quantum computer. Our algorithms compress the representations of quantum wave functions using matrix product states, which are able to capture states with low to moderate entanglement very accurately. This compression introduces a finite error rate ε so that the algorithms closely mimic the behavior of real quantum computing devices. The computing time of our algorithm increases only linearly with N and D in sharp contrast with exact simulation algorithms. We illustrate our algorithms with simulations of random circuits for qubits connected in both one- and two-dimensional lattices. We find that ε can be decreased at a polynomial cost in computing power down to a minimum error ε∞. Getting below ε∞ requires computing resources that increase exponentially with ε∞/ε. For a two-dimensional array of N=54 qubits and a circuit with control-Z gates, error rates better than state-of-the-art devices can be obtained on a laptop in a few hours. For more complex gates such as a swap gate followed by a controlled rotation, the error rate increases by a factor 3 for similar computing time. Our results suggest that, despite the high fidelity reached by quantum devices, only a tiny fraction (∼10−8) of the system Hilbert space is actually being exploited.8 MoreReceived 19 February 2020Revised 22 September 2020Accepted 5 October 2020DOI:https://doi.org/10.1103/PhysRevX.10.041038Published 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 AreasQuantum computationTechniquesDensity matrix methodsCondensed Matter, Materials & Applied Physics
Accurate RGB-D Salient Object Detection via Collaborative Learning

Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are still two limitations. One hand is that the pooling and upsampling operations in FCNs might cause blur object boundaries. On the other hand, using an additional depth-network to extract depth features might lead to high computation and storage cost. The reliance on depth inputs during testing also limits the practical applications of current RGB-D models. In this paper, we propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way, which solves those problems tactfully. The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries. Depth and saliency learning is innovatively integrated into the high-level feature learning process in a mutual-benefit manner. This strategy enables the network to be free of using extra depth networks and depth inputs to make inference. To this end, it makes our model more lightweight, faster and more versatile. Experiment results on seven benchmark datasets show its superior performance.
A blockchain-empowered crowdsourcing system for 5G-enabled smart cities

With the development of 5G(5th generation mobile networks) technology, smart cities are an inevitable trend in modern city development. Among them, smart city services are the foundation of 5G-enabled smart cities. As an emerging and informational city service model, crowdsourcing has been widely used in our daily life. However, in the existing crowdsourcing systems, the requesters and the workers are usually required to use the crowdsourcing platform as the trust center, and the payment depends on the third-party central payment institutions, which have a massive security risk. Once these centers are attacked or do evil, it will bring higher losses to the crowdsourcing parties. These problems will negatively affect the further development of 5G-enabled smart cities. To address these issues, we propose a blockchain-empowered and decentralized trusted service mechanism for the crowdsourcing system in 5G-enabled smart cities. In the proposed mechanism, the crowdsourcing service process is divided into nine stages: initialization, task submission, task publication, task reception, scheme submission, scheme arbitration, payment, task rollback, and service compensation. The smart contract controls the execution of each step in each stage, and the payment is completed by blockchain without the involvement of third-party central institutions. Finally, we develop smart contracts to conduct experiments based on Ethereum and compare it with the existing crowdsourcing system. The experimental results show the effectiveness and applicability of the crowdsourcing system service mechanism without the central institutions.
Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing

Recently, some visually secure image encryption schemes using compressive sensing (CS) have been developed to protect images with visual security, where the images are first encrypted and compressed concurrently, and then embedded into a carrier image. However, existing schemes have some performance limitations in the quality of the reconstructed and cipher images and the efficiency. To address above issues, this work proposes a new visually secure image encryption scheme. First, we devise an adaptive-thresholding sparsification to greatly improve the quality of the reconstructed image. Second, we design a new parallel CS technique to tremendously improve the processing efficiency. Further, a matrix encoding strategy is finally employed to significantly reduce the number of changed bits in embedding process. Simulations and comparisons show that our proposed scheme has a high security level. Meanwhile, it is also more efficient, and achieves higher quality of the reconstructed and cipher images than some newly developed schemes.
Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis

Despite the great success of Generative Adversarial Networks (GANs) in synthesizing images, there lacks enough understanding of how photo-realistic images are generated from the layer-wise stochastic latent codes introduced in recent GANs. In this work, we show that highly-structured semantic hierarchy emerges in the deep generative representations from the state-of-the-art GANs like StyleGAN and BigGAN, trained for scene synthesis. By probing the per-layer representation with a broad set of semantics at different abstraction levels, we manage to quantify the causality between the layer-wise activations and the semantics occurring in the output image. Such a quantification identifies the human-understandable variation factors that can be further used to steer the generation process, such as changing the lighting condition and varying the viewpoint of the scene. Extensive qualitative and quantitative results suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize various concepts in a hierarchical manner: the early layers tend to determine the spatial layout, the middle layers control the categorical objects, and the later layers render the scene attributes as well as the color scheme. Identifying such a set of steerable variation factors facilitates high-fidelity scene editing based on well-learned GAN models without any retraining (code and demo video are available at https://genforce.github.io/higan ).
How artificial intelligence will affect the future of retailing

Artificial intelligence (AI) will substantially impact retailing. Building on past research and from interviews with senior managers, we examine how senior retailing managers should think about adopting AI, involving factors such as the extent to which an AI application is customer-facing, the amount of value creation, whether the AI application is online, and extent of ethics concerns. In addition, we highlight that the near-term impact of AI on retailing may not be as pronounced as the popular press might suggest, and also that AI is likely to be more effective if it focuses on augmenting (rather than replacing) managers’ judgments. Finally, while press coverage typically involves customer-facing AI applications, we highlight that a lot of value can be obtained by adopting non-customer-facing applications. Overall, we remain very optimistic as regards the impact of AI on retailing. Finally, we lay out a research agenda and also outline implications for practice.
Automating Procurement Contracts in the Healthcare Supply Chain Using Blockchain Smart Contracts

Effectively managing the healthcare supply chain (HCSC) process is crucial for healthcare providers not only during pandemics such as COVID-19 but also in their normal operations. Despite significant advances in new technologies and treatment options providers still suffer from poor procurement, ordering, forecasting, and distribution practices. Group Purchasing Organizations (GPOs) are an important stakeholder in HCSC and benefit providers with cost savings, volume discounts, and vendor selection. However, the current GPO contract process is time-consuming and lacks efficiency. Hence, our proposed solution integrates blockchain technology and decentralized storage to promote transparency, streamlines communication with stakeholders, and minimize the procurement timeline while avoiding pricing discrepancies and inaccuracies. Our solution connects all the stakeholders such as manufacturer, GPO, distributor, and provider using Ethereum network. In this paper, we propose a blockchain solution using smart contracts to automate the GPO contract process. We propose a generic framework for contracting process in the HCSC with detailed algorithms depicting various interactions among HCSC stakeholders. The smart contract code was developed and tested using Remix IDE and the code is publicly shared via Github. We discuss various security risks and present detailed cost analysis of various transactions incurred by the stakeholders. Our analysis demonstrates that the proposed blockchain-based solution is economically feasible as only a minimal transaction fee is expended by the stakeholders in the distributed network.
Industry-scale Knowledge Graphs: Lessons and Challenges

This article looks at the knowledge graphs of five diverse tech companies, comparing the similarities and differences in their respective experiences of building and using the graphs, and discussing the challenges that all knowledge-driven enterprises face today. The collection of knowledge graphs discussed here covers the breadth of applications, from search, to product descriptions, to social networks.
Medical image analysis based on deep learning approach

Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.
End-to-End Object Detection with Fully Convolutional Network

Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS) post-processing, which impedes fully end-to-end training. In this paper, we give the analysis of discarding NMS, where the results reveal that a proper label assignment plays a crucial role. To this end, for fully convolutional detectors, we introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection, which obtains comparable performance with NMS. Besides, a simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. With these techniques, our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets. The code is available at https://github.com/Megvii-BaseDetection/DeFCN.
MUSIQ: Multi-scale Image Quality Transformer

Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often compromised by the fixed shape constraint in batch training. To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation. To address this, we design a multi-scale image quality Transformer (MUSIQ) to process native resolution images with varying sizes and aspect ratios. With a multi-scale image representation, our proposed method can capture image quality at different granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale embedding is proposed to support the positional embedding in the multi-scale representation. Experimental results verify that our method can achieve state-of-the-art performance on multiple large scale IQA datasets such as PaQ-2-PiQ [41], SPAQ [11], and KonIQ-10k [16]. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD co-saliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2-D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed framework.
Emotion Recognition on Twitter: Comparative Study and Training a Unison Model

