Reviewerly/exHarmony huggingface dataset:
Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors

Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real-time human volumetric capture of complex scenarios, especially using light-weight setups, remains challenging. In this paper, we propose a human volumetric capture method that combines temporal volumetric fusion and deep implicit functions. To achieve high-quality and temporal-continuous reconstruction, we propose dynamic sliding fusion to fuse neighboring depth observations together with topology consistency. Moreover, for detailed and complete surface generation, we propose detailpreserving deep implicit functions for RGBD input which can not only preserve the geometric details on the depth inputs but also generate more plausible texturing results. Results and experiments show that our method outperforms existing methods in terms of view sparsity, generalization capacity, reconstruction quality, and run-time efficiency.
A Generalized Loss Function for Crowd Counting and Localization

Previous work [40] shows that a better density map representation can improve the performance of crowd counting. In this paper, we investigate learning the density map representation through an unbalanced optimal transport problem, and propose a generalized loss function to learn density maps for crowd counting and localization. We prove that pixel-wise L2 loss and Bayesian loss [29] are special cases and suboptimal solutions to our proposed loss function. A perspective-guided transport cost function is further proposed to better handle the perspective transformation in crowd images. Since the predicted density will be pushed toward annotation positions, the density map prediction will be sparse and can naturally be used for localization. Finally, the proposed loss outperforms other losses on four large-scale datasets for counting, and achieves the best localization performance on NWPU-Crowd and UCF-QNRF.
FSDR: Frequency Space Domain Randomization for Domain Generalization

Domain generalization aims to learn a generalizable model from a ‘known’ source domain for various ‘unknown’ target domains. It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features. However, most existing randomization methods use GANs that often lack of controls and even alter semantic structures of images undesirably. Inspired by the idea of JPEG that converts spatial images into multiple frequency components (FCs), we propose Frequency Space Domain Randomization (FSDR) that randomizes images in frequency space by keeping domain-invariant FCs (DIFs) and randomizing domain-variant FCs (DVFs) only. FSDR has two unique features: 1) it decomposes images into DIFs and DVFs which allows explicit access and manipulation of them and more controllable randomization; 2) it has minimal effects on semantic structures of images and domain-invariant features. We examined domain variance and invariance property of FCs statistically and designed a network that can identify and fuse DIFs and DVFs dynamically through iterative learning. Extensive experiments over multiple domain generalizable segmentation tasks show that FSDR achieves superior segmentation and its performance is even on par with domain adaptation methods that access target data in training.
Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation

Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; On the other hand, it can remind radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis. Yet, this task remains a challenging job for data-driven neural networks, due to the serious visual and textual data biases. To this end, we propose a Posterior-and-Prior Knowledge Exploring-and-Distilling approach (PPKED) to imitate the working patterns of radiologists, who will first examine the abnormal regions and assign the disease topic tags to the abnormal regions, and then rely on the years of prior medical knowledge and prior working experience accumulations to write reports. Thus, the PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD). In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias; PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias. The explored knowledge is distilled by the MKD to generate the final reports. Evaluated on MIMIC-CXR and IU-Xray datasets, our method is able to outperform previous state-of-the-art models on these two datasets.
A novel version of Cuckoo search algorithm for solving optimization problems

In this paper, a Cuckoo search algorithm, namely the New Movement Strategy of Cuckoo Search (NMS-CS), is proposed. The novelty is in a random walk with step lengths calculated by Lévy distribution. The step lengths in the original Cuckoo search (CS) are significant terms in simulating the Cuckoo bird's movement and are registered as a scalar vector. In NMS-CS, step lengths are modified from the scalar vector to the scalar number called orientation parameter. This parameter is controlled by using a function established from the random selection of one of three proposed novel functions. These functions have diverse characteristics such as; convex, concave, and linear, to establish a new strategy movement of Cuckoo birds in NMS-CS. As a result, the movement of NMS-CS is more flexible than a random walk in the original CS. By using the proposed functions, NMS-CS achieves the distance of movement long enough at the first iterations and short enough at the last iterations. It leads to the proposed algorithm achieving a better convergence rate and accuracy level in comparison with CS. The first 23 classical benchmark functions are selected to illustrate the convergence rate and level of accuracy of NMS-CS in detail compared with the original CS. Then, the other Algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Grey Wolf Optimizer (GWO) are employed to compare with NMS-CS in a ranking of the best accuracy. In the end, three engineering design problems (tension/compression spring design, pressure vessel design and welded beam design) are employed to demonstrate the effect of NMS-CS for solving various real-world problems. The statistical results show the potential performance of NMS-CS in a widespread class of optimization problems and its excellent application for optimization problems having many constraints. Source codes of NMS-CS is publicly available at http://goldensolutionrs.com/codes.html.
Real-Time Underwater Maritime Object Detection in Side-Scan Sonar Images Based on Transformer-YOLOv5

To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image.
Applications of distributed ledger technology (DLT) and Blockchain-enabled smart contracts in construction

