Reviewerly/exHarmony huggingface dataset:
Accurate Segmentation of Cerebrovasculature From TOF-MRA Images Using Appearance Descriptors

Analyzing cerebrovascular changes can significantly lead to not only detecting the presence of serious diseases e.g., hypertension and dementia, but also tracking their progress.Such analysis could be better performed using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images, but this requires accurate segmentation of the cerebral vasculature from the surroundings.To achieve this goal, we propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that have the ability to capture the appearance of macro and micro-vessels in ToF-MRA.The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets.The appearance of the cerebral vasculature is also represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) that its parameters are estimated by using a modified Expectation-Maximization (EM) algorithm.The extracted appearance features are separable and can be classified by any classifier, as demonstrated by our segmentation results.To validate the accuracy of our algorithm, we tested the proposed approach on invivo data using 270 data sets, which were qualitatively validated by a neuroradiology expert.The results were quantitatively validated using the three commonly used metrics for segmentation evaluation: the Dice coefficient, the modified Hausdorff distance, and the absolute volume difference.The proposed approach showed a higher accuracy compared to two of the existing segmentation approaches.
Blockchain-based Supply Chain for the Automation of Transaction Process: Case Study based Validation

The transaction issue in the area of supply chain management plays a vital role among stakeholders. For the reasonable processes of transportation and logistics in the supply chain, the sharing of the transaction is very crucial. The current supply chain management has several shortcomings in security and trust issues in the transaction process. Therefore, information is shared on paper-based and a semi digitalizes way. In this paper, we analyze a trust issue in supply chain and design a new propose scheme based on blockchain technology for resolving the problem in the supply chain and automate the whole payment process through a smart contract. The case study based validation is performed. The result shows that blockchain improves the transaction process.
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications

Recently, adversarial attacks can be applied to the physical world, causing practical issues to various Convolutional Neural Networks (CNNs) powered applications. Most existing physical adversarial attack defense works only focus on eliminating explicit perturbation patterns from inputs, ignoring interpretation to CNN's intrinsic vulnerability. Therefore, they lack expected versatility to different attacks and thereby depend on considerable data processing costs. In this paper, we propose LanCe - a comprehensive and lightweight CNN defense methodology against different physical adversarial attacks. By interpreting CNN's vulnerability, we find that non-semantic adversarial perturbations can activate CNN with significantly abnormal activations and even overwhelm other semantic input patterns' activations. We improve the CNN recognition process by adding a self-verification stage to detect the potential adversarial input with only one CNN inference cost. Based on the detection result, we further propose a data recovery methodology to defend the physical adversarial attacks. We apply such defense methodology into both image and audio CNN recognition scenarios and analyze the computational complexity for each scenario, respectively. Experiments show that our methodology can achieve an average 91% successful rate for attack detection and 89% accuracy recovery. Moreover, it is at most 3× faster compared with the state-of-the-art defense methods, making it feasible to resource-constrained embedded systems, such as mobile devices.
Multi-label Log-Loss function using L-BFGS for document categorization

Text mining, which fundamentally involves quantitative tactics to analyze textual data, can be used for discovering knowledge and to achieve scholarly research goals. For large-scale data such as corpus text, intelligent learning methods have been effectively approached. In this paper, an artificial neural network with a quasi-Newton updating procedure is presented for multi-label multi-class text classification. This numerical unconstrained training technique, the Multi-Label extension of Log-Loss function using in Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (ML4BFGS), provides a noteworthy opportunity for text mining and leads to a significant improvement in text classification performances. The ML4BFGS training approach is applied to allocate some (one or multi) of the classes to each corresponding sentence from different available labels. We evaluate this method on English translations of the Holy Quran. These religious texts have been chosen for experiments of this manuscript because each verse (sentence) usually has multiple labels (topics) and different translations of each verse should have the same labels. Experimental results show that ML4BFGS is talented for multi-label multi-class classification in the Quranic corpus. Evaluation criteria of some advanced updating methods such as ITCG, BFGS, L-BFGS-B, L3BFGS as well as some other multi-label approaches such as ML-k-NN, and well-known SVM are compared with the proposed ML4BFGS and the outcomes are fully-described in this study. The performance measures including the Hamming loss, recall, precision, and F1 score show that the ML4BFGS achieves the best results in extracting related classes for each verse, while the proposed network takes the least epochs compared to the other training approach for completing learning or training phase. Simultaneously, the elapsed time for ML4BFGS is just 78% (in seconds) of the best experience of this term. Compared with the applicability of some state-of-the-art algorithms, ML4BFGS has a less computational cost, faster convergence rate, and much accuracy in corpus analysis.
Are Online Haters Psychopaths? Psychological Predictors of Online Hating Behavior

