Reviewerly/exHarmony huggingface dataset:
FakeLocator: Robust Localization of GAN-Based Face Manipulations

Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media forensics area, detecting and ultimately locating the image forgery has become an imperative task. In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization. Based on this basic observation, we have proposed a novel approach, termed <i>FakeLocator</i>, to obtain high localization accuracy, at full resolution, on manipulated facial images. To the best of our knowledge, this is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness map that preserves more information of fake regions. To improve the universality of <i>FakeLocator</i> across multifarious facial attributes, we introduce an attention mechanism to guide the training of the model. To improve the universality of <i>FakeLocator</i> across different DeepFake methods, we propose partial data augmentation and single sample clustering on the training images. Experimental results on popular FaceForensics&#x002B;&#x002B;, DFFD datasets and seven different state-of-the-art GAN-based face generation methods have shown the effectiveness of our method. Compared with the baselines, our method performs better on various metrics. Moreover, the proposed method is robust against various real-world facial image degradations such as JPEG compression, low-resolution, noise, and blur.
Topic-Enhanced Capsule Network for Multi-Label Emotion Classification

Identifying multiple emotions in a piece of text is an important research topic in the NLP community.Existing methods usually model the task as a multi-label classification problem, while these work has two issues. First, these methods fail to leverage the topic information of the text, which has been shown to be effective for sentiment analysis task. Second, different parts of the text can contribute differently to predicting different emotion labels, so the proposed model needs to capture effective features for each corresponding emotion, which is not considered by existing models. To tackle these problems, we propose a topic-enhanced capsule network, which contains two main parts: a variational autoencoder and a capsule module, for multi-label emotion detection task. Specifically, the variational autoencoder can learn the latent topic information of the text, and the capsule module can capture rich features for corresponding emotion. Experimental results on two benchmark datasets show that the proposed model achieves the current best performance, outperforming previous methods and strong baselines by a large margin.
DeepDeform: Learning Non-Rigid RGB-D Reconstruction With Semi-Supervised Data

Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. Unfortunately, this method fails for important cases such as highly non-rigid deformations. We first address this problem of lack of data by introducing a novel semi-supervised strategy to obtain dense inter-frame correspondences from a sparse set of annotations. This way, we obtain a large dataset of 400 scenes, over 390,000 RGB-D frames, and 5,533 densely aligned frame pairs; in addition, we provide a test set along with several metrics for evaluation. Based on this corpus, we introduce a data-driven non-rigid feature matching approach, which we integrate into an optimization-based reconstruction pipeline. Here, we propose a new neural network that operates on RGB-D frames, while maintaining robustness under large non-rigid deformations and producing accurate predictions. Our approach significantly outperforms existing non-rigid reconstruction methods that do not use learned data terms, as well as learning-based approaches that only use self-supervision.
Learning to See Through Obstructions

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera. Our method leverages the motion differences between the background and the obstructing elements to recover both layers. Specifically, we alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network. The learning-based layer reconstruction allows us to accommodate potential errors in the flow estimation and brittle assumptions such as brightness consistency. We show that training on synthetically generated data transfers well to real images. Our results on numerous challenging scenarios of reflection and fence removal demonstrate the effectiveness of the proposed method.
An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain Databases

In recent decades, mobile or the Internet of Thing (IoT) devices are dramatically increasing in many domains and applications. Thus, a massive amount of data is generated and produced. Those collected data contain a large amount of interesting information (i.e., interestingness, weight, frequency, or uncertainty), and most of the existing and generic algorithms in pattern mining only consider the single object and precise data to discover the required information. Meanwhile, since the collected information is huge, and it is necessary to discover meaningful and up-to-date information in a limit and particular time. In this paper, we consider both utility and uncertainty as the majority objects to efficiently mine the interesting high expected utility patterns (HEUPs) in a limit time based on the multi-objective evolutionary framework. The benefits of the designed model (called MOEA-HEUPM) can discover the valuable HEUPs without pre-defined threshold values (i.e., minimum utility and minimum uncertainty) in the uncertain environment. Two encoding methodologies are also considered in the developed MOEA-HEUPM to show its effectiveness. Based on the developed MOEA-HEUPM model, the set of non-dominated HEUPs can be discovered in a limit time for decision-making. Experiments are then conducted to show the effectiveness and efficiency of the designed MOEA-HEUPM model in terms of convergence, hypervolume and number of the discovered patterns compared to the generic approaches.
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling

Hyperspectral super-resolution (HSR) aims at fusing a pair of hyperspectral and multispectral images to recover a super-resolution image (SRI). This work revisits coupled tensor decomposition (CTD)-based HSR. The vast majority of the HSR approaches take a low-rank matrix recovery perspective. The challenge is that theoretical guarantees for recovering the SRI using low-rank matrix models are either elusive or derived under stringent conditions. A couple of recent CTD-based methods ensure recoverability for the SRI under relatively mild conditions, leveraging algebraic properties of the canonical polyadic decomposition (CPD) and the Tucker decomposition models, respectively. However, the latent factors of both the CPD and Tucker models have no physical interpretations in the context of spectral image analysis, which makes incorporating prior information challenging-but using priors is often essential for enhancing performance in noisy environments. This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r,L_r,1)$ terms (i.e., the ${\mathsf {LL1}}$ model) and formulates the HSR problem as a coupled ${\mathsf {LL1}}$ tensor decomposition problem. Similar to the existing CTD approaches, recoverability of the SRI is shown under mild conditions. More importantly, the latent factors of the ${\mathsf {LL1}}$ model can be interpreted as the key constituents of spectral images, i.e., the endmembers' spectral signatures and abundance maps. This connection allows us to incorporate prior information for performance enhancement. A flexible algorithmic framework that can work with a series of structural information is proposed to take advantages of the model interpretability. The effectiveness is showcased using simulated and real data.
Mediated trust: A theoretical framework to address the trustworthiness of technological trust mediators

This article considers the impact of digital technologies on the interpersonal and institutional logics of trust production. It introduces the new theoretical concept of technology-mediated trust to analyze the role of complex techno-social assemblages in trust production and distrust management. The first part of the article argues that globalization and digitalization have unleashed a crisis of trust, as traditional institutional and interpersonal logics are not attuned to deal with the risks introduced by the prevalence of digital technologies. In the second part, the article describes how digital intermediation has transformed the traditional logics of interpersonal and institutional trust formation and created new trust-mediating services. Finally, the article asks as follows: why should we trust these technological trust mediators? The conclusion is that at best, it is impossible to establish the trustworthiness of trust mediators, and that at worst, we have no reason to trust them.
Spatiotemporal chaos in cross coupled map lattice with dynamic coupling coefficient and its application in bit-level color image encryption

This paper proposes a novel tent-dynamic cross coupled map lattice (TDCCML) by introducing the dynamic coupling coefficient. A large number of theoretical analysis and experiments prove that it has a larger parameter space and better chaotic property compared with the original cross coupled map lattice (CCML), which is highly suitable for chaos-based image encryption. Moreover, this paper proposes a bit-level color image encryption scheme using TDCCML, and demonstrates the good chaotic behavior of TDCCML from the aspect of encryption performance against the common types of attacks.
Person search: New paradigm of person re-identification: A survey and outlook of recent works

Person Search (PS) has become a major field because of its need in community and in the field of research among researchers. This task aims to find a probe person from whole scene which shows great significance in video surveillance field to track lost people, re-identification, and verification of person. In last few years, deep learning has played unremarkable role for the solution of re-identification problem. Deep learning shows incredible performance in person (re-ID) and search. Researchers experience more flexibility in proposing new methods and solve challenging issues such as low resolution, pose variation, background clutter, occlusion, viewpoints, and low illumination. Specially, convolutional neural network (CNN) achieves breakthrough performance and extracts useful patterns and characteristics. Development of new framework takes substantial efforts; hard work and computation cost are required to acquire excellent results. This survey paper includes brief discussion about feature representation learning and deep metric learning with novel loss functions. We thoroughly review datasets with performance analysis on existing datasets. Finally, we are reviewing current solutions for further consideration.
CenterFace: Joint Face Detection and Alignment Using Face as Point

Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power. This paper proposes a one-stage method named CenterFace to simultaneously predict facial box and landmark location with real-time speed and high accuracy. The proposed method also belongs to the anchor-free category. This is achieved by (a) learning face existing possibility by the semantic maps, (b) learning bounding box, offsets, and five landmarks for each position that potentially contains a face. Specifically, the method can run in real time on a single CPU core and 200 FPS using NVIDIA 2080TI for VGA-resolution images and can simultaneously achieve superior accuracy (WIDER FACE Val/Test-Easy: 0.935/0.932, Medium: 0.924/0.921, Hard: 0.875/0.873, and FDDB discontinuous: 0.980 and continuous: 0.732).
Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs

This article investigates the problem of testing independence of two random vectors of general dimensions. For this, we give for the first time a distribution-free consistent test. Our approach combines distance covariance with the center-outward ranks and signs developed by Marc Hallin and collaborators. In technical terms, the proposed test is consistent and distribution-free in the family of multivariate distributions with nonvanishing (Lebesgue) probability densities. Exploiting the (degenerate) U-statistic structure of the distance covariance and the combinatorial nature of Hallin's center-outward ranks and signs, we are able to derive the limiting null distribution of our test statistic. The resulting asymptotic approximation is accurate already for moderate sample sizes and makes the test implementable without requiring permutation. The limiting distribution is derived via a more general result that gives a new type of combinatorial noncentral limit theorem for double- and multiple-indexed permutation statistics. Supplementary materials for this article are available online.
Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks

In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem is central to quantitative finance. After a detailed introduction to the problem, we implement a number of quantum and quantum-inspired algorithms on different hardware platforms to solve its discrete formulation using real data from daily prices over 8 years of 52 assets, and do a detailed comparison of the obtained Sharpe ratios, profits, and computing times. In particular, we implement classical solvers (Gekko, exhaustive), D-wave hybrid quantum annealing, two different approaches based on variational quantum eigensolvers on IBM-Q (one of them brand-new and tailored to the problem), and for the first time in this context also a quantum-inspired optimizer based on tensor networks. In order to fit the data into each specific hardware platform, we also consider doing a preprocessing based on clustering of assets. From our comparison, we conclude that D-wave hybrid and tensor networks are able to handle the largest systems, where we do calculations up to 1272 fully-connected qubits for demonstrative purposes. Finally, we also discuss how to mathematically implement other possible real-life constraints, as well as several ideas to further improve the performance of the studied methods.
Improving neighbor-based collaborative filtering by using a hybrid similarity measurement

Memory-based collaborative filtering is one of the recommendation system methods used to predict a user's rating or preference by exploring historic ratings, but without incorporating any content information about users or items. It can be either item-based or user-based. Taking item-based Collaborative Filtering (CF) as an example, the way it makes predictions is accomplished in 2 steps: first, it selects based on pair-wise similarities a number of most similar items to the predicting item from those that the user has already rated on. Second, it aggregates the user's opinions on those most similar items to predict a rating on the predicting item. Thus, similarity measurement determines which items are similar, and plays an important role on how accurate the predictions are. Many studies have been conducted on memory-based CFs to improve prediction accuracy, but none of them have achieved better prediction accuracy than state-of-the-art model-based CFs. In this paper, we proposed a new approach that combines both structural and rating-based similarity measurement. We found that memory-based CF using combined similarity measurement can achieve better prediction accuracy than model-based CFs in terms of lower MAE and reduce memory and time by using less neighbors than traditional memory-based CFs on MovieLens and Netflix datasets.
RSDehazeNet: Dehazing Network With Channel Refinement for Multispectral Remote Sensing Images

Multispectral remote sensing (RS) images are often contaminated by the haze that degrades the quality of RS data and reduces the accuracy of interpretation and classification. Recently, the emerging deep convolutional neural networks (CNNs) provide us new approaches for RS image dehazing. Unfortunately, the power of CNNs is limited by the lack of sufficient hazy-clean pairs of RS imagery, which makes supervised learning impractical. To meet the data hunger of supervised CNNs, we propose a novel haze synthesis method to generate realistic hazy multispectral images by modeling the wavelength-dependent and spatial-varying characteristics of haze in RS images. The proposed haze synthesis method not only alleviates the lack of realistic training pairs in multispectral RS image dehazing but also provides a benchmark data set for quantitative evaluation. Furthermore, we propose an end-to-end RSDehazeNet for haze removal. We utilize both local and global residual learning strategies in RSDehazeNet for fast convergence with superior performance. Channel attention modules are incorporated to exploit strong channel correlation in multispectral RS images. Experimental results show that the proposed network outperforms the state-of-the-art methods for synthetic data and real Landsat-8 OLI multispectral RS images.
Address Clustering Heuristics for Ethereum

