Reviewerly/exHarmony huggingface dataset:
FORECASTING OF COVID-19 DAILY CASES IN INDONESIA USING ARIMA MODEL

COVID-19 (Coronavirus Disease 2019) continues to be a global issue. The disease began to spread due to direct contact with the seafood market in Wuhan, Hubei Province, China. COVID-19 cases globally and especially in Indonesia, are still increasing as well. Therefore, it is important to forecast future cases as a form of vigilance and materials to formulate strategies in controlling the spread and procurement of health systems. This study aims to predict daily cases of COVID-19 in Indonesia. This research includes non-reactive studies by collecting daily case data on COVID-19 from October 1st to December 31st, 2020 from the COVID-19 Task Force website in Indonesia. The results showed that the model that is fit to describe COVID-19 cases in Indonesia is ARIMA [5,1,0] with a model significance of 0.000 and constant of 0.049 (p value &lt;0.05), Ljung-Box Q of 0.880 (p value &gt;0.05) and residual normality of 0.330 (p value &gt;0.05). The three months forecasting (from January to March 2021) showed a number that tended to increase. The increase in cases occurred due to environment, behavior, health services, and genetics. Therefore, it is necessary to increase cooperation between the government and the community so that efforts to suppress the growth of COVID-19 cases are optimal.
Hierarchical Triple Model of Hybrid Neural Machine Translation

For more than a decade, PMT and SMT models have dominated the field of machine translation, and neural machine translation has emerged as a new paradigm for machine translation by the 2015. Neural machine translation provides a simple modeling mechanism that is easy to use in practice and science. Thus, it does not require concepts such as word ranking, a key component of the statistical machine translation. While this simplicity may be seen as an advantage, on the other hand, the lack of careful spelling is to lose control of the translation. Even tough, the neural machine translation is more flexible in terms of translations that don’t exactly match the training data. This provides more opportunities for such models, but exempts translation from pre-determined restrictions. Failure to connect specific words can make it difficult to connect the target words you create to the original word. The widespread use of neural machine translation system has the advantage of allowing users to translate certain terms and translate uneducated data to a certain extent. In some cases, however, the structure and the grammar boundary of a sentence is often distorted. The paper is intended to address issues such as the control of neural machine translation, more accurate translation of unidentified data, the accuracy of sentence structure and grammar boundaries. To solve this problem, modern translation theory led to the hybrid model of machine translation. Our model is expansion of this hybrid model with a sentence and a grammar boundary. We named this model as hierarchical triple model (HTM).
Improving Hindi POS Tagger Accuracy Through Domain Adaptation

The paper presents a comparative evaluation report on multi-domain Hindi taggers. Two taggers are trained in this experiment with the objective of detecting the accuracy rate of the tagger after adapting Cricket domain. The multi-domain tagger, trained as part of ILCI project, includes our major domain (Health, Tourism, Entertainment and Agriculture) presently and adapting Cricket as a new domain was recently proposed in Pandey (2017) which was calculated with a difference of approx. 6% in the tagger accuracy. Statistically, the accuracy of four domain tagger (without Cricket) is 85% and for five domain tagger (with Cricket) is approx. 93% which is 1% lower than the pre-existing Hindi tagger. This paper deals mainly with evaluation of the Hindi tagger (with and without Cricket as one of the domains). Author also attempts at finding the difference in terms of POS tagging issues in the output and the linguistic analysis of the errors found.
Implementation of an Eduliterasi Application for Children

During the current Covid-19 pandemic, digital technology plays an active role in online learning. In online learning, we can take advantage of technology even though it is limited by distance. Currently, we need a digital literacy system that helps childrens learning, especially elementary school children. A child can learn online anywhere using a computer. Digital literacy can help childrens development in terms of reading. Reading is a complex activity in students that can produce a new mindset after reading. However, there are not a few students who rarely read and there are also those whose reading interest has decreased due to the change in the learning system to distance learning. Educators can take advantage of digital literacy in increasing students reading interest so that students can maximize online learning for learning development. This study focuses on how to design a web-based literacy information system to support childrens learning to increase childrens interest in reading and knowledge, which is called the “Eduliterasi” system. Utilizing this application can provide a direct reading experience and with many topics that can be read.
Efforts to Improve Avatar Technology for Sign Language Synthesis

