Reviewerly/exHarmony huggingface dataset:
Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems

Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective. Healthcare 4.0 mainly consisted of periodic medical data sensing, aggregation, data transmission, data sharing, and data storage. The sensitive and personal data of patients lead to several challenges while protecting it from hackers. Therefore storing, accessing, and sharing the patient medical information on the cloud needs security attention that data should not be compromised by the authorized user's components of E-healthcare systems. To achieve secure medical data storage, sharing, and accessing in cloud service provider, several cryptography algorithms are designed so far. However, such conventional solutions failed to achieve the trade-off between the requirements of EHR security solutions such as computational efficiency, service side verification, user side verifications, without the trusted third party, and strong security. Blockchain-based security solutions gained significant attention in the recent past due to the ability to provide strong security for data storage and sharing with the minimum computation efforts. The blockchain made focused on bitcoin technology among the researchers. Utilizing the blockchain which secure healthcare records management has been of recent interest. This paper presents the systematic study of modern blockchain-based solutions for securing medical data with or without cloud computing. We implement and evaluate the different methods using blockchain in this paper. According to the research studies, the research gaps, challenges, and future roadmap are the outcomes of this paper that boost emerging Healthcare 4.0 technology.
Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.
Benefits of Taking a Virtual Field Trip in Immersive Virtual Reality: Evidence for the Immersion Principle in Multimedia Learning

This study describes and investigates the immersion principle in multimedia learning. A sample of 102 middle school students took a virtual field trip to Greenland via a head mounted display (HMD) or a 2D video as an introductory lesson within a 6-lesson inquiry-based climate change intervention. The HMD group scored significantly higher than the video group on presence (d = 1.43), enjoyment (d = 1.10), interest (d = .57), and retention in an immediate (d = .61) and delayed posttest (d = .70). A structural equation model indicated that enjoyment mediated the pathway from instructional media to immediate posttest, and interest mediated the pathway from instructional media to delayed posttest score, indicating that these factors may play different roles in the learning process with immersive media. This work contributes to the cognitive affective model of immersive learning, and suggests that immersive lessons can have positive longitudinal effects for learning.
Finding Multiple Roots of Nonlinear Equation Systems via a Repulsion-Based Adaptive Differential Evolution

Finding multiple roots of nonlinear equation systems (NESs) in a single run is one of the most important challenges in numerical computation. We tackle this challenging task by combining the strengths of the repulsion technique, diversity preservation mechanism, and adaptive parameter control. First, the repulsion technique motivates the population to find new roots by repulsing the regions surrounding the previously found roots. However, to find as many roots as possible, algorithm designers need to address a key issue: how to maintain the diversity of the population. To this end, the diversity preservation mechanism is integrated into our approach, which consists of the neighborhood mutation and the crowding selection. In addition, we further improve the performance by incorporating the adaptive parameter control. The purpose is to enhance the search ability and remedy the trial-and-error tuning of the parameters of differential evolution (DE) for different problems. By assembling the above three aspects together, we propose a repulsion-based adaptive DE, called RADE, for finding multiple roots of NESs in a single run. To evaluate the performance of RADE, 30 NESs with diverse features are chosen from the literature as the test suite. Experimental results reveal that RADE is able to find multiple roots simultaneously in a single run on all the test problems. Moreover, RADE is capable of providing better results than the compared methods in terms of both root rate and success rate.
Forecasting-Based Power Ramp-Rate Control Strategies for Utility-Scale PV Systems

Passing cloud results in rapid changes of irradiance. The intermittency of photovoltaic (PV) power output has drawn serious concern especially for utility-scale PV system. Consequently, power ramp-rate control (PRRC) has been introduced to avoid significant PV power fluctuations. PRRC is usually implemented either by curtailing active power output or implementing energy storage system (ESS). However, current active power curtailment cannot deal with the irradiance ramp-down fluctuations, and the high cost of the ESS is still hindering its extensive application. In this paper, two innovative PRRC strategies are presented, which utilize the short-term forecasting. The first solution does not require any ESS, during the power ramp-down event, the PV generation will be curtailed before the actual shading occurs. The second solution requires only one-quarter of the energy capacity of the conventional ESS control strategy. To provide the PV generation forecasts, a dynamic model based on spatio-temporal theory is formulated. The effectiveness of the proposed forecasting model and control strategies have been verified through experiment and case studies.
Clinical text classification research trends: Systematic literature review and open issues

