Reviewerly/exHarmony huggingface dataset:
Structuring Abstraction to Achieve Ontology Modularisation

Large and complex ontologies lead to usage difficulties, thereby hampering the ontology developers' tasks. Ontology modules have been proposed as a possible solution, which is supported by some algorithms and tools. However, the majority of types of modules, including those based on abstraction, still rely on manual methods for modularisation. Toward filling this gap in modularisation techniques, the authors systematised abstractions and selected five types of abstractions relevant for modularisation for which they created novel algorithms, implemented them, and wrapped them in a GUI, called NOMSA, to facilitate their use by ontology developers. The algorithms were evaluated quantitatively by assessing the quality of the generated modules. The quality of a module is measured by comparing it to the benchmark metrics from an existing framework for ontology modularisation. The results show that the module's quality ranges between average to good, whilst also eliminating manual intervention.
Extending WordNet with UFO Foundational Ontology

WordNet is a large lexical database used by an uncountable number of applications for computational linguistics. Many proposals have attempted to better describe it in a semantic perspective, especially addressing synonymy, taxonomy and mereology properties, which led to very good results in domain-specific applications. A philosophical shift on this semantic description could, however, improve the scope of these results across different domains. In this direction, this work extends WordNet's semantic knowledge by addressing philosophical meta-properties. Specifically, we apply the notion of Semantic Types to propose mapping rules between the noun synsets of Wordnet and the top-level constructs of a foundational ontology. For this task we have chosen the Uni ed Foundational Ontology (UFO), which explicitly exposes philosophical meta-properties of concepts in its structure, leading to a well-founded semantically-enriched version of Wordnet. The proposed rules were validated through an experiment over approximately 5; 200 sample mappings, obtaining an average accuracy of 93.5% Furthermore, to show its applicability, the proposal was applied to the task of automatically learning a well-founded domain ontology.
Causes and Effects of the Division Algorithm Applied in Ecuadorian Education

The objective of this research was to analyze the causes and consequences of the systematic process of teaching and learning of the division of whole numbers, a study aimed at students and teachers of the 3rd and 4th level of basic education. For its development the qualitative paradigm was used through the modality of field research. As informants, twenty (20) students and two (2) teachers participated. All participants are enrolled in the 3rd and 4th level of the Giordano Bruno School located in the city of Quito, Ecuador. To collect the information, the observation technique and the semi-structured interview were used, and the instruments were subjected to a validity process, where the triangulation was applied. The results were analyzed to give an answer to the proposed objective of the manuscript, where the teaching-learning method applied to the students is presented as a critical-constructivist model, but in reality, a mechanic-academicist education system is used. It was evidenced that teachers shorten the logical processes of the division of whole numbers, by the time-space factor.
On the Consistency of Maximum Likelihood Estimators for Causal Network Identification

We consider the problem of identifying parameters of a particular class of Markov chains, called Bernoulli Autoregressive (BAR) processes. The structure of any BAR model is encoded by a directed graph. Incoming edges to a node in the graph indicate that the state of the node at a particular time instant is influenced by the states of the corresponding parental nodes in the previous time instant. The associated edge weights determine the corresponding level of influence from each parental node. In the simplest setup, the Bernoulli parameter of a particular node's state variable is a convex combination of the parental node states in the previous time instant and an additional Bernoulli noise random variable. This letter focuses on the problem of edge weight identification using Maximum Likelihood (ML) estimation and proves that the ML estimator is strongly consistent for two variants of the BAR model. We additionally derive closed-form estimators for the aforementioned two variants and prove their strong consistency.
ELK Stack – Improving the Computing Clusters at DFCTI Through Log Analysis

The full stack logging service provided by Elastic™ has become a powerful tool within the highperformance computing community due to its ease of use, lightweight impact on the machines, performance speeds, and scalability. In the current work, we attempt to deploy such a stack on a server inside our department, which will be used for ingesting, parsing, and analyzing logs coming from multiple clusters. By analyzing the overall performance of each machine that is under continuous monitoring, we can provide immediate support in case of any issues that might occur, and more importantly, we can improve the computing power of our clusters through optimizations in terms of system management, networking, and other specific features.
Artificial Intelligence and Internet of Things in Instrumentation and Control in Waste Biodegradation Plants: Recent Developments

