Reviewerly/exHarmony huggingface dataset:
DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal

Rain removal is an important but challenging computer vision task as rain streaks can severely degrade the visibility of images that may make other visions or multimedia tasks fail to work. Previous works mainly focused on feature extraction and processing or neural network structure, while the current rain removal methods can already achieve remarkable results, training based on single network structure without considering the cross-scale relationship may cause information drop-out. In this paper, we explore the cross-scale manner between networks and inner-scale fusion operation to solve the image rain removal task. Specifically, to learn features with different scales, we propose a multi-sub-networks structure, where these sub-networks are fused via a cross-scale manner by Gate Recurrent Unit to inner-learn and make full use of information at different scales in these sub-networks. Further, we design an inner-scale connection block to utilize the multi-scale information and features fusion way between different scales to improve rain representation ability and we introduce the dense block with skip connection to inner-connect these blocks. Experimental results on both synthetic and real-world datasets have demonstrated the superiority of our proposed method, which outperforms over the state-of-the-art methods. The source code will be available at https://supercong94.wixsite.com/supercong94.
Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle Re-identification

Vehicle re-identification (Re-Id) is a challenging task due to the inter-class similarity, the intra-class difference, and the cross-view misalignment of vehicle parts. Although recent methods achieve great improvement by learning detailed features from keypoints or bounding boxes of parts, vehicle Re-Id is still far from being solved. Different from existing methods, we propose a Parsing-guided Cross-part Reasoning Network, named as PCRNet, for vehicle Re-Id. The PCRNet explores vehicle parsing to learn discriminative part-level features, model the correlation among vehicle parts, and achieve precise part alignment for vehicle Re-Id. To accurately segment vehicle parts, we first build a large-scale Multi-grained Vehicle Parsing (MVP) dataset from surveillance images. With the parsed parts, we extract regional features for each part and build a part-neighboring graph to explicitly model the correlation among parts. Then, the graph convolutional networks (GCNs) are adopted to propagate local information among parts, which can discover the most effective local features of varied viewpoints. Moreover, we propose a self-supervised part prediction loss to make the GCNs generate features of invisible parts from visible parts under different viewpoints. By this means, the same vehicle from different viewpoints can be matched with the well-aligned and robust feature representations. Through extensive experiments, our PCRNet significantly outperforms the state-of-the-art methods on three large-scale vehicle Re-Id datasets.
A novel combined multi-task learning and Gaussian process regression model for the prediction of multi-timescale and multi-component of solar radiation

A novel combined multi-task learning and Gaussian process regression (MTGPR) model is proposed to predict the multi-time scale (daily and monthly mean daily) and multi-component (global and diffuse) solar radiation simultaneously. Compared to conventional Gaussian process regression (GPR) which can only be used for specific solar radiation component prediction on a specific timescale, the MTGPR can utilize the correlated information between different tasks to improve the model generalization and accuracy. Meteorological data from ten stations in China were used to train and validate the GPR and MTGPR models for daily global, monthly mean daily global, daily diffuse and monthly mean daily diffuse solar radiation prediction. The results showed that the GPR and MTGPR models are highly accurate in estimating daily and monthly mean daily solar radiation with coefficient of determination (R2), root mean square error (RMSE), relative root mean square error (rRMSE) and mean bias error (MBE) of GPR in ranges of 0.4623–0.9892, 0.5542–4.1591 MJm−2d−1, 4.70–39.75% and −1.1750–1.5347 MJm−2d−1, respectively. Because the MTGPR learned the intercorrelated information between different tasks, compared to GPR models, the MTGPR models performed better. For daily prediction, the average R2, RMSE and rRMSE of the MTGPR improved by 0.19–0.48%, 0.57–0.65% and 0.51–0.52%, respectively. In terms of monthly mean daily prediction, the corresponding values of MTGPR improved by 2.62–2.65%, 5.50–12.07% and 5.21–12.08%, respectively. This paper provides a compact guide for the simultaneous prediction of combined parameters.
Reconsidering Self-Moderation