The pervasive use of electronic health databases has increased the accessibility of free-text clinical reports for supplementary use. Several text classification approaches, such as supervised machine learning (SML) or rule-based approaches, have been utilized to obtain beneficial information from free-text clinical reports. In recent years, many researchers have worked in the clinical text classification field and published their results in academic journals. However, to the best of our knowledge, no comprehensive systematic literature review (SLR) has recapitulated the existing primary studies on clinical text classification in the last five years. Thus, the current study aims to present SLR of academic articles on clinical text classification published from January 2013 to January 2018. Accordingly, we intend to maximize the procedural decision analysis in six aspects, namely, types of clinical reports, data sets and their characteristics, pre-processing and sampling techniques, feature engineering, machine learning algorithms, and performance metrics. To achieve our objective, 72 primary studies from 8 bibliographic databases were systematically selected and rigorously reviewed from the perspective of the six aspects. This review identified nine types of clinical reports, four types of data sets (i.e., homogeneous–homogenous, homogenous–heterogeneous, heterogeneous–homogenous, and heterogeneous–heterogeneous), two sampling techniques (i.e., over-sampling and under-sampling), and nine pre-processing techniques. Moreover, this review determined bag of words, bag of phrases, and bag of concepts features when represented by either term frequency or term frequency with inverse document frequency, thereby showing improved classification results. SML-based or rule-based approaches were generally employed to classify the clinical reports. To measure the performance of these classification approaches, we used precision, recall, F-measure, accuracy, AUC, and specificity in binary class problems. In multi-class problems, we primarily used micro or macro-averaging precision, recall, or F-measure. Lastly, open research issues and challenges are presented for future scholars who are interested in clinical text classification. This SLR will definitely be a beneficial resource for researchers engaged in clinical text classification.
Identity-based key-exposure resilient cloud storage public auditing scheme from lattices

With the rapid development of cloud auditing services, key exposure has been highlighted as a serious security issue. Using the exposed private key of a client, cloud servers can forge previous auditing proofs to cheat auditors. To date, a few pairing-based cloud storage auditing schemes addressing key exposure have been proposed. However, they are not secure from quantum attacks, and they rely on public key infrastructure (PKI), which involves complex certificate management. In this paper, we propose an efficient identity-based key-exposure resilient public auditing scheme from lattice assumptions in cloud storage. Our scheme is not only quantum-resistant, but eliminates the need to establish a PKI. We employ lattice basis delegation technique to update a client’s private key flexibly, keeping the private key size constant. Based on the hardness of lattice assumptions, we prove the forward security of storage correctness guarantee against malicious cloud servers in detail, and that the proposed scheme preserves privacy against curious auditors. Furthermore, we conduct a performance comparison to demonstrate that our scheme is much more efficient and practical for post-quantum secure cloud storage.
Detecting Abnormal Events in Video Using Narrowed Normality Clusters

We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machines (SVM) to eliminate outliers. In the feature extraction stage, we propose to augment spatio-temporal cubes with deep appearance features extracted from the last convolutional layer of a pre-trained neural network. After extracting motion and appearance features from the training video containing only normal events, we apply k-means clustering to find clusters representing different types of normal motion and appearance features. In the first stage, we consider that clusters with fewer samples (with respect to a given threshold) contain mostly outliers, and we eliminate these clusters altogether. In the second stage, we shrink the borders of the remaining clusters by training a one-class SVM model on each cluster. To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality. If the test sample does not fit well in any narrowed normality cluster, then it is labeled as abnormal. We compare our method with several state-of-the-art methods on three benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve better results in most cases, while processing the test video in real-time at 24 frames per second on a single CPU.
Towards cross-modal organ translation and segmentation: A cycle- and shape-consistent generative adversarial network

