Reviewerly/exHarmony huggingface dataset:
A General Outpatient Triage System Based on Dynamic Uncertain Causality Graph

In order to reduce the misdiagnosis caused by outpatient triage error and help triage nurses to improve their triage accuracy, a general outpatient triage system based on the hybrid Dynamic Uncertain Causality Graph (hybrid DUCG) is presented. The hybrid DUCG is a combination of the single-valued DUCG (S-DUCG) and the multivalued DUCG (M-DUCG). In this paper, the M-DUCG is utilized to construct the basic outpatient triage knowledge base. In the knowledge base, each department is modeled as one sub-M-DUCG. It describes the causal relations between the department and its associated clinical features. All the sub-M-DUCGs are compiled into one M-DUCG as the basic outpatient triage knowledge base. The S-DUCG is employed to adjust the basic knowledge base to match the variant triage requirements for various hospitals. To validate the triage system, we constructed a basic triage knowledge base and adjusted it to match the triage requirement of a county-level hospital. Verification experiments were performed based on the adjusted triage knowledge base. The result showed that the triage system has high triage accuracy, and it can effectively assist triage nurses in outpatient triage.
Handwritten Text Recognition using Deep Learning with TensorFlow

Character recognition is one in all the emerging fields within the computer vision.The most abilities of humans are they will recognize any object or thing.The hand transcription can easily identify by humans.Different languages have different patterns to spot.Humans can identify the text accurately.The hand transcription cannot be identified by the machine.It's difficult to spot the text by the system.During this text recognition, we process the input image, extraction of features, and classification schema takes place, training of system to acknowledge the text.During this approach, the system is trained to seek out the similarities, and also the differences among various handwritten samples.This application takes the image of a hand transcription and converts it into a digital text.
Detection of Malignant and Benign Breast Cancer Using the ANOVA-BOOTSTRAP-SVM

Abstract Purpose The aim of this research is to propose a modification of the ANOVA-SVM method that can increase accuracy when detecting benign and malignant breast cancer. Methodology We proposed a new method ANOVA-BOOTSTRAP-SVM. It involves applying the analysis of variance (ANOVA) to support vector machines (SVM) but we use the bootstrap instead of cross validation as a train/test splitting procedure. We have tuned the kernel and the C parameter and tested our algorithm on a set of breast cancer datasets. Findings By using the new method proposed, we succeeded in improving accuracy ranging from 4.5 percentage points to 8 percentage points depending on the dataset. Research limitations The algorithm is sensitive to the type of kernel and value of the optimization parameter C. Practical implications We believe that the ANOVA-BOOTSTRAP-SVM can be used not only to recognize the type of breast cancer but also for broader research in all types of cancer. Originality/value Our findings are important as the algorithm can detect various types of cancer with higher accuracy compared to standard versions of the Support Vector Machines.
Consuming and Persistent Types for Classical Logic

We prove that type systems are able to capture exact measures related to dynamic properties of functional programs with control operators, which allow implementing intricate continuations and backtracking. Our type systems give the number of evaluation steps to normal form as well as the size of this normal form without any evaluation being needed.
Solution of Ruin Probability for Continuous Time Model Based on Block Trigonometric Exponential Neural Network

The ruin probability is used to determine the overall operating risk of an insurance company. Modeling risks through the characteristics of the historical data of an insurance business, such as premium income, dividends and reinvestments, can usually produce an integral differential equation that is satisfied by the ruin probability. However, the distribution function of the claim inter-arrival times is more complicated, which makes it difficult to find an analytical solution of the ruin probability. Therefore, based on the principles of artificial intelligence and machine learning, we propose a novel numerical method for solving the ruin probability equation. The initial asset u is used as the input vector and the ruin probability as the only output. A trigonometric exponential function is proposed as the projection mapping in the hidden layer, then a block trigonometric exponential neural network (BTENN) model with a symmetrical structure is established. Trial solution is set to meet the initial value condition, simultaneously, connection weights are optimized by solving a linear system using the extreme learning machine (ELM) algorithm. Three numerical experiments were carried out by Python. The results show that the BTENN model can obtain the approximate solution of the ruin probability under the classical risk model and the Erlang(2) risk model at any time point. Comparing with existing methods such as Legendre neural networks (LNN) and trigonometric neural networks (TNN), the proposed BTENN model has a higher stability and lower deviation, which proves that it is feasible and superior to use a BTENN model to estimate the ruin probability.
Segmentation Of Pap Smear Images For Cervical Cancer Detection

