Reviewerly/exHarmony huggingface dataset:
Respondents’ View on the Study of Ethnocultural Subjects in General Education Organizations of the Khanty-Mansi Autonomous Okrug-Yugra: On the Example of the Cities of Kogalym and Langepas

The article considers the need of educational institutions of the Khanty-Mansi Autonomous Okrug – Yugra (on the example of the cities of Kogalym and Langepas) to teach ethnocultural subjects. This problem is also relevant for the region under consideration, where the indigenous small peoples of the North of Khanty, Mansi and Forest Nenets live. The purpose of this work is to study the state of studying the native language and ethnocultural subjects in general education institutions of the Khanty-Mansi Autonomous Okrug – Yugra, the request of parents of schoolchildren from among the indigenous small peoples of the North for ethnocultural subjects. The tasks of the work are to identify the state of study of the native language and ethnocultural subjects in general education institutions and to determine the need of parents of schoolchildren from among the indigenous small peoples of the North for ethnocultural subjects. The object of the study was the parents of schoolchildren from among the indigenous small peoples of the North living in the cities of the Autonomous Okrug. The main method of our research is questionnaire. Results: just over half of respondents (50.9%) believe that teaching their native language and subjects with ethnocultural content will to some extent help preserve the traditional culture of the indigenous minorities peoples of the North. Among the respondents in Langepas, pessimistic assessments prevail; in Kogalym (68.9%), the majority of respondents believe in the role of teaching ethnocultural subjects. Only 36.8% of respondents want their children to study ethnocultural subjects at school.
Distributed and Decentralized Attribute Based Access Control for Smart Health Care Data

Most access control solutions today provide the ability for centralized authorities, whether governments, manufacturers, or service providers to gain unauthorized access to and control devices by collecting and analyzing user’s data. Data owners (DO) experience a necessity to concentrate on their own medical data and manage them. As an alternative, Blockchain promotes healthcare, including health care data, which actually may cause legal and privacy issues and solves the problem of sharing with third parties. Blockchain technology provides data owners with comprehensive, immutable records, and access to Smart Health Care (SHC) data free from service providers and websites. This paper presents a scheme, in which the data owner endorses the message based on attributes without leaking data other than the appended proof. The proposed model is Distributed and Decentralized scheme for Access Control.
UML Conceptual Analysis of Smart Contract for Health Claim Processing

Block chain and current economic scenario allow decentralization of data profile over peer to peer network. In this research work authors take the help of UML (Unified modeling language) to build a framework for health insurance claim processing that is using block chain to built smart contract. Smart contract application in health insurance industry incorporate user profile, electronic medical record, health insurance profile and linked bank agencies to automate claim settlement walk out on any third-party control. Smart contract is providing a promising solution to support insurance data validation over peer to peer transaction. Health insurance industry is untangling power of smart contract. Embedding UML analysis for block chain network build intelligence that showcase a conceptual processing of insurance data transaction. Though substantial research work in this regard is going on in numerous fields but still it needs more conceptual analysis before it’s real time implementation. So, in this experimentation authors pinpoint block chain basic UTXO (Unspent transaction output) and UML test bed analysis for smart contract in health insurance service. This meticulous research outcome is justified by factual analysis of block chain. Moreover, it is given with context aware sequence analysis of smart contract in health insurance.
Fine-grained part-of-speech tagging in Nepali text

Part-of-speech (POS) tagging is essential in Natural Language Processing (NLP) and text analysis applications. POS-tagging is a well-researched problem, but there have been limited efforts in fine-grained Nepali POS-tagging. Highly inflectional languages like Nepali encodes information like gender, person, number, mood, and aspect within their words. Accurate disambiguation of these inflected word forms is essential in Nepali text analysis. This work shows that neural network models are great at disambiguating fine-grained morphological information encoded by words in Nepali texts. We experiment with three neural network-based architectures: BiLSTM, BiGRU, and BiLSTM-CRF. Our results show that deep-learning based models can capture fine-grained morphological information encoded by Nepali words given a large enough corpus. We trained all the models in this work using two embeddings: pre-trained multi-lingual BERT and randomly initialized Bare embeddings. We found that training a randomly initialized Bare embedding is better than the ones trained using large pre-trained multi-lingual BERT embedding for downstream tasks in Nepali like POS tagging. Among the tested models, the BiLSTM-CRF with the Bare embedding performed the best and achieved a new state-of-the-art F1 score of 98.51% for fine-grained Nepali POS tagging.
Advanced Code-Based Electronic Digital Signature Scheme

