Reviewerly/exHarmony huggingface dataset:
Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency

Blockchain based decentralized Cloud Manufacturing-as-a-Service (CMaaS) platforms enable customers to gain access to a large capacity of manufacturing nodes over cryptographically secure networks. In recent times, the Ethereum network has emerged as a popular blockchain framework for providing provenance and traceability of proprietary manufacturing data in decentralized CMaaS. However, the Ethereum ecosystem was only designed to store and transmit low volume financial transaction data and little has been done to make it an efficient repository of large manufacturing data streams in CMaaS systems. In this paper, the authors build on their previous work and report the design, implementation, and validation of middleware software architectures that allow Ethereum based distributed CMaaS platforms to harness the benefits of the secure asset models of the Ethereum ecosystem and the immutable big data storage capabilities of the decentralized BigchainDB database platform. A novel hybrid blockchain architecture enabled by efficient communication protocols and blockchain oracles is proposed. This architecture allows the transfer and immutable storage of large manufacturing data streams onto global BigchainDB nodes allowing data rich manufacturing transactions to bypass the transaction fees of the Ethereum ecosystem. Additionally, a machine learning based time series inference model is proposed which enables the forecast of Ethereum gas price into the future. This allows the CMaaS platform smart contracts to judiciously assign gas price limits and hence save on transactions ensuing from transfer or creation of assets. The outcomes of this research show that the designed hybrid architecture can lead to the reduction of significant number of computational steps and hence transaction fees on Ethereum by offloading large volume data onto BigchainDB nodes. A Random Forest regressor based time series inference model has been shown to exhibit superior performance in the prediction of Ethereum gas price, that allows the CMaaS to avoid executing transactions in periods of high gas prices within the Ethereum ecosystem.
Multinomial naïve Bayesian classifier with generalized Dirichlet priors for high-dimensional imbalanced data

The data-level approach can be used to balance the class distribution in a data set, while generating synthetic instances in a high-dimensional space is an awful task. Naïve Bayesian classifier with multinomial model, called multinomial naïve Bayesian classifier, is a popular classification algorithm for high-dimensional data because of its computational efficiency. However, this algorithm generally cannot have a better performance than the other algorithms on high-dimensional imbalanced data. Generalized Dirichlet distribution that is defined on unit simplex can be priors for multinomial naïve Bayesian classifier. This study proposes methods to find noninformative generalized Dirichlet priors for multinomial naïve Bayesian classifier so that its performance on high-dimensional imbalanced data can be largely improved. The methods are tested on seven high-dimensional imbalanced data sets to demonstrate that the multinomial naïve Bayesian classifier with generalized Dirichlet priors can significantly outperform not only the original multinomial naïve Bayesian classifier, but also random forest and Ripper algorithm.
Blockchain in Healthcare System: Security Issues, Attacks and Challenges

The Healthcare system as an organization has the important requirements corresponding to security and privacy, for example, protecting patients’ medical information from unauthorized access, secure drug tracking, secure communication with transport like ambulance, secure and smart e-health monitoring. But due to lack of expert design of security protocols, the healthcare system is facing many security threats such as interoperability, authenticity, data sharing, the conveying of medical data, and deliberations of mobile health. In such a scenario, blockchain may help because of its unique properties. The blockchain ledger, formed by applying digital signature schemes, consensus mechanisms, and chain of hashing, offers highly reliable storage capabilities. Due to the aforementioned characteristics, blockchain provides various services like integrity, traceability, security, and non-repudiation, by storing the information in a public decentralized environment so that privacy preservation can be maintained. It is required to explore the security issues, different attacks and challenges when blockchain is applied in healthcare, which can help to design a more strong security protocol in a distributed environment. Therefore, this book chapter focuses on (a) different applications of healthcare where blockchain can be applied, (b) requirements of blockchain in such applications, (c) different possible security attacks in the blockchain-based healthcare system, (d) security issues and challenges to design protocols using blockchain, and (e) future directions to the research to be explored more.
Application of improved sparrow search algorithm in SVM optimization

Abstract Sparrow search algorithm has good global search performance, but there is still a probability of falling into local optimum. In order to optimize the algorithm, K-means is proposed to make the population evenly distributed and improve the efficiency at the beginning. Then, the sine-cosine search and adaptive local search strategies are introduced to reduce the probability of falling into the local optimum in the middle and late stages, so that the optimal solution can be obtained easily. Finally, the two strategies are discussed and applied to SVM parameter optimization. The UCI dataset classification results show that the algorithm with the two strategies is suitable for SVM parameter optimization, and the algorithm with sine-cosine search has better optimization ability and stability.
The accuracy versus interpretability trade-off in fraud detection model