Research in online content moderation has a long history of exploring different forms that moderation can take, including both user-driven moderation models on community-based platforms like Wikipedia, Facebook Groups, and Reddit, and centralized corporate moderation models on platforms like Twitter and Instagram. In this work I review different approaches to moderation research with the goal of providing a roadmap for researchers studying community self-moderation. I contrast community-based moderation research with platforms and policies-focused moderation research, and argue that the former has an important role to play in shaping discussions about the future of online moderation. I provide six guiding questions for future research that, if answered, can support the development of a form of user-driven moderation that is widely implementable across a variety of social spaces online, offering an alternative to the corporate moderation models that dominate public debate and discussion.
Coping with the New Normal Imposed by the COVID-19 Pandemic: Lessons for Technology Management and Governance

The reliance of many facets of society on technology and online platforms has grown exponentially during the COVID-19 pandemic. This drastic change has impacted all industries and service sectors alike including the health-care sector, education, retail, travel, food, and government agencies. The observations made in the last six months provide us with valuable insights for technology management and information systems. In this piece, we discuss some of these key insights.
BDoS: Blockchain Denial-of-Service

Proof-of-work (PoW) cryptocurrency blockchains like Bitcoin secure vast amounts of money. Their operators, called miners, expend resources to generate blocks and receive monetary rewards for their effort. Blockchains are, in principle, attractive targets for Denial-of-Service (DoS) attacks: There is fierce competition among coins, as well as potential gains from short selling. Classical DoS attacks, however, typically target a few servers and cannot scale to systems with many nodes. There have been no successful DoS attacks to date against prominent cryptocurrencies. We present Blockchain DoS (BDoS), the first incentive-based DoS attack that targets PoW cryptocurrencies. Unlike classical DoS, BDoS targets the system's mechanism design: It exploits the reward mechanism to discourage miner participation. Previous DoS attacks against PoW blockchains require an adversary's mining power to match that of all other miners. In contrast, BDoS can cause a blockchain to grind to a halt with significantly fewer resources, e.g., 21% as of March 2020 in Bitcoin, according to our empirical study. We find that Bitcoin's vulnerability to BDoS increases rapidly as the mining industry matures and profitability drops. BDoS differs from known attacks like Selfish Mining in its aim not to increase an adversary's revenue, but to disrupt the system. Although it bears some algorithmic similarity to those attacks, it introduces a new adversarial model, goals, algorithm, and game-theoretic analysis. Beyond its direct implications for operational blockchains, BDoS introduces the novel idea that an adversary can manipulate miners' incentives by proving the existence of blocks without actually publishing them.
Fast Bi-Layer Neural Synthesis of One-Shot Realistic Head Avatars

We propose a neural rendering-based system that creates head avatars from a single photograph. Our approach models a person’s appearance by decomposing it into two layers. The first layer is a pose-dependent coarse image that is synthesized by a small neural network. The second layer is defined by a pose-independent texture image that contains high-frequency details. The texture image is generated offline, warped and added to the coarse image to ensure a high effective resolution of synthesized head views. We compare our system to analogous state-of-the-art systems in terms of visual quality and speed. The experiments show significant inference speedup over previous neural head avatar models for a given visual quality. We also report on a real-time smartphone-based implementation of our system.
Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?

Extensive research has been conducted on sentiment analysis for software engineering (SA4SE). Researchers have invested much effort in developing customized tools (e.g., SentiStrength-SE, SentiCR) to classify the sentiment polarity for Software Engineering (SE) specific contents (e.g., discussions in Stack Overflow and code review comments). Even so, there is still much room for improvement. Recently, pre-trained Transformer-based models (e.g., BERT, XLNet) have brought considerable breakthroughs in the field of natural language processing (NLP). In this work, we conducted a systematic evaluation of five existing SA4SE tools and variants of four state-of-the-art pre-trained Transformer-based models on six SE datasets. Our work is the first to fine-tune pre-trained Transformer-based models for the SA4SE task. Empirically, across all six datasets, our fine-tuned pre-trained Transformer-based models outperform the existing SA4SE tools by 6.5-35.6% in terms of macro/micro-averaged F1 scores.
Fermionic Partial Tomography via Classical Shadows