The contribution of distributed ledger technology (DLT) (e.g. blockchain) and smart contracts to the digitalisation and digital transformation of the construction sector is nascent but rapidly gaining traction. ‘Systematic reviews’ of DLT and smart contract applications that are specific to the construction sector are missing. This paper performs an extensive systematic review of 153 DLT and smart contract papers specific to the design, construction and operation of built assets. The protocols and processes of a systematic review were adopted to ensure full transparency, accountability, reproducibility, and updateability of the results. Through thematic analysis, we identify eight distinct themes of applications for DLT and smart contracts in construction: information management, payments, procurement, supply chain management, regulations and compliance, construction management and delivery, dispute resolution, and technological systems. Each theme identified was analysed to understand current capabilities, applications, and future developments. A cross-themes discussion revealed that DLT and smart contracts are ‘supplementary’ technologies that are used in combination with other technologies (e.g. BIM, IoT, cloud computing) as part of ‘technological systems’ that need to co-evolve in order to enable the themes' applications identified. Research into DLT and smart contracts in construction is rapidly moving from theoretical insights and frameworks into developing proofs-of-concept studies (27 studies) and testing them in case studies (20 studies). The next stage of research involving wider academic communities and industry-wide engagement is expected to begin uncovering the anticipated benefits of DLT and smart contracts through investments into technological systems and testing in real-world pilot studies. The discussion of the themes identified from technology, policy, process, and society perspectives exposed the need for an extended socio-technical approach to the solution in order to deliver the necessary change and impact from the adoption of DLT and smart contracts at speed and scale. The results of this systematic review provide a noteworthy reference point for academics, practitioners and policy makers interested in the future development of DLT and smart contract applications in construction.
Creating and detecting fake reviews of online products

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.
ICT for sustainable development: Global comparative evidence of globalisation thresholds

The objectives of this paper are to investigate the effect of ICT on sustainable development and the mechanisms through which the effect is modulated. The focus is on a sample of 140 countries around the globe for the period 2000–2019. The methodology involves the: (i) Fixed Effects estimator to control for individual heterogeneity, (ii) Driscoll and Kraay estimator to control for cross-section dependence between panels, (iii) the Mean Group estimator to take into account the averages between panel groups, (iv) the system GMM to correct for unobserved heterogeneity and simultaneity bias and (v) the instrumental variable Fixed Effects Tobit to take in to account the limited range in our dependent variable. The results show that ICT has a positive and significant effect on sustainable development. Whereas overall net effects are positive, the findings are contingent on the choice of the ICT measurement, the geographical location of the economy and the income group category. The study recommends policy makers to take into account ICT and the advantages it offers in the elaboration of measures for the sustainable development agenda.
AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis

Generating high-fidelity talking head video by fitting with the input audio sequence is a challenging problem that receives considerable attentions recently. In this paper, we address this problem with the aid of neural scene representation networks. Our method is completely different from existing methods that rely on intermediate representations like 2D landmarks or 3D face models to bridge the gap between audio input and video output. Specifically, the feature of input audio signal is directly fed into a conditional implicit function to generate a dynamic neural radiance field, from which a high-fidelity talking-head video corresponding to the audio signal is synthesized using volume rendering. Another advantage of our framework is that not only the head (with hair) region is synthesized as previous methods did, but also the upper body is generated via two individual neural radiance fields. Experimental results demonstrate that our novel framework can (1) produce high-fidelity and natural results, and (2) support free adjustment of audio signals, viewing directions, and background images. Code is available at https://github.com/YudongGuo/AD-NeRF.
Instances as Queries

We present QueryInst, a new perspective for instance segmentation. QueryInst is a multi-stage end-to-end system that treats instances of interest as learnable queries, enabling query based object detectors, e.g., Sparse RCNN, to have strong instance segmentation performance. The attributes of instances such as categories, bounding boxes, instance masks, and instance association embeddings are represented by queries in a unified manner. In QueryInst, a query is shared by both detection and segmentation via dynamic convolutions and driven by parallellysupervised multi-stage learning. We conduct extensive experiments on three challenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in object detection, instance segmentation, and video instance segmentation tasks. For the first time, we demonstrate that a simple end-to-end query based framework can achieve the state-of-the-art performance in various instance-level recognition tasks. Code is available at https://github.com/hustvl/QueryInst.
Causal Directed Acyclic Graphs

This JAMA Guide to Statistics and Methods discusses the basics of causal directed acyclic graphs, which are useful tools for communicating researchers’ understanding of the potential interplay among variables and are commonly used for mediation analysis.
Beyond the bubble: Will NFTs and digital proof of ownership empower creative industry entrepreneurs?