Detection and segmentation of nuclei in pap smear cell images is necessary for automatic screening of cervical cancer. The methodology proposed here is based on K means clustering for segmentation of preprocessed images and is carried out on the Herlev dataset. IoU values of segmentation results with the ground truth are checked and various shape features are extracted from the segmented nucleus. The classification of the nuclei on the basis of the shape features is done with the help of Random Forest Classifier and comparison is made with other classifiers and their results on this dataset.
Achieving Stronger Compaction for DCT-Based Steganography: A Region-Growing Approach

The strong energy compaction property of the Discrete Cosine Transform (DCT) has inspired researchers to utilize the DCT transform technique in steganography. The DCT transform can effectively represent a signal within a few significant coefficients leaving a large area with insignificant coefficients that can be safely replaced by a substantial amount of secret data. It has been proven that this strong energy compaction property has a relation with the correlation of the signal. The higher the correlation, the stronger the compaction, and thus more data can be hidden. Therefore, several state-of-the-art image steganography techniques tend to segment the cover image into homogeneous segments to exploit the strong compaction property of the DCT. In this paper, a precise segmentation process using the region-growing segmentation method is applied to maximize the homogeneity level between pixels in a segment, and thus, maximizing the hiding capacity while achieving improved stego quality.
Optimizing the prototypes with a novel data weighting algorithm for enhancing the classification performance of fuzzy clustering

Fuzzy clustering is regarded as an unsupervised learning process that constitutes a prerequisite for many other data mining techniques. Deciding how to classify data efficiently and accurately has been one of the topics pursued by many researchers. We anticipate that the classification performance of the clustering is strongly dependent on the boundary data (viz. data located at the boundaries of the clusters). The boundary data hold some levels of uncertainties and as such contain more information than others. Usually the greater the uncertainty, the more information contained in such data. To improve the quality of clustering, this study develops an augmented scheme of fuzzy clustering, in which a novel weighted data-based fuzzy clustering is proposed. In the introduced scheme, a dataset is composed of boundary data and non-boundary data. The partition matrix is used to determine the boundary data and the non-boundary data to be next considered in the clustering process. Then, we assign different weights to each datum to construct the weighted data. During this process, we make the weights for the boundary data and the non-boundary data different, which makes the contributions of the boundary data and the non-boundary data to the prototypes being reduced and enhanced, respectively. Furthermore, we build a weighting function to determine the weights of the data. The weighted data are used to optimize the prototypes. With the optimized prototypes, the partition matrix can be refined, which ultimately makes the boundaries of the clusters optimized. Finally, the classification performance of fuzzy clustering is enhanced. We offer a thorough analysis of the developed scheme. Comprehensive experimental studies involving synthetic and publicly available datasets are reported to demonstrate the performance of the proposed approach.
Social Media User Geolocation via Hybrid Attention

Determining user geolocation is vital to various real-world applications on the internet, such as online marketing and event detection. To identify the geolocations of users, their behaviors on social media like published posts and social interactions can be strong evidence. However, most of the existing social media based approaches individually learn from text contexts and social networks. This separation can not only lead to sub-optimal performance but also ignore the distinct importance of two resources for different users. To address this challenge, we propose a novel end-to-end framework, Hybrid-attentive User Geolocation (HUG), to jointly model post texts and user interactions in social media. The hybrid attention mechanism is introduced to automatically determine the importance of texts and social networks for each user while social media posts and interactions are modeled by a graph attention network and a language attention network. Extensive experiments conducted on three benchmark geolocation datasets using Twitter data demonstrate that HUG significantly outperforms competitive baseline methods. The in-depth analysis also indicates the robustness and interpretability of HUG.
Revisiting Higher-Order Dependency Parsers

Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parsers.This has led to a belief that neural encoders can implicitly encode structural constraints, such as siblings and grandparents in a tree.We tested this hypothesis and found that neural parsers may benefit from higher-order features, even when employing a powerful pre-trained encoder, such as BERT.While the gains of higher-order features are small in the presence of a powerful encoder, they are consistent for long-range dependencies and long sentences.In particular, higher-order models are more accurate on full sentence parses and on the exact match of modifier lists, indicating that they deal better with larger, more complex structures.
Out of the Echo Chamber: Detecting Countering Debate Speeches

