Reviewerly/exHarmony huggingface dataset:
Deep Learning Strategy to Recognize Kannada Named Entities

Entity representatives are useful in understanding the natural language tasks including the semantics of the Kannada sentences into various entities. In this paper, we have come up with new pertained tag based representative learning of words and entities based on the bidirectional parsing. The proposed research works on segmenting the sentences of Kannada words into various taken, where every token makes various contributions in understanding the semantics of Kannada Sentences which treats words and entities in a given text as independent tokens, and outputs tagged entities based on representative learning mechanism. The research also has focused its attention towards achieving the results of good classification accuracy while recognizing the entities are through the tagging mechanism that is an extension of the general self-tagging mechanism of the Supervised Machine Learning Technique, and considers the types of tokens (words or entities) when computing attention scores. The erected research work has given its significant contribution in terms of good results over a standard benchmark datasets. In particular, it obtains state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question answering) as well as Kannada Named Entity Recognition of Central Institute of Indian Languages.
Anatomical approach of team formation in the study of the collective

Recently, in the practice and theory of management of general education systems, much attention has been paid to the formation and work with "teams” within a team of teachers. In the teaching environment, the concept of "team" is understood positively and is used very often, but not always in its direct meaning. By the thesis "teaching team" we mean a group of teachers who are prepared to achieve a common goal for the sake of joint activities and are responsible for the results obtained. Defining a group of teachers as a team does not mean creating it in reality, and the desire of management to force teachers to act as a team is not enough.
E-Ticket Application as Supporting Technology During COVID-19 Pandemic in Baluran National Park

The COVID-19 outbreak has a major impact, especially on tourism. Closure of tourist attractions must be enforced to prevent the spread of the virus. People are required to maintain a safe distance and avoid crowds (physical distancing). Some policies and guidelines are created to adapt to the new normal era to drive and stabilize the people's economy during this pandemic. Tourist attractions start operating with government guidelines, including limiting the number of visitors and implementing supporting technology to prevent queues of visitors when purchasing entrance tickets. This study develops a web-based system to facilitate ordering tickets through a web-based system, uploading proof of transfer, and obtaining e-tickets to be shown to ticket officers. Extreme Programming model was used. The e-ticket system has been tested at Baluran National Park using the Blackbox method, showing that the system has the functionality to meet user needs.
eTREE: Learning Tree-structured Embeddings

Matrix factorization (MF) plays an important role in a wide range of machine learning and data mining models. MF is commonly used to obtain item embeddings and feature representations due to its ability to capture correlations and higher-order statistical dependencies across dimensions. In many applications, the categories of items exhibit a hierarchical tree structure. For instance, human diseases can be divided into coarse categories, e.g., bacterial, and viral. These categories can be further divided into finer categories, e.g., viral infections can be respiratory, gastrointestinal, and exanthematous viral diseases. In e-commerce, products, movies, books, etc., are grouped into hierarchical categories, e.g., clothing items are divided by gender, then by type (formal, casual, etc.). While the tree structure and the categories of the different items may be known in some applications, they have to be learned together with the embeddings in many others. In this work, we propose eTREE, a model that incorporates the (usually ignored) tree structure to enhance the quality of the embeddings. We leverage the special uniqueness properties of Nonnegative MF (NMF) to prove identifiability of eTREE. The proposed model not only exploits the tree structure prior, but also learns the hierarchical clustering in an unsupervised data-driven fashion. We derive an efficient algorithmic solution and a scalable implementation of eTREE that exploits parallel computing, computation caching, and warm start strategies. We showcase the effectiveness of eTREE on real data from various application domains: healthcare, recommender systems, and education. We also demonstrate the meaningfulness of the tree obtained from eTREE by means of domain experts interpretation.
The Influence Of Educational Resources, Geographic Conditions, Demographic Conditions, Economic Conditions, Population Capacity On Junior High School Gross Participation Rate

The success of education development will be largely determined by the availability of educational resources as the main variable in providing educational services. Other factors are geographical conditions, demographic conditions, economic conditions, and the capacity of the population itself. This study examines the correlational-contributive relationship between these factors with the Gross Participation Rate (GER) in junior high schools in West Java. The data studied were 2015-2019 (5 years). Quantitative research methods with descriptive analysis. The results showed that the educational resources variable can explain the effect of 88, 14% on the total GER. The geographical condition variable can explain the effect of 63.96% on the total GER. The demographic condition variable explains the effect of 94.2% on the total GER and the KE variable contributes approximately 89.21% to the achievement of the total GER for SMP. The population capacity variable gave a valuable contribution of 37.96%.
Don’t Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data

