Reviewerly/exHarmony huggingface dataset:
NUICNet: Non-Uniform Illumination Correction for Underwater Image Using Fully Convolutional Network

Absorption and scattering in aqueous media would attenuate light and make imaging difficult.Therefore, an artificial light source is usually utilized to assist imaging in the deep ocean.However, the artificial light source typically alters the light conditions to a large extent, resulting in the non-uniform illumination of images.To solve this problem, we propose a non-uniform illumination correction algorithm based on a fully convolutional network for underwater images.The proposed algorithm model the original image as the addition of the ideal image and a non-uniform light layer.We replace the traditional pooling layer with dilated convolution to expand the receptive field and achieve higher accuracy in non-uniform illumination recognition.To improve the perception ability of the network effectively, the original image and parameters which pre-trained on the ImageNet are concentrated.The concentrated information is used as input to the network.Due to the color shift and blurred details of the underwater image, we design the novel loss function, which includes three parts of feature loss, smooth loss, and adversarial loss.Moreover, we built a dataset of the underwater image with non-uniform illumination.Experiments show that our method performs better in subjective assessment and objective assessment than some traditional methods.
Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations

Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision. Recent theoretical results suggest that despite their excellent practical performance, in various cases, a substitution with basis expansions (e.g., polynomials) can yield significant benefits from both the optimization and generalization perspective. Unfortunately, the existing results remain limited to networks with a couple of layers, and the practical viability of these results is not yet known. Motivated by some of these results, we explore the use of Hermite polynomial expansions as a substitute for ReLUs in deep networks. While our experiments with supervised learning do not provide a clear verdict, we find that this strategy offers considerable benefits in semi-supervised learning (SSL) / transductive learning settings. We carefully develop this idea and show how the use of Hermite polynomials based activations can yield improvements in pseudo-label accuracies and sizable financial savings (due to concurrent runtime benefits). Further, we show via theoretical analysis, that the networks (with Hermite activations) offer robustness to noise and other attractive mathematical properties.
Modeling Biological Immunity to Adversarial Examples

While deep learning continues to permeate through all fields of signal processing and machine learning, a critical exploit in these frameworks exists and remains unsolved. These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus signal by an imperceptible amount. The attack takes advantage of statistical irregularities within the training data, where the added perturbations can move the image across deep learning decision boundaries. What is even more alarming is the transferability of these attacks to different deep learning models and architectures. This means a successful attack on one model has adversarial effects on other, unrelated models. In a general sense, adversarial attack through perturbations is not a machine learning vulnerability. Human and biological vision can also be fooled by various methods, i.e. mixing high and low frequency images together, by altering semantically related signals, or by sufficiently distorting the input signal. However, the amount and magnitude of such a distortion required to alter biological perception is at a much larger scale. In this work, we explored this gap through the lens of biology and neuroscience in order to understand the robustness exhibited in human perception. Our experiments show that by leveraging sparsity and modeling the biological mechanisms at a cellular level, we are able to mitigate the effect of adversarial alterations to the signal that have no perceptible meaning. Furthermore, we present and illustrate the effects of top-down functional processes that contribute to the inherent immunity in human perception in the context of exploiting these properties to make a more robust machine vision system.
Feature Transformation for Neural Ranking Models

Although neural network models enjoy tremendous advantages in handling image and text data, tree-based models still remain competitive for learning-to-rank tasks with numerical data. A major strength of tree-based ranking models is the insensitivity to different feature scales, while neural ranking models may suffer from features with varying scales or skewed distributions. Feature transformation or normalization is a simple technique which preprocesses input features to mitigate their potential adverse impact on neural models. However, due to lack of studies, it is unclear to what extent feature transformation can benefit neural ranking models. In this paper, we aim to answer this question by providing empirical evidence for learning-to-rank tasks. First, we present a list of commonly used feature transformation techniques and perform a comparative study on multiple learning-to-rank data sets. Then we propose a mixture feature transformation mechanism which can automatically derive a mixture of basic feature transformation functions to achieve the optimal performance. Our experiments show that applying feature transformation can substantially improve the performance of neural ranking models compared to directly using the raw features. In addition, the proposed mixture transformation method can further improve the performance of the ranking model without any additional human effort.
Quantized genetic resampling particle filtering for vision-based ground moving target tracking