Despite recent successes of deep learning in many fields of natural language processing, previous studies of emotion recognition on Twitter mainly focused on the use of lexicons and simple classifiers on bag-of-words models. The central question of our study is whether we can improve their performance using deep learning. To this end, we exploit hashtags to create three large emotion-labeled data sets corresponding to different classifications of emotions. We then compare the performance of several word- and character-based recurrent and convolutional neural networks with the performance on bag-of-words and latent semantic indexing models. We also investigate the transferability of the final hidden state representations between different classifications of emotions, and whether it is possible to build a unison model for predicting all of them using a shared representation. We show that recurrent neural networks, especially character-based ones, can improve over bag-of-words and latent semantic indexing models. Although the transfer capabilities of these models are poor, the newly proposed training heuristic produces a unison model with performance comparable to that of the three single models.
EARS: Emotion-aware recommender system based on hybrid information fusion

Recommender systems suggest items that users might like according to their explicit and implicit feedback information, such as ratings, reviews, and clicks. However, most recommender systems focus mainly on the relationships between items and the user’s final purchasing behavior while ignoring the user’s emotional changes, which play an essential role in consumption activity. To address the challenge of improving the quality of recommender services, this paper proposes an emotion-aware recommender system based on hybrid information fusion in which three representative types of information are fused to comprehensively analyze the user’s features: user rating data as explicit information, user social network data as implicit information and sentiment from user reviews as emotional information. The experimental results verify that the proposed approach provides a higher prediction rating and significantly increases the recommendation accuracy.
Online Worker Fraud and Evolving Threats to the Integrity of MTurk Data: A Discussion of Virtual Private Servers and the Limitations of IP-Based Screening Procedures

ABSTRACT We identify a pervasive, yet previously undocumented threat to the reliability of MTurk data—and discuss how this issue is symptomatic of opportunities and incentives that facilitate fraudulent behavior within online recruitment platforms. In doing so, we explain how IP addresses were never intended to identify individuals and are likely insufficient to identify and mitigate emergent risks around data integrity. We discuss MTurk samples for two studies that include alarming proportions of participants who circumvent an entire set of conventional sample screening methods—and provide disturbingly low-quality responses. These “bad actors” exploited inherent limitations of IP screening procedures by using virtual private servers (VPS) that concealed the IP address of their local devices. While service providers now help target this abuse, the underlying limitations of IP screening procedures remain. Our findings emphasize the importance of continued diligence within the research community to identify and mitigate evolving threats to data integrity.
A survey on blockchain cybersecurity vulnerabilities and possible countermeasures

Summary Blockchain technology has attracted considerable attention owing to its wide range of potential applications. It first appeared as a cryptocurrency, called Bitcoin, but has since been used in many other business and nonbusiness applications. Unlike most existing systems that are based on centralized frameworks, this new technology utilizes peer‐to‐peer networks and distributed systems which includes blockchain registers to store transactions. Its structure is designed as a digital log file and stored as a series of linked groups, called blocks. Each individual block is locked cryptographically with the previous block. Once a block has been added, it cannot be altered. Many security experts speculate that the inherent cryptographic nature of the blockchain system is sufficient to withstand constant hacking and security threats. However, previous studies on the security and privacy of blockchain technology have shown that many applications have fallen victim to successful cyberattacks. Owing to the increasing demand for cryptocurrency and its current security challenges, previous studies have not focused on blockchain technology cybersecurity vulnerabilities extensively. Here, our study extends upon the previous studies on vulnerabilities and investigates the types of potential attacks. Our study then provides further direction to highlight possible countermeasures against blockchain technology vulnerability to cybersecurity.
You Said That?: Synthesising Talking Faces from Audio

We describe a method for generating a video of a talking face. The method takes still images of the target face and an audio speech segment as inputs, and generates a video of the target face lip synched with the audio. The method runs in real time and is applicable to faces and audio not seen at training time. To achieve this we develop an encoder–decoder convolutional neural network (CNN) model that uses a joint embedding of the face and audio to generate synthesised talking face video frames. The model is trained on unlabelled videos using cross-modal self-supervision. We also propose methods to re-dub videos by visually blending the generated face into the source video frame using a multi-stream CNN model.
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems

We show that end-to-end learning of communication systems through deep neural network autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we elaborate how an attacker can craft effective physical black-box adversarial attacks. Due to the openness (broadcast nature) of the wireless channel, an adversary transmitter can increase the block-error-rate of a communication system by orders of magnitude by transmitting a well-designed perturbation signal over the channel. We reveal that the adversarial attacks are more destructive than the jamming attacks. We also show that classical coding schemes are more robust than the autoencoders against both adversarial and jamming attacks.