Reviewerly/exHarmony huggingface dataset:
RBN: enhancement in language attribute prediction using global representation of natural language transfer learning technology like Google BERT

Transfer learning can replace the long and costly data collection, labeling and training session by effective and the most efficient representations. BERT, trained by Google, is a language representation generator and is far more global to be effectively determine the representations of natural languages and create the numerical version of grammatical structures and inter-dependencies of language attributes. In this work, we introduced recurrent BERT network and singular BERT network and have demonstrated the effective way of utilization of BERT for applications like parts-of-speech tagging and phrase tagging, which are integral part of understanding languages structure and interpretation of message. We have achieved extraordinarily high accuracy for prediction with these models and have done a comparative study using different datasets and is aimed at applications related to sentence generation. We created an accuracy of 96.65% for parts-of-speech detection and 95.24% for phrase prediction for Penn Tree Bank sentences and 99.64% and 97.94% for MSCOCO dataset sentences. Different origin sentences will ensure that while human generated sentences are complex for high accuracy and prone to errors, effective machine generated sentence attribute detection provision must be kept open for progress in language understanding.
A Blockchain-Based Approach for Usage Based Insurance and Incentive in ITS

With the increasing number of vehicles on the road, the insurance market for vehicular insurance is also increasing. There has been a massive proliferation in the number of policy taken by the drivers over the year. The traditional vehicular insurance processes used by the insurance companies rely on analyzing the history of the behaviors of the drivers for deciding the suitable premium amount to be paid by the vehicles. Usage-Based Insurance (UBI), which is based on telematics, turns out to be a modern and effective approach for providing insurance to the vehicles. Unlike the traditional approach, the premiums in the UBI are calculated based on the current behavior of the drivers. Moreover, there exists a lack of transparency in the processing of the claims, which not only results in delays of receiving the claims but also leads to a number of frauds. Decentralized technology such as blockchain turns out to be an effective solution for the problems mentioned above. In this work, we propose a blockchain-based framework for vehicular UBI and incentives in ITS. We demonstrate and analyze the feasibility of our work with proper experimental testbed setup.
Sub-policy Adaptation for Hierarchical Reinforcement Learning

Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Leaving the skills fixed can lead to significant sub-optimality in the transfer setting. In this work, we propose a novel algorithm to discover a set of skills, and continuously adapt them along with the higher level even when training on a new task. Our main contributions are two-fold. First, we derive a new hierarchical policy gradient with an unbiased latent-dependent baseline, and we introduce Hierarchical Proximal Policy Optimization (HiPPO), an on-policy method to efficiently train all levels of the hierarchy jointly. Second, we propose a method for training time-abstractions that improves the robustness of the obtained skills to environment changes. Code and results are available at sites.google.com/view/hippo-rl
Interval forecasts based on regression trees for streaming data

In forecasting, we often require interval forecasts instead of just a specific point forecast. To track streaming data effectively, this interval forecast should reliably cover the observed data and yet be as narrow as possible. To achieve this, we propose two methods based on regression trees: one ensemble method and one method based on a single tree. For the ensemble method, we use weighted results from the most recent models, and for the single-tree method, we retain one model until it becomes necessary to train a new model. We propose a novel method to update the interval forecast adaptively using root mean square prediction errors calculated from the latest data batch. We use wavelet-transformed data to capture long time variable information and conditional inference trees for the underlying regression tree model. Results show that both methods perform well, having good coverage without the intervals being excessively wide. When the underlying data generation mechanism changes, their performance is initially affected but can recover relatively quickly as time proceeds. The method based on a single tree performs the best in computational (CPU) time compared to the ensemble method. When compared to ARIMA and GARCH modelling, our methods achieve better or similar coverage and width but require considerably less CPU time.
Estimating counterfactual treatment outcomes over time through adversarially balanced representations

Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods.
A Novel Technique for Data Steganography

In this paper, a novel stego-method will be introduced, which can be used to hide any secret message in any holding color image. The proposed method will be implemented and tested and the calculated parameters will be compared with the LSB method parameters. It will be shown that the proposed method provides a high-security level by using two keys to extract the secret message from the holding image, making it very difficult to hack.
Efficacy of Mastocheck for Screening of Early Breast Cancer: Comparison with Screening Mammography