This paper concentrates on the quantized measurements fusion problem. Because of the non-Gaussian property of quantized measurements, classical nonlinear filters become not applicable, especially when the quantization noise is large. Motivated by the above problem, we propose a novel particle-based filtering named quantized genetic resampling particle filtering (QGRPF) in order to fuse quantized measurements optimally and improve estimation accuracy. Unlike the Gaussian assumption in traditional filers, the probability density function (PDF) of sensor measurement is modeled here as a discrete PDF that consists of a series of Dirac impulses. The particle filtering is employed to estimate the state, where the posterior of the state based on quantized measurements is approximated and updated by a set of weighted particles. As the likelihood function is a multi-dimensional integral of Gaussian density, which has no analytical solution, Genz's transformation and quasi-Monte Carlo method are combined to calculate the integral numerically. By integrating genetic resampling method into the particle filtering method, the proposed method can avoid particle degeneracy, in the meantime, guarantee particle diversity. The proposed QGRPF is demonstrated with an illustrative vision-based ground moving target (GMT) tracking example. Simulation results show the proposed approach is effective in fusing quantized measurements.
Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation

Given an occurred event, human can easily predict the next event or reason the preceding event, yet which is difficult for machine to perform such event reasoning. Event representation bridges the connection and targets to model the process of event reasoning as a machine-readable format, which then can support a wide range of applications in information retrieval, e.g., question answering and information extraction. Existing work mainly resorts to a joint training to integrate all levels of training loss in event chains by a simple loss summation, which is easily trapped into a local optimum. In addition, the scenario knowledge in event chains is not well investigated for event representation. In this paper, we propose a unified fine-tuning architecture, incorporated with scenario knowledge for event representation, i.e., UniFA-S, which mainly consists of a unified fine-tuning architecture (UniFA) and a scenario-level variational auto-encoder (S-VAE). In detail, UniFA employs a multi-step fine-tuning to integrate all levels of training and S-VAE applies a stochastic variable to implicitly represent the scenario-level knowledge. We evaluate our proposal from two aspects, i.e., the representation and inference abilities. For the representation ability, our ensemble model UniFA-S can beat state-of-the-art baselines for two similarity tasks. For the inference ability, UniFA-S can outperform the best baseline, achieving 4.1%-8.2% improvements in terms of accuracy for various inference tasks.
A multi-frame super-resolution based on new variational data fidelity term

The main idea of multi-frame super-resolution (SR) algorithm is to recover a single high-resolution (HR) image from a sequence of low resolution ones of the same scene. Since the restoration step of super-resolution algorithms is always an ill-posed problem, the choice of the fidelity term and the regularization are always crucial. In this paper, we propose a new variational SR framework based on an automatic selection of the weighting parameter that control the balance between the L1 and L2 fidelity terms, which handle different type of noise distributions. Concerning the regularization, we use the combined total variation (TV) and the total variation of the first derivatives (TV2) model with a new implementation of the Primal-dual algorithm to solve the corresponding discretized problem. The obtained results are compared with some competitive algorithms and confirm that the proposed method has much benefices over the others in avoiding some undesirable artifacts.
Performance Evaluation of Different Neural Network Classifiers for Sanskrit Character Recognition

Handwritten character recognition (HCR) is one of the significant issues in today’s emerging world. It is very difficult to identify the characters from a handwritten document using optical character recognition (OCR) technique. In our work, geometrical feature extraction and neural network computational algorithm are used for recognizing the offline handwritten Sanskrit characters. Initially the binarization and denoising processes are performed on the scanned handwritten document. Later, skeletonization, skewness detection, and correction processes are performed. Image is segmented and required features are extracted and fed into the different classifiers for character recognition. Then, the comparative study of the Sanskrit character recognition is done by employing the RCS with BPNN, BPNN with RBF, and MLP. The proposed character recognition system deploys precision, mean square error rate, recall, false error rate, false-positive error rate, sensitivity, specificity, and accuracy for effective analysis of the handwritten Sanskrit characters.
Inertial measurement unit aided extrinsic parameters calibration for stereo vision systems