Abstract Like a hydra, fraudsters adapt and circumvent increasingly sophisticated barriers erected by public or private institutions. Among these institutions, banks must quickly take measures to avoid losses while guaranteeing the satisfaction of law-abiding customers. Facing an expanding flow of operations, effective banking relies on data analytics to support established risk control processes, but also on a better understanding of the underlying fraud mechanism. In addition, fraud being a criminal offence, the evidential aspect of the process must also be considered. These legal, operational, and strategic constraints lead to compromises on the means to be implemented for fraud management. This paper first focuses on the translation of practical questions raised in the banking industry at each step of the fraud management process into performance evaluation required to design a fraud detection model. Secondly, it considers a range of machine learning approaches that address these specificities: the imbalance between fraudulent and nonfraudulent operations, the lack of fully trusted labels, the concept-drift phenomenon, and the unavoidable trade-off between accuracy and interpretability of detection. This state-of-the-art review sheds some light on a technology race between black box machine learning models improved by post-hoc interpretation and intrinsic interpretable models boosted to gain accuracy. Finally, it discusses how concrete and promising hybrid approaches can provide pragmatic, short-term answers to banks and policy makers without swallowing up stakeholders with economical and ethical stakes in this technological race.
Determination of halogens in geological reference materials using neutron irradiation noble gas mass spectrometry

We determined the heavy halogen (Cl, Br, and I) concentrations of fourteen powdered geological reference materials distributed by the Geological Survey of Japan (GSJ) and the US Geological Survey, and seven original blocks of GSJ geological reference materials, using neutron irradiation noble gas mass spectrometry (NI-NGMS). The halogen concentrations obtained from the powder and original blocks of geological reference materials are systematically lower than their reference values for low-concentration samples. This discrepancy can be explained by contamination on the sample surface during the preparation of the reference materials. Contaminated halogen-derived noble gases were, at least partially, removed before analysis during preheating of the samples under vacuum after sample loading. The presence of the contaminated component in the reference materials is not a matter for other analytical methods as long as the contaminated component is completely homogenized in the powder reference materials and not removed before analysis. However, the present results indicate that the standard materials used in NI-NGMS should be selected more carefully than those used in other methods. In addition, we propose an F-determination method using NI-NGMS based on Ne systematics of neutron-irradiated samples. • Geological reference materials and their original blocks were analyzed using NI-NGMS. • Halogens are contaminated on the surface of some geological reference materials. • A fluorine determination method using NI-NGMS is proposed.
COVID-19 Face Mask Live Detection Using OpenCV

Severe acute respiratory syndrome coronavirus (SARS-CoV) is recognized, and very first person infected is from the Guangdong province of southern China in 2002 while the virus that causes COVID-19 (Corona VIrus Disease-2019) is known as SARS-CoV-2. World Health Organization (WHO) named it as "COVID-19" on February 11, 2020. Currently, the COVID-19 has frightened the whole world of human beings and pushed into the pandemic. This coronavirus affects the respiratory system by entering into the human body through the droplets of saliva and mucus. It takes 14 days to observe the symptoms of the virus attack. In the meantime, the virus affected person may spread the virus to the coexisting people in the abode unknowingly. Also, it takes 48 h to confirm if a person is virus attacked after the test sample is collected. So, there is a serious need to wear a face mask that covers the nose and mouth besides maintaining the social distance to break the chain of massive increase. This paper attempts to detect if an individual wears a mask, using OpenCV. The accurate identification of landmarks of face in the image is an imperative challenge. Being instinctive it is simple for a human to detect the object, but it took years of research to raise the accessibility of quality datasets and a remarkable progress. The purpose of the paper is identifying the count of faces with the mask in the image and count of faces without a mask on live webcam.
Electronic Medical Record Sharing System Based on Hyperledger Fabric and InterPlanetary File System

Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With the continuous development of data sharing, big data applications and other technologies, new requirements are proposed for the storage of medical privacy data. At present, medical data have the characteristics of obstructed sharing and exchange, difficult data tracing and data sensitivity, which increases the cost and difficulty of each diagnosis. Moreover, patients lack the right to know and control their own medical data, which is easy to cause data fraud or data leakage. As a decentralized, tamper proof and traceable distributed ledger system, blockchain brings new solutions to these problems. This paper designs a medical data fusion and distributed privacy management system based on the Hyperledger Fabric blockchain development platform and InterPlanetary File System. It can not only solve the problem of mutual distrust in the traditional system, but also realize the safe data storage and the safe sharing of medical record data between medical organizations which is a beneficial attempt to overcome the disadvantages of traditional hospital data separation. At the same time, according to the requirements of different levels and types of medical users or groups for data security and personal privacy protection, the access control technology of users is studied to ensure personal privacy data.
An ensemble classification model for fake feedback detection using proposed labeled CloudArmor dataset

