Reviewerly/exHarmony huggingface dataset:
Lie to Me: A Soft Threshold Defense Method for Adversarial Examples of Remote Sensing Images

Adversarial examples fool the models into predicting wrong results through generated perturbations, demonstrating the vulnerability of convolutional neural networks (CNNs). Recent studies also show that many CNNs applied to remote sensing image (RSI) scene classification are still subject to adversarial example attacks. Through further analysis of adversarial examples of RSIs, it is found that the misclassified classes are not random, and these adversarial examples have demonstrated attack selectivity. Based on this finding, we propose a soft threshold defense method. First, we take the images with the correct prediction of each class as positive samples and adversarial examples as negative samples. Then, we take their output confidence as input and get the decision boundaries by the logistic regression algorithm. Finally, the confidence threshold of each class can be further obtained based on the decision boundary. It is the soft threshold used for defense, which can determine whether the image is an adversarial example or not. When the model predicts the new RSI, the input is an original image if the output confidence is higher than the soft threshold of the corresponding class, and the opposite is an adversarial example. Our proposed algorithm does not require modification of the model structure and is computationally uncomplicated, and it is simple and effective. For the FGSM, BIM, Deepfool, and C&W attack algorithms, their fooling rates are reduced by an average of 97.76%, 99.77%, 68.18%, and 97.95% in several scenarios. The soft threshold defense method can effectively defend against adversarial examples.
Adversarial Risk via Optimal Transport and Optimal Couplings

Modern machine learning algorithms perform poorly on adversarially manipulated data. Adversarial risk quantifies the error of classifiers in adversarial settings; adversarial classifiers minimize adversarial risk. In this paper, we analyze adversarial risk and adversarial classifiers from an optimal transport perspective. We show that the optimal adversarial risk for binary classification with 0-1 loss is determined by an optimal transport cost between the probability distributions of the two classes. We develop optimal transport plans (probabilistic couplings) for univariate distributions such as the normal, the uniform, and the triangular distribution. We also derive optimal adversarial classifiers in these settings. Our analysis leads to algorithm-independent fundamental limits on adversarial risk, which we calculate for several real-world datasets. We extend our results to general loss functions under convexity and smoothness assumptions.
Biomedical Text Similarity Evaluation Using Attention Mechanism and Siamese Neural Network

It is a crucial component to estimate the similarity of biomedical sentence pair.Siamese neural network (SNN) can achieve better performance for non-biomedical corpora.However, SNN alone cannot obtain satisfactory biomedical text similarity evaluation results due to syntactic complexity and long sentences.In this paper, a cross self-attention (CSA) is proposed to design a new attention mechanism, namely self2self-attention(S2SA). Then the S2SA is introduced into SNN to construct a novel self2selfattentive siamese neural network, namely S2SA-SNN.In the S2SA-SNN, self-attention is used to learn the different weights of words and complex syntactic features in a single sentence.The means of the CSA are used to learn inherent interactive semantic information between sentences, and it employs self-attention instead of global attention to perform cross attention between sentences.Finally, three biomedical benchmark datasets of Pearson Correlation of 0.66 and 0.72/0.66 on DBMI and CDD-ful/-ref are used to test and prove the effectiveness of the S2SA-SNN.The experiment results show that the S2SA-SNN can achieve better performances with pre-trained word embedding and obtain better generalization ability than other compared methods.
Managing Cybersecurity Risk Using Threat Based Methodology for Evaluation of Cybersecurity Architectures