We propose a tomographic protocol for estimating any $k$-body reduced density matrix ($k$-RDM) of an $n$-mode fermionic state, a ubiquitous step in near-term quantum algorithms for simulating many-body physics, chemistry, and materials. Our approach extends the framework of classical shadows, a randomized approach to learning a collection of quantum-state properties, to the fermionic setting. Our sampling protocol uses randomized measurement settings generated by a discrete group of fermionic Gaussian unitaries, implementable with linear-depth circuits. We prove that estimating all $k$-RDM elements to additive precision $ϵ$ requires on the order of $(\genfrac{}{}{0ex}{}{n}{k}){k}^{3/2}\mathrm{log}(n)/{ϵ}^{2}$ repeated state preparations, which is optimal up to the logarithmic factor. Furthermore, numerical calculations show that our protocol offers a substantial improvement in constant overheads for $k\ensuremath{\ge}2$, as compared to prior deterministic strategies. We also adapt our method to particle-number symmetry, wherein the additional circuit depth may be halved at the cost of roughly 2--5 times more repetitions.
Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization

Image enhancement has been widely applied to medical science, industry, agriculture, and military, for which it is very important to preserve brightness and structures. In order to overcome the shortcomings of over-enhancement and over-smoothing of traditional histogram equalization methods, this paper proposes an effective image enhancement strategy named as contrast limited dynamic quadri-histogram equalization (CLDQHE) which includes three steps: (1) the original histogram of an image is divided by a proposed threshold scheme into four sub-histograms. (2) An adaptive histogram clipping method is proposed to control the enhancement rate, which can adjust the clipping threshold. (3) Each sub-histogram is mapped to a new dynamic range and is equalized independently. Both quantitatively and qualitatively experimental results demonstrate that the proposed CLDQHE method can yield pleasing results with the preservation of brightness and structures, and even outperforms the state-of-the-arts.
A comprehensive review of past and present image inpainting methods

Images can be described as visual representations or likeness of something (person or object) which can be reproduced or captured, e.g. a hand drawing, photographic material. However, for images on photographic material, images can have defects at the point of captured, become damaged, or degrade over time. Historically, these were restored by hand to maintain image quality using a process known as inpainting. The advent of the digital age has seen the rapid shift image storage technologies, from hard-copies to digitalised units in a less burdensome manner with the application of digital tools. This paper presents a comprehensive review of image inpainting methods over the past decade and the commonly used performance metrics and datasets. To increase the clarity of our review, we use a hierarchical representation for the past state-of-the-art traditional methods and the present state-of-the-art deep learning methods. For traditional methods, we divide the techniques into five sub-categories, i.e. Exemplar-based texture synthesis, Exemplar-based structure synthesis, Diffusion-based methods, Sparse representation methods and Hybrid methods. Then we review the deep learning methods, i.e. Convolutional Neural Networks and Generative Adversarial Networks. We detail the strengths and weaknesses of each to provide new insights in the field. To address the challenges raised from our findings, we outline some potential future works.
Performance of Metaheuristic Optimization Algorithms based on Swarm Intelligence in Attitude and Altitude Control of Unmanned Aerial Vehicle for Path Following

Nowadays, it is very important for the success of the determined missions or operations that the Unmanned Aerial Vehicles (UAVs), which are used extensively in the performance of many civil and military tasks, follow the predetermined path with high accuracy at the determined altitude. The fact that the UAV performs its mission by adhering to the predetermined height and path enables the UAV to spend less energy and therefore fly for a longer time. Many traditional control algorithms, especially Proportional-Integral-Derivative (PID), are used in the attitude and altitude control of UAV for path following. Unlike other studies, in this study, metaheuristic optimization algorithms based on swarm intelligence estimate the parameters of the control algorithm proposed for UAV. Using meta-heuristic optimization algorithms such as Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO), both attitude and altitude control of the quadrotor have been performed for path following in routes with different geometries such as rectangle, circle, and lemniscate. The performance of each control algorithm in the study for the specified routes has been tested and the test results obtained have been compared with each other. Considering the features such as simplicity, flexibility, ability to search randomly and avoiding local optima, a new control algorithm whose K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</sub> , K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sub> , and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> parameter values optimized by HHO, have been proposed for UAV's attitude and altitude control.
Donkey and smuggler optimization algorithm: A collaborative working approach to path finding

