Reviewerly/exHarmony huggingface dataset:
KernelNet: A Blind Super-Resolution Kernel Estimation Network

Recently developed deep neural network methods have achieved remarkable performance in the Super Resolution problem when applied to Low Resolution (LR) images that are obtained from High Resolution (HR) images with ideal and predefined downsampling processing, i.e., convolution with a known blurring kernel that is followed by subsampling (e.g., Bicubic). However, when these algorithms are applied to real-world images whose downsampling pattern is unknown, unlike synthetically generated LR-HR image pairs, their performance drops drastically. Blind SR problem can be defined as real-world image SR when the down-sampling blurring kernel (SR kernel) is unknown. The recent SR kernel estimation techniques like KernelGAN have shown promising results in this direction. However, their limited recovery performance and high computational complexity make them unsuitable for real-time usage, like for applications in mobile cameras. This paper proposes a modular and interpretable neural network structure, KernelNet, for the blind SR kernel estimation problem. The proposed model outperforms the state-of-the-art SR kernel estimator, KernelGAN, by a significant margin in SR kernel reconstruction accuracy. Moreover, to the best of our knowledge, the proposed algorithm is the first one that can estimate the SR kernel in real-time by performing O(1k) times faster than KernelGAN.
Finding Good Proofs for Description Logic Entailments using Recursive Quality Measures

Abstract Logic-based approaches to AI have the advantage that their behavior can in principle be explained to a user. If, for instance, a Description Logic reasoner derives a consequence that triggers some action of the overall system, then one can explain such an entailment by presenting a proof of the consequence in an appropriate calculus. How comprehensible such a proof is depends not only on the employed calculus, but also on the properties of the particular proof, such as its overall size, its depth, the complexity of the employed sentences and proof steps, etc. For this reason, we want to determine the complexity of generating proofs that are below a certain threshold w.r.t. a given measure of proof quality. Rather than investigating this problem for a fixed proof calculus and a fixed measure, we aim for general results that hold for wide classes of calculi and measures. In previous work, we first restricted the attention to a setting where proof size is used to measure the quality of a proof. We then extended the approach to a more general setting, but important measures such as proof depth were not covered. In the present paper, we provide results for a class of measures called recursive, which yields lower complexities and also encompasses proof depth. In addition, we close some gaps left open in our previous work, thus providing a comprehensive picture of the complexity landscape.
Semi-Supervised Image Deraining Using Gaussian Processes

Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data and hence, generalize poorly to real-world images. The use of real-world data in training image deraining networks is relatively less explored in the literature. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images. More specifically, we model the latent space vectors of unlabeled data using Gaussian Processes, which is then used to compute pseudo-ground-truth for supervising the network on unlabeled data. The pseudo ground-truth is further used to supervise the network at the intermediate level for the unlabeled data. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain200L and DDN-SIRR), we show that the proposed method is able to effectively leverage unlabeled data thereby resulting in significantly better performance as compared to labeled-only training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results in superior performance as compared to the existing methods. Code is available at: https://github.com/rajeevyasarla/Syn2Real.
Nonlinear diffusion equation with selective source for binarization of degraded document images

Binarization has always been a very challenging task for degraded document images because of the variety and complexity of degradations. This paper proposed a nonlinear diffusion equation with selective source for restoration of degraded document images, followed by a binary projection for binarization. The source is composed of two parts: one is used for the restoration of contaminated background, and another is responsible for the fidelity of texts. The evolution equation is numerically solved by the simplest finite differencing. The proposed method is tested on publicly available datasets (DIBCO (Document Image Binarization Competition) 2009–2014, 2016) and is compared with five models based on partial differential equation (PDE). The experimental results show that the proposed method is effective for degraded document images and has achieved generally the best performance of binarization compared to the other five PDE methods.
Joint emotion label space modeling for affect lexica

Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, vocabulary coverage issues, differences in construction method and discrepancies in emotion framework and representation result in a heterogeneous landscape of emotion detection resources, calling for a unified approach to utilizing them. To combat this, we present an extended emotion lexicon of 30,273 unique entries, which is a result of merging eight existing emotion lexica by means of a multi-view variational autoencoder (VAE). We showed that a VAE is a valid approach for combining lexica with different label spaces into a joint emotion label space with a chosen number of dimensions, and that these dimensions are still interpretable. We tested the utility of the unified VAE lexicon by employing the lexicon values as features in an emotion detection model. We found that the VAE lexicon outperformed individual lexica, but contrary to our expectations, it did not outperform a naive concatenation of lexica, although it did contribute to the naive concatenation when added as an extra lexicon. Furthermore, using lexicon information as additional features on top of state-of-the-art language models usually resulted in a better performance than when no lexicon information was used.
Online Performance Prediction of Perception DNNs by Multi-Task Learning With Depth Estimation

Online performance prediction (or: observation) of deep neural networks (DNNs) in highly automated driving presents an unsolved task until now, as most DNNs are evaluated offline requiring datasets with ground truth labels. In practice, however, DNN performance depends on the used camera type, lighting and weather conditions, and on various other kinds of domain shift. Also, the input to DNN-based perception systems can be perturbed by adversarial attacks requiring means to detect these input perturbations. In this work we propose a method to mitigate these problems by a multi-task learning approach with monocular depth estimation as a secondary task, which enables us to predict the DNN's performance for various other (primary) tasks by evaluating only the depth estimation task with a physical depth measurement provided, e.g., by a LiDAR sensor. We show the effectiveness of our method for the primary task of semantic segmentation using various training datasets, test datasets, model architectures, and input perturbations. Our method provides an effective way to predict (observe) the performance of DNNs for semantic segmentation even on a single-image basis and is transferable to other primary DNN-based perception tasks in a straightforward manner.
Application of Chi-square discretization algorithms to ensemble classification methods

Classification is one of the important tasks in data mining and machine learning. Classification performance depends on many factors as well as data characteristics. Some algorithms are known to work better with discrete data. In contrast, most real-world data contain continuous variables. For algorithms working with discrete data, these continuous variables must be converted to discrete ones. In this process called discretization, continuous variables are converted to their corresponding discrete variables. In this paper, four Chi-square based supervised discretization algorithms ChiMerge(ChiM), Chi2, Extended Chi2(ExtChi2) and Modified Chi2(ModChi2) were used. In the literature, the performance of these algorithms is often tested with decision trees and Naïve Bayes classifiers. In this study, differently, four sets of data discretized by these algorithms were classified with ensemble methods. Classification accuracies for these data sets were obtained through using a stratified 10-fold cross-validation method. The classification performance of the original and discrete data sets of the methods is presented comparatively. According to the results, the performance of the discrete data is more successful than the original data.
A Configurable Multilingual Model is All You Need to Recognize All Languages

Multilingual automatic speech recognition models have shown great promise in recent years because of the simple model training and deployment process. Conventional methods either train a universal multilingual model without taking any language information or with a 1-hot language ID (LID) vector to guide the recognition of the target language. In practice, a multilingual user can be prompted to preselect several languages he/she can speak. The multilingual model without LID cannot well utilize the language information set by the user while the multilingual model with 1-hot LID can only handle one pre-selected language. In this paper, we propose a novel configurable multilingual model (CMM) which is trained only once but can be configured as different models based on users’ choices by extracting language-specific modules together with a universal module from the trained CMM. Particularly, a single CMM can be deployed to any user scenario where the users can pre-select any combination of languages. Trained with 75K hours of transcribed anonymized Microsoft multilingual data and evaluated with 10-language test sets, the proposed CMM improves from the universal multilingual model by 26.0%, 16.9%, and 10.4% relative word error reduction when the user selects 1, 2, or 3 languages, respectively.
How personal characteristics impact phishing susceptibility: The mediating role of mail processing