Synthesized medical images have several important applications. For instance, they can be used as an intermedium in cross-modality image registration or used as augmented training samples to boost the generalization capability of a classifier. In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 2D/3D images without needing paired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) more importantly, improving volume segmentation by using synthetic data for modalities with limited training samples. We show that these goals can be achieved with an end-to-end 2D/3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shape-consistency loss (supervised by segmentors) to reduce the geometric distortion. From the segmentation view, the segmentors are boosted by synthetic data from generators in an online manner. Generators and segmentors prompt each other alternatively in an end-to-end training fashion. We validate our proposed method on three datasets, including cardiovascular CT and magnetic resonance imaging (MRI), abdominal CT and MRI, and mammography X-rays from different data domains, showing both tasks are beneficial to each other and coupling these two tasks results in better performance than solving them exclusively.
Online streaming feature selection using adapted Neighborhood Rough Set

Online streaming feature selection, as a new approach which deals with feature streams in an online manner, has attracted much attention in recent years and played a critical role in dealing with high-dimensional problems. However, most of the existing online streaming feature selection methods need the domain information before learning and specifying the parameters in advance. It is hence a challenge to select unified and optimal parameters before learning for all different types of data sets. In this paper, we define a new Neighborhood Rough Set relation with adapted neighbors named the Gap relation and propose a new online streaming feature selection method based on this relation, named OFS-A3M. OFS-A3M does not require any domain knowledge and does not need to specify any parameters in advance. With the “maximal-dependency, maximal-relevance and maximal-significance” evaluation criteria, OFS-A3M can select features with high correlation, high dependency and low redundancy. Experimental studies on fifteen different types of data sets show that OFS-A3M is superior to traditional feature selection methods with the same numbers of features and state-of-the-art online streaming feature selection algorithms in an online manner.
DIGITALITY, VIRTUAL REALITY AND THE ‘EMPATHY MACHINE’

The man who works recognizes his own product in the World that has actually been transformed by his work: He recognizes himself in it, he sees in it his own human reality, in it he discovers and reveals to others the objective reality of his humanity, of the originally abstract and purely subjective idea he has of himself. Alexandre Kojève (1980 Kojève, Alexandre . 1980. Introduction to the Reading of Hegel. Ithaca: Cornell University Press. [Google Scholar], 27) The essay critiques an aspect of the so-called post-mobile wave of technological change that claims, through the vector of virtual reality (VR), to have created an ‘empathy machine’ that will form the basis of a new journalism. Through VR devices deployed by news organisations such as the New York Times, and multilateral institutions such as the United Nations, users will be so powerfully immersed in, for example, a street demonstration, or a refugee camp, that the empathy they feel may constitute a new strengthening of the fourth estate’s civic role in informing and enlightening the public, to the extent that it can go beyond subjective empathy to develop a shared basis for political participation in civil society. The essay considers these claims from the overarching context of what is called digitality. It argues that human agents are analogue agents from an analogue world. Digitality, by contrast, is an essentially alienating sphere wherein digital media cannot replicate analogue communication processes without generating gaps, voids, and ‘missing information’. It further argues, extending insights from Guy Debord, that what VR does produce is a powerful ‘integrated spectacle’ that is a pale substitute for the form of interactive experience needed for the generation of empathy. Taken together, the essay concludes that empathy, a contestable term in its common understanding to begin with, cannot be generated from a digital source. Moreover, should VR become the next dominant post-mobile technological wave as the tech giants predict, then people, users and consumers of VR products in the fourth estate news context, will be further distanced from the analogue reality of the actual world.
Domain-specific interpretation of eye tracking data: towards a refined use of the eye-mind hypothesis for the field of geometry

