Reviewerly/exHarmony huggingface dataset:
Designing Semantic Web Service Based on OAS 3.0 through Relational Database

The web service description becomes the interface that must be provided by the service provider to expose the web service capabilities it offers. To increase human and machine understanding of web service descriptions, it is necessary to add meaning that can avoid ambiguity and ease of learning by clients who do not have sufficient knowledge. The semantic web is becoming a technology that can be adopted to assign meaning to resources described in service descriptions. Collaboration between web service description and semantic web presents a new term known as semantic web services (SWS). In service composition domain, SWS become crutial elemen to support automation in service discovery and selection. The complexity of creating web service semantics is caused by, among others, difficulties in identifying resources, the relationship between resources, and determining the knowledge that should be added. This study contributes to the activities used to create web service semantics from the OAS 3.0 document. OAS 3.0 is a standard that is widely adopted today by web service developers using the REST architecture. As a result, the RDF scheme can be structured by following the activities and techniques presented in the research methodology.
Braid: Weaving Symbolic and Statistical Knowledge into Coherent Logical Explanations.

Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of (the knowledge acquisition problem). These issues are particularly severe for the Natural Language Understanding (NLU) task, where we often use implicit background to understand and reason about text, resort to fuzzy alignment of concepts and relations during reasoning, and constantly deal with ambiguity in representations. To address these issues, we devise a novel FOL-based reasoner, called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid-BC (the backchaining component of Braid), and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query in a scalable manner. We use a simple QA example from a children's story to motivate Braid-BC's design and explain how the various components work together to produce a coherent logical explanation.
On derivative consequence argument

The article gives critical remarks to the derivative consequence argument proposed by Bogdan Faul. The first critical remark concerns the ontological status of the laws of nature, the second remark deals with the term «physicalism». It is demonstrated that conceptual vagueness of the terms used by B. Faul in his article undermines the validity of the proposed derivative argument
Significance of Literature Review in the Research of Social Sciences

Literature review is a preparatory work that enables a researcher to take up motivating future research. Literature review often makes a researcher an expert specialist of specific subject. Literature review is not just a report that summaries articles or books but it provides a description, summary as well as the critical evaluation of a scholarly work. The purpose of literature review is to provide background information, to establish importance, to demonstrate reliability and to carve out a space for further addition to research. Review of the literature is very essential and important for the proposed research writing. It’s always helpful for the researcher throughout his research work. Without surveying the relevant literature, no one can identify the research problem. So review of literature is the backbone of the research problem. Most of the other elements of the proposal are related with it. Reviewing the literature provides the guideline to the researcher to complete his proposed research oriented studies. Research is a process of knowing causes and effects of the events. Research is essential to know the fact; because seeing, hearing and feeling may be different than the reality. Knowing from such activities does not have the proof and such knowledge would not be believable to the others. But the knowledge gained from a researcher is proved by evidences and that is believed by others. Such work is known as a scientific research and it provides us reliability, authenticity and validity of the relevant social phenomenon.
SLAM-SEAN for Higher Autonomy of Mobile Robots in Unknown Environment

SLAM-SEAN (Simultaneous Localization and Mapping-Simultaneous Exploration and Navigation) framework for a mobile robot to work autonomously in unknown environments is proposed. An implementation of the framework was completed by a combination of a local-minimum-free artificial potential method and a frontier-based approach suggested in the previous work where a particle filter SLAM is involved. The validity of the implementation was verified through simulations. Some other conditions to see applicability of the method to various environments were also discussed.
Literature Review: Data Quality Assessment Methods Of Electronic Tuberculosis Recording And Reporting

In the world, number of tuberculosis (TB) cases reaches 10 million people with deaths reaching 1.7 million people. Data quality of recording and reporting needs to be considered as one of TB control strategies. To optimize TB control, some countries conduct data quality assessments using a method by assessing several dimensions. This study aims to examine several methods of assessing data quality of electronic TB recording and reporting through literature review with simplified approach. Search of indexed and reputable articles is done on Google Scholar, Scopus, PubMed, Web of Science, CrossRed, PROQUEST databases. Articles review show that there are 3 methods used to assess data quality, namely Centers for Disease Control and Prevention (CDC) tool (85%), Routine Data Quality Assessment (RDQA) (5%), and independently developed methods (40 %). Based on used of several methods, data quality dimensions most frequently assessed were completeness (85%), followed by dimensions of timeliness (40%) and consistency (20%). There were differences from 3 methods, specifically CDC tool was suitable for assessing data completeness, RDQA was suitable for assessing data reported routinely, and independently developed methods were used according to district conditions. Assessment of TB data quality is still limited, a health institution needs to conduct assessment of data quality regularly so TB control efforts are more optimal.
Gender Determination Using Voice Data