A promising method for increasing Deaf accessibility is the technology of sign language synthesis, which can be used to automate the translation of spoken or written language to sign language through the manipulation of an avatar. Efforts to automatically translate spoken language to sign have lagged behind spoken-to-spoken translation. Avatars are used in various capacities for sign language display, including translation and educational tools. Though the ability of avatars to portray acceptable sign language producing believable human-like motion has improved in recent years, many still lack the naturalness and supporting motions of human motion. This paper presents current efforts being made at the DePaul University ASL Lab to improve the methods of sign synthesis using avatar technology and create believable human-like motion.
Strategy for the Effectiveness of the Implementation of Academic Information Systems in College in Efforts to Improve Information Quality

The success of information systems research has begun to decline in the past few years, but existing research is still inaccurate in its definition and in the factors that affect the success of the information system. The purpose of this research is to find out, study, and analyze the effectiveness of implementing academic information systems in universities to improve the quality of information. The research was conducted using a qualitative approach and case studies, namely problem formulation, study proposition, analysis unit, data linkage to the proposition, the criteria for interpreting the findings, and making conclusions and recommendations. From the results of the research data analysis, several conclusions were obtained, namely, the academic information system running in universities has not met expectations. The performance of academic information systems that can support the smooth operation of the universities is not well understood. It is necessary to formulate an optimal academic information system strengthening strategy that can support the smooth operation of the universities.
Innovative Media Space of the Modern Library: Axiological Guidelines of Functioning

The aim of the research is to outline the axiological guidelines for the functioning of the innovative media space of a modern library. The research methodology is based on general scientific methods of generalisation, systematisation and forecasting. The hypothetical method made it possible to define the «innovative media space of a modern library» based on a value-based approach in library science. The scientific novelty of the research lies in the substantiation of the value approach to the creation and functioning of the innovative media space of a modern library. The author’s definition of the concept of «innovative media space of a modern library» is a set of technical, technology and software tools of library activities formed in the process of digital transformations of the library aimed at meeting the information and social and communicative needs of citizens, due to their professional, social activities, education, leisure, life, tasks of personal growth and communication. Conclusions. In the context of digital transformations of libraries, the issue of avoiding technology determinism and the need to adapt the humanistic function of the library to innovative shifts remains relevant. Technology and social-communicative components of innovative media space of modern libraries should be considered in the relationship and interaction following the axiological guidelines of the library, including the implementation of social, educational, value-oriented, moral and ethical, memorial, environmental recreational functions. The theory and methodology of research on the digital technology issues in libraries is a value approach, which consists of the joint study of humanitarian issues (library’s purpose) and technology issues (ways to implement the library’s purpose). From the point of view of the value approach, the innovative media space of a modern library is a set of technical, technology and software tools of library activity formed in the digital transformations of the library aimed at meeting the information and social and communicative needs of citizens, due to their professional, social activities, education, leisure, life, tasks of personal growth and communication.
Multiview Latent Representation Learning with Feature Diversity for Clustering

To analyze and manage data, multiple features are extracted for robust and accurate description. Taking account of the complementary nature of multiview data, it is urgent to find a low-dimensional compact representation by leveraging multiple views and generating a better performance for clustering. In this paper, we present multiview latent representation learning with feature diversity for clustering (MvLRFD). To get rid of noise and maintain the information of each view, matrix factorization is adopted to get the latent representation. For fusing the diverse latent representations, we argue that the final representation is constructed by concatenating the latent representation of each view. With the help of the Hilbert–Schmidt Independence Criterion (HSIC), the diversity of features in the final representation is maximized to exploit the complementary information. A new coregularization strategy is introduced to maximize the distance between the similarity between data of the final representation and the latent one. The partition entropy regularization is adopted to control the uniformity level of the value of the similarity matrix and the weight of each view. To verify the availability of MvLRFD compared to state-of-the-art algorithms, several experiments are conducted on four real datasets.
A Study on the Efficiency of Recognition of Specific Workers based on Deep Learning through Wearing a Work Vest

This paper studies the method of detecting and tracking a moving target (person) among workers. The target’s noticeable features, such as its shape or color, are some of the requirements that guarantee the reliable performance of human detection and tracking methods. These features, however, are not adequate for the target tracking methods among workers because they all have a similar shape. Moreover, their clothes vary from day to day. Accordingly, we overcame these issues by devising a method in which the target wears a work vest, thus creating a distinctive feature. The contribution of this paper is as follows: 1) A work vest can be used as a noticeable feature to detect and track a target among workers. 2) Even if the target changes, the proposed method can be performed without additional learning. 3) Finally, by implementing the prototype system, the possibility of the proposed methods was experimentally verified.
Semantics for Combinatory Logic With Intersection Types