To manage limited resources available to protect against cybersecurity threats, organizations must use risk management approach to prioritize investments in protection capabilities. Currently, there is no commonly accepted methodology for cybersecurity professionals that considers one of the key elements of risk function – threat landscape – to identify gaps (blinds spots) where cybersecurity protections do not exist and where future investments are needed. This paper discusses a new, threat-based approach for evaluation of cybersecurity architectures that allows organizations to look at their cybersecurity protections from the standpoint of an adversary. The approach is based on a methodology developed by the Department of Defense and further expanded by the Department of Homeland Security. The threat-based approach uses a cyber threat framework to enumerate all threat actions previously observed in the wild and scores protections (cybersecurity architectural capabilities) against each threat action for their ability to: a) detect; b) protect against; and c) help in recovery from the threat action. The answers form a matrix called capability coverage map – a visual representation of protections coverage, gaps, and overlaps against threats. To allow for prioritization, threat actions can be organized in a threat heat map – a visual representation of threat actions’ prevalence and maneuverability that can be overlaid on top of a coverage map. The paper demonstrates a new threat modeling methodology and recommends future research to establish a decision-making framework for designing cybersecurity architectures (capability portfolios) that maximize protections (described as coverage in terms of protect, detect, and respond functions) against known cybersecurity threats.
Natural language processing for web browsing analytics: Challenges, lessons learned, and opportunities

In an Internet arena where the search engines and other digital marketing firms’ revenues peak, other actors still have open opportunities to monetize their users’ data. After the convenient anonymization, aggregation, and agreement, the set of websites users visit may result in exploitable data for ISPs. Uses cover from assessing the scope of advertising campaigns to reinforcing user fidelity among other marketing approaches, as well as security issues. However, sniffers based on HTTP, DNS, TLS or flow features do not suffice for this task. Modern websites are designed for preloading and prefetching some contents in addition to embedding banners, social networks’ links, images, and scripts from other websites. This self-triggered traffic makes it confusing to assess which websites users visited on purpose. Moreover, DNS caches prevent some queries of actively visited websites to be even sent. On this limited input, we propose to handle such domains as words and the sequences of domains as documents. This way, it is possible to identify the visited websites by translating this problem to a text classification context and applying the most promising techniques of the natural language processing and neural networks fields. After applying different representation methods such as TF–IDF, Word2vec, Doc2vec, and custom neural networks in diverse scenarios and with several datasets, we can state websites visited on purpose with accuracy figures over 90%, with peaks close to 100%, being processes that are fully automated and free of any human parametrization.
Joint scene and object tracking for cost-Effective augmented reality guided patient positioning in radiation therapy

BACKGROUND AND OBJECTIVE: The research done in the field of Augmented Reality (AR) for patient positioning in radiation therapy is scarce. We propose an efficient and cost-effective algorithm for tracking the scene and the patient to interactively assist the patient's positioning process by providing visual feedback to the operator. Up to our knowledge, this is the first framework that can be employed for mobile interactive AR to guide patient positioning. METHODS: We propose a point cloud processing method that combined with a fiducial marker-mapper algorithm and the generalized ICP algorithm tracks the patient and the camera precisely and efficiently only using the CPU unit. The alignment between the 3D reference model and body marker map is calculated employing an efficient body reconstruction algorithm. RESULTS: Our quantitative evaluation shows that the proposed method achieves a translational and rotational error of 4.17 mm/0.82 deg at 9 fps. Furthermore, the qualitative results demonstrate the usefulness of our algorithm in patient positioning on different human subjects. CONCLUSION: Since our algorithm achieves a relatively high frame rate and accuracy employing a regular laptop (without the usage of a dedicated GPU), it is a very cost-effective AR-based patient positioning method. It also opens the way for other researchers by introducing a framework that could be improved upon for better mobile interactive AR patient positioning solutions in the future.
Mercurial Signatures for Variable-Length Messages

Abstract Mercurial signatures are a useful building block for privacy-preserving schemes, such as anonymous credentials, delegatable anonymous credentials, and related applications. They allow a signature σ on a message m under a public key pk to be transformed into a signature σ ′ on an equivalent message m ′ under an equivalent public key pk′ for an appropriate notion of equivalence. For example, pk and pk′ may be unlinkable pseudonyms of the same user, and m and m ′ may be unlinkable pseudonyms of a user to whom some capability is delegated. The only previously known construction of mercurial signatures suffers a severe limitation: in order to sign messages of length ℓ , the signer’s public key must also be of length ℓ . In this paper, we eliminate this restriction and provide an interactive signing protocol that admits messages of any length. We prove our scheme existentially unforgeable under chosen open message attacks (EUF-CoMA) under a variant of the asymmetric bilinear decisional Diffie-Hellman assumption (ABDDH).
Video recoloring via spatial-temporal geometric palettes