The rapid advancement of today's technologies, it is tried to facilitate whichever system will be used by using voice features such as person recognition and speech recognition by making use of the voices of the users. Organizations serving in these systems need less manpower and facilitate the operation by helping users faster. The decision-making process using sound features is a very challenging process. With gender recognition, which is one of these steps, it is possible to address the user by gender. In this study, it is aimed to define the genders according to the voices in terms of both forensic informatics and the rapid and accurate progress of the processes. In this study, 3168 male and female voice samples were taken as a dataset. Sound samples were first analyzed by acoustic analysis in R using seewave and tuneR packages. Artificial neural networks were used in the classification stage. In order to increase the classification accuracy, the dataset was divided into 10 parts and each part was excluded from training for testing and used for retesting. Average classification success was found by taking the arithmetic mean of the results. In the classification made with artificial neural networks, male and female voices could be distinguished from each other with a success of 97.9%.
The 4-Adic Complexity of A Class of Quaternary Cyclotomic Sequences with Period 2p

In cryptography, we hope a sequence over $\mathbb{Z}_m$ with period $N$ having larger $m$-adic complexity. Compared with the binary case, the computation of 4-adic complexity of knowing quaternary sequences has not been well developed. In this paper, we determine the 4-adic complexity of the quaternary cyclotomic sequences with period 2$p$ defined in [6]. The main method we utilized is a quadratic Gauss sum $G_{p}$ valued in $\mathbb{Z}_{4^N-1}$ which can be seen as a version of classical quadratic Gauss sum. Our results show that the 4-adic complexity of this class of quaternary cyclotomic sequences reaches the maximum if $5\nmid p-2$ and close to the maximum otherwise.
Knowledge bases and description logics applications to natural language texts analysis

The article describes some ways of knowledge bases application to natural language texts analysis and solving some of their processing tasks. The basic problems of natural language processing are considered, which are the basis for their semantic analysis: problems of tokenization, parts of speech tagging, dependency parsing, correference resolution. The basic concepts of knowledge bases theory are presented and the approach to their filling based on Universal Dependencies framework and the correference resolution problem is proposed. Examples of applications for knowledge bases filled with natural language texts in practical problems are given, including checking constructed syntactic and semantic models for consistency and question answering.
Joint gender and age estimation based on speech signals using x-vectors and transfer learning

In this paper we extend the x-vector framework for the task of speaker's age estimation and gender classification. In particular, we replace the baseline multilayer-TDNN architecture with QuartzNet, a convolutional architecture that has gained success in the field of speech recognition. We further propose a two-staged transfer learning scheme, utilizing large scale speech datasets: VoxCeleb and Common Voice, and usage of multitask learning to allow for joint age estimation and gender classification with a single system. We train and evaluate the performance on the TIMIT dataset. The proposed transfer learning scheme yields consecutive performance improvements in terms of both age estimation error and gender classification accuracy and the best performing system achieves new state-of-the-art results on the task of age estimation on the TIMIT TEST dataset with MAE of 5.12 and 5.29 years and RMSE of 7.24 and 8.12 years for male and female speakers respectively while maintaining a gender classification accuracy of 99.6%.
Concept Identification with Sequence-to-Sequence Models in Abstract Meaning Representation Parsing

A variety of NLP tasks require semantic information from sentence input. In order to capture meaning through Abstract Meaning Representation parsing, concept identification is required. In this research, the task of concept identification is approached using sequence-to-sequence models. Building upon an encoder-decoder architecture with attention mechanism, concepts are separated into verbs and non-verbs. Different alignment strategies are applied in order to increase the amount of train and test data for the model, resulting in a merged data-set with twice as many available instances. Furthermore, the training data increase induces an improvement of 10% for the sequence-to-sequence model.
VM Matters: A Comparison of WASM VMs and EVMs in the Performance of Blockchain Smart Contracts

