Reviewerly/exHarmony huggingface dataset:
Learning Mri Contrast-Agnostic Registration

We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to magnetic resonance imaging (MRI) contrast. While classical methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning methods are fast at test time but limited to images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency using a generative strategy that exposes networks to a wide range of images synthesized from segmentations during training, forcing them to generalize across contrasts. We show that networks trained within this framework generalize to a broad array of unseen MRI contrasts and surpass classical state-of-the-art brain registration accuracy by up to 12.4 Dice points for a variety of tested contrast combinations. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images during training.
Instance Segmentation for Autonomous Vehicle

To detect the objects around an autonomous vehicle is the most important thing to drive it carefully without danger. This paper delivers a presentation on detecting and classifying the objects for assisting autonomous driving. In autonomous driving systems, the task of detecting the vehicle itself is one of the most important prerequisites to autonomous navigation. Deep learning, one of the computer vision tasks, performs object detection very effectively compared to earlier methods and this project is to segment the objects like vehicles, persons. Instance segmentation an extension of object detection is used to identify the objects in the image by assigning different labels for each instance of the objects belonging to the same class. Instance segmentation method is used for solving both object detection and semantic segmentation in parallel. In the existing system like Deep Neural Network (DNN) is used for segmenting the papers in the medical field and in other places. But when it comes to detect for autonomous vehicle it is not detecting clearly. To get better from this problem in the proposed system Instance segmentation is performed by using the method known as Mask RCNN. Compared to earlier detection approaches Mask RCNN shows improvement in detection accuracy and in time complexity.
A Note on the Computation of the Modular Inverse for Cryptography

In literature, there are a number of cryptographic algorithms (RSA, ElGamal, NTRU, etc.) that require multiple computations of modulo multiplicative inverses. In this paper, we describe the modulo operation and we recollect the main approaches to computing the modulus. Then, given a and n positive integers, we present the sequence (zj)j≥0, where zj=zj−1+aβj−n, a&lt;n and GCD(a,n)=1. Regarding the above sequence, we show that it is bounded and admits a simple explicit, periodic solution. The main result is that the inverse of a modulo n is given by a−1=⌊im⌋+1 with m=n/a. The computational cost of such an index i is O(a), which is less than O(nlnn) of the Euler’s phi function. Furthermore, we suggest an algorithm for the computation of a−1 using plain multiplications instead of modular multiplications. The latter, still, has complexity O(a) versus complexity O(n) (naive algorithm) or complexity O(lnn) (extended Euclidean algorithm). Therefore, the above procedure is more convenient when a&lt;&lt;n (e.g., a&lt;lnn).
Comparative Analysis of Text Extraction from Color Images using Tesseract and OpenCV

Image-based Text Extraction has a growing requirement in today's generation. Students, doctors, and engineers generate a lot of images every day. It is very important to extract text from these images in a simple yet effective manner. We can obtain useful information by testing these images. We aim is to summarize the visual information and retrieve its content. The Optical Recognition System involves several algorithms that fulfill this purpose. Text Extraction involves a lot of processes from text detection, localization, segmentation and, text recognition. Tesseract is the most optimized OCR Engine build by HP Labs and owned by Google. Text Detection involves the recognition of text from desired input images. Text Localization involves identifying the position of text on the images. Tesseract works pretty well on the light-colored background but unable to recognize text on darker shades. We have tried to apply various image processing techniques. This method will allow us to recognize text from most types of background. We propose to provide methods for easy text extraction. Track bar allows the user to adjust various parameters to extract a required text from an Image. This method is gaining huge importance in years to come. For Automation, we can use a set of image processing techniques such as edge detection, filtering and, blurring for better results. A series of these steps will enable us to extract text from images efficiently. This experiment compares the optimized result by two methods for efficient Text Extraction.
Graph matching as a graph convolution operator for graph neural networks

Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classification context. However, in the way they were formalised, CNNs are bound to operate on euclidean spaces. Indeed, convolution is a signal operation that are defined on euclidean spaces. This has restricted deep learning main use to euclidean-defined data such as sound or image. And yet, numerous computer application fields (among which network analysis, computational social science, chemo-informatics or computer graphics) induce non-euclideanly defined data such as graphs, networks or manifolds. In this paper we propose a new convolution neural network architecture, defined directly into graph space. Convolution and pooling operators are defined in graph domain thanks to a graph matching procedure between the input signal and a filter. We show its usability in a back-propagation context. Experimental results show that our model performance is at state of the art level on simple tasks. It shows robustness with respect to graph domain changes and improvement with respect to other euclidean and non-euclidean convolutional architectures.
Deep Learning Models for Image Segmentation