Eye tracking is getting increasingly popular in mathematics education research. Studies predominantly rely on the so-called eye-mind hypothesis (EMH), which posits that what persons fixate on closely relates to what they process. Given that the EMH was developed in reading research, we see the risk that implicit assumptions are tacitly adopted in mathematics even though they may not apply in this domain. This article investigates to what extent the EMH applies in mathematics—geometry in particular—and aims to lift the discussion of what inferences can be validly made from eye-tracking data. We use a case study to investigate the need for a refinement of the use of the EMH. In a stimulated recall interview, a student described his original thoughts perusing a gaze-overlaid video recorded when he was working on a geometry problem. Our findings contribute to better a understanding of when and how the EMH applies in the subdomain of geometry. In particular, we identify patterns of eye movements that provide valuable information on students’ geometry problem solving: certain patterns where the eye fixates on what the student is processing and others where the EMH does not hold. Identifying such patterns may contribute to an interpretation theory for students’ eye movements in geometry—exemplifying a domain-specific theory that may reduce the inherent ambiguity and uncertainty that eye tracking data analysis has.
Using google translate in EFL drafts: a preliminary investigation

This study investigates the impact on extemporaneous English-language first drafts by using Google Translate (GT) in three different tasks assigned to Chinese sophomore, junior, and senior students of English as a Foreign Language (EFL) majoring in English. Students wrote first in Chinese (Step 1), then drafted corresponding texts in English (Step 2), and translated the Chinese into English using the 2016 GT version (Step 3), and finally compared their self-written (SW) English texts drafted in Step 2 and their GT English texts translated from the Chinese texts in Step 3. Both English drafts were analyzed using two types of online computational assessments to compare and evaluate grammatical components of writing quality and lexical features. Results indicate that the GT English texts presented a number of components of significantly higher writing quality than those of students’ SW texts, by having more words, fewer mistakes in spelling and grammar, and fewer errors per words. In addition, there were more advanced-level words in the students’ GT texts than in their SW ones. A follow-up questionnaire survey indicated that EFL students found satisfaction with using Google Translate in their English writing, especially in finding vocabulary items and enhancing the completion of English writing.
Multiple histograms based reversible data hiding by using FCM clustering

Reversible data hiding algorithm (RDH) has been widely used in multimedia’s copyright protection and content integrity authentication. As a typical RDH scheme, histogram shifting (HS) is extensively investigated due to its high quality of stego-image. Most existing HS based RDH schemes utilize prediction and sorting techniques to build single sharp histogram, which exploit the smooth areas in cover image for data hiding. To take advantages of the correlation among image contents of different texture characteristics, several multiple histograms based RDHs (MH_RDH) are proposed recently, which resort on some rigid rules, e.g. single feature based sorting followed by uniform segmentation of sorted sequence, to construct the multiple histograms. In this paper, the clustering algorithm, i.e. Fuzzy C-means (FCM) clustering, is introduced for the construction of multiple histograms. The FCM equipped with deliberately designed features is employed to classify the cover carriers, e.g. prediction errors, into different clusters with similar traits, which are then used to build the multiple histograms for efficient data embedding. Experimental results demonstrate the superior performance of the proposed scheme over other state-of-the-art ones.
Corporate social responsibility and the role of rural women in sustainable agricultural development in sub‐Saharan Africa: Evidence from the Niger Delta in Nigeria

Abstract Low productivity among female farmers when compared with their male counterparts is considered an outcome of limited access to agricultural land and inputs. The objective of this investigation was to assess the impact of multinational oil companies' (MOCs') corporate social responsibility (CSR) on rural women's access to modern agricultural inputs in the Niger Delta, Nigeria. A total of 700 rural female farmers were sampled across the region. Results from the use of a logit model indicated that CSR recorded significant success in agricultural development generally, but has undermined equality. This implies that if a woman's agricultural productivity is continuously hindered by unequal access to agricultural resources (or opportunities) and widespread inequality will limit poverty reduction efforts in Nigeria. The results also showed that women depended on CSR of MOCs for policy dialogue and advocacy for women's access to agricultural land and inputs. Supporting agricultural initiatives that focus on empowering women would boost food security in sub‐Saharan Africa.