WebAssemly is an emerging runtime for Web applications and has been supported in almost all browsers. Recently, WebAssembly is further regarded to be a the next-generation environment for blockchain applications, and has been adopted by Ethereum, namely eWASM, to replace the state-of-the-art EVM. However, whether and how well current eWASM outperforms EVM on blockchain clients is still unknown. This paper conducts the first measurement study, to measure the performance on WASM VM and EVM for executing smart contracts on blockchain. To our surprise, the current WASM VM does not perform in expected performance. The overhead introduced by WASM is really non-trivial. Our results highlight the challenges when deploying WASM in practice, and provide insightful implications for improvement space.
Control And Path Planning Of AUVs Robot Using Krill Herd Optimization Algorithm And Learning Automata

Control And Path Planning Of AUVs Robot Using Krill Herd Optimization Algorithm And Learning Automata
Diversification of Higher Educational Institutions as a Factor of Sustainable Development of Education

The article explores the processes of diversification of higher educational institutions in the context of the sustainable society, which poses specific requirements to the quality of training of new generations of professionals for economy, social sphere and culture. The authors believe that the co-evolution of higher education and the society of sustainable development may evolve along the line of horizontal diversification of higher educational institutions. In the framework of this type of diversification, many industry-specific institutions of higher education (academies, institutes, technological universities) launch educational programs beyond their specialized status and implement ICT education across all curricula. The authors emphasize that for higher education to be sustainable, besides being continuous, it needs to move in the direction of fundamentalization, universality, flexibility, informatization and increasing the role of the humanities. To be sustainable, higher education has to be innovative, based on a harmonious unity of teaching, research and industry-based training. Today, university curricula should be flexible and show a good balance between fundamental and applied disciplines, on the one hand, and so-called general disciplines, the humanities, social sciences and specialist, profession-specific disciplines on the other. The humanities must be granted a proper place in the content of university education, if we want universities to shape high cultural and moral values in their graduates.
Edit distance-based search approach for retrieving element-wise prosody/rhymes in Hindi-Urdu poetry

Background: Prosody (rhyming words) is a connatural element of poetry, throughout its reach, across thousands of languages in the world. Since medieval era, the Indic poetry (principally the Hindi/Urdu poetry) has created an impactful flamboyance w.r.t the subjects, styles, and other creative aspects in poetry. Besides the message of heartfelt poetry, we see the Qafiya (i.e., rhyming words) is the core element, without which we may not consider anything Hindi/Urdu poetry but merely a piece of writing; alongside it, Radif (i.e., a phrasal suffix to qafiya) is also considered next to the intrinsic part in Ghazals. In this regard, the contributions of this paper are one–the development of an optimal technique for the prosodic (qafiya) suggestions/retrieval in Hindi/Urdu poetry; and two–the qafiya suggestions based on the attached subsequent radif. Methods: The work in this paper involves usage of a 13.46 M tokens tri-script corpus of poetry. Instead of phonetic value matching, the proposed methodology employs four different Edit Distances (i.e., Levenshtein, Damerau–Levenshtein, Jaro–Winkler, and Hamming distance) as the comparison measures for prosodic suggestions. Findings: The proposed work shows better results in comparison to ‘Qaafiya Dictionary’ powered by rekhta.org. Moreover, w.r.t the inter-metric similarity and running time Jaro–Winkler appears to be the most optimal algorithm for the rhyme suggestion, whereas the Levenshtein distance is the laziest technique. Novelty/Applications: This work benefits researchers of Indic natural language processing for lexical look-ups and analysis of creative literature, especially poetry.
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution.
Identifying Cultural Values of the Narrative Texts in an English Textbook for Tenth Grade of Senior High School

Many English teachers assumed that culture cannot be separated from English learning. This paper aimed to investigate the cultural values of the narrative texts in English textbook for grade tenth of senior high school. The data of the research were collected from narrative reading passage in English textbook for senior high school entitled “Bahasa Inggris”, English textbook Curriculum 2013 (henceforth, K13) published by The Ministry of Education of Republic of Indonesia. To reveal what culture values represented in the narrative reading passage in the textbook, the cultural values were categorized into several source cultures such as visual representation of the cultures, and the objectives of the lesson related to cultural values in narrative texts. Moreover, the findings of this research discovered that there were three cultural values identified in the narrative reading passages in the textbook such as respect for the right indigenous people, finding peace and nature and with all forms of life, and appreciation of cultural products.
Efficient Information-Theoretic-Statistical (ITSM) Equation for Face Recognition Technique: Comparison with Statistical Technique and Information-Theoretic Technique

