Reviewerly/exHarmony huggingface dataset:
A nonparametric updating method to correct clinical prediction model drift

Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models.We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test's properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions.In simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correction under changing outcome rates, and refitting under shifted predictor-outcome associations. The recommended updates provided superior or similar calibration to that achieved with more complex updating. In the case study, however, small update sets lead the test to recommend simpler updates than may have been ideal based on subsequent performance.Our test's recommendations highlighted the benefits of simple updating as opposed to systematic refitting in response to performance drift. The complexity of recommended updating methods reflected sample size and magnitude of performance drift, as anticipated. The case study highlights the conservative nature of our test.This new test supports data-driven updating of models developed with both biostatistical and machine learning approaches, promoting the transportability and maintenance of a wide array of clinical prediction models and, in turn, a variety of applications relying on modern prediction tools.
An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors

Trajectory replanning for quadrotors is essential to enable fully autonomous flight in unknown environments. Hierarchical motion planning frameworks, which combine path planning with path parameterization, are popular due to their time efficiency. However, the path planning cannot properly deal with nonstatic initial states of the quadrotor, which may result in nonsmooth or even dynamically infeasible trajectories. In this article, we present an efficient kinodynamic replanning framework by exploiting the advantageous properties of the B-spline, which facilitates dealing with the nonstatic state and guarantees safety and dynamical feasibility. Our framework starts with an efficient B-spline-based kinodynamic (EBK) search algorithm, which finds a feasible trajectory with minimum control effort and time. To compensate for the discretization induced by the EBK search, an elastic optimization approach is proposed to refine the control point placement to the optimal location. Systematic comparisons against the state-of-the-art are conducted to validate the performance. Comprehensive onboard experiments using two different vision-based quadrotors are carried out showing the general applicability of the framework.
Efficient algorithms for densest subgraph discovery

Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph G , DSD aims to find a subgraph D of G with the highest density (e.g., the number of edges over the number of vertices in D ). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that the densest subgraph can be accurately found through a k -core (a kind of dense subgraph of G ), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based- and general pattern-based density. We have performed extensive experimental evaluation on both real and synthetic datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.
An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine

Maintaining secured patient credentials in telemedicine is becoming a critical task. Image watermarking is one of the solutions to this problem. It is extensively used to protect and block the content alteration. Medical images may acquaint with tampers during transit in telemedicine. Before taking a prior decision about referring for diagnosis, the reliability of region of interest (ROI) of the watermarked medical test image must be tested to avoid faulty diagnosis. In this paper, tamper recognition and authenticity were obtained by concealing the dual watermarks into the region of non-interest (RONI) blocks of the medical image. These blocks are chosen by the characteristics of Human Visual System (HVS) with the integration of Discrete Wavelet Transform (DWT) and Schur transform along with the Particle Swarm Bacterial Foraging Optimization algorithm (PSBFO). The major focus of the PSBFO algorithm is to select the threshold value for obtaining optimum results in terms of imperceptibility and robustness against attacks. The dual watermarks are compressed by Lempel-Ziv-Welch (LZW) lossless compression algorithm to increase the payload capacity. Simulation outcomes conducted on different types of medical images disclose that the proposed scheme demonstrates superior transparency and robustness against signal and compression attacks compared with the related hybrid optimized algorithms. It also recognizes the existence of tampers inside the portion of ROI with 100% precision. The proposed scheme is also able to retrieve the original ROI without losing any information and provides optimum security capability when compared with the state-of-the-art algorithms.
Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization

Particle swarm optimization (PSO) is a stochastic computation technique motivated by intelligent collective behavior of some animals, which has been widely used to address many hard optimization problems. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima when dealing with complex multimodal problems. In this paper, we propose a chaotic particle swarm optimization with sigmoid-based acceleration coefficients (abbreviated as CPSOS). On the one hand, the frequently used logistic map is applied to generate well-distributed initial particles. On the other hand, the sigmoid-based acceleration coefficients are formulated to balance the global search ability in the early stage and the global convergence in the latter stage. In particular, two sets of slowly varying function and regular varying function embedded update mechanism in conjunction with the chaos based re-initialization and Gaussian mutation strategies are employed at different evolution stages to update the particles during the whole search process, which can effectively keep the diversity of the swarm and get out of possible local optima to continue exploring the potential search regions of the solution space. To validate the performance of CPSOS, a series of experiments are conducted and the simulation results reveal that the proposed method can achieve better performance compared to several state-of-the-art PSO variants in terms of solution accuracy and effectiveness.
PASS: Panoramic Annular Semantic Segmentation