An educated and informed consumption of media content has become a challenge in modern times.With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views.We suggest a novel task aiming to alleviate some of these concerns -that of detecting articles that most effectively counter the arguments -and not just the stance -made in a given text.We study this problem in the context of debate speeches.Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it.We provide a large dataset of 3685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community.We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research.All data collected during this work is freely available for research 1 .
Unsupervised Paraphrase Generation using Pre-trained Language Models

Large scale Pre-trained Language Models have proven to be very powerful approach in various Natural language tasks. OpenAI's GPT-2 \cite{radford2019language} is notable for its capability to generate fluent, well formulated, grammatically consistent text and for phrase completions. In this paper we leverage this generation capability of GPT-2 to generate paraphrases without any supervision from labelled data. We examine how the results compare with other supervised and unsupervised approaches and the effect of using paraphrases for data augmentation on downstream tasks such as classification. Our experiments show that paraphrases generated with our model are of good quality, are diverse and improves the downstream task performance when used for data augmentation.
Combinatorial t-designs from special functions

A special function is a function either of special form or with a special property. Special functions have interesting applications in coding theory and combinatorial t-designs. The main objective of this paper is to survey t-designs constructed from special functions, including quadratic functions, almost perfect nonlinear functions, almost bent functions, bent functions, bent vectorial functions, and planar functions. These combinatorial designs are not constructed directly from such functions, but come from linear codes which are constructed with such functions. As a byproduct, this paper also surveys linear codes from certain special functions.
Extensions and limitations of randomized smoothing for robustness guarantees

Randomized smoothing, a method to certify a classifier's decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. It operates in a black-box and so certification is not constrained by the size of the classifier's architecture. Here, we extend the work of Li etal. [26], studying how the choice of divergence between smoothing measures affects the final robustness guarantee, and how the choice of smoothing measure itself can lead to guarantees in differing threat models. To this end, we develop a method to certify robustness against any ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (p ∈ N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;0</sub> ) minimized adversarial perturbation. We then demonstrate a negative result, that randomized smoothing suffers from the curse of dimensionality; as p increases, the effective radius around an input one can certify vanishes.
Evaluation of Fiber Orientation by X-Ray Diffraction on Carbon Fiber Reinforced Polyamide 6

Fiber orientation is important for the development of carbon fiber reinforced thermoplastics (CFRTPs). The purpose of this work is the development of an easy and effective technique to analyze carbon fiber (CF) orientation in CFRTPs in a short time. For this purpose, we investigated the CF orientations in CFRTPs reinforced with polyamide 6 (PA6) as observed by X-ray diffraction (XRD), and discuss the suitability of XRD for the measurement of the CF orientation in CFRTPs. The 200, 002, and 202 reflections of PA6; and the 002 reflection of graphite were observed in the XRD pattern of the CFRTPs. The XRD pattern of unidirectional CFRTP (UD-CFRTP) in the diffraction angle (2θ) range from 25 ̊to 33̊was almost entirely dominated by contributions from CF. The dependence of the 002 XRD intensity in the 2θ range from 25̊ to 33̊ on the azimuth (I(φ)) of UD-CFRTP and cloth CFRTP (c-CFRTP) showed the CF orientations in those. The direction and distribution of the CF orientations in the discontinuous CF reinforced thermoplastics (D-CFRTP) could be estimated based on the I(φ) obtained by the XRD. Each test piece showed a variable direction and distribution of CF orientation. Fiber orientation order parameters of D-CFRTPs were estimated by I(φ) obtained from the XRD of D-CFRTP and that of UD-CFRTP. The CFs in the D-CFRTP were almost oriented along the molding direction. The tensile modulus was correlated with the orientation order parameters estimated from the XRD patterns of the D-CFRTP. We could obtain these results from studying a UD-CFRTP,a c-CFRTP and a D-CFRTP with performing easy measurements using a general XRD system for 10min. Therefore, XRD may be the one of the most effective methods for the estimation of CF orientations in CFRTPs reinforced with PA6.
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors

When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in the posterior. Here we propose an approach using parallel runs of MCMC, variational, or mode-based inference to hit as many modes or separated regions as possible and then combine these using Bayesian stacking, a scalable method for constructing a weighted average of distributions. The result from stacking efficiently samples from multimodal posterior distribution, minimizes cross validation prediction error, and represents the posterior uncertainty better than variational inference, but it is not necessarily equivalent, even asymptotically, to fully Bayesian inference. We present theoretical consistency with an example where the stacked inference approximates the true data generating process from the misspecified model and a non-mixing sampler, from which the predictive performance is better than full Bayesian inference, hence the multimodality can be considered a blessing rather than a curse under model misspecification. We demonstrate practical implementation in several model families: latent Dirichlet allocation, Gaussian process regression, hierarchical regression, horseshoe variable selection, and neural networks.
SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization

Most prior work in the sequence-to-sequence paradigm focused on datasets with input sequence lengths in the hundreds of tokens due to the computational constraints of common RNN and Transformer architectures. In this paper, we study long-form abstractive text summarization, a sequence-to-sequence setting with input sequence lengths up to 100,000 tokens and output sequence lengths up to 768 tokens. We propose SEAL, a Transformer-based model, featuring a new encoder-decoder attention that dynamically extracts/selects input snippets to sparsely attend to for each output segment. Using only the original documents and summaries, we derive proxy labels that provide weak supervision for extractive layers simultaneously with regular supervision from abstractive summaries. The SEAL model achieves state-of-the-art results on existing long-form summarization tasks, and outperforms strong baseline models on a new dataset/task we introduce, Search2Wiki, with much longer input text. Since content selection is explicit in the SEAL model, a desirable side effect is that the selection can be inspected for enhanced interpretability.
On Anisotropic Optical Flow Inpainting Algorithms

This work describes two anisotropic optical flow inpainting algorithms.The first one recovers the missing flow values using the Absolutely Minimizing Lipschitz Extension partial differential equation (also called infinity Laplacian equation) and the second one uses the Laplace partial differential equation, both defined on a Riemmanian manifold.The Riemannian manifold is defined by endowing the plane domain with an appropriate metric depending on the reference video frame.A detailed analysis of both approaches is provided and their results are compared on three different applications: flow densification, occlusion inpainting and large hole inpainting.
Object Recognition with Hybrid Deep Learning Methods and Testing on Embedded Systems

Object recognition applications can be made with deep neural networks.However, this process may require intensive processing load.For this purpose, hybrid object recognition algorithms that can be created for the recognition of an object in the image and the comparison of the working time of these algorithms on various embedded systems are emphasized.While Haar Cascade, Local Binary Pattern (LBP) and Histogram Oriented Gradients (HOG) algorithms are used for object detection, Convolutional Neural Network (CNN) and Deep Neural Network (DNN) algorithms are used for classification.As a result, six hybrid structures such as Haar Cascade+CNN, LBP+CNN, HOG+CNN and Haar Cascade+DNN, LBP+DNN, HOG+DNN are developed.In this study, these 6 hybrid algorithms were analyzed in terms of success percentage and time, then compared with each other.Microsoft COCO dataset was used to train and test all these hybrid algorithms.Object recognition success of CNN was 76.33%.Object recognition success of Haar Cascade+CNN, one of the hybrid methods we recommend, with a success rate of 78.6% is higher than CNN and other hybrid methods.LBP+CNN method recognized objects in 0.487 seconds which is faster than any other hybrid methods.In our study, Nvidia Jetson TX2, Asus TinkerBoard, Raspbbery Pi 3 B+ were used as embedded systems.As a result of these tests, Haar Cascade+CNN method on Nvidia Jetson TX2 was detected in 0.1303 seconds, LBP+DNN and Haar Cascade+DNN methods on Asus Tinker Board were detected in 0.2459 seconds, and HOG+DNN method on Raspberry Pi 3 B+ was detected in 0.7153 seconds..
Blockchain Tree as Solution for Distributed Storage of Personal ID Data and Document Access Control

This paper introduces a new method of Blockchain formation for reliable storage of personal data of ID-card holders. In particular, the model of the information system is presented, the new structure of smart ID-cards and information on these cards are proposed. The new structure of Blockchain, “Blockchain Tree”, allows not only to store information from ID-cards but also to increase the level of security and access control to this information. The proposed Subchains system allows us to integrate Blockchain of the lower level to Blockchain of the higher level, allowing us to create a multilevel protected system.
TableBank: A Benchmark Dataset for Table Detection and Recognition

We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models are available at \url{https://github.com/doc-analysis/TableBank}.