Calibration of a stereo vision system is essential for three-dimensional shape and deformation measurements. Although the target-based calibration technique is a good solution, it comes with limitations in a large field of view (FOV) measurement due to the target size and difficulties in operation. In this paper, a method using the inertial measurement unit (IMU) has been proposed to calibrate stereo vision systems for large-scale measurements. The prerequisite for this method is to calibrate the camera intrinsic parameters in advance. With the alignment of IMU sensor with the camera, the relative rotation between the two cameras can be determined through coordinate transform. An angular differential method has been developed to improve accuracy in determining the yaw angle. After the epipolar geometry is constructed upon stereo scene correspondences, the translation vector can be obtained by solving a set of linear equations, since the intrinsic parameters and the relative rotation are predefined. The proposed method does not require any known calibration target, leading to easy operation in practice. Two laboratory experiments are presented to demonstrate the use of the method. The experimental results show that the accuracy of the proposed method is comparable to that resulted from the well-known Zhang's method, and it can also be applied to measurement with a large FOV.
Erasable pattern mining based on tree structures with damped window over data streams

Several pattern mining methods have been proposed to process dynamic data streams because the data generated in industrial fields is continually accumulated. Erasable pattern mining techniques for processing dynamic data streams are needed to discover erasable patterns from dynamic data streams. In previous erasable pattern mining approaches suggested for dynamic data streams, all data are considered to have the same importance regardless of its timestamp. However, dynamic data streams have the characteristic that the new data is relatively more significant than the old data. In erasable pattern mining, one of the desired techniques is an approach in consideration of such characteristic of data streams. For this reason, we propose an erasable pattern mining algorithm over dynamic data streams based on the damped window model. Since the suggested technique considers the new data more important than the previous data, it can find more useful erasable patterns. In addition, erasable pattern mining based on the damped window model is conducted efficiently by employing the tree and table structures. In performance test, we present that our pruning techniques remove unnecessary operations related to invalid erasable patterns efficiently from damped-window-based data streams. Performance evaluation results using real datasets and synthetic datasets show that the proposed approach has good performance with regard to as execution time, pattern generation, and scalability by comparing between the suggested technique and the state of the art algorithms.
Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer’s disease and cancers

Abstract Background Predictive patient stratification is greatly emerging, because it allows us to prospectively identify which patients will benefit from what interventions before their condition worsens. In the biomedical research, a number of stratification methods have been successfully applied and have assisted treatment process. Because of heterogeneity and complexity of medical data, it is very challenging to integrate them and make use of them in practical clinic. There are two major challenges of data integration. Firstly, since the biomedical data has a high number of dimensions, combining multiple data leads to the hard problem of vast dimensional space handling. The computation is enormously complex and time-consuming. Secondly, the disparity of different data types causes another critical problem in machine learning for biomedical data. It has a great need to develop an efficient machine learning framework to handle the challenges. Methods In this paper, we propose a fast-multiple kernel learning framework, referred to as fMKL-DR, that optimise equations to calculate matrix chain multiplication and reduce dimensions in data space. We applied our framework to two case studies, Alzheimer’s disease (AD) patient stratification and cancer patient stratification. We performed several comparative evaluations on various biomedical datasets. Results In the case study of AD patients, we enhanced significantly the multiple-ROIs approach based on MRI image data. The method could successfully classify not only AD patients and non-AD patients but also different phases of AD patients with AUC close to 1. In the case study of cancer patients, the framework was applied to six types of cancers, i.e., glioblastoma multiforme cancer, ovarian cancer, lung cancer, breast cancer, kidney cancer, and liver cancer. We efficiently integrated gene expression, miRNA expression, and DNA methylation. The results showed that the classification model basing on integrated datasets was much more accurate than classification model basing on the single data type. Conclusions The results demonstrated that the fMKL-DR remarkably improves computational cost and accuracy for both AD patient and cancer patient stratification. We optimised the data integration, dimension reduction, and kernel fusion. Our framework has great potential for mining large-scale cohort data and aiding personalised prevention.
Blockchain-Based Searchable Encryption Scheme With Fair Payment

In order to solve the problem that most of the existing blockchain-based searchable encryption schemes only support exact keyword search and mutual distrust between the cloud server and the user, a searchable encryption scheme based on blockchain, that has functions such as file update dynamically, search results verification, fuzzy keyword search, and fair payment, is proposed.We use edit distance to generate the fuzzy keywords set.According to the un-modification characteristic of Ethereum blockchain and the security of the RSA accumulator, our scheme can verify the search results by the smart contract and realize the service-payment fairness between the user and the cloud server.The security analysis shows that the proposed scheme achieves non-adaptive semantic security.Performance analysis and experiment show that our scheme is efficient and meets the demand of search application in the cloud environment.
Topic modeling in short-text using non-negative matrix factorization based on deep reinforcement learning