Artificial Intelligence and deep learning models have evolved rapidly in the last decade and successfully applied to face recognition, autonomous driving, satellite imaging, robotics, and many more. Computer vision tasks often require adequate segmentation of an image that helps to understand the patterns and information. The adequate segmentation makes the analysis of each part of an image easier. Traditional segmentation techniques are often applied for image segmentation, but they are less efficient than deep learning techniques. Using deep learning approaches, it is possible to obtain hierarchical feature representations directly from the images, and hence, it eliminates the requirement of handcrafted features. This paper covers the fundamentals of image segmentation and deep learning, deep learning models for image segmentation, some successful implementations of deep learning models for image segmentation, and available open and benchmark datasets for image segmentation tasks.
Predictive Modeling of Hospital Readmission: Challenges and Solutions

Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.
Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion.
Phishing Website Detection Using Novel Features And Machine Learning Approach

Phishing, a type of digital assault adversely affects individuals where the client is coordinated to counterfeit sites and tricked to uncover their delicate and individual data which incorporates passwords of records, bank subtleties, ATM pin-card subtleties and so forth Subsequently shielding touchy data from malwares or web real; irregular woods phishing is troublesome. Inferable from the restrictions of existing advances in identifying a phishing site, anticipating that the users should notice and can decide if a URL is phishing or genuine would be unreasonable, wasteful and mistaken. Consequently, in tending to these difficulties, a robotized approach should be considered for phishing site recognition. This research aims at detecting phishing website using novel features and machine learning algorithm. The input URL websites are first feature extracted using Convolutional auto encoder. Then those features are sent to deep neural network classifier for better classification of Phishing and legitimate URL’s. The system is tested for its accuracy and detection rate. It shows that the implemented system is best in detecting phishing websites with 89% accuracy.
Introduction to the Special Issue on Decentralized Blockchain Applications and Infrastructures for Next Generation Cyber-Physical Systems

introduction Introduction to the Special Issue on Decentralized Blockchain Applications and Infrastructures for Next Generation Cyber-Physical Systems Share on Editors: Kim Kwang Raymond Choo University of Texas at San Antonio University of Texas at San AntonioView Profile , Uttam Ghosh Vanderbilt University Vanderbilt UniversityView Profile , Deepak Tosh University of Texas El Paso University of Texas El PasoView Profile , Reza M. Parizi Kennesaw State University Kennesaw State UniversityView Profile , Ali Dehghantanha University of Guelph University of GuelphView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 2June 2021 Article No.: 38epp 1–3https://doi.org/10.1145/3464768Online:15 June 2021Publication History 0citation68DownloadsMetricsTotal Citations0Total Downloads68Last 12 Months68Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Expectation-aware planning: A unifying framework for synthesizing and executing self-explaining plans for human-aware planning

In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over existing approximate approaches that unnecessarily try to search in the space of models while also failing to facilitate the full gamut of behaviors enabled by our framework.
Token-Aware Virtual Adversarial Training in Natural Language Understanding

Gradient-based adversarial training is widely used in improving the robustness of neural networks, while it cannot be easily adapted to natural language processing tasks since the embedding space is discrete. In natural language processing fields, virtual adversarial training is introduced since texts are discrete and cannot be perturbed by gradients directly. Alternatively, virtual adversarial training, which generates perturbations on the embedding space, is introduced in NLP tasks. Despite its success, existing virtual adversarial training methods generate perturbations roughly constrained by Frobenius normalization balls. To craft fine-grained perturbations, we propose a Token-Aware Virtual Adversarial Training method. We introduce a token-level accumulated perturbation vocabulary to initialize the perturbations better and use a token-level normalization ball to constrain these perturbations pertinently. Experiments show that our method improves the performance of pre-trained models such as BERT and ALBERT in various tasks by a considerable margin. The proposed method improves the score of the GLUE benchmark from 78.3 to 80.9 using BERT model and it also enhances the performance of sequence labeling and text classification tasks.
Toward Privacy Protection Composition Framework on Internet of Vehicles

As an emerging computing paradigm, the Internet of Vehicles (IoV) brings a lot of benefits for drivers and consumers, such as route recommendation, reducing traffic congestion, and parking difficulty. However, participants in IoV are inevitably exposed to privacy threats because their data are outsourced to a third party. This article presents a comprehensive analysis of the root causes of privacy risks in IoV and the potential solutions. We begin by introducing the concrete privacy challenges, which are tied to the structural properties of IoV. Then, we present a unified framework of data privacy preservation in IoV, which integrates multiple privacy techniques, including encryption, anonymity, and perturbation. Next, we investigate the possible research directions for route recommendation, charging deployment, traffic prediction under different privacy requirements in detail. Finally, we discuss emerging techniques and environments (e.g., blockchain, smart grid, and 5G communication) to strengthen the ability and application of IoV under strong protection.
Variable Importance Measure System Based on Advanced Random Forest

