Reviewerly/exHarmony huggingface dataset:
Positional Encoding as Spatial Inductive Bias in GANs

SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translationinvariant convolutional generator could capture the global structure with just a spatially i.i.d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improves SinGAN for more versatile image manipulation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-balanced Samplings

Long-tailed data distribution is common in many multi-label visual recognition tasks and the direct use of these data for training usually leads to relatively low performance on tail classes. While re-balanced data sampling can improve the performance on tail classes, it may also hurt the performance on head classes in training due to label co-occurrence. In this paper, we propose a new approach to train on both uniform and re-balanced samplings in a collaborative way, resulting in performance improvement on both head and tail classes. More specifically, we design a visual recognition network with two branches: one takes the uniform sampling as input while the other takes the re-balanced sampling as the input. For each branch, we conduct visual recognition using a binary-cross-entropy-based classification loss with learnable logit compensation. We further define a new cross-branch loss to enforce the consistency when the same input image goes through the two branches. We conduct extensive experiments on VOC-LT and COCO-LT datasets. The results show that the proposed method significantly outperforms previous state-of-the-art methods on long-tailed multi-label visual recognition.
Cryptocurrency Mining from an Economic and Environmental Perspective. Analysis of the Most and Least Sustainable Countries

There are different studies that point out that the price of electricity is a fundamental factor that will influence the mining decision, due to the cost it represents. There is also an ongoing debate about the pollution generated by cryptocurrency mining, and whether or not the use of renewable energies will solve the problem of its sustainability. In our study, starting from the Environmental Performance Index (EPI), we have considered several determinants of cryptocurrency mining: energy price, how that energy is generated, temperature, legal constraints, human capital, and R&amp;D&amp;I. From this, via linear regression, we recalculated this EPI by including the above factors that affect cryptocurrency mining in a sustainable way. The study determines, once the EPI has been readjusted, that the most sustainable countries to perform cryptocurrency mining are Denmark and Germany. In fact, of the top ten countries eight of them are European (Denmark, Germany, Sweden, Switzerland, Finland, Austria, and the United Kingdom); and the remaining two are Asian (South Korea and Japan).
The moderating effect of managerial discretion on blockchain technology and the firms’ innovation quality: Evidence from Chinese manufacturing firms

Along with the flourishing development of digital technology, blockchain technology emerged and has been developed for a broad spectrum of applications. As an emerging digital technology, blockchain provides firms with new ways of creating and capturing value, new information exchange mechanisms and trading systems, as well as new organizational forms. In recent years, Some studies have noted that blockchain technology plays an imperative role in manufacturing firms' innovation quality. However, relevant research considering the effect of blockchain technology on innovation quality is still poorly elaborated. At the same time, we put forward that managerial discretion also has considerable implications for innovation. Given that blockchain technology is still in the experimental stage for business use, managers would rather adopt other technologies that bring profits in the short term than blockchain technology as far as their stakeholders' satisfaction is concerned. For this reason, managers with higher managerial discretion are more likely to leverage blockchain technology to facilitate innovation. Yet, little research has focused on the critical role of managerial discretion on firms’ innovation quality. Taking together the foregoing discussions, this paper is designed to analyze the impact of blockchain technology on innovation quality. We also divide managerial discretion into environmental discretion, organizational discretion, and additional discretion in the Chinese context, analyzing their moderating effects on the relationship between blockchain technology and innovation quality respectively. Further, relevant hypotheses are tested empirically using a panel data set of Chinese listed manufacturing firms involved in the blockchain business from 2012 to 2019. The findings are listed as follows: With an increasing expenditure on blockchain technology, firms' innovation quality exhibits an initially decreasing trend and then an increasing trend. Additionally, it is proved that environmental discretion, organizational discretion and additional discretion in the Chinese context all positively moderate the relationship between blockchain technology and firms' innovation quality.
Generative Hierarchical Features from Synthesizing Images

Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from solving the task of image generation are applicable to other vision tasks remains seldom explored. In this work, we show that learning to synthesize images can bring remarkable hierarchical visual features that are generalizable across a wide range of applications. Specifically, we consider the pre-trained StyleGAN generator as a learned loss function and utilize its layer-wise representation to train a novel hierarchical encoder. The visual feature produced by our encoder, termed as Generative Hierarchical Feature (GH-Feat), has strong transferability to both generative and discriminative tasks, including image editing, image harmonization, image classification, face verification, landmark detection, and layout prediction. Extensive qualitative and quantitative experimental results demonstrate the appealing performance of GH-Feat. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections

Image super-resolution technology successfully overcomes the limitation of excessively large pixel size in infrared detectors and meets the increasing demand for high-resolution infrared image information. In this paper, the super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections is reported. The introduction of global residual learning and local residual learning reduces computational complexity and accelerates network convergence. Multiple convolution layers and deconvolution layers respectively implement the extraction and restoration of the features in infrared images. Skip connections and channel fusion are introduced to the network to increase the number of feature maps and promote the deconvolution layers to restore image details. Compared with the other previously proposed methods for infrared information restoration, our proposed method shows obvious advantages in the ability of high-resolution details acquisition.
Empirical evaluation of smart contract testing: what is the best choice?