• Ensemble model has been proposed to classify reviews for Cloud Service provider (CSP) posted by a cloud customer (CC) as fake or genuine on labeled CloudArmour dataset. • Labeling is achieved through: Number of Reviews by a CC to single CSP; Number of CSPs reviewed by a CC; & Frequency of feedbacks published. • Proposed ensemble model is compared with various classifiers. • Ensemble model has depicted highest values as: 97.51%; 98.19%; 93.65%; & 95.86% respectively in terms of accuracy, precision, recall and F1-Score. With an increase in the use of Cloud Services, Cloud Customers (CCs) have started browsing reviews about various Cloud Service Providers (CSPs) online provided by numerous CCs to gather their sentiments. This pattern has proved to be fruitful, but it can become ineffective if fake reviews are posted. This article aims at introducing an ensemble model to classify reviews as fake or genuine on the proposed labeled CloudArmour dataset. Labeling has been achieved through these aspects: Number of Reviews provided by a CC to single CSP; Number of CSPs reviewed by a CC; & Frequency of feedbacks published. Additionally, we have compared the performance of the proposed ensemble model with these supervised machine learning classification techniques: KNN; Logistic_Regression; and SVM_rbf on the basis of the following parameters: accuracy; precision, recall; and F1-Score. Ensemble model has yielded highest values as: 97.51%; 98.19%; 93.65%; & 95.86% respectively. Thus, we conclude that the ensemble model has outperformed other models. Graphical abstract
Banking sector credit, inflation and growth in sub-Saharan African countries

The banking sector in the past decade has been known to play a germane role in the development of any economy through its financial intermediation function. However, the financial intermediation of banks which is a major prerequisite for growth can be hindered by inflation. This study examines the effect of banking sector credit on inflation and growth in thirty-five sub-Saharan African economies for the periods 2000 and 2016. The panel vector error correction model (PVECM) technique was employed in analyzing the data. The study outcome indicates that banking sector credit affects inflation positively and growth negatively in SSA economies. Besides, the study found a bi-directional causality between banking sector credits and inflation. Hence, the government in sub-Saharan Africa countries should continue to design and implement an appropriate monetary policy to ensure optimal bank credit that will enhance growth in the region while keeping inflation level under control. The study also recommends the need for financial sector policy reform that will promote fiscal discipline, deepen the financial market, enhance credit availability, and keep inflation under control.
Inference About Complex Relationships Using Peak Height Data from DNA Mixtures

Abstract In both criminal cases and civil cases, there is an increasing demand for the analysis of DNA mixtures involving relationships. The goal might be, for example, to identify the contributors to a DNA mixture where the donors may be related, or to infer the relationship between individuals based on a mixture. This paper introduces an approach to modelling and computation for DNA mixtures involving contributors with arbitrarily complex relationships. It builds on an extension of Jacquard's condensed coefficients of identity, to specify and compute with joint relationships, not only pairwise ones, including the possibility of inbreeding. The methodology developed is applied to two casework examples involving a missing person, and simulation studies of performance, in which the ability of the methodology to recover complex relationship information from synthetic data with known ‘true’ family structure is examined. The methods used to analyse the examples are implemented in the new KinMix R package that extends the DNAmixtures package to allow for modelling DNA mixtures with related contributors.
An Improved Large-Field Microscopic Speckle Interferometry System for Dynamic Displacement Measurement of MEMS

The traditional microscopic speckle interferometer has limited applications in engineering due to its small field of view. In this paper, we propose a large-field microscopic speckle interferometer which embeds two doublet lens groups in the improved Mach–Zehnder optical path structure to expand its field of view. At the same time, the new system can reduce the coherent noise of reflected light in the optical path. We use this new system to measure the dynamic displacement process of the entire surface of the microchips. The experimental results show that our improved measurement system can achieve large-field, real-time and high-precision dynamic measurement of micro-electromechanical systems (MEMS).
Gesture recognition in somatosensory game via <scp>Kinect</scp> sensor

