Reviewerly/exHarmony huggingface dataset:
Unsupervised Deep Video Denoising

Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, we propose an Unsupervised Deep Video Denoiser (UDVD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ), a CNN architecture designed to be trained exclusively with noisy data. The performance of UDVD is comparable to the supervised state-of-the-art, even when trained only on a single short noisy video. We demonstrate the promise of our approach in real-world imaging applications by denoising raw video, fluorescence-microscopy and electron-microscopy data. In contrast to many current approaches to video denoising, UDVD does not require explicit motion compensation. This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy. A gradient-based analysis reveals that UDVD automatically adapts to local motion in the input noisy videos. Thus, the network learns to perform implicit motion compensation, even though it is only trained for denoising.
Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach

In this paper, we build an empirical specification that helps to explain bitcoin volatility and to characterize phases of the bitcoin bubble using information derived from investors’ emotions and sentiment that captures investment intentions and investors’ aversion to risk. To this end, we investigated the bilateral relations between bitcoin volatility and investor emotions between 2018 and 2021, a period characterized by significant changes in bitcoin prices as well as wide disparities in investor emotions, especially in the context of the ongoing COVID-19 pandemic. The study was based on a linear and nonlinear Vector Autoregressive (VAR) model that we applied to data related to bitcoin prices and market sentiment as expressed by the Fear and Greed index. Overall, our results evince the key role played by collective emotions in the formation and collapse of the bitcoin bubble. Two findings in particular stand out. First, our model shows significant time-varying lead-lag effects between bitcoin volatility and investor sentiment that come into play bilaterally and help to characterize the dynamics of bitcoin volatility. Second, these interactions exhibit asymmetry and nonlinearity as the sign and size of collective emotions (resp. bitcoin volatility) vary with the regime and market state under consideration (calm state versus period of bubble formation, etc.). In other words, the power of sentiment has a time-varying effect on the market. Indeed, in the first regime (“calm state”), where bitcoin volatility is relatively low and the market shows evidence of stability, collective emotions have a negative impact on bitcoin volatility, prompting a stabilizing strength. However, in the second regime (“bubble formation”), the effect of emotions turns significantly positive as investors gradually become less fearful and more reassured, which can simultaneously increase volatility and destabilize the market. Finally, in the third regime (“bubble collapse”), when bitcoin reaches a high level of value and experiences impressive volatility excess, the effect of emotions again turns negative, resulting in further switching behavior that pushes investor action to provoke a bitcoin price correction, moving it toward a new state of stability. Our conclusion helps improve predictions of bitcoin price dynamics informed by the information provided by investor emotions.
Learning to Adversarially Blur Visual Object Tracking

Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i.e., adversarial blur attack (ABA). Our main objective is to online transfer input frames to their natural motion-blurred counterparts while misleading the state-of-the-art trackers during the tracking process. To this end, we first design the motion blur synthesizing method for visual tracking based on the generation principle of motion blur, considering the motion information and the light accumulation process. With this synthetic method, we propose optimization-based ABA (OP-ABA) by iteratively optimizing an adversarial objective function against the tracking w.r.t. the motion and light accumulation parameters. The OP-ABA is able to produce natural adversarial examples but the iteration can cause heavy time cost, making it unsuitable for attacking real-time trackers. To alleviate this issue, we further propose one-step ABA (OS-ABA) where we design and train a joint adversarial motion and accumulation predictive network (JAMANet) with the guidance of OP-ABA, which is able to efficiently estimate the adversarial motion and accumulation parameters in a one-step way. The experiments on four popular datasets (e.g., OTB100, VOT2018, UAV123, and LaSOT) demonstrate that our methods are able to cause significant accuracy drops on four state-of-the-art trackers with high transferability. Please find the source code at https://github.com/tsingqguo/ABA
Domain Generalization via Gradient Surgery

In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains. When the aim is to make predictions on distributions different from those seen at training, we incur in a domain generalization problem. Methods to address this issue learn a model using data from multiple source domains, and then apply this model to the unseen target domain. Our hypothesis is that when training with multiple domains, conflicting gradients within each mini-batch contain information specific to the individual domains which is irrelevant to the others, including the test domain. If left untouched, such disagreement may degrade generalization performance. In this work, we characterize the conflicting gradients emerging in domain shift scenarios and devise novel gradient agreement strategies based on gradient surgery to alleviate their effect. We validate our approach in image classification tasks with three multi-domain datasets, showing the value of the proposed agreement strategy in enhancing the generalization capability of deep learning models in domain shift scenarios.
An adaptive image steganography method based on integer wavelet transform using genetic algorithm