Security of smart contracts has attracted increasing attention in recent years. Many researchers have devoted themselves to devising testing tools for vulnerability detection. Each published tool has demonstrated its effectiveness through a series of evaluations on their own experimental scenarios. However, the inconsistency of evaluation settings such as different data sets or performance metrics, may result in biased conclusion.
AgileGAN

Portraiture as an art form has evolved from realistic depiction into a plethora of creative styles. While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images. Such StyleGAN-based methods have focused on finding the best latent inversion mapping for reconstructing input images; however, our key insight is that this does not lead to good generalization to different portrait styles. Hence we propose AgileGAN, a framework that can generate high quality stylistic portraits via inversion-consistent transfer learning. We introduce a novel hierarchical variational autoencoder to ensure the inverse mapped distribution conforms to the original latent Gaussian distribution, while augmenting the original space to a multi-resolution latent space so as to better encode different levels of detail. To better capture attribute-dependent stylization of facial features, we also present an attribute-aware generator and adopt an early stopping strategy to avoid overfitting small training datasets. Our approach provides greater agility in creating high quality and high resolution (1024×1024) portrait stylization models, requiring only a limited number of style exemplars (~100) and short training time (~1 hour). We collected several style datasets for evaluation including 3D cartoons, comics, oil paintings and celebrities. We show that we can achieve superior portrait stylization quality to previous state-of-the-art methods, with comparisons done qualitatively, quantitatively and through a perceptual user study. We also demonstrate two applications of our method, image editing and motion retargeting.
Technical Sustainability of Cloud-Based Blockchain Integrated with Machine Learning for Supply Chain Management

Knowing the challenges of keeping and manipulating more and more immutable transaction records in a blockchain network of various supply chain parties and the opportunities of leveraging sophisticated analyses on the big data generated from these records, design of a robust blockchain architecture based on a cloud infrastructure is proposed. This paper presents this technical design with consideration of the technical sustainability in terms of scalability and big data processing and analytics. A case study was used to illustrate how the technical sustainability is achieved by applying the proposed technical design to the real-time detection of the maritime risk management. This case also illustrates how machine learning mechanism helps to reduce maritime risk by guiding a cargo ship to adjust to the planned or safe route from a detour to a danger zone. This paper also discusses the implications for further research direction.
Computation Offloading for Edge-Assisted Federated Learning

When applying machine learning techniques to the Internet of things, aggregating massive amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed learning framework called federated learning has been proposed. Due to the parallel training structure, the performance of federated learning suffers from the straggler effect. In this paper, to mitigate the straggler effect, we propose a novel learning scheme, edge-assisted federated learning (EAFL), which utilizes edge computing to reduce the computational burdens for stragglers in federated learning. It enables stragglers to offload partial computation to the edge server, and leverages the server's idle computing power to assist clients in model training. The offloading data size is optimized to minimize the learning delay of the system. Based on the optimized data size, a threshold-based offloading strategy for EAFL is proposed. Moreover, we extend EAFL to a dynamic scenario where clients may be offline after several update rounds. By grouping clients into different sets, we formulate the new EAFL delay optimization problem and derive the corresponding offloading strategy for the dynamic scenario. Simulation results are presented to show that EAFL has lower system delay than the original federated learning scheme.
Digital Contact Tracing Applications during COVID-19: A Scoping Review about Public Acceptance

Digital contact tracing applications (CTAs) have been one of the most widely discussed technical methods of controlling the COVID-19 outbreak. The effectiveness of this technology and its ethical justification depend highly on public acceptance and adoption. This study aims to describe the current knowledge about public acceptance of CTAs and identify individual perspectives, which are essential to consider concerning CTA acceptance and adoption. In this scoping review, 25 studies from four continents across the globe are compiled, and critical topics are identified and discussed. The results show that public acceptance varies across national cultures and sociodemographic strata. Lower acceptance among people who are mistrusting, socially disadvantaged, or those with low technical skills suggest a risk that CTAs may amplify existing inequities. Regarding determinants of acceptance, eight themes emerged, covering both attitudes and behavioral perspectives that can influence acceptance, including trust, privacy concerns, social responsibility, perceived health threat, experience of and access to technologies, performance expectancy and perceived benefits, and understanding. Furthermore, widespread misconceptions about the CTA function are a topic in need of immediate attention to ensure the safe use of CTAs. The intention-action gap is another topic in need of more research.
Nearly tight Trotterization of interacting electrons