The variable importance measure (VIM) can be implemented to rank or select important variables, which can effectively reduce the variable dimension and shorten the computational time. Random forest (RF) is an ensemble learning method by constructing multiple decision trees. In order to improve the prediction accuracy of random forest, advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees. Referring to the Mean Decrease Accuracy (MDA) index based on Out-of-Bag (OOB) data, the single variable, group variables and correlated variables importance measures are proposed to establish a complete VIM system on the basis of advanced random forest. The link of MDA and variance-based sensitivity total index is explored, and then the corresponding relationship of proposed VIM indices and variance-based global sensitivity indices are constructed, which gives a novel way to solve variance-based global sensitivity. Finally, several numerical and engineering examples are given to verify the effectiveness of proposed VIM system and the validity of the established relationship.
Usability of End-to-End Encryption in E-Mail Communication

This paper presents the results of a usability study focused on three end-to-end encryption technologies for securing e-mail traffic, namely PGP, S/MIME, and Pretty Easy Privacy (pEp). The findings of this study show that, despite of existing technology, users seldom apply them for securing e-mail communication. Moreover, this study helps to explain why users hesitate to employ encryption technology in their e-mail communication. For this usability study, we have combined two methods: 1) an online survey, 2) and user testing with 12 participants who were enrolled in tasks requiring e-mail encryption. We found that more than 60% of our study participants (in both methods) are unaware of the existence of encryption technologies and thus never tried to use one. We observed that above all, users 1) are overwhelmed with the management of public keys and 2) struggle with the setup of encryption technology in their e-mail software. Nonetheless, 66% of the participants consider secure e-mail communication as important or very important. Particularly, we found an even stronger concern about identity theft among e-mail users, as 78% of the participants want to make sure that no other person is able to write e-mail on their behalf.
Frequency Domain-Based Detection of Generated Audio

Attackers may manipulate audio with the intent of presenting falsified reports, changing an opinion of a public figure, and winning influence and power. The prevalence of inauthentic multimedia continues to rise, so it is imperative to develop a set of tools that determines the legitimacy of media. We present a method that analyzes audio signals to determine whether they contain real human voices or fake human voices (i.e., voices generated by neural acoustic and waveform models). Instead of analyzing the audio signals directly, the proposed approach converts the audio signals into spectrogram images displaying frequency, intensity, and temporal content and evaluates them with a Convolutional Neural Network (CNN). Trained on both genuine human voice signals and synthesized voice signals, we show our approach achieves high accuracy on this classification task.
Synthetic data for annotation and extraction of family history information from clinical text

Abstract Background The limited availability of clinical texts for Natural Language Processing purposes is hindering the progress of the field. This article investigates the use of synthetic data for the annotation and automated extraction of family history information from Norwegian clinical text. We make use of incrementally developed synthetic clinical text describing patients’ family history relating to cases of cardiac disease and present a general methodology which integrates the synthetically produced clinical statements and annotation guideline development. The resulting synthetic corpus contains 477 sentences and 6030 tokens. In this work we experimentally assess the validity and applicability of the annotated synthetic corpus using machine learning techniques and furthermore evaluate the system trained on synthetic text on a corpus of real clinical text, consisting of de-identified records for patients with genetic heart disease. Results For entity recognition, an SVM trained on synthetic data had class weighted precision, recall and F 1 -scores of 0.83, 0.81 and 0.82, respectively. For relation extraction precision, recall and F 1 -scores were 0.74, 0.75 and 0.74. Conclusions A system for extraction of family history information developed on synthetic data generalizes well to real, clinical notes with a small loss of accuracy. The methodology outlined in this paper may be useful in other situations where limited availability of clinical text hinders NLP tasks. Both the annotation guidelines and the annotated synthetic corpus are made freely available and as such constitutes the first publicly available resource of Norwegian clinical text.
A Semiautomated Deep Learning Approach for Pancreas Segmentation

Accurate pancreas segmentation from 3D CT volumes is important for pancreas diseases therapy. It is challenging to accurately delineate the pancreas due to the poor intensity contrast and intrinsic large variations in volume, shape, and location. In this paper, we propose a semiautomated deformable U-Net, i.e., DUNet for the pancreas segmentation. The key innovation of our proposed method is a deformable convolution module, which adaptively adds learned offsets to each sampling position of 2D convolutional kernel to enhance feature representation. Combining deformable convolution module with U-Net enables our DUNet to flexibly capture pancreatic features and improve the geometric modeling capability of U-Net. Moreover, a nonlinear Dice-based loss function is designed to tackle the class-imbalanced problem in the pancreas segmentation. Experimental results show that our proposed method outperforms all comparison methods on the same NIH dataset.