Reviewerly/exHarmony huggingface dataset:
Blockchain Application in Healthcare Industry: Attacks and Countermeasures

Blockchain technology is a collection of records called blocks, and it is characterized by several features including transparency, which means that everyone in the network can view all information stored in the blockchain, and blockchain technology is also decentralized, meaning that there is no authority within it. This technology is used in many areas, including digital identities. Digital auctions as well as cryptocurrency and so on. Blockchain technology plays a large role in healthcare in terms of its many services, as it helps healthcare researchers decode the genetic code and securely transmit patient medical records, and it also manages the drug supply chain. All Blockchain-based systems are exposed to various attacks by attackers, which threatens the security of information and data stored on blockchain systems, and among these systems are blockchain-based healthcare systems. This paper focuses on the recent attacks and countermeasures of blockchain technology in the healthcare field. Several expected attacks are discussed and countermeasures that can be used to prevent or reduce the risk of these attacks are mentioned.
Hearables: Making Sense from Motion Artefacts in Ear-EEG for Real-Life Human Activity Classification

Ear-worn devices are rapidly gaining popularity as they provide the means for measuring vital signals in an unobtrusive, 24/7 wearable and discrete fashion. Naturally, these devices are prone to motion artefacts when used in out-of-lab environments, various movements and activities cause relative movement between user's skin and the electrodes. Historically, these artefacts are seen as nuisance resulting in discarding the segments of signal wherever such artefacts are present. In this work, we make use of such artefacts to classify different daily activities that include sitting, speaking aloud, chewing and walking. To this end, multiple classification techniques are employed to identify these activities using 8 features calculated from the electrode and microphone signal embedded in a generic multimodal in-ear sensor. The results show an overall training accuracy of 93% and 90% and a testing accuracy of 85% and 80% when using a KNN and a 2-layer neural network respectively, thus providing a much needed, simple and reliable framework for real-life human activity classification.
Multimodal optimization via dynamically hybrid niching differential evolution

A multimodal optimization problem (MMOP) can be referred to as a single-objective optimization involving multiple global and/or local optima. Solving a MMOP is often a complex task that involves finding as many optimal and accurate solutions as possible in a rough search space and providing more alternative solutions for decision makers. Generally, locating more peaks and improving convergence accuracy are two very challenging tasks. In this paper, a dynamically hybrid niching-based differential evolution (DE) with two archives is proposed to try to solve the MMOPs effectively. The proposed method is referred to as DHNDE, which can be featured as: (i) Two niching techniques, i.e., crowding and speciation, are dynamically used during the run. (ii) A secondary archive is introduced to save the inferior offspring. This archive is integrated into crowding-based DE to promote the diversity. And (iii) an improved neighborhood speciation-based DE (INSDE) is presented to improve the convergence. In INSDE, the extremely similar individuals are identified and removed from population to save the computational resources. Additionally, the optimal solutions are stored into an optimal solution archive to avoid losing them during the run. Based on the proposed dynamically hybrid niching technique, DHNDE can make a good tradeoff between the diversity and the convergence. Of the 20 MMOPs presented in CEC-2013 are chosen as the test suite, the DHNDE can stably find all the global optimal solutions on functions F1–F12. Experimental results indicate that DHNDE provides highly competitive results, especially for the MMOPs with a large number of global optima when comparing with 17 related methods.
Overview of Business Innovation and Research Probability on Blockchain and Introduction to its Exclusive Version

Blockchain is a very extraordinary innovation after the internet. Blockchain has become known as a public ledger for all transactions. It solves the problem of double spending by combining the technology with public key cryptography. However, globally, there will be more and more innovations created related to current innovations. One is a digital process compatible business, the former will help solve trust issues more efficiently over the network. We will propose that although blockchain has enabled Bitcoin, the most successful digital currency, its wide adoption in finance and other business sectors will represent many business discoveries and many research opportunities. This paper focuses on an overview of blockchain research development and also introduces specific issues.
SparseShift-GCN: High precision skeleton-based action recognition