With the development of computer technology, the means of human‐computer interaction are constantly updated. The development trend is from human to computer to constantly adapt human habits. In the human‐computer interaction, the somatosensory sensor technology is used to track and analyze human actions through computer vision technology. This paper adopts somatosensory sensor to implement gesture recognition, which is an important way in human‐computer interaction. The somatosensory sensor can obtain the depth information and bone information in the human‐computer interaction. First, the gesture is decomposed into a gesture sequence consisting of micro gestures to describe different directions. Then, the gesture sequence is compared with the gesture templates. The gesture is determined by the pattern matching result. The experimental results show that more than 92% gestures can be identified by proposed recognition method.
Self-Guided Deep Multiview Subspace Clustering via Consensus Affinity Regularization

Multiview subspace clustering (MVSC) leverages the complementary information among different views of multiview data and seeks a consensus subspace clustering result better than that using any individual view. Though proved effective in some cases, existing MVSC methods often obtain unsatisfactory results since they perform subspace analysis with raw features that are often of high dimensions and contain noises. To remedy this, we propose a self-guided deep multiview subspace clustering (SDMSC) model that performs joint deep feature embedding and subspace analysis. SDMSC comprehensively explores multiview data and strives to obtain a consensus data affinity relationship agreed by features from not only all views but also all intermediate embedding spaces. With more constraints being cast, the desirable data affinity relationship is supposed to be more reliably recovered. Besides, to secure effective deep feature embedding without label supervision, we propose to use the data affinity relationship obtained with raw features as the supervision signals to self-guide the embedding process. With this strategy, the risk that our deep clustering model being trapped in bad local minima is reduced, bringing us satisfactory clustering results in a higher possibility. The experiments on seven widely used datasets show the proposed method significantly outperforms the state-of-the-art clustering methods. Our code is available at https://github.com/kailigo/dmvsc.git.
EPICURE: Ensemble Pretrained Models for Extracting Cancer Mutations from Literature

To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type. Named entity recognition (NER) is a core step in the text mining pipeline which facilitates mining valuable cancer information from the scientific literature. However, due to the scarcity of related datasets, previous NER attempts in this domain either suffer from low performance when deep learning based models are deployed, or they apply feature-based machine learning models or rule-based models to tackle this problem, which requires intensive efforts from domain experts, and limit the model generalization capability. In this paper, we propose EPICURE, an ensemble pre-trained model equipped with a conditional random field pattern (CRF) layer and a span prediction pattern (Span) layer to extract cancer mutations from text. We also adopt a data augmentation strategy to expand our training set from multiple datasets. Experimental results on three benchmark datasets show competitive results compared to the baseline models, validating our model's effectiveness and advances in generalization capability.
Converged AI, IoT, and blockchain technologies: a conceptual ethics framework

The convergence of AI, IoT, and blockchain as the next disruptive technology is inevitable. Studying the ethics of these converged technologies is crucial for the steady development and adoption of the applications. Even though this topic is crucial, it is still not yet touched in the literature. This work is the first attempt to tackle this issue by developing a conceptual ethics framework for converged AI, IoT, and blockchain. The framework is based on a systematic literature review and categorizes the ethical issues into three layers namely micro (ethical issue involving the technology itself), meso (ethical issue involving the applications), and macro (ethical issue involving the society and institutions).
Neighbourhood semantics and labelled calculus for intuitionistic infinitary logic

Abstract Neighbourhood semantics for intuitionistic logic extended with countable conjunctions and disjunctions is introduced and shown equivalent to topological semantics, with an indirect completeness proof as payoff. The new semantics is used to obtain a labelled sequent calculus with good structural properties. In particular, admissibility of weakening and contraction, invertibility with preservation of height for each rule and cut elimination are shown. Finally, a direct Tait–Schütte–Takeuti style form of completeness via the extraction of a countermodel from a failed proof search is proved.
FRI-miner: fuzzy rare itemset mining

Data mining is a widely used technology for various real-life applications of data analytics and is important to discover valuable association rules in transaction databases. Interesting itemset mining plays an important role in many real-life applications, such as market, e-commerce, finance, and medical treatment. To date, various data mining algorithms based on frequent patterns have been widely studied, but there are a few algorithms that focus on mining infrequent or rare patterns. In some cases, infrequent or rare itemsets and rare association rules also play an important role in real-life applications. In this paper, we introduce a novel fuzzy-based rare itemset mining algorithm called FRI-Miner, which discovers valuable and interesting fuzzy rare itemsets in a quantitative database by applying fuzzy theory with linguistic meaning. Additionally, FRI-Miner utilizes the fuzzy-list structure to store important information and applies several pruning strategies to reduce the search space. The experimental results show that the proposed FRI-Miner algorithm can discover fewer and more interesting itemsets by considering the quantitative value in reality. Moreover, it significantly outperforms state-of-the-art algorithms in terms of effectiveness (w.r.t. different types of derived patterns) and efficiency (w.r.t. running time and memory usage).