Reviewerly/exHarmony huggingface dataset:
Ontology-based knowledge management in income tax of Nepal

Organisational knowledge management in the government agencies is crucial to create a common understanding about data, system and procedures for stakeholders. Main purpose of this work is to create a knowledge repository for income tax of Nepal using domain ontology. To create this ontology, we have used 'Methontology' a broadly used ontology development methodology. After the knowledge acquisition; competency questions were created and conceptualisation was specified. A class hierarchy, object properties and data propertied are identified and specified. Further, ontology was created by using the Protégé tool. Consistency of the developed ontology was evaluated by correctly answering all the competency questions using SPARQL. The ontology can be used by the Inland Revenue Department as a stepping stone towards knowledge management through ontology. The work can be a foundation for the future works in this domain in good governance, data sharing, interoperability and transparency for the government of Nepal.
Feature Selection for Breast Cancer Detection using Machine Learning Algorithms

Cancer has been portrayed as a heterogeneous disease comprising of a wide range of subtypes. The early diagnosis of a cancer type is very important to determine the course of medical treatment required by the patient. The significance of classifying cancerous cells into benign or malignant has driven many research studies, in the biomedical and the bioinformatics field. In the past years researchers have been encouraged to use different machine learning (ML) techniques for cancer detection, as well as prediction of survivability and recurrence. What's more, ML instruments can be used to distinguish key highlights from complex datasets and uncover their significance. An assortment of these procedures, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Random Forest Methods (RVMs) and Decision Trees (DTs) has been usually used in cancer research for the development of predictive models, resulting in successful and exact decision making. Although it is obvious that the usage of machine learning techniques can enhance our comprehension of cancer detection, progression, recurrence and survivability, a proper level of accuracy is required for these strategies to be considered in the ordinary clinical practice. The predictive models talked about here depend on different administered ML strategies and on various input features and data samples. We have used Naïve-Bayes classifier, Neural Networks method, Decision Tree and Logistic Regression algorithm to detect the type of breast cancer (Benign or Malignant) and selection of features which are more relevant for prediction. We have made a comparative study to find out the best algorithm of the above four, for prediction of cancer type. With a high level of accuracy, any of these methods can be used to predict the type of breast cancer of any particular patient.
A Review on Negation Role in Twitter Sentiment Analysis

Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.
Measuring digital pathology throughput and tissue dropouts

Digital pathology operations that precede viewing by a pathologist have a substantial impact on costs and fidelity of the digital image. Scan time and file size determine throughput and storage costs, whereas tissue omission during digital capture ("dropouts") compromises downstream interpretation. We compared how these variables differ across scanners.A 212 slide set randomly selected from a gynecologic-gestational pathology practice was used to benchmark scan time, file size, and image completeness. Workflows included the Hamamatsu S210 scanner (operated under default and optimized profiles) and the Leica GT450. Digital tissue dropouts were detected by the aligned overlay of macroscopic glass slide camera images (reference) with images created by the slide scanners whole slide images.File size and scan time were highly correlated within each platform. Differences in GT450, default S210, and optimized S210 performance were seen in average file size (1.4 vs. 2.5 vs. 3.4 GB) and scan time (93 vs. 376 vs. 721 s). Dropouts were seen in 29.5% (186/631) of successful scans overall: from a low of 13.7% (29/212) for the optimized S210 profile, followed by 34.6% (73/211) for the GT450 and 40.4% (84/208) for the default profile S210 profile. Small dislodged fragments, "shards," were dropped in 22.2% (140/631) of slides, followed by tissue marginalized at the glass slide edges, 6.2% (39/631). "Unique dropouts," those for which no equivalent appeared elsewhere in the scan, occurred in only three slides. Of these, 67% (2/3) were "floaters" or contaminants from other cases.Scanning speed and resultant file size vary greatly by scanner type, scanner operation settings, and clinical specimen mix (tissue type, tissue area). Digital image fidelity as measured by tissue dropout frequency and dropout type also varies according to the tissue type and scanner. Dropped tissues very rarely (1/631) represent actual specimen tissues that are not represented elsewhere in the scan, so in most cases cannot alter the diagnosis. Digital pathology platforms vary in their output efficiency and image fidelity to the glass original and should be matched to the intended application.
A forensic-driven data model for automatic vehicles events analysis