Skeleton-based action recognition is widely used due to its advantages of lightweight and strong anti-interference. Recently, graph convolutional networks (GCNs) have been applied to action recognition and have made breakthrough progress. The shift convolution operator can effectively replace the spatial convolution and greatly reduce the computational complexity of the algorithm. This article first applies the Conv-Shift-Conv (CSC) module and the Shift-Conv-Shift-Conv (SC2) module to replace the Shift-Conv-Shift (SCS) module in spatial graph convolution of Shift-GCN respectively. This design can reorder the shifted channels more effectively. The experimental results show that the CSC module has the best effect and effectively improves accuracy of model. After that, this article proposes to replace the shift module in the original Shift-GCN with a sparse shift module and named SparseShift-GCN. This structure can reduce the redundancy of features, prevent overfitting and improve the generality of the model. Based on the improvement in the previous step, better results have been achieved. Finally, this paper uses OHEM Loss and Weighted Loss to carefully design the loss function of the model and introduces it into the model proposed in this paper. Experimental results show that OHEM Loss further improves the accuracy of algorithm. After a series of improvements, our proposed model has improved the accuracy of 4 different streams to varying degrees, which improves the overall performance of the network.
Classification of sport actions using principal component analysis and random forest based on three-dimensional data

Due to the rapid development of motion capture technology, a large number of Three-Dimensional (3D) motion data are produced. Research on classification and recognition based on sports data has received great attention. In this study, a combination approach of Principal Component Analysis (PCA), Random Forest (RF) and Genetic Algorithm (GA) are proposed to classification sport action by using 114 dimensions indicators, and the accuracy and effectiveness of the proposed model is analyzed by five evaluation measures. First, sports action cases that based 3D data with three actions are collected. Then, a hybrid model PCA-RF is proposed and, several parameters are optimized by the GA. Finally. The results show that the proposed PCA-RF-GA classification model is outstanding at sport action classification accuracy than the benchmark methods in overall three actions. Additionally, Due to PCA processing advantages, it be used to reduce the dimension of independent variable data without retaining the features of the original data, which improves the overall sport action classification accuracy. Moreover, the classification results indicate that applying model parameters optimization by GA has successfully improved the classification accuracy. Therefore, our proposed method provides a new and effective way to classification the people actions in the field of sport.
Dictionary learning for integration of evidential layers for mineral prospectivity modeling

Machine learning and deep learning anomaly detectors have been successfully used to map mineral prospectivity. However, the establishment of machine learning or deep learning anomaly detection models for mineral prospectivity mapping often requires the determination of a set of initialization parameters in the absence of ground truth data. Improper initialization parameters will degrade the performance of these mineral prospectivity mapping models. Different from most machine learning and deep learning algorithms, dictionary learning is a “white-box” algorithm involving only linear transformations, and building a dictionary learning model requires only an empirical definition of the total number of atoms. Therefore, in this paper, two dictionary learning anomaly detectors were established for mineral prospectivity modeling based on the least angle regression-Lasso (LARS-Lasso) algorithm and the iterative shrinkage-thresholding algorithm (ISTA). The following five-step procedure was proposed for mineral prospectivity modeling using the dictionary learning techniques: (a) an overcomplete dictionary is constructed based on the input data; (b) each data point is transformed into sparse coefficients according to the overcomplete dictionary; (c) the sparse representation of each data point is calculated based on the overcomplete dictionary and the sparse coefficients; and (d) the Euclidean norm of the difference between each data point and its sparse representation is calculated and used as the mineral potential of the data point. The dictionary learning models were established to model gold prospectivity in the Jinchanggouliang district, Inner Mongolia, China, and compared with the logistic regression model and one-class support vector machine model in gold exploration targeting. The result shows that (a) the performances of the dictionary learning models are no less than that of the logistic regression (LGR) model and better than that of the one-class support vector machine (OCSVM) model, and (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. Therefore, the dictionary learning algorithms are high-performance mineral prospectivity modeling techniques. It is worth to further test the effectiveness of the dictionary learning algorithms for different types of mineral deposits in different areas in mineral exploration targeting.
Dynamic relevance and interdependent feature selection for continuous data