Abstract Spontaneous recognition of human faces is a challenging problem that has install important concern from signal processing researchers in Last years. This is owing to its many uses in various fields, including security and forensic analysis. Notwithstanding this interest, face recognition is yet one of the most challenging troubles. Up to this time, no way gives a good solution to all attitudes. In this paper we present a neoteric mathematical technicality for face recognition. which we call, (ITSM), is Accredit on our lately disseminated efficient information-theoretic-statistical equation (ITSM), which Merge three mathematically balanced equations. The first one is entropic equation (EE), the second one is histogram equation (HE), and the third one is the standard statistic (SSIM). (ITSM) Tested against versus (SSIM) and (ITSSIM) beneath Gaussian noise, so we got good results even beneath a large scale of PSNR. The face recognition with (ITSM) certified on both above measures of a test image and a database images. We performed the performance evaluation with (MATLAB) using part of the Famous (AT&amp;T) gray Image Database that made up of (49) face images, from which we chose seven person and for each one we chose seven Perspectives (poses) with different facial emotions. The Target of this paper is to present an efficient technicality for face recognition that may work in real-time milieu. Through the implementation of our information, facial recognition has been proven with a method (ITSM) Hybrid (information - theoretic-statistical) that surpasses the known statistical technicality of face recognition (SSIM) and a technicality based on information theory known as (ISSIM).
Super-resolution of satellite images: Feasibility of deep learning techniques

The work is devoted to studying the feasibility of applying the convolutional neural networks with deep learning to the problems of super-resolution of satellite images. The main aim is to enhance the image details and delete the artifacts. The algorithms for resolution enhancement were studied. The training set of satellite images was prepared. The neural network was constructed and trained using the PyTorch library for the Python language and the NVIDIA Tesla K40m graphics processors. Comparison of constructed network with the classic interpolation algorithms was carried out for the reference satellite images. It was shown that the neural network gives a better quality of the images.
Dominant Influence of Linguistic Knowledge in EFL Reading Comprehension: Schemata Analysis

Many studies imply that EFL students broadly use their schemata or background knowledge in comprehending the reading texts, including their formal and content schemata.Therefore, this study aims to know the influence of these schemata especially the formal schemata, grammatical and vocabulary knowledge, in reading comprehension and further determine the dominant influence between grammatical and vocabulary knowledge.Samples of 40 EFL sixth semester students of Makassar Muhammadiyah University were selected by using the Simple Random Sampling Technique.The researcher obtained the data through proficiency tests of grammar and vocabulary and reading comprehension tests.The use of regression and correlation analysis is to analyze the collected data.The results indicated that the students' grammatical and vocabulary knowledge influence their reading comprehension significantly.Moreover, their grammatical schemata are more dominant than their vocabulary knowledge, showing the new aspect of this study from the latest research, i.e., the study of [6].The result described that the schemata about grammar play a significant role in the EFL students' comprehension processing.The correlation analysis showed that the better grammatical competence students have, the better comprehension they do.Their understanding depended more on their competence about tenses (syntactic knowledge) than on their vocabulary knowledge.The contribution of this study is to give a new perspective about grammar and vocabulary schema in the EFL students using as cognitive structures in understanding English reading texts.It may also support developing a method that helps students use their linguistic knowledge to activate their background knowledge appropriately in understanding the different English reading texts.
Casual Real-World VR using Light Fields

Virtual reality (VR) would benefit from more end-to-end systems centered around a casual capturing procedure, high-quality visual results, and representations that are viewable on multiple platforms. We present an end-to-end system that is designed for casual creation of real-world VR content, using a smartphone. We use an AR app to capture a linear light field of a real-world object by recording a video sweep around the object. We predict multiplane images for a subset of input viewpoints, from which we extract high-quality textured geometry that are used for real-time image-based rendering suitable for VR. The round-trip time of our system, from guided capture to interactive display, is typically 1–2 minutes per scene.