Reviewerly/exHarmony huggingface dataset:
Software Development Dengan Extreme Programming (XP) Pada Aplikasi Deteksi Kemiripan Judul Skripsi Berbasis Android

To determine the topic or research title for the student's thesis, it is possible that the similarity of the title to the previous title, either accidentally or unintentionally. Therefore, the relevant section must check the titles submitted by students, so as to avoid plagiarism. The development of a thesis title similarity detection application is one of the efforts that can be made in solving this problem. The development of sosftware requires the right method to develop applications according to user needs. Extreme Programming (XP) is a part of the agile development approach based on iterative development, which is based on needs and solutions through collaboration among the development team. So, in this study, the development of a similarity detection application for the title of a thesis with the XP software development approach was carried out. Keywords - agile development, extreme programming, thesis
Open government data and environmental science: a federal Canadian perspective

Governments worldwide are releasing data into the public domain via open government data initiatives. Many such data sets are directly relevant to environmental science and complement data collected by academic researchers to address complex and challenging environmental problems. The Government of Canada is a leader in open data among Organisation for Economic Co-operation and Development countries, generating and releasing troves of valuable research data. However, achieving comprehensive and FAIR (findable, accessible, interoperable, reusable) open government data is not without its challenges. For example, identifying and understanding Canada’s international commitments, policies, and guidelines on open data can be daunting. Similarly, open data sets within the Government of Canada are spread across a diversity of repositories and portals, which may hinder their discoverability. We describe Canada’s federal initiatives promoting open government data, and outline where data sets of relevance to environmental science can be found. We summarize research data management challenges identified by the Government of Canada, plans to modernize the approach to open data for environmental science and best practices for data discoverability, access, and reuse.
Enabling Clustering for Privacy-Aware Data Dissemination Based on Medical Healthcare-IoTs (MH-IoTs) for Wireless Body Area Network

There is a need to develop an effective data preservation scheme with minimal information loss when the patient's data are shared in public interest for different research activities. Prior studies have devised different approaches for data preservation in healthcare domains; however, there is still room for improvement in the design of an elegant data preservation approach. With that motivation behind, this study has proposed a medical healthcare-IoTs-based infrastructure with restricted access. The infrastructure comprises two algorithms. The first algorithm protects the sensitivity information of a patient with quantifying minimum information loss during the anonymization process. The algorithm has also designed the access polices comprising the public access, doctor access, and the nurse access, to access the sensitivity information of a patient based on the clustering concept. The second suggested algorithm is K-anonymity privacy preservation based on local coding, which is based on cell suppression. This algorithm utilizes a mapping method to classify the data into different regions in such a manner that the data of the same group are placed in the same region. The benefit of using local coding is to restrict third-party users, such as doctors and nurses, when trying to insert incorrect values in order to access real patient data. Efficiency of the proposed algorithm is evaluated against the state-of-the-art algorithm by performing extensive simulations. Simulation results demonstrate benefits of the proposed algorithms in terms of efficient cluster formation in minimum time, minimum information loss, and execution time for data dissemination.
Entangled N-photon states for fair and optimal social decision making

Abstract Situations involving competition for resources among entities can be modeled by the competitive multi-armed bandit (CMAB) problem, which relates to social issues such as maximizing the total outcome and achieving the fairest resource repartition among individuals. In these respects, the intrinsic randomness and global properties of quantum states provide ideal tools for obtaining optimal solutions to this problem. Based on the previous study of the CMAB problem in the two-arm, two-player case, this paper presents the theoretical principles necessary to find polarization-entangled N -photon states that can optimize the total resource output while ensuring equality among players. These principles were applied to two-, three-, four-, and five-player cases by using numerical simulations to reproduce realistic configurations and find the best strategies to overcome potential misalignment between the polarization measurement systems of the players. Although a general formula for the N -player case is not presented here, general derivation rules and a verification algorithm are proposed. This report demonstrates the potential usability of quantum states in collective decision making with limited, probabilistic resources, which could serve as a first step toward quantum-based resource allocation systems.
Trade finance in Qatar: blockchain and economic diversification