Digital vision technologies emerged exponentially in all living areas to watch, play, control, or track events. Security checkpoints have benefited also from those technologies by integrating dedicated cameras in studied locations. The aim is to manage the vehicles accessing the inspection security point and fetching for any suspected ones. However, the gathered data volume continuously increases each day, making their analysis very hard and time-consuming. This paper uses semantic-based techniques to model the data flow between the cameras, checkpoints, and administrators. It uses ontologies to deal with the increased data size and its automatic analysis. It considers forensics requirements throughout the creation of the ontology modules to ensure the records' admissibility for any possible investigation purposes. Ontology-based data modeling will help in the automatic events search and correlation to track suspicious vehicles efficiently.
Frequency considerations in GIC applications

Earth and Space Science Open Archive This work has been accepted for publication in Space Weather. Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]Frequency considerations in GIC applicationsAuthorsLarisaTrichtchenkoiDSee all authors Larisa TrichtchenkoiDCorresponding Author• Submitting AuthorNatural Resources CanadaiDhttps://orcid.org/0000-0001-8179-2510view email addressThe email was not providedcopy email address
Vehicle localisation by altitude data matching in spatial domain and its fusion with dead reckoning

In this paper, localisation algorithm based on altitude profiles is proposed. It is a kind of landmark-based localisation algorithm in which altitude profile which is obtained while driving a vehicle are treated as landmarks. The author proposes an algorithm for measuring the location of vehicles by using altitude data in locations where GNSS positioning is difficult. Specifically, altitude is estimated from the pressure information obtained from a barometer, in other words, pressure sensor. This is a position estimation algorithm that attempts to identify the current position by matching altitude reference data prepared in advance with altitude data currently acquired. A sensor fusion with wheel pulse speed data is also proposed as an extension to the original altitude similarity matching algorithm to make it more applicable to the cases when variation in altitude is little. The effectiveness of the algorithm is presented with several experimental results obtained with real data.
Locomotion Mode Recognition Using Sensory Data With Noisy Labels: A Deep Learning Approach

Availability of various sensors in the smartphone makes it easier and convenient to collect the data of human locomotion activities. A recognition approach can utilize this sensory data for recognizing a locomotion mode of a user such as a bicycle, bike, car, etc. Such recognition of locomotion modes helps in the precise estimation of transportation expenditure, travel time, and appropriate journey planning. The accuracy of the recognition approaches heavily relies on the training dataset having correctly annotated labels. These labels are usually assigned using crowdsourcing or web-based queries for economic and fast annotation. However, the annotation generates abundant noisy labels in the dataset. This paper proposes a locomotion mode recognition approach capable of handling noisy labels in the training dataset. The approach builds an ensemble model by developing three different deep learning-based models, namely conventional, noise adaptive, and noise corrective, to handle different concentrations of noisy labels. The ensemble model not only improves the recognition performance but also helps in estimating the concentration of noisy labels. Experimental results demonstrate the effectiveness of the proposed approach on collected and existing datasets.
Augmenting MetaMask to Support TLS-endorsed Smart Contracts

Users in blockchain systems are exposed to address replacement attacks due to the weak binding between websites and smart contracts, as they have no way to verify the authenticity of obtained addresses. Prior research introduced TLS-endorsed Smart Contracts (TeSC) that equip Smart Contracts with authentication information, proving the relation to the domain name of the respective website. For an efficient and user-friendly approach, this technology needs to be integrated with wallets. Based on the analysis of browser warnings regarding TLS-certificates, we augment MetaMask with the ability to detect TeSC and warn users if attack scenarios are detected. To evaluate our work, we conduct a study with 40 participants to show the effectiveness of TeSC to prevent address-replacement attacks and ensure the safe interaction of users and addresses.
Neural Network and Deep Learning-Based Resource Allocation Model for Multilayered Cloud

In the past few decades cloud itself has emerged as a computing model where all kinds of services are virtually provided to the users. Nowadays it is no longer necessary to have physically existing resources as cloud can provide services to a small infrastructure or a large infrastructure as required. As businesses are migrating to the cloud so the challenges to maintain performance, integrity and security are also increasing. There is no conclusive definition of cloud as everyone has their own definition. The National Institute of Standards and Technology, USA (NIST), define cloud computing as “loud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction”. Gartner defines cloud computing as a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies.
Diabetes Prediction Using Quantum Neurons with Preprocessing Based on Hypercomplex Numbers