Feature Selection (FS) techniques are employed in expert system for selecting relevant and eliminating redundant features. Information-theoretic based FS methods measure non-linear correlation and have gained popularity recently. However, these methods work with discrete data, leading to an additional overhead of discretization while dealing with continuous data. In order to resolve this, the paper proposes a FS method namely, Dynamic Relevance Interdependent Feature Selection (DRIFS) for continuous data which retains the essence of information-theoretic based methods. DRIFS incorporates two major aspects of FS that are ignored by many state of the art methods. First, the method updates the relevance of every candidate features after a new feature is selected and second, the method discriminates between redundant and interdependent features. The performance of DRIFS is compared with six other FS methods, CIFE, CMIM, DISR, ICAP, IGFS and MRMR. Ten real world datasets, which include seven high-dimensional microarray datasets are used for experimental validation. DRIFS performs better in terms of classification accuracy and the number of feature selected. Experimental results also show an improvement in classification accuracy after the application of DRIFS.
A Novel Approach for Malicious URL Detection Based on the Joint Model

The number of malicious websites is increasing yearly, and many companies and individuals worldwide have suffered losses. Therefore, the detection of malicious websites is a task that needs continuous development. In this study, a joint neural network algorithm model combining the attention mechanism, bidirectional independent recurrent neural network (Bi-IndRNN), and capsule network (CapsNet) is proposed. The word vector tool word2vec trains the character- and word-level uniform resource locator (URL) static embedding vector features. At the same time, the algorithm will also extract texture fingerprint features that can compare the content differences of different malicious web URL binary files. Then, the extracted features are fused and input into the joint neural network algorithm model. First, the multihead attention mechanism is used to extract contextual semantic features by adjusting weights and Bi-IndRNN. Second, CapsNet with dynamic routing is used to extract deep semantic information. Finally, the sigmoid classifier is used for classification. This study uses different methods from different angles to extract more comprehensive features. From the experimental results, the method proposed in this study improves the classification accuracy of malicious web page detection compared with other researchers.
Remote teaching and learning of software testing using active methodologies in the COVID-19 pandemic context

This Research for the Practice of Full Paper shows that in the face of the COVID-19 pandemic, the immense challenge of continuing the teaching-learning process without face-to-face classes arose. This required a rapid shift to distance education in order to enable continuity of university activities. Distance education still needs the help of other factors, as some subjects, for example software testing, may need other forms of active approaches and methodologies to make their teaching and learning process more efficient. In teaching some computer science subjects, there is a need for learning through the application of practical activities. The use of distance learning entails an even greater aggravation of this need, given the numerous limitations that exist in this modality, requiring good planning and rigorous monitoring before, during and after classes. Thus, this paper aims to identify and discuss the findings, good practices, recommendations and perceptions used in a software testing subject, in addition to analyzing the motivations and perceptions of students in a distance learning environment.
Improving Deep Learning based Automatic Speech Recognition for Gujarati

We present a novel approach for improving the performance of an End-to-End speech recognition system for the Gujarati language. We follow a deep learning-based approach that includes Convolutional Neural Network, Bi-directional Long Short Term Memory layers, Dense layers, and Connectionist Temporal Classification as a loss function. To improve the performance of the system with the limited size of the dataset, we present a combined language model (Word-level language Model and Character-level language model)-based prefix decoding technique and Bidirectional Encoder Representations from Transformers-based post-processing technique. To gain key insights from our Automatic Speech Recognition (ASR) system, we used the inferences from the system and proposed different analysis methods. These insights help us in understanding and improving the ASR system as well as provide intuition into the language used for the ASR system. We have trained the model on the Microsoft Speech Corpus, and we observe a 5.87% decrease in Word Error Rate (WER) with respect to base-model WER.
Exploring User Acceptance Determinants of COVID-19-Tracing Apps to Manage the Pandemic

Tracing infectious individuals and clusters is a major tactic for mitigating the pandemic. This paper explores the factors impacting the intentions and actual use of COVID-19 contact tracing apps based on a technology acceptance model. A partial least squares structural equation model has been applied to understand determinants for the usage of tracing apps based on a large sample (N = 2,398) from more than 30 countries (mainly from Germany and USA). Further, the paper presents a classification of COVID-19 apps and users. Through that, the study provides insights for technologists and designers of tracing apps as well as policy makers and practitioners to work toward enhancing user acceptance. Moreover, the results are abstracted to general social participation with apps in order to manage future strategies. The theoretical contribution of this work includes the results of our acceptance model and a classification of COVID-19 tracing and tracking apps.
Multi-Objective optimization of solar park design under climatic uncertainty