There is a plethora of semantics of computational models, nevertheless, the semantics of combinatory logic are among the less investigated ones. In this paper, we propose semantics for the computational system of combinatory logic with intersection types. We define extensional applicative structures endowed with special elements corresponding to primitive combinators. We prove two soundness and completeness results. First, the equational theory of untyped combinatory logic is proven to be sound and complete with respect to the proposed semantics. Second, the system of the combinatory logic with intersection types is proven to be sound and complete with respect to the proposed semantics. The usual approach to the semantics for calculi with types that can be found in the literature is based on models for the untyped calculus endowed with a valuation of type variables which enables the interpretation of types to be defined inductively. We propose, however, a different approach. In the semantics we propose, the interpretation of types is represented as a family of subsets that satisfies certain properties, whereas for a given valuation of term variables, the interpretation of terms is defined inductively. Due to the wide applicability of semantics of computational models, the presented approach could be further developed to other computational models and beyond—to current and foreseen application of semantics to large distributed systems and new challenging technologies.
Password Management Against Versatile Attacks

Passwords play a very critical role in authentication, information and security of web applications. Securing and managing passwords is a significant problem for most of the people in the modern world. As the number of accounts increases managing and securing of the passwords gets complicated. As a consequence, many users tend to adopt weak password management schemes which can significantly reduce the security of the systems. Prior to the research it confirms that security experts recommend Password managers to help users generate, store and enter strong unique passwords. In this research, after checking Password habits of users accounts, it allowed us to understand the password habits of users according to sensitive data of their account, and also revealed some critical issues associated with password choice. So we propose PasswordAgent(password hashing mechanism).It utilizes both salt repository(salt-sequence of randomly generated bytes) and a browser plug in to secure logins with strong passwords. Password hashing is a technique that allows user to remember simple low entropy passwords and have them hashed to create high-entropy secure passwords. PasswordAgent is less vulnerable to offline attacks, and it provides stronger protection against password theft.
Professional development of specialists of education management bodies

The article discusses the current situation with the training of education management specialists from the perspective of current research and regulatory aspects; foreign experience of a similar sphere is presented, as well as approaches to building professional development programs for specialists in educational authorities.
Finding Collusive Spam in Community Question Answering Platforms: A Pattern and Burstiness Based Method

Community question answering (CQA) websites have become very popular platforms attracting numerous participants to share and acquire knowledge and information in Internet However, with the rapid growth of crowdsourcing systems, many malicious users organize collusive attacks against the CQA platforms for promoting a target (product or service) via posting suggestive questions and deceptive answers. These manipulate deceptive contents, aggregating into multiple collusive questions and answers (Q&#x0026;As) spam groups, can fully control the sentiment of a target and distort the decision of users, which pollute the CQA environment and make it less credible. In this paper, we propose a Pattern and Burstiness based Collusive Q&#x0026;A Spam Detection method (PBCSD) to identify the deceptive questions and answers. Specifically, we intensively study the campaign process of crowdsourcing tasks and summarize the clues in the Q&#x0026;As&#x2019; vocabulary usage level when collusive attacks are launched. Based on the clues, we extract the Q&#x0026;A groups using frequent pattern mining and further purify them by the burstiness on posting time of Q&#x0026;As. By designing several discriminative features at the Q&#x0026;A group level, multiple machine learning based classifiers can be used to judge the groups as deceptive or ordinary, and the Q&#x0026;As in deceptive groups are finally identified as collusive Q&#x0026;A spam. We evaluate the proposed PBCSD method in a real-world dataset collected from Baidu Zhidao, a famous CQA platform in China, and the experimental results demonstrate the PBCSD is effective for collusive Q&#x0026;A spam detection and outperforms a number of state-of-art methods.
Prediction novelty implements breast cancer disease detection using machine learning techniques

Breast cancer is an exceptionally heterogeneous sickness. Bosom Cancer Diagnosis and Prognosis are two clinical difficulties the specialists in the field of clinical exploration. Bosom self-test and mammography can assist with discovering early findings of bosom disease. This is conceivable when in some circumstance or stage, the treatment is conceivable. Therapy might comprise radiation, lumpectomy, mastectomy, and chemical treatment. The essential dataset of bosom disease is done from the UCI dataset store with the end goal of exploratory work. These exploratory works legitimize the issue definition of the clinical examination utilizing distinctive order methods. Bosom Cancer Diagnosis and Prognosis are two clinical applications that represent an extraordinary test for specialists. In this paper, we have described the prediction of breast cancer disease using the proposed algorithm using the weka, and the Jupiter anaconda navigator simulates the tool and generates accuracy with efficiency.
Normative framework for the regional teacher skills development training system modelling