The use of properties that are intrinsic to quantum mechanics has made it possible to build quantum algorithms with greater efficiency than classical algorithms to solve problems whose classically efficient solution either does not exist or is not known. There are quantum neurons that can carry an exponential amount of information to a linear number of quantum information units (qubits) using the quantum property of superposition. In this paper, we compare the performance of three of these quantum neuron models applied to the diabetes classification problem. We also propose the use of different data preprocessing strategies. Quantum neurons were simulated using the IBM Qiskit tool. We compare the preprocessing approaches applied to two toy problems (1) simulating the XOR operator and (2) solving a generic nonlinear problem. The results of the experiments shows that a single quantum neuron is capable of achieving an accuracy rate of 100% in the XOR problem and an accuracy rate of 100% in a non-linear dataset, demonstrating that the quantum neurons with real weights are capable of modeling non-linearly separable problems. In the problem of diagnosing diabetes, quantum neurons achieved an accuracy rate of 76% and AUC-ROC of 88%, while its classic version, the perceptron, reached only 63% accuracy and the artificial neural network reached 80% AUC-ROC. These results indicate that a single quantum neuron performs better than its classical version and even the artificial neural network for AUC-ROC, demonstrating potential for use in healthcare applications in the near future.
New paradigms of personnel management in the concept of E. Deming as a factor of improving the quality of higher education in Russia

Some principles of the philosophy of quality of the founder of the quality management system-the American scientist E. Deming, concerning the change of the attitude to the personnel are stated. These principles are an important factor in improving the quality of any activity, including education. The author gave an interpretation of the above principles regarding the higher education system in Russia and showed ways to improve the quality of educational activities by “humanizing” work with academic staff.
Towards Better Representation of Context Into Recommender Systems

Context-aware recommender systems (CARSs) are attracting more and more attention from both the academic community and from industry. Users' contextual situations (e.g., location, time, companion, etc.) which can influence their ratings on items, are taken into consideration. Therefore, more accurate and personalized recommendations can be generated. The integration of contextual information in recommender systems to better model users' preferences under different contextual situations is a key research topic. In this paper, the authors propose a new method for representing contextual situations in recommender systems based on the influence of contextual conditions on ratings using Pearson Correlation Coefficient. The authors show the effectiveness of the proposed method compared to state-of-art methods by experiments on three different datasets widely used in CARSs research community.
Security and privacy issues of data-over-sound technologies used in IoT healthcare devices

Internet of things (IoT) healthcare devices, like other IoT devices, typically use proprietary protocol communications. Usually, these proprietary protocols are not audited and may present security flaws. Further, new proprietary protocols are desgined in the field of IoT devices, like data-over-sound communications. Data-over-sound is a new method of communication based on audio with increasing popularity due to its low hardware requirements. Only a speaker and a microphone are needed instead of the specific antennas required by Bluetooth or Wi-Fi protocols. In this paper, we analyze, audit and reverse engineer a modern IoT healthcare device used for performing electrocardiograms (ECG). The audited device is currently used in multiple hospitals and allows remote health monitoring of a patient with heart disease. For this auditing, we follow a black-box reverse-engineering approach and used STRIDE threat analysis methodology to assess all possible attacks. Following this methodology, we successfully reverse the proprietary data-over-sound protocol used by the IoT healthcare device and subsequently identified several vulnerabilities associated with the device. These vulnerabilities were analyzed through several experiments to classify and test them. We were able to successfully manipulate ECG results and fake heart illnesses. Furthermore, all attacks identified do not need any patient interaction, being this a transparent process which is difficult to detect. Finally, we suggest several short-term solutions, centred in the device isolation, as well as long-term solutions, centred in involved encryption capabilities.
The effectiveness of agile management on traditional projects within public organizations

Abstract After the coronavirus pandemic, we must look for the notion of an agile methodology as one of the ways that supports the restoration of our businesses to what they were and even better; this is critical in order to remedy and grow the global economy once again. The notion of agility has been traditionally present, however, it was engaged only for IT and software projects. As this concept has been validated further during the pandemic, this methodology can be increasingly applied in different types of projects, such as construction projects and other infrastructure projects. With the growing adoption of agile methods, project managers at public organizations increasingly need to realize its applicability to their projects and the aspects that drive key project performance features. On the one hand, many firms confirmed that agile methods solved their difficulties during the pandemic, particularly in addressing changes due to unstable and unpredictable needs. On the other hand, some other firms have continued to struggle, arguably because of not practicing agile approaches. Consequently, this paper attempts to examine the effect of agile methodologies on the processes involved in traditional project management within public organizations, which it does through applying a qualitative method. Furthermore, this paper will explore how this methodology can allow project managers to evaluate the applicability of using an agile method within public organizations, especially for construction projects and other infrastructure projects.
Using Information Technology to Manage the COVID-19 Pandemic: Development of a Technical Framework Based on Practical Experience in China (Preprint)