Color correction and color grading are important steps in film production. Recent palette-based approaches to image recoloring have shown that a small set of representative colors provide an intuitive set of handles for color adjustment. However, a single, static palette cannot represent the time-varying colors in a video. We introduce a spatial-temporal geometry-based approach to video recoloring. Specifically, its core is a 4D skew polytope with a few vertices that approximately encloses the video pixels in color and time, which implicitly defines time-varying palettes through slicing of the 4D skew polytope at specific time values. Our geometric palette is compact, descriptive, and provides a correspondence between colors throughout the video, including topological changes when colors merge or split. Experiments show that our method produces natural, artifact-free recoloring.
Inference and Learning Methodology of Belief Rule Based Expert System to Assess Chikungunya

Chikungunya virus (CHIKV) causes Chikungunya disease in human, transmitted by the Aedes species of mosquito. To this date, no vaccines are available to cure this disease as a result doctors rely on symptomatic treatment. This treatment is significantly based on recording the observation of signs and symptoms related to CHIKV in patients before prescribing medication. However, this disease can be misdiagnosed as its signs are similar to diseases like dengue or zika. This, in turn, gives rise to an uncertain diagnosis of Chikungunya. In addition, it is necessary to develop a system that will not only ensure the accurate assessment of Chikungunya but diagnose it within its early stages of infection. Since expert systems are adequate for handling uncertain data as well as generating more precise and accurate inferences, it is applied to this research. A Belief Rule Base Expert System (BRBES) is proposed in this research for performing the assessment of Chikungunya in its early stages of infection in patients. BRBES is reckoned to obliterate human labor and is computationally faster and less expensive in implementation and inferencing the assessment of CHIKV. This research is carried out using real-world data and inference of Chikungunya has been made by using Receiver Operating Characteristics Curves (ROC). In order to increase the accuracy of assessment, an optimal learning model of BRBES is developed with respect to various combinations of sets of training perimeters. In order to prove trained BRBES is an optimal learning model, it’s performance is differentiated with the initial BRBES. Further, this trained BRBES is also compared with a deep learning model such as Convolutional Neural networks (CNN) and machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Network (ANN).
Stair-Step Feature Pyramid Networks for Object Detection

Feature Pyramid Networks have solved scale variation problems in object detection by developing multi-level features with different scales from backbone networks. Although this network achieved promising performance without affecting model complexity, they still suffer feature-level imbalance between multi-level features, i.e., low-level features and high-level features in each stage of the backbone. Moreover, the detection head predicts classification scores and offset regression independently on each feature of multi-level features, which leads to inconsistency among the detection branch. Hence, this paper releases this problem by introducing simple but effective Stair-step Feature Pyramid Networks (SFPN) to harmonize information between multi-level features. Further, the Offset Adaption Module (OA Module) is proposed to improve feature representation by adapting the feature of the classification branch with regressed offsets of the regression branch. On the MS-COCO dataset, the proposed method increases by 1.2% Average Precision when comparing with baseline FCOS without bells and whistles.
MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images.Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically.In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part.The number of output feature maps increases with the network layers in contracting path of encoder, which is consistent with the characteristics of dense blocks.Using dense blocks can decrease the number of network parameters, deepen network layers, strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields.The residual blocks were used in the decoder to replace the convolution neural block of original U-Net, which made the network performance better.Our proposed approach was trained and validated on the BraTS2019 training and validation data set.We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumor core respectively on the BraTS2019 validation data set.Our method has the better performance than the original 3D U-Net.The results of our experiment demonstrate that compared with some state-of-the-art methods, our approach is a competitive automatic brain tumor segmentation method.
A Proposed Genetic Algorithm Attack for Public Key Cryptosystem

