Reviewerly/exHarmony huggingface dataset:
A Neutrosophic AHP Analysis for Using Video Conferences in Smart Learning: A Systematic Review

There is evidence that students feel less involved in distance learning, which needs more work from their educators. While distance education allows learners to feel less reluctant andor timid to ask questions, there is also evidence that students feel less interested in distance education. Students may obtain information and knowledge at their own pace and convenience via the use of distance education. Also, it may assist in reaching the same overall performance and ultimate score as would be achieved via face-to-face instruction. The goal of this study analyzes the performance of video conferences in smart learning. This analysis contains many criteria, so the concept of multi-criteria decision-making is integrated with the neutrosophic sets to overcome uncertain information. We used the neutrosophic AHP to show the importance of each criterion. There are seven criteria used in this paper. The findings demonstrated that the utilisation of video conferences enabled students to gain access to information and educational resources, had a positive effect on the students' constructing knowledge and critical reflection, and resulted in the students reporting an extremely positive learning experience in the context of smart learning.
Geology, Petrography, and Mineralization of Sedimentary Hosted Strata-Bounded Barite Deposit at Gunga, Khuzdar District Balochistan, Pakistan

Gunga deposit is a type of sedimentary exhalative (SEDEX) deposit located in the northwest of Khuzdar Knot within the Kirthar Fold Belt (KFB) in the south-west of Khuzdar city. This deposit is formed during the late Triassic-Jurassic period due to rifting of the Gondwana supercontinent. being hosted with the Anjira Member of Jurassic Shirinab Formation, hosted the Strata bounded barite mineralization in these deposits contact with the siliceous and iron-rich gossan zones. The Baritic zone indicates the complex replacement of silica with the continuous silicification which is followed by massive to brecciated type Barite. Petrographically, barite has a very fine to coarse grain texture, anhedral to euhedral crystal shape, and forms dendritic crystal structure In the Back Scattered Electron (BSE) images, the crystal morphology of Barite exhibits well-developed elongated crystal structures with medium to coarse grain texture. Energy Dispersive X-rays (EDX) graphs indicates the high peaks of Ba, S, and O elements associated with sub-peaks rock-forming mineral elements (Si, Al, Na, K, and Ca) along with sub-peaks of ore-forming mineral elements (Pb, Zn, Fe, P, and Ni).
Ontology of approved Egyptian theses in the field of documents and archives in the period: :( 1956-2021): Applied bibliometric study

Since the fifties of the last century, Egypt has been keen to study the field of records&archives scientifically. This study has tried to follow developments in this field in Egypt by collecting theses of records and archives and describing them by using Functional Requirements for Bibliographic Records aligned Bibliographic Ontology(FaBiO ); to create a semantically linked bibliography; It has enhanced the availability and retrieval of these theses, and supported their bibliometric analysis. The researcher, in this study, has relied on descriptive and bibliometric approaches and found that: 1-the field of records&archives in Egypt has witnessed various scientific and technological developments in the period (1956-2021) that distanced it from scientific stagnation.2- There is a direct relationship between the increase in the number of Egyptian records and archives theses and the opening of academic departments of records & archives and libraries in Egypt. In the end, the researcher has prepared an ontologic bibliography of Egyptian records&archives theses and published it on Diplomatics website.
Language, Gender, and Ideology: Media-induced Linguistic Innovation in Female Address Terms in China

As we enter the 21st century, we find ourselves living in intensified globalization, characterized by global cultural flows of people, technologies, money, images, and ideas (Appadurai 2020). Language is evolving in response to socio-cultural changes. As such, linguistic innovations via mass media offer a particularly interesting locus to track such global flows. This paper aims to study how popular lexicons in female address terms have emerged out of digital communication and have been widely used and interpreted by different communities interacting with mass media in contemporary China. As China is increasingly integrated into the global economy, the widespread of media networks, such as WeChat, QQ and Microblogs, has increasingly provided Chinese citizens with access to new words and new ways of using old forms. The study thus enquires as to the origin of these linguistic innovations, the linguistic resources required to bring about such changes, the motives for developing such online resources, and the responses by Chinese citizens to these media-induced language changes. By addressing these issues, this paper is oriented toward exploring the role of mass media in language change as well as the relationship between language, identity and ideology, in China, in the context of globalization. Our findings suggest that Chinese female address terms have emerged via mass media, by coining, borrowing, reapprorpiating older forms for new meanings, and by employing multimodality. These media-induced language innovations are not simple responses to the broader socio-cultural changes occurring inside and outside of China. Instead, Chinese citizens, through creating, using, or promulgating new popular lexicons, are able to construct, negotiate, and make sense of multiple selves across those digital spaces. Therefore, Chinese mass media has generated a network of “figured worlds”, within which individuals' identities and agencies form dialectically and dialogically in global cultural processes (Holland et al. 1998). In particular, the circulation of certain female address terms across digital spaces involves the enregisterment of words as part of a sexist register, which has perpetuated the ideologies of male dominance in contemporary China. Both individual and institutional efforts have been made to respond to such sexism and reconstruct gender images and identities.
Improving Genome Compression Performance by Extending Reference Sequences