<sec> <title>BACKGROUND</title> The coronavirus disease (COVID-19) epidemic poses an enormous challenge to the global health system, and governments have taken active preventive and control measures. The health informatics community in China has actively taken action to leverage health information technologies for epidemic monitoring, detection, early warning, prevention and control, and other tasks. </sec> <sec> <title>OBJECTIVE</title> The aim of this study was to develop a technical framework to respond to the COVID-19 epidemic from a health informatics perspective. </sec> <sec> <title>METHODS</title> In this study, we collected health information technology–related information to understand the actions taken by the health informatics community in China during the COVID-19 outbreak and developed a health information technology framework for epidemic response based on health information technology–related measures and methods. </sec> <sec> <title>RESULTS</title> Based on the framework, we review specific health information technology practices for managing the outbreak in China, describe the highlights of their application in detail, and discuss critical issues to consider when using health information technology. Technologies employed include mobile and web-based services such as Internet hospitals and Wechat, big data analyses (including digital contact tracing through QR codes or epidemic prediction), cloud computing, Internet of things, Artificial Intelligence (including the use of drones, robots, and intelligent diagnoses), 5G telemedicine, and clinical information systems to facilitate clinical management for COVID-19. </sec> <sec> <title>CONCLUSIONS</title> Practical experience in China shows that health information technologies play a pivotal role in responding to the COVID-19 epidemic. </sec>
Virtual Reality-Supported Drilling Strategy In Teaching English Vocabulary To Young Learners

This present research intent to discover the implementation of Virtual Reality (VR) media in supporting drilling strategy to teach English vocabulary to young learners modeled by a current teacher who is taking Independent Learning-Campus Merdeka (Merdeka Belajar-Kampus Merdeka or MBKM) as the participant of this study. She is holding a responsibility to teach English lesson to a school partnership. The school that is in partnership is Trikarya Public Elementary School of Cipanas district. This research collected the data through observation on teaching praxis using VR media. As the data were collected, the writer analyzed them. There were six steps in analyzing the data; (1) preparing the data to be analyzed, (2) reading or viewing all data, (3) coding the data, (4) generalizing the code, (5) creating tables based on the data code, and (6) interpret the data code. Over the observation the writers found a new procedure on implementing VR media in teaching English vocabulary to elementary students. There are three stages in this procedure that is called PMP. PMP stands for Pre-Activity, Main Activity, and Post-Activity. Furthermore, there are four activities in the main stage; Watch, Collect, Sharing, and Practice and hereafter is called WCSP. The last finding uncovers that the teacher is using drilling as the teaching strategy to activate the students.
Human Capital Development: Innovative Learning in the Post-Pandemic Era

The paper demonstrates the feasibility of innovative learning, which contributes to human capital. During the 2020-21 academic year, Kazakhstani universities rapidly moved to online learning due to the pandemic: teachers and students gain significant experience in digital technologies influencing human capital, which, in turn, contributes to the effectiveness of the educational institutions and the education as a whole. The use of digital technologies helps to preserve the educational infrastructure; on the other hand, digital credentials are developing. There are advantages and disadvantages to distant and blended learning approaches that influence student satisfaction. Student satisfaction scores could be insightful in extending the blended learning approach in educational programs after the pandemic.The study aims to determine the role of innovative technologies to support the education-relevant elements of human capital during the pandemic. The literature review of articles is based on Elsevier, Springer, and Science Direct databases. To search the literature, we used the keywords “human capital,” “innovation,” and “online learning in the context of the COVID-19 pandemic.” Qualitative and quantitative research was carried out among teachers and students of the Faculty of International Relations of al-Farabi KazNU. The research results demonstrate the attitude of teachers and students to distance learning. The introduction of innovative teaching methods for Kazakhstan in the face of blended learning can significantly reduce risks in an unpredictable environment. It is important to note that innovative educational technologies make the learning process flexible and adaptive. Credentials and skills acquired through online learning will be essential in the post-pandemic era.