Reviewerly/exHarmony huggingface dataset:
Analysis of Online News Coverage on Earthquakes Through Text Mining

News agencies work around the clock to report critical news such as earthquakes. We investigate the relationship between online news articles and seismic events that happen around the world. We have utilised computer text mining tools to automatically harvest, identify, cluster and extract information from earthquake-related reports and carry out cross-validation on the mined information. Earthquake parameters retrieved from the United States Geological Survey (USGS) Application Programming Interface (API) are organised into earthquake events, with each event consisting of daily earthquake readings taking place in a particular geographical location. The results are then visualised on a user-friendly dashboard. 268,182 news reports published by 23 news agencies from different parts of the world and 14,677 earthquakes of magnitude ranging from 4 to 8.2 listed in the bulletin were processed during a 1-year study between 2018 and 2019 resulting in 732 news clusters. The use of multilingual news sources from across six continents gives the advantage of reducing potential news bias originating from English written newspapers only. Our results confirm that the number of online published news articles for an earthquake depends on the event magnitude, on the duration of the seismicity linked to the mainshock, on the damage caused, and on the degree of the location's inhabitation. We propose a general global relationship between the number of news agencies, the earthquake magnitude and the anticipated number of published articles. Such analysis can be used to plan and coordinate global campaigns in cases of relief efforts for disaster-struck areas or educational promotion for earthquake preparedness.
Medical Video Watermarking Scheme for Telemedicine Applications

Recent advancements in a new era of e-health and telemedicine technologies present newer opportunities for sharing medical data when an open communication channel is used. However, this exchange is facing critical challenges of copyright protection, content authentication, and integrity verification. Therefore, due to the high demand for safe manipulation of patient's medical information, digital watermarking is required, as a value-added tool, for secure medical data management. In this study, a new reversible watermarking technique for the security of medical videos is proposed. The patient identity is embedded, as a watermark, into the host medical videos. An algorithm is adopted for selecting the optimal frames in each video shot. This approach includes the use of polynomial transform methods, in order to identify the best watermark embedding areas in the optimal frames. Experimental results demonstrate high imperceptibility of the watermark and strong robustness against different attacks. Hence, our algorithm validates the security measures that ensure an effective manipulation of medical videos.
Research on English grammar recognition system based on combination of genetic algorithm and KNN algorithm

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
A generalised bound for the Wiener attack on RSA

Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N, it has been a general belief that the Wiener attack works for d<N14. On the contrary, in [1], it was shown that the bound d<N14 is not accurate as it has been thought of. Specifically, for the standard assumption of the two primes p and q that q < p < 2q, the Wiener continued fraction technique is proven to work for d≤1184N14. In this paper, we consider a general condition on the RSA primes, namely q < p < α q, and we give the corresponding bound for the Wiener attack to work, which is d≤α42(α+1)N14. In a special case when α=2, this general bound agrees with the result of [1].
Network projection-based edge classification framework for signed networks

Many real-world networks have signed relationships between the nodes. Identification of these relationships is an important aspect of decision making. The existing signed relationships in a network may impact the relationships between the other nodes, hence learning from the existing signed relationships in a network can be used for decision making in various mining tasks. These signed networks are getting attention in recent years due to their relevance to many applications such as categorization, recommendation, and relationship discovery in various domains for decision support such as biological, social network analysis, communication and making knowledge graphs. In this work, we focus on edge classification (sign/label prediction for edges) in unweighted and undirected signed networks where the task is to predict the label of the unlabeled edges. Edge classification is a challenging problem as in real-world signed networks, edges are scarcely labeled. In our work, we are using labeled edges to predict the sign of unlabeled edges (classification) with the help of structural information. In this work, we have proposed a novel framework named NPECF for the classification of unlabeled edges. The proposed framework is novel in its way of utilizing the existing information in the signed network to predict the label of unlabeled edges. The utilization of the unlabeled edges in NPECF using three spanning subgraph projections of the given network minimizes the information loss. The experiments have been performed on four real-world datasets from different domains to demonstrate the effectiveness of the proposed framework.
Graphical Perception for Immersive Analytics