In recent times, millions of researches have been directed to exploring the potentials of Artificial Intelligence (AI) and Internet of Things (IoT). With enhanced capabilities offered through the integration of artificial intelligence, big data, and the cloud, the future might be descending into a new era defined and determined by artificial intelligence and Internet of Things. Across various jurisdictions and socio-political systems, the perspective on artificial intelligence technologies is rapidly converging. Vladimir Putin, the Russian President, in a statement published on 4 September 2017 by the Verge, had stated unequivocally that “Artificial intelligence is the future and that the nation that leads in artificial intelligence research would certainly dominate in global affairs.” Thus, it appears that every facet of governance including environmental management is very likely to be redefined by artificial intelligence and allied technologies. In today’s traditional and highly urbanized cities, waste management has remained a matter of great concern among decision makers and public sector operatives. In the past, a number of traditional approaches such as incineration, concentration and subsequent deposition in landfills, recycling, gasification, etc. had been employed but are becoming increasingly unsustainable owing to side-effects they pose ranging from secondary pollution, diseases and epidemics, high cost of maintenance, climate change, etc. The quest for sustainable, safer, and eco-friendly alternatives coupled with current advances in nanotechnology, biotechnology, artificial intelligence and internet of things, soft systems such as biodegradation plants that utilize microbial agents under controlled conditions, have become a more prospective alternative compared to customary channels. This chapter adopts a swift narrative and analytical approach in articulating the critical and recent advances in the exploitation of artificial intelligence and internet of things technologies in the design, deployment, and management of waste biodegradation plants.
Reconsidering Lost Votes by Mail

A “lost vote” occurs when a voter does all that is asked of her, and yet her vote is uncounted in the final tally. Estimating the magnitude of lost votes in American presidential elections has followed the work of the Caltech/MIT Voting Technology Project, which initially estimated the magnitude of lost votes in the 2000 presidential election, due to failures of voter registration, polling-place management, and voting technologies, to be between 4 and 6 million out of 107 million cast that year. Because of data and conceptual limitations, lost vote estimates have tended to focus on in-person voting, ignoring lost votes due to mail voting. This paper revisits the one previous effort to estimate lost votes, by considering data available from the 2016 presidential election. Conceptually, the paper highlights how differing mail-ballot legal regimes produce lost mail votes in different ways, and at different rates, on account of differing laws, regulations, and practices. Empirically, the paper draws on administrative records and surveys to provide an estimated number of lost mail votes in 2016. That estimate works out to approximately 1.4 million votes in 2016 — 4.0% of mail ballots cast and 1.0% of all ballots. These estimates are relevant in light of efforts to expand mail balloting in the 2020 presidential election. States that will see the greatest growth in mail ballots tended to have higher lost vote rates than those with vote-by-mail systems. This implies that a doubling or tripling of the number of mail ballots in 2020 will result in a disproportionate growth in the number of lost votes due to mail ballots.
A blockchain-based authentication scheme for the machine-to-machine communication of a cyber physical system

Today, the manner in which we communicate has greatly advanced. The technology is not just about machines, but people with technology together. Machine-to-machine (M2M) communication is unavoidable in the Internet of things. However, at the same time, there are more attacks against the M2M system. Therefore, a reliable and secure authentication mechanism is required. Blockchain technology is decentralized and highly secure while being tamper-proof. This protects M2M service providers by eliminating the single point of failures. This paper proposes a blockchain-based authentication scheme that uses a practical Byzantine fault tolerance (pBFT) consensus mechanism for M2M security in cyber physical systems. By implementing a blockchain to an M2M system, it provides an ID for devices on the blockchain. Simulation results have shown that the data on the chain cannot be altered. A pBFT consensus algorithm also ensures that the blockchain network is able to come to a consensus with faults.
Adversarial examples with transferred camouflage style for object detection

Abstract Most of existing adversarial examples attacking methods for object detection models aim at generating subtle perturbation which is invisible to human vision. However, some perturbations with stronger intensity are equally effective and can even make the objects in adversarial examples more invisible. In this paper, an attack model based on the Generative Adversarial Network and style transfer method is designed to craft and add perturbation with camouflage style to the object area in the image. Experiments on PASCAL VOC 2012 dataset demonstrated that Adversarial examples generated from our model can attack both proposal-based detectors and regression-based object detection models effectively, and reduce the visual saliency of the objects.
Pedestrain Detection in Infrared Images with Improved YOLOv2 Network