Despite growing prevalence of derogatory online behaviors, still little is known about psychological factors underlying this negative phenomenon. In the present study, we aimed to compare characteristics of persons who post hating and non-hating comments about Polish sports players during Winter Olympic Games in Pyeongchang (2018) on the Internet. Ninety-four Internet users (41% women) participated in the study, among which 46 posted hating comments. After 1 month, participants were invited to take part in a psychological survey, and filled the Dark Triad questionnaire, the Satisfaction with Life Scale, the Scale of Frustration, and the Scale of Envy. Results showed that high scores in Psychopathy subscale were significant predictors of posting hating comments online; high scores on the Envy Scale were marginally significant. Our findings provide initial evidence that persons who engage in derogatory online behavior have a high level of Psychopathy, but, contrary to previous studies, do not have elevated levels of other traits, commonly associated with disruptive behavior. Our research is one of the first to establish a psychological background of online haters, while setting a clear line between online hating and other derogatory online behaviors (e.g., trolling, cyber-bullying, or hatred speech).
A comprehensive study on face recognition: methods and challenges

Face Recognition is the process of identifying and verifying the faces. Face recognition has vast importance in the field of Security, Healthcare, Banking, Criminal Identification, Payment, and Advertising. In this paper, we have reviewed various techniques and challenges for the face recognition. Illumination, pose variation, facial expressions, occlusions, aging, etc. are the key challenges to the success of face recognition. Pre-processing, Face Detection, Feature Extraction, Optimal Feature Selection, and Classification are primary steps in any face recognition system. This paper provides a detailed review of each. Feature extraction techniques can be classified as appearance-based methods or geometry-based methods, such method may be local or global. Feature extraction is the most crucial stage for the success of the face recognition system. However, deep learning methods have freed the user from handcrafting the features. In this article, we have surveyed state-of-the-art methods of last few decades and the comparative study of various feature extraction methods is provided. Article also describes the current challenges in the area.
Differential Hoare Logics and Refinement Calculi for Hybrid Systems with Isabelle/HOL

We present simple new Hoare logics and refinement calculi for hybrid systems in the style of differential dynamic logic. (Refinement) Kleene algebra with tests is used for reasoning about the program structure and generating verification conditions at this level. Lenses capture hybrid program stores in a generic algebraic way. The approach has been formalised with the Isabelle/HOL proof assistant. Several examples explain the workflow with the resulting verification components.
A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification

Breast cancer is the most common cancer type attacking women worldwide. Also, breast cancer has been phenotypically classified into five subtypes. Each subtype group has unique characteristics that demonstrate the heterogeneity present within the breast cancer tumour. In 2012, the American Association for Cancer Research provided a population based molecular integrative clusters for the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) dataset, resulting in ten subtypes. Previous work on the METABRIC dataset used only gene expression data to figure out the effective genes for each subtype, without applying integration to benefit from all data sources. The objective of this paper is to present a breast cancer subtype classification model that applies feature fusion on the METABRIC datasets, namely clinical, gene expression, Copy Number Aberrations (CNA), Copy Number Variations (CNV), and histopathological images. State-of-the-art machine learning classifiers were applied on different data profiles, including Linear-SVM, Radial-SVM, Random Forests (RF), Ensemble SVM (E-SVM), and Boosting. The highest accuracy achieved for IntClust subtyping was 88.36% using Linear-SVM, applied on the data profile with features fused from the clinical, gene expression, CNA, and CNV datasets, with a Jaccard and Dice scores of 0.802 and 0.8835, respectively. On the other hand, for the Pam50 subtyping, an accuracy of 97.1% was achieved, Jaccard score ranging from 0.9439 to 0.9472, and Dice score of 0.971, using Linear-SVM and E-SVM classifiers, with several data profiles that include features from histopathological images. Conclusively, the significance of our study is to validate that using feature fusion from various METABRIC datasets improves breast cancer subtypes classification performance. Moreover, histopathological images give promising results on Pam50 subtypes, and it is expected to improve the accuracy for IntClust subtyping when applied on a higher population.
Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models