Immersive Analytics (IA) uses immersive virtual and augmented reality displays for data visualization and visual analytics. Designers rely on studies of how accurately people interpret data in different visualizations to make effective visualization choices. However, these studies focus on data analysis in traditional desktop environments. We lack empirical grounding for how to best visualize data in immersive environments. This study explores how people interpret data visualizations across different display types by measuring how quickly and accurately people conduct three analysis tasks over five visual channels: color, size, height, orientation, and depth. We identify key quantitative differences in performance and user behavior, indicating that stereo viewing resolves some of the challenges of visualizations in 3D space. We also find that while AR displays encourage increased navigation, they decrease performance with color-based visualizations. Our results provide guidelines on how to tailor visualizations to different displays in order to better leverage the affordances of IA modalities.
Coronary Artery Disease Diagnosis Using Feature Selection Based Hybrid Extreme Learning Machine

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) that cause millions of deaths worldwide due to heart failure, heart attack, and angina. The symptoms of the CAD do not appear in the early stage of the disease and it causes deadly conditions; therefore, accurate and early diagnosis of CAD is necessary to take appropriate and timely action for preventing or minimizing such conditions. Angiography, being the most accurate method for diagnosis of CAD, is often used by the clinicians to diagnose the CAD but this is an invasive procedure, costly, and may cause side effects. Therefore, researchers are trying to develop alternative diagnostic modalities for the efficient diagnosis of CAD. To that end, machine learning and data mining techniques have been widely employed. This paper proposes and develops hybrid Particle swarm optimization based Extreme learning machine (PSO-ELM) for diagnosis of CAD using the publicly available Z-Alizadeh sani dataset. To enhance the performance of the proposed model, a feature selection algorithm, namely Fisher, is used to find more discriminative feature subset. In the training period, the PSO algorithm is used to calibrate the ELM input weights and hidden biases. Further, the performance of the proposed model is compared with the basic ELM in terms of accuracy, Pearson correlation coefficient (R2) and Root mean square error (RMSE) goodness-of-fit functions. The results show that the performance of the proposed model is better than the basic ELM. The obtained CAD classification performance in terms of sensitivity, accuracy, specificity, and F1-measure is competitive to the known approaches in the literature.
Decentralized Reputation System on a Permissioned Blockchain for E-Commerce Reviews

E-commerce has experienced rapid growth and adoption over the decade and continues to do so. Customer reviews of products have been a driving force in making purchasing decisions but credibility of these reviews have been put into question as a result of influx of fraudulent reviews. Blockchain is a technology that promises a decentralized trust based environment where records are managed in an immutable manner. This paper presents a novel decentralized reputation system that utilizes smart contracts on a permissioned blockchain. We present a background on current reputation systems with a focus on the current limitations and security vulnerabilities. Our approach will address the centralization and credibility of E-commerce reviews in current reputation systems. We then discuss the limitations of such a system and highlight solutions to these limitations.
An efficient similarity measure to alleviate the cold-start problem

The objective of developing a recommender system is to aid users by recommending products that might be of interest to them. In this, the collaborative filtering technique is one of the widely used methods where similarities are calculated among users/items to provide personalized recommendation. In order to calculate the similarity, various similarity measures are used. Most of these similarity methods do not perform satisfactorily in the presence of cold start users. A user is considered cold start if he/she has rated less than twenty items. In case of such users, the minimum available ratings data has to be utilized to recommend items. To resolve this problem, we propose a new similarity measure based on both City Block (CB) and Jaccard measure (CBJ). The co-rated items are considered by City Block while Jaccard measure considers the common items for similarity computation. Thus, when both of these measures are combined, they consider all the co-rated and common items. The main advantage of using CBJ is the reduced computational complexity involved in finding the similarity as compared to other similarity methods. To validate CBJ, we conduct experiments on Film Trust and MiniFilm data sets. The recommendation results on Film trust data set having 872 cold start users out of1508 users and MiniFilm data set having all the 55 cold start users reveal that the proposed CBJ method outperforms other existing methods.
Genuine tripartite nonlocality for random measurements in Greenberger-Horne-Zeilinger-class states and its experimental test

We present a comprehensive numerical analysis of violations of local realism by tripartite generalized Greenberger-Horne-Zeilinger states. As an indicator of nonlocality we use the nonlocal fraction which describes the probability of violation of local realism under randomly sampled observables. We compare two kinds of local realism based on standard and hybrid local--nonlocal models. As a result, we show a great disproportion of the results determined for both models. Although the nonlocal fraction for standard local realism and tripartite generalized Greenberger-Horne-Zeilinger states significantly increases compared to the bipartite Clauser-Horne-Shimony-Holt scenario, the genuine tripartite nonlocality (the strongest form of nonlocal correlations) is observed with probability much smaller than its bipartite counterpart. Furthermore, when the effects of decoherence on these states are introduced, such disproportion becomes significantly greater. Finally, we present the statistical relevance of various classes of tight Bell inequalities as they are of paramount importance for practical experimental investigation of all problems discussed in this paper. We also propose the nonlocal fraction of hybrid local--nonlocal realism as a measure of genuine tripartite entanglement.
Solving Large Scale Classification Problems with Stochastic Based Optimization