High-performing machine translation (MT) systems can help overcome language barriers while making it possible for everyone to communicate and use language technologies in the language of their choice.However, such systems require large amounts of parallel sentences for training, and translators can be difficult to find and expensive.Here, we present a data collection strategy for MT which, in contrast, is cheap and simple, as it does not require bilingual speakers.Based on the insight that humans pay specific attention to movements, we use graphics interchange formats (GIFs) as a pivot to collect parallel sentences from monolingual annotators.We use our strategy to collect data in Hindi, Tamil and English.As a baseline, we also collect data using images as a pivot.We perform an intrinsic evaluation by manually evaluating a subset of the sentence pairs and an extrinsic evaluation by finetuning mBART (Liu et al., 2020) on the collected data.We find that sentences collected via GIFs are indeed of higher quality.
Enhanced parallel mining algorithm for frequent sequential rules

Sequential rule mining is an important data mining technique that discovers relationships between occurrences of sequential patterns. The main challenge is to avoid time-consuming, especially in large search spaces. This can be achieved through developing an efficient sequential rule mining algorithm without redundancy in a long sequence dataset. In this paper, an algorithm named PNRD-CloGen is proposed. It can be used to mine sequential rules from both frequent closed dynamic bit vectors with sequential generator patterns at the same time. It helps in speedily eliminating uninteresting candidates and compact the representations. Additionally, we apply a parallel approach utilizing multi-core architecture. An experimental evaluation was performed using five real sequence datasets: BMSWebView1, Sign, FIFA, Korsarak, and MSNBC. The proposed algorithm has been compared with two non-redundant sequential rule algorithms called: TRuleGrowth, and NRD-DBV algorithm. Experimental results show the time saving, especially for large sequence datasets and low minimum support threshold.
Regulating Blockchain, DLT and Smart Contracts: A Technology Regulator’s Perspective

Blockchain, Smart Contracts and other forms of Distributed Ledger Technology provide means to ensure that processes are verifiable, transparent, and tamper-proof. Yet the very same enabling features that bring decentralisation also pose challenges to providing protection for the various users and stakeholders. Most jurisdictions which have implemented regulatory frameworks in this area have focused on regulating the financial aspects of cryptocurrency-based operations. However, they have not addressed technology assurance requirements. In this paper we present a world-first technology regulatory framework.
A Comparative Study of the TEA, XTEA, PRESENT and Simon lightweight cryptographic schemes

With the current fast paced growth in the number of devices connected to the internet, many of these having limited computational capability, security concerns are of increasing importance. To meet the necessity of providing secure encryption to constrained devices many lightweight cryptographic schemes have been developed. This paper provides a comparative study of four of these schemes, namely TEA, XTEA, PRESENT and Simon, explaining how they work and discussing their vulnerabilities and performances. This paper specifically considers the performance of Application Specific Integrated Circuit (ASIC) implementations with regards to their size, throughput, power usage and energy usage per bit encrypted. TEA and PRESENT were found to have potentially problematic vulnerabilities, while no attacks on XTEA and Simon are known that do not reduce the number of rounds used during encryption. Simon and PRESENT were found to perform well, while XTEA implementations were found to be too large for constrained devices. Out of the four, Simon seems the most promising for use in constrained devices. As such, further research into its vulnerabilities is advisable.
The Foundation of Classical Mechanics

Mechanics is the science of the equilibrium and motion of bodies subject to forces. The adjective classical, hence Classical Mechanics, was added in the 20th century to distinguish it from relativistic mechanics which studies motion with speed close to light speed and quantum mechanics which studies motion at a subatomic level.
Application project task scheduling using dolphin swarm technology

Project task scheduling, which is part of software project management, has great importance in the success or failure of projects as 40% of projects are subject to failure due to poor planning and misuse of human resources, andthis greatly confirms that the wise decisions that are made at this stage leadto reduced Obstacles facing work and leading tosuccess. The work and decision-making is the responsibility of the project manager as he faces all the complexity and critical phases of this critical phase and deals with all its related processes which consist of defining the project tasks and assigning the employees responsible for each task and then preparing project plans. In this research we dealt with project scheduling using The smart dolphins warm algorithm, the project scheduling process is essential in the software engineering industry nowadays and the possibility of finding good solution swhich saves the software company a lot of time and money.&lt;div&gt; &lt;/div&gt;
Representing the Self Through the Visualization of Personal Data