Purpose This study was designed to verify the effectiveness of Mastocheck, a new diagnostic tool developed with proteomics techniques using plasma proteins. In addition, checked the current state of mammography used for breast cancer screening. Methods A total of 305 cases were analyzed (normal 122, breast cancer 183) and used for validation after Mastocheck development. First, screening mammograms of normal patients and pre-diagnosis mammography of cancer patients were reviewed retrospectively. The results were compared with Mastocheck, a newly developed blood test. Imaging tests were blinded and analyzed by dividing the readings of breast specialists and non-breast specialists among radiologists. We confirmed how much better the results would be if only the mammography was used and if both tests were used together. Results The sensitivity, specificity, and accuracy of mammography alone, reviewed by non-breast specialists among radiologists, were 63.0%, 85.7%, and 71.3%, respectively. In dense breasts, the values were 59.2%, 84.8% and 69.0%, which were too low to be considered interpretable. The sensitivity, specificity and accuracy of the test was 93.9%, 83.8%, and 90.2% when using mammography and Mastocheck together. From these results, an improvement in sensitivity of about 30% and an improvement in accuracy of about 15% or more in concomitant use than mammography alone can be seen. Conclusion Mastocheck can be widely used for screening breast cancer, especially in dense breasts, patients with low accuracy in mammography, and patients with mammography side effects. In addition, it has the advantage of increasing the diagnosis rate when used with mammography, the current screening method of choice. Keywords: Breast neoplasms; Diagnosis; Early detection of cancer
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets

A number of approaches to causal discovery assume that there are no hidden confounders and are designed to learn a fixed causal model from a single data set. Over the last decade, with closer cooperation across laboratories, we are able to accumulate more variables and data for analysis, while each lab may only measure a subset of them, due to technical constraints or to save time and cost. This raises a question of how to handle causal discovery from multiple data sets with non-identical variable sets, and at the same time, it would be interesting to see how more recorded variables can help to mitigate the confounding problem. In this paper, we propose a principled method to uniquely identify causal relationships over the integrated set of variables from multiple data sets, in linear, non-Gaussian cases. The proposed method also allows distribution shifts across data sets. Theoretically, we show that the causal structure over the integrated set of variables is identifiable under testable conditions. Furthermore, we present two types of approaches to parameter estimation: one is based on maximum likelihood, and the other is likelihood free and leverages generative adversarial nets to improve scalability of the estimation procedure. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.
Monocular pedestrian orientation estimation based on deep 2D-3D feedforward

Accurate pedestrian orientation estimation of autonomous driving helps the ego vehicle obtain the intentions of pedestrians in the related environment, which are the base of safety measures such as collision avoidance and prewarning. However, because of relatively small sizes and high-level deformation of pedestrians, common pedestrian orientation estimation models fail to extract sufficient and comprehensive information from them, thus having their performance restricted, especially monocular ones which fail to obtain depth information of objects and related environment. In this paper, a novel monocular pedestrian orientation estimation model, called FFNet, is proposed. Apart from camera captures, the model adds the 2D and 3D dimensions of pedestrians as two other inputs according to the logic relationship between orientation and them. The 2D and 3D dimensions of pedestrians are determined from the camera captures and further utilized through two feedforward links connected to the orientation estimator. The feedforward links strengthen the logicality and interpretability of the network structure of the proposed model. Experiments show that the proposed model has at least 1.72% AOS increase than most state-of-the-art models after identical training processes. The model also has competitive results in orientation estimation evaluation on KITTI dataset.
Simulation of PMSM Vector Control System Based on Fuzzy PI Controller

PMSM has the characteristics of nonlinearity, strong coupling, and time-varying parameters. The parameters of PI controller cannot make self-tuning with the change of system state. This paper proposes a new fuzzy control rule. The proportion factor can be adjusted online. The simulation results show that fuzzy PI controller speed regulation system with improved fuzzy rules has faster response speed, small overshoot and strong anti-interference ability. The control system with improved rules has better control performance.
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation

With the rapid proliferation of online media sources and published news, headlines have become increasingly important for attracting readers to news articles, since users may be overwhelmed with the massive information. In this paper, we generate inspired headlines that preserve the nature of news articles and catch the eye of the reader simultaneously. The task of inspired headline generation can be viewed as a specific form of Headline Generation (HG) task, with the emphasis on creating an attractive headline from a given news article. To generate inspired headlines, we propose a novel framework called POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG). PORL-HG exploits the extractive-abstractive architecture with 1) Popular Topic Attention (PTA) for guiding the extractor to select the attractive sentence from the article and 2) a popularity predictor for guiding the abstractor to rewrite the attractive sentence. Moreover, since the sentence selection of the extractor is not differentiable, techniques of reinforcement learning (RL) are utilized to bridge the gap with rewards obtained from a popularity score predictor. Through quantitative and qualitative experiments, we show that the proposed PORL-HG significantly outperforms the state-of-the-art headline generation models in terms of attractiveness evaluated by both human (71.03%) and the predictor (at least 27.60%), while the faithfulness of PORL-HG is also comparable to the state-of-the-art generation model.
An Optimized Registration Method Based on Distribution Similarity and DVF Smoothness for 3D PET and CT Images

A fusion image combining both anatomical and functional information obtained by registering medical images of two different modalities, Positron Emission Tomography (PET) and Computed Tomography (CT), is of great significance for medical image analysis and diagnosis. Medical image registration relies on similarity measure which is low between PET/CT image voxels and therefore PET/CT registration is a challenging task. To address this issue, this paper presents an unsupervised end-to-end method, DenseRegNet, for deformable 3D PET/CT image registration. The method consists of two stages: (1) predicting 3D displacement vector field (DVF); and (2) registering 3D image. In the 3D DVF prediction stage, a two-level similarity measure together with a deformation regularization is proposed as loss function to optimize network training.In the image registration stage, a resampler and a spatial transformer are utilized to obtain the registration results. In this paper, 663 pairs of Uptake Value (SUV) and Hounsfield Unit (Hu) patches of 106 patients, 227 pairs of SUV and Hu patches of 35 patients and 259 pairs of SUV and Hu patches of 35 patients are randomly selected as training, validation and test set, respectively. Normalized cross correlation (NCC), intersection over union (IoU) of liver bounding box and euclidean distance (ED) on landmark points are used to evaluate the registration results. Experiment results show that the proposed method, DenseRegNet, achieves the best results in terms of liver bounding box IoU and ED, and the second highest value of NCC. For a trained model, given a new pair of PET/CT images, the registration result can be obtained with only one forward calculation within 10 seconds. Through qualitative and quantitative analyses, we demonstrate that, compared with other deep learning registration models, the proposed DenseRegNet achieves improved results in the challenging deformable PET/CT registration task.
Fuzzy Hysteresis Smoothing: A New Approach for Image Denoising

Hysteresis smoothing (HS) is one of the recently proposed image denoising schemes, which employs hard threshold levels to model the hysteresis process. Nevertheless, hard thresholding is in contrast with what is observed from the behavior of the hysteresis phenomenon in nature, and this implies that the HS may lead to a suboptimal model. On the other hand, it is proved that fuzzy logic is a promising research field in solving various problems by applying the uncertainty characteristics. Hence, in this article, we develop a novel HS methodology based on the fuzzy norms, which, in addition to incorporating the advantages of the HS, also manages the undesired effects of hard thresholding. In this method, which is called fuzzy HS (FHS), pointwise hard thresholding is replaced by an interval soft manner, which allows the threshold levels to be determined commensurate with the fuzzy norm's free parameter. It is shown that among the classical fuzzy norms, the Yager one can carry out the FHS relatively well. However, this norm causes oversaturation in the hysteresis loop, which leads to an oversmoothing problem in the image denoising process. To alleviate this, a new fuzzy norm with logarithmic nature, named expansion norm, has been proposed, which improves the accuracy of the FHS. The comparative simulations demonstrate the significant superiority of the FHS in terms of both objective and subjective criteria over the classical HS. Besides, the results indicate that the proposed scheme has competitive denoising performance in comparison with some well-known algorithms.
Robot Path Planning Using Differential Evolution

The differential evaluationJain, Sanjay (DE) algorithm is an evolutionary algorithm.Kumar Sharma, Vivek It is a popular metaheuristics that efficientlyKumar, Sandeep solved various complex optimization problems. This paper studied some recent modifications in DE and Robot Path Planning Problem. The problem are studied with various constraints and results are compared with competitive nature-inspired algorithms.
The Design and Implementation of Responsive Web Page Based on HTML5 and CSS3