The Public Key Cryptosystem is considered as new direction of cryptography since 1976. It contains two main problems; factorization Problem and One-way function problem. In 1984, there was a new approach under the umbrella of public key cryptography, which is called Elliptic Curve cryptography. The RSA Cryptosystem is one candidate on the Factorization problem, that is widely used in the special secure commercial communication systems. Although these systems have higher security, but they are attacked with many attacking methods. One of the new attacking methods is the genetic algorithm. Such attack had success against the knapsack public key cryptosystem (a candidate on the one-way problem), but till know, there are no publications about using such attack against RSA Type Public Key cryptosystem.This paper provides a proposed investigation of applying genetic algorithm in cryptanalysis of RSA public key cryptosystem using cipher text only attack in order to enhance the attack overall performance. The method provides good results. Experimental results show that G.A. enhanced the overall performance & memory consumption.
TF-DM: Tool for Studying ML Model Resilience to Data Faults

Machine learning (ML) is widely deployed in safety-critical systems (e.g. self-driving cars). Failures can have disastrous consequences in these systems, and hence ensuring the reliability of its operations is important. Mutation testing is a popular method for assessing the dependability of applications and tools have recently been developed for ML frameworks. However, the focus has been on improving the quality of test data. We present an open source data mutation tool, TensorFlow Data Mutator (TF-DM), which targets different kinds of data faults for any ML program written in TensorFlow 2. TF-DM supports different types of data mutators so users can study model resilience to data faults. We explain how different fault models are mapped to mutators in TF-DM, and present a detailed evaluation and resiliency analysis of 6 ML models and 3 datasets.
Dynamically Adjusting Diversity in Ensembles for the Classification of Data Streams with Concept Drift

A data stream can be defined as a system that continually generates a lot of data over time. Today, processing data streams requires new demands and challenging tasks in the data mining and machine learning areas. Concept Drift is a problem commonly characterized as changes in the distribution of the data within a data stream. The implementation of new methods for dealing with data streams where concept drifts occur requires algorithms that can adapt to several scenarios to improve its performance in the different experimental situations where they are tested. This research proposes a strategy for dynamic parameter adjustment in the presence of concept drifts. Parameter Estimation Procedure (PEP) is a general method proposed for dynamically adjusting parameters which is applied to the diversity parameter (λ) of several classification ensembles commonly used in the area. To this end, the proposed estimation method (PEP) was used to create Boosting-like Online Learning Ensemble with Parameter Estimation (BOLE-PE), Online AdaBoost-based M1 with Parameter Estimation (OABM1-PE), and Oza and Russell’s Online Bagging with Parameter Estimation (OzaBag-PE), based on the existing ensembles BOLE, OABM1, and OzaBag, respectively. To validate them, experiments were performed with artificial and real-world datasets using Hoeffding Tree (HT) as base classifier. The accuracy results were statistically evaluated using a variation of the Friedman test and the Nemenyi post-hoc test. The experimental results showed that the application of the dynamic estimation in the diversity parameter (λ) produced good results in most scenarios, i.e., the modified methods have improved accuracy in the experiments with both artificial and real-world datasets.
Gradient-based Algorithms for Machine Teaching

The problem of machine teaching is considered. A new formulation is proposed under the assumption of an optimal student, where optimality is defined in the usual machine learning sense of empirical risk minimization. This is a sensible assumption for machine learning students and for human students in crowdsourcing platforms, who tend to perform at least as well as machine learning systems. It is shown that, if allowed unbounded effort, the optimal student always learns the optimal predictor for a classification task. Hence, the role of the optimal teacher is to select the teaching set that minimizes student effort. This is formulated as a problem of functional optimization where, at each teaching iteration, the teacher seeks to align the steepest descent directions of the risk of (1) the teaching set and (2) entire example population. The optimal teacher, denoted MaxGrad, is then shown to maximize the gradient of the risk on the set of new examples selected per iteration. MaxGrad teaching algorithms are finally provided for both binary and multiclass tasks, and shown to have some similarities with boosting algorithms. Experimental evaluations demonstrate the effectiveness of MaxGrad, which outperforms previous algorithms on the classification task, for both machine learning and human students from MTurk, by a substantial margin.
Bayesian Fully Convolutional Networks for Brain Image Registration

The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.