Topic modeling for short texts is a challenging and interesting problem in the machine learning and knowledge discovery domains. Nowadays, millions of documents published on the internet from various sources. Internet websites are full of various topics and information, but there is a lot of simila rity between topics, contents, and total quality of sources, which causes data repetition and gives the user the same information. Another issue is data sparsity and ambiguity because the length of the short text is limited, which causes unsatisfactory results and give irrelevant results to end-users. All these mentioned issues in short texts made an interesting topic for researchers to use machine learning and knowledge discovery techniques to discover underlying topics from a massive amount of data. In this paper, we propose a combination of deep reinforcement learning (RL) and semantics-assisted non-negative matrix factorization model to extract meaningful and underlying topics from short document contents. The main objective of this work is to reduce the problem of repetitive information and data sparsity in short texts to help the users to get meaningful and relevant contents. Furthermore, our propose model reviews an issue of the Seq2Seq approach based on the reinforcement learning perspective and provides a combination of reinforcement learning and SeaNMF formulation using the block coordinate descent algorithm. Moreover, we compare different real-world datasets by using numerical calculation and present a couple of state-of-art models to get better performance on short text document topic modeling. Based on experimental results and comparative analysis, our propose model outperforms the state of art techniques in terms of short document topic modeling.
JSNet: A simulation network of JPEG lossy compression and restoration for robust image watermarking against JPEG attack

Deep learning-based watermarking methods have achieved a better performance in capacity and invisibility than some traditional methods. However, their robustness against JPEG lossy compression attack is still to be improved. To enhance the robustness and construct an end-to-end method, it is urgent to simulate the JPEG lossy compression by a neural network and then introduce it into the deep learning-based watermarking methods. In this paper, a JPEG simulation network JSNet is proposed to reappear the whole procedure of the JPEG lossy compression and restoration except entropy encoding as realistically as possible. The steps of sampling, DCT, and quantization are modeled by the max-pooling layer, convolution layer, and 3D noise-mask, respectively. The proposed JSNet can simulate JPEG lossy compression with any quality factors. To verify the proposed JSNet in improving the robustness against JPEG compression attack, a CNN-based robust watermarking network (CRWNet) is proposed as an application example. The end-to-end CRWNet contains three subnetworks, i.e., embedding subnetwork, JSNet, and extraction subnetwork. Here. the JSNet is regarded as an attack module in the pipeline. Experimental results on two publicly available datasets (ImageNet, BossBase) demonstrate that: (a) the proposed JSNet can well simulate JPEG lossy compression under any quality factors with small Root mean square error (RMSE) values; (b) the proposed CRWNet considering JSNet has achieved an average 30.6 percent advantage over the basic model without consideration of JSNet.
Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions

*Department of Pathology, The Ohio State University Medical Center, Columbus, OH †Department of Pathology and Laboratory Medicine, University of Tennessee Health Science Center, Memphis, TN A.V.P. serves on the advisory board for Contextvision and provides advice on deep learning and artificial intelligence applications in Anatomic Pathology. M.B.A. serves on the advisory board for Ibex and is a consultant for Advanced Clinical providing consultations for artificial intelligence applications in Anatomic Pathology for both these organizations. Reprints: Anil V. Parwani, MD, PhD, MBA, Department of Pathology, The Ohio State University Wexner Medical Center, 410 W.10th AVE, Columbus, OH 43210 (e-mail: [email protected]).
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes

As humans, we inherently perceive images based on their predominant features, and ignore noise embedded within lower bit planes.On the contrary, Deep Neural Networks are known to confidently misclassify images corrupted with meticulously crafted perturbations that are nearly imperceptible to the human eye.In this work, we attempt to address this problem by training networks to form coarse impressions based on the information in higher bit planes, and use the lower bit planes only to refine their prediction.We demonstrate that, by imposing consistency on the representations learned across differently quantized images, the adversarial robustness of networks improves significantly when compared to a normally trained model.Present stateof-the-art defenses against adversarial attacks require the networks to be explicitly trained using adversarial samples that are computationally expensive to generate.While such methods that use adversarial training continue to achieve the best results, this work paves the way towards achieving robustness without having to explicitly train on adversarial samples.The proposed approach is therefore faster, and also closer to the natural learning process in humans.