The representation of identity in digital media does not necessarily have to be conceived on the basis of criteria that mimic physical reality. This article presents a model for representing individual identity, based on the recording of human experience in the form of personal data, as an alternative to the common forms of mimetic portraiture. As such, the authors developed the project Data Self-Portrait that aims to explore the creative possibilities associated with the concept of data portrait. It can be described as a means of representing and expressing identity through the application of data visualization techniques to the domain of portraiture, according to an exploratory design approach, based on visualizing the digital footprint. It thus seeks to develop design proposals for representing identity that respond to the growing dematerialization of human activities and explores the representational and expressive role of data visualization, according to a creative use of computational technologies.
Problems of students’ assessment in the process of distance learning (on the example of the EU countries)

The purpose is to determine the effectiveness of distance assessment, positive and problematic positions on the introduction of distance assessment in European higher education. The basic method of the research is the experimental method; empirical, statistical and theoretical methods have been also used. In order to carry out diagnostics on the effectiveness of the research, the survey method has been used. The basic hypothesis lies in the fact that changes in the students’ assessment system are positive, adaptable; prepared and properly implemented system will provide quality and objective control of knowledge and skills of students. The result of the research is proving of the effectiveness and prospects of the assessment system in the context of distance education; along with this, the difficulties and shortcomings are insignificant.
MetaFlow: a meta-learning-based network for optical flow estimation

Convolutional neural networks (CNNs) have achieved success in optical flow estimation using labeled datasets, but they fail to build an internal representation to fast adapt to the specific task. On the other hand, limited by the lack of ground truth, existing CNNs-based methods suffer from high noise sensitivity and inferior generalization performance. We integrate the meta-learning technique with optical flow estimation, which can learn internal features to search optimal initial state parameters of the network. Meanwhile, we devise an enhanced network termed MetaFlow to further improve performance. MetaFlow extracts per-pixel features, builds correlation volumes for all pairs of pixels, and iteratively updates optical flow through optical flow predictor using meta-learning. In addition, we propose a meta-transfer pretraining approach to obtain initial network weights, which can efficiently avoid network overfitting. Empirical experiments on MPI Sintel and KITTI benchmarks have shown that the proposed MetaFlow achieves the state-of-the-art results and performs outstanding in challenging scenarios such as textureless regions and abrupt motions.
War Field Robot with Night Vision Camera

Considering the current scenario of warfare between India and China, we got inspired by the thought of various ways technology can help our hardworking soldiers by reducing the human loss by using an application that can spy on the enemy, and also for security purpose. This project’s main purpose is to deal with difficult situations like where humans cannot go through scenarios like darkness, entering narrow areas and detecting hidden bombs etc. The robot serves as a perfect machine for the defense sector in order to reduce the human life loss and will also help in prevention of illegal activities. The robot is self-powered, with a backtracking facility, in case a situation arises where there is connection loss from the base station. Wireless cameras sends back real-time video and audio inputs that can be seen on a monitor in the base station and action can be taken accordingly.
Comparative analysis of machine learning algorithms on gender classification using Hindi speech data

A speech signal consists of salient paralinguistic details, which include the language, gender, age, and emotion of the speaker. This information from speech signals has various practical applications in crimeanalysis, security & monitoring, and brain-computer interface. The primary and most important step for these applications is to identify gender using speech data. Preprocessing plays a crucial role in the development of speech identification system due to the presence of background noise. In this study, we have compared the performance of four Machine Learning (ML) algorithms to classify gender using Hindi speech data. The data was pre-processed using Speech Endpoint Detection (SED) algorithm and windowing process from which the Mel-Frequency Cepstrum Coefficients (MFCC) and Pitch Range features were extracted. It was observed that the Random Forest algorithm outperformed other ML classification algorithms – Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Logistic regression and achieved an accuracy of 78.84%.
Literary, Socio-Political, and Material Everyday Life of the Editorial Board of the Russkoye Bogatstvo Magazine, Its Authors, and Staff between 1896 and 1900