We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved by simultaneously exploiting commutativity of the target Hamiltonian, sparsity of interactions, and prior knowledge of the initial state. We achieve this using Trotterization for a class of interacting electrons that encompasses various physical systems, including the plane-wave-basis electronic structure and the Fermi-Hubbard model. We estimate the simulation error by taking the transition amplitude of nested commutators of the Hamiltonian terms within the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>η</mml:mi></mml:math>-electron manifold. We develop multiple techniques for bounding the transition amplitude and expectation of general fermionic operators, which may be of independent interest. We show that it suffices to use<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>5</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup><mml:msup><mml:mi>η</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>2</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mfrac><mml:mo>+</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>4</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup><mml:msup><mml:mi>η</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>2</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>o</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:msup></mml:math>gates to simulate electronic structure in the plane-wave basis with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>spin orbitals and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>η</mml:mi></mml:math>electrons, improving the best previous result in second quantization up to a negligible factor while outperforming the first-quantized simulation when<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mi>η</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>2</mml:mn><mml:mo>−</mml:mo><mml:mi>o</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:msup></mml:math>. We also obtain an improvement for simulating the Fermi-Hubbard model. We construct concrete examples for which our bounds are almost saturated, giving a nearly tight Trotterization of interacting electrons.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. The proposed model acts as a backbone and the learned features from this model can be used for downstream tasks such as point cloud semantic and panoptic segmentation or 3D detection. In this paper, we benchmark our model on these three tasks. For semantic segmentation, we evaluate the proposed model on several large-scale datasets, i.e., SemanticKITTI, nuScenes and A2D2. Our method achieves the state-of-the-art on the leaderboard of SemanticKITTI (both single-scan and multi-scan challenge), and significantly outperforms existing methods on nuScenes and A2D2 dataset. Furthermore, the proposed 3D framework also shows strong performance and good generalization on LiDAR panoptic segmentation and LiDAR 3D detection.
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling

Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolution (HR) images from the low-resolution (LR) image rather than learning a deterministic mapping. For image rescaling, it achieves high accuracy by jointly modelling the downscaling and upscaling processes. While existing approaches employ specialized techniques for these two tasks, we set out to unify them in a single formulation. In this paper, we propose the hierarchical conditional flow (HCFlow) as a unified framework for image SR and image rescaling. More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously. In particular, the high-frequency component is conditional on the LR image in a hierarchical manner. To further enhance the performance, other losses such as perceptual loss and GAN loss are combined with the commonly used negative log-likelihood loss in training. Extensive experiments on general image SR, face image SR and image rescaling have demonstrated that the proposed HCFlow achieves state-of-the-art performance in terms of both quantitative metrics and visual quality.
Online Knowledge Distillation for Efficient Pose Estimation

Existing state-of-the-art human pose estimation methods require heavy computational resources for accurate predictions. One promising technique to obtain an accurate yet lightweight pose estimator is knowledge distillation, which distills the pose knowledge from a powerful teacher model to a less-parameterized student model. However, existing pose distillation works rely on a heavy pre-trained estimator to perform knowledge transfer and require a complex two-stage learning procedure. In this work, we investigate a novel Online Knowledge Distillation framework by distilling Human Pose structure knowledge in a one-stage manner to guarantee the distillation efficiency, termed OKDHP. Specifically, OKDHP trains a single multi-branch network and acquires the predicted heatmaps from each, which are then assembled by a Feature Aggregation Unit (FAU) as the target heatmaps to teach each branch in reverse. Instead of simply averaging the heatmaps, FAU which consists of multiple parallel transformations with different receptive fields, leverages the multi-scale information, thus obtains target heatmaps with higher-quality. Specifically, the pixel-wise Kullback-Leibler (KL) divergence is utilized to mini-mize the discrepancy between the target heatmaps and the predicted ones, which enables the student network to learn the implicit keypoint relationship. Besides, an unbalanced OKDHP scheme is introduced to customize the student networks with different compression rates. The effectiveness of our approach is demonstrated by extensive experiments on two common benchmark datasets, MPII and COCO.
A binary moth search algorithm based on self-learning for multidimensional knapsack problems

A key issue for moth search algorithm (MS) is the ability to maintain sufficient diversity in the population so as to be able to track a dynamically changing landscape. However, for the original MS algorithm, the implicit learning mechanism of flight straightly operator focuses mainly on the global best individual, which inevitably causes a deteriorated evolution. To increase the population diversity and enhance the global search ability of MS, a binary moth search algorithm based on self-learning (SLMS) is proposed to solve an NP-hard combinatorial optimization problem with many diverse applications, the 0–1 multidimensional knapsack problem (MKP). In SLMS, a self-learning flight straightly operator is proposed to make each individual learn from any one better than itself, not just the global best individual. The performance of the proposed SLMS is validated by testing 89 different widely used benchmark instances. Experimental results show the effectiveness of the proposed SLMS in solving the MKP problem and the self-learning flight straightly operator can enhance population diversity and global search ability. The effect of transfer functions on the performance of SLMS is also investigated.