We propose an efficient referential genome compression algorithm called RCCG. It extends reference genomes by its reverse complementation and uses coprime window sampling to detect the maximum matches (MEMs) between two genome sequences. After the assessment, those selected matches will be united to form mutation-containing matches (MCMs). The average compression ratio of the proposed algorithm is higher than that of the state-of-the-art genome compression algorithms.
AI Based Smart Surveillance System

The AI Based smart surveillance system is gaining huge attention because of rise in demand for safety and security. The surveillance system is designed to analyse the video, image, and audio or any kind of surveillance data automatically without any human involvement. The developments that happened in recent years in the computer vision, sensor devices and Auto ML is playing a keen role in accrediting such intelligent system. There are many kinds of surveillance and security systems present in the market, but there is no live decision making and predictive analysis surveillance i.e., automated self-decision making system which helps the different public departments like police, health, fire and many more to track and reach the particular location where the incident is happened. In this proposed project we provide the AI based intelligent surveillance and security system which analyses and takes the decision immediately by itself based on the present parametric conditions as per the modules trained by us, It helps in providing the alerts quicker compared to the traditional alert system. The system which is proposed here will react automatically based on the situations occurs as it is trained in various modes. This paper is intending to provide the generalised outline of the AI based smart surveillance system and its functionalities. This paper also consists of information regarding the core processing steps of AI based surveillance system such as tracking, object detection and classification, and behavioural analysis.
Inverse reinforcement learning through logic constraint inference

Autonomous robots start to be integrated in human environments where explicit and implicit social norms guide the behavior of all agents. To assure safety and predictability, these artificial agents should act in accordance with the applicable social norms. However, it is not straightforward to define these rules and incorporate them in an agent's policy. Particularly because social norms are often implicit and environment specific. In this paper, we propose a novel iterative approach to extract a set of rules from observed human trajectories. This hybrid method combines the strengths of inverse reinforcement learning and inductive logic programming. We experimentally show how our method successfully induces a compact logic program which represents the behavioral constraints applicable in a Tower of Hanoi and a traffic simulator environment. The induced program is adopted as prior knowledge by a model-free reinforcement learning agent to speed up training and prevent any social norm violation during exploration and deployment. Moreover, expressing norms as a logic program provides improved interpretability, which is an important pillar in the design of safe artificial agents, as well as transferability to similar environments.
Study of Out-of-School Youth Issues through Topic Modeling

본 연구는 교육부가 시･도교육감의 학교 밖 청소년 지원 책임을 명시한 법 제정을 추진하고 있는 시점에서, 현재 정부에서 학교 밖 청소년에게 주로 제공하는 지원 외에 제도적으로 확장할 수 있는 지원 분야에는 무엇이 있는지 알아보고자 하였다. 이를 위해 2018년부터 2022년까지 학교 밖 청소년을 주제어로 한 뉴스 기사 1,799건을 대상으로 텍스트마이닝 기법인 LDA를 기반으로 한 토픽모델링 분석을 실시하여 언론에서 주로 다루고 있는 토픽을 분석하였다. 그 결과 ‘검정고시 및 대입지원’, ‘직업체험훈련’, ‘건강검진 및 전용공간 지원’, ‘문화예술 프로그램’, ‘공립 대안학교 등 지원정책 수립’, ‘창업･자립 지원’, ‘후원금 기부’, ‘교육재난지원금 지급’, ‘교육수당지급 등 교육청 지원’, ‘지역사회 상호 재능기부’ 등 총 10개의 토픽이 도출되었다. 이 중 현 정부에서 주로 지원 중인 분야를 제외하면, ‘문화예술 프로그램’, ‘공립 대안학교’, ‘교육재난지원금 및 교육수당 지급’, ‘지역사회 재능기부’ 등에 관한 내용이 새로운 토픽으로 등장하였음을 알 수 있다. 분석 결과에 따라 새롭게 등장한 토픽에 대해 학교 밖 청소년 지원 제도에 반영할 수 있는 방안을 제시하고 추가적으로 이루어져야 할 연구를 제안하였다.This study aimed to find out what support areas that can be extended institutionally besides the support currently provided by the government to out-of-school youth. To this end, topic modeling analysis based on LDA, a text mining technique, was conducted on 1,799 news articles with out-of-school youth as the theme from 2018 to 2022 to analyze topics mainly covered by the media. As a result, ‘GED and college entrance support’, ‘Job experience training’, ‘Health checkup and dedicated space support’, ‘Cultural arts program’, ‘Public alternative school support policy establishment’, ‘Start-up and self-reliance support’, ‘Sponsor money Donation’, ‘Education disaster support fund’, ‘Education allowance payment’, ‘Mutual talent donation’ a total of 10 topics were derived. Among them, excluding the fields mainly supported by the current government, new topics such as ‘culture and arts programs’, ‘public alternative schools’, ‘educational disaster subsidies and payment of education allowances’, and ‘community talent donation’ have emerged as new topics. According to the results of the analysis, new topics that have emerged were proposed to be reflected in the out-of-school youth support system, and additional research to be done was suggested.
Tuning Language Processing Approaches for Pashto Texts Classification

Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the need for an automatic text categorization system for local languages is felt. The main aim of this study is to establish a Pashto automatic text classification system. In order to pursue this work, we built a Pashto corpus which is a collection of Pashto documents due to the unavailability of public datasets of Pashto text documents. Besides, this study compares several models containing both statistical and neural network machine learning techniques including Multilayer Perceptron (MLP), Support Vector Machine (SVM), K Nearest Neighbor (KNN), decision tree, gaussian na\"ive Bayes, multinomial na\"ive Bayes, random forest, and logistic regression to discover the most effective approach. Moreover, this investigation evaluates two different feature extraction methods including unigram, and Time Frequency Inverse Document Frequency (IFIDF). Subsequently, this research obtained average testing accuracy rate 94% using MLP classification algorithm and TFIDF feature extraction method in this context.
Image stitching based on FABEB and parallax tolerance

In order to improve the efficiency of image stitching and solve the problem of visual artifacts, an image stitching algorithm based on FABEB and parallax tolerance is proposed. Firstly, the FABEB algorithm is proposed to efficiently obtain robust feature points by combining scale-space keypoint detection of FAST (Features From Accelerated Segment Test) and boosted efficient binary local image descriptor (BEBLID). In the feature point registration algorithm, KNN algorithm is first used for general screening. Marginalizing sample consensus algorithm (MAGSAC) is used for further screening, to get the geometric relation between the images. In order to solve the artifact problem in large parallax image stitching, this paper proposes an optimal seam-cutting fusion algorithm based on visual saliency. And it adds saliency feature into the seam-cutting strategy to make the results more consistent with the perception of human eyes. Experimental results show that compared with the traditional algorithm, the proposed image stitching strategy can extract and match feature points more efficiently and accurately, and eliminate stitching artifacts to get a better visual experience.
Human Activity Recognition Vision Based Pose Detection

Human activity detection systems in the modern era are mostly trained and operated on video feeds and picture data. This data contains modifications to the functioning and operation of data that exhibits comparable or related behaviours. Recognizing human activity is critical for both human-to-human and interpersonal interactions. Operating systems manually is time-consuming and expensive. The project's objective is to develop a minimal cost, high-speed human behaviour detection system that relies on vision and can process video and photographs to determine what is going on. Examples include surveillance, the sports industry, and healthcare. In addition to being cost-effective, the system is a time saving, user-directed system that can be integrated into a variety of useful applications for a wide variety of activities that require cognitive processing with extraordinary precision. In addition, it can help blind people use their knowledge of their surroundings.
Diversity in Software Design and Construction Teaching: A Systematic Literature Review

Teaching in computing faces challenges concerning technological changes and strategies to encourage student engagement with the teaching–learning process. In software engineering, specifically in the construction of the solution, these challenges are even greater due to technological changes and the evolution of applications. Based on this context, the objective of this work is to present the results of a systematic literature review on the knowledge areas that are central to the software construction process, the Software Design and Construction. The methodology for developing the systematic literature review followed a review protocol in which 51 studies were selected out of 302 studies found by executing a search string in the Association for Computing Machinery and Institute of Electrical and Electronic Engineers databases. As a main result, the diversity of teaching strategies applied to the teaching of Software Design and Construction was identified. In addition, amongst the selected studies, we identified that active methodologies are more frequent in the literature.
Scrutinizing the temporal dimension: physical constraints of photonic time crystals and time-varying metamaterials

Although photonic time crystals and time-varying metamaterials have been widely studied theoretically and numerically, experimental progress at optical frequencies has so far been limited. In this talk, we show that, despite the often-invoked space-time duality, the characteristics of the temporal dimension, particularly its relation to causality and energy conservation, can lead to various challenges and physical constraints that are unique to time-varying systems. Specifically, we discuss how temporal dispersion can lower the effective temporal modulation strength seen by the probe wave and we show that realizing large variations of the material permittivity in time may require large amounts of energy.
Distance education in a pandemic