This article introduces into scholarly circulation documents kept in the funds of the Scientific Research Department of Manuscripts of the RSL and the Russian State Archive of Literature and Art relating to literary, socio-political, and material life of the Russkoye Bogatstvo magazine. Along with documentary evidence of literary activity, these documents serve to clarify human preferences, assessments, relationships, and reveal the internal mechanisms of the editorial life of the magazine. The chronological period which the archival materials date back to (1896–1900) is a landmark in the history of the magazine when it first became a legal print medium of the Narodniks and, second, recovered the cooperative principle of its publication. Finally, during the period in question, the relations between the members of the editorial board began to be regulated progressively. Also, to a greater extent than before, all financial flows that passed through the editorial board started to be regulated legally organising the work of the editorial board which allowed the magazine to continue to exist despite the many difficulties — censorious, political, repressive — it had to face on its long and difficult way to the reader. The materials presented in the article tell about some episodes of the fate of the famous magazine under the changing historical conditions and clarify facts of human, literary, and financial relations in the editorial board, while a brief real commentary restores the historical and literary context of the facts described in these documents. The subject of the published documents is specific circumstances including economic ones of the editorial office of Russkoye Bogatstvo, which until now have rarely been the subject of historical and literary publications and articles.
Research on Methods of Improving Teaching Efficiency of Physical Training Based on Sports Biomechanics

Today, with the continuous development of science and technology, sports biomechanics has also made some progress. The application of various new materials and technologies has made the test of sports biomechanics more accurate and fast, especially the application of computer technology has accelerated the development of sports biomechanics automation. Sports biomechanics plays its own unique role in physical education, and it is an important part of physical education. Studying sports biomechanics is helpful to speed up the reform process of physical education. This spaper introduces the methods of improving the efficiency of physical training teaching. Applying sports biomechanics to physical education and training in Colleges and universities is conducive to students to understand and grasp the essential rules of movement and improve the learning and training effect. Therefore, we should carry out the teaching reform of sports biomechanics course, and apply it to the physical education and training of colleges and universities better, so as to improve the sports technology level of college students.
SURVEY PAPER ON SMART ESSAY GRADER

In order to optimize Human-Machine agreement for automatic evaluation of textual summaries or essays, automated essay grading has been a research field. With a growing number of people taking multiple exams such as the GRE, TOEFL, and IELTS, grading each paper would become more challenging, not to mention the challenge for humans to maintain a consistent mindset. In this situation, it is extremely difficult to rate a large number of essays in a short amount of time. This project aims to address this issue by developing a stable interface that will aid humans in grading essays. This study served as a medium for us to extract features such as the Bag of Words, numerical features such as the count of sentences and words, as well as their average lengths, structure, and organization, in order to rate the essay with the highest level of accuracy. This algorithm was chosen because it works well for small datasets.
A NEW MODEL ON AUTOMATIC TEXT SUMMARIZATION FOR TURKISH

The amount of data available in the electronic environment is increasing day by day with the development of technology. It becomes tough and time consuming for the users to access the information they desire within this increasing amount of data. Automatic text summarization systems have been developed to reach the desired information within texts in a shorter time than that of manual text summarization. In this paper, a new extractive text summarization model is proposed. In the proposed model, the inclusion of sentences of a given text in the summary is decided based on a classification approach. Also, the effectiveness of widely used features for automatic text summarization in the Turkish language is evaluated using sequential feature selection methods. The evaluations were carried out specifically for Turkish texts in the categories of economy, art, and sports. The experimental work justified the performance of the proposed text summarization method and revealed how effective the features are.
IMPLEMENTASI MDLC (MULTIMEDIA DEVELOPMENT LIFE CYCLE) DALAM PEMBUATAN MULTIMEDIA PEMBELAJARAN KITAB SAFINAH SUNDA

Safinah Book is a book that contains material jurisprudence, the work of Sheikh Salim Bin Sumair Al-Hadhromiy, until now it is still frequently studied by Muslims both in villages and in cities. Learning the Safinah Book is an important thing for Muslims.This book consists of 6 chapters, namely: chapter of monotheism, chapter of thaharoh, chapter of prayer, chapter of janazah, chapter of zakat and chapter of fasting. Because it is supported by rapidly increasing technological developments, finally all human activities can be assisted by digital media, this is certainly true in the field of Islamic religion. Therefore, researchers realized the importance of this occasion, to create an application that can be used anywhere and anytime as a learning tool for the Safinah book independently. This application development using methods developed MDLC (Multimedia Development Life Cycle) version of the Luther-Sutopo. This research produces a product multimedia learning of the Safinah Sunda book based on Android designed using Adobe Animate software. Keywords : Book of Safinah, Multimedia Learning, MDLC, Android, Adobe Animate . DOI : http://doi.org/10.5281/zenodo.4958220