Non-fungible Tokens (NFTs) are blockchain-enabled cryptographic assets that represent proof-of-ownership for digital objects. The use of NFTs has been pioneered by creative industry entrepreneurs who have sought to generate new revenue streams and modes of stakeholder engagement. Despite rapid growth in popularity, concerns have been raised around the legal ownership of NFT assets and the prevalence of speculation and fraud associated with NFT trading. In this rapid response article, we explore the value of NFTs for creative industry entrepreneurs. First, we examine the novel digital affordances of the technology; second, we analyse NFTs through the prism of the recent Initial Coin Offering (ICO) boom and bust; and finally, we take a longer-term historical perspective to consider how past speculative waves inform the present NFT economy. While we identify some potentially valuable artistic and financial opportunities for creative industry entrepreneurs, we conclude that NFTs should be approached with caution.
Consensus reaching process in large-scale group decision making based on bounded confidence and social network

Consensus reaching process (CRP) is a dynamic and interactive method used to reach a group decision. Now that social networks and mobile internet are prominent features in daily life, more experts are able to take participate in decision making in the network and their opinions are influenced by others in the decision-making process. Therefore, how to use the difference of opinions and the relationships between experts to promote the consensus is an important issue. This paper proposed a CRP approach for large-scale group decision-making based on bounded confidence and social network to manage experts’ opinions. Firstly, a fast unfolding algorithm was used to reduce the dimension of the large-scale and the experts’ weights were obtained by social network analysis. Secondly, the CRP was built based on the Manhattan distance, and the feedback mechanism was developed to adjust experts’ opinions based on bounded confidence and social network when the experts did not reach a consensus. A numerical example was used to show the feasibility of the proposed approach and the results illustrated that the consensus speed was faster and the information managed more efficiently. The comparisons with other approaches showed the advantage of our proposed approach. Finally, simulation experiments were given to verify the effectiveness of our proposed approach.
A New Service Mechanism for Profit Optimizations of a Cloud Provider and Its Users

In this paper, we try to design a service mechanism for profit optimizations of both a cloud provider and its multiple users. We consider the problem from a game theoretic perspective and characterize the relationship between the cloud provider and its multiple users as a Stackelberg game, in which the strategies of all users are subject to that of the cloud provider. The cloud provider tries to select and provision appropriate servers and configure a proper request allocation strategy to reduce energy cost while satisfying its cloud users at the same time. We approximate its servers selection space by adding a controlling parameter and configure an optimal request allocation strategy. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value under the strategy of the cloud provider. We formulate the competitions among all users as a generalized Nash equilibrium problem (GNEP). We solve the problem by employing variational inequality (VI) theory and prove that there exists a generalized Nash equilibrium solution set for the formulated GNEP. Finally, we propose an iterative algorithm (IA), which characterizes the whole process of our proposed service mechanism. We conduct some numerical calculations to verify our theoretical analyses. The experimental results show that our IA algorithm can benefit both of a cloud provider and its multiple users by configuring proper strategies.
Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking

Tracking-by-detection is one of the most popular approaches to tracking multiple objects in which the detector plays an important role. Sometimes, detector failures caused by occlusions or various poses are unavoidable and lead to tracking failure. To cope with this problem, we construct a heterogeneous association graph that fuses high-level detections and low-level image evidence for target association. Compared with other methods using low-level information, our proposed heterogeneous association fusion (HAF) tracker is less sensitive to particular parameters and is easier to extend and implement. We use the fused association graph to build track trees for HAF and solve them by the multiple hypotheses tracking framework, which has been proven to be competitive by introducing efficient pruning strategies. In addition, the novel idea of adaptive weights is proposed to analyze the contribution between motion and appearance. We also evaluated our results on the MOT challenge benchmarks and achieved state-of-the-art results on the MOT Challenge 2017.
A semantic similarity-based perspective of affect lexicons for sentiment analysis

Lexical resources are widely popular in the field of Sentiment Analysis, as they represent a resource that directly encodes sentimental knowledge. Usually sentiment lexica are used for polarity estimation through the matching of words contained in a text and their associated lexicon sentiment polarities. Nevertheless, such resources have limitations in vocabulary coverage and domain adaptation. Besides, many recent techniques exploit the concept of distributed semantics, normally through word embeddings. In this work, a semantic similarity metric is computed between text words and lexica vocabulary. Using this metric, this paper proposes a sentiment classification model that uses the semantic similarity measure in combination with embedding representations. In order to assess the effectiveness of this model, we perform an extensive evaluation. Experiments show that the proposed method can improve Sentiment Analysis performance over a strong baseline, being this improvement statistically significant. Finally, some characteristics of the proposed technique are studied, showing that the selection of lexicon words has an effect in cross-dataset performance.
Financial development, income inequality, and country risk

This study explores the non-linear effects of both finance development and country risks on income inequality when countries’ risk changes through a broad sample of 59 countries over the period 1985–2015. Using the panel smooth transition regression model, our results present evidence of the different relationships among income inequality, finance development, and country risks under different degrees of country risk. For the full sample, we find evidence of the inequality-widening hypothesis under unstable economic, stable financial, and stable political environments. For the sub-samples, under stable economic and financial environments, high-income countries’ income inequality can be improved through financial development. Moreover, we find a positive relationship between financial development and income inequality for low-income countries.
An image encryption algorithm based on modified logistic chaotic map

In order to improve the problems of small key space and poor security when the one-dimension logistic chaotic map is applied in image encryption, we propose a modified Logistic chaotic map. Based on the modified Logistic chaotic map, a new image encryption algorithm is introduced. The experimental simulation proves the feasibility of the proposed algorithm. The safety performance analysis shows that the algorithm has infinite key space and high security, and can resist the attack of the exhaustive method and statistical analysis.