Steganography is the science and art of concealing the existence of communication. The idea proposed in this paper is that the coefficients of the high-frequency subbands are more suitable for embedding, as they are surrounded by larger coefficients and represent the image's edge areas. In this method, the appropriate embedding coefficients are selected using an edge intensity criterion based on a local neighborhood analysis. The proposed method begins by blocking the image and then applying a wavelet transform to each block. Several coefficients in each high-frequency subband are identified, and then, using a genetic algorithm, the coefficients are selected so that the generated stego image has the highest PSNR value. The proposed method is more successful when the Integer Wavelet Transform (IWT) is used rather than the Discrete Wavelet Transform (DWT). The proposed method produces a higher-quality stego image than existing methods, and it is secure against steganalysis attacks.
A Comparative study of Hyper-Parameter Optimization Tools

Most of the machine learning models have associated hyper-parameters along with their parameters. While the algorithm gives the solution for parameters, its utility for model performance is highly dependent on the choice of hyperparameters. For a robust performance of a model, it is necessary to find out the right hyper-parameter combination. Hyper-parameter optimization (HPO) is a systematic process that helps in finding the right values for them. The conventional methods for this purpose are grid search and random search and both methods create issues in industrial-scale applications. Hence a set of strategies have been recently proposed based on Bayesian optimization and evolutionary algorithm principles that help in runtime issues in a production environment and robust performance. In this paper, we compare the performance of four python libraries, namely Optuna, Hyper-opt, Optunity, and sequential model-based algorithm configuration (SMAC) that has been proposed for hyper-parameter optimization. The performance of these tools is tested using two benchmarks. The first one is to solve a combined algorithm selection and hyper-parameter optimization (CASH) problem The second one is the NeurIPS black-box optimization challenge in which a multilayer perceptron (MLP) architecture has to be chosen from a set of related architecture constraints and hyper-parameters. The benchmarking is done with six real-world datasets. From the experiments, we found that Optuna has better performance for CASH problem and HyperOpt for MLP problem.
SSH: A Self-Supervised Framework for Image Harmonization

Image harmonization aims to improve the quality of image compositing by matching the "appearance" (e.g., color tone, brightness and contrast) between foreground and background images. However, collecting large-scale annotated datasets for this task requires complex professional retouching. Instead, we propose a novel Self-Supervised Harmonization framework (SSH) that can be trained using just "free" natural images without being edited. We reformulate the image harmonization problem from a representation fusion perspective, which separately processes the foreground and background examples, to address the background occlusion issue. This framework design allows for a dual data augmentation method, where diverse [foreground, background, pseudo GT] triplets can be generated by cropping an image with perturbations using 3D color lookup tables (LUTs). In addition, we build a real-world harmonization dataset as carefully created by expert users, for evaluation and benchmarking purposes. Our results show that the proposed self-supervised method outperforms previous state-of-the-art methods in terms of reference metrics, visual quality, and subject user study. Code and dataset are available at https://github.com/VITA-Group/SSHarmonization.
Digital payment fraud detection methods in digital ages and Industry 4.0

The advent of the digital economy and Industry 4.0 enables financial organizations to adapt their processes and mitigate the risks and losses associated with the fraud. Machine learning algorithms facilitate effective predictive models for fraud detection for Industry 4.0. This study aims to identify an efficient and stable model for fraud detection platforms to be adapted for Industry 4.0. By leveraging a real credit card transaction dataset, this study proposes and compares five different learning models: logistic regression, decision tree, k-nearest neighbors, random forest, and autoencoder. Results show that random forest and logistic regression outperform the other algorithms. Besides, the undersampling method and feature reduction using principal component analysis could enhance the results of the proposed models. The outcomes of the studies positively ascertain the effectiveness of using features selection and sampling methods for tackling business problems in the new age of digital economy and industrial 4.0 to detect fraudulent activities.
Automated quality assessment of large digitised histology cohorts by artificial intelligence

Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at [Formula: see text] magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall 'usability' (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86-90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research.
Enablers to the adoption of blockchain technology in logistics supply chains: evidence from an emerging economy

This study identifies the factors that can enable blockchain technology (BCT) implementation in the logistics sector and reveals the interdependencies, hierarchical structure, and causalities between them. This study uses a three-phased research framework to identify thirteen enablers to BCT adoption in the Logistics Supply Chain (LSC) and analyse the hierarchical and cause-effect relationships using an integrated Interpretive Structural Modelling and Decision-Making Trial and Evaluation Laboratory (ISM-DEMATEL) technique. This study classifies the enablers into three categories: prominent enablers, influencing enablers, and resulting enablers. 'Real-time connectivity and information flow' were identified as the most influencing enabler, whereas traceability was found to be the most prominent and resulting enabler. This study contributes to the literature by identifying the critical BCT enablers and modelling their hierarchical and causal relationships to address the absence of an appropriate decision-making framework that aids managers in understanding why and how BCT can be adopted in LSC, especially in an emerging economy like India. Strategic recommendations on how BCT can help overcome some select challenges faced by LSC organizations are also presented.
Robust multi-view subspace clustering based on consensus representation and orthogonal diversity