Trade finance helps businesses deal with abnormal cash flows whilst managing counterparty risk and enhancing confidence in commercial transactions. It also allows parties to overcome trust barriers that may inhibit commercial activity in both a domestic and international commercial context. Globally, particularly among micro, small and medium enterprises (MSMEs), there exists a significant and widening unmet demand for documentary finance. Securing trade finance is laborious and time-consuming. For MSMEs, the trade finance application process alone, can be an insurmountable barrier that usually ends in rejection. By its nature, trade finance arrangements engage with decentralized stakeholders and diffused information sources across supply chains. Issuers and underwriters of trade finance instruments are required to draw on disparate elements of information, not merely during the application phase, but indeed throughout the life of a transaction. Blockchain technology is similarly decentralized and can capture information in a secure, transparent and immutable manner potentially improving and reinvigorating the trade finance space. As Qatar embarks on a strategy of widening its economic base away from a singular reliance on the hydrocarbon fuel sector, the introduction of blockchain technology holds the potential to overcome the transactional friction associated with trade finance. A more efficient and accessible trade finance sector will ultimately enhance the competitiveness of MSMEs whilst simultaneously fostering the growing FinTech sector in Qatar.
Modeling viscosity of crude oil using k-nearest neighbor algorithm

Oil viscosity is an important factor in every project of the petroleum industry. These processes can range from gas injection to oil reservoirs to comprehensive reservoir simulation studies. Different experimental approaches have been proposed for measuring oil viscosity. However, these methods are often time taking, cumbersome and at some physical conditions, impossible. Therefore, development of predictive models for estimating this parameter is crucial. In this study, three new machine learning based models are developed to estimate the oil viscosity. These approaches are genetic programing, k-nearest neighbor (KNN) and linear discriminant analysis. Oil gravity and temperature were the input parameters of the models. Various graphical and statistical error analyses were used to measure the performance of the developed models. Also, comparison study between the developed models and the well-known previously published models was conducted. Moreover, trend analysis was performed to compare the predictions of the models with the trend of experimental data. The results indicated that the developed models outperform all of the previously published models by showing negligible prediction errors. Among the developed models, the KNN model has the highest accuracy by showing an overall mean absolute error of 8.54%. The results show that the new developed models in this study can be potentially utilized in reservoir simulation packages of the petroleum industry. Cited as : Mahdiani, M.R., Khamehchi, E., Hajirezaie, S., Hemmati-Sarapardeh, A. Modeling viscosity of crude oil using k-nearest neighbor algorithm. Advances in Geo-Energy Research, 2020, 4(4): 435-447, doi: 10.46690/ager.2020.04.08
Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia

Background: Detailed pathology analysis and morphological quantification is tedious and prone to errors. Automatic image analysis can help to increase objectivity and reduce time. Here, we present the evaluation of the DeePathology STUDIO™ for automatic analysis of histological whole-slide images using machine learning/artificial intelligence. Objective: To evaluate and validate the use of DeePathology STUDIO for the analysis of histological slides at high resolution. Methods: We compared the DeePathology STUDIO and our current standard method using macros in AxioVision for the analysis of amyloid-β (Aβ) plaques and microglia in APP-transgenic mice at different ages. We analyzed density variables and total time invested with each approach. In addition, we correlated Aβ concentration in brain tissue measured by ELISA with the results of Aβ staining analysis. Results: DeePathology STUDIO showed a significant decrease of the time for establishing new analyses and the total analysis time by up to 90%. On the other hand, both approaches showed similar quantitative results in plaque and activated microglia density in the different experimental groups. DeePathology STUDIO showed higher sensitivity and accuracy for small-sized plaques. In addition, DeePathology STUDIO allowed the classification of plaques in diffuse- and dense-packed, which was not possible with our traditional analysis. Conclusion: DeePathology STUDIO substantially reduced the effort needed for a new analysis showing comparable quantitative results to the traditional approach. In addition, it allowed including different objects (categories) or cell types in a single analysis, which is not possible with conventional methods.
Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering

In computer aided diagnosis of liver tumor detection, tumor segmentation from the CT image is an important step.The majority of methods are not able to give an integrated structure for finding fast and effective tumor segmentation.Hence segmentation of tumor is most difficult task in diagnosing.In this paper, CT abdominal image is segmented using Superpixel-based fast Fuzzy C Means clustering algorithm to decrease the time needed for computation and eradicate the manual interface.In this algorithm, a superpixel image with perfect contour can be obtain using a Multiscale morphological gradient reconstruction operation.Superpixel is presegmentation algorithm and is employed to obtain segmentation accuracy.FCM with modified object is used to obtain the color segmentation.This method is examined on 20 CT images gathered from liveratlas database, results shows that this approach is fast and accurate compared to most of segmentation algorithms.Statistical parameters which include accuracy, precision, sensitivity, specificity, dice, rfn and rfp are calculated for segmented image.The results shows that this algorithm gives high accuracy of 99.58% and improved rfn value of 8.34% compared with methods discussed in the literature.
Impulse denoising based on noise accumulation and harmonic analysis techniques