In this article, we consider the problem of tracking the eye gaze of individuals while they engage in reading. In particular, we develop the ways to accurately track the line being read by an individual using commercially available eye-tracking devices. Such an approach will enable futuristic functionalities, such as comprehension evaluation, interest level detection, and user-assisting applications such as hand-free navigation and automatic scrolling. Furthermore, the proposed approach will pave the way to develop technology that may generate valuable feedback to content makers, such as web designers, authors, educators, and social media users. The existing commercial eye trackers provide an estimated location of the eye-gaze points every few milliseconds. However, these estimated gaze points are not sufficient to quantify reading progression—a specific eye-gaze activity. In this article, we propose algorithms to bridge the commercial gaze tracker outputs and informative eye-gaze patterns while reading. The proposed system consists of Kalman filters and hidden Markov models to parameterize these statistical models and to accurately detect the line being read. The proposed approach is shown to yield an improvement of 27.1% in line detection accuracy over line tracking using estimated eye-gaze points alone by the eye tracker.
C-3PR: A Bot for Fixing Static Analysis Violations via Pull Requests

Static analysis tools are frequently used to detect common programming mistakes or bad practices. Yet, the existing literature reports that these tools are still underused in the industry, which is partly due to (1) the frequent high number of false positives generated, (2) the lack of automated repairing solutions, and (3) the possible mismatches between tools and workflows of development teams. In this study we explored the question: “How could a bot-based approach allow seamless integration of static analysis tools into developers' workflows?” To this end we introduce C-3PR, an event-based bot infrastructure that automatically proposes fixes to static analysis violations through pull requests (PRs). We have been using C-3PR in an industrial setting for a period of eight months. To evaluate C-3PR usefulness, we monitored its operation in response to 2179 commits to the code base of the tracked projects. The bot autonomously executed 201346 analyses, yielding 610 pull requests. Among them, 346 (57%) were merged into the projects' code bases. We observed that, on average, these PRs are evaluated faster than general-purpose PRs (2.58 and 5.78 business days, respectively). Accepted transformations take even shorter time (1.56 days). Among the reasons for rejection, bugs in C-3PR and in the tools it uses are the most common ones. PRs that require the resolution of a merge conflict are almost always rejected as well. We also conducted a focus group to assess how C-3PR affected the development workflow. We observed that developers perceived C-3PR as efficient, reliable, and useful. For instance, the participants mentioned that, given the chance, they would keep using C-3PR. Our findings bring new evidence that a bot-based infrastructure could mitigate some challenges that hinder the wide adoption of static analysis tools.
A Dual Camera System for High Spatiotemporal Resolution Video Acquisition

This paper presents a dual camera system for high spatiotemporal resolution (HSTR) video acquisition, where one camera shoots a video with high spatial resolution and low frame rate (HSR-LFR) and another one captures a low spatial resolution and high frame rate (LSR-HFR) video. Our main goal is to combine videos from LSR-HFR and HSR-LFR cameras to create an HSTR video. We propose an end-to-end learning framework, AWnet, mainly consisting of a FlowNet and a FusionNet that learn an adaptive weighting function in pixel domain to combine inputs in a frame recurrent fashion. To improve the reconstruction quality for cameras used in reality, we also introduce noise regularization under the same framework. Our method has demonstrated noticeable performance gains in terms of both objective PSNR measurement in simulation with different publicly available video and light-field datasets and subjective evaluation with real data captured by dual iPhone 7 and Grasshopper3 cameras. Ablation studies are further conducted to investigate and explore various aspects, such as reference structure, camera parallax, exposure time, etc) of our system to fully understand its capability for potential applications.
Review on the Application of Knowledge Graph in Cyber Security Assessment

Abstract The development of artificial intelligence technology has advanced by leaps and bounds and made significant progress in many areas. Many researchers have begun to apply artificial intelligence technology to the cyber security domain. Knowledge graphs can describe the concepts, entities and their relationships in the objective world in a structured way. Applying knowledge graph to the cyber security domain can organize, manage, and utilize massive amounts of information in cyberspace in a better way. In this paper, the common cyber security assessment models and their shortcomings is summarized, the research progress of ontology-based knowledge representation is discussed, thus leading to a conclusion that ontology-based knowledge representation can completely and accurately represent the complex knowledge of heterogeneous systems in the cyber security domain. Then we introduce the concept of knowledge graph, summarize the application progress of knowledge graphs in the cyber security domain, and discuss directions of future research.
A Motion Deblur Method Based on Multi-Scale High Frequency Residual Image Learning