The main purpose of multi-view subspace clustering is to reveal the intrinsic low-dimensional architecture of data points according to their multi-view characteristics. Exploring the potential relationship from views is one of the most essential research focuses of the multi-view task. To better utilize the complementary and consistency information from distinct views, we propose a novel robust subspace clustering approach based on consensus representation and orthogonal diversity (RMSCCO). A novel defined orthogonality term is adopted to improve the diversity and decrease the redundance of learning subspace representation. The consensus representation and subspace learning are integrated into one unified framework to characterize the consistency from views. The grouping-enhanced representation is utilized to maintain the local geometric architecture in the original data space. The ℓ2,1-norm regularizer constraint to the noise is applied to improve the robustness. Finally, an optimization algorithm is exploited to solve RMSCCO with the Alternating Direction Method of Multipliers (ADMM). Extensive experimental results on six challenging datasets demonstrate that our approach has accomplished highly qualified performance.
EVFL: An explainable vertical federated learning for data-oriented Artificial Intelligence systems

Vertical federated learning (VFL), as one of the latest advances of security in the data-oriented Artificial Intelligence (AI) systems, facilitates better keeping the AI systems converge faster with higher performance and security. Since a large amount of data from these systems is often of low quality, the training data needs to be interpreted and evaluated. While there have been some research efforts, they still have significant shortcomings, such as high computational complexity and impracticality. Considering the characteristics of the data, the interpretation of machine learning models allows for data cleansing, which can improve data quality and help regulators understand the decision-making process. In this paper, we propose an explainable vertical federated learning (EVFL) framework, including the credibility assessment strategy, the federated counterfactual explanation and the importance rate (IR) metric. Furthermore, we initialize the knowledge-based counterfactual instance based on prior knowledge and retrain the federated counterfactual method for feasible counterfactual features. We report experimental results obtained on the Lending Club and Zhongyuan datasets for implementing our framework to show that our approach is significantly effective. Notably, on the Lending Club dataset, our method can have a +4.9% improvement over other selections.
Mobile behavioral biometrics for passive authentication

Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb,1 one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval.
Financial management of construction projects: Hyperledger fabric and chaincode solutions

This paper proposes a novel interconnected financial management system based on Hyperledger fabric and chaincode solutions to address endemic problems with financial management practices adopted within construction projects. Increasingly complex construction projects have necessitated a corresponding evolvement of financial management tools and systems to augment security and control. A decentralised financial management system is introduced to deal with all financial tasks across various construction stages using blockchain technology. Under different delivery approaches and payment methods, the proposed system enables parties to record/invoke their transactions safely and automatically, with no third party involvement. Moreover, the proposed approach allows non-owner parties to control remaining financial rights during the Defects Liability Period (DLP) automatically through a pre-agreed endorsement policy. The proposed system is tested on a real-life case project, where results corroborate its ability and workability in providing a secure and scalable platform for all project parties.
Underwater Image Restoration via Information Distribution and Light Scattering Prior

Underwater images suffer from blurred details, color cast, and contrast reduction, caused by absorption and scattering of light. To address these degradation issues, we propose a novel underwater image restoration method based on information distribution and light scattering prior. Concretely, we first estimate the background light according to the information distribution and light scattering characteristics of the background light area. Then, the scene depth map and transmission map are obtained by the relationship between the brightness information and color attenuation. Finally, the underwater image formation model is inversed to obtain the restored image. Extensive experiments indicate that our method can effectively remove the color cast and increase contrast. Furthermore, compared with several state-of-the-art methods, the proposed method yields better results. The qualitative and quantitative results demonstrate that our approach is superior to other comparison methods.
Watching VR advertising together: How 3D animated agents influence audience responses and enjoyment to VR advertising

With the popularity of virtual reality (VR) devices, advertisers are starting to seek the potential advertising tactics and effects in VR. This study investigates the influence of introducing 3D animated agents on video ad effectiveness in a VR context (i.e., brand/product attitude, brand/product recall, purchase intention, and ad-skipping behavior). Findings from a between-subjects experiment in Study 1 confirmed that when 2D video ads are played in a VR environment, the presence of 3D animated agents will make the audiences enjoy the ad-viewing experience more and promote the audiences’ positive attitudes toward the brand and the product and their purchase intention indirectly. In Study 2, employing a large industry dataset of VR users, we found that the presence of 3D animated agents can also decrease the ad-skipping rate while the 2D ads are playing. This study provides empirical support for the use of animated agents as an effective adversiting tactic in a VR context. How these findings link to previous research and their practical implications are discussed as future directions.