Development of efficient neural network classifier topology involves preprocessing of training data to reduce noise and discrepancies, utilizing a number of hidden layers, choice of correct activation functions etc. In this research we propose using a combination of first order Stochastic Gradient Descent (SGD) optimizers for Artificial Neural Networks (ANNs) with Deep Learning (DL) and Deep Learning Multilayered Perceptron DLMLP with Back Propagation (BP) for classification of multiclass real datasets with large dimensions. DLMLP topologies require varying the number of hidden layers and their neurons, the development of specific activation and loss functions, batch processing, normalization, implementation of different gradient methods, approaches to avoid overfitting/underfitting as well as evaluation of training and classification results. Our focus is on optimization of stochastic objectives and parameters as well as implementation of different activation functions. K-fold cross validation is utilized to compare the performance of each model with the respective set of parameters. Experiments shown here include Cover Type, Mushrooms, EEG and Plastic Detection datasets where the loss and accuracy metrics are compiled and visualized.
Novelty Detection Review State of Art and Discussion of New Innovations in The Main Application Domains

Novelty detection is the identification of new or unknown data or signals that a machine learning system is not aware of during training. It is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. This survey aims to provide an updated and structured overview of recent studies and approaches of novelty detection that have appeared in the machine learning and deep learning after the last review of Novelty detection in 2014. We surveyed on various techniques and applications of novelty detection in the main application domains.
Calibration methods for a camera with a tilted lens and a three-dimensional laser scanner in the Scheimpflug condition

A three-dimensional laser scanner has been designed and widely utilized in many fields. The lens plane is tilted according to the Scheimpflug condition and the optical axis is not perpendicular to the charge-coupled device plane. In this case, depth of view can be extended significantly. In this paper, analytical models for a camera with a tilted lens and a laser scanner meeting the Scheimpflug condition are presented. Based on these models, a calibration procedure is detailed. We propose a simple calibration method to determine the intrinsic parameters of a Scheimpflug camera. Meanwhile, two calibration methods for a laser scanner in the Scheimpflug condition are detailed. According to the obtained intrinsic parameters, the laser scanner can be calibrated directly. Moreover, a simple calibration for a three-dimensional laser scanner without the help of a precise positioning system is described. Experimental results show the effectiveness and high measurement accuracy of our calibration methods.
An Improved Robot Path Planning Algorithm for a Novel Self-adapting Intelligent Machine Tending Robotic System

Autonomous industrial mobile manipulation systems (AIMMS) are widely used in manufacturing processes. AIMMS can help with part handling and delivering, part insertion and extraction, loading and unloading, and some other auxiliary tasks in a machine tending workshop environment. However, nowadays most AIMMS cannot truly realize the complete automation because the path for the mobile platform needs setting in advance in terms of different environment and inspection need to be executed to ensure safety due to lack of proper path planning algorithms. Therefore, this paper proposes an improved path-planning algorithm based on Rapidly-exploring Random Tree (RRT) and the quintic B-spline curve technique to generate a collision-free and smoother path for our designed Novel Self-adapting Intelligent Machine Tending Robotic System in the workspace. In the end, the proposed algorithm is demonstrated to generate paths for five different scenarios to test its performance and reliability.
Analyzing CSP Trustworthiness and Predicting Cloud Service Performance

Analytics firm Cyence estimated Amazon's four-hour cloud computing outage in 2017 “cost S&P 500 companies at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \$ $</tex-math></inline-formula> 150 million” and traffic monitoring firm Apica claimed “54 of the top 100 online retailers saw site performance slump by at least 20 percent”. According to Ponemon, 2015 data center outages cost Fortune 1000 companies between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \$ $</tex-math></inline-formula> 1.25 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \$ $</tex-math></inline-formula> 2.5 billion. Despite potential risks, the cloud computing industry continues to grow. For example, Internet of Things, which is projected to grow 266% between 2013 and 2020, will drive increased demand on cloud computing as data across multiple industries is collected and sent back to cloud data centers for processing. RightScale estimates enterprises will continue to increase cloud demand with 85% having multi-cloud strategies. This growth and dependency will influence risk exposure and potential for impact (e.g. availability, performance, security, financial). The research in this paper and proposed solution calculates cloud service provider (CSP) trustworthiness levels and predicts cloud service and cloud service level agreement (SLA) availability performance. Evolving industry standards (e.g. NIST, ISO/IEC) for cloud SLAs and existing work regarding CSP trustworthiness will be leveraged as regression-based predictive models are constructed to analyze CSP cloud computing services, SLA performance and CSP trustworthiness.
Multiclass histology image retrieval, classification using Riesz transform and local binary pattern features