The article focuses on standard regulation of the regional model of teacher skills development system. Key actors of regional teacher training system and their roles are discussed. Relevant legal and regulatory mechanisms for such a system are presented.
Ecology of interaction of region educational space subjects

The authors propose a mechanism for the formation of ecological interactions’ fields between the subjects of the region educational space, the activity of which contribute to the creation of network nodes of the educational space with a new quality (eventfulness, environmental friendliness), and ultimately affect the sociocultural situation and educational space of the studied territory.
Hurddle Detection for Special (Blind) People

Abstract: Ongoing headway in advancement is making the life flourish, more straightforward and simpler for normal person. The World Health Organization (WHO) insights demonstrate that a lot of individuals experience visual misfortunes, in view of which they experience numerous troubles in regular positions. Subsequently, we want to structure humble, secure, wearable, and flexible system for outwardly impeded to help them in their day-to-day schedules. For this, the arrangement is to make a successful framework which will help outwardly debilitated individuals through hindrance recognition and scenes characterization. The proposed strategy uses Arduino board (UNO or Nano), Ultrasonic Sensor and Arduino, mounted on the stick of the person. Tensor Flow Object Detection calculation. The said methods are utilized to distinguish objects.
Libraries post Covid 19 pandemic: Considerations from Mexico

The objective of this paper is to show a general description of the first actions that librarians carried out in Mexico in the face of the Covid 19 pandemic, both to respond to the user in their demands for information, and to carry out their professional and leisure activities. The actions undertaken by librarians of academic libraries in the context of the pandemic are presented. Libraries in Mexico are one of the main sources of access to documentary information. In general, all types of libraries had to suspend their services during the pandemic and initiate protocols to restart functions. In this paper we have specifically addressed the contingency measures implemented in university libraries. Academic libraries had to adapt quickly to continue offering documentary and information services and with this, continue to carry out their functions in the development of professional training activities, technological developments, research, administration, and extension of culture. Both university libraries and users (students, professors, researchers, and administrative staff) had to adapt to the contingency since in most cases protocols were established both for prevention to avoid contagion and for user care.&#x0D; This document is the result of the research carried out based on the information answered by 40 libraries studied, to whom an instrument was applied. The literature on current topics was consulted in various databases and information from the websites of the libraries themselves. Information was also collected from approximately 20 academic events organized by library associations and other information professionals, such as the AMBAC (Civil Association of Librarians), the Library College of Mexico and the IFLA (International Federation of Library Associations and Institutions).
Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI

Automatic breast and fibro-glandular tissue (FGT) segmentation in breast MRI allows for the efficient and accurate calculation of breast density. The U-Net architecture, either 2D or 3D, has already been shown to be effective at addressing the segmentation problem in breast MRI. However, the lack of publicly available datasets for this task has forced several authors to rely on internal datasets composed of either acquisitions without fat suppression (WOFS) or with fat suppression (FS), limiting the generalization of the approach. To solve this problem, we propose a data-centric approach, efficiently using the data available. By collecting a dataset of T1-weighted breast MRI acquisitions acquired with the use of the Dixon method, we train a network on both T1 WOFS and FS acquisitions while utilizing the same ground truth segmentation. Using the "plug-and-play" framework nnUNet, we achieve, on our internal test set, a Dice Similarity Coefficient (DSC) of 0.96 and 0.91 for WOFS breast and FGT segmentation and 0.95 and 0.86 for FS breast and FGT segmentation, respectively. On an external, publicly available dataset, a panel of breast radiologists rated the quality of our automatic segmentation with an average of 3.73 on a four-point scale, with an average percentage agreement of 67.5%.
Reading comprehension in the development of communication skills in high school students

The objective of the research was study reading comprehension in the development of communicative skills, to respond to the lack of attention and concentration in reading in high school students, a situation for which students present difficulties when reading or interpreting what they read causing a loss in the comprehension of texts and poor communication. The methodology used was through the quantitative method applying different techniques such as: induction, deduction, analysis, synthesis, and statistics, where a survey was applied to a population of 1644, including parents, managers, teachers, students, staff administrative and service. It was obtained as a result that a good understanding and development of communicative skills improves learning and develops the obtaining of general ideas of a text, distinguishing main ideas, critically evaluating a text and finally developing cognitive skills.