Pixel-wise semantic segmentation is capable of unifying most of driving scene perception tasks, and has enabled striking progress in the context of navigation assistance, where an entire surrounding sensing is vital. However, current mainstream semantic segmenters are predominantly benchmarked against datasets featuring narrow Field of View (FoV), and a large part of vision-based intelligent vehicles use only a forward-facing camera. In this paper, we propose a Panoramic Annular Semantic Segmentation (PASS) framework to perceive the whole surrounding based on a compact panoramic annular lens system and an online panorama unfolding process. To facilitate the training of PASS models, we leverage conventional FoV imaging datasets, bypassing the efforts entailed to create fully dense panoramic annotations. To consistently exploit the rich contextual cues in the unfolded panorama, we adapt our real-time ERF-PSPNet to predict semantically meaningful feature maps in different segments, and fuse them to fulfill panoramic scene parsing. The innovation lies in the network adaptation to enable smooth and seamless segmentation, combined with an extended set of heterogeneous data augmentations to attain robustness in panoramic imagery. A comprehensive variety of experiments demonstrates the effectiveness for real-world surrounding perception in a single PASS, while the adaptation proposal is exceptionally positive for state-of-the-art efficient networks.
Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells

Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.
Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks

Abstract Primary management for head and neck cancers, including squamous cell carcinoma (SCC), involves surgical resection with negative cancer margins. Pathologists guide surgeons during these operations by detecting cancer in histology slides made from the excised tissue. In this study, 381 digitized, histological whole-slide images (WSI) from 156 patients with head and neck cancer were used to train, validate, and test an inception-v4 convolutional neural network. The proposed method is able to detect and localize primary head and neck SCC on WSI with an AUC of 0.916 for patients in the SCC testing group and 0.954 for patients in the thyroid carcinoma testing group. Moreover, the proposed method is able to diagnose WSI with cancer versus normal slides with an AUC of 0.944 and 0.995 for the SCC and thyroid carcinoma testing groups, respectively. For comparison, we tested the proposed, diagnostic method on an open-source dataset of WSI from sentinel lymph nodes with breast cancer metastases, CAMELYON 2016, to obtain patch-based cancer localization and slide-level cancer diagnoses. The experimental design yields a robust method with potential to help create a tool to increase efficiency and accuracy of pathologists detecting head and neck cancers in histological images.
Towards secure and efficient energy trading in IIoT-enabled energy internet: A blockchain approach

Nowadays, with the rapid development and wide application of Internet of Things technology, Industrial Internet of Things (IIoT) has become an essential part of industrial systems. A large number of IoT devices has enabled the diversification and decentralization of energy systems, and the Energy Internet makes it possible to interconnect distributed energy sources and consumers. However, as the number of entities involved in the energy transactions grows and the forms of transactions diversify, the traditional centralized energy trading model faces the problems such as insufficient scalability, vulnerabilities to cyber-attack, and low processing efficiency. To address these challenges, we propose BC-ETS, a secure and efficient BlockChain based Energy Trading Scheme. In BC-ETS, the energy trading model is divided into two levels, which can not only protect privacy but also achieve the balance between power supply and demand. Additionally, to adapt to the relatively weak computing power of the underlying IoT equipment in energy internet, a credibility-based equity proof mechanism is designed to greatly improve the system availability. The analysis shows that BC-ETS can meet the security requirements and has a better performance compared with other similar energy trading schemes.
A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization

In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heuristic algorithm to automatically adjust the tunable parameter based on a novel fitness function. Fitness function contains two different objective functions, which use edge, entropy, gray level co-occurrence matrix (GLCM) contrast, and GLCM energy of image for best visual, contrast enhancement and improved different characteristic information of the anatomical images. This method is compared with a different state of the art methods to check the viability and vigorous of the scheme and salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed approach. The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity.
Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation

Computer is a part and parcel in our day to day life and used in various fields. The interaction of human and computer is accomplished by conventional input devices like mouse, keyboard etc. Hand gestures can be a useful medium of human-computer interaction and can make the interaction easier. Gestures vary in orientation and shape from person to person. So, non-linearity exists in this problem. Recent research has proved the supremacy of Convolutional Neural Network (CNN) for image representation and classification. Since, CNN can learn complex and non-linear relationships among images, in this paper, a static hand gesture recognition method deploying CNN was proposed. Data augmentation like re-scaling, zooming, shearing, rotation, width and height shifting was applied to the dataset. The model was trained on 8000 images and tested on 1600 images which were divided into 10 classes. The model with augmented data achieved accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%).
Detecting Cyberbullying and Cyberaggression in Social Media

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future.
Good News, Everyone! Context Driven Entity-Aware Captioning for News Images

Current image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret images by integrating several sources of prior knowledge of the world. In this work, we aim to take a step closer to producing captions that offer a plausible interpretation of the scene, by integrating such contextual information into the captioning pipeline. For this we focus on the captioning of images used to illustrate news articles. We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image. Our model is able to selectively draw information from the article guided by visual cues, and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source. Furthermore we introduce ``GoodNews'', the largest news image captioning dataset in the literature and demonstrate state-of-the-art results.
Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation

Deep learning models have shown their vulnerabilities to universal adversarial perturbations (UAP), which are quasi-imperceptible. Compared to the conventional supervised UAPs that suffer from the knowledge of training data, the data-independent unsupervised UAPs are more applicable. Existing unsupervised methods fail to take advantage of the model uncertainty to produce robust perturbations. In this paper, we propose a new unsupervised universal adversarial perturbation method, termed as Prior Driven Uncertainty Approximation (PD-UA), to generate a robust UAP by fully exploiting the model uncertainty at each network layer. Specifically, a Monte Carlo sampling method is deployed to activate more neurons to increase the model uncertainty for a better adversarial perturbation. Thereafter, a textural bias prior to revealing a statistical uncertainty is proposed, which helps to improve the attacking performance. The UAP is crafted by the stochastic gradient descent algorithm with a boosted momentum optimizer, and a Laplacian pyramid frequency model is finally used to maintain the statistical uncertainty. Extensive experiments demonstrate that our method achieves well attacking performances on the ImageNet validation set, and significantly improves the fooling rate compared with the state-of-the-art methods.
A probability density function generator based on neural networks

In order to generate a probability density function (PDF) for fitting the probability distributions of practical data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be utilised as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of the trained deep learning model can be used to estimate the PDF. Numerical experiments with single and mixed distributions are conducted to evaluate the performance of the proposed method. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method.
Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications

It has been reported that some specially designed recurrent neural networks and their related neural dynamics are efficient for solving quadratic programming (QP) problems in the real domain. A complex-valued QP problem is generated if its variable vector is composed of the magnitude and phase information, which is often depicted in a time-dependent form. Given the important role that complex-valued problems play in cybernetics and engineering, computational models with high accuracy and strong robustness are urgently needed, especially for time-dependent problems. However, the research on the online solution of time-dependent complex-valued problems has been much less investigated compared to time-dependent real-valued problems. In this article, to solve the online time-dependent complex-valued QP problems subject to linear constraints, two new discrete-time neural dynamics models, which can achieve global convergence performance in the presence of perturbations with the provided theoretical analyses, are proposed and investigated. In addition, the second proposed model is developed to eliminate the operation of explicit matrix inversion by introducing the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Moreover, computer simulation results and applications in robotics and filters are provided to illustrate the feasibility and superiority of the proposed models in comparison with the existing solutions.
Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss

Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color. We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method.