This article is devoted to code-based cryptosystems. They provide high security level even in the conditions of quantum cryptographic analysis. A new improved code-based electronic signature scheme is proposed in this article. Due to the improved decoding scheme, we are able to significantly reduce the signature generation time. Also, quantitative estimates of the speed of the new cryptoalgorithm are provided. Practical studies have shown that the use of the proposed decoder can significantly reduce the number of attempts required to decode the syndrome, and, consequently, reduce the time required by the digital signature algorithm. Due to the fact that additional calculation of additional syndromes and re-decoding requires fewer calculations than increasing the value of the counter and performing all steps from hashing values to decoding again.
Semi-supervised Semantic Segmentation of Cataract Surgical Images based on DeepLab v3+

Microscopic surgical image analysis is very important in surgical skill analysis, workflow recognition, and autonomous robotic surgery. Semantic segmentation of microscopic image is a prerequisite. Currently, supervised deep convolutional neural network (CNN) has become the state-of-the-art image segmentation method. However, manual contouring of thousands of images is needed for training a supervised CNN. Semi-supervised or weakly supervised learning can greatly reduce the need for labeling, and the semantic segmentation results are also close to or slightly better than those of supervised learning. However, most existing semi-supervised methods have disadvantages of poor segmentation accuracy and poor robustness. The existing semi-supervised methods can only be close to the segmentation accuracy of the existing supervised methods, and can only be used in large-scale data sets. There are still many limitations to the use of the field. In this article, we proposed a novel semi-supervised learning method based on principle of cross-consistency in microscopic images segmentation. To leverage unlabeled images, we use consistency principle between main decoder and auxiliary decoder. The auxiliary decoder is a variety of interference examples generated by different interference functions, which can improve the accuracy and robustness of the overall output of the network. The network also has good task scalability, and has high segmentation accuracy in fields such as natural scenes and medical surgical scenes. The experimental results of the CATARACTS-Semantic-Segmentation 2020 data set proved the effectiveness of the deep neural network. The proposed method is better than the baseline model in terms of accuracy. The experimental setting 1 segmentation is increased by 1.71%, and the experimental setting 2 segmentation accuracy is improved by 22.34%.
Quality of Institutions, ICT Diffusion and Financial Development: An Empirical Evidence from Developing Countries

The aim of this study is to analyze the link between the quality of institutions and the financial development using a panel data that include 93 developing countries for the period 2002–2017 with a special focus on MENA region. Empirical results show that improving the quality of institutions i.e., strengthening the institutional infrastructure, can promote financial development. These results are robust for all financial development indicators selected in this study. The non-linear relationship between institutional quality and financial development, depends on use of ICT showing that more use of ICT can help corruption control and boost financial development.
Contextual Bandit Learning With Reward Oracles and Sampling Guidance in Multi-Agent Environments

Learning action policy for autonomous agents in a decentralized multi-agent environment has remained an interesting but difficult research problem. We propose to model this problem in a contextual bandit setting with delayed reward signals, in particular an individual short-term reward signal and a shared long-term reward signal. Our algorithm utilizes an approach with reward oracles to directly model these delayed reward signals and also relies on a learning scheme benefiting from the sampling guidance of an expert-designed policy. This algorithm is expected to apply to a wide range of problems, including those with constraints on accessing state transitions and those with implicit reward information. A demonstration, deployed by deep learning regressors, shows the effectiveness of the proposed algorithm in learning offensive action policy in the RoboCup Soccer 2D Simulation (RCSS) environment against a well-known adversary benchmark team compared to a baseline policy.
Collaborative Filtering With Awareness of Social Networks

In this article, we present the NetRec method to leverage the social network data of users in collaborative filtering. We formulate two new network-related terms and obtain convex optimization problems that incorporate assumptions regarding users’ social connections and preferences about products. Our theory demonstrates that this procedure leads to a sharper error bound than before, as long as the observed social network is well structured. We point out that the larger the noise magnitude in the observed user preferences, the larger the reduction in the magnitude of the error bound. Moreover, our theory shows that the combination of the network-related term and the previously used term of nuclear norm gives estimates better than those achieved by any of them alone. We provide an algorithm to solve the new optimization problem and prove that it is guaranteed to find a global optimum. Both simulations and real data experiments are carried out to validate our theoretical findings. The application of the NetRec method on the Yelp data demonstrate its superiority over a state-of-the-art social recommendation method.
Design and Performance Evaluation of an Embedded EDHOC Module