The responsive web design solves the compatibility problems of web pages displayed at different resolutions, different platforms, and different screen sizes, and also brings high-quality experience to users. Based on the research on responsive web design, and related technologies of HTML5 and CSS3, this paper expounds the design ideas and key technologies of responsive design with a responsive enterprise website. Response web design based on HTML5 and CSS3 has proved to be feasible and effective.
Augmented reality using features accelerated segment test for learning tajweed

Currently, education forms students to think creatively and critically, this can be supported by the multimedia technology for education, including Islamic religious education. Islam requires all of its Muslim to read the Qur'an. Tajweed is an important because it is related to the articulation of reading the Qur'an properly and correctly. This article discusses the application of augmented reality (AR) as one of the multimedia technologies that can be used as an interactive educational medium to help study the tajweed of Qur'an. The method used in this research is Features from accelerated segment test (FAST) corner detection. The testing result with 31 tajweed objects show that FAST is able to recognize all Tajweed objects and display their AR. Besides, based on a survey with questionnaires to several students, the result shows that 88.2% of students responded very well and judged that it was sufficient to help study the tajweed.
Tampered and Computer-Generated Face Images Identification Based on Deep Learning

Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that are used to generate human facial images have made it more challenging for even humans to detect the tampered one. The spread of those images on the internet can cause severe ethical, moral, and legal issues if the manipulated images are misused. As a result, much research has been conducted to detect facial image manipulation based on applying machine learning algorithms on tampered face datasets in the last few years. This paper introduces a deep learning-based framework that can identify manipulated facial images and GAN-generated images. It is comprised of multiple convolutional layers, which can efficiently extract features using multi-level abstraction from tampered regions. In addition, a data-based approach, cost-sensitive learning-based approach (class weight), and ensemble-based approach (eXtreme Gradient Boosting) is applied to the proposed model to deal with the imbalanced data problem (IDP). The superiority of the proposed model that deals with an IDP is verified using a tampered face dataset and a GAN-generated face dataset under various scenarios. Experimental results proved that the proposed framework outperformed existing expert systems, which has been used for identifying manipulated facial images and GAN-generated images in terms of computational complexity, area under the curve (AUC), and robustness. As a result, the proposed framework inspires the development of research on image forgery identification and enables the potential to integrate these models into practical applications, which require tampered facial image detection.
A Reversible Steganography Method With Statistical Features Maintained Based on the Difference Value

Reversible data hiding (RDH) is a technique that slightly alters digital media (e.g.images or videos) to embed secret messages while the original digital media can be completely recovered without any error after the hidden messages have been extracted.In the past more than one decade, hundreds of RDH algorithms have been reported, and among these algorithms, the difference histogram shifting (DHS) based methods have attracted much attention.With DHS-based RDH, high capacity and low distortion can be achieved efficiently.But there occurs one problem that, with DHS, the difference values to embed secret bits are explored, and the other difference values are shifted to create vacant spaces, it will cause the difference value histogram changing significantly and draw the attention of steganalyzers.So, this paper proposed a new idea for RDH based on the difference value and with statistical features maintained (SFM) with simple implementation and high scalability, we embed the secret messages by keeping the difference values that need to be modified in the original range, and the other difference values would not be shifted.In addition, we need the original difference values as the key to extract the secret messages.In order to expand the embedding capacity further, we designed two algorithms that embed message in two different difference values and four different difference values, and these two methods are named SFM_A and SFM_B respectively.SFM_B can support greater amount of embedded message than SFM_A, but brings greater changes to the original image, which could lead to the decline of PSNR and SSIM.The experimental results show that through our method, the histogram of difference values is well maintained, and the degree of distortion of the image is improved at the same time.
Automating the Detection of Dynamically Triggered Earthquakes via a Deep Metric Learning Algorithm

Abstract Detecting subtle signals from small earthquakes triggered by transient stresses from the surface waves of large magnitude earthquakes can contribute to a more general understanding of how earthquakes nucleate and interact with each other. However, searching for signals from such small earthquakes in thousands of seismograms is overwhelming, and discriminating them from a miscellany of noise is challenging. Here, we explore how we can automate the detection of such dynamically triggered earthquakes using a simple, diagnostic signal-to-noise ratio (SNR) threshold as well as a convolutional deep metric learning network. Our analysis shows that the deep learning network was more reliable at detecting small earthquakes than the SNR method.