Abstract Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face &amp; Suicide, and Face &amp; Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems. Highlights Donkey and Smuggler Optimization Algorithm (DSO) is a novel optimization algorithm. The DSO is inspired by Donkeys' behaviors. It has been evaluated against other algorithms. DSO solves different problems such as TSP, packet routing, and ambulance routing. The outcomes on the tests exhibit a greater manipulation of DSO. The outcomes prove that DSO has a greater searching ability.
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis

We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several enhancements. We find that low temperature scaling is a performance-critical component and explain why it works. Besides, we also discover that Global Max Pooling works better in general when compared to Global Average Pooling. Additionally, our proposed fast moving proxies also addresses small gradient issue of proxies, and this component synergizes well with low temperature scaling and Global Max Pooling. Our enhanced model, called ProxyNCA++, achieves a 22.9% point average improvement of Recall@1 across four different zero-shot retrieval datasets compared to the original ProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the CUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2, 90.1, 81.4, and 90.9, respectively.
Satellite quantum communications: Fundamental bounds and practical security

Satellite quantum communications are emerging within the panorama of quantum technologies as a more effective strategy to distribute completely-secure keys at very long distances, therefore playing an important role in the architecture of a large-scale quantum network. In this work, we apply and extend recent results in free-space quantum communications to determine the ultimate limits at which secret (and entanglement) bits can be distributed via satellites. Our study is comprehensive of the various practical scenarios, encompassing both downlink and uplink configurations, with satellites at different altitudes and zenith angles. It includes effects of diffraction, extinction, background noise and fading, due to pointing errors and atmospheric turbulence (appropriately developed for slant distances). Besides identifying upper bounds, we also discuss lower bounds, i.e., achievable rates for key generation and entanglement distribution. In particular, we study the composable finite-size secret key rates that are achievable by protocols of continuous variable quantum key distribution, for both downlink and uplink, showing the feasibility of this approach for all configurations. Finally, we present a study with a sun-synchronous satellite, showing that its key distribution rate is able to outperform a ground chain of ideal quantum repeaters.
Linguistic Structure Guided Context Modeling for Referring Image Segmentation

Referring image segmentation aims to predict the foreground mask of the object referred by a natural language sentence. Multimodal context of the sentence is crucial to distinguish the referent from the background. Existing methods either insufficiently or redundantly model the multimodal context. To tackle this problem, we propose a “gather-propagate-distribute” scheme to model multimodal context by cross-modal interaction and implement this scheme as a novel Linguistic Structure guided Context Modeling (LSCM) module. Our LSCM module builds a Dependency Parsing Tree suppressed Word Graph (DPT-WG) which guides all the words to include valid multimodal context of the sentence while excluding disturbing ones through three steps over the multimodal feature, i.e., gathering, constrained propagation and distributing. Extensive experiments on four benchmarks demonstrate that our method outperforms all the previous state-of-the-arts.
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation

Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt convolutions for feature extraction. However, as affected by the inherent receptive field, convolution based feature extraction inevitably mixes up the foreground features and the background features, resulting in ambiguities in the subsequent instance association. In this paper, we propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation. Our method generates a new tracking-by-points paradigm where discriminative instance embeddings are learned from randomly selected points rather than images. Furthermore, multiple informative data modalities are converted into point-wise representations to enrich point-wise features. The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5.4% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS). Evaluations across three datasets demonstrate both the effectiveness and efficiency of our method. Moreover, based on the observation that current MOTS datasets lack crowded scenes, we build a more challenging MOTS dataset named APOLLO MOTS with higher instance density. Both APOLLO MOTS and our codes are publicly available at https://github.com/detectRecog/PointTrack .
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets

State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded scenes, however, detectors often fail to obtain accurate detections due to heavy occlusions and high crowd density. In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and trajectories of multiple targets over the whole video. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to errors in crowded scenes, or rely on a suboptimal two-step process using heuristic density-aware point-tracks for matching targets.Our approach yields promising results on public benchmarks of various domains including people tracking, cell tracking, and fish tracking.