Structure and distribution of cells in histological images cause considerable variability among the tissue patterns and make the retrieving and classification as challenging tasks for this type of medical images. In this paper, we propose to extract four local features, namely local energetic information, local structural information, local geometric information, and local patterns to represent the huge texture variability of the histological images in a feature space. For extracting these features, the Riesz transform and Monogenic local binary patterns are used. Riesz transform is an extension of the Hilbert transform for 2-D signals. It provides a monogenic representation for an image where we can capture some local structure information. We used these local features for classification and retrieving purposes. We validated our proposed method on two multiclass archives of histological images, namely Kimiapath24 and Kather datasets. We achieved performance of more than 90% for classification and retrieval in two datasets.
Public Security Surveillance System Using Blockchain Technology and Advanced Image Processing Techniques

National security is a top priority to mitigate intrusions and criminal acts. Governments require robust national surveillance system that can cover all geographical areas, including the blind spots that may hold violence and criminal incidents' triggers i.e. malls, stadiums, airports, and other key sites. Integrating existing surveillance infrastructures rather than creating centralized solutions will have great potential on scalability as well as providing more liberal framework that is not run by a single point of control. However, this definitely requires establishing secure communication and mutual trust amongst these entities, which is a real challenge. Towards this end, we propose an efficient smart surveillance architecture that combines machine learning and Blockchain technologies to facilitate the exchange of relevant surveillance events as admitted transactions into a permissioned Hyperledger fabric Blockchain. We conducted comprehensive analysis to demonstrate the feasibility of blockchain and the efficiency of the machine learning-based face recognition and matching for real-time surveillance of suspects using heterogeneous surveillance infrastructure. The proposed architecture proved scalability and real-time behavior after putting the system through multiple test cases. With very high matching accuracy, and end-to-end latency of less than 12.8 seconds, the system proves to be scalable, and fast enough for a smart surveillance use case.
Remote Evaluation of Short Videos Captured by Static Telecytological Applications for Obtaining Expert Opinions

Over the last decade, cytopathology laboratories wishing to achieve an automated and seamless workflow process, to diminish turnaround times and to improve their diagnostic accuracy have successfully adopted information technologies and automation. New types of cameras and microscopes, connected to computers, have made possible image capture and transmission (telecytology). New innovative information technologies, including e-health and telemedical applications, constitute a valuable tool for interlaboratory collaboration and quality improvement. New applications are expected to enhance the opportunities for improvement in the field of cytological data management and sharing. In this article, we emphasize on the possible use of short videos captured by static telecytology applications in a modern cytopathology laboratory wishing to establish an effective interlaboratory comparison system and obtain expert opinions in diagnostically challenging cases from distant consultants with well recognized diagnostic competence and experience in the field of thyroid cytology.
Analyzing student understanding of cryptography using the SOLO taxonomy

Although cryptography is monumentally important in keeping our information safe and secure, this interdisciplinary field of study can also be used as a powerful teaching tool by putting mathematics in a dramatic and realistic setting. Cryptography also allows for a natural way to introduce topics such as modular arithmetic, matrix operations, and elementary group theory. This paper reports the results of a preliminary qualitative study of students’ thinking and understanding of cryptography using both the SOLO taxonomy and open coding as they participated in task-based interviews. Students interviewed were assessed on how they made connections to various areas of mathematics through solving cryptography problems. Analyzing these interviews showed that students have a strong foundation in number-theoretic concepts such as divisibility and modular arithmetic. Also, students were able to use probability intuitively to make sense of and solve problems. Finally, participants’ SOLO levels ranged from uni-structural to extended abstract, with the multi-structural level being the most common (three participants). This gives evidence to suggest that students should be given the opportunity to make mathematical connections early and often in their academic careers. Results indicate that although cryptography need not be a required course for mathematics majors, concepts from this field could be introduced in courses such as linear algebra, abstract algebra, number theory, and discrete mathematics.