The rapid increase of the number of resource-constrained devices connected to the Internet of Things (IoT) through shared infrastructures poses certain challenges for the achievement of robust and manageable secure communications, especially in critical environments. The efficient realization of end-to-end, application layer security protocols supporting also public key cryptography mechanisms is for a long time a quest in the world of resource constrained embedded devices. The Ephemeral Diffie-Hellman over COSE (EDHOC) is a recently developed key establishment protocol that can work over any layer. The main scope of this work is to present the design and performance evaluation of an embedded EDHOC module developed for the open source Contiki-NG OS, investigating the runtime and memory footprint overhead associated with an implementation for modern, ARM-based resource constrained devices.
Designing a Vocational Immersive Storytelling Training and Support System to Evaluate Impact on Working and Episodic Memory

Working memory and episodic memory are mainly responsible for the storage and recollection of information, whereas Storytelling is the most ancient and effective way of relaying this information to the user. We have designed an interactive and immersive storytelling system using Augmented Reality to improve episodic and working memory. This paper presents an overview of the tasks we used to improve these 2 sections of memory and also presents a study design on how we plan to evaluate our system to prove its effectiveness in comparison to desktop 2D-based training.
Guided Filter Bank

Guided filter can perform edge-preserving smoothing by utilizing the structures of a guidance image. However, it is difficult to obtain two images with different contents from the same scenario. Therefore, we focus on the case that the input image and the guidance image are identical. In this case, the direction of the gradient of the output image is the same as the guidance image. Based on this discovery, we change the regularization term of guided filter and develop a more general model which can generate a bank of guided filters. To take examples, we pick up three filters from this bank, where $$L_1$$ guided filter and $$L_{0.5}$$ guided filter are newly proposed filters. Mathematical and experimental analysis are performed to demonstrate that the new filters have totally different properties from the guided filter. $$L_1$$ guided filter is very suitable for edge-preserving and texture-removing tasks, while $$L_{0.5}$$ guided filter can do enhancement automatically. We applied them to a variety of image processing applications, including image smoothing, image denoising, edge detection, image detail enhancement and X-ray image enhancement. The experimental results reveals the effectiveness of the newly proposed filters and their state of the art performance. We also believe that more interesting filters can be developed from our model.
Monitoring and Alarming Activity of Islamic Prayer (Salat) Posture Using Image Processing

This paper introduced a Salat Inspection and Training System based on Machine Vision and Image Processing Subject. In Islam, prayer (i.e., Salat) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures' perspective, there are predefined human postures that must be performed in a precise manner. There are lots of materials on the Internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer's postures. Many features of images are being extracted and analyzed. Methods for image comparison and pattern matching are used to study the system's effectiveness by using several combining algorithms, such as Euclidean Distance, Template Matching and Grey-Level Correlation, to compare the images of the user and the database. The experiments' results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat. Limitations of the system, such as lighting, is discussed regarding how it affects the system's performance.
Improving researcher’s area of expertise identification using TF-IDF Characters N-grams

As the academic information on the internet became broadly available in the shape of academic social networks and academic profiles, its usage to help to resolve tasks like the discovery of specialists in a given area, identification of potential scholarship holders, or suggestion of collaborators, for example, had a growth in importance and relevance. In the case of academic social networks, the Brazilian government created the Lattes Platform in order to manage academic data from Brazilian researchers as well as use it to help in the evaluation of researchers and groups of researchers. However, in order to use the Lattes Platform information to help in the aforementioned tasks, it is important to check the quality of the data, because most of it is declared by the users and does not have any verification of its veracity, especially regarding the declared main expertise area. Thus, this article explores the usage of machine learning techniques to recognize the main areas of expertise of researchers using several numerical representations to represent its scientific production titles as data source for the algorithms. We have been able to surpass the current state-of-art results to resolve this problem by using a TF-IDF character n-gram representation for the text in the titles, achieving an accuracy of 95.91%.
An abstract interface for large-scale continuous optimization decomposition methods