With the progress of unmanned driving technology and the attention of security problems, pedestrian detection is getting more and more researchers' participation. Since 2012, the application of deep convolutional network has greatly improved the accuracy and detection speed of pedestrian detection. However, it is difficult to detect pedestrians at night, the detection accuracy of small objects and obstructed pedestrians is poor, which are problems that researchers are solving. This paper presents infrared image detection of pedestrain based on improved YOLOv2 detection network. It improves the detection accuracy of small volume and blocked pedestrian in the night.
THE RELATIONSHIP BETWEEN INFORMATION SEARCH AND PROVISION OF INTERLIBRARY SERVICES

The search for the requested document is one of the most important processes in the provision of interlibrary services. At the same time, it becomes quite difficult to conduct search procedures in a variety of information retrieval systems (IRS) that operate according to certain rules. This problem requires constant study as new technical search solutions are emerging that need to be mastered by experienced users and novices in the expanding digital workspace. The purpose of the article is to show the existing possibilities for improving the quality of information retrieval in the IRS to meet user orders. The objectives of the research are to uncover the main causes of users’ dissatisfaction with information search in interactive systems and to identify ways to improve them. A brief overview of the history of development and transformations in the use of the reference and retrieval apparatus in interlibrary services is given. Positive changes were noted in the search for documents related to the use of information and communication technologies, as well as the contribution of employees of the information and library sphere to the creation and operation of electronic catalogs. The profound interrelationship of the processes of interlibrary services, the relevance of searching and the implementation of high technology is emphasized. Special attention is paid to the analysis of foreign scientific publications 2015–2019. They covered the use of IRS, search models, the dissatisfaction with search, the improvement of efficiency and modernization of systems, the prospects for their development. The article summarizes scientific and practical results of information search in relation to interlibrary services in domestic and foreign online systems of information and library institutions. The novelty of the article is that it focuses on the study of the effectiveness of IRS, which fundamentally affects users’ satisfaction with interlibrary services.
Implementation of Augmented Reality into Student Practical Skills Training

Augmented reality is eligible to be used in educational environments, and many applications successfully apply it to improve learning. It provides new possibilities for different spheres of education, but in these environments it is not yet investigated in its entirety. This paper’s objective is to present experience with utilization of augmented reality in professional skills training at a secondary technical school and to discuss its advantages and limits.
Neotectonic vertical movements of the South Sikhote-Alin and characteristics of self-similarity of the stream network

Morphostructural analysis of the relief and fractal analysis of the stream network of the South of the Sikhote-Alin orogenic belt were carried out. The formation of the relief at the neotectonic stage occurred in several stages, which was reflected in the stream network pattern: 1) during pre-Oligocene time there was a general uplift of Sikhote-Alin; 2) in the Pliocene there was an activation of vertical neotectonic movements, most intense to the East of the Central Sikhote-Alin fault and synchronous whith basalt volcanism; 3) in the Pleistocene vertical movements of significant amplitude did not occur, at this time the modern erosion-denudation relief of the region was formed; 4) at the end of the Pleistocene and in Holocene there was a slight activation of vertical movements to the East of the Central Sikhote-Alin fault, which was reflected in the peculiarities of residual relief. Comparison of morphological and fractal analysis results showed, that the maximum of complex parameter of self-similarity PRNS coincide with the areas of greatest increments in elevation and the minima is the smallest increment of relief or whith the areas whith most significant erosion. In regions with the stage character of neotectonic development during fractal analysis of stream network it is necessary to consider additional factors due to the peculiarities of development of the stream network at each stage of development, and conservative of its pattern, reflecting features of the development of the relief in different stages.
Managing Container QoS with Network and Storage Workloads over a Hyperconverged Platform

Container resource management is non-trivial over hyperconverged platforms where the storage is shared among host servers. Therefore, the same backbone network is used by storage and regular network traffic. In this paper, we first characterize this problem by analyzing the nature of the traffic from storage workloads and its impact on the network workloads in a container-based virtualization environment. Accordingly, we develop CONtrol, a resource management approach for assuring network workloads' performance in the presence of storage workloads. CONtrol uses a proportional-integral-derivative controller to dynamically decide the bandwidth redistribution among various workloads. Additionally, it uses a container migration strategy for balancing the workloads across different servers of a hyperconverged platform. We have implemented CONtrol over a hyperconverged platform with 5 physical servers. Thorough testing indicates that it can significantly improve the performance of various benchmark applications over a containerized hyper-converged platform.
A Two Tier Iterative Ensemble Method To Tackle Imbalance In Multiclass Classification