The scarcity of land near energy demand poses the challenge of designing multi-functional solar parks in terms of land use in some countries. This requires solutions accounting for multiple conflicting objectives, e.g., power generation and multi-functional use of the land (agricultural, construction, ecological). Moreover, the performance of solar park projects in terms of these criteria is subject to uncertainties, e.g., meteorological aspects impacted by climate change, electricity prices, grid infrastructure availability. In this work we present a framework for multi-objective optimization under uncertainty to aid in the development of smart solar park configurations accounting for multi-purpose land use. A solar park simulator and a techno-economic model are combined to evaluate key performance indicators serving as objective functions. Meteorological uncertainty throughout the park lifetime is characterized through an ensemble of scenarios generated based on the variability of historical data, instead of the current practice of assessing the performance of solar park using a deterministic profile of an average meteorological year. The developed workflow is demonstrated through a case study where power yield and agricultural land use are two conflicting objectives being optimized with the orientation, tilt angle, spacing and height of the modules as the optimization variables. A series of optimization experiments with varying importance weights between the objectives is performed. Obtained solutions show solar park designs which double the available farming area without compromising the levelized cost of energy. These results showcase the value generated through an integrated framework for multi-objective optimization under uncertainty leading to optimized solar parks.
The uncertainties involved in measuring national solar photovoltaic electricity generation

The measurement of near real-time national solar PV electricity output is an increasingly important component for managing electricity systems. PV systems embedded in the distribution network are problematic for system operators since their output is not directly visible through existing transmission system metering. Typically, national solar PV output is measured indirectly by upscaling the output from a sample of reference PV systems using an estimate of the national capacity. National PV output is then used, along with similar measurements for other embedded technologies such as wind, to train and validate electricity forecasts which ensure efficient electricity market operation. For the first time and using Great Britain as a case study, we investigate the accuracy of this general approach by characterising different sources of uncertainty in national PV output measurements. We find that the capacity error, at ±5%, dominates the yield calculation error, at < ±1% and leads to an overall error in GB solar PV output estimates of ±5.1%. We conclude that solar PV measurements, and consequently national electricity demand forecasts, are currently limited by the state of national PV capacity registers. To make PV output measurements more accurate nations must develop a coherent and comprehensive approach to PV system registration, and output measurement methods must move away from using static solar PV capacity registers towards estimating an operational grid-connected solar PV capacity. This could involve moving away from capacity registers altogether and instead estimating capacity from other sources, for example, from network power flows, satellite imagery, or more likely, a combination of complementary datasets.
Online Recruitment Fraud Detection using ANN

Online recruitment provides job-seekers an efficient search and reach for jobs. It also helps recruiters searching for qualified candidates which improves the recruitment process. However, employment scam has emerged as a critical issue. Some job posts are legitimate, and others are fraud. In this paper, an Artificial Neural Network based model is proposed to detect fraud job posts. The public Employment Scam Aegean Dataset (EMSCAD) is used with proper text preprocessing techniques for training and testing the proposed model. Our model has precision, recall, and f-measure of 91.84%, 96.02%, and 93.88% respectively. The results show that the proposed ANN-based model outperforms similar existing models in detecting fraud jobs.
Modifying Transactional Databases to Hide Sensitive Association Rules

Although firms recognize the value in sharing data with supply chain partners, many remain reluctant to share for fear of sensitive information potentially making its way to competitors. Approaches that can help hide sensitive information could alleviate such concerns and increase the number of firms that are willing to share. Sensitive information in transactional databases often manifests itself in the form of association rules. The sensitive association rules can be concealed by altering transactions so that they remain hidden when the data are mined by the partner. The problem of hiding these rules in the data are computationally difficult (NP-hard), and extant approaches are all heuristic in nature. To our knowledge, this is the first paper that introduces the problem as a nonlinear integer formulation to hide the sensitive association rule while minimizing the alterations needed in the data set. We apply transformations that linearize the constraints and derive various results that help reduce the size of the problem to be solved. Our results show that although the nonlinear integer formulations are not practical, the linearizations and problem-reduction steps make a significant impact on solvability and solution time. This approach mitigates potential risks associated with sharing and should increase data sharing among supply chain partners.