Decomposition methods are valuable approaches to support the development of divide-and-conquer metaheuristics. When the problem structure is unknown, such as in black-box problems, this structure can be inferred through several decomposition mechanisms. In the context of continuous optimization, the most efficient meta-heuristics to deal with a large number of decision variables involve decomposition methods. However, choosing a suitable decomposition method is not a trivial task since each strategy requires an appropriate set of parameters. In this context, this paper proposes a C++ library called Continuous Optimization Problem Decomposition (COPD) that provides the most recent decomposition methods, interfaces for new methods, and integration with solvers. Furthermore, the proposed library can aid related studies since the decomposition for continuous optimization problems can be easily applied with different methods. The experimental results demonstrate a high grouping accuracy for most methods on large-scale problems.
Utilising OpenCV with Tesseract to extract Bill of Materials (BOM) from Isometric Drawings

Quality assurance is often a time-consuming and error prone process for organisations. However, it is increasingly important for companies that produce fabricated products for integration into safety critical environments. For example, creating pipe systems for the pharmaceutical industry will include additional risks. As a result, increased regulation is required, which has resulted in further paperwork and validation for companies operating in this sector.A lot of the isometric drawings provided to companies for fabrication remain in paper format (or scanned paper documents). This provides an administrative burden on these companies as the average project could generate up to 5,000 isometric drawings. This research explores techniques that could be utilised to automatically extract Bill of Materials (BOM) information from these isometric drawings.Tesseract has failed to perform OCR accurately on the extracted Region of Interest (ROI) data containing the BOM information, achieving a mean average of 43.8%. This paper explores different pre-processing techniques to increase the accuracy of recognition. Techniques such as binarisation, erosion, noise reduction and contouring were employed to increase this accuracy. In the study, the accuracy increased to a mean average of 81.2%. This has demonstrated that effective use of pre-processing can have an impact on character recognition.
A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students

Summaries of scientific publications enable readers to gain an overview of a large number of studies, but users’ preferences have not yet been explored. In this paper, we conduct two user studies (i.e., short- and long-term studies) where Japanese university students read summaries of English research articles that were either manually written or automatically generated using text summarization and/or machine translation. In the short-term experiment, subjects compared and evaluated the two types of summaries of the same article. We analyze the characteristics in the generated summaries that readers regard as important, such as content richness and simplicity. The experimental results show that subjects are mainly judged based on four criteria, including content richness, simplicity, fluency, and format. In the long-term experiment, subjects read 50 summaries and answered whether they would like to read the original papers after reading the summaries. We discuss the characteristics in the summaries that readers tend to use to determine whether to read the papers, such as topic, methods, and results. The comments from subjects indicate that specific components of scientific publications, including research topics and methods, are important to judge whether to read or not. Our study provides insights to enhance the effectiveness of automatic summarization of scientific publications.
Machine Learning and Value Generation in Software Development: A Survey

Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews the literature between 2000 and 2019 on the use the learning models that have been employed for programming effort estimation, predicting risks and identifying and detecting defects. This work is meant to serve as a starting point for practitioners willing to add ML to their software development toolbox. It categorises recent literature and identifies trends and limitations. The survey shows as some authors have agreed that industrial applications of ML for SD have not been as popular as the reported results would suggest. The conducted investigation shows that, despite having promising findings for a variety of SD tasks, most of the studies yield vague results, in part due to the lack of comprehensive datasets in this problem domain. The paper ends with concluding remarks and suggestions for future research.
Research on Perceptual Cues of Interactive Narrative in Virtual Reality

From words, images, films to games, the development of narrative is inseparable from the evolution of media. With the advent of virtual reality, the traditional paradigms of storytelling need to be expanded. VR can simulate the real world vividly to an unprecedented extent, in which users can choose their own perspectives freely during their exploration. However, in addition to great immersion and presence, this powerful new technology also brings new challenges to interactive storytellers who are familiar with the traditional "frame-bound" media. To keep users focused on the storyline efficiently and naturally in VR, new ways of perceptual cues need to be explored and verified. Through a comparative experiment, we tested how well the existing perceptual cues could meet our expectations using "Secondary Mission", an original interactive narrative VR game, as a case for study. Based on our findings, design strategies for planning perceptual cues in VR interactive narratives were proposed to help in improving users' experience.