For many years, address clustering for the identification of entities has been the basis for a variety of graph-based investigations of the Bitcoin blockchain and its derivatives. Especially in the field of fraud detection it has proven to be useful. With the popularization and increasing use of alternative blockchains, the question arises how to recognize entities in these new systems. Currently, there are no heuristics that can directly be applied to Ethereum’s account balance model. This drawback also applies to other smart contract platforms like EOS or NEO, for which previous transaction network analyses have been limited to address graphs. In this paper, we show how addresses can be clustered in Ethereum, yielding entities that are likely in control of multiple addresses. We propose heuristics that exploit patterns related to deposit addresses, multiple participation in airdrops and token authorization mechanisms. We quantify the applicability of each individual heuristic over the first 4 years of the Ethereum blockchain and illustrate identified entities in a sample token network. Our results show that we can cluster 17.9% of all active externally owned account addresses, indicating that there are more than 340,000 entities that are likely in control of multiple addresses. Comparing the heuristics, we conclude that the deposit address heuristic is currently the most effective approach.
Neuro-fuzzy systems in construction engineering and management research

Neuro-fuzzy systems (NFS) can explicitly represent and model the input–output relationships of complex problems and non-linear systems, like those inherent in real-world construction engineering and management (CEM) problems. This paper contributes three things previously lacking in CEM literature: a systematic review and content analysis of published articles related to NFS topics in CEM research; identification of criteria to evaluate different NFS; and recommendations to researchers and industry practitioners in choosing a suitable subset of NFS techniques for solving different types of CEM problems. The literature review reveals that NFS classification methods are based on NFS architecture, learning algorithm, fuzzy method, and application area. This paper systematically categorizes CEM application domains (decision making, prediction/forecasting, evaluation/assessment, system modeling and analysis, simulation, and optimization) and maps them to NFS based on their suitability, which is determined using the performance evaluation criteria of convergence speed, computational complexity, interpretability, accuracy, and local minima trapping.
Riding the Wave of Crypto-Exuberance: The Potential Misusage of Corporate Blockchain Announcements

Cryptocurrencies have been broadly scrutinised in recent times for a host of concerning regulatory and cybercriminality issues. Although steps have been taken to promote regulatory sufficiency in the near future, we examine the avenues through which this extremely high-risk industry can derive potentially devastating contagion channels, influencing both unwilling and unsuspecting investors. We focus this research on the expressions of interest by publicly traded companies across the world to utilise cryptocurrency and blockchain projects. We find evidence that there exists a substantial stock price premium and sustained increase in volatility in the aftermath of blockchain announcements, with emphasis on highly-speculative motives such as coin creation and corporate name changes. Changes in price discovery and information flows are found to be largely determined from cryptocurrency-based pricing sources in the aftermath of speculative announcements. We discuss the inherent ethical and legal issues, considering as to whether such announcements are simply an attempt to artificially manipulate share prices and take part in the current phase of crypto-exuberance.
Effects of aerosols and water vapour on spatial-temporal variations of the clear-sky surface solar radiation in China

Clear-sky solar radiation is an important indicator describing the potential of solar energy. The aerosols and water vapour, apart from geometrical/astronomical ones, are the main factors resulting in the losses of the surface solar radiation (SSR) under clear sky conditions. This study uses satellite aerosol data, reanalysis water vapour data, and the Mesoscale Atmospheric Irradiance Code to create the clear-sky SSR dataset for China in 2001–2015. The aerosol direct radiative effect (ADRE) and water vapour radiative effect (WVRE) are calculated to study the clear-sky SSR losses due to aerosols and water vapour. The coefficient of determination (R2) of the observations and simulations is 0.944. The mean absolute error (MAE) and root mean square error (RMSE) are 6.67% and 9.00%, respectively. The strongest attenuation in clear-sky SSR due to aerosols occurs in April in most areas of China, while in June in North China and the Yangtze River Delta, water vapour most strongly attenuates the clear-sky SSR in July, and the monthly solar radiation losses due to water vapour are higher than those due to aerosols. The high aerosol and water vapour contents are the reasons why the clear-sky SSR in North China and the Yangtze River Delta in June are lower than those in May. From 2001 to 2015, the clear-sky trend with the greatest increase of up to 0.389 W m−2 per year (2.48% in 15 years) occurs over central Inner Mongolia, which is related to the reduction in dust. The increasing concentration of particulate matter in the atmosphere results in the most significant decreasing clear-sky trend in North China (−0.510 W m−2 year−1, 3.35% in 15 years). Generally, the ADRE variations are the main factor controlling the annual variations in clear-sky SSR.