In the article, we propose a robust impulse denoising method based on noise accumulation and harmonic analysis techniques (NAHAT filter). Noise accumulation technique is used to improve the ability to detect noisy pixels, and a harmonic function is used to gain the accuracy of restoring gray values of the detected noisy pixels. The proposed method can remove impulse noise very effectively even under high-density impulse noise levels. Moreover, the proposed method can preserve image structures such as the edges and textures well. Based on experimental results, the proposed method obtains good performance under various noise levels as well as outperforms other similar state-of-the-art impulse denoising methods.
A Privacy-Protection Model for Patients

The collection and analysis of patient cases can effectively help researchers to extract case feature and to achieve the objectives of precision medicine, but it may cause privacy issues for patients. Although encryption is a good way to protect privacy, it is not conducive to the sharing and analysis of medical cases. In order to address this problem, this paper proposes a federated learning verification model, which combines blockchain technology, homomorphic encryption, and federated learning technology to effectively solve privacy issues. Moreover, we present a FL-EM-GMM Algorithm (Federated Learning Expectation Maximization Gaussian Mixture Model Algorithm), which can make model training without data exchange for protecting patient’s privacy. Finally, we conducted experiments on the federated task of datasets from two organizations in our model system, where the data has the same sample ID with different subset features, and this system is capable of handling privacy and security issues. The results show that the model was trained by our system with better usability, security, and higher efficiency, which is compared with the model trained by traditional machine learning methods.
The Impact of Digital Transformation Process of the Russian University

The research topic of this paper is the impact of digital transformation process of the Russian university.The relevance of this study is determined by the role of education on the world stage during the period of a single cohesive large-scale digitalization, which is aimed at improving the quality of life and guaranteeing further prosperity, both for society and for each individual separately.A comprehensive coverage of issues related to digitalization in higher education is raised, including the disclosure of the essence of digitalization, consideration of the transformation that digitalization leads to, coverage of the features of the presentation of digitized information, challenges for Russian higher education.The paper examines learner's vision towards digital transformation in higher education.Identification of the key factors which have a major impact on the successful implementation of the transformation process in the education will be presented within the paper.The results of qualitative part of research indicate the influence of key factors on students and demonstrate their attitude to the digitalization process in the Peter the Great St.
Strategizing Education: Ecosystem Transition

Introduction. The authors of the article, based on numerous studies on the transformation of the education system, offer a practical model of education, developed with reference to a view of education from a constructed image of the future, as well as to global trends. The essential basis of the model are productive processes of education and social engineering, as well as the folding of the ecosystem with the value-normative basis for matching the educational needs of different stakeholders of educational activities. The relevance of the article is determined by the need to respond to the discrepancy with the world trends of the education system, formed to ensure the reproduction of existing cultural norms and socio-productive relations in society in order to maintain its stability. The aim of the study is to develop an educational model, the key form of organization of which is the educational ecosystem. The model content is formed as a reflection of the process of strategizing based on the fundamental development trends. Materials and Methods. Using analysis and synthesis methods, groups of topical world trends are identified and their impact on the projected image of education is shown. Methods of focus groups and case studies are utilized to study advanced educational practices. Hypotheses on the projected educational model are generalized and advanced. The hypotheses are tested. The elements of the new model of education proposed by the authors are tested. Results. The authors substantiated the concept of a new model of education as a result of value and semantic bases of strategy of consent of different social groups participating in the process of education. Methodological approach to education development strategy as if looking from the future differs from the dominant methodological method of future formation by means of its present modification and reworking. The authorʼs concept of the model of education from the future rests on the value and meaning foundation, which distinguishes it from the dominant position associated with the justification of only requirements to learning outcomes and content of education. The new vision of “education from the future” will contribute to the successful implementation of practices of transformation of education taking into account global trends based on the vision of education from the future, namely, a collective-wanted image of the future, rather than a continued “reworkedˮ present. Discussion and Conclusion. This article is useful for organizers of changes in educational systems at different levels (region, city, secondary school), as well as for researchers in the field of modern models, approaches and educational technologies that arise under the influence of global trends.
Application of modern technologies in the management of records in public libraries