In the phishing email literature, recent researchers have given much attention to individual differences in phishing susceptibility from the perspective of the Big Five personality traits. Although the effectiveness and advantages of the phishing susceptibility measures in the signal detection theory (SDT) framework have been verified, the cognitive mechanisms that lead to individual differences in these measures remain unknown. The current study proposed and examined a theoretical path model to explore how the Big Five personality traits, related knowledge and experience and the cognitive processing of emails (i.e., mail elaboration) influence users’ susceptibility to phishing emails. A sample of 414 Chinese participants completed the 44-item Big Five Personality Inventory (BFI-44), Mail Elaboration Scale (MES), Web Experience Questionnaire, Experience with Electronic Mail Scale, Knowledge and Technical Background Test and a demographic questionnaire. The phishing susceptibility measures were calculated after the participants finished an email legitimacy task in a role-playing scenario. The results showed that the general profile of the “victim personality” included low conscientiousness, low openness and high neuroticism, and Internet experience and computer and web knowledge played an important role. All of these factors have significant indirect effects on phishing susceptibility by influencing mail elaboration. Moreover, the probabilities of checking for further information or deleting the email reflect the sensitivity of email judgment. These findings reveal the mediating role of cognitive processing between individual factors and phishing susceptibility. The theoretical implications of this study for the phishing susceptibility literature and its applications to phishing risk interventions or training programs are discussed.
A Combinatorial Auction Resource Trading Mechanism for Cybertwin-Based 6G Network

Cybertwin is a promising technique in the 6G network to cope with an exponentially increasing demand for the mobile data traffic and growing number of required network service appliances. Multiple resources are required to fulfil various personalized services of mobile users while protecting their privacy. In this article, we propose a progressive adaptive user selection environment (PAUSE)-based combinatorial auction resource trading mechanism to allocate the resource efficiently and securely. Cybertwins can request the various resources as a bid according to its corresponding user and proceed the auction in a fully distributed manner. Not only the user’s privacy can be well protected but also its cost can be saved due to the transparency of this mechanism. Simulation results validate the effectiveness of our mechanism in comparison with the centralized benchmark.
Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.
Heterogeneous Information Network Embedding with Adversarial Disentangler

Heterogeneous information network (HIN) embedding has gained considerable attention in recent years, which learns low-dimensional representation of nodes while preserving the semantic and structural correlations in HINs. Many of existing methods which exploit meta-path guided strategy have shown promising results. However, the learned node representations could be highly entangled for downstream tasks; for example, an author's publications in multidisciplinary venues may make the prediction of his/her research interests difficult. To address this issue, we develop a novel framework named HEAD (i.e., HIN Embedding with Adversarial Disentangler) to separate the distinct, informative factors of variations in node semantics formulated by meta-paths. More specifically, in HEAD, we first propose the meta-path disentangler to separate node embeddings from various meta-paths into intrinsic and specific spaces; then with meta-path schemes as self-supervised information, we design two adversarial learners (i.e., meta-path and semantic discriminators) to make the intrinsic embedding more independent from the designed meta-paths while the specific embedding more meta-path dependent. To comprehensively evaluate the performance of HEAD, we perform a set of experiments on four real-world datasets. Compared to the state-of-the-art baselines, the maximum 15% improvement of performance demonstrates the effectiveness of HEAD and the benefits of the learned disentangled representations.
A Study on the effects of recursive convolutional layers in convolutional neural networks

To overcome problems with the design of large networks, particularly with respect to the depth of the network, this paper presents a new model of convolutional neural networks (CNN) which features fully recursive convolutional layers (RCLs). An RCL is a generalization of the classic one-stage feedforward convolutional layer (CL) to fully direct feedback connections between the outputs of the CL and its inputs. A traditional deep CNN consisting of many CLs, can then be generalized to include some CLs, and some RCLs in the intermediate stages. We call the corresponding network a Convolutional Neural Network with Fully Recursive Perceptron Network (C-FRPN). Through an analysis of results obtained from applications of the C-FRPN to three benchmark image classification datasets: CIFAR-10, SVHN, ISIC, it is found that (i) in general, the performance of a C-FRPN, even with only one RCL, is better than the performance of the corresponding deep CNN with all CLs, under the constraint of having the same number of unknown parameters; (ii) the performance of the C-FRPN varies with respect to (a) where the RCLs are located, and (b) the number of RCLs in the C-FRPN; and, (iii) the effectiveness of the RCLs depends on the size of the training dataset. The results suggest that: (a) it is advisable to use RCLs particularly when training very large sets of data, (b) it is best to prioritize placement of RCLs close to the input layer of the C-FRPN, and (c) it is advisable to increase the number of RCLs as long as the training dataset can sustain without overfitting being observed.
S3Net: A Single Stream Structure for Depth Guided Image Relighting

Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map. To the best of our knowledge, this task is a new challenge that has not been addressed in the previous literature. To address this issue, we propose a deep learning-based neural Single Stream Structure network called S3Net for depth guided image relighting. This network is an encoder-decoder model. We concatenate all images and corresponding depth maps as the input and feed them into the model. The decoder part contains the attention module and the enhanced module to focus on the relighting-related regions in the guided images. Experiments performed on challenging benchmark show that the proposed model achieves the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> highest SSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge.
Reducing corruption and bribery in Africa as a target of the sustainable development goals: applying indicators for assessing performance

Purpose The purpose of this paper is to assess African performance for substantially reducing all forms of corruption and bribery on the continent by 2030, through the indicators for achieving Target 16.5 of the sustainable development goals (SDGs). Design/methodology/approach Drawing on the available and accessible relevant data from credible sources, this work quantifies, outlines and analyses the relationship between corruption/bribery and sustainable development as it applies primarily to sub-Saharan Africa; assesses the trends in the region through the official indicators for achieving Target 16.5 of the SDGs; and recommends other indicators for assessing ethical behaviour in African political, administrative and business leadership and institutions for achieving sustainable development and improved ethical performance towards significant reductions in all manifestations of bribery and corruption on the continent by 2030. Findings Corruption and bribery are found to affect all SDG-related sectors, undermining development outcomes and severely compromising efforts to achieve the SDGs in Africa. Consequently, prioritising corruption reduction including from money laundering, bribery and other illegal activities is a necessary requirement for achieving sustainable development, good governance, building effective and inclusive institutions as required by SDG 16, and funding the achievement of the SDGs. Originality/value The main value of the paper is the insights it provides through the very comprehensive compilation of statistical information that quantifies, and with analysis, the corruption/bribery avenues and the resultant deleterious effects on sustainable development in Africa.
The extensive hydrocarbon-mediated fixation of hydrothermal gold in the Witwatersrand Basin, South Africa

There is a close spatial relation between high-grade gold mineralization in the Witwatersrand basin and carbonaceous nodules, veins and seams. Hydrocarbons thus may well have been essential in ore genesis. We have sampled four major gold-, uranium- and hydrocarbon-bearing ore horizons, namely the Carbon Leader, Vaal, B and Black reefs, to determine the role of hydrocarbons in the accumulation and hydrothermal fixation of gold. Our multipronged approach included high-resolution scanning and transmission electron microscopy (SEM, TEM), nanotomography with video clips, and geochemical modeling. Post-depositional hydrothermal activity at the peak of regional metamorphism produced an assemblage of quartz, phyllosilicates, brannerite, crandallite, florencite, monazite and gold in all four reefs. The gold, hydrocarbons and associated mineral assemblages are closely related on the micro to nano scale. Gold deposition occurred in interstices, as fracture fillings in detrital minerals, and on the surface of migrated solid hydrocarbon residues. The spherical to elliptical inclusions in the gold consist of an outer pyrobitumen phase and a central void space, partially associated with nanometric gold, uraninite, coffinite and silica. The hydrocarbon-bearing inclusion likely formed by the entrapment of a fossil liquid oil precursor during gold precipitation. The oil was subsequently thermally altered and converted into the final pyrobitumen and gaseous residues. Geochemical calculations to simulate the interaction of an invading hot hydrothermal fluid with the hydrocarbons in the reefs reveal that a very small amount of hydrocarbons will drastically decrease the aqueous solubility of gold and hence cause its instant precipitation. We extend our genetic model for the epigenetic formation of gold in the Witwatersrand. Regional metamorphism promoted the extensive and likely basin-wide circulation of hydrothermal fluids; these were capable of mobilizing substantial amounts of gold. The liquid, gaseous and solid hydrocarbons in the reefs acted as efficient chemical traps for the concentration of gold. Being strong chemical reductants, they caused the rapid precipitation and accumulation of gold on the surface of the fossil oil droplets and already solidified hydrocarbons. The release of the gases from accessible hydrocarbons into the sediments away from their source buffered the redox state of the hydrothermal solutions even at a considerable distance from the pyrobitumen seams and veins, likely resulting in the deposition of gold in the absence of visible hydrocarbons. Although our findings do not explain the ultimate origin and exceptional endowment of gold in the Witwatersrand, we do provide intriguing evidence for the large-scale hydrothermal mobilization, accumulation and fixation of gold mediated by hydrocarbons during post-depositional metamorphism.