The research defines the pecularities of online learning, implemented at the time of acute respiratory disease COVID 19 pandemic, caused by coronavirus SARS-CoV-2. The paper presents survey results aimed at defining the attitude of students and academic staff towards online learning, highlighting the drawbacks of mentioned above type of education as well as development and implementation of required online resources based on methods and tools that can be used during online learning in order to enhance students’ academic performance. The influential peculiarities of distance learning have been suggested and a comparative analysis of traditional and distance learning under the conditions of quarantine has been carried out. Based on the performed analysis, the most important peculiarities have been identified.
Aapplication of image compression technology based on local change rate in image printing

A predictive coding algorithm for image lossless compression is introduced. In the prediction stage, the algorithm uses the local change rate of the pixel value to adjust the prediction model adaptively, and in the coding stage, the error feedback technology is used to further reduce the information entropy of the error image. The simulation test results on standard images show that the performance of the algorithm is significantly better than the standard lossless compression algorithm. The compression algorithm we proposed uses the local change rate of the image in the decorrelation phase to improve the prediction accuracy, and in the coding phase, the algorithm uses error feedback technology to further reduce the error.
A survey on self-assessment of “soft skills” formation in bachelors majoring in nursing

The development of “soft skills” applicable to healthcare professionals is a necessary factor for the formation of a highly qualifed specialist engaged in medical activity. Aim. The aim of the survey is to study the formation of “soft skills” while undergraduates at Nursing Institute obtain higher medical education. Materials and methods. It has led to the analysis of four key competencies “4K”. The comparative analysis was used to describe the names of universal competencies included into the Federal State Educational Standard for Bachelor’s degree in “Nursing” and the meaning of “4K” competencies. Fulltime students of Saratov State Medical University named after V.I. Razumovsky majoring in “Nursing” were engaged into the questionnaire. It has been revealed that the development of “4K” competencies is important for medical professional staﬀ. Results. The present study confrmed that the training for “Nursing” at the Bachelor level following the Federal State Educational Standard contributes to the development of “soft skills”. Conclusion. Based on the data obtained from the survey, we may conclude that the four key competencies “4K” are well formed in Bachelor of Nursing. However, the lack of information on the concept and structure of “soft skills” among students has been determined. More detailed information can be found by the use of special educational technologies and methods for mastering the “soft skills”.
Stages of Digital Transformation of Educational Institutions in the System of Sustainable Development of the Region

The purpose of the article is to model the main stages of sustainable development through the digital transformation of educational institutions in the region.World trends in the development of society, education, science and economy are aimed at the transition to digital transformation.Successful digitalization is becoming a key condition for the successful functioning of both the region and the entire country.Accordingly, today the successful sustainable development of the region is impossible without effective digitalization of higher education.Based on the results of the study, paired models of sustainable development of the region were formed with the help of digital transformations, taking into account the characteristics of the educational environment.The study has a number of limitations related to the inability to cover more than one region.Subsequent research should be related to ensuring the sustainable development of the region through the digitalization of other areas of activity.
Contrasting Neural Click Models and Pointwise IPS Rankers

Inverse-propensity scoring and neural click models are two popular methods for learning rankers from user clicks that are affected by position bias. Despite their prevalence, the two methodologies are rarely directly compared on equal footing. In this work, we focus on the pointwise learning setting to compare the theoretical differences of both approaches and present a thorough empirical comparison on the prevalent semi-synthetic evaluation setup in unbiased learning-to-rank. We show theoretically that neural click models, similarly to IPS rankers, optimize for the true document relevance when the position bias is known. However, our work also finds small but significant empirical differences between both approaches indicating that neural click models might be affected by position bias when learning from shared, sometimes conflicting, features instead of treating each document separately.
Imitation Learning with Sinkhorn Distances

Imitation learning algorithms have been interpreted as variants of divergence minimization problems. The ability to compare occupancy measures between experts and learners is crucial in their effectiveness in learning from demonstrations. In this paper, we present tractable solutions by formulating imitation learning as minimization of the Sinkhorn distance between occupancy measures. The formulation combines the valuable properties of optimal transport metrics in comparing non-overlapping distributions with a cosine distance cost defined in an adversarially learned feature space. This leads to a highly discriminative critic network and optimal transport plan that subsequently guide imitation learning. We evaluate the proposed approach using both the reward metric and the Sinkhorn distance metric on a number of MuJoCo experiments. For the implementation and reproducing results please refer to the following repository https://github.com/gpapagiannis/sinkhorn-imitation .