Many organisations, including libraries and information centres are struggling to manage their records. Effective management of records ensures the public library’s ability to function effectively and provides documentary evidence of scholars and citizens. Recently, modern technologies such as blockchain, cloud computing and Internet of Things (IoT) have increasingly been adopted by various organisations to manage records while other organisations are slow to adopt them. Public libraries are positioning themselves to take this advantage by implementing innovative technologies to manage their records. This study utilised literature review to investigate the application of modern technologies in managing records in public libraries with the view to highlight how these technologies can revolutionise library practices. The study established that although public libraries acknowledge the importance of modern technologies such as blockchain, cloud computing and Internet of Things in managing their records, these technologies are being slowly adopted due to lack of information technology infrastructure, technical support, and knowledge and skills. Therefore, all these challenges made it difficult for librarians and information professionals to maximise the benefits of these technologies and they struggle to see how these technologies can be incorporated into their institutions. Public libraries thus need to better understand best practices for records management, which may go a long way in influencing library policy to support records-management processes. The study recommends that public libraries consider exploring collaborating with other sectors such as archival services to implement modern technologies for the purpose of managing records.
The Language of Emoji in Social Media

The very fast development of information technology which is characterized by an&#x0D; influx of industry 4.0 has changed the way of human and behavior in language. The&#x0D; grammar which is a phenomenon of interest to language is examined along with&#x0D; behavior change language in the internet world. A phenomenon in language online&#x0D; is the emergence of the use of visual language emoji in conducting conversations in&#x0D; social media. This paper aims to discuss the phenomenon of visual language emoji&#x0D; among internet users in social media (WhatsApp). The aspects that will be emphasized&#x0D; are language (grammar) of emoji. Research methods carried out is observation and&#x0D; descriptive. Method of data collection is the division of the questionnaire online, and&#x0D; communications in WA screenshot that uses emoji icons. The research result show&#x0D; that emoji is a language (grammar) used in communicating in social media. Emoji&#x0D; language has dominated the conversation or message written on the social media&#x0D; and emoji (WA) as a language (syntax, semantics, and pragmatics) is part of the&#x0D; sentence, punctuation, expression, expressing feelings and thoughts to the opponent&#x0D; talk. The language of emoji expression indicates that the emoji can represent the&#x0D; thoughts and feelings instead of using verbal language. Thus, emoji is composed of&#x0D; two directions, i.e. language and parole. The language of emoji is the social institution&#x0D; of emoji (grammar) in social media, and the individual is the parole act, an actualized&#x0D; manifestation of the function of the emoticons language in syntactic, semantic and&#x0D; pragmatic.
Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal

Digital watermark is a commonly used technique to protect the copyright of medias. Simultaneously, to increase the robustness of watermark, attacking technique, such as watermark removal, also gets the attention from the community. Previous watermark removal methods require to gain the watermark location from users or train a multi-task network to recover the background indiscriminately. However, when jointly learning, the network performs better on watermark detection than recovering the texture. Inspired by this observation and to erase the visible watermarks blindly, we propose a novel two-stage framework with a stacked attention-guided ResUNets to simulate the process of detection, removal and refinement. In the first stage, we design a multi-task network called SplitNet. It learns the basis features for three sub-tasks altogether while the task-specific features separately use multiple channel attentions. Then, with the predicted mask and coarser restored image, we design RefineNet to smooth the watermarked region with a mask-guided spatial attention. Besides network structure, the proposed algorithm also combines multiple perceptual losses for better quality both visually and numerically. We extensively evaluate our algorithm over four different datasets under various settings and the experiments show that our approach outperforms other state-of-the-art methods by a large margin.
Conceptualising a Library Chatbot using Open Source Conversational Artificial Intelligence

Conversational software, or chatbots in popular parlance, has been in vogue amongst organizations for a few decades now. In sync with the trend, various libraries and knowledge resource centres have also adopted them within their technological fold, with an aim to provide improved services to patrons. The only bottleneck towards such implementation has been the dearth of open source conversational software platforms. The purpose of the paper is to conceptualize a library chatbot using a recently developed, artificial intelligence-powered open source conversational software platform named Rasa Stack. It introduces the essence of chatbot technology and their present day application in libraries, illustrates how the technical underpinnings of Rasa Stack can be leveraged to develop a library chatbot, and reflects on the potential future research in this direction.