Non-uniform blind deblurring of dynamic scenes has always been a challenging problem in image processing because of the diverse of blurring sources.Traditional methods based on energy minimization cannot make accurate kernel estimation.It leads to that some high frequency details cannot be fully recovered.Recently, many methods based on convolution neural networks (CNNs) have been proposed to improve the overall performance.Followed by this trend, we first propose a two-stage deblurring module to recover the blur images of dynamic scenes based on high frequency residual image learning.The first stage performs initial deburring with the blur kernel estimated by the salient structure.The second stage calculates the difference of input image and initially deblurred image, referred to as residual image, and adopt an encoder-decoder network to refine the residual image.Finally, we can combine the refined residual image with the input blurred image to obtain the latent image.To increase deblurring performance, we further propose a coarse-to-fine framework based on the deblurring module.It performs the deblurring module many times in a multi-scale manner which can gradually restore the sharp edge details of different scales.Experiments conducted on three benchmark datasets demonstrate the proposed method achieves competitive performance of state-of-art methods.
When Are Fuzzy Extractors Possible?

Fuzzy extractors (Dodis et al., SIAM J. Computing 2008) convert repeated noisy readings of a high-entropy secret into the same uniformly distributed key. A minimum condition for the security of the key is the hardness of guessing a value that is similar to the secret, because the fuzzy extractor converts such a guess to the key. We quantify this property in a new notion called fuzzy min-entropy. We ask: is fuzzy min-entropy sufficient to build fuzzy extractors? We provide two answers for different settings. 1) If the construction is provided a description of the probability distribution W that defines the noisy source then fuzzy min-entropy is a sufficient condition for information-theoretic key extraction from W . 2) A more ambitious goal is to design a single extractor that works for all possible sources. This more ambitious goal is impossible: there is a family of sources with high fuzzy min-entropy for which no single fuzzy extractor is secure. This is true in three settings: a) for standard fuzzy extractors, b) for fuzzy extractors that are allowed to sometimes be wrong, c) and for secure sketches, which are the main ingredient of most fuzzy extractor constructions.
Facial Expression Recognition in Videos: An CNN-LSTM based Model for Video Classification

Facial Expressions are an integral part of human communication. Therefore, correct classification of facial expression in image and video data has been an important quest for researchers and software development industry. In this paper we propose the video classification method using Recurrent Neural Networks (RNN) in addition to Convolution Neural Networks (CNN) to capture temporal as well spatial features of a video sequence. The methodology is tested on The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Since no other results were available on this dataset using only visual analysis, the proposed method provides the first benchmark of 61% test accuracy on given dataset.
hHHO-IGWO: A New Hybrid Harris Hawks Optimizer for Solving Global Optimization Problems

This paper proposes a hybrid meta-heuristic optimization technique that combines the benefits of Harris Hawks's optimization and Improved Grey wolf optimization. An attempt is made to develop a more efficient method for reaching an exploration to the exploitation phase. Also, hHHO-IGWO provides more scope for finding the best possible solution for solving the various optimization problems. The algorithm is tested for 23 standard benchmarked test functions and the results are verified by a comparative study with nine renowned Optimizer. The results of suggested hHHO- IGWO algorithm is found to be more efficient to provide better results in terms of convergence.
A novel efficient camera calibration approach based on K-SVD sparse dictionary learning

Camera calibration is essential for accurate product visual inspection. In this paper, a novel efficient camera calibration approach based on K-Singular Value Decomposition (K-SVD) sparse dictionary learning is presented, in which, (1) the nonlinear optimization model with ten calibration parameters is formulated, (2) a large amount of images of checkerboards are acquired offline at different locations in the working volume of the camera to construct a sparse dictionary through K-SVD sparse dictionary learning, which enables such non-linear optimization model appropriately initialized and efficiently converges, and (3) in addition, the sparse dictionary can be further updated with the new acquired image so that it can be used to timely characterize the camera variation due to lens aging, and still be used to obtain appropriate initial values. Finally, three experiments are demonstrated to validate the efficiency of the presented approach. The results show that (1) the camera can be accurately calibrated with only one image, which can save almost more than 60 percent calibration time, and is very beneficial to online calibration for quality inspection, and (2) the calibration is much more accurate than that of traditional approaches with only one image having identical grid pixels.