In real world applications, it is very common that the data skewness occurs among multiple classes. Several studies and various attempts were made in the past to overcome this imbalance problem which is a serious issue to the standard machine learning techniques especially classification and regression but, still there exists a need to handle the imbalance problem effectively. Datasets which are imbalanced generally include safe and unsafe minority samples. Our proposed approach is a classifier independent two tier iterative ensemble approach which focuses the rare minority sample's influence on learning from imbalanced datasets. Most of the informed oversampling techniques like SMOTE and its variants cannot be applied directly on rare class samples especially when the count of rare samples is too low. To alleviate this problem, in our proposed approach to learn from rare and outlying samples we proposed a hybrid oversampling technique used at different levels and make them balanced. The goal is to tone down the data imbalance at the data preprocessing stage itself by correcting or balancing the training data sets before moving to the learning part which makes the classifier to focus on its primary role and thereby it improves the learning process. The proposed two tier iterative ensemble approach shows a much significant improvement in the learning process among the multiclass imbalanced data which is clearly evident with the experimental results.
Poker Watcher: Playing Card Detection Based on EfficientDet and Sandglass Block

We present a neural network to detect playing cards in real poker scenes through a camera, where the playing card area represents only 0.7% of the shot table area. In the acquired images, the suits of cards are fuzzy and difficult to identify, even to the naked eye. Because of the relatively few pixels corresponding to the cards, traditional image processing and pattern recognition methods struggle to detect them. Therefore, we use deep learning methods to detect, which have shown to be easy-to-use, faster and more accurate in a broad range of computer vision applications over the years. Inspired by the sandglass block, we improved the current state-of-the-art neural network architecture for object detection, EfficientDet, to retain more features. Experiments have been conducted to evaluate the performance of our improved EfficientDet model and showed that it achieved considerable performance improvement compared with the other deep learning models.
Ethnic Diversity and Inequality in Sub-Saharan Africa: Do Institutions Reduce the Noise?

Studies on the causes of income differences between the rich and the poor have received an extensive attention in the inequality empirics. While ethnic diversity has also been identified as one of the fundamental causes of income inequality, the role of institutions as a mediating factor in the ethnicity-inequality nexus has not received the scholarly attention it deserves. To this end, this study complements the existing literature by investigating the extent to which institutional framework corrects the noisy influence originating from the nexus between “ethnic diversity” and inequality in 26 sub-Saharan African countries for the period 1996-2015. The empirical evidence is based on pooled OLS, fixed effects and system GMM estimators. The main findings reveal that the mediating influences of institutional settings are defective, thus making it extremely difficult to modulate the noisy impacts of ethno-linguistic and religious heterogeneity on inequality. In addition, the negative influences orchestrated by ethnolinguistic and religious diversities on inequality fail to attenuate the impact of income disparity even when interacted with institutions. On the policy front, institutional reforms tailored toward economic, political and institutional governances should be targeted.
Empirical Research And Auxiliary Tool For Custom Android ROMs

As the most popular and the most widely used mobile operating system, Android OS is open source with rich features. Many developers customize it to fulfill their needs or what the end users demand. The advantages of custom ROMs are analyzed systematically in this paper. The customized ROMs are experimentally demonstrated to be better than the stock ROMs in terms of performance, storage, user interface etc.
Toward an Intelligent Hybrid System Based on Data Analysis and Preprocessing Method

In recent years, artificial intelligence (AI) represents a crucial domain or technology that can be found everywhere, it can solve many problems facing the researchers. Hence, the powerfulness of Artificial intelligence contributes enormously to the sustainable growth of various domains (e.g., medical, agriculture, data analysis, and so forth). For that, in this paper, we propose the artificial neural network (ANN), and in particular, the paradigm of the Counter propagation artificial neural network. Our objective is to improve the standard Counter propagation artificial neural network in terms of results and classification accuracy by using the principal component analysis (PCA) for making a modified Counter propagation network through the hybridization of PCA and CPN. So, the PCA is a method among data analysis methods. It can reduce the dimensionality space of original data in small data that contained the new elements or objects, and also with this method we can eliminate the dependence and the obstacles between inputs which allowed to obtain the good classification, this hybridization is used in the data analysis field. Thus, the result shows that the proposed approach of modified CPN gave the best performance than the standard CPN in